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Irrigation Futures for the Murray Basin – Technical

Documentation M Ejaz Qureshi, Mohammed Mainuddin, Steve Marvanek, Amgad Elmahdi, Jeff Connor and Stuart Whitten

March 2012

Water for a Healthy Country Flagship Report series ISSN: 1835-095X

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Citation: Qureshi ME, Mainuddin M, Marvanek M, Elmahdi A, Connor J and Whitten S. 2012. Irrigation futures for the Murray Basin – Technical documentation. CSIRO: Water for a Healthy Country National Research Flagship. 33pp.

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File: Photo irrigation system Description: Centre pivot irrigation system in a pasture farm near Wagga Wagga, NSW. Photographer: M Ejaz Qureshi © 2012 CSIRO

Irrigation futures for the Murray Basin – Technical documentation iii

CONTENTS

Acknowledgments ........................................................................................................ v

Executive Summary ..................................................................................................... vi

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

2. Methodology ........................................................................................................ 2 2.1. Irrigation sector impacts of climate change ............................................................... 3

2.2. Modelling crop yield response to water and deficit irrigation .................................... 5

2.3. Modelling irrigation efficiency response .................................................................... 6

2.4. Modelling water trade and water prices .................................................................... 6 2.4.1. Estimation of regional water price .......................................................................... 7 2.4.2. Water price impact on regional profitability .......................................................... 10

2.5. Model solution algorithm ......................................................................................... 11

3. Data Collection Procedures .............................................................................. 11 3.1. Land use data acquisition ....................................................................................... 11

3.1.1. Cropping and horticulture data ............................................................................ 12 3.1.2. Livestock data ...................................................................................................... 13 3.1.3. Estimating irrigated area by activity ..................................................................... 13

3.2. Water allocations data acquisition .......................................................................... 14

3.3. Water salinity data acquisition ................................................................................ 19

3.4. Estimation of actual crop evapotranspiration, effective rainfall and irrigation requirements ........................................................................................................... 21

3.5. Economic information .............................................................................................. 24

4. Model Calibration ............................................................................................... 25 4.1. Irrigated areas and gross values comparison ......................................................... 26

5. Summary and Conclusions ............................................................................... 29

References ................................................................................................................... 30

LIST OF FIGURES

Figure 1 Historical water allocation and temporary water market price ....................... 9 Figure 2 Water allocations and predicted water prices .............................................. 10 Figure 3 A schematic representation of the steps involved in land use data scaling in southern connected Murray-Darling Basin ................................................................. 12 Figure 4 Map of the Murray-Darling Basin southern catchments ............................... 15 Figure 5 Difference between the NRM regions and the current study model catchments ................................................................................................................. 27

LIST OF TABLES

Table 1: Parameters used in the model ....................................................................... 3 Table 2 Minimum water requirement threshold (proportion) of full irrigation ................ 6 Table 3 Irrigation efficiencies by crop type and irrigation technology ........................... 6 Table 4 Water prices ($/ML) predicted with regression for climate change - base case and climate dry scenarios with associated states of nature ......................................... 8

Irrigation futures for the Murray Basin – Technical documentation iv

Table 5 Water prices ($/ML) predicted with regression for climate change – climate medium and wet scenarios with associated states of nature ....................................... 8 Table 6 Adjusted water prices ($/ML) predicted with regression for alternative climate change scenario and states of nature ........................................................................ 10 Table 7 Land use area (hectares) by major agricultural activities across the catchments in the southern MDB ............................................................................... 13 Table 8 Predicted available water for diversion, associated probabilities and change from the long term base case scenario expected mean (Base case expected mean = 6146 GL) .................................................................................................................... 16 Table 9 Expected base case water allocation (ML) of each activity across catchments in the SMDB ............................................................................................................... 17 Table 10 Water allocation for different states of nature across the catchments of the southern MDB - base case and climate dry scenarios (GL) ....................................... 17 Table 11 Water allocation for different states of nature across the catchments of the southern MDB - climate medium and wet scenarios (GL) .......................................... 18 Table 12 Salinity (EC) of water for different states of nature across the catchments of the southern MDB - base case and climate dry scenarios with associated states of nature ......................................................................................................................... 19 Table 13 Salinity (EC) of water for different states of nature across the catchments of the southern MDB - climate medium and wet scenarios with associated states of nature ......................................................................................................................... 20 Table 14 Estimated SMDB maximum crop water requirement (millimetres) and revised average crop yields (tonnes or litres/ha) of major agricultural activities ........ 22 Table 15 Base case expected mean crop water requirements (ET) in milimeters ..... 22 Table 16 Climate dry expected mean crop water requirements (ET) in milimeters ... 22 Table 17 Climate medium expected mean crop water requirements (ET) in milimeters ................................................................................................................................... 22 Table 18 Climate wet expected mean crop water requirements (ET) in milimeters ... 23 Table 19 Base case expected crop effective rainfall in milimeters ............................. 23 Table 20 Climate dry expected crop effective rainfall in milimeters ........................... 23 Table 21 Climate medium expected crop effective rainfall in milimeters ................... 24 Table 22 Climate wet expected crop effective rainfall in milimeters ........................... 24 Table 23 Economic parameters and their values for major agricultural activities ...... 25 Table 24 Catchment-wise model simulated land and water use versus ABS 2005-06 land and Base Case water diversion .......................................................................... 26 Table 25 Crop-wise model simulated land and water use versus ABS 2005-06 land and Base Case water diversion ................................................................................. 26 Table 26 ABS 2005-06 irrigated areas and gross values across catchments ............ 28 Table 27 Model estimated expected irrigated areas and gross values across catchments ................................................................................................................. 28

Irrigation futures for the Murray Basin – Technical documentation v

ACKNOWLEDGMENTS

The Authors would like to thank those individuals for useful discussions and insights and/or those who provided the data presented in this report: Neil Armstrong, Kyabram Research Centre, Ellinbank, DPI Victoria; David Pocock, PIRSA, Adelaide, SA; Scott Keyworth, CSIRO; Mike Stone, Murray Valley Winegrowers Inc. Mildura; Julie Mount, Australian Wine and Brandy Corporation, Adelaide; Vesna Simic, Australian Citrus Growers Inc. Mildura, Victoria; Mark Hickey, NSW Agriculture, Leeton, NSW; Deborah Kerr, Ricegrowers Association of Australia Inc, Leeton, NSW; Riaz Ahmed, Irrigator, Leeton, NSW; and Anna Lukasiewicz, Latrobe University, Wodonga, Vic.; Bob O’Brien, Per Cat Water, Awadhesh, Prasad, Jason Alexander, Anil Dhir and Oscar Mamalai, MDBA; Mike Young, University of Adelaide; Orion Sanders , ABARE and Rodney Coulton, NWC.

Irrigation futures for the Murray Basin – Technical documentation vi

EXECUTIVE SUMMARY The irrigation sector in the Murray-Darling Basin (MDB) is facing several issues including droughts, over allocation, decreased water supply and changed reliability of water. These issues will lead to a changed sharing of water between cities, the environment and the irrigation industry. Furthermore, changing world food markets and rapidly evolving water policy provide opportunities for changed investments supporting irrigation infrastructure and water allocation and water markets and trading. The most important policy question is how the water available in the basin should be shared among various users including irrigators and the environment. Key policy responses that contribute to resolving these issues include organising future water policy to increase water value and enhance the resilience of the irrigation industry in the light of potential future changes in water availability and enhancing the value of public investment. The focus of this report is to present an analytical framework with linked hydrologic, agronomic and economic components and data collection procedures. The analytical framework is critical in assessing impacts of various climate change scenarios on effective rainfall, irrigation water requirements and water allocations for irrigation and on productivity and profitability of various agricultural activities in different catchments/regions of the southern Murray-Darling Basin. The developed framework is also essential in assessing impacts of water policy mechanisms in mitigating impacts of reduced rainfall and water allocations. The implications of less water for irrigation and alternative basin-management strategies are presented in Qureshi et al. (paper in review) and Qureshi and Whitten (paper in preparation). While overview of data and trends in basin water allocation, trade patterns, price and volumes of water traded disaggregated by major catchments in the Murray complied from a series of recent reports are presented in another complementary report (Kaczan et al, 2011). The analytical framework is an integrated biophysical and economic model developed by linking land use, rainfall and water allocations and crop water requirements information/data along with economics of producing different agricultural activities. In the crop water yield function, deficit irrigation is allowed subject to minimum crop water thresholds. The crop yield function accounts for total quantity of water required for the crop, effective rainfall, irrigation system efficiency and net irrigation water used. The impact of salinity on crop water requirement is also linked for salt leaching purpose and the crop yield is maintained at higher crop water requirement. This function ensures that below a certain proportion of evapotranspiration (i.e. crop threshold), less water application results in zero yield. Actual crop evapotranspiration, effective rainfall and net irrigation requirements of different crops grown in the southern MDB are estimated using a soil water balance simulation model. The analytical framework is used to estimate impacts on agricultural profitability and implications of different climate change and policy scenarios on eight major agricultural activities in different catchments (regions) of the southern MDB. These catchments include: Murrumbidgee, Ovens, Goulburn & Broken, Campaspe, Loddon & Avoca, Wimmera & Avon, Murray Riverina NSW, Murray Riverina Vic, Mallee Vic, Lower Murray NSW and Lower Murray SA. Eight major agricultural activities considered in the analysis include: cereals, rice, pasture for dairy, beef and sheep, vegetables, citrus fruits, deciduous fruits, stone fruits and grapes. Later in the analysis, the maximum water use constraint is relaxed and decisions to buy and sell annual water allocations among regions are allowed. The model is run

Irrigation futures for the Murray Basin – Technical documentation vii

with the option of water trade to evaluate the impact of water trading, especially as water availability changes in different climate change scenarios. ABS agricultural statistics were collected at a Statistical Local Area or SLA level and processed using GIS. These SLA based data were disaggregated in order to estimate the agricultural activity occurring within the reporting catchments. The Bureau of Rural Sciences (BRS) land use grid was used to determine what proportion of a broad land use category occurring in a given SLA fell into a given reporting catchment. The NRM level ABS data was used to determine what proportion of a given aggregate land use class was irrigated in a given NRM region. Water diversion data at different reaches for different catchments for four scenarios including: i) base case scenario of historical development and historical climate change, ii) historical development and future climate change dry scenario, iii) historical development and future climate change median scenario and iv) historical development and future climate change wet scenario, was obtained from CSIRO Sustainable Yield data files. Alignment of these catchments with NRM regions accounting for ABS SLAs was critical for linking biophysical and economic information and for the scenarios and policy mechanism analysis. Further, given the differences in water entitlements and allocation rules and management regimes across the jurisdictions, it was essential to split the Murray catchment into sub-catchments to represent its area in each of the three states, i.e. Vic, NSW and SA. The linking and alignment process resulted in agricultural land use area for 12 catchments. Later, Lower Murray NSW catchment was merged with Murray Riverina NSW catchment, for simplicity. Despite the alignment effort, there was a mismatch between irrigated land use and simulated water diverted in most catchments due to an under/overestimation of land use or water diversion data calculated for each catchment or due to not accounting for groundwater use. We aligned land use data with regional base case (or initial) water allocations data by accounting for expected mean crop evapotranspiration (ETa), expected mean effective rainfall, individual on-farm crop irrigation efficiency and estimating net irrigation requirements, and redistributing and allocating water to each region by multiplying proportion of total water use by total water available for irrigation in each of the four climate scenarios. We distributed each catchment’s water to individual agricultural activities after accounting for the land use data of each activity in the respective catchment, crop water requirement, effective rainfall, on-farm irrigation efficiency and net irrigation requirement. MSM-BIGMOD was used to estimate the impact of climate change scenarios on salinity by maintaining a salt balance. Conversion of electrical conductivity values to total dissolved solids is carried out using a multiplication factor and net salinity in diverted water for catchments in the Murray basin is estimated. Average salinity concentration across these catchments was used to reflect salinity of Murrumbidgee. Historical prices of individual commodities were obtained from ABARE and ABS and other publications as well as from state agricultural departments and their mean values were used in the analysis. Water charging regime varies across regions of the southern MDB. For simplicity, a constant water pumping charge is used for each activity across catchments. When inter-regional water trading was allowed, the irrigators who sold water increased their revenue while those who purchased water increased their cost of irrigation. A non linear programming structure is used to account for the nonlinearities involved in the agricultural activities crop water production functions, irrigation water salinity and

Irrigation futures for the Murray Basin – Technical documentation viii

crop production functions. A positive mathematical programming or PMP approach (Howitt, 1995) is applied to address the overspecialisation problem faced by mathematical models and to calibrate the model simulated irrigated land use against a reference period. The PMP approach assuming a profit-maximising equilibrium in the reference period recovers the missing or additional information from the observed activity levels and by specifying a non-linear objective function, the resulting model exactly produces the observed behaviour of farmers (Cortignani and Severini, 2009). By taking the steps in PMP approach, we calibrated model simulated land use data to ABS estimated 2005-06 land use data and reproduced 2005-06 conditions with reasonable accuracy. We also compared the model estimated gross values of agricultural production in the base case with the ABS 2005-06 estimated and observed gross values for each catchment and found close match which assures the model’s reliability in assessing climate change scenarios and water policy options.

Irrigation futures for the Murray Basin – Technical documentation 1

1. INTRODUCTION In most of the last decade, the Murray-Darling Basin (MDB) has faced severe drought and reduction in rainfall. A decrease in rainfall has a disproportionately severe impact on basin inflows which have been at record low. Subsequently, the volume of water held in many major storages has also fallen to record low levels. Many of these storages were less than 20% of their full capacity. As a result, water available for diversion and allocations of irrigation water were significantly lower than licensed entitlements in most regulated river valleys of the MDB (MDBA, various reports including MDBC, 2008-2009). The high security water entitlements received allocations of less than 100% for the first time in 2002. In 2007-08, high security water allocations received by irrigators varied from 30% to 55% of entitlements. Low security entitlements remained at or close to zero for most of this year. In fact, the 2200 GL that flowed into the Murray system in 2007-08 was the sixth lowest in 117 years of records. The preceding year was even drier with only 970 GL flowing into the river system (NWC, 2008). Given the severe climate change circumstances, diversions in 2007-08 fell to only 40% of the average diversions between 1997 and 2008 (MDBA, 2009). The timing of inflows and water allocations (availability) is critical for the irrigated agriculture and can greatly influence the business as usual (Ashton, Hooper and Oliver, 2010). In addition to reduction in irrigation water diversions and allocations, there are concerns that the river systems in the MDB, especially in Murray River (one of the two major rivers of the MDB) is facing multiple threats, including changes to flow regimes. One indicator of changed river management in the Murray River is its median annual flow to the sea. Between January and March 2009, the inflows in Murray River were the lowest in 117 years of records. Inflows between 2006 and 2009 were only 46% of the historical three-year minimum (MDBA, 2009). Climate change is believed to exacerbate the water over-allocation challenges in the MDB. CSIRO (2008a) estimated a substantial reduction in rainfall and surface water availability in the south of the MDB. The study considered the historical climate and current development as a baseline scenario and compared other climate and development scenarios against it. The study found that the diversions in driest years will fall by more than 10% in most NSW regions, around 20% in the Murrumbidgee and Murray regions and from around 35% to over 50% in Victorian regions. Further, the uncertainty in rainfall can affect on crop effective rainfall in a region and less crop effective rainfall means more crop water needs. Along with reduced rainfall and water allocations, climate change is also expected to increase crop water requirement by increasing evapotranspiration and irrigation water salinity concentration. The combined impact of these factors will result in a greater water demand and reduced allocations and potentially reduced agricultural production and possible impact on global food supply (Hanjra and Qureshi, 2010; Qureshi and Hanjra, paper in review). Addressing these issues will require a changed sharing of water between cities, the environment and the sustainable irrigation industry. Furthermore, changing world food markets and rapidly evolving water policy provide opportunities for changed investments supporting irrigation infrastructure and allocation and water markets and trading. The most important policy question is what is the economic impact of reduced water allocations across catchments and how the water available in the basin should be shared. Key policy responses that contribute to resolving these questions include organising future water policy to enhance the water value and the resilience of the


irrigation industry in the light of potential changes in future water availability and enhancing the value of public investment by using future scenarios. The purpose of this study is to present documentation of the positive mathematical programming (PMP) model and its key components including description of hydrologic, agronomic and economic components. The following section presents a discussion on the integrated biophysical and economic PMP model developed to link biophysical and economic information and assess economic impacts of various climate change scenarios and water policy mechanisms. Section 3 presents information about data collection procedures. In section 4, a discussion on model calibration is presented. Summary and concluding remarks are provided in the final section.

2. METHODOLOGY The analytical framework or methodology used in this analysis extends the previous mathematical modelling approaches (Qureshi et al., 2007; Adamson et al., 2009; Hafi et al., 2009; ABARE-BRS, 2010; Grafton and Jiang, 2010; Mallawaarachchi et al., 2010) by extending the analysis and explicitly considering several climate induced water resource impacts and adaptation options in the southern MDB that have not been considered in a single analysis. It is anticipated that the combined impact of climate change on irrigated agriculture may be greater than assessed in the previous studies. This is because water availability reductions are compounded by direct impacts of reduced rainfall, greater evapotranspiration and increased salinity. However, adaptation options and institutional policies (including crop/land use decisions, deficit irrigation, and opportunities to trade water) generate the opposite effect by ameliorating the impacts of reduced water availability. Thus incorporating a wider range of adaptation and institutional responses, as undertaken in this study, yields a more thorough understanding of the differential impacts and will inform a more sound policy response. The positive mathematical programming (PMP) approach (Howitt, 1995) has been applied to address the problem of overspecialisation in agriculture often faced using the mathematical programming approaches (Howitt et al., 2010). The PMP approach assumes a profit-maximising equilibrium in the reference period and based on an assumption of unobserved information recovers additional information from observed activity levels. Following the steps described in the PMP approach results in the model exactly producing the observed behaviour of farmers (Cortignani and Severini, 2009) without introducing artificial constraints (Heckelie and Britz, 2005) and makes it a widely accepted method for policy analysis (Griffin, 2006; Howitt et al., 2010, Merel and Bucaram, 2010; Qureshi et al., paper in review). The model has been used to estimate gross values and profits as well as capital and operating costs of investing in different agricultural activities across the southern MDB using different water allocations, rainfall and irrigation water salinity data under different climate scenarios. The catchments included in the model are: Murrumbidgee (Murrum), Ovens, Goulburn & Broken (GB), Campaspe (Campas), Loddon & Avoca (LodAvo), Wimmera & Avon (WimAvon), Murray Riverina NSW (MRivNSW), Murray Riverina Vic (MRivVic), Mallee Vic (MalleeVic), Lower Murray NSW (LMurrayNSW) and Lower Murray SA (LMurraySA). Eight agricultural activities included are: cereals, rice, pasture activities (including dairy, beef and sheep), vegetables (represented by potato), citrus fruits (cFruits), deciduous fruits (dFruits), stone fruits (sFruits) and vines (grape). Dairy represents areas of the all the pasture related activities including dairy, beef and sheep.


Later, trading of water was allowed between regions to examine its impact on land and water allocations, profitability and impact in mitigating costs of reduction in water allocations due to climate change.

2.1. Irrigation sector impacts of climate change At the core of the integrated stochastic biophysical and economic analytical framework is a model of the irrigation response that would be expected under the alternative climate change (i.e. water demand and supply) scenarios. The objective function of the model is to maximise profits π under each scenario and state of nature for the MDB after accounting for amortised annual establishment costs and operating or variable costs subject to available land and water. The objective function of maximising regional profit is expressed algebraically in Equation (1). All of the variables and parameters used in equation 1 and subsequent equations of the model are defined in Table 1. The first expression in equation (1) characterises the long-run on-farm irrigation and agricultural activity infrastructure capital investment choices that can vary across but not within a growing season. The second two expressions characterise the short decisions that can be varied within a season (such as the variable or operating input levels, and the water application rates) after stochastically determined factors affecting production are revealed (in this case levels of water allocation and salinity). Therefore, variable costs including water use related cost and crop and revenue are dependent on water allocation of an individual year.

s j hsrjhrrjs

j hsrjhjjh AREAVCostAREACECostIECost R

srjhIWWChargeProb

s j hsrjhsrjhrjs YIELDob AREACPricePr

(1)

Table 1: Parameters used in the model

s Each state of nature reflecting very low, low, high and very high rainfall and water allocations

r Irrigation catchment or region (11 catchments in southern MDB) j Irrigated cropping activity (rice, cereals, vegetables, and fruit) h On-farm irrigation efficiency system

srjhAREA

Irrigated area (ha)

srjhYIELD

Crop yield (t/ ha) (i.e. crop response to water)

RsrjhET

Crop water requirement (mm)

srjERain

Regional crop effective rainfall

rja

Intercept of the yield response function (t/ha or l/ha)

rjb

Slope coefficient of the yield response functions (t/ML or l/ML)

rjc

Other (quadratic) coefficient of yield response function (t. ha/ML2 or l.ha/ML)

rjhIEff

On-farm crop irrigation system efficiency

srTWat Total amount of water available after accounting for conveyance losses in each region

rCLoss Water conveyance losses in each region


RsrjhIW

Irrigation water use (ML/ha) for revenue purpose

CsrjhIW

Irrigation water use (ML/ha) after accounting for leaching for cost purpose

jhIECost

Irrigation establishment cost ($/ha);

jCECost

Crop establishment cost ($/ha)

sProb Probability (prop) of water allocations/supply

rjCPrice

Crop Price ($/ha)

DRevenue Dry revenue ($/ha)

rjVCost

Variable cost ($/ha) of each agricultural activity

rWCharge Water charges or price of water ($/ML)

srphdMaxlY

Maximum crop yield (t/ ha)

srnWAllocatio The fraction of an irrigator’s entitlement

srWPrice Water market price ($/ML)

srTEC Total electrical conductivity

1TH

2TH Thresholds defined for TECsr by agricultural activity depending on salinity tolerance

The water availability constraint is given in Equation (2) and a land availability constraint in Equation (3). The water availability constraint ensures that for each state of nature s and region r, the sum of the amount of water required will not exceed the total amount of water available srTWat after accounting for conveyance losses

rCLoss in each region. The land availability constraint ensures that for each region r, the sum of the land areas required will not exceed the total available area for irrigation rTArea in a region

rs,TWatCLossAREAIW srrsrjhj h

Csrjh 1 (2)

srTAreaAREAj h

rsrjh , (3)

The portion of the total available area not irrigated is considered dryland and represented by Equation (4). The dryland constraint is used to release irrigated land towards dryland activities if it is not economic to irrigate given water allocation and market conditions.

j h

srjhrr sr,AREA-TAreaDAREA (4)

Evidence from actual water market transactions suggests that the area of lower value annual crops, particularly pasture, tends to contract in years of low allocations and high water price. Dairy farmers have adapted to capture moisture through opportunistic cropping activities or substituting towards purchased feed and/or reducing herd numbers when prices are too high. When water allocations are high, farmers produce their own fodder or pasture. During wet season and high allocations, dairy farmers had plans to increase their herd numbers (Mallawaarachchi and Foster, 2009). As a result, they expanded their pasture irrigated areas.


2.2. Modelling crop yield response to water and deficit irrigation

srjhYIELD is a function of crop evapotranspiration RsrjhET which is crop water

requirement including irrigation water use RsrjhIW and effective rainfall srjERain and

accounting for on-farm irrigation system efficiency rjhIEff . A quadratic yield response

function is used of the form:

2Rsrjhrj

Rsrjhrjrj

Rsrjhsrjh ETcETba)(ETYIELD f (5)

where:

rja = intercept of the yield response function (t/ha or l/ha)

rjb = slope coefficient of the yield response functions (t/ML or l/ML)

rjc = other (quadratic) coefficient of yield response function (t. ha/ML2 or l.ha/ML)

A minimum threshold is imposed on crop water requirement in those yield (production) functions where the intercept rja is not zero to deal with negative yield

occurrence at zero crop water usage.

The amount of irrigation water use R

srjhIW required to meet a specific crop’s RsrjhET will depend on the srjERain and the

rjhIEff as shown:

rjhsrjRsrjh

Rsrjh IEffERainETI W

(5a) The irrigation water salinity can affect crop productivity and requires additional water to maintain crop productivity.1 As a result, this causes an increase in total irrigation water

use CsrjhIW shown as:

rjhsrjCsrjh

Csrjh IEffERainETIW

(5b)

where CsrjhET is the total crop water requirement which is greater than R

srjhET (i.e. R

srjhC

srjh ETET ) because of the salinity impact. It should be noted that CsrjhET

is used for

cost purpose while yield is obtained using RsrjhET . C

srjhET is obtained as:

2122

21121

11

THTECifETTHTHTHTECx

THTECTHifETTHTHTHTECx

THTECifETTHTECx

ET

srR

srjhsr33

srR

srjhsr22

srR

srjhsr11

Csrjh

(5c)

1 Irrigation water salinity is accounted for in the form of two components, i.e. concentration

rSC and electrical conductivity srEC . The values of these two components result in total

electrical conductivity srTEC (i.e. srrsr ECSCTEC ) of available water for each scenario

and in each catchment/region.


where 1x , 2x and 3x are numbers (factors) and their values start from 1 (i.e. 1x =1)

and increases with srTEC . 1 , 2 and 3 are proportions, which also increase as the

value of srTEC increases. 1

TH and 2TH are two thresholds defined for srTEC which

vary across agricultural activities depending on the salinity water tolerance. Inclusion of a crop water production function allows modelling of deficit irrigation or applying less than the full crop water requirement and accepting less than the greatest possible yield. By reducing the water use per hectare, a greater area can be irrigated. However, the level of deficit irrigation depends on the type of crops. In general, cereals and grapes are tolerant to water stress to some extent. Rice is sensitive to water stress particularly at the flowering and the second half of vegetative period (Doorenbos and Kassam 1979). Thus, the current model allows deficit irrigation subject to a certain threshold of minimum water requirements (i.e. allowed proportion of reduction in evapotranspiration at maximum yield) for each agricultural activity, as shown in Table 2.

Table 2 Minimum water requirement threshold (proportion) of full irrigation

Activity Rice Dairy and

Cereals

Grapes, deciduous fruits, citrus fruits and

stone fruits Vegetables

Minimum threshold (proportion)

0.80 0.50 0.60 0.75

2.3. Modelling irrigation efficiency response The plausible on-farm irrigation efficiency systems included in the model and the assumed irrigation efficiency of each system is shown in Table 3. The values are based on anecdotal evidence, by visiting farms of annual and perennial activities and by discussing with irrigators and irrigation scientists along with considering the findings of previous studies (Hafi et al., 2001; Qureshi, et al., 2007).

Table 3 Irrigation efficiencies by crop type and irrigation technology

Rice Dairy

Cereals Grapes, deciduous fruits, citrus fruits and

stone fruits

Vegetables

Flood 0.70 0.70 0.70 0.70 0.75 CP NA 0.80 0.80 NA 0.80 Drip NA NA NA 0.87 0.87

2.4. Modelling water trade and water prices Later in the analysis, the model is run with a water trade treatment so as to evaluate the value of water trading, especially as water availability changes. For the trading treatment, the water constraint presented in Equation (2) is modified to allow trade of water among regions across the basin, as shown in Equation (6). Those regions which have no physical linkage with other catchments are excluded from the interregional water trading market. Water trading equates the value of marginal product to shadow price of the water and results in optimal allocation across the


trading regions. At the basin scale analysis, no water price is included as the value of water trades represents a transfer payment within the basin therefore does not impact on aggregate results. For convenience, we assume that there is no restriction on interregional water trading and zero transaction costs of trade.

sTWatCLossAREAIWr

srrsrjhr j h

Csrjh 1 (6)

In the current analysis no limit on water trading is imposed though any limit can be applied to prevent greater trade in the light of farm expansion capacity, and use of a certain volume of water on farm and product-market-demand constraints, along with fulfilling regional administrative rules and regulations on water trading. Therefore, the regional trading gains could be considered the maximum possible gains of water markets.

2.4.1. Estimation of regional water price

To estimate the impact of water trading on each region’s total gross value and profitability, equation (7a) is included in the objective function (equation 1). While in equation (7b), the parameters srWPrice and srnWAllocatio represent water market price

($/ML) and water allocation (ML) for each catchment, respectively. When the term in bracket in the equation (7b) is positive, water is brought into the region through water purchases. When negative, water is transferred out of the region through water sales.

sTWatCLossIAREAIWr

srrsrjhr j h

Csrjh 1 (7a)

s jsr

hsrjh

Csrjhsrs nWAllocatioIAREAIWWPriceProb

(7b)

In developing the relationship between water allocation and water prices, we follow Brennan (2006) who uses regression analysis to estimate such a relationship. The resulting equation (R2 = 0.89), which uses annual temporary water price and water allocation data from 1998 to 2004, is as follows:

srsrsr EffRain0086.0TWatProp48466.00333.7)WPriceln( (8)

Each irrigator in the region has an entitlement to be delivered an amount of water denominated in megalitres. Depending on dam storage levels the water authority chooses a percentage of entitlement )TWatProp( sr up to 100% to distribute to

irrigators. This fraction of entitlement srTWatProp is known as an irrigator’s annual

allocation. Finally, srEffRain in equation (8) represents the cumulative season rainfall

in millimetres. We estimated water prices that each region confronts with this equation and the water allocations and rainfall levels assumed for each climate change scenario. The results are presented in Table 4 Water and Table 5 Water prices ($/ML) predicted with regression for climate change – climate medium and wet scenarios with associated states of nature .


Table 4 Water prices ($/ML) predicted with regression for climate change - base case and climate dry scenarios with associated states of nature

Very low

state Low state High state

Very high state Mean

Base Case Murrumbidgee 185 89 39 16 58 Ovens 87 36 14 5 22 Goulburn-Broken 111 54 24 10 35 Campaspe 186 94 42 17 61 Lodon-Avoca 275 147 72 33 101 Wimmera-Avon 281 156 86 44 115 Upper Murray & Kiewa 65 26 11 5 17 Murray Riverina NSW 321 166 78 30 110 Murray Riverina Vic 327 168 80 32 113 Mallee Vic 368 220 130 63 165 Lower Murray SA 402 244 146 66 182 Murray Basin average 237 127 66 29 89 Climate Dry Murrumbidgee 303 151 70 27 100 Ovens 143 57 22 7 34 Goulburn-Broken 182 87 37 14 56 Campaspe 302 149 66 25 96 Lodon-Avoca 406 212 102 46 145 Wimmera-Avon 440 245 142 75 185 Upper Murray & Kiewa 124 52 22 9 34 Murray Riverina NSW 498 265 135 55 183 Murray Riverina Vic 496 264 137 58 185 Mallee Vic 557 334 201 101 254 Lower Murray SA 592 360 221 105 274 Murray Basin average 367 198 105 47 140

Table 5 Water prices ($/ML) predicted with regression for climate change – climate medium and wet scenarios with associated states of nature

Very low



Climate medium Murrumbidgee 196 92 39 15 58 Ovens 97 38 14 5 23 Goulburn-Broken 118 56 24 9 36 Campaspe 200 99 43 17 63 Lodon-Avoca 295 155 75 33 106 Wimmera-Avon 319 174 94 47 127 Upper Murray & Kiewa 72 28 11 4 17 Murray Riverina NSW 357 182 83 31 118 Murray Riverina Vic 364 184 85 33 121 Mallee Vic 406 236 134 62 173 Lower Murray SA 440 262 153 66 193 Murray Basin average 260 137 69 29 94 Climate wet Murrumbidgee 171 79 33 12 49 Ovens 78 29 11 3 17 Goulburn-Broken 112 55 23 9 35


Campaspe 192 96 41 16 61 Lodon-Avoca 272 141 67 29 95 Wimmera-Avon 278 150 81 40 109 Upper Murray & Kiewa 59 23 9 3 14 Murray Riverina NSW 303 150 66 24 96 Murray Riverina Vic 308 152 68 25 99 Mallee Vic 346 201 115 52 148 Lower Murray SA 382 225 130 54 164 Murray Basin average 227 118 58 24 81 As expected, the lower the water allocation, the greater the market price for water and vice versa. Technically, water price predictions with this regression relationship are only valid within the range of supply, demand and price used in the original regression. To address the question whether this equation is accurate outside of the range of values used in the original regression, we compared these values with the price of water observed in the last one decade across catchments of the southern MDB (Kaczan et al., 2011). The average of the predicted water prices for all these scenarios and states of nature was about 1/3rd of the average price of water observed in the last one decade in the southern MDB. This indicates shortcoming of this regression in determine water price for more severe climate change scenarios as the predicted water prices lie outside the original regression sample range. To overcome the water price underestimation problem, we examined the relationship between the historical water allocations and both temporary allocation price and permanent entitlement price. In both these cases, we found a strong negative correlation between the water allocations and the price data. The estimated correlation between the water allocations and the temporary water market price was 0.91. Figure 1 shows the clear relationship between the two variables (i.e. water allocations and the temporary water market price).

Figure 1 Historical water allocation and temporary water market price

We fitted trend lines of these two variables and got the functions equations. We used these functions and built a linear relationship function, shown in Figure 2. Assuming a similar relationship between the future water allocation and prices and by utilising the linear function we predicted water prices against a range of water allocations including water prices of water allocations in three scenarios and their four states of nature.


Figure 2 Water allocations and predicted water prices

The predicted water prices for the four scenarios and respective states of nature are shown in Table 6. These values are close to what we observed in dry years of the last decade. For example, average temporary water allocation market prices in 2006-07 and 2007-08 (as shown in Figure 1) was $410 and $630, respectively. As a point of interest, our dry scenario (very low state of nature water availability of 2096 GL) and medium scenario (very low state of nature water availability of 3933 GL) are a reasonable correspondence to allocations in the droughts of 2006-07 and 2007-08 when the water allocations in the southern connected MDB were about 1/3rd of the long term average, i.e. 4409 GL in 2006-07 and 3332 GL in 2007-08 (MDBA, 2010). While the predicted water price for the two scenarios and states of nature are $697/ML and $497/ML, respectively. Also, our predicted expected mean price ($271/ML) for the base case scenario is close to the long term average SMDB price ($279/ML). Given these prices are close to the observed water market prices, we used the predicted prices in our interregional water markets for the four scenarios and states of nature and accounted for water revenue and cost in gross value estimation of water selling or buying regions.

Table 6 Predicted water prices ($/ML) for alternative climate change scenarios and states of nature

Base case

Climate dry

Climate medium

Climate wet

Very Low 445 697 497 436

Low 315 488 349 305

High 211 337 232 206

Very High 145 207 148 140

Expected Mean 271 422 298 263

2.4.2. Water price impact on regional profitability

Water trading equates the value of marginal product across the trading regions and endogenously determines shadow price (or opportunity cost) of water by its optimal allocation across the regions. In the current analysis, first, water trading impacts (positive or negative) for each region are estimated and compared with base case when no trading was allowed. Considering endogenous prices and accounting for transaction costs can provides benefits of water trading at a whole basin level with an objective of (global) maximum benefits. If a region reduces its water usage as a result of trading, its production and revenue decrease. While a region which increases its water usage, its profitability increases due to increase in production and revenue. This process is only applicable when there is a pool of water available that


needs an economically desirable level of water allocation. However, in reality, water entitlements have already been assigned and regions get allocations depending on available water for diversion based on types of entitlements, water sharing plans and other factors. This means if an irrigator within a region sells water to another region he gets price and regional revenue increases. While a region which buys water from another region increases its production and revenue but pays water price which increases its costs of production. If the incremental benefits are greater than the incremental costs, then the regional profitability increases. Therefore, accounting for the increase in water usage and associated production in one region and decrease in water usage and production and increase in water revenue proceeds in another region was critical to understand regional trading gains. We used the water prices given in Table 6 above to evaluate the potential gains from water markets and inter-regional trade on the individual catchments.

2.5. Model solution algorithm A non linear programming structure is used instead of the more common linear programming approach because of the nonlinearities involved in the agricultural activities crop water production functions, irrigation water salinity and crop production functions. The PMP model has been coded in the General Algebraic Modelling System (GAMS) (Brooke et al. 2004).

3. DATA COLLECTION PROCEDURES

3.1. Land use data acquisition Land use data were obtained from the Australian Bureau of Statistics (ABS) agricultural statistics. Data were collected at Statistical Local Area (or SLA) level and were disaggregated across watershed (catchment) boundaries in order to estimate the agricultural activity occurring within the reporting catchments. The Bureau of Rural Sciences (BRS) land use grid (BRS, 2004) was used to determine what proportion of a broad land use category occurring in a given SLA fell into a given reporting catchment. A schematic representation of the steps involved in land use data scaling in southern connected Murray-Darling Basin is shown in Figure 3.


Figure 3 A schematic representation of the steps involved in land use data scaling in southern connected Murray-Darling Basin

3.1.1. Cropping and horticulture data

Agricultural commodities in the Australian Bureau of Statistics (ABS) data were categorised according to the SPREAD land use categories represented in the BRS land use grid. For example, ABS “Wheat” commodity mapped to the SPREAD class “Cereals excluding rice”, “Apricot” mapped to “Stone fruit” etc. For each SLA and SPREAD class combination, the proportion of cells in the Land Use grid that fell into a given Catchment was determined and used attribute that proportion of commodity area as reported in the ABS stats for the corresponding SLA to that Catchment. For example if: 1) According to AgStats the area of “wheat” in SLA “X” = 10,000 ha; 2) According to the land use grid, SLA “X” contains 25 pixels coded to the SPREAD class “Cereals excluding rice”; 3) When overlaying the SLA boundaries x Land Use grid x Catchment boundaries in the GIS, 10 of those 25 “Cereal” pixels fall into Catchment Y. Then 10/25 x 10,000ha (i.e. 4000ha) of wheat are attributed to Catchment Y. This is repeated for all other SLAs that intersect Catchment Y and for all commodities using the corresponding Landuse (SPREAD) coding in the Land Use grid. Then the fractional SLA contributions to Catchment Y are summed to get the Catchment total for that commodity. It is to be noted that for specific tree crops, AgStats reports tree numbers rather than hectares at the commodity level (e.g. Number of orange trees, No. of lemon trees etc). For the above disaggregation process, tree numbers were converted to hectares based on the orchard tree density estimates used in the 2001 NLWRA. However ABS does report some aggregate areal statistics for tree crops (e.g. “Total citrus – area” which was used to cross check commodity level tree crop areas derived from tree numbers.

Input region scale data

A (ha)

B (ha)

Input region data spatially rescaled to

Study catchment; e.g.

A x 3/10

+

x

Input regionalisation for rescaling

(SLA level crop area)

(NRM level irrigation area)

Study catchment boundary Spatially explicit land use

(1.1km raster)

x

Overlay

A

BC

A

BC

NRM boundaries

region contributions

SLA boundaries

INPUT REGIONS

STUDY CATCHMENTS

1.1km

Landuse raster

Input data

regionalisations used


3.1.2. Livestock data

ABS does not report livestock areas but rather numbers of dairy cattle, beef cattle and sheep per SLA. Also, while the land use grid spatially depicts pasture areas (vis SPREAD classes “Sown pasture” and “Natural pastures”), it does not specify livestock types. To address Dairy, Beef, and Sheep pastures, the ABS areal statistic “Total grazing land” was disaggregated within that SLA to area of Dairy cattle, Beef cattle and sheep pastures based on the DSE (Dry Sheep Equivalent) proportions of livestock numbers reported in that SLA. The DSE rates used to standardise livestock to reflect differential pasture stocking rates are as follows. Dairy cattle = 10 DSE, Beef cattle = 8 DSE and Sheep = 1.5 DSE. These derived SLA based livestock areas are then distributed to Catchments as described above for the crop and horticulture commodities, using the SPREAD pasture category cells of the land use grid to rescale the SLA statistics to the reporting catchment areas.

3.1.3. Estimating irrigated area by activity

The aforementioned method for re-scaling SLA level Agricultural commodity statistics to Catchments gives us the total area of crop. The ABS does not comprehensively report irrigation area by commodity type at the SLA level, (presumably) because of commercial sensitivity considerations. Instead, it reports irrigation areas (rounded to the nearest 1000ha) of aggregated land use classes (e.g. “Cereal crops”, “Fruit trees, nut tree, plantation or berry fruits”) at the smaller scale regionalisation of NRM boundaries.). Again, a classification and spatial disaggregation method was used to derive the area of irrigated commodities by Catchment. The NRM level ABS data was used to determine what proportion of a given aggregate land use class was irrigated in a given NRM region. For example, for the North Central NRM region, ABS reports 572,000ha of “Cereal crops”, of which 5,000ha is irrigated, (i.e. 3.50%). This “proportion of aggregated land use class irrigated” is computed for each natural resource management (NRM) region that intersects with the Catchments and, similar to the spatial disaggregation method use to rescale SLA based commodity area data to Catchments, the land use grid is used to rescale “proportion of aggregated land use irrigated” to the reporting catchments. This catchment scaled proportion is in turn applied to the relevant commodity area total (as calculated from SLA derived ABS statistics) to arrive at the area of irrigated commodity. For example, the agricultural area (as depicted spatially by the Land use grid) of the Loddon Avoca reporting Catchment falls 80% in the NRM region North Central and 20% in the NRM region Mallee. From the ABS NRM level data, 3.50% of North Central “Cereal crops” are irrigated and 0.20% of Mallee “Cereal crops” are irrigated. It is therefore computed that in the reporting catchment of Loddon Avoca 2.84% of “Cereal crops” are irrigated (i.e. 3.5% x 0.8 + 0.2% x 0.2). This percentage is applied to the catchment scale area of “Wheat” to arrive at area of wheat irrigated. A summary of land use area (hectares) by each crop across the catchments is provided in Table 7. In the southern MDB, pasture for dairy, beef and sheep is the major activity (53%) followed by cereals (21%), rice (11%), grapes (8%) and fruits (5%).

Table 7 Land use area (hectares) by major agricultural activities across the catchments in the southern MDB

Cereals Rice

Pasture activities1 Vegetables

Citrus fruits

Deciduous fruits

Stone fruits Grapes

Total

Murrum 115194 54261 100189 5014 5786 39 2908 14152 297544Ovens 0 0 4085 0 0 0 20 1829 5935GB 4569 49 142305 1400 0 0 9107 2524 159954


Campas 562 0 24786 734 0 0 0 395 26477LodAvo 19162 0 88810 2398 0 0 1823 3096 115288WimAvon 4192 0 11190 269 323 0 0 2068 18042UmurrayK 62 0 19280 0 0 0 151 547 20041MRivNSW 35532 37168 58920 942 623 0 0 897 134081MRivVic 485 635 32395 380 0 0 4102 98 38095MalleeVic 596 0 2383 1766 2635 5259 3374 20457 36470LMurraySA 1021 0 13504 7140 5788 2919 2448 29670 62491Total 181375 92113 497848 20043 15155 8217 23932 75733 9144171Pasture activities including dairy, beef and sheep.

3.2. Water allocations data acquisition Water allocation to different sub-catchments, actual diversion for irrigation, urban, domestic and other uses from different river reaches, conveyance losses, and possible diversion for four scenarios, namely a) base case scenario of historical development and historical climate change; b) historical development and future climate change dry scenario; c) historical development and future climate change median scenario and historical development and future climate change wet scenario (CSIRO 2008). Details of these scenarios, data, methods, and models used can be found in the CSIRO Sustainable Yield regional reports (CSIRO, 2008a; CSIRO, 2008b; CSIRO, 2008c; CSIRO, 2008d; CSIRO, 2008e; CSIRO, 2008f; CSIRO, 2008g; CSIRO, 2008h). The simulated runoff and consequent water diversion data accounted for existing water sharing plans rules where environment is disadvantaged in dry periods (CSIRO, 2006). The CSIRO Sustainable Yield (SY) project reported water diversion data for each catchment. However, these catchments were not aligned with NRM regions and/or ABS SLA or SD (statistical division) levels. Alignment of these catchments with NRM regions accounting for ABS SLAs was critical for linking biophysical and economic information and for scenario and policy analysis. For example, in the SY project, Murray was considered as a single catchment which crossed over three states, NSW, Vic and SA. Given the differences in water entitlements and allocation rules and management regimes across the jurisdictions, it was essential to split the Murray catchment into sub-catchments to represent its area in each of the three states Vic, NSW and SA. As a result we ended up with 12 catchments, nine in Vic, two in NSW and one in SA, as shown in Figure 4. Later in the analysis, Lower Murray NSW was merged with Murray Riverina NSW, for simplicity.


Figure 4 Map of the Murray-Darling Basin southern catchments

The alignment of scaling with NRM regions also required water diversion data at each diversion point. CSIRO SY project water diversion data was obtained at each diversion point and aggregated and/or distributed to each catchment in the Basin depending on the location of the diversion point and where water was being diverted. While analysing the water diversion data, it was found that in some water diversion reaches, diverted water included water for both irrigation and urban as well as conveyance losses, while in other cases diverted water was only for irrigation without accounting for conveyance losses. For consistency, urban water was deducted from the diverted water and conveyance losses were included to represent total water for irrigation diversion. Later, conveyance losses were deducted to get net water available for irrigation. The expected net irrigation water for diversion after accounting for urban water and conveyance losses was about 6146 GL. While in climate dry and medium scenarios, the volume of water for irrigation was 4708 GL and 5882 GL, respectively. Climate wet scenario had slightly above the base case scenario allocation (i.e. 6216 GL). However, for dry and medium scenarios across the basin there was an overall expected reduction in allocations of about 25% and 5%, respectively. As far as the individual catchment water diversion or allocation is concerned, despite the alignment effort, there was mismatch between the observed irrigated land use in 2005-06 and the simulated water diverted in many of these catchments. This could be due to an under/overestimation of land use or water diversion data calculated for each catchment (i.e. inclusion and exclusion of more spatial area) or due to not accounting for groundwater use. We thought of using the historical water allocation proportion of each catchment to distribute the total southern MDB water allocation to individual catchments. However, the water entitlement and allocation regimes (depending on level of water security, dam storage and rainfall) vary across catchments and there are assessments that some catchments will face more


reduction in their water diversion (supply or allocations) than others in some scenarios. We aligned irrigation water available for diversion data with regional land use data by accounting for expected mean crop water requirement (evapotranspiration or ETa), expected mean effective rainfall, individual on-farm crop irrigation efficiency and estimating net irrigation requirements. We distributed water allocation data to each catchment by multiplying proportion of the total water use by total water available for irrigation in each of the four climate scenarios. For simplicity, we assumed constant reductions in water availability for all the catchments keeping the total water for irrigation diversion for the whole southern MDB constant depending on the scenario. To represent the variability in supply in the scenarios, we broke the data into four distinct categories or, states of nature. A very dry state of nature represents the 12% of driest years, a dry state of nature represents the next 38% of dry years, a wet state of nature represents the next 38% of relative wet years, and a very wet state of nature represents the 12% of the wettest years in the 111 year simulation. The level of allocation associated with each state of nature changes depending on the base case and each of three climate change scenarios. For example, low water allocation years become more common as the climate moves from no change to severe change. The estimated impacts of the climate change four scenarios on irrigation water allocations along with their reliability are presented in Table 8.

Table 8 Predicted available water for diversion, associated probabilities and change from the long term base case scenario expected mean (Base case expected mean = 6146 GL)

States of Nature

Climate Scenario Very low

(p=12%)

Low

(p=38%)

High

(p=38%)

Very high

(p=12%)

Southern MDB water available for diversion (GL)

(Percentage Change from Base Case Expected Mean)

Base Case 4495 (-27) 5727 (-7) 6708 (9) 7339 (19)

Climate Dry 2096 (-66) 4083 (-34) 5513 (-10) 6748 (10)

Climate Medium 3993 (-35) 5401 (-12) 6511 (6) 7305 (19)

Climate Wet 4578 (-26) 5818 (-5) 6761 (10) 7388 (20)

Historical average water diversion for all the irrigation areas in the southern MDB was about 7000 GL with a maximum diversions of about 9000 GL in years 2000-01 and 2001-02 and least diversion of slightly over 4000 in 2006-07 and 2829 GL in 2007-08. The dry scenario represents a future with a 12% probability of water allocations equal to 2096 GL, a reasonable corresponds to allocations in the current drought of 2628 GL in 2006-07, and 1468 GL in 2008-09. Arguably, this dry scenario may even understate the future expectation of drought given that storages are very low going into the 2009-10 year and allocations even lower than in 2008-09 are likely. Nevertheless, the scenario sufficies to provide some general insight into the nature of adaptive response


and economic impacts that can be expected in a future with less and more variable water allocations for irrigation. The water allocation for each catchment was distributed to each agricultural activity after accounting for the 2006 land use data of individual agricultural activities in the respective catchments, crop water requirements, effective rainfall, on-farm irrigation efficiency and net irrigation requirements. The final data set was on-farm water available for irrigation for each agricultural activity in each catchment and for each scenario and state of nature. Expected water allocation of each activity across each catchment is presented in Table 9. Across catchments, Murrumbidgee got highest allocation (33%) followed by MurrayRivNSW (17%) and GB (13%). Across agricultural activities, pasture related activities (including dairy, sheep and beef) got highest allocation (51%) followed by rice (22%) and grapes (10%).

Table 9 Expected base case water allocation (ML) of each activity across catchments in the SMDB

Cereals Rice Pasture activities Vegetables

Citrus fruits

Deciduous fruits

Stone fruits Grapes

Total

Murrum 350566 790798 680282 25689 31148 418 31259 109292 2019453Ovens 16567 158 10315 27041 GB 9044 579 697562 5522 77426 15559 805692 Campas 1656 175562 3955 3196 184369 LodAvo 57880 625158 12382 18822 23233 737475 WimAvon 12793 81058 1434 1935 15859 113080 UMurrayK 94 67835 1094 2881 71904 MRivNSW 115272 505547 426947 4895 3725 6807 1063193MRivVic 1536 6731 254210 2151 41804 709 307141 MalleeVic 2083 19226 10071 17862 58496 37527 166932 312198 LMurraySA 3744 112519 41182 40693 32937 27615 245252 503941 Total 554668 1322881 3147770 97211 95364 91851 235705 600036 6145486

The predicted water available in the four scenarios and the respective states of nature is given in Table 10 and Table 11. The predicted water available in the base case high state of nature (assuming it representative of the long term average rainfall and irrigation water allocation year) is given in Table. The change in water available for irrigation in each scenario and state of nature from the base case high state of nature is shown in Table 2. Table 2 Percentage change in water available for irrigation in alternative scenarios and states of nature compared to the Base Case High state of nature water allocation

Base case Climate dry Climate medium Climate wet

Very Low -33% -69% -40% -32%

Low -15% -39% -19% -13%

High 0% -18% -3% 1%

Very High 9% 1% 9% 10%

Table 10 Water allocation for different states of nature across the catchments of the southern MDB - base case and climate dry scenarios (GL)

Very low



Base Case


Murrumbidgee 1330 1695 1986 2173 818 Ovens 19 25 29 32 512 Goulburn-Broken 675 860 1007 1102 505 Campaspe 130 166 195 213 549 Lodon-Avoca 618 788 923 1009 441 Wimmera-Avon 88 112 132 144 99 Upper Murray & Kiewa 64 81 95 104 667 Murray Riverina NSW 828 1055 1236 1352 694 Murray Riverina Vic 254 324 379 415 282 Mallee Vic 167 213 249 273 348 Lower Murray SA 321 409 479 524 194 Climate Dry Murrumbidgee 620 1209 1632 1997 534 Ovens 9 18 24 29 21 Goulburn-Broken 315 613 828 1013 271 Campaspe 61 118 160 196 1068 Lodon-Avoca 288 562 758 928 263 Wimmera-Avon 41 80 108 132 550 Upper Murray & Kiewa 30 58 78 96 125 Murray Riverina NSW 386 752 1015 1243 804 Murray Riverina Vic 119 231 312 382 170 Mallee Vic 78 152 205 251 116 Lower Murray SA 150 291 393 481 761

Table 11 Water allocation for different states of nature across the catchments of the southern MDB - climate medium and wet scenarios (GL)

Very low



Climate medium Murrumbidgee 1182 1599 1927 2162 1609 Ovens 17 23 28 32 23 Goulburn-Broken 599 811 977 1096 816 Campaspe 116 157 189 212 158 Lodon-Avoca 549 743 896 1005 748 Wimmera-Avon 78 106 128 143 107 Upper Murray & Kiewa 57 77 92 104 77 Murray Riverina NSW 735 995 1199 1346 1001 Murray Riverina Vic 226 305 368 413 307 Mallee Vic 148 201 242 272 202 Lower Murray SA 285 385 465 521 388 Climate wet Murrumbidgee 1355 1722 2001 2187 817 Ovens 20 25 29 32 27 Goulburn-Broken 687 873 1015 1109 414 Campaspe 133 169 196 214 1072 Lodon-Avoca 630 800 930 1016 394 Wimmera-Avon 90 114 133 145 557 Upper Murray & Kiewa 65 82 96 105 136 Murray Riverina NSW 843 1072 1245 1361 970 Murray Riverina Vic 259 329 382 418 222 Mallee Vic 170 216 251 275 150 Lower Murray SA 327 415 482 527 814


3.3. Water salinity data acquisition In this project MSM-BIGMOD was used to understand and provide information regarding the climate change impact and reduced flows on River Murray salinity across catchments in the southern MDB. MSM is a monthly simulation model used for modelling river flows, and BIGMOD is a daily flow and salinity routing model used prior to the adoption of MSM-BIGMOD for daily river operation. It is also used independently to produce weekly flow and salinity forecasts for the mid reaches of the River Murray. MSM-BIGMOD has been calibrated and accredited by the MDBC for managing the lower Murray system. MSM-BIGMOD simulates the River Murray system by dividing the river into a number of river reaches. In each river reach, the major processes modelled are the routing of flow and salinity, losses, inflows, extractions, the operation of storages and weirs based on specified rules and the diversion of water into branches. Salinity is routed in the model by tracking parcels of water. The salinity of the inlet channel is dependent on the history of flow. The model maintains a salt balance even when a reach ceases to flow and the dead storage evaporates. Moreover, in the model, conversion of electrical conductivity (EC) values to total dissolved solids is carried out using a multiplication factor of 0.6 (MDBC, 1999). In this research MSM-BIGMOD model was used to understand the climate change and reduced flows effects on river salinity. This process involved: Estimating weekly/monthly/annual river salinity under baseline (benchmark)

climatic conditions (1975-2000) and for four representative recent years: one representing a very low water allocation year, one a moderately low water allocation year, one a moderately high water allocation year and one a very high water allocation year.

Estimating weekly/monthly/annual river salinity under the different climatic conditions (mild, moderate and severe climate change) is briefly described below (detail is given by Elmahdi et al., 2008, Chiew, 2006 and Connor et al., 2008): a) Baseline run (MDBC Benchmark case); b) Mild climate change event (tributary inflows reduced by 0.13); c) Moderate climate event (tributary inflows reduced by 0.38) and Severe climate event (tributary inflows reduced by 0.63).

Following the above procedure, we estimated salinity (EC) data for 25 years (i.e. 1975 to 2000) at different reaches across the Murray catchments. Average of salinity data at different reaches is used for those catchments which receive water from more than one reach. These data were ranked in the ascending order and assigned to four states of nature (i.e. more salinity in dry years and less in wet years) within a climate scenario. A previous study (Beal, et al., 2000) predicted salinity concentration for NSW rivers including Murrumbidgee and found salinity in Murrumbidgee catchment varying from 200 EC to 300 EC. Average salinity concentration across Murray catchments was used to reflect salinity of Murrumbidgee for all the scenarios and states of nature. A summary of the salinity data of four scenarios and states of nature for all the catchments is presented in Table 12 and Table 13. These salinity values for each scenario have been used as inputs into the economic model to estimate the impact of salinity on crop water use requirements across catchments of the Southern MDB.

Table 12 Salinity (EC) of water for different states of nature across the catchments of the southern MDB - base case and climate dry scenarios with associated states of nature

Very low




Base Case Murrumbidgee 275 328 352 382 337 Ovens 55 60 63 67 62 Goulburn-Broken 63 70 73 79 72 Campaspe 78 95 98 102 95 Lodon-Avoca 85 108 115 123 111 Wimmera-Avon 157 272 299 333 283 Upper Murray & Kiewa 53 57 59 64 59 Murray Riverina NSW 59 62 65 68 64 Murray Riverina Vic 63 70 73 79 72 Mallee Vic 492 613 626 672 606 Lower Murray SA 361 459 537 576 502 Climate Dry Murrumbidgee 300 460 577 752 620 Ovens 58 65 76 124 76 Goulburn-Broken 70 81 98 175 98 Campaspe 80 106 115 177 113 Lodon-Avoca 90 120 134 193 130 Wimmera-Avon 218 280 348 418 316 Upper Murray & Kiewa 55 60 68 90 66 Murray Riverina NSW 62 71 87 172 87 Murray Riverina Vic 70 81 98 175 98 Mallee Vic 309 817 935 958 808 Lower Murray SA 737 777 998 1196 935

Table 13 Salinity (EC) of water for different states of nature across the catchments of the southern MDB - climate medium and wet scenarios with associated states of nature

Very low



Climate medium Murrumbidgee 300 345 377 409 358 Ovens 58 63 69 73 66 Goulburn-Broken 70 75 83 87 79 Campaspe 83 94 104 113 98 Lodon-Avoca 91 106 119 138 114 Wimmera-Avon 173 278 330 355 294 Upper Murray & Kiewa 54 59 63 67 61 Murray Riverina NSW 61 67 71 75 69 Murray Riverina Vic 70 75 83 87 79 Mallee Vic 522 659 720 749 670 Lower Murray SA 490 571 638 748 614 Climate wet Murrumbidgee 279 330 352 388 339 Ovens 54 60 64 68 63 Goulburn-Broken 62 70 73 81 73 Campaspe 78 93 98 107 95 Lodon-Avoca 84 108 115 125 110 Wimmera-Avon 152 268 321 344 284 Upper Murray & Kiewa 52 57 60 63 59 Murray Riverina NSW 55 62 66 68 64 Murray Riverina Vic 62 70 73 81 73 Mallee Vic 288 607 654 700 621


Lower Murray SA 399 482 551 644 526

3.4. Estimation of actual crop evapotranspiration, effective rainfall and irrigation requirements

We have estimated actual crop evapotranspiration (ETa), effective rainfall and net irrigation requirements of different crops grown in the southern MDB using a soil water balance simulation model. The model is based on the FAO Irrigation and Drainage Paper 56 (Allen et al. 1998), and is similar to that of the CROPWAT model developed by FAO. The model simulates soil water for upland crops and ponding water depth and soil water for rice at a 10-day time step. The inputs of the model are monthly rainfall and reference crop evapotranspiration (ETo), crop coefficients, rooting depth, crop planting time and growing period, length of growth stages, soil properties such as field capacity, wilting point, saturated moisture content, depletion factor, ponding water depth and percolation rate for rice. The model can simulate both irrigated and rainfed crops. The outputs of the model are ETa, potential crop evapotranspiration at well-watered condition, irrigation requirement (for irrigated crops), and effective rainfall during the cropping period. The model has been used previously to estimate ETa and irrigation water requirements for a range of crops grown in the Murray-Darling Basin and the Mekong River Basin in Southeast Asia (Mainuddin et al., 2007; Qureshi et al., 2007; Mainuddin and Kirby, 2009). In the simulation model, crop coefficients, growth stages, rooting depths and depletion factors were taken from Allen et al. (1998). Crop planting time and growing periods were mostly based on the Australian Field Crops by Lovett and Lazzenby (1987). We ran the model with a range of soil types which exist in the basin and used average values in the analysis. We have used spatial average (based on small sub-catchment) historical (1885 – 2006) rainfall and potential evapotranspiration (PET) data available from the Murray-Darling Basin Sustainable Yield (MDBSY) study (Chiew et al. 2008). The source of the 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 0.05° x 0.05° (~ 5 km x 5 km) grid cells interpolated from point measurements made by the Australian Bureau of Meteorology (Chiew et al. 2008). Rainfall and PET data for future climate scenario were also taken from the data used in the MDBSY study. For the MDBSY study, the future climate scenario is used to assess the range of possible climate conditions around the year 2030. Three global warming scenarios for ~2030 relative to ~1990 are used: high, medium and low. These three scenarios are inferred from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4) (IPCC, 2007) and the latest climate change projections for Australia (CSIRO and Australian Bureau of Meteorology, 2007). Forty-five future climate variants, each with 112 years of daily climate sequences for 0.05° x 0.05° grid cells across the Murray-Darling Basin (MDB), are used for the rainfall-runoff modelling in the MDBSY. The future climate variants come from scaling the 1895 to 2006 climate data to represent the climate around 2030, based on analyses of 15 global climate models and three global warming scenarios. More details about this can be found in Chiew et al. (2008). Estimated maximum crop water requirements or ET (in millimetres) for eight major agricultural activities are presented in Table 14. Estimated expected mean crop water requirements of these activities in four scenarios are given in Table 15, Table 16, Table 17 and Table 18. Higher concentration of CO2 may lead to enhanced plant


growth through a range of effects (Drake, Gonzalez-Meler, and Long 1997) and could affect crop yield in different climate scenarios. However, the CO2 impact is expected to be minimal for the crops grown in catchments of the MDB and there is lack of information about these impacts for all the crops considered in the analysis. Therefore, an average yield of each crop was sourced from various agricultural departments and agencies of NSW and Vic and SA and industry reports. These values were adjusted after cross checking with farmers, agronomists and irrigation scientists (Dan Armstrong, DPI Vic, pers. Comm. Ikram, Leeton). The revised yields of the individual activities used in the analysis are also shown in Table 14.

Table 14 Estimated SMDB maximum crop water requirement (millimetres) and revised average crop yields (tonnes or litres/ha) of major agricultural activities


Citrus fruits

Deciduous fruits

Stone fruits Grapes

ET (mm) 449 1214 1237 674 1176 1007 1007 673 Yield (t/ha) 6 10 21 24 29 2.2 18 20

Table 15 Base case expected mean crop water requirements (ET) in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes

Murrum 389 1071 1107 591 1047 894 894 594 Ovens 354 1001 1021 590 963 827 827 552 GB 353 990 1014 576 957 820 820 547 Campas 351 981 1006 559 950 814 814 543 LodAvo 369 1027 1057 560 1000 856 856 569 WimAvon 370 1017 1052 553 995 849 849 564 UmurrayK 346 966 988 587 931 798 798 533 MRivNSW 390 1086 1119 577 1059 906 906 602 MRivVic 385 1073 1105 572 1046 895 895 595 MalleeVic 396 1058 1108 556 1048 890 890 587 LMurraySA 410 1080 1137 560 1076 910 910 599

Table 16 Climate dry expected mean crop water requirements (ET) in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes


Table 17 Climate medium expected mean crop water requirements (ET) in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes



Table 18 Climate wet expected mean crop water requirements (ET) in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes


Estimated expected mean crop effective rainfall for the major agricultural activities across the catchments for the four scenarios is given in Table 19, Table 20, Table 21 and Table 22.

Table 19 Base case expected crop effective rainfall in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes

Murrum 201 248 456 165 448 297 297 245Ovens 284 390 593 246 575 414 414 323GB 251 310 531 203 518 360 360 286Campas 212 241 448 156 438 290 290 234LodAvo 173 180 371 112 365 224 224 186WimAvon 170 163 354 98 348 207 207 172UmurrayK 285 437 634 280 614 454 454 352MRivNSW 156 177 355 109 350 216 216 179MRivVic 155 174 351 106 346 212 212 176MalleeVic 137 129 293 71 290 163 163 136LMurraySA 124 122 273 66 271 151 151 128

Table 20 Climate dry expected crop effective rainfall in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes

Murrum 172 196 388 124 383 240 240 202Ovens 257 323 550 216 536 378 378 298GB 219 254 480 171 470 320 320 255


Campas 183 195 396 125 388 248 248 197LodAvo 141 154 330 93 326 199 199 157WimAvon 146 122 296 64 292 158 158 133UmurrayK 262 329 556 214 542 377 377 304MRivNSW 132 132 292 72 289 162 162 138MRivVic 134 130 291 69 287 160 160 137MalleeVic 111 98 239 46 238 121 121 103LMurraySA 100 94 223 44 223 112 112 96

Table 21 Climate medium expected crop effective rainfall in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes


Table 22 Climate wet expected crop effective rainfall in milimeters


Citrus fruits

Deciduous fruits

Stone fruits Grapes


3.5. Economic information The key economic data are commodity and input prices and costs of investment and production. To deal with temporal variation in price, historical prices of individual commodities were obtained from ABARE and ABS (ABARE, 2007; ABS, 2006) and other publications as well as from state agricultural departments. These data were analysed to examine whether there were historical trends. Since these nominal data reflected inflationary factors, they were deflated to get real prices and the average prices of individual commodities were used in the analysis. These prices are shown in Table 23. The cost of irrigation application systems and crop or agricultural activity establishment costs as well as production (or variable) costs were also obtained from different sources including industry reports, agriculture departments and after cross checking with local farmers were adjusted. The capital costs were annualised


assuming appropriate life and using a 5% discount rate. These costs are also shown in Table 23. Irrigators of the five horticulture crops (including citrus fruits, stone fruits, deciduous fruits, grapes and vegetables) generally rely on more reliable high security water entitlements. In contrast, the irrigators of the annual activities (i.e. rice, cereals and dairy) generally rely on general security water entitlements or purchase water in a temporary market and incur cost based on the units of water usage. In the current analysis no distinction is made between the low security and high security water entitlements. For simplicity, an average water pumping charge of $20/ML is used for each activity. When inter-regional water trading takes place, the irrigators who sell water get a price which increases their revenue while those who purchase water pay the price, increase their production with increase in their irrigation cost. Average land price is about $1,000/ha and with a perpetual right using 5% discount rate is an annualised value of $50/ha. This is kept constant for both the annual and perennial activities and used in the analysis.

Table 23 Economic parameters and their values for major agricultural activities

($/ha)


Citrus fruits

Deciduous fruits

Stone fruits Grapes

Mean

product

price ($/ha

or $/l) 202 341 320 300 610 6000 900 711

Capital

costs

($/ha/year) 160 241 472 659 1292 730 1477 1609

Variable

costs

($/ha/year) 234 990 4400 4180 8042 8403 6285 6018

4. MODEL CALIBRATION As mentioned above, we used a PMP approach to calibrate the model. The PMP approach recovered the missing information from the observed activity levels and by specifying a non-linear objective function the model exactly produced the observed behaviour of farmers. The 2005/06 year was considered a suitable base year for calibration because irrigation allocations were close to historical averages. Therefore we calibrated the model simulated land and water use to the irrigated land use observed in 2005/06 and the water available for diversion in the Base Case climate scenario for the high allocation state of nature. We used constant effective rainfall and average prices of individual commodities for all the catchments. As shown in Table 24, simulated irrigated land and water use


calibrates well with the ABS 2005-06 land use and the water available for diversion in the Base Case, and demonstrates the PMP model’s predictive capability to assess impact of climate change scenarios and water policies. Murrumbidgee, Goulbern-Broken and Murray-NSW have highest irrigated area respectively. These three catchments are also the highest irrigation water users across the MDB. Dairy, cereal and rice have highest irrigated land. Dairy is also highest irrigation water user followed by rice and cereals.

Table 24 Catchment-wise model simulated land and water use versus ABS 2005-06 land and Base Case water diversion

Model

simulated area ABS 2005-06 observed area

Model simulated water

CSIRO base case high state

water Murrum 288 298 1986 1986 Ovens 6 6 27 29 GB 160 160 977 1007 Campas 26 26 192 195 LodAvo 115 115 923 923 WimAvon 17 18 132 132 UmurrayK 20 20 87 95 MRivNSW 131 134 1236 1236 MRivVic 38 38 379 379 MalleeVic 32 36 249 249 LMurraySA 58 62 479 479 The model allocated land also matches with the observed irrigated land for each crop in the SMDB (shown in Table 25). Pasture for dairy production has the highest irrigated land area followed by cereals, rice and grapes, respectively. Pasture for dairy is also the highest irrigation water use activity followed by rice, cereals and grapes, respectively.

Table 25 Crop-wise model simulated land and water use versus ABS 2005-06 land and Base Case water diversion

Model simulated

area (1000 ha)

ABS 2005-06 observed

area (1000 ha)

Model simulated water (GL)

CSIRO base case high state

water (GL) Cereals 181 181 468 454 Rice 80 92 1111 1107 Dairy 490 498 4132 4305 Vegetables 19 20 122 107 Citrus fruits 15 15 149 135 Decidious fruits 8 8 85 64 Stone fruits 24 24 195 139 Grapes 76 76 404 398

4.1. Irrigated areas and gross values comparison In addition to model calibration against the base case 2005-06 land use, it was also desirable to compare the model estimated gross values of agricultural production in the base case with the existing literature. However, there was no other study which


has carried out analysis at a scale what is used in the current study. ABS (2005-06) has estimated irrigated areas and associated gross values at NRM region and statistical division level. As shown in Figure 5, the current study’s catchments though close to NRM regions but do not exactly match with them due to overlaps. Some NRM catchments may have more than one study/modelled catchments. For example, Mallee catchment has areas of Mallee (Vic) and Loddon Avoca region. In some cases, NRM catchment is smaller than our region/s. For example, Murrumbidgee region covers both Murrumbidgee NRM region as well as a portion of the Murray catchment. Nevertheless, in table 6 we have provided irrigated areas and associated gross values of NRM catchments (which are part of the southern MDB) and estimated gross values of the model’s base case scenario.

Figure 5 Difference between the NRM regions and the current study model catchments

Table 26 shows ABS 2005-06 irrigated areas and gross values across catchments. Total irrigated areas in NSW, Vic and SA are 588,000 ha, 531,000 ha and 71,000 ha in the southern MDB. In contrast total optimal irrigated areas estimated by the model in the base case scenario shown in Table 27 for NSW, Vic and SA are 420,000 ha (47%), 415,000 ha (46%) and 58,000 ha (7%), respectively. According to ABS, total area across the catchments is 1,190,000 ha while total model estimated area is 893,000 ha, which is about 85% of the ABS area. A comparison of ABS gross (local) values with the model estimated gross values is also shown in Table 26 and Table 27. ABS reported gross values for NSW, Vic and SA are $2383 million, $4271 million and $1,271 million, respectively. While the model estimated gross values for NSW, Vic and SA are $3,221 million, $2,965 million and $479 million, respectively. Total ABS gross value is $7,925 million in the whole southern MDB while the model estimated gross value is $6,665 million which is about 84% of the ABS estimates. About one per cent reduction in estimated gross value of the SMDB is due to less area being irrigated in the base case compared to the ABS


irrigated area. At a state level, the irrigated area reduction in NSW, Vic and SA is by 29%, 22% and 18%, respectively. At a state level, gross value of NSW is 35% higher than the ABS estimates while gross values of Vic and SA are 31% and 62% lower than the ABS estimated values, respectively. In the case of Vic, the reason for the reduction in gross value is due to reduction in irrigated area though the irrigated area reduction is lower than the gross value reduction. This means some irrigated area which was part of the NRM regions was not part of our study catchments. Another reason could be that ABS estimates are of total agricultural value rather than only irrigated land values. This could be the case, especially in SA where irrigated area reduction is 17% while reduction in gross value is 62% of the ABS estimates for SA MDB region. This comparison though does not provide the true picture of the discrepancy. Nevertheless, is a useful indication that the model behaves reasonably well, especially when impacts of water reduction across the whole basin is desirable.2

Table 26 ABS 2005-06 irrigated areas and gross values across catchments

NRM region ABS 2005-06 irrigated

area (1000 ha)

Proportion of total state

irrigated area (%)

Gross value (million)

Proportion of total state gross

value (%) Murrumbidgee 281 0.48 1403 0.59 Murray NSW 307 0.52 981 0.41 Total NSW 588 1.00 2383 1.00 GB 223 0.42 1320 0.31 Mallee 44 0.08 830 0.19 North Central 237 0.45 1216 0.28 North East 16 0.03 321 0.08 Wimmera 11 0.02 585 0.14 Total Vic 531 1.00 4271 1.00 SA MDB 71 1 1271 1 Total SMDB 1190 7925

Table 27 Model estimated expected irrigated areas and gross values across catchments

Model catchment

Model estimated base case

irrigated area (1000 ha)


irrigated area (%) Gross Value

(million)


gross value (%)

Murrum 288 0.65 1986 0.68

MRivNSW 131 0.35 1236 0.32

Total NSW 420 1 3221 1

Ovens 6 0.01 27 0.02

GB 160 0.28 977 0.37

Campas 26 0.05 192 0.06

LodAvo 115 0.2 923 0.25

WimAvon 17 0.03 132 0.04

2 Ideally, it would be useful to examine ABS commodity prices and yields data for different catchments across the SMDB but time does not allow for such a spatial analysis and is beyond the scope of the current study.


UMurrayK 20 0.04 87 0.04

MRivVic 38 0.33 379 0.09

MalleeVic 32 0.07 249 0.13

Total Vic 415 1 2965 1

LMurraySA 58 1 479 1

Total SMDB 893 6665

The above presented results of the land and water allocations across catchments and their comparison with the ABS estimated values, local observation and/or anecdotal evidence clearly indicate that actual response under different scenarios and policy mechanisms could be reproduced with the model reasonably accurately. These results indicate that the model is calibrated reasonably well and can predict the economic impact of different climate change scenarios and water policies. The calibrated model was later used to assess the impacts of the climate change scenarios and reduced water allocations (mentioned above). The economic implications of the climate change and adaptation options including water markets and trading at the SMDB level are presented in Qureshi et al. (paper in review) while the implication on individual sectors and in individual catchments is presented in Qureshi and Whitten (paper in preparation).While Qureshi and Hanjra (paper in review) assessed the impacts of climate change on irrigated areas and crop yields of the major agricultural activities in climate dry scenario very low state of nature and compared them with the irrigated areas and crop yields in the climate base case expected mean state.

5. SUMMARY AND CONCLUSIONS This report documents the positive mathematical programming modelling framework and data collection procedures used to assess alternative climate change scenarios and implications of various policy mechanisms. The PMP modelling framework or approach has linked key hydrologic, agronomic and economic components critical for assessing impacts of various climate change scenarios on effective rainfall, irrigation water requirements and water allocations. The integrated approach has assessed the impact on productivity and profitability of various agricultural activities in different catchments/regions of the southern Murray-Darling Basin. The model accounts for both annual and perennial activities. In the crop water yield functions, deficit irrigation is allowed subject to minimum crop water requirement thresholds. The crop yield function accounts for the total quantity of water required for the crop, effective rainfall, irrigation system efficiency and net irrigation water. The model allows for the deficit irrigation above the crop threshold. The impact of water salinity on crop productivity is also linked for leaching salt purpose and to maintain crop productivity. An increase in water salinity requires greater evapotranspiration and as a result greater crop water use (ML/ha). Later in the analysis, the maximum water use constraint is relaxed and the decision to buy and sell annual water allocations among regions is allowed. The model is run with and without the option of water trade so as to evaluate this policy option, especially as water availability changes. This model is used to estimate impacts on agricultural profitability and implications of different climate change and policy scenarios on eight major agricultural activities in different catchments of the southern MDB.


Land use data is obtained using ABS 2006 land use data using GIS. The data at SLA level were disaggregated in order to estimate the agricultural activity occurring within the reporting catchments using BRS land use grid. The NRM level ABS data was used to calculate irrigated area for each agricultural activity. Water diversion data at different reaches for different sub-catchments for four climate change scenarios were obtained from CSIRO Sustainable Yield data files. These catchments were aligned with NRM regions for the scenarios and the policy analysis. The Murray catchment was split into sub-catchments to represent its area in each of the three states. Despite the alignment effort, there was a mismatch between irrigated land use and simulated water diverted in most catchments due to an under/overestimation of land use or water diversion data calculated for each catchment or due to not accounting for groundwater use. We aligned land use data with regional base case (or initial) water allocations data water by accounting for expected mean crop evapotranspiration (ETa), expected mean effective rainfall, individual on-farm crop irrigation efficiency and estimating net irrigation requirements, and redistributing and allocating water to each region by multiplying proportion of total water use by total water available for irrigation in each of the four climate scenarios. The water allocated data for each catchment was distributed to each agricultural activity after accounting for the 2006 land use data of individual agricultural activities in the respective catchments, crop water requirements, effective rainfall, on-farm irrigate efficiency and net irrigation requirements. MSM-BIGMOD was used to estimate the impact of climate change scenarios on salinity by maintaining a salt balance. Total dissolved salt is estimated using a multiplication factor and net salinity in diverted water for each catchment was estimated. The salinity data was estimated for 25 years (i.e. 1975 to 2000) at different reaches across the Murray catchments. To estimate salinity data for Murrumbidgee catchment, estimate by a previous study along with the average salinity concentration across Murray catchments were used to reflect salinity of Murrumbidgee. The PMP model was used to calibrate and compare the predicted land and crop water use with ABS estimated irrigated land use for 2005-06. The water supply was calibrated to predict CSIRO climate base case his state of nature water allocations by crop and region. The results indicate that by calibrating water allocation to actual land use and estimating per hectare water use accounting for effective rainfall and on-farm irrigation efficiency, actual land use response to 2005-06 conditions could be reproduced with reasonable accuracy. Estimated irrigated areas and gross values of agricultural production in the base case were also compared with the ABS 2005-06 irrigated areas and associated gross values for NRM regions. The comparison indicates reasonable representation of irrigated areas and gross values in the southern MDB. In summary, the results indicate that the model is calibrated reasonably well and is a useful analytical tool in assessing various climate change scenarios and water policy mechanisms.

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