CSIRO LAND AND WATER

Optimisation of water quality
for sustainable irrigation withcoal seam gas produced water

Dirk Mallants, Jirka Simunek, Saeed Torkzaban
December 2015

CSIRO Land and Water

Citation
Mallants, D., Šimůnek J., Torkzaban, S. 2015. Optimisation of water quality for sustainable irrigation withcoal seam gas produced water. CSIRO Land and Water, Canberra, 53 pp.

Copyright and disclaimer© 2015 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication
covered by copyright may be reproduced or copied in any form or by any means except with the written
permission of CSIRO.

Important disclaimer
CSIRO advises that the information contained in this publication comprises general statements based on
scientific research. The reader is advised and needs to be aware that such information may be incomplete
or unable to be used in any specific situation. No reliance or actions must therefore be made on that
information without seeking prior expert professional, scientific and technical advice. To the extent
permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for
any consequences, including but not limited to all losses, damages, costs, expenses and any othercompensation, arising directly or indirectly from using this publication (in part or in whole) and any
information or material contained in it.

iiContents
Optimisation of water quality for sustainable irrigation with coal seam gas produced wateriContents ....................................................................................................................................................... 3
List of Figures ................................................................................................................................................ 5
List of Tables ................................................................................................................................................. 6
Acknowledgments ........................................................................................................................................ 7
Executive summary

8

1

Introduction9

1.1

Beneficial use of produced water for irrigation .............................................................................. 9

1.2

Potential impact on soil health (soil structure, permeability) ...................................................... 11

1.3

Potential impact on vegetation health ......................................................................................... 14

1.4

Scope of the study......................................................................................................................... 17

2

Modelling process

2.1

Conceptual model ......................................................................................................................... 18
2.1.1 Soil hydrological model ....................................................................................................... 18
2.1.2 Soil geochemical model ...................................................................................................... 20

2.2

Numerical model for multi‐component reactive transport modelling in saturated/unsaturated
media ............................................................................................................................................. 22
2.2.1 HYDRUS ............................................................................................................................... 22
2.2.2 UNSATCHEM ....................................................................................................................... 22

2.3

Model implementation ................................................................................................................. 24
2.3.1 Soil hydrological characteristics .......................................................................................... 24
2.3.2 Soil geochemical Model ...................................................................................................... 24
2.3.3 Chemical composition of the irrigation water .................................................................... 26
2.3.4 Vegetation parameters ....................................................................................................... 27
2.3.5 Summary of simulation cases ............................................................................................. 29

3

Detailed description of the simulation cases

3.1

Scenario 1: Base case .................................................................................................................... 31

3.2

Scenario 2: Major ion chemistry ................................................................................................... 34

3.3

Scenario 3: Major ion chemistry including effect of salinity on soil hydraulic properties ............ 38

3.4

Scenario 4: Amended produced water ......................................................................................... 41

3.5

Scenario 5: Treated produced water ............................................................................................ 43

3.6

Summary of soil water balance calculations ................................................................................. 47

18

31

3

4

Conclusions

References

4

49
51

List of Figures
Figure 1.1 Estimated volumes of produced water in Queensland (Source: DNRM 2012). ........................ 10
Figure 2.1 Soil water retention curves for three soil layers (based on parameters from Table 2.1). ........ 19
Figure 2.2 Irrigation, potential transpiration and evaporation rates applied during the simulations. ...... 20
Figure 2.3 Finite element grid with soil layers (left) and location of four observation nodes (right). ...... 24
Figure 2.4 Schematic of the plant water stress response function, α(h), as used by a) Feddes et al. (1978)and b) van Genuchten (1987). The Feddes et al. function was used in this study: for definition ofparameters, see Table 2.8 (h1 = P0, h2 = POpt, h3 = P2L/P2H, h4 = P3). .................................................. 28
Figure 2.5 Plant water stress response function, α(h), for pasture and alfalfa as applied in this study. ... 29
Figure 3.1 Simulated water content at three depths in the soil profile. Only first 100 days are shown.
Arrows show times at which results at observation nodes are outputted for producing profile plots (at30, 60, 90 days)........................................................................................................................................... 31
Figure 3.2 Simulated pressure head at three depths in the soil profile. Only first 100 days are shown. .. 32
Figure 3.3 Simulation of tracer movement through soil. Tracer is applied through the irrigation water. 32
Figure 3.4 Simulated solute concentration following infiltration of solute‐containing irrigation water.
Comparison of models including ET (solid lines), excluding T (transpiration, dashed lines) or excluding ET(evapotranspiration, symbols) (left). Comparison of simulated soil water content for models includingET, excluding T or excluding ET (right)........................................................................................................ 33
Figure 3.5 Cumulative potential and actual surface (infiltration and evaporation) and transpiration fluxand cumulative actual bottom flux for pasture and alfalfa (scenario 1). Cumulative potential and actual
surface flux are identical. ........................................................................................................................... 34
Figure 3.6 Solute tracer concentration profiles for scenario 1 (left), 2 (middle) and scenario 3 (right). ... 35
Figure 3.7 Water content profiles for scenario 2 (left) and scenario 3 (right). .......................................... 35
Figure 3.8 Simulated SAR (left) and electrical conductivity (right) for scenario 2. ANZECC trigger value for
SAR and EC (see Table 1.3). ........................................................................................................................ 36
Figure 3.9 Simulated concentrations of dissolved major ions Ca, Mg, Na, K, and Cl. ANZECC trigger value
for sodium (see Table 1.3). ......................................................................................................................... 37
Figure 3.10 Calcite concentration for scenario 2 (left). Calcite mineral volume after 365 days of irrigation(right). ......................................................................................................................................................... 37
Figure 3.11 Cumulative potential and actual surface (infiltration and evaporation) and transpiration fluxand cumulative actual bottom flux for scenario 2. .................................................................................... 38
Figure 3.12 Simulated SAR (left) and electrical conductivity (right) for scenario 3. .................................. 39
Figure 3.13 Cumulative potential and actual surface (infiltration and evaporation) and transpiration fluxand cumulative actual bottom flux for scenario 3. .................................................................................... 39
Figure 3.14 Root water uptake function for scenario 1 (top left), scenario 2 (top right) and scenario 3(bottom left). Pressure head exceeds the POpt = ‐25 cm threshold after 300 days (bottom right). ......... 40
Figure 3.15 Simulated SAR (left) and electrical conductivity (right) for scenario 4. ANZECC trigger value
for SAR and EC (see Table 1.3).................................................................................................................... 41
Figure 3.16 Cumulative potential and actual surface (infiltration and evaporation) and transpiration fluxand cumulative actual bottom flux for scenario 4, in comparison with scenario 3. .................................. 42
Figure 3.17 Simulated SAR (left) and electrical conductivity (right) for scenario 5. ANZECC trigger value
for SAR (light clay) and EC (0.65 dS/m) (see Table 1.3). ............................................................................. 43
5

Figure 3.18 Simulated concentrations of dissolved major ions Ca, Mg, Na, K, and Cl. .............................. 44
Figure 3.19 Surface species concentration versus soil depth during irrigation cycles. Total adsorbed
concentration for Ca, Mg, Na, and K is constant (279 meq/kg). ................................................................ 45
Figure 3.20 Calculated soil water content and pressure head at 10 cm depth (scenario 5; pasture). Every
five days a 24‐hour irrigation event takes place. POpt = pressure head below which root water uptake is
optimal, and P2H = limiting pressure head below which root water uptake is no longer optimal(according to Figure 2.4)............................................................................................................................. 46

List of Tables
Table 1.1 Estimated water production rates in the main coal seam gas areas in Queensland and New
South Wales. ................................................................................................................................................. 9
Table 1.2 Irrigation trials in Queensland and New‐South Wales using untreated and RO treated produced
water. ......................................................................................................................................................... 11
Table 1.3 Permissible levels of irrigation water quality parameters to preserve soil and plant health. ... 12
Table 1.4 SAR values for produced water for selected locations in Australia. Calculated SAR values basedon reported concentrations for Na+, Ca2+, and Mg2+ expressed in milliequivalents/litre. ......................... 14
Table 1.5 Water quality parameters for Gloucester basin (Tiedeman Dam, NSW) and Surat basin(Woleebee Creek, QLD) produced water. .................................................................................................. 16
Table 2.1 van Genuchten soil hydraulic properties for Vertosols (average across the Cox’s Creak
Catchment area, NSW) (source: Bennett, 2012). ....................................................................................... 19
Table 2.2 Initial concentrations of exchangeable cations (source: Ringrose‐Voase 2004). ....................... 21
Table 2.3 Initial soil solution composition used to calculate initial solution in equilibrium with the cationexchange complex. Data represents composition of rainwater in Wagga‐Wagga (Source: Crosbie et al.
2012). .......................................................................................................................................................... 21
Table 2.4 Chemical species included in the UNSATCHEM major ion module of HYDRUS‐1D. ................... 23
Table 2.5 Defined variable boundary conditions. ...................................................................................... 24
Table 2.6 Gapon Exchange Constants (Source: Jacques et al. 2012). ........................................................ 25
Table 2.7 Composition of irrigation waters. Kenya/rain water = ratio of treated produced water to rainwater composition...................................................................................................................................... 26
Table 2.8 Root water uptake reduction parameters without solute stress (based on Wesseling et al.
1991). .......................................................................................................................................................... 28
Table 2.9 Root water uptake reduction parameters with solute stress for alfalfa. ................................... 29
Table 2.10 Description of scenarios used. .................................................................................................. 30
Table 3.1 Water balance components for a 365 day simulation period. Negative infiltration values means
inflow; negative net drainage values means outflow. ............................................................................... 47
Table 3.2 Chemical mass fluxes (gram/m2) through upper and lower model boundary for a 365 day
simulation period. For upper model boundary negative fluxes are input to the soil; for lower model
boundary negative values refer to fluxes leaving the soil. For each element and each scenario, first row isflux through upper boundary; second row is flux through lower boundary. ............................................. 48

6

Acknowledgments
The authors would like to acknowledge Sarah Bennett (formerly University of NSW) for providing detailed
soil hydraulic property data on the Cox Creak Catchment (NSW). We equally acknowledge the Land andWater Business Unit for funding the strategic appropriation research project “Next generation methods andcapability for multi‐scale cumulative impact and management”. Professor Jirka Šimůnek (University ofCalifornia Riverside) received a travel grant through this project. This report was subject to internal peer
review during its production.

7

Executive summary

Coal seam gas production in Australia generates large volumes of so‐called produced water that contains
elevated concentrations of naturally occurring inorganic and organic compounds, and usually has a highsalinity. Treated coal seam gas water may be utilised for beneficial or alternative uses, including agriculturaluses (irrigation and stock), urban uses (town water supply) and industrial uses (construction andprocessing). For instance, assessment of alternative use for treated coal seam gas water for the Central
Condamine Alluvium (Queensland) indicates the region has capacity to deliver 854 GL, or around 35% of
the historic depletion, by 2050 to irrigators and the Chinchilla Weir water supply scheme. Most coal seamgas operators in Australia are considering some kind of agricultural uses for their coal seam gas water,
including irrigation.
Coal seam gas produced water is generally high in total dissolved solids and has a high salinity which may
affect soil and plant health if used untreated for irrigation. For example, waters with a high sodium
absorption ratio (SAR), if used for irrigation, will result in salt accumulation in the soil profile, potentiallykilling off plant species that are not salt tolerant. Use of irrigation water with high SAR values may further
result in dispersion of the soil clay minerals and, once dry, soils may become structureless destroying
natural particle aggregation. This degrades soil properties by making the soil more dispersible and erodible,
restricting water entry and reducing hydraulic conductivity.
Prior to any use of such produced water for irrigation purposes, an in‐depth assessment has to beundertaken of the potential long‐term effects of dissolved ions on soil and plant health. We here simulate
coupled processes of variably saturated water flow, plant water uptake and coupled transport of multiple
major ions in soils irrigated with produced water featuring different water qualities. By coupling major ion
soil chemistry to unsaturated flow and plant water uptake, and by explicitly incorporating effects of salt
concentrations on soil hydraulic properties and on root water uptake (so‐called salinity stress), critical soil
processes required for salinity risk assessment associated with coal seam gas produced water are
effectively included in our analysis. Simulations with different irrigation water qualities provided detailed
results regarding chemical indicators of soil and plant health, i.e. SAR, EC and sodium concentrations. By
comparing such indicators in the soil profile with permissible ANZECC values, an assessment was made of
the risk to soil and plant health. We also evaluated the effect of high salt concentrations in the soil profileon plant salinity stress, a condition which reduces the capacity for plants to uptake water causing yield
reduction. Finally, the simulations also allowed to test if soil hydraulic properties, in particular the hydraulicconductivity, are negatively impacted by high salt concentrations, especially by the accumulation of themonovalent cations sodium and potassium.
Results from a base case scenario indicated that use of untreated irrigation water would cause SAR and EC
values to significantly exceed the ANZECC guide values. The purpose of this what‐if scenario was to provide
an upper bound impact against which alternative scenarios with improved irrigation water quality would betested. While it is highly unlikely that such highly saline water will be used for long‐term irrigation, thesimulations do provide useful insights in the type of coupled processes that might occur, and what thepotential impacts are on soil and plant health. For example, by using highly saline irrigation water, wedemonstrated that accumulation of monovalent cations occurs in the soil, resulting in a decrease in
hydraulic conductivity. This in turn caused water stagnation, leading to plant water stress and reduced
transpiration. Calcite precipitation in the top soil layer made the available pore space decrease by 3%.
In case amended irrigation water was used (a mixture of produced and surface water), the calculated soil
SAR values were much lower and generally acceptable for sandy to sandy‐loam soil. Permissible SAR values
for light clay were still exceeded for most of the soil profile. Calcite formation does still occur, but only inthe top of the soil profile and with a negligible impact on soil porosity. The use of treated produced water
yielded an acceptable salinity profile not exceeding guide values for SAR and EC; plant water stress waslimited as there was no additional salinity stress associated with the low level of salts.
81 Introduction

1.1 Beneficial use of produced water for irrigation
Production of coal seam gas requires de‐pressurisation of the coal seam layers by extracting groundwater.
Reducing the hydrostatic pressure on the gas in the pores allows gas to flow from high pressure areas tothe area with the lowest pressure (i.e. the production well). Coal layers generally have a sufficiently highpermeability (i.e. ability to transmit fluids, including gas) to facilitate water and gas flow without the needfor improving the coal permeability by means of hydraulic fracturing. In the least permeable coal measures,
coal seam gas operations require the use of hydraulic fracturing as a means to increase coal permeability
and connectivity between naturally occurring fractures in coal beds. In Australia, coal measures in the
Bowen Basin (Queensland) have much lower permeability than, for example, the Surat Basin (Queensland)
and are therefore likely to require increased levels of hydraulic fracturing.
A summary of the water extraction rates in the main coal seam gas areas in Australia is provided in Table
1.1. It is acknowledged that the forecasts of water production will vary over time and geographically (QWC
2012). For example, Figure 1.1 shows estimates of expected volumes of produced water per development
area and the whole‐of‐industry forecast in Queensland. Much of the variation is due to different
assumptions about the size and rate of coal seam gas industry development over the 50‐year time framethat is generally assumed for forecasting water production (DNRM 2012). By 2050, most coal seam gas
areas will have reached the end of their production stage.
Table 1.1 Estimated water production rates in the main coal seam gas areas in Queensland and New South Wales.
Water production rate

Area

Comment

Source0.2 – 0.7 GL/year

Gloucester (New South
Wales)

Average total water extraction
for an estimated resource life of
14 years

RPS 2011

0.028 GL/year

Clarence‐Moreton (NewSouth Wales)

Average total water extraction
assuming 697 ML over a 25‐year
periodRPS 2011

95 GL/year

Surat Basin (Queensland)

Average total water extraction
over the life of the industry
based on a regional model
predictions

QWC 2012

75 to 98 GL/year

Surat Basin (Queensland)

Average total water extraction
over the life of the industry
based on predictions fromindustry and DNRM (2012)

QWC 2012

140 GL/ySurat Basin (Queensland)

Peak water production ratearound the year 2026

QWC 2012

140 to 281 GL/yQueensland’s Surat andsouthern Surat Basins.

Peak water production rates

DNRM 2012;
CWiMi 2008

150 to 180 GL/ySurat Basin (Queensland)

Peak water production rate
based on different scenarios forcoal seam gas production andassuming operations ceased
either in year 2040 or 2060

DNRM 20129

Figure 1.1 Estimated volumes of produced water in Queensland (Source: DNRM 2012).

In Australia, like in the US, there is increasing use of treated and untreated produced water from the coalseam gas industry for agriculture and forestry (Table 1.2). The Gloucester Coal Seam Gas Project in NSW
under the operation of Lucas Energy has used low salinity produced water for dust suppression andreceived approval to irrigate using this water (AGL 2008). Produced water from some of the Stratford Pilot
wells with EC of about 3000 S/cm was used for pasture irrigation at the Tiedman property, but it was notseen as a viable long‐term option without improving the quality of this water (AGL 2008; Lucas 2008). AGL
Energy Ltd reports pasture irrigation trials have been undertaken as part of the Gloucester Gas Project in
NSW in FY2010, but not in FY2011 as the irrigation approval lapsed (AGL 2011). In June 2011, AGL
commissioned a review of environmental factors for the Gloucester irrigation project (Tiedman), which
recommended blending produced water with fresh river water as a contingency measure if soil salinity
increased by more than 50% in the root zone (Parson Brinkerhoff 2011). The AGL (2012) report describes
the proposal to irrigate pasture, using up to 70 ML of produced water blended with water from the Avon
River (at a ratio of about 3 parts river water: 1 part produced water) over a maximum area of 40 ha over athree‐year period at the Tiedman site. Table 1.5 provides typical water quality data for untreated surface
water‐amended produced water (calculated based on a 3:1 ratio of surface water:groundwater).
Assessment of alternative use for treated coal seam gas water for the Central Condamine Alluvium(Queensland) indicates the region has capacity to deliver 854 GL, or around 35% of the historic depletion,by 2050 to irrigators and the Chinchilla Weir water supply scheme (DNRM 2013).
Other irrigation projects in Australia where produced water is used include the large‐scale forestry bySantos in Queensland’s Bowen Basin and the legume plantation by Origin Energy (RPS 2011). In the Bowen
Basin, up to 8 ML/day of reverse osmosis (RO) treated produced water was used to irrigate 234 ha pasture
crop and an 800,000 tree timber plantation in the Fairview Project Area (Santos 2009; Santos 2010). Santos
also trialled hardwood drip‐irrigation in the Fairview Field where a 2000 ha plantation of eucalyptus trees
were irrigated with approximately 14 ML of “amended” water per day (Coordinator‐General 2010). The
amendment process did not involve salt removal, but rather reducing the pH level and adding calcium andmagnesium to balance excess sodium (i.e. to reduce the SAR (Coordinator‐General 2010)). In Spring Gully

10

Field, Queensland, Origin Energy irrigates 300 ha of oilseed‐bearing legume tree (Pongamia pinnata)
plantation with RO treated produced water (Parsons 2010).
Table 1.2 Irrigation trials in Queensland and New‐South Wales using untreated and RO treated produced water.

Geographic
area

Specifics of irrigation trials

Reference

NSW ‐Gloucester

Stratford project: untreated produced water from trial
wells; EC = 3000 S/cm; pasture; 0.02 ML/day

AGL 2008;
Lucas 2008

Tiedman project: blending river water with produced
water at a ratio of 3:1 (river:produced); EC between
1500 and 200 S/cm; salt tolerant crops (sorghum,
lucerne, cereals); 40 ha; 70 ML blended water over 3
years

ParsonBrinkerhoff(2011); AGL(2012)

Fairview project: 8 ML/day RO water; 234 ha forage
crops and timber trees

Santos 2009;
Santos 2010

2000 ha; 14 ML of “amended” water per day;eucalyptus plantation (Chinchilla White Gum)

Coordinator‐
General 2010

Spring Gully Field; RO treated produced water; 300 haof oilseed‐bearing legume tree;

Parsons 2010

QLD – Bowen
Basin

In a recent report to the Office of NSW Chief Scientist and Engineer the “risk of salinization of soil ordestruction of flora and stream habitats if untreated or partially treated produced water is used for
irrigation or surface dispersal” was highlighted as one of several community concerns over the recovery ofcoal seam gas in New South Wales (Fell 2014). The potential impacts on soil and plant health from use of
irrigation water with a high SAR1 are discussed in section 1.2 and section 1.3.

1.2 Potential impact on soil health (soil structure, permeability)
Coal seam gas produced water is generally high in total dissolved solids and may contain organic andinorganic compounds that may affect soil and plant health if used untreated for irrigation. For example,
coal seam gas waters are typically high in sodium but relatively low in calcium and magnesium, andtherefore yield a high sodium absorption ratio (SAR). If such produced water is used for irrigation, it leads
to sodium displacing calcium and magnesium in the soil and salt accumulation in the soil profile. This willcause a decrease in the ability of the soil to form stable aggregates and results in a loss of soil structure.
This in turn will result in a decrease in infiltration leading to problems with crop production.
Prior to any use of such produced water for irrigation purposes, an in‐depth assessment has to beundertaken of the long‐term effects of dissolved ions on soil and plant health. In an accompanying study
the effect of leaching of alkaline produced water through soil on the migration of inorganic compounds
naturally present in soil is discussed (Mallants et al. 2015).
The minimum quality of irrigation water is defined by several national and state regulations. The Australianand New Zealand Guidelines for Fresh and Marine Water Quality (ANZECC 2000) provide rationale andbackground information on quality of irrigation and general water uses, stock drinking water, aquaculture
and human consumers of aquatic foods. For instance, for different soil types, permissible levels of chloride,sodium and SAR have been defined (Table 1.3). The permissible SAR levels decrease with increasing clay
content: high SAR values mean a high proportion of sodium in soil. This results in dispersion of the clay

1

Sodium adsorption ratio (SAR) = [Na+] / {([Ca2+] + [Mg2+]) / 2}1/2 where [Na+], [Ca2+], and [Mg2+] are in milliequivalents/litre
11minerals and, once dry, soils may become dense, cloddy and structureless, destroying natural particle
aggregation. This degrades soil properties by making the soil more dispersible and erodible, restricting
water entry and reducing hydraulic conductivity.
Although no trigger values have been defined for bicarbonate, elevated levels in irrigation waters canadversely affect soil structure. If the solubility of calcium carbonate is exceeded, precipitation of calcium
carbonate in soil can occur. This will increase the soil SAR (when calcium carbonate precipitates,
concentration of exchangeable calcium decreases) and may lead to structural issues.
Plant salt tolerances are provided in ANZECC 2000. Concentrations of metals and metalloids in irrigationwater should be less than the recommended trigger values.
Table 1.3 Permissible levels of irrigation water quality parameters to preserve soil and plant health.

Parameter

Maximum (DEPH 2014)

ANZECC 2000

Electrical conductivity(µS/cm)@< 950#< 650

SAR< 6# for heavy soils; < 12#
for light soils>20a; 20‐8b; 13‐6c; 11‐5d

Bicarbonate (mg/L)

100&

No trigger value recommended

Fluoride (mg/L)

2pH

6 – 8.51#

6‐9

Chloride (mg/L)< 175e; 175‐350f; 350 – 700g; >700h

Sodium (mg/L)< 115e; 115‐230f; 230‐460g;>460h

Heavy metals andmetalloids (trigger
values in mg/L; short‐term use up to 20
years)Al (20); As (2); Cd (0.05); Cr (1); Co (0.1); Cu (5); Fe (10); Li(2.5); Pb (5); Mn (10); Hg (0.002); Mb (0.05); Ni (2); Zn (5)#95th percentile over a one‐year period; &DERM 2010; @1000 µS/cm = 1 dS/m; a: sand‐sandy loam; b: loam‐
silty loam; c: clay loam; d: light clay; e: sensitive crop; f: moderately sensitive crop; g: moderately tolerant
crop; h: tolerant crop

Produced water is generally unsuitable for direct surface discharge or irrigation without any treatment oramendment (Stearns et al. 2005; Beletse et al. 2008; Nghiem et al. 2011). While sodium ions cause soil
particles to disperse, particularly if the soils contain montmorillonite clays, most ions increase theaggregation of soil particles (Nghiem et al. 2011). Irrigation water with a high SAR can lead to a decrease ininfiltration and deterioration of the soil structure. SAR values greater than 13 pose a risk to the soil
ecosystem (Stearns et al. 2005), and even SAR values between 5 and 8 have been shown to cause
irreversible plugging of soil pores and swelling (Mace and Amrhein 2001). Traditional treatments tomitigate saline‐sodic irrigation water can be used, for example the addition of gypsum and elemental sulfur(S) (Vance et al. 2008; Šimůnek et al. 2006).
Studies have been undertaken to assess the feasibility of using sodium‐rich produced water for salt‐tolerant
crop production (Johnston et al. 2008; Vance et al. 2008; Beletse et al. 2008). Vance et al. (2008) usedsaline‐sodic coal seam gas produced water with a SAR between 17 and 57 for irrigating grasslands andhayfields in the Powder River Basin, Wyoming. For the use of produced water to be effective, the method12further required applications of gypsum and elemental S to provide calcium ions and an acidified soil
environment to promote calcite dissolution. Water produced during the depressurisation phase of coalseam gas mining in Waterberg district, South Africa, is highly saline and dominated by sodium bicarbonate.
With careful management, Beletse et al. (2008) determined that coal seam gas irrigation water with SAR
values of 85 could be used to grow certain crops, but additions of gypsum and organic matter to the soil
were necessary to counteract infiltration problems that arose due to the excessive sodium that had
accumulated in the soil.
In Australia, the Queensland Department of Environment and Heritage Protection (DEHP) recommends thatparties proposing to use produced water for irrigation purposes should seek professional advice tounderstand and manage site‐specific soil‐water interaction, agronomic, monitoring and irrigation
management issues (DEHP 2014). Furthermore, among the minimum standards for using produced water
for irrigation purposes, the guidelines indicate that the electrical conductivity (EC) shall be less than 950
S/cm or 0.95 dS/m (95th percentile over a one‐year period), and the SAR shall be less than 6 for heavy soils
and less than 12 for light soils (95th percentile over a one‐year period) (DEHP 2014).
There are broader requirements in Queensland regarding management of salinity risks that are relevant touse of produced water. In particular the requirement under Schedule B – Basin Salinity Management toPart 12 Schedule 1 of the Water Act 2007 (Commonwealth) to not permit the undertaking of any actionthat may have a significant effect on stream salinity in the Murray‐Darling Basin, which supports
assessment of salinity risk out to 100 years (Biggs et al. 2012).
To support the sustainable management of soil and landscapes under irrigated agriculture, Biggs et al.
(2012) have developed a framework to assess the salinity risk associated with the use of coal seam gas
water for irrigation in the Queensland Murray‐Darling Basin (QMDB). Biggs et al. (2012) identified thedescription of the unsaturated zone as the key knowledge and data gap when conducting salinity risk
assessments. In particular, unsaturated zone‐related gaps include:











Soil and regolith (unsaturated zone) properties, including i) studies of critical soil attributes(porosity, initial soil water content, substrate permeability/conductivity) undertaken to depths
below the “root zone”, ii) defining the root zone for different crops, and iii) quantifying lateral flow
processes.
Better rainfall salinity data is required to improve the input side of the salt balance equation.
A need for a more strategic approach to install gauging stations for various monitoring purposesthat will provide long‐term benefits to the assessment of cumulative risk in catchments
Considerable research is needed to better define critical thresholds for agronomic and degradation
purposes (e.g. acceptable root zone salinity) across landscapes
Process understanding: understanding of the fate of excess water (lateral flow or drainage), deeper
soil/regolith processes and properties
Further detailed assessment is needed to delineate emerging salt expressions and to investigate
existing salt expressions
Detailed shallow groundwater monitoring is required to allow detection of drainage fluxes
Capture past and current land management practices as these affect historic and current deep
drainage/recharge rates
Drainage rates from modelling need to be tested against measured deep drainage data to provide
reliable long‐term estimates of deep drainage.

The current study endeavours to address at least those knowledge gaps that relate to better process
understanding (e.g. critical soil properties, critical thresholds) using the unique features of the HYDRUS‐1D
simulator that are relevant for salinity risk assessment (Jacques et al. 2013; Šimůnek et al. 2006, 2009).
SAR values based on cation concentration data have been compiled from a selection of CSG company
reports (Table 1.4). In the Fairview project area (Surat and Bowen Basins), the coal seam gas target is theBandanna Formation, which is not considered to be a significant regional aquifer (Santos 2010). The coal13

seams within the Bandanna Formation are recognised as fractured aquifers based on the well‐cleated coal,and the seams are separated by siltstone and sandstone that restrict vertical leakage between the seams
and the overlying and underlying units (Santos 2010). In the Woleebee Creek block, the produced water isfrom the Walloon Coal Measures, which are heterogeneous formations with coal seams dispersed in an
aquitard (QGC 2011). Interconnectivity between the Walloon Coal Measures and over‐ and underlying
aquifers due to faulting is not apparent within the tenement (QGC 2011). The produced water data fromArrow Energy are also from the Walloon Coal Measures in its southern tenements in Queensland in theSurat Basin. These include gas production from Daandine, Tipton West, Stratheden and Kogan North near
Dalby (AE 2011).
Table 1.4 SAR values for produced water for selected locations in Australia. Calculated SAR values based onreported concentrations for Na+, Ca2+, and Mg2+ expressed in milliequivalents/litre.
Location

SAR

Comment

Reference

Fairview Project Area, Surat
and Bowen Basins, Qld

16‐567(Q75)

Range reported for CSG water sampled from operations(ponds and wells)

Santos 2010

Fairview Project Area, Surat
and Bowen Basins, Qld

512

Average reported for CSG water from operations (ponds
and wells)

Santos 2010

Fairview Project Area, Surat
and Bowen Basins, Qld

181

Calculated based on maximum cation concentrations for
CSG water sampled from operations (ponds and wells)

Santos 2010

Woleebee Creek Block, Surat
Basin, Qld

77

Calculated based on average concentrations of cations
sampled from raw CSG water from ponds

QGC 2011

Surat Basin, Arrow Energy
southern tenements, Qld

3

Calculated based on minimum cation concentrations in
CSG water sampled from wells

AE 2011

Surat Basin, Arrow Energy
southern tenements, Qld

61

Calculated based on maximum cation concentrations in
CSG water sampled from wells

AE 2011

Surat Basin, Arrow Energy
southern tenements, Qld

196

Calculated based on average concentrations of cations
sampled from an aggregation pond of untreated CSG
water that feeds the Daandine RO facility

AE 2011All produced waters, except one, exceed the DEPH and ANZECCC trigger values for SAR. This indicates
short‐term irrigation (20 years) with untreated produced water is not a viable option to maintain soil
health.

1.3 Potential impact on vegetation health
Irrigation with high SAR water will effectively killing off plant species that are not salt tolerant (Moore
2012). A key publication on soil remediation standards for salt ions is a set of technical reports by Brightand Addison (2002a; 2002b) to inform policy decisions in British Columbia, Canada. A major catalyst for thisstudy was to develop standardised guidance to assist spill response and soil remediation in northern British
Colombia at sites where salt‐containing produced water is released as part of oil and gas exploration andextraction activities (Bright and Addison 2002b). Generic soil quality standards for human health, aquatic
life, and soil ecological functioning, as manifested through soil invertebrate and plant responses, weredeveloped for salt ions (Bright and Addison 2002b). Whilst the impairment of metabolic functioning of soil
microbes is recognised as playing a major role in nutrient cycling and other processes important toterrestrial ecosystems, there is insufficient data available to define a threshold for salt ions; instead, amicrobial functional impairment standard is used that is developed from nutrient and energy cycling data
(CCME 1996; Bright and Addison 2002b). The report provides a model for predicting concentrations ofchloride in soils that are protective of groundwater and surface water uses based on categories of site
properties. The categorisation of site types considers soil texture (coarse versus fine grained, or low versus
high Water Holding Capacity) and climatic conditions (Bright and Addison 2002a). As discussed in the14

report, the major controlling influences on salt concentration enroute to the saturated groundwater zone
are:




the leachate concentration in and immediately below the soil source area
the water flux as the driving force for migration
the soil type as an important factor defining the amount of salt that can adsorb on the clay and silt
particles.

Another potential issue when produced water is used for irrigation is the relatively high boron content. If
not removed, boron can be damaging to some flora. The ANZECC guide value for long‐term irrigation (up to
100 y) is 0.5 mg/L (Table 1.5). An analysis of the migration of boron in soil was beyond the scope of thisstudy.
Overall salt concentrations do not significantly decrease while migrating through the soil because salt ionsdo not biodegrade, volatilise or undergo photolysis (Bright and Addison 2002b).
An investigation of soil and vegetation recovery over a ten‐year period after a gas well blowout and salt
release in British Columbia conducted by Leskiw et al. (2012) documents the natural recovery of salt‐
affected plots and discusses the advantages of allowing for natural recovery over imposing soil reclamation
measures. This study analysed whether leaching of salts from natural precipitation events were sufficient toimprove the soil on the basis of certain soil and vegetation parameters (i.e. sodium adsorption ratio, soil EC,
pH, soluble ions, and vegetation recovery). Natural attenuation from above normal rainfall in the first few
years after the blowout was effective in removing salt from the root zone and subsoil (Leskiw et al. 2012).
These results are obtained for a humid climate; it is expected that natural attenuation under arid or semi‐arid climates would be much less effective.
In a study conducted by Dewalle and Galeone (1990), gas well brine was applied to forest land during thedormant season to test the feasibility of wastewater disposal in forested catchments in Pennsylvania.
Different loading rates for brine were applied to the ground surface on different plots and macro‐ andmicropore soil water chemistry changes were monitored at a depth of 70 cm. Concentrations of Cl, Ba, Pb,
As, Se and Cd exceeded drinking water guidelines for several months following treatment on all plots at 70
cm depth (Dewalle and Galeone 1990). High mobility of trace metals (Cd and Pb) in the soil appeared to beenhanced by complexation of these metals with chloride in the brine (see earlier discussions on themobility enhancement of metals by several anions). These interactions between metals and inorganic
complexing species may have also mobilised metals naturally present in soil profiles, leading to:
1. concentrations in the soil pore‐water that were higher than acceptable
2. migration to groundwater at a faster rate.
The study suggests treating forest soil to raise soil pH, pretreating brines to remove toxic elements or usingbrines with lower chloride concentrations as options to reduce trace metal mobility problems (Dewalle andGaleone 1990).
Adams (2011) describes the land application of shale gas flowback water in a deciduous forest in West
Virginia, which showed a 50‐fold increase in concentrations of sodium and chloride in the surface soil
within the treatment area that declined over time. The composition of the hydraulic fracturing fluid was
not made publicly available; however, Adams (2011) surmised based on the study results that Na and Cl
were the main constituents in the hydraulic fracturing fluid, and thus in the flowback water.

15

Table 1.5 Water quality parameters for Gloucester basin (Tiedeman Dam, NSW) and Surat basin (Woleebee Creek, QLD) produced water.

Gloucester basin (NSW) water quality*

Surat basin(QLD) water
quality**

ANZECC 2000
guideline for
irrigation$

Chemicalparameter

Site 1
producedwater

Site 2
produced
water

Avon riversurfacewater

Amendedirrigationwater@

Site 4pH

9.60‐9.86#

8.39‐9.77#

6.13‐7.38

6.70‐7.98

9.14

6‐9; 6‐9

Electricalconductivity(S/cm)4180 ‐ 4280

2610 ‐ 2790

158‐324771‐940‐650

Total alkalinity(mg/L)

872‐989395‐577

29‐52

120‐183

1706A

NG

SO4 (mg/L)

<5‐14

22‐29

11‐440#13.8‐337

22.8

NG

Cl (mg/L)

569‐663425‐489

24‐56

124‐164593

NG

Ca (mg/L)

<1‐2

3‐5

5‐12

4.5‐10.3

10.41

NG

Mg (mg/L)

<1

1‐2

3‐8

2.5‐6.5

8.96

NG

Na (mg/L)

768‐776602‐624

16‐38

162‐1841406

NG

K (mg/L)

317‐323

40‐43

6‐32

14.5‐34.8

5.61

0.05; 0.8

Al (mg/L)

0.13‐0.48

0.05‐0.07

0.02‐0.22

0.03‐0.08

18.18

5; 20

Ba (mg/L)

0.086‐0.228

0.241‐0.265

0.043‐0.08

0.38‐0.17‐

NG

B (mg/L)

0.79‐0.83

0.70

0.06‐0.14

0.22‐0.18

0.9

0.5; 0.5 ‐ 15

* Parson Brinkerhoff 2011; ** raw water pond average (QGC 2011); #2014 data (Parson Brinkerhoff 2015); @calculated based on a 3:1 ratio of surface
water:groundwater using Site 1 produced water and Avon surface water; & Dawson Valley Beneficial Use Scheme;$ long‐term use (up to 100 y); short‐term use (upto 20 y); NG = no guideline available; Abicarbonate alkalinity16

1.4 Scope of the study
The aim of this study is to simulate coupled processes of variably saturated water flow, plant water uptake
and coupled transport of multiple major ions in soils irrigated with produced water featuring different
water qualities. By coupling major ion soil chemistry to unsaturated flow and plant water uptake, and by
explicitly incorporating effects of salt concentrations on soil hydraulic properties and on root water uptake(so‐called salinity stress), critical soil processes required for salinity risk assessment associated with coalseam gas produced water are effectively included in our analysis. In doing so, the current study addresses
several of the knowledge gaps that are pivotal for an improved salinity risk assessment (Biggs et al. 2012).
Simulations are based on the finite element code HYDRUS‐1D (Šimůnek et al. 2008). The major ion
chemistry module Unsatchem is implemented to simulate the simultaneous movement of multiple ionspresent in irrigation water in a vegetated soil profile.
The produced water continuously emerges from the well for its life‐time after a hydraulic fracturing process(Vidic et al. 2013; Ward et al. 2015). It has a high load of dissolved solutes (Batley et al. 2012; Ward et al.
2015), a high alkalinity (Ward et al. 2015) and contains organic and inorganic chemicals (Batley et al. 2012).
The subject of this study are the non‐metal and non‐metalloid inorganic substances of the produced waterthat define its high salinity.

17

2 Modelling process

2.1 Conceptual model
2.1.1 SOIL HYDROLOGICAL MODEL
Simulation of variably saturated flow in soil requires a mathematical relationship between i) the soil watercontent (θ) and the soil pressure head (h), and ii) either the water content or the pressure and theunsaturated hydraulic conductivity, respectively K(θ) and K(h). The most commonly used mathematicalexpression for the soil water retention curve, θ(h), is the van Genuchten equation (van Genuchten 1980)
since it permits a relatively good description of θ(h) for many soils using only a limited number ofparameters. The van Genuchten soil moisture retention characteristic is defined as:|

Equation 1|

where θr is the residual water content [cm3/cm3], θs is the saturated water content [cm3/cm3], and α [1/m],n [‐] and m (= 1 – 1/n) [‐] are curve shape parameters specifically for the van Genuchten equation.
The second important soil hydraulic property is the unsaturated hydraulic conductivity function, K(h). The
hydraulic conductivity characterises the ability of a soil to transmit water, and as such is inversely related to
the resistance to water flow. The hydraulic conductivity depends on many factors, including the pore‐size
distribution of the porous medium, and the tortuosity, shape, roughness, and degree ofinterconnectedness of pores. The hydraulic conductivity decreases considerably as soil becomesunsaturated and less pore space is filled with water. The unsaturated hydraulic conductivity function gives
the dependency of the hydraulic conductivity on the water content, K(), or pressure head, K(h). The
conceptual model that views the soil as a bundle of capillaries of different radii may be used to evaluate thehydraulic conductivity function. By adding the conductivity of all capillaries that are filled with water at aparticular water content or pressure head, one obtains the hydraulic conductivity of the complete set ofcapillaries, and consequently of the soil itself.
Similarly, as for the soil water retention curve, analytical models are often used also for the unsaturated
hydraulic conductivity function, K(h). The van Genuchten (1980) retention function is coupled mostly withthe model of Mualem (1976) to give1/

1

Equation 2

where
1

1

,

1

Ks is saturated hydraulic conductivity, and Se = (θ ‐ θr)/( θs ‐ θr) is the effective saturation. The pore‐connectivity parameter (l) was estimated by Mualem (1976) to be about 0.5 as an average for many soils.
Also in this study the l parameter was fixed at 0.5.
The soil profile is assumed 1‐m deep, with three soil layers: 0‐0.3 m, 0.3‐0.7 m, and from 0.7‐1.0 m. The van
Genuchten soil hydraulic properties were taken from Bennet (2012). These soil hydraulic properties were
calculated on the basis of the pedotransfer functions of Minasny and McBratney (2002) using soil data
points within the Cox’s Creek catchment, NSW. For three soil horizons particle size distribution and the bulk
density from the SALIS (New South Wales Soil And Land Information System) dataset were used as
predictor variables in the pedotransfer functions. From six soil types (Tenosols, Dermosols, Vertosols,
Chromosols, Sodosols, Kandosols), Vertosols were most prominent with 84 out of 143 soil profiles (59%).
18

Vertosol hydraulic properties were therefore selected here for the simulations (Table 2.1). The mean soil
water retention curves together with their uncertainty bands (mean ± 2 standard deviation) are shown in
Figure 2.1.

Θr[cm3/cm3]

Θs[cm3/cm3]

α
[1/m]

n
[‐]

Ks
[m/day]l[‐]1 (0‐0.3 m)

0.059

0.515

8.3

1.217

6.04

0.5

2 (0.3‐0.7 m)

0.066

0.538

7.4

1.190

4.51

0.5

3 (0.7‐1.0 m)

0.07

0.515

7.9

1.150

3.35

0.5

Matric head (cm)Soil layer

Matric head (cm)Matric head (cm)

Table 2.1 van Genuchten soil hydraulic properties for Vertosols (average across the Cox’s Creak Catchment area, NSW)
(source: Bennett, 2012).

Figure 2.1 Soil water retention curves for three soil layers (based on parameters from Table 2.1).

In order to reach the (pseudo) steady‐state concentrations profile, the following hypothetical irrigation
scheme was used. The potential ET is equal to 1 cm/d (summer conditions in Queensland2), divided into
potential evaporation (2 mm/d) and potential transpiration (8 mm/d) (Figure 2.2). Irrigation of 6 cm isapplied each 5 days, i.e., 20% above potential ET (6/5). The annual total irrigation of 438 cm is much higher
than the combined rainfall and irrigation in irrigation areas of the Northern Darling Basin for crops such as
cotton (up to nearly 140 cm) or sorghum (up to 110 cm) (Silburn and Montgomery 2004). However,
irrigation of pasture, especially for grazing (beef and lamb), uses more water: for example, nearly 1500 mm
was recorded in a flood irrigated pasture in the South East of South Australia (Horizon 2006). The
application rate used in this study therefore corresponds to the equivalent applied water of two to three
growing seasons. Our purpose was not to have the most realistic irrigation regime, rather did we apply asimplified water application rate that would simplify the analysis of complex coupled chemical‐hydrologicalprocesses.
No precipitation was considered at this time; while this is a simplification of a soil’s hydrological balanceand does not account for effects of rainfall salinity on the salt mass balance, it does allow focus on effectsof irrigation only when coupled chemical‐hydrological‐biological processes are considered. The root zone
was assumed to be 0.50 m deep and root distribution was assumed to decrease linearly with depth. The
initial water content at the beginning of each simulation was assumed to be ‐100 cm throughout the soil
profile. Top boundary condition was specified as atmospheric BC with surface runoff. At the bottom of the
profile a free drainage BC was imposed. The soil water and chemical balance was calculated for one year.

2

http://www.bom.gov.au/climate/dwo/IDCJDW4128.latest.shtml
19

Figure 2.2 Irrigation, potential transpiration and evaporation rates applied during the simulations.

2.1.2 SOIL GEOCHEMICAL MODEL
In the first scenario (Run3a), the standard Hydrus “General Solute Transport” was used, with salinity
modelled as a tracer with a unit input concentration. This is the base case which serves as a yardstickagainst which the effects of saline irrigation water can be compared. Simulations consider effects on soil
water chemical composition and on vegetation (i.e. crops). Only the effect of water stress on plant water
uptake was considered (for details see section 2.3.4).
The same scenario was rerun (Run3b) using the Hydrus “Major Ion Chemistry” module and concentrationsof major ions (for details see section 2.3.3). Salinity was explicitly modelled with major ions in irrigationwater (Ca, Mg, Na, K, alkalinity, Cl, SO4) accounted for. In addition to effects on soil water chemicalcomposition, this scenario further calculates the effects of both water and salinity stress on plant water
uptake.
A third scenario (Run3c) uses the Hydrus “Major Ion Chemistry” module and considers the reduction of
hydraulic conductivity due to the solution composition (McNeal 1968, 1974). The accumulation ofmonovalent cations, such as sodium and potassium, often leads to clay dispersion, swelling, flocculationand overall poor soil physico‐mechanical properties. These processes have an adverse effect on the soil
hydraulic properties including hydraulic conductivity, infiltration rates and soil water retention as a result ofswelling and clay dispersion. These negative effects are usually explained based on the diffuse double layer
theory.
Scenario four (Run3d) considers amended produced water while a final fifth scenario (Run3e) uses treatedproduced water (see section 2.3.3 for chemical composition of irrigation water)
The initial concentrations of soil exchangeable cations (Table 2.2) are taken from a lysimeter study by
Ringrose‐Voase (2004). The lysimeter is located in paddock C1 at the Australian Cotton Research Institute(ACRI) near Narrabri in northern New South Wales (30° 11.53’ South, 149° 36.31’ East). The soil is a Haplic,
Self‐mulching, Grey Vertosol. The bulk density for different soil depths was estimated to range between
201.32 and 1.45 (Ringrose‐Voase 2004). Because the total cation exchange capacity is similar for all three soil
layers, the cation exchange capacity and the concentration of exchangeable cations of the first layer was
assigned to all three layers.
Table 2.2 Initial concentrations of exchangeable cations (source: Ringrose‐Voase 2004).

Soil layer

Bulk density (g/cm3)

CEC (meq/kg soil)

1 (0‐0.3 m)

1.32

275

2 (0.3‐0.7 m)

1.42

278

3 (0.7‐1.0 m)

1.45

274
Exchangeable cations for layer 1

Ions

Ca

Mg

Na

K

mmol/kg

86.3

44

4.6

9.4

meq/kg

173

88

4.6

9.4

The initial solution composition in the soil profile (Table 2.3) was defined using the solution composition of
rainwater measured in Wagga‐Wagga (Crosbie et al. 2012). Note that this solution will be automaticallybrought into equilibrium with the cation exchange complex.

Table 2.3 Initial soil solution composition used to calculate initial solution in equilibrium with the cation exchange
complex. Data represents composition of rainwater in Wagga‐Wagga (Source: Crosbie et al. 2012).

Solution composition (meq/L)
Ca

Mg

Na

K

Alkalinity

SO4

Cl

0.012

0.00660.021

0.00280.00098

0.00690.018

21

2.2 Numerical model for multi‐component reactive transport modellingin saturated/unsaturated media
2.2.1 HYDRUS
HYDRUS‐1D (Šimůnek et al. 2008, 2009) is a public domain computer software package(www.hydrus2d.com) that simulates the one‐dimensional movement of water, heat, and multiple solutes invariably saturated media. The program uses finite elements to numerically solve the Richards equation forsaturated‐unsaturated water flow and Fickian‐based advection‐dispersion equations for both heat andsolute transport. The flow equation includes a sink term to account for water uptake by plant roots as afunction of both water and salinity stress. The heat transport equation considers conduction as well as
advection with flowing water. The solute transport equations assume advective‐dispersive transport in theliquid phase, and diffusion in the gaseous phase. The transport equations also include provisions fornonlinear and/or nonequilibrium reactions between the solid and liquid phases, linear equilibrium
reactions between the liquid and gaseous phases, zero‐order production, and two first‐order degradation
reactions: one which is independent of other solutes, and one which provides the coupling between solutes
involved in the sequential first‐order decay reactions. The program may be used to analyse water andsolute movement in unsaturated, partially saturated, or fully saturated media. The flow region itself may
consist of non‐uniform (layered) soils. The unsaturated soil hydraulic properties (the constitutive
relationships) are described using van Genuchten (1980) or Brooks and Corey (1964, 1966) type analytical
functions, or modified van Genuchten type functions that produce a better description of the hydraulicproperties near saturation. HYDRUS‐1D incorporates hysteresis by assuming that drying scanning curves are
scaled from the main drying curve, and wetting scanning curves from the main wetting curve. Root growthis simulated by means of a logistic growth function. Water and salinity stress response functions can also be
considered. HYDRUS‐1D was recently modified to also allow simulations of the transport of viruses,
colloids, and bacteria (Schijven and Šimůnek 2002; Bradford et al. 2003). HYDRUS‐1D uses a Microsoft
Windows based Graphics User Interface (GUI) to manage the input data required to run the program, as
well as for nodal discretization and editing, parameter allocation, problem execution, and visualization of
results. All spatially distributed parameters, such as those for various soil horizons, the root water uptake
distribution, and the initial conditions for water, heat and solute movement, are specified in a graphicalenvironment. The program offers graphs of the distributions of the pressure head, water content, waterand solute fluxes, root water uptake, temperature and solute concentrations in the soil profile at pre‐selected times. Also included are a small catalogue of unsaturated soil hydraulic properties (Carsel andParish 1988), as well as pedotransfer functions based on neural networks (Schaap et al. 2001).

2.2.2 UNSATCHEM
The solute transport module of HYDRUS‐1D as described above is limited to single ions, or ions subject torelatively simple first‐order consecutive decay reactions (e.g., nitrification‐denitrification chains, orradionuclide decay chains). As an alternative to this approach, we recently implemented into HYDRUS‐1D
also a major ion chemistry module based on the UNSATCHEM model (Šimůnek and Suarez 1994, 1997;Šimůnek et al. 1996, 2008). This module considers the transport of major ions and carbon dioxide in soils.
CO2 transport is assumed to be governed by diffusion in both the liquid and gas phases, and by advection in
the liquid phase. The major variables of the chemical system are Ca, Mg, Na, K, SO4, Cl, NO3, H4SiO4,
alkalinity, and CO2 (Table 6). The model accounts for equilibrium chemical reactions between thesecomponents such as complexation, cation exchange and precipitation‐dissolution. For the precipitation‐
dissolution of calcite and the dissolution of dolomite, either equilibrium or multicomponent kinetic
expressions are used, including both forward and backward reactions. Other precipitation‐dissolution
reactions considered involve gypsum, hydromagnesite, nesquehonite, and sepiolite. Since the ionicstrength of soil solutions can vary considerably in time and space and often reach high values, both themodified Debye‐Hückel and Pitzer expressions were incorporated into the model, thus providing options
for calculating single‐ion activities. This module also considers the effects of solution composition on the22unsaturated soil hydraulic properties. The UNSATCHEM module of HYDRUS‐1D enables quantitative
predictions of processes involving major ions, such as simulations of the effects of salinity on plant growthand estimating the amount of water and amendment required to reclaim soil profiles to desired levels of
salinity and ESP (exchangeable sodium percentage).

Table 2.4 Chemical species included in the UNSATCHEM major ion module of HYDRUS‐1D.

23

2.3 Model implementation
2.3.1 SOIL HYDROLOGICAL CHARACTERISTICS
The soil water redistribution in the soil profile will be discussed for scenario 1, the base case (Run3a)against which changes in the other scenarios will be compared. Boundary conditions are summarised inTable 2.5. The following features were simulated with Hydrus:Time series of soil water content, matric head, and tracer concentrations
Depth profiles of soil water content, matric head, and tracer concentrations
Water fluxes bottom (drainage) and top (actual root water uptake) of soil profile.

Table 2.5 Defined variable boundary conditions.

Time

Flux [cm/day]

Boundary solution

Every 5 days

6 cm/dayIrrigation water

Constant

0.8 cm/day

Potential transpiration

Constant

0.2 cm/day

Potential evaporation

The 1‐m numerical domain was discretised in 100 finite elements, each 0.01 m long. The numerical grid, thethree soil layers and four observations nodes are shown in Figure 2.3.

Figure 2.3 Finite element grid with soil layers (left) and location of four observation nodes (right).

2.3.2 SOIL GEOCHEMICAL MODEL
Migration of chemicals in soil is primarily by advection (sometimes referred to as convection) anddispersion (ignoring gaseous transport). By mathematically solving the solute transport oradvection‐ dispersion equation (ADE when advection is used, or CDE when convection is used), predictionsof solute concentrations at different depths and times can be obtained (Mallants et al. 2011). For transportof inert, non‐adsorbing contaminants during transient water flow the ADE is as follows (Toride et al. 1995):
Equation 3
24

where c is solute concentration in the water phase (mg/L), t is time (s), D is hydrodynamic dispersion (total
dispersion, which is the sum of molecular diffusion and mechanical dispersion) (m2/s), z is soil depth (m),and v is pore‐water velocity (m/s). In the unsaturated zone, advective transport usually becomes animportant component of the overall transport, hence the two‐component dispersion coefficient (D) (m2/s)
has to be used. Molecular diffusion (Dp) (m2/s) is one component of the hydrodynamic dispersion, but
unless water flow is extremely slow, molecular diffusion is of secondary importance3 in the migration ofelements in soils and permeable aquifer sediments. The second component of hydrodynamic dispersion ismechanical dispersion,  x v, as shown in the following equation:
Equation 4
where D is hydrodynamic dispersion, or simply dispersion,  is dispersivity4 (m), and v is pore‐water velocity(m/s). Water content θ is needed to obtain pore‐water velocities (v) from water fluxes (q) following:q/θ

Equation 5

Diffusion or molecular diffusion is transport of solutes from an area of higher concentration to an area oflower concentration; it occurs as long as such a concentration gradient exists, even if the water is not
flowing. Mechanical dispersion is a transport process due to heterogeneous distribution of water flow
velocities within and between different soil pores (Mallants et al. 2011). The result is that some soluteparticles will be ahead of the solute front whereas others will lag behind, leading to solute mixing andgenerally a bell‐shaped distribution of velocities and thus of arrival times, typical of a breakthrough curve.
The processes of molecular diffusion and mechanical dispersion are incorporated into one parameter, thehydrodynamic dispersion coefficient D. Phenomenologically, dispersion has two effects: it increases thepassage time of a complete solute pulse and it decreases the maximum concentration. In the case of a toxic
contaminant for example, it leads to a longer exposure time but also to a lower maximum concentration.
Dispersivity is a transport parameter that is often obtained experimentally by fitting measured
breakthrough curves with analytical solutions of the advection‐dispersion equation (Seuntjens et al. 2001).
In this study a dispersivity = 0.1 m was taken, or one tenth of the total travel distance from top to bottomof the soil profile.
Partitioning between the solid and solution phases is described with the Gapon equation (White andZelazny 1986) provided in the Major Ion Chemistry module. This requires definition of the Gapon Exchange
Constants for exchange of calcium and magnesium, calcium and potassium, and calcium and sodium. The
Gapon Exchange Constant are taken from Jacques et al. (2012). The exchange coefficients can beinterpreted as follows: for the Ca2+/Mg2+ exchange, Ca2+ is the preferred ion relative to Mg2+. For theCa2+/Na+ exchange, Ca2+ is the preferred ion relative to Na+. Finally, for the Ca2+/K+ exchange, K+ is thepreferred ion relative to Ca2+.
Table 2.6 Gapon Exchange Constants (Source: Jacques et al. 2012).

Soil layer

K [Ca/Mg]

K [Ca/Na]K [Ca/K]

1

1.2

2.9

0.2

2

1.2

2.9

0.2

3

1.2

2.9

0.2

3 Mechanical dispersion in most subsurface transport problems dominates molecular diffusion in the liquid phase, except when thefluid velocity becomes relatively small or is negligible. Diffusion dominated transport occurs in low permeability media, such as
clays and rock matrices.
4
Longitudinal dispersivity (αL) is a term used to indicate dispersion along the main direction of flow25

2.3.3 CHEMICAL COMPOSITION OF THE IRRIGATION WATER
Three different chemical compositions of the irrigation water were assumed; the first corresponds tountreated produced water (scenario 2 and 3), the second to surface water amended produced water(scenario 4), and the third to treated produced water (scenario 5). The first (untreated) and third (treated)compositions represent end‐members that can be mixed to various degrees to obtain fit‐for‐purpose
irrigation water. Unlike the amended and treated produced waters that are actually being tested and/or
being used for irrigation, the untreated water would almost certainly not receive any permission for use as
irrigation water owing to its high potassium and SAR values (Table 2.7). It is included here only todemonstrate effects of several interacting processes, such as major ion composition triggering a reduction
in soil hydraulic conductivity and subsequent modification of water redistribution in the soil profile andhow root water uptake responds to such changes.
It is worth noting that the treated water is still much more saline than rainfall (see Table 2.3 for rainfall
composition). In other words, even irrigation with treated produced water is likely to have some effect on
the soil’s chemical balance.
Table 2.7 Composition of irrigation waters. Kenya/rain water = ratio of treated produced water to rain watercomposition.

Alkalinity

Cl

SO4

Ca

K

Mg

Na

SAR

Untreated produced water (Surat basin, QLD)*
mg/L

1706

593

22.8

10.4

5.6

8.9

1406

meq/L

41.9

16.7

0.47

0.52

0.14

0.74

61.1

77

EC@
(µS/cm)

1245

1278

38

31

11

39

3064

Total EC
= 5705

%EC

21.8

22.4

0.7

0.5

0.2

0.7

53.7

100%

Amended produced water (3:1 ratio surface water:produced water) (Gloucester basin,
NSW)#
mg/L

183

164

337

10.30

34.8

6.5

184

meq/L

4.5

4.6

7.0

0.51

0.89

0.53

8.0

11

EC@
(µS/cm)

134

353

561

31

65

28

401

Total EC
= 1573

%EC

8.5

22.5

35.7

1.9

4.2

1.8

25.5

100%

Treated produced water (Kenya Water Treatment Facility, Surat basin, QLD)**
mg/L

25

67

1

10.33

0.43

7.6

28.7

meq/L

0.61

1.89

0.02

0.52

0.011

0.62

1.25

1.6

EC@
(µS/cm)

18.2

144.3

1.67

30.7

0.81

33

62.5

Total EC
= 291

%EC

6.3

49.6

0.6

10.5

0.3

11.3

21.5

100%

Kenya/
rain water

417

103

3

43

4

95

60

Electrical conductivity for ions as trace concentration in water at 25ᴼC
S.cm2/mol

26

44.5

76.35

160

119

73.5

106

50.1

*site 4 from Table 1.5 (QGC 2011); # maximum values from Table 1.5; ** from SunWater 2014 (average
across 3 sampling dates between July 2013 to 30 June 2014); @calculated as Ʃ[ECi (S.cm2/mol)xCi(mmol/L)](Appelo and Postma 2004 Table 1.7)
It is important to note that from all the ions considered in the simulation, only potassium (K) has an
ANZECCC guide value for irrigation (ANZECCC 2000). For long term use (up to 100 years), the guide value for
K is 0.05 mg/L. For short term use (up to 20 years), the guide value is 0.8 mg/L. The calculated SAR for theuntreated produced water is 77 (Table 2.7); this is considerably higher than the DEHP maximum guide valueof 12 for light soils (95th percentile over a one‐year period) (DEHP 2014).
The scenarios 2 (simulation Run3b) and 3 (simulation Run3c) differ only in the way effects of major ion
chemistry on soil hydraulic properties are treated: only in scenario 3 is reduction in hydraulic conductivity
due to major ion composition and corresponding pore space blockage accounted for. Any major ion
composition in scenario 2 will not affect hydraulic conductivity.
The effect of solution chemistry on the hydraulic conductivity in the major ion chemistry module iscalculated as follows:
,

,

,

,

,

Equation 6

where SAR is the sodium adsorption ratio, C0 is the total salt concentration of the ambient solution (meql‐1),
and r is a scaling factor which represents the effect of the solution composition on the final hydraulicconductivity [‐], and which is related to pH, SAR and salinity (Šimůnek et al. 2009). Conceptually the effects
relate to clay swelling as a result of changes in ion composition here expressed via the parameters SAR andC0.
We note that other effects of solution chemistry on hydraulic properties such as mineral precipitation
resulting in a decrease of pore space and hence decrease in hydraulic conductivity may be equallyimportant to determine a soil’s water balance under irrigation in arid and semi‐arid regions. Examples of amore general approach to couple major ion chemistry with soil hydraulic and solute transport parameters
are available from Jacques et al. (2013). These authors implemented a Kozeny‐Carman type equation torelate the permeability to the porosity in the coupled HYDRUS‐PHREEQC simulator which accommodates
effects of mineral dissolution and/or precipitation; other relationships were implemented that relate
tortuosity (and subsequently diffusion coefficient) to porosity changes.
Both scenarios 2 and 3 use three solution compositions for three consecutive periods:Solution 1 is rainwater (used as initial solution, Table 2.3),
Solution 2 is untreated produced water (used for irrigation, Table 2.7),
Solution 3 is again rainwater (not currently used, but may be used if precipitation is considered).

2.3.4 VEGETATION PARAMETERS
To simulate the effect of i) water stress separately and ii) both water stress and salinity, two cases are
considered. First, the effect of water stress separately was analysed using Feddes’ model (Feddes et al.
1978) (see Figure 2.4 for a conceptual representation and Table 2.8 for relevant parameters). Plant water
stress response functions specific for pasture and alfalfa are displayed in Figure 2.5
Whether HYDRUS selects P2H or P2L as the limiting pressure head depends on the value of the potential
transpiration rate, Tp. If Tp > r2H (0.5 cm/day), then P2H (‐200 cm) is selected. If Tp < r2L (0.1 cm/day), thenP2L (‐800 cm) is selected. If Tp is in between r2H and r2L, linear interpolation between P2H and P2l is
applied. In this study Tp = 0.8 cm/day, hence P2H is selected.
When salinity stress is considered in HYDRUS, a selection must be made whether the effect of salinity stressis additive or multiplicative to water stress. Here the multiplicative Threshold Model function according to
Maas (1990) was selected. The Threshold Model has two parameters, the threshold parameter and theslope parameter (Table 2.9). The first parameter represents the value of the minimum osmotic head (thesalinity threshold) above which root water uptake occurs without a reduction. The second parameter is the27slope of the curve determining the fractional root water uptake decline per unit increase in salinity below
the threshold. Root water uptake reduction parameters for alfalfa were used to investigate combined
water and salinity stress as no salinity stress parameters are available for pasture (all other root water
uptake parameters are those for pasture).
Table 2.8 Root water uptake reduction parameters without solute stress (based on Wesseling et al. 1991).

Feddes’parameters

PasturealfalfaParameter definition

P0 (cm)‐10‐10

Value of the pressure head below which roots start to extract
water from the soil

P0pt (cm)

‐25

‐25

Value of the pressure head below which roots extract water at
the maximum possible rate

P2H (cm)

‐200

‐1500

Value of the limiting pressure head, below which roots cannot
longer extract water at the maximum rate (assuming a
potential transpiration rate of r2H)

P2L (cm)

‐800

‐1500

As above, but for a potential transpiration rate of r2LP3 (cm)

‐8000

‐8000

Value of the pressure head, below which root water uptake
ceases (usually taken at the wilting point)r2H (cm/day)

0.5

0.5

Potential transpiration rate [LT‐1] (currently set at 0.5 cm/day)

r2L (cm/day)

0.1

0.1

Potential transpiration rate [LT‐1] (currently set at 0.1 cm/day)Figure 2.4 Schematic of the plant water stress response function, α(h), as used by a) Feddes et al. (1978) and b) van
Genuchten (1987). The Feddes et al. function was used in this study: for definition of parameters, see Table 2.8 (h1= P0, h2 = POpt, h3 = P2L/P2H, h4 = P3).

28

Figure 2.5 Plant water stress response function, α(h), for pasture and alfalfa as applied in this study.
Table 2.9 Root water uptake reduction parameters with solute stress for alfalfa.

Threshold Model

cm

dS/m

Threshold5‐1473.52 (cm)2

Slope69.57854e‐005 (1/cm)

7.3

Osmotic coefficient

N/A

N/A

2.3.5 SUMMARY OF SIMULATION CASES
A total of five different scenarios are considered in the numerical analysis to evaluate (Table 2.10):

A base case scenario against which other scenarios can be compared: the base case involves soil
water balance calculations without i) effects of major ion chemistry on hydraulic properties, ii)
effects of salinity stress on root water uptake, iii) effects of competing adsorption between major
ions on ion migration in soil;
Effect of major ion chemistry on hydraulic conductivity (scenarios 2 and 3);
Effect of three different irrigation water qualities (scenarios 2/3, 4, and 5).

5

Threshold = ‐3.8106*(Th_ECe*2)+0.507 (m), where Th_ECe = 2 dS/m for alfalfa; the electrical conductivityof the saturation extract is converted into electrical conductivity of the soil water through themultiplication factor 2
6

Slope= (Sl_ECe/2)/(381.06) (1/m), where SI_ECe = 7.3 dS/m for alfalfa
29

Table 2.10 Description of scenarios used.

30

Scenario#

Scenario description (main processes)

Irrigation water quality

1

Base case: general solute transport module withsingle component. Comparison between pastureand alfalfa root water uptake without solutestressTracer only (6 cm/day irrigation rate)

2

Major ion chemistry solute transport without
reduction in hydraulic conductivity; root water
uptake with multiplicative solute stress

Untreated produced water (6 cm/day
irrigation rate)

3

Major ion chemistry solute transport with
reduction in hydraulic conductivity; root water
uptake with multiplicative solute stress

Untreated produced water (4 cm/day
irrigation rate to avoid water ponding
due to reduced hydraulic conductivity)

4

Major ion chemistry solute transport with
reduction in hydraulic conductivity; root water
uptake with multiplicative solute stress

Surface water‐amended produced
water (6 cm/day irrigation rate)

5

Major ion chemistry solute transport with
reduction in hydraulic conductivity; root water
uptake with multiplicative solute stress

Treated produced water (6 cm/day
irrigation rate)3 Detailed description of the simulation cases

3.1 Scenario 1: Base case
The base case scenario represents a simplified conceptualisation of water flow and solute transport underirrigation boundary conditions involving water containing only a non‐reactive tracer. This scenario does notconsider major ion chemistry, root water uptake affected salinity stress, and soil hydraulic properties
influenced by salinity. The calculated soil water balance, including root water uptake and actual planttranspiration, will serve as a reference against which other scenarios that do include additional coupledprocesses will be compared.
Simulated water content (Figure 3.1) and pressure head (Figure 3.2) time series show typical patterns
linked to alternating periods of soil wetting during and following irrigation and drying periods in theintermittent periods between irrigation events. At deeper depths in the soil profile, the variations become
smaller as the water redistribution becomes increasingly buffered as it moves through soil. Total simulation
time for the base case is 30 sec on Personal Computer with an Intel Core i5‐2540M CPU and 2.6 GHz.

Figure 3.1 Simulated water content at three depths in the soil profile. Only first 100 days are shown. Arrows show
times at which results at observation nodes are outputted for producing profile plots (at 30, 60, 90 days).

31

Figure 3.2 Simulated pressure head at three depths in the soil profile. Only first 100 days are shown.

The solute accumulation in the soil profile at different depths is shown in Figure 3.3. Note that a unit tracer
concentration of 1 mg/L was assumed in the infiltration water. At the shallower depths of 10 and 20 cm thecyclic variation in solute concentration reflects the variation in water content seen in Figure 3.1. Print time
steps indicate the time when detailed profile results are outputted to produce graphs like Figure 3.4.

Figure 3.3 Simulation of tracer movement through soil. Tracer is applied through the irrigation water.

Solute concentration profiles are shown in Figure 3.4 at the end of the time period with transpiration andevaporation (lasting four days), before irrigation starts. During this period, as a result of evaporation, watercontent at the soil surface decreases (Figure 3.1), creating at the same time the pressure head gradient,32

which produces upward water flow towards the soil surface. Water flowing towards the soil surface carries
with it solute ions, which accumulate at the soil surface during evaporation. Note that during evaporation,water leaves the soil profile while leaving solute ions behind. As a result of decreasing water contents andupward water flow towards the surface (bringing additional solute ions), concentration of solute ionsincreases significantly during this time period.

Soil depth (cm)Soil depth (m)As a whole, the solute profiles reflect the equilibrium between infiltration with irrigation water, water andsolute redistribution due to transpiration and evaporation, and downward leaching (Figure 3.4). The
prominent effects of transpiration and evaporation on the solute profile become further apparent whenthese processes are suppressed (Figure 3.4). When transpiration is suppressed during the simulations amuch smaller solute concentration across the entire soil profile becomes apparent. If subsequently also soil
evaporation is suppressed, the effect is much smaller although some further decrease in soluteconcentration is observed after 365 days. This effect is mainly caused by differences in soil water content
between the different models: highest water content is observed when both transpiration and evaporation
are suppressed, whereas lowest water contents exist when those processes are included (Figure 3.4). As
expected, the effect of suppressing transpiration is much greater than suppressing evaporation (daily
potential transpiration rate is 0.8 cm/day while daily potential evaporation rate is 0.2 cm/day). At the top of
the soil profile, the water content without ET is 1.7 times larger than when ET is included. This ratio almost
corresponds to the ratio of 2.2 obtained by dividing solute concentration with ET and without ET. This
demonstrates that higher solute concentration are indeed due to lower water contents following water loss
mainly due to transpiration and to a lesser degree due to soil evaporation.

Figure 3.4 Simulated solute concentration following infiltration of solute‐containing irrigation water. Comparison ofmodels including ET (solid lines), excluding T (transpiration, dashed lines) or excluding ET (evapotranspiration,
symbols) (left). Comparison of simulated soil water content for models including ET, excluding T or excluding ET(right).

Cumulative fluxes are compared for pasture and alfalfa (only water stress functions applied for both crops).
As can be observed from Figure 3.5, there is no noticeable difference between the two crops. For both
crops, the actual (281 cm) and potential (292 cm) cumulative transpiration flux are very similar, indicating
there was little water stress during the simulations (Figure 3.5). The cumulative potential and actual surface
flux (infiltration and evaporation) are both negative (downward) and identical, meaning potential andactual evaporation are equal. The cumulative actual bottom flux is slightly negative, meaning a small
drainage flux is exiting the soil profile. This is expected as the irrigation rate = 1.2 × potential ET (6 cm
irrigation over 5 days/5 cm potential ET over 5 days).
33

Figure 3.5 Cumulative potential and actual surface (infiltration and evaporation) and transpiration flux andcumulative actual bottom flux for pasture and alfalfa (scenario 1). Cumulative potential and actual surface flux are
identical.

3.2 Scenario 2: Major ion chemistryThis scenario and all subsequent scenarios consider major ion chemistry to simulate behaviour of multiple
solutes added to the soil profile through use of produced water (untreated in scenario 2). Consideration ofmajor ion chemistry provides insight in the distribution of individual ions (both in the aqueous phase andadsorbed on the solid phase), the electrical conductivity and the sodium adsorption ratio in soil. The
simulations also provide concentrations of minerals such as calcite, dolomite or gypsum. Scenario 2 further
links the soil chemistry (i.e. salinity) to salinity stress of plants (in addition to water stress invoked in
scenario 1), allowing calculation of reduction in water uptake by vegetation. The reduction in water uptake
in turn has an effect on the water and solute distribution in the soil profile (see discussion in Section 3.1),
and thus on the drainage flux across the bottom of the profile. Unlike scenario 3, 4, and 5, effects of salinity
on hydraulic properties are not considered in scenario 2.
The tracer profile developed over one year simulation is quite different across the three scenarios 1, 2 and
3 (Figure 3.6). If only water stress is accounted for in absence of major ion chemistry (scenario 1), the tracer
concentration gradually builds up over the entire soil profile, with a maximum concentration at a soil
surface due to a “concentration” effect (Figure 3.14). As a result of soil evaporation (E) and planttranspiration (T), water contents in the top layer are minimal, resulting in an increase of concentrations.
Also note that the output is obtained just before irrigation starts (at the end of a period with irrigation at
day 1 followed by 4 days of ET), when water contents in the top layer are low (resulting in an increase inconcentration). When major ion chemistry is accounted for (scenario 2 and 3), salinity stress is induced
resulting in a significant reduction in the root water uptake (Figure 3.14). This in turn changes the water
distribution as exemplified by the changes in the solute profile (Figure 3.6): the lower concentrations in thebottom part of the soil profile are due to the higher drainage flux causing dilution of the tracer. In the caseof scenario 3 (reduction in hydraulic conductivity due to the soil solution composition), the hydraulicconductivity decreased and caused the water to pond on the soil surface. This caused the tracer toaccumulate at the 40 to 50 cm depth for the first 150 days of the simulation.
34

Figure 3.6 Solute tracer concentration profiles for scenario 1 (left), 2 (middle) and scenario 3 (right).

Water content at a particular depth in the soil profile seems quite constant during the simulation (Figure3.7); however, this is only so because we print water content profiles at the same time of the irrigation
cycle (i.e., just before irrigation starts). On the other hand, there is a gradual increase of water content withdepth as a result of the plant water uptake in the top 50 cm.
The simulated SAR and electrical conductivity evolution illustrate high SAR values of over 100 progressing
downward the soil profile. The rate at which SAR moves through soil is determined by the migration of Na
and Ca. Because Na migration is retarded, SAR migration will be retarded too (Figure 3.9). The precipitationof Ca in the top of the profile further influences the migration of SAR (Figure 3.10). The electricalconductivity progresses much faster because it is mainly determined by chloride (Figure 3.9), and reaches
values beyond 10 dS/m (Figure 3.8). Electrical conductivity migration will be retarded if mineral
precipitation happens.
The SAR ANZECC trigger value of 20 (sandy‐sandy loam soil, see Table 1.5) is exceeded relatively quickly, i.e.
down to 40 cm after 150 days. The EC ANZECC trigger value of 0.65 dS/m (see Table 1.5) is exceeded very
quickly, i.e. down to 70 cm after 30 days.

Figure 3.7 Water content profiles for scenario 2 (left) and scenario 3 (right).

35

Figure 3.8 Simulated SAR (left) and electrical conductivity (right) for scenario 2. ANZECC trigger value for SAR and EC(see Table 1.3).

From the six minerals considered in the Major Ion Chemistry (calcite, dolomite, gypsium, nesquehonite,hydromagnesite, sepolite), only calcite is formed. The concentration of calcite gradually increases withdepth as more irrigation water entered the soil (Figure 3.10). The effect of calcite precipitation is clearlyreflected in the concentration of dissolved calcium ions (Figure 3.9): close to the surface, dissolved calcium
is nearly absent as a result of precipitation of calcite. Using a calcite mineral density 2.72 g/cm3, total
porosity from Table 2.1, and soil bulk density from Table 2.2, the calculated mineral volume of precipitatedcalcite after 365 days was shown to have increased to approximately 0.015 cm3 cm‐3 at the soil surface; themineral phase occupies nearly 3% of total soil porosity. While this is a relatively small fraction of theavailable pore space, this volume is expected to increase as irrigation continues for many years. The long‐term effects of salt formation on soil hydrology and plant response needs further corroboration.
There are clear differences in the way the major cations Ca, Mg, Na and K migrate through soil (Figure 3.9).
Calcium is mainly affected by precipitation in the top soil; ahead of the precipitation front calcium is only
weakly retarded compared to chloride. Magnesium displays accumulation at the soil surface due to adecrease in soil water owing to ET; the similarity between magnesium and chloride indicates the former islittle retarded. Sodium displays the largest retardation as is expected from the Gapon cation exchange
constant (see Table 2.6). The migration of potassium is somewhat similar to Mg, although it exhibits nearly
constant values in the top 30 cm of the soil profile.

36

Figure 3.9 Simulated concentrations of dissolved major ions Ca, Mg, Na, K, and Cl. ANZECC trigger value for sodium(see Table 1.3).

Figure 3.10 Calcite concentration for scenario 2 (left). Calcite mineral volume after 365 days of irrigation (right).

37

Figure 3.11 Cumulative potential and actual surface (infiltration and evaporation) and transpiration flux andcumulative actual bottom flux for scenario 2.

The sodium ANZECC trigger value of 20 meq/L (460 mg/L, see Table 1.5) is exceeded relatively quickly, i.e.
down to 60 cm after 150 days.
Due to the combined effect of water and salinity stress on root water uptake, the actual transpiration isconsiderably lower than the potential transpiration (Figure 3.11). The lower transpiration results in morewater remaining in the soil and more water draining through the bottom of the profile (123 cm versus 84
cm for scenario 1).

3.3 Scenario 3: Major ion chemistry including effect of salinity on soil
hydraulic properties
Scenario 3 was run (Run3c) using the Major Ion Chemistry module and concentrations of major ionsassociated with irrigation of untreated produced water, while now also considering reduction of hydraulicconductivity due to solution composition (based on the McNeal model).
During the simulations and based on an irrigation rate of 6 cm/day once in five days, the hydraulicconductivity was reduced automatically due to the saliniry effect to the point that water could not get into
the profile without causing ponding. Therefore, we reduced the irrigation rate to 4 cm/day once in fivedays to avoid ponding conditions. Under those new soil hydrological conditions, major ion chemistry
resulted in the following observations. Compared to the two previous scenarios, the SAR moves downward
about two times slower, while also the electrical conductivity reaches lower values at the same depths(Figure 3.12).

38

Soil depth (cm)Figure 3.12 Simulated SAR (left) and electrical conductivity (right) for scenario 3.

Figure 3.13 Cumulative potential and actual surface (infiltration and evaporation) and transpiration flux andcumulative actual bottom flux for scenario 3.

The actual transpiration becomes severely reduced, i.e. to 188 cm versus 240 cm for scenario 2 (Figure3.13). The decrease in root water uptake between ‐20 and ‐40 cm soil deth is not due to salinity stresscaused by a too high EC. This occurs at the very end of the simulation, when the SAR front reaches thesecond soil layer, causing the hydraulic conductivity reduction which in turn causes water logging in thisarea. The reduction in water uptake then is due to a too high pressure head (> ‐25 cm, see parameter POptin Table 2.8) to allow water uptake at an optimal rate (Figure 3.14).

39

Figure 3.14 Root water uptake function for scenario 1 (top left), scenario 2 (top right) and scenario 3 (bottom left).
Pressure head exceeds the POpt = ‐25 cm threshold after 300 days (bottom right).

40

3.4 Scenario 4: Amended produced water
Scenario 4 (run3d) considers use of amended irrigation water with chemical composition defined in Table
1.5, while all other processes and parameters are identical to those of scenario 3. The simulated SAR
profiles shown in Figure 3.15 demonstrate that the SAR ANZECC trigger value (SAR=20) for sand‐sandy loam
is exceeded after 365 days for a small section of the profile. The SAR ANZECC trigger value (SAR=5) for lightclay is exceeded in the top 30 cm after 150 days, whereas after 365 days it is exceeded for the top 50 cm.
The EC ANZECC trigger value of 0.65 dS/m is exceeded across the entire soil profile after 90 days. The
relatively high EC is due to the EC of the irrigation water (1.57 dS/m). In terms of contributions of individual
ions in the amended irrigation water to the EC, the most important contributors to EC are sulfate (36%contribution to total EC), sodium (26% contribution to total EC) and chloride concentration (23%contribution to total EC) (Table 1.2).
Optimisation of irrigation water quality can be done by reducing concentration of those ions that
contribute most to the total EC, in the above example that is sulfate, sodium, and chloride, where thecontribution of each ion to the total EC is calculated using the procedure given in Table 1.2.

Figure 3.15 Simulated SAR (left) and electrical conductivity (right) for scenario 4. ANZECC trigger value for SAR andEC (see Table 1.3).

The cumulative potential and actual surface and bottom fluxes are shown in Figure 3.16. The use of lower
salinity irrigation water results in reduced salinity stress: the actual transpiration is slightly higher. As aresult, the drainage across the bottom of the soil profile is lower too. This scenario results in a more
efficient use of the applied irrigation water.

41

Figure 3.16 Cumulative potential and actual surface (infiltration and evaporation) and transpiration flux andcumulative actual bottom flux for scenario 4, in comparison with scenario 3.

42

3.5 Scenario 5: Treated produced water
In scenario 5 (run3e) irrigation water has the chemical composition of treated produced water as defined inTable 1.5, while all other processes and parameters are identical to those of scenario 3 and 4. The
simulated SAR profiles now remain below the lowest SAR ANZECC trigger value (SAR=5) for light clay (Figure3.17). This result is as expected, given the low SAR value (i.e. 1.6) of the irrigation water and the absence of
any initial salt built‐up in the soil profile (initial SAR value is 1.25 throughout entire soil profile) or without
other salt inputs into the soil profile (e.g. from saline shallow groundwater or later inflow from water
discharging at the break‐of‐slope (Biggs et al. 2012)).

Soil depth (cm)The EC ANZECC trigger value of 0.65 dS/m is generally not exceeded across the entire soil profile, except atthe very top few centimetres. Although the EC of the irrigation water is low (0.29 dS/m), EC in the top of
the soil profile increase to a maximum value of 0.73 dS/m due to evapotranspiration leading to dry soil
conditions triggering salt concentration effects. In terms of contributions of individual ions in the treatedirrigation water to the EC, the most important contributors to EC are chloride (49.6% contribution to total
EC), sodium (21.5% contribution to total EC) and magnesium (11.3% contribution to total EC) (Table 2.7).

Figure 3.17 Simulated SAR (left) and electrical conductivity (right) for scenario 5. ANZECC trigger value for SAR (lightclay) and EC (0.65 dS/m) (see Table 1.3).

Depth distributions of aqueous concentrations of major cations Ca, Mg, Na and K in the soil profile areshown in Figure 3.18. Calcium, magnesium and sodium display similar patterns; accumulation at the soil
surface due to a decrease in soil water owing to ET and gradual accumulation at deeper depths due todownward leaching. Sodium displays the largest retardation as is expected from the Gapon cation exchange
constant (see Table 2.6). The migration of potassium is somewhat different in the top 30 cm of the soil
profile, without a distinctive accumulation as observed with the other ions.
The sodium ANZECC trigger value of 20 meq/L (460 mg/L, see Table 1.5) is not exceeded anywhere in thesoil profile.
Figure 3.19 shows concentrations of sorbed/exchangeable cations on the cation exchange sites. While theconcentration front for exchangeable Na moves relatively quickly down the soil profile, the front reaching
the bottom of the soil profile, other concentration fronts (i.e., for Ca and Mg) move much slower. This ismainly due to large amounts of Na being added with irrigation water and relatively small buffering capacity
43

with respect to Na (note that the initial concentration for exchangeable Na was only about 2.6 meq/kg). On
the other hand, the initial concentrations of exchangeable Ca and Mg were about 175 and 89 meq/kg, i.e.,significantly larger than for Na, resulting in a significantly larger buffering capacity for these two cations.

Figure 3.18 Simulated concentrations of dissolved major ions Ca, Mg, Na, K, and Cl.

From the six minerals considered in the Major Ion Chemistry (calcite, dolomite, gypsium, nesquehonite,hydromagnesite, sepolite), only calcite is formed. The concentration of calcite is highest in the top soil layer
where it reaches nearly 0.5 meq/kg (results not shown). Calcite formation in the top layers explains thenearly depth‐invariance in aqueous calcium concentration, a behaviour quite different from that ofmagnesium and sodium.
Time series of soil water content and pressure head at 10 cm depth display wetting‐drying cycles as a resultof the applied irrigation regime and water redistribution due to evapotranspiration and drainage (Figure3.20). Every irrigation event adds solutes to the soil which partly migrate downwards while another part
accumulates in the top of the soil profile (Figure 3.18).

44

The pressure head data indicates that the minimum pressure (~ ‐15 cm) is slightly smaller than the pressure
head below which root water uptake is optimal (POpt = ‐25 cm) (Figure 3.20). On the other hand, themaximum pressure head (~ ‐272 cm) exceeds the limiting pressure head below which root water uptake is
no longer optimal (P2H = ‐200 cm). In other words, the water uptake by the grass vegetation consideredhere occurs at a maximum rate for about 3 out of 4 days, whereas at the 4th day it is suboptimal (seeschematic root water uptake function in Figure 2.4). A more optimal irrigation regime would involve
starting irrigation at day 4 to avoid the soil from becoming too dry.

Figure 3.19 Surface species concentration versus soil depth during irrigation cycles. Total adsorbed concentration
for Ca, Mg, Na, and K is constant (279 meq/kg).

45

Figure 3.20 Calculated soil water content and pressure head at 10 cm depth (scenario 5; pasture). Every five days a
24‐hour irrigation event takes place. POpt = pressure head below which root water uptake is optimal, and P2H =limiting pressure head below which root water uptake is no longer optimal (according to Figure 2.4).

46

3.6 Summary of soil water balance calculations
For each scenario water balance components were calculated based on a 365 day simulation period.
Cumulative infiltration, actual transpiration (root water uptake) and evaporation and net drainage (outflow‐ inflow) values are shown for each of five scenarios evaluated (Table 3.1). The cumulative actual
transpiration and evaporation are each expressed as percentage of their respective cumulative potential
values (292 and 73 cm, respectively).
For all scenarios except scenario 3 cumulative infiltration equals the imposed value of 438 cm based on a 6
cm/day irrigation rate once every 5 days. Scenario 3 had a reduced irrigation rate of 4 cm/day once every 5days resulting in a cumulative infiltration of 292 cm. The lower irrigation rate was required to avoid ponding
conditions as a result of the reduction in hydraulic conductivity when untreated produced water wasapplied.
Cumulative actual evaporation reached its maximum value of 73 cm (0.2 cm/day) in all but one scenario(i.e. scenario 3 yielded 69 cm or 95% of the potential value owing to the reduced infiltration generating
drier soil moisture conditions and hence reduced evaporation).
The cumulative actual transpiration is a useful metric to evaluate combined effects of soil chemical and soil
physical processes on the soil water balance and on plant water stress. In all scenario is the cumulative
actual transpiration less than the potential; for scenario 1, 4, and 5 transpiration reaches 281 cm or 96% of
the potential value. This very small water stress is most likely due to the sub‐optimal irrigation regime
which generates soil pressure heads slightly outside the optimal range (see Figure 3.20). Larger differencesin actual transpiration exist for scenario 2 (82% of potential value) and 3 (64% of potential value). Scenario
2 applied the combined water and solute stress model – in combination with the major ion chemistry
module ‐ to control root water uptake: this resulted in additional stress on plant water uptake, mainly
caused by salinity stress. The smaller actual transpiration for scenario 3 (188 cm) is due in part to i) the
increased water stress as a result of a drier soil (4 cm/day rather than 6 cm/day irrigation rate, every 5days), ii) sup‐optimal pressure head conditions owing to a decrease in hydraulic conductivity generatingnear‐saturated conditions within the root zone (Figure 3.14), and iii) to salinity stress.
Table 3.1 Water balance components for a 365 day simulation period. Negative infiltration values means inflow;
negative net drainage values means outflow.

Scenario

Cumulative
infiltration(cm)

Cumulative
actualtranspiration(cm)

Cumulative
actualevaporation(cm)

Cumulative
net
drainage(cm)

1: pasture – UPW‐438@

281 [96%]

73 [100%]‐84

1: alfalfa – UPW‐438@

281 [96%]

73 [100%]‐83

2: pasture ‐ UPW ‐ MIC ‐ WSS‐438@

240 [82%]

73 [100%]‐123

3: pasture ‐ UPW ‐ MIC ‐ WSS ‐ KRM‐292#

188 [64%]

69 [95%]‐29

4: pasture ‐ SAPW ‐ MIC ‐ WSS ‐ KRM‐438@

281 [96%]

73 [100%]‐84

5: pasture ‐ TPW ‐ MIC ‐ WSS – KRM‐438@

280 [96%]

73 [100%]‐84

UPW : untreated produced water; MIC: major ion chemistry; WSS: water and solute stress model; KRM:
hydraulic conductivity reduction model; SAPW: surface water amended produced water; TPW: treatedproduced water; @ 0.06 cm/day once every 5 days; # 0.04 cm/day once every 5 days;

Cumulative net drainage (difference between cumulative outflow and inflow from shallow groundwater) is
identical for scenarios 1, 4, and 5. The higher drainage for scenario 2 (‐123 cm) is due to the lower
transpiration leaving more water in the soil profile that is available for drainage. The lower drainage for47

scenario 3 (‐29 cm) is the result of the smaller irrigation rate. Note that the decrease in irrigation by ~33%
results in a decreased in drainage of ~ 65%. Calculated net drainage from Table 3.1 is considerably higher
than calculated values (e.g. 10–20 cm/year) for furrow irrigation on cracking clay soils as reported by (Biggs
et al. 2012). Our drainage values are not surprising, given the high irrigation rate applied constantly for a
full year.
A comparison of scenario 1 and 2 is interesting from another perspective: it illustrates the effect ofincluding or excluding certain coupled soil processes on water balance components. Scenario 1 has astandard advection‐dispersion solute transport model without major ion chemistry. Scenario 2 on the otheraccounts not only for major ion chemistry, but also calculates salinity effects on root water uptake. This wasa link is made between soil geochemistry, soil hydrology and plant water uptake. Accounting for suchcoupled processes resulted in i) a decrease of the actual transpiration to 82% of its potential value, and ii)
an increase in net drainage by 46%. This is a clear demonstration that soil water balance modelling
involving major ion chemistry should account for the above interacting processes. Note that theimportance of such interacting processes decreases as the applied irrigation water has a less extreme
composition (such as the amended and treated produced water).
Mass fluxes of major cations and anions were calculated for upper and lower model boundary (Table 3.2).
For all scenarios the mass flux for calcium out of the soil is larger than the mass of calcium added via
irrigation. This results from calcium being replaced on the adsorption complex by mono‐charged sodium
cations whose added concentration is two orders of magnitude larger than that of calcium. As the sodium
concentration in the irrigation water decreases when untreated produced water is replaced by surface
water amended produced water (scenario 4) or treated produced water (scenario 5), the calcium flux
through the bottom of the soil profile also decreases due to decreased competition for adsorption sites
between calcium and sodium.
For magnesium the mass flux through the bottom of the soil profile is larger than that added via irrigation
only when untreated produced water is used (scenario 2 and 3). For all other scenarios magnesium
accumulates in the soil (input flux is larger than output flux via drainage).
The use of treated produced water generates the overall smallest mass flux (for all ions except HCO3) fromsoil to groundwater.
Table 3.2 Chemical mass fluxes (gram/m2) through upper and lower model boundary for a 365 day simulation
period. For upper model boundary negative fluxes are input to the soil; for lower model boundary negative values
refer to fluxes leaving the soil. For each element and each scenario, first row is flux through upper boundary;second row is flux through lower boundary.

Scenario

Ca

Mg

Na

K

HCO3

SO4

Cl

2: pasture ‐ UPW ‐MIC – WSS

‐4.56E+01‐3.93E+01‐6.18E+03‐2.41E+01‐5.64E+03‐9.99E+01‐2.61E+03‐6.76E+02‐7.91E+01‐6.90E+02‐7.35E+01‐1.44E+01‐7.92E+01‐2.07E+03

3: pasture ‐ UPW ‐MIC ‐ WSS – KRM‐3.02E+01‐2.59E+01‐4.07E+03‐1.59E+01‐3.72E+03‐6.58E+01‐1.72E+03‐4.42E+02‐4.99E+01‐1.69E+02‐3.19E+01‐2.92E+00‐3.80E+01‐9.93E+02

4: pasture ‐ SAPW ‐MIC ‐ WSS – KRM‐4.52E+01‐2.86E+01‐8.09E+02‐1.53E+02‐6.04E+02‐1.48E+03‐7.20E+02‐2.06E+02‐2.07E+01‐5.52E+01‐2.55E+01‐1.01E+01‐9.13E+02‐4.43E+02

5: pasture ‐ TPW ‐MIC ‐ WSS ‐ KRM‐4.54E+01‐3.33E+01‐1.26E+02‐1.90E+00‐8.24E+01‐4.39E+00‐2.95E+02‐5.70E+01‐6.27E+00‐2.80E+01‐1.63E+01‐1.17E+01‐2.74E+00‐1.75E+02

UPW : untreated produced water; MIC: major ion chemistry; WSS: water and solute stress model; KRM:
hydraulic conductivity reduction model; SAPW: surface water amended produced water; TPW: treatedproduced water; @ 0.06 cm/day once every 5 days; # 0.04 cm/day once every 5 days;48

4 Conclusions

Coal seam gas production in Australia will generate vast amounts of produced water with an estimated150‐180 GL/y peak production rate in Queensland alone. This water may be used, when treated oramended with e.g. surface water, for irrigation and stock, town water supply and construction andprocessing. For instance, assessment of alternative use for treated coal seam gas water for the Central
Condamine Alluvium (Queensland) indicates the region has capacity to deliver 854 GL, or around 35% of
the historic depletion, by 2050 to irrigators and the Chinchilla Weir water supply scheme. If the water
quality is acceptable, also untreated coal seam gas water can be used for agricultural purposes.
An in‐depth analysis has been undertaken to inform optimisation of water quality scenarios that will yield
minimal long‐term effects of dissolved ions on soil and plant health. We evaluated three irrigation
scenarios, whereby each scenario applied a different water quality based on whether the produced CSG
water was amended with surface water, treated or untreated, and two additional scenarios with differing
conceptualisation of water quality evolution in the soil. In four out of five scenarios the HYDRUS‐1D codeand its major ion chemistry module Unsatchem were implemented to simulate the simultaneous
movement of multiple major ions (Na, K, Ca, Mg, Cl, SO4, alkalinity) present in irrigation water in avegetated soil profile (pasture or alfalfa). The fifth scenario served as a baseline and did not consider
addition and transport of multiple major ions.
Simulations with different irrigation water qualities provided detailed results regarding chemical indicatorsof soil and plant health, i.e. SAR, EC and sodium concentrations. By comparing such indicators in the soil
profile with ANZECC water quality guideline values, an assessment was made of the suitability of theapplied produced water for long‐term irrigation. We also evaluated the build‐up of salt in the soil profile
and its effect on plant salinity stress, a condition which reduces the capacity for plants to uptake water
causing yield reduction. Finally, the simulations also allowed to test if soil hydraulic properties, in particular
the hydraulic conductivity, are negatively impacted by salt concentrations owing to clay dispersion.
Results based on five scenarios differing in applied irrigation water quality and invoked coupled processes
indicated that use of untreated irrigation water would cause SAR and EC values to significantly exceed the
ANZECC guide values. One of the three irrigation scenarios considered use of untreated produced water (EC= 5705 µS/cm and SAR = 77). Because irrigation with produced water is strictly regulated, the purpose ofthis scenario is to provide useful insights in the type of coupled chemical‐hydrological processes that mightoccur, and what the potential impacts would be on soil and plant health should such water end up in thesoil. For example, by using saline irrigation water, we demonstrated that accumulation of monovalent ionscauses clay swelling, resulting in a decrease in hydraulic conductivity of a particular soil layer. This in turncaused water stagnation above this layer (pressure head exceeds threshold for optimum water uptake byplant roots), leading to additional plant water stress and reduced transpiration (188 cm versus 240 cm
without water stress, or more than 20% less). Due to the lower transpiration, more water resides in the soil
profile after irrigation causing drainage to increase by 46% (‐123 cm compared to 84 cm).
In case surface water amended produced water was used (at a ratio of 1:3 with EC = 1573 µS/cm and SAR =11), the calculated soil SAR values were much lower and generally acceptable for sandy to sandy‐loam soil
(i.e. less than 20). Permissible SAR values for light clay (i.e. less than 5) were still exceeded for most of thesoil profile. Results also demonstrated the formation of calcite in the top of the soil profile owing to soluteconcentration effects under dry soil moisture conditions. This scenario did not result in additional stress onplant water uptake as a result of the acceptable salinity levels.
A final scenario used treated produced water with a relatively low concentration of major ions (EC = 291µS/cm, SAR = 1.6). The permissible SAR values for light clay were not exceeded, and all other ions remained
at tolerable levels. Under these conditions, actual transpiration was not reduced by additional salinity
stress, meaning that near optimal soil hydrological and chemical conditions existed for plant growth.
49

Our analysis further illustrate that accounting for coupled geochemical, hydrological and plant water
uptake processes resulted in i) a decrease of the actual transpiration to 82% of its potential value, and ii) an
increase in net drainage by 46% when compared to a water balance calculation where such interacting
processes were disregarded. This is a clear demonstration that soil water balance modelling involving major
ion chemistry should account for the above interacting processes.
The major ion chemistry module Unsatchem implemented in HYDRUS‐1D is a very power tool to assess in avery detailed way potential effects of different irrigation water qualities on soil and plant health. This offers
a cost‐effective way to optimise the quality of irrigation water derived from coal seam gas water and thus
ensure soils are managed in a sustainable manner. To further underpin such assessments, field trials
providing detailed soil chemical and physical data need to be consider to test the validity of the simulations
and to improve process parameterisation. One of the processes that needs improvement and testing is thechange in hydraulic properties as a result of mineral precipitation/dissolution causing partial or full pore
blockage. Based on a simulation period of a single year, calcite precipitation was found to occupy up to 3%of the soil’s pore space in the top of the soil profile. The long‐term evolution of salinity and its effect on soil
hydraulic properties, however needs further corroboration.

50

References

Adams MB (2011) Land Application of Hydrofracturing Fluids Damages a Deciduous Forest Stand in West Virginia. Journal ofEnvironmental Quality 40, 1340‐44.
AE (Arrow Energy) (2011) Surat Basin Coal Seam Gas Water Management Plan, Doc no: ENV11‐133, Revision 2. Arrow Energy.
AGL (2008) Gloucester Coal Seam Gas Project ‐ Water factsheet, community information fact sheet no. 5, NSW.
AGL (2011) Sustainability Performance Report.
AGL (2012) Water Management Plan for the Tiedman Irrigation Program ‐ Gloucester, NSW.
Appelo T and Postma D (2004) Geochemistry, groundwater, and pollution. Second Edition.
ANZECC (2000) Australian and New Zealand Guidelines for Fresh and Marine Water Quality. Volume 1: The Guidelines (Chapters 1–
7).
Batley GE and Kookana RS (2012) Environmental issues associated with coal seam gas recovery: managing the fracking boom.
Environmental Chemistry 9, 425–428.
Beletse YG, Annandale JG, Steyn JM, Hll I and Aken ME (2008) Can crops be irrigated with sodium bicarbonate rich CBM deep
aquifer water? Theoretical and field evaluation. Ecological Engineering 33, 26‐36.
Bennett S (2012) Key Spatiotemporal Factors Determining Uncertainty of Deep Drainage in a Semi‐Arid Area. PhD thesis ‐ The
University of New South Wales.
Biggs A, Witheyman SL, Williams KM, Cupples N, de Voil CA, Power RE, Stone BJ (2012) Assessing the salinity impacts of coal seamgas water on landscapes and surface streams. August 2012. Final report of Activity 3 of the Healthy HeadWaters Coal Seam
Gas Water Feasibility Study. Department of Natural Resources and Mines, Toowoomba.
Bradford SA, Šimůnek J, Bettehar M, van Genuchten MTh and Yates SR (2003) Modeling colloid attachment, straining, and exclusion
in saturated porous media. Environmental Science & Technology 37, 2242‐2250.
Bright DA and Addison J (2002a) Derivation of matrix standards for salt under the British Columbia contaminated sites regulation.
Royal Roads University.
Bright DA and Addison J (2002b) Derivation of matrix standards for salt under the British Columbia contaminated sites regulation.
Addendum A: Technical Options Analysis for a Soil Chloride Standard for Drinking Water Protection (Aesthetic), Applied
Research Division, Royal Roads University, Victoria, B.C.
Brooks RH and Corey AT (1964) Hydraulic properties of porous media, Hydrol. Paper No. 3, Colorado State Univ., Fort Collins, CO.
Brooks RH and Corey AT (1966) Properties of porous media affecting fluid flow. Journal of Irrigation and Drainage Division, ASCE
Proceedings 72, 61‐88.
Carsel RF and Parrish RS (1988) Developing joint probability distributions of soil water retention characteristics. Water Resources
Research 24, 755‐769.
CCME (1996) A Protocol for the Derivation of Environmental and Human Health Soil Quality Guidelines. Report CCME‐ EPC ‐101 E.
National Contaminated Sites Remediation Program.
Coordinator‐General (2010) Coordinator General’s evaluation report for an environmental impact statement; Gladstone Liquified
Natural Gas‐GLNG project.
Crosbie R, Morrow D, Cresswell R, Leaney F, Lamontagne S, Lefournour M (2012) New insights to the chemical and isotopic
composition of rainfall across Australia. Water for a Healthy Country Flagship Report series ISSN: 1835‐095X. CSIRO.
CWiMi (Centre for Water in the Minerals Industry) (2008) Groundwater Impacts of Coal Seam Gas Development – Assessment andMonitoring. University of Queensland, Brisbane.
DERM (2010) Guideline: approval of coal seam gas water for beneficial use. Queensland Department of Environment and Resource
Management.
DEPH (2014) General Beneficial Use Approval—Irrigation of Associated Water (including coal seam gas water).
Dewalle DR and Galeone DG (1990) One‐time Dormant Season Application of Gas‐Well Brine on Forest Land. Journal ofEnvironmental Quality 19, 288‐95.
DNRM (QLD Department of Natural Resources Management) (2012) Forecasting coal seam gas water production in Queensland's
Surat and southern Bowen basins, report prepared by Klohn Crippen Berger: Healthy HeadWaters Coal Seam Gas Water
Feasibility Study, September 2012.
DNRM (Department of Natural Resources and Mines) (2013) Assessment of alternative use options for coal seam gas waterproposed for Central Condamine Alluvium recharge schemes. Healthy HeadWaters Coal Seam Gas Water Feasibility Study.
Feddes RA, Kowalik PJ, and Zaradny H (1978) Simulation of Field Water Use and Crop Yield, John Wiley & Sons, New York, NY.
Fell C (2014) Water treatment and coal seam gas. Discussion Paper for Office of NSW Chief Scientist and Engineer: 59.
Horizon (2006) Irrigated pasture systems comparative project, Stage 3 Final Report.
Jacques D, Smith C, Šimůnek J, and Smiles D (2012) Inverse optimization of hydraulic, solute transport, and cation exchange
parameters using HP1 and UCODE to simulate cation exchange. Journal of Contaminant Hydrology 142‐143, 109‐125.
Jacques D, Šimůnek J, Mallants D, van Genuchten MTh (2013) The HPx reactive transport models: summary of recent developments
and applications. 4th International conference “Hydrus software applications to subsurface flow and contaminant transport
problems”, Czech University., pp. 7‐16.
Johnston CR, Vance GF & Ganjegunte GK (2008) Irrigation with coalbed natural gas co‐produced water. Agricultural Water
Management 95, 1243‐52.
51

Leskiw LA, Sedor RB, Welsh CM & Zeleke TB (2012) Soil and vegetation recovery after a well blowout and salt water release innortheastern British Columbia. Canadian Journal of Soil Science 92, 179‐90.
Lucas (2008) Review of environmental factors for exploration boreholes and production evaluation testing. Document #G‐STP‐L‐AP‐
A‐080818‐REF. Lucas Energy Pty Ltd.
Maas EV (1990) Crop salt tolerance. In "Agricultural salinity assessment and management", K. K.Tanji (ed.), ASCE Manuals andReports on Engineering practice, No 71, New York.
Mace JE and Amrhein C (2001) Leaching and reclamation of a soil irrigated with moderate SAR waters. Soil Science Society ofAmerica Journal 65, 199‐204.
Mallants D, van Genuchten MTh, Šimůnek J, Jacques D. and Seetharam S (2011) Leaching of contaminants to groundwater –Chapter 18. In (F.Swartjens, Editor) Dealing with Contaminated Sites. From Theory towards Practical Application, ISBN 978‐90‐481‐9756‐9, Springer Verlag, Dordrecht, The Netherlands, 2011. pp. 787‐850. doi: 10.1007/978‐90‐481‐9757‐6_1.
Mallants D, Buter A, and Simunek J (2015) Modelling heavy metal and major ion geochemistry in soil subjected to coal seam gas
produced water. CSIRO Land and Water, Canberra.
McNeal BL (1968) Prediction of the effect of mixed‐salt solutions on soil hydraulic conductivity. Soil Science Society of America
Proceedings 32, 190‐193.
McNeal BL (1974) Soil salts and their effects on water movement, In: Drainage for Agriculture, Edited by J. van Schilfgaarde,
Agronomy No 17, Am. Soc. Agr., Madison, WI.
Minasny B, McBratney A (2002b) The neuro‐m method for fitting neural network parametric pedotransfer functions. Soil Science
Society of America Journal 66, 352‐361.
Moore TA (2012) Coalbed methane: a review. International Journal of Coal Geology 101, 36‐81.
Mualem Y (1976) A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research
12, 513‐522.
Nghiem LD, Ren T, Aziz N, Porter I and Regmi G (2011) Treatment of coal seam gas co‐produced water for beneficial use inAustralia: A review of best practices. Desalination and Water Treatment 32, 316‐23.
Parsons W (2010) Adaptive associated water management, volume 2, chapter 12 in Australia Pacific LNG Project Environmental
Impact Statement, Brisbane: report to Origin and Conoco Phillips (Asia Pacific LNG Joint Venture).
Parson Brinkerhoff (2011) Gloucester Exploration Program – Irrigation Proposal Review of Environmental Factors for AGL Energy
Ltd. Parson Brinckerhoff Australia Pty Ltd.
Parson Brinkerhoff (2015) Tiedman Irrigation Program ‐ Water Compliance Report for the Period 5 July to 31 December 2014
Subtitle: Gloucester Gas Project. Parson Brinckerhoff Australia Pty Ltd.
QGC (2011) Interim Soil Management Plan Woleebee Creek Block PL276 (QCLNG‐BB‐24‐ENV‐PLN‐000001) Appendix 3.
QWC (2012) Underground water impact report for the Surat cumulative management area. July 2012. Queensland Water
Commission.
Ringrose‐Voase AJ (2004) Water Balance and deep drainage under irrigated cotton. ‘WaterPak.’ (Eds H Dugdale, G Harris, J Neilsen
D Richards, G Roth, D Williams). Cotton Research and Development Corporation: Narrabri, New South Wales: pp. 17‐28.
RPS (2011) Onshore co‐produced water: extent and management. In: LTD, R. A. P. (ed.) Waterlines Report Series No.54, Canberra:
National Water Commission.
Santos (2009) Sustainability report 2009: the energy for sustainability, Australia.
Santos (2010) Upstream‐Fairview Project Area Environmental Management Plan; Appendix B Fairview Project Area Coal Seam Gas
Water Management Plan.
Schaap MG, Leij FJ, and van Genuchten MTh (2001) Rosetta: a computer program for estimating soil hydraulic parameters withhierarchical pedotransfer functions. Journal of Hydrology 251, 163‐176.
Schijven J and Šimůnek J (2002) Kinetic modeling of virus transport at the field scale. Journal of Contaminant Hydrology 55, 113‐135.
Seuntjens P, Mallants D, Toride N, Cornelis C, and Geuzens P (2001) Grid lysimeter study of steady‐state chloride transport in two
Spodosol types using TDR and wick samplers. Journal of Contaminant Hydrology 51, 13‐39.
Silburn DM and Montgomery J (2004) Deep drainage under irrigated cotton in Australia: a review. In WATER‐PAK a guide for irrigation
management in cotton. Cotton Research and Development Corporation and Australian Cotton Cooperative Research Centre,
Narrabri. pp 29–40.
Šimůnek J and Suarez DL (1994) Two‐dimensional transport model for variably saturated porous media with major ion chemistry.
Water Resources Research 30, 1115‐1133.
Šimůnek J and Suarez DL (1997) Sodic soil reclamation using multicomponent transport modelling. ASCE Journal of Irrigation andDrainage Engineering 123, 367‐376.
Šimůnek J, Suarez DL, and Šejna M (1996) The UNSATCHEM software package for simulating one‐dimensional variably saturated
water flow, heat transport, carbon dioxide production and transport, and multicomponent solute transport with major ion
equilibrium and kinetic chemistry, Version 2.0, Research Report No. 141, U.S. Salinity Laboratory, USDA, ARS, Riverside, CA.
Šimůnek J, van Genuchten MTh, and Šejna M (2008) Development and applications of the HYDRUS and STANMOD software packages
and related codes. Vadose Zone Journal, Special Issue “Vadose Zone Modeling” 7, 587‐600. doi:10.2136/VZJ2007.0077
Šimůnek J, Jacques D, van Genuchten MT and Mallants D (2006) Multicomponent geochemical transport modelling using Hydrus‐1Dand HP1. Journal of the American Water Resources Association 42, 1537‐47.
Šimůnek J, Šejna M, Saito H, Sakai M, van Genuchten MTh (2009) The HYDRUS‐1D Software Package for Simulating the One‐
Dimensional Movement of Water, Heat, and Multiple Solutes in Variably‐Saturated Media, Version 4.08. Department ofenvironmental Sciences University of California Riverside, CA.
Stearns M, Tindall J, Cronin G, Friedel M and Bergquist E (2005) Effects of coal‐bed methane discharge waters on the vegetation andsoil ecosystem in Powder River Basin, Wyoming. Water Air and Soil Pollution 168, 33‐57.
SunWater (2014) Chinchilla Beneficial Use Scheme Annual Report 18 July 2013 – 30 June 2014.
52

Toride N, Leij FJ, and van Genuchten MTh (1995) The CXTFIT code for estimating transport parameters from laboratory or field tracer
experiments. Version 2.0, Riverside, CA, U. S. Salinity Laboratory, USDA, ARS.
Vance GF, King LA and Ganjegunte GK (2008) Soil and plant responses from land application of saline‐sodic waters: Implications ofmanagement. Journal of Environmental Quality 37, S139‐S148.
van Genuchten MTh (1987) A numerical model for water and solute movement in and below the root zone. Research Report No 121,
U.S. Salinity laboratory, USDA, ARS, Riverside, CA.
van Genuchten MTh (1980) A Closed‐form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Science
Society of America Journal 44, 892‐898.
Vidic RD, Brantley SL, Vandenbossche JM, Yoxtheimer D, Abad JD (2013) Impact of Shale Gas Development on Regional Water
Quality. Science 340:1235009. doi:10.1126/science.1235009.
Ward RS, Stuart ME, Bloomfield JP (2015) The Hydrogeological Aspects of Shale Gas Extraction in the UK. Issues in environmental
Science and Technology, 39: Fracking. Royal Society of Chemistry.
Wesseling JG, Elbers JA, Kabat P, and van den Broek BJ (1991) SWATRE: instructions for input, Internal Note, Winand Staring Centre,
Wageningen, the Netherlands.
White N and Zelazny LW (1986) Charge properties in soil colloids, In: Soil Physical Chemistry, edited by D. L. Sparks, CRC Press, BOCA
Raten, FL.

53

CONTACT US

FOR FURTHER INFORMATION

t 1300 363 400+61 3 9545 2176
e enquiries@csiro.au
w www.csiro.auLand and Water Business Unit/Water Resource
Management Program
Dirk Mallants
t +61 8 8303 8595
e dirk.mallants@csiro.au
w www.csiro.au

YOUR CSIRO
Australia is founding its future on
science and innovation. Its national
science agency, CSIRO, is a powerhouseof ideas, technologies and skills forbuilding prosperity, growth, health andsustainability. It serves governments,
industries, business and communities
across the nation.

54