Sustainable expansion of irrigated agriculture and ......Appendix 1 Soil hydraulic properties | 101 NAP-14, 30-60cm 0.371 0.312 0.215 0.113 0.098 0.087 0.064 NAP-14, 60-90cm 0.362

Sustainable expansion of irrigated agriculture and horticulture

in Northern Adelaide Corridor: Task 2 ‐ Modelling nutrient and

chemical fate to support the long‐term sustainability of the use

of recycled water

APPENDICES 1 to 4

Dirk Mallants, Vinod Phogat, Danni Oliver, Jackie Ouzman,

Yousef Beiraghdar, Jim Cox

Goyder Institute for Water Research

Technical Report Series No. 19/15

www.goyderinstitute.org

Appendix 1 Soil hydraulic properties | 98

APPENDIX 1 Soil hydraulic properties

Contents 1 Direct measurement ...................................................................................................99

1.1 Methodology ...............................................................................................................99

1.2 Data ...........................................................................................................................100

2 Mid infrared predictions ...........................................................................................123

2.1 Methodology .............................................................................................................123

2.2 Data ...........................................................................................................................125

2.3 Pedotransfer function predictions ............................................................................132 2.3.1 Methodology ................................................................................................132 2.3.2 Input data from site specific soil cores ........................................................132 2.3.3 Input data from regional database ..............................................................136

3 Validation of PTF .......................................................................................................138

3.1 Methodology .............................................................................................................138

3.2 Laboratory moisture retention data .........................................................................138

4 Summary of hydraulic properties ..............................................................................142

References ..............................................................................................................................145


1 Direct measurement

1.1 Methodology

Undisturbed soil cores were allowed to gradually saturate from the base upward over

approximately 7 days. When samples were deemed to be fully saturated, the hydraulic

conductivity was measured according to the constant head method of Youngs (2001).

When all measurements of saturated hydraulic conductivity were completed, the soil cores were

disconnected from their hydraulic extensions and weighed to obtain their saturated weights.

The cores were then placed onto saturated ceramic plates1 connected to either hanging water

columns, or subjected to positive gas (nitrogen) pressures in sealed chambers. Following their

saturation (nominated to be 0.1 kPa), the cores were exposed to a series of sequentially

increasing suctions (negative pressure) via the hanging water column method (4 and 8 kPa),

and hydrostatic pressures (positive gas pressure) via sealed chambers (33, 60 and 100 kPa).

When equilibrium was deemed to have been achieved (based upon experience and

measurement) each soil core was weighed, re-saturated and placed back onto a ceramic plate

for the next pressure step. After weighing the samples at 100 kPa, they were ovendried at 105

°C, then re-weighed (and the tare weight of the stainless steel ring, mesh and elastic band

recorded). Separate pieces of soil (saved from trimming each core) were used to measure the

water content of the soil at 1500 kPa. To achieve this, small duplicate samples were placed

directly onto the surface of saturated ceramic plates (capacity 15 bar) and exposed to 1500 kPa

of nitrogen gas pressure in steel chambers for 6 weeks. All measured soil water retention data

are shown in Table 1. The corresponding fitted moisture retention curves using the RETC code

are shown in Figure 1 to Figure 16; fitted parameters for individual soil samples are listed in Table

2. Soil group mean moisture retention parameters are shown in Table 3. Measured saturated

hydraulic conductivity is listed in Table 4, whereas soil group mean hydraulic conductivities are

shown in Table 5.

1 A thin layer of fine contact material was placed between the sample and the plate to ensure continuous

hydraulic connectivity


1.2 Data

Table 1 Soil water retention data measured with suction plates. Soil group Sample code,

depth Soil water content (cm3/cm3) at increasing matric potentials

0.01 kpa

4 kpa

8 kpa

33 kpa

60 kpa

100 kpa

1500 kpa

Deep uniform to gradational

NAP-8, 0-10cm 0.394 0.373 0.365 0.355 0.284 0.284 0.082

NAP-8, 10-30cm 0.473 0.460 0.455 0.449 0.433 0.394 0.252

Hard red brown

NAP-10, 0-10cm 0.447 0.391 0.380 0.331 0.283 0.269 0.134

NAP-10, 10-30cm 0.374 0.337 0.323 0.295 0.273 0.258 0.129

NAP-12, 0-10cm 0.512 0.461 0.427 0.358 0.327 0.301 0.133

NAP-12, 10-30cm 0.367 0.350 0.330 0.300 0.282 0.249 0.145

NAP-13, 0-10cm 0.422 0.382 0.358 0.319 0.284 0.255 0.120

NAP-13, 10-30cm 0.360 0.317 0.303 0.254 0.235 0.228 0.141

NAP-13, 30-60cm 0.465 0.370 0.332 0.307 0.291 0.288 0.219

NAP-15, 0-10cm 0.530 0.465 0.446 0.404 0.362 0.363 0.246

NAP-15, 10-30cm 0.567 0.549 0.521 0.456 0.429 0.421 0.322

NAP-18, 0-10cm 0.424 0.383 0.377 0.333 0.286 0.281 0.182

NAP-18, 10-30cm Rep1

0.436 0.404 0.389 0.348 0.324 0.319 0.263

NAP-20, 0-10cm 0.465 0.400 0.390 0.356 0.326 0.322 0.226

NAP-20, 10-30cm 0.543 0.464 0.442 0.409 0.390 0.385 0.322

Sand over clay NAP-9, 0-10cm 0.436 0.408 0.340 0.147 0.114 0.103 0.068

NAP-9, 10-30cm 0.481 0.408 0.355 0.258 0.217 0.202 0.112

NAP-11, 0-10cm 0.399 0.357 0.317 0.188 0.155 0.145 0.104

NAP-11, 10-30cm 0.435 0.367 0.315 0.277 0.245 0.238 0.165


0.402 0.386 0.333 0.289 0.261 0.256 0.172


0.462 0.408 0.320 0.255 0.226 0.218 0.162

NAP-14, 0-10cm 0.401 0.347 0.175 0.095 0.063 0.053 0.015

NAP-14, 10-30cm 0.396 0.334 0.135 0.069 0.066 0.047 0.025


NAP-14, 30-60cm 0.371 0.312 0.215 0.113 0.098 0.087 0.064

NAP-14, 60-90cm 0.362 0.344 0.124 0.079 0.071 0.065 0.061

Calcareous NAP-16, 0-10cm 0.485 0.397 0.360 0.282 0.249 0.244 0.105

NAP-16, 10-30cm 0.476 0.366 0.321 0.261 0.228 0.215 0.150

NAP-17, 0-10cm 0.452 0.402 0.389 0.349 0.312 0.307 0.196


0.484 0.393 0.368 0.314 0.290 0.280 0.215

NAP-19, 0-10cm 0.504 0.412 0.377 0.293 0.248 0.239 0.165


0.487 0.411 0.378 0.293 0.269 0.257 0.213


Table 2 Soil water retention parameters fitted with RECT based on data measured with suction plates. Note: pressure data from Table 1 have units of kPa (negative pressure or suction), which were converted into cm of water column for fitting purposes, with 100 kPa = 1 atmosphere = 10 m of water column. When pressure (i.e. suction when negative) units used in RETC and Hydrus are cm, the water retention parameter α has units (cm-1).

Soil group Sample code, depth

van Genuchten parameters

α (cm-1)

n (-)

θr

(cm3/cm3) θs

(cm3/cm3)


NAP-8, 0-10cm

0.0018 1.580 0.041 0.394

NAP-8, 10-30cm

0.0014 1.329 0.126 0.473

Hard red brown

NAP-10, 0-10cm

0.0138 1.2671 0.067 0.447

NAP-10, 10-30cm

0.008 1.2675 0.0645 0.374

NAP-12, 0-10cm

0.0143 1.2238 0.0665 0.512

NAP-12, 10-30cm

0.0067 1.1958 0.0725 0.367

NAP-13, 0-10cm

0.0106 1.2244 0.0013 0.422

NAP-13, 10-30cm

0.0263 1.2092 0.0705 0.36

NAP-13, 30-60cm

0.2816 1.1327 0.1095 0.465

NAP-15, 0-10cm

0.0361 1.168 0.123 0.53

NAP-15, 10-30cm

0.0157 1.1703 0.161 0.567

NAP-18, 0-10cm

0.0147 1.2237 0.091 0.424


0.037 1.1357 0.1315 0.436

NAP-20, 0-10cm

0.0502 1.1498 0.113 0.465

NAP-20, 10-30cm

0.2304 1.0925 0.131 0.543

Sand over clay NAP-9, 0-10cm 0.0116 1.8195 0.034 0.436


NAP-9, 10-30cm 0.0285 1.3294 0.056 0.481

NAP-11, 0-10cm 0.0169 1.478 0.052 0.399

NAP-11, 10-30cm

0.0222 1.5753 0.0825 0.435


0.0207 1.2217 0.086 0.402


0.0435 1.2675 0.0811 0.4615

NAP-14, 0-10cm 0.018 3.7681 0.0531 0.4008

NAP-14, 10-30cm

0.0231 2.0793 0.0125 0.3963

NAP-14, 30-60cm

0.0248 1.619 0.0320 0.3713

NAP-14, 60-90cm

0.0167 6.3259 0.0305 0.3620

Calcareous NAP-16, 0-10 cm

0.036 1.2628 0.0525 0.485

NAP-16, 10-30 cm

0.086 1.2365 0.075 0.476

NAP-17, 0-10cm 0.0216 1.1943 0.098 0.452


0.1076 1.1692 0.1075 0.484

NAP-19, 0-10cm 0.0465 1.2593 0.0825 0.504


0.06 1.2178 0.1065 0.487


Table 3 Mean soil water retention parameters fitted with RECT (from Table 2) based on data measured with suction plates.


Mean van Genuchten parameters

α (cm-1)

n (-)

θr

(cm3/cm3) θs

(cm3/cm3)


0-10 cm 0.0018 1.580 0.041 0.394

10-30 cm 0.0014 1.329 0.126 0.473

Hard red brown

0- 10 cm 0.025 1.217 0.073 0.469

10 -30 cm 0.059 1.187 0.101 0.446

30 – 60 cm 0.282 1.133 0.109 0.465

Sand over clay 0- 10 cm 0.016 2.355 0.046 0.412

10 -30 cm 0.025 1.661 0.050 0.437

30 – 60 cm 0.023 1.420 0.059 0.387

60 – 90 cm 0.030 3.797 0.056 0.412

Calcareous 0- 10 cm 0.035 1.239 0.078 0.480

10 -30 cm 0.085 1.208 0.096 0.482


Table 4 Measured saturated hydraulic conductivitiy. Soil group Soil sample Mean Ksat

(m/s) Std dev Ksat

(m/s)


NAP-8, 0-10 cm 4.52E-06 4.14E-06

NAP-8, 10-30 cm 3.20E-06 4.27E-06

Hard red brown

NAP-10, 0-10 cm 3.49E-06 9.17E-07

NAP-10, 10-30 cm 1.75E-05 2.42E-05

NAP-12, 0-10 cm 7.30E-07 2.31E-07

NAP-12, 10-30 cm 5.51E-07 1.13E-07

NAP-13, 0-10 cm 1.25E-05 1.65E-05

NAP-13, 10-30 cm 8.66E-06 1.02E-05

NAP-13, 30-60 cm 4.97E-05 6.89E-05

NAP-15, 0-10 cm 1.76E-06 2.04E-06

NAP-15, 10-30 cm 3.53E-07 3.21E-08

NAP-18, 0-10 cm 1.63E-05 2.20E-05

NAP-18, 10-30 cm Rep1 2.07E-07 NC

NAP-20, 0-10 cm 2.21E-07 2.49E-08

NAP-20, 10-30 cm 7.62E-07 8.05E-07

Sand over clay

NAP-9, 0-10 cm 2.63E-05 6.26E-06

NAP-9, 10-30 cm 1.24E-05 6.29E-06

NAP-11, 0-10 cm 1.08E-05 5.48E-07

NAP-11, 10-30 cm 1.04E-05 6.26E-07

NAP-11, 30-60 Rep1 1.74E-6 NC

NAP-11, 60-90 cm Rep1 2.19E-05 NC

NAP-14, 0-10 cm 7.07E-05 4.61E-06

NAP-14, 10-30 cm 1.23E-05 1.52E-06

NAP-14, 30-60 cm 1.48E-05 4.54E-06

NAP-14, 60-90 cm 2.29E-05 2.45E-05

Calcareous NAP-16, 0-10 cm 1.71E-05 1.15E-05

NAP-16, 10-30 cm 3.54E-05 2.35E-05


NAP-17, 0-10 cm 2.75E-05 3.69E-05

NAP-17, 10-30 cm Rep1 1.16E-06 NC

NAP-19, 0-10 cm 2.66E-05 1.00E-05

NAP-19, 10-30 cm Rep1 2.54E-05 NC

Table 5 Mean measured saturated hydraulic conductivitiy (based on data from Table 4). Soil Group Depth (cm) Average Ks (m/s)

Deep uniform to gradational 0-10 4.52E-06

10-30 3.20E-06

Hard red brown 0-10 5.8E-06

10-30 4.7E-06

30-60 5.0E-05

Sand over clay 0-10 3.6E-05

10-30 1.2E-05

30-60 8.3E-06

60-90 2.2E-05

Calcareous 0-10 2.4E-05

10-30 2.1E-05


Figure 1 Fitted retention curve using the van Genuchten model (soil profile NAP8 – deep uniform to gradational). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Values for θr were fixed to 50% of the measured water content at 1500 kpa. Top: soil depth 10-30 cm; bottom: soil depth 0-10 cm.

0 0.1 0.2 0.3 0.4Soil water content (cm3/cm3)

0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

Data (0-10 cm)Theta_r = 0.041

0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5


NAP8


Figure 2 Fitted retention curve using the van Genuchten model (soil profile NAP10 – hard red brown). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Values for θr were fixed to either 50% of the measured water content at 1500 kpa (orange curve) or fixed to a value > 0.001 (black line) (each time θr reaches 0.001 forces RECT to put θr = 0, which is to be avoided). Top: soil depth 10-30 cm; bottom: soil depth 0-10 cm.

0 0.1 0.2 0.3 0.4 0.5Soil water content (cm3/cm3)

0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

Data (0-10 cm)Theta_r = 0.0023Theta_r = 0.067

0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5


NAP10



0 0.2 0.4 0.6Soil water content (cm3/cm3)

0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5


NAP12




0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5


NAP13


Figure 5 Fitted retention curve using the van Genuchten model (soil profile NAP13 – hard red brown). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Values for θr were fixed to either 50% of the measured water content at 1500 kpa (orange curve) or fixed to a value > 0.001 (black line) (each time θr reaches 0.001 forces RECT to put θr = 0, which is to be avoided). Soil depth 30-60 cm.

0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)



NAP13




0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.2 0.4 0.6


NAP15


Figure 7 Fitted retention curve using the van Genuchten model (soil profile NAP16 – hard red brown). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Values for θr were fixed to 50% of the measured water content at 1500 kpa. Top: soil depth 10-30 cm; bottom: soil depth 0-10 cm.


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5


NAP16




0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.2 0.4 0.6


NAP18




0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.2 0.4 0.6


NAP20


Figure 10 Fitted retention curve using the van Genuchten model (soil profile NAP9 – sand over clay). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Value for θr was fixed to 50% of the measured water content at 1500 kpa. Top: soil depth 10-30 cm; bottom: soil depth 0-10 cm.


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5


NAP9


Figure 11 Fitted retention curve using the van Genuchten model (soil profile NAP11 – sand over clay). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Value for θr was fixed to 50% of the measured water content at 1500 kpa (except for 0-10 data where θr was fitted). Top: soil depth 10-30 cm; bottom: soil depth 0-10 cm.


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5

Data (10-30 cm)Theta_r = 0.0825

NAP11


Figure 12 Fitted retention curve using the van Genuchten model (soil profile NAP11 – sand over clay). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Value for θr was fixed to 50% of the measured water content at 1500 kpa (except for 0-10 data). Top: soil depth 60-90 cm; bottom: soil depth 30-60 cm.


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5


NAP11




0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5

Data (10-30 cm)Theta_r = 0.0125

NAP14




0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5

Data (60-90 cm)Theta_r = 0.0305

NAP14


Figure 15 Fitted retention curve using the van Genuchten model (soil profile NAP17 – calcareous). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Value for θr was fixed to 50% of the measured water content at 1500 kpa. Top: soil depth 10-30 cm; bottom: soil depth 0-10 cm.


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5

Data (10-30 cm)Theta_r = 0.1075

NAP17


Figure 16 Fitted retention curve using the van Genuchten model (soil profile NAP19 – calcareous). Saturated and residual water content were fixed during the optimisation with RETC (van Genuchten et al. 1991). Value for θr was fixed to 50% of the measured water content at 1500 kpa. Top: soil depth 10-30 cm; bottom: soil depth 0-10 cm.


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)


0.1

1

10

100

1000

10000

100000

Soil

suct

ion

(cm

)

0 0.1 0.2 0.3 0.4 0.5

Data (10-30 cm)Theta_r = 0.1065

NAP19


2 Mid infrared predictions

2.1 Methodology

Mid-infrared (MIR) spectroscopy was used as an alternative to conventional soil analysis

methods to generate relationships that allow the prediction of hydraulic properties at locations

where soil samples were collected but direct estimation of the hydraulic properties was not

carried out (see Section 1). MIR analysis is rapid and inexpensive compared to conventional

wet chemistry and soil physical methods, especially because it can provide us with simultaneous

prediction of various chemical and physical properties using only soil IR spectra.

Soil samples were obtained from field soils and glass house soils. For field soils, the collected

soil material was available to depths of 1.2 m, air-dried and passed through a 2mm sieve in

preparation for MIR spectral analysis (for details, see Task 1 Report). The calibration models

for predicting soil properties were developed between IR spectra (i.e. predictor variables) and

reference soil property values (i.e. response variables). The response variables were available

from shallow soils depths as discussed in Section 1. The MIR method was used to predict water

content at different soil suctions (0.01, 4, 8, 33, 60, 100, and 1500 kpa) and saturated hydraulic

conductivity. The reliability of the MIR predictions can be asserted from the cross plot in Figure

17; water contents are very well predicted for soil suction of 8 KPa and higher, while the water

contents at low suction are somewhat less good predicted. This is not a surprise: at the 0.01 and

4 kPa soil suction the soil structure is the dominant factor determining soil water content.

Because soil structure is relatively difficult to measure with MIR, the soil water content

predictions are somewhat less reliable. At the higher suctions (8 kPa and higher), soil texture is

the dominant factor in determining soil water content. Because soil texture is relatively easily

predicted with MIR, the water content is too. The predicted saturated hydraulic conductivity

compares reasonably well with the measured values, especially in the wetter range (Figure 18).

The resulting predicted soil water retention data are discussed in Section 2.2.

For glasshouse soils, two soils were samples at three locations and two depths (0-10, 20-30 cm).

Core analysis was the same as for the field soils.


Figure 17 Comparison between measured and MIR predicted soil water content.

0 0.1 0.2 0.3 0.4 0.5 0.6Measured soil water content (cm3/cm3)

0

0.1

0.2

0.3

0.4

0.5

0.6

Pred

icte

d so

il w

ater

con

tent

(cm

3 /cm

3 )

Water retention at 0.01 kPaWater content at 4 kPaWater content at 8 kPaWater content at 33 kPaWater content at 60 kPaWater content at 100 kPaWater content at 1500 kPa

Line of perfect agreement

R2 = 0.73 (0.01 kPa)R2 = 0.73 (4 kPa)R2 = 0.95 (8 kPa)R2 = 0.95 (33 kPa)R2 = 0.95 (60 kPa)R2 = 0.96 (100 kPa)R2 = 0.95 (1500 kPa)

1:1


Figure 18 Comparison between measured and MIR predicted saturated hydraulic conductivity.

2.2 Data

Field soils

MIR predicted soil water contents for four Soil Groups are shown in Table 6 to Table 9. The van

Genuchten parameters were then obtained with the optimisation code RETC; average van

Genuchten parameters are shown in Table 10. We only show values for soil depths for which no

direct measurements were available (see Section 1), up to 1.2 m depth (for deeper depths

pedotransfer function predictions will be used). These values are used in the HYDRUS

simulations.

1E-07 1E-06 1E-05 1E-04Measured saturated hydraulic conductivity (m/s)

1E-07

1E-06

1E-05

0.0001Pr

edic

ted

satu

rate

d hy

drau

lic c

ondu

ctiv

ity (m

/s)

R2 = 0.77


Table 6 Soil water retention data predicted with mid-infrared model for deep uniform to gradational soils.

Soil depth (cm)

Water content (cm3/cm3) at KSAT

(m/s) 0.01 kPa

4 kPa

8 Kpa

33 Kpa

60 Kpa

100 Kpa

1500 Kpa

0-10 min 0.387 0.370 0.361 0.255 0.224 0.202 0.072 1.59E-06

max 0.496 0.444 0.436 0.379 0.354 0.332 0.217 1.04E-05

mean 0.037 0.021 0.021 0.034 0.033 0.031 0.039 2.49E-06

median 0.412 0.392 0.373 0.355 0.292 0.287 0.107 7.06E-06

10-30 min 0.397 0.390 0.342 0.279 0.257 0.230 0.121 1.77E-07

max 0.487 0.470 0.465 0.453 0.435 0.394 0.265 1.32E-05

mean 0.453 0.427 0.406 0.367 0.347 0.316 0.199 7.92E-06

median 0.463 0.417 0.400 0.350 0.309 0.288 0.222 8.50E-06

30-60 min 0.392 0.380 0.366 0.275 0.243 0.236 0.161 4.69E-06

max 0.497 0.413 0.403 0.391 0.369 0.343 0.229 2.65E-05

mean 0.442 0.401 0.384 0.336 0.319 0.297 0.205 1.98E-05

median 0.435 0.406 0.377 0.342 0.324 0.302 0.216 2.45E-05

60-90 min 0.422 0.385 0.370 0.312 0.304 0.281 0.188 1.28E-05

max 0.492 0.454 0.438 0.430 0.406 0.399 0.296 4.15E-05

mean 0.455 0.418 0.402 0.360 0.344 0.322 0.228 2.70E-05

median 0.454 0.424 0.403 0.357 0.330 0.318 0.219 2.91E-05

90-120 min 0.445 0.425 0.400 0.328 0.324 0.296 0.208 1.59E-05

max 0.535 0.468 0.433 0.370 0.390 0.363 0.356 5.65E-05

mean 0.478 0.445 0.421 0.355 0.350 0.324 0.269 3.15E-05

median 0.455 0.441 0.428 0.366 0.336 0.314 0.243 2.22E-05

Table 7 Soil water retention data predicted with mid-infrared model for hard red brown soils. Soil depth (cm)


(m/s) 0.01 kPa

4 kPa

8 Kpa

33 Kpa

60 Kpa

100 Kpa

1500 Kpa


0-10 min 0.409 0.364 0.302 0.174 0.168 0.149 0.071 ND

max 0.543 0.467 0.452 0.408 0.370 0.372 0.283 3.19E-05

mean 0.463 0.410 0.389 0.336 0.300 0.284 0.155 4.83E-06

median 0.457 0.398 0.383 0.338 0.297 0.282 0.140 1.59E-06

10-30 min 0.357 0.316 0.256 0.154 0.174 0.159 0.067 ND

max 0.573 0.550 0.527 0.462 0.437 0.428 0.375 3.46E-05

mean 0.455 0.406 0.376 0.327 0.312 0.296 0.215 4.77E-06

median 0.460 0.403 0.368 0.321 0.301 0.287 0.205 4.23E-07

30-60 min 0.435 0.358 0.324 0.263 0.245 0.239 0.207 ND

max 0.555 0.512 0.487 0.434 0.427 0.413 0.378 9.84E-05

mean 0.479 0.424 0.388 0.350 0.343 0.327 0.278 2.03E-05

median 0.469 0.410 0.392 0.347 0.347 0.328 0.274 1.63E-05

60-90 min 0.413 0.370 0.343 0.265 0.278 0.247 0.201 1.12E-05

max 0.519 0.450 0.428 0.397 0.384 0.369 0.341 4.79E-05

mean 0.459 0.414 0.381 0.345 0.339 0.321 0.273 3.12E-05

median 0.447 0.409 0.378 0.345 0.342 0.320 0.278 2.83E-05

90-120 min 0.439 0.385 0.349 0.245 0.247 0.220 0.198 2.72E-05

max 0.504 0.439 0.417 0.354 0.358 0.332 0.304 3.64E-05

mean 0.467 0.416 0.386 0.298 0.300 0.275 0.255 2.98E-05

median 0.463 0.421 0.389 0.297 0.296 0.275 0.258 2.78E-05

Table 8 Soil water retention data predicted with mid-infrared model for sand over clay soils. Soil depth (cm)


(m/s) 0.01 kPa

4 kPa

8 Kpa

33 Kpa

60 Kpa

100 Kpa

1500 Kpa

0-10 min 0.393 0.345 0.164 0.089 0.061 0.050 0.015 1.04E-05

max 0.471 0.418 0.346 0.197 0.159 0.152 0.107 7.40E-05

mean 0.418 0.371 0.279 0.144 0.114 0.103 0.064 3.31E-05

median 0.403 0.364 0.313 0.152 0.122 0.107 0.077 2.19E-05


10-30 min 0.376 0.328 0.132 0.062 0.064 0.045 0.025 7.94E-06

max 0.483 0.410 0.361 0.294 0.255 0.250 0.175 1.68E-05

mean 0.437 0.370 0.268 0.201 0.176 0.162 0.101 1.17E-05

median 0.435 0.367 0.315 0.254 0.217 0.202 0.112 1.10E-05

30-60 min 0.369 0.308 0.209 0.091 0.077 0.067 0.049 1.74E-06

max 0.536 0.386 0.340 0.289 0.261 0.256 0.173 2.41E-05

mean 0.434 0.357 0.293 0.213 0.182 0.178 0.107 1.44E-05

median 0.412 0.374 0.328 0.249 0.203 0.207 0.084 1.54E-05

60-90 min 0.352 0.318 0.119 0.044 0.060 0.054 0.032 5.57E-06

max 0.551 0.408 0.387 0.347 0.287 0.309 0.162 5.25E-05

mean 0.441 0.356 0.256 0.203 0.176 0.180 0.109 2.92E-05

median 0.439 0.362 0.292 0.227 0.198 0.195 0.113 2.75E-05

90-120 min 0.379 0.331 0.160 0.080 0.074 0.062 0.058 2.68E-05

max 0.381 0.342 0.178 0.106 0.095 0.089 0.083 2.75E-05

mean 0.380 0.337 0.169 0.093 0.084 0.075 0.071 2.71E-05

median 0.380 0.337 0.169 0.093 0.084 0.075 0.071 2.71E-05

Table 9 Soil water retention data predicted with mid-infrared model for calcareous soils. Soil depth (cm)


(m/s) 0.01 kPa

4 kPa

8 Kpa

33 Kpa

60 Kpa

100 Kpa

1500 Kpa

0-10 min 0.441 0.392 0.355 0.274 0.242 0.237 0.084 1.38E-06

max 0.506 0.413 0.394 0.358 0.319 0.314 0.201 5.35E-05

mean 0.480 0.404 0.375 0.308 0.270 0.264 0.155 2.37E-05

median 0.484 0.403 0.377 0.293 0.253 0.246 0.165 2.24E-05

10-30 min 0.446 0.328 0.292 0.243 0.212 0.205 0.141 1.16E-06

max 0.506 0.411 0.378 0.330 0.318 0.298 0.234 5.20E-05

mean 0.475 0.387 0.346 0.289 0.264 0.251 0.188 2.22E-05

median 0.477 0.395 0.353 0.286 0.260 0.247 0.189 2.04E-05


30-60 min 0.470 0.337 0.269 0.269 0.235 0.239 0.123 8.03E-06

max 0.513 0.383 0.341 0.316 0.289 0.283 0.185 3.01E-05

mean 0.485 0.352 0.296 0.288 0.261 0.259 0.152 1.69E-05

median 0.481 0.346 0.286 0.285 0.259 0.261 0.146 1.32E-05

60-90 min 0.463 0.308 0.236 0.246 0.210 0.222 0.093 4.37E-06

max 0.504 0.376 0.333 0.317 0.291 0.283 0.186 3.07E-05

mean 0.480 0.345 0.289 0.284 0.252 0.254 0.142 1.91E-05

Median 0.475 0.349 0.294 0.287 0.254 0.256 0.145 2.07E-05

Table 10 Soil water retention parameters fitted with RETC based on MIR data.


Mean van Genuchten parameters

α (cm-1)

n (-)

θr

(cm3/cm3) θs

(cm3/cm3)

Hard red brown 60- 90 cm 0.0891 1.1216 0.1365 0.4588

Calcareous 30- 60 cm 0.2781 1.1639 0.0758 0.485

60 - 90 cm 0.2305 1.1382 0.0001 0.481


30- 60 cm 0.0219 1.1886 0.102 0.442

60 - 90 cm 0.0184 1.1781 0.1139 0.4545

Glasshouse soils

Using the same methodology as for field soils, the water content at increasing soil suction was

predicted using MIR (Table 11). Fitted water retention parameters are available from Table 12.

Table 11 Soil water retention data predicted with mid-infrared model for glasshouse soils.

Soil depth (cm)


(m/s) 0.01 kPa

4 kPa

8 Kpa

33 Kpa

60 Kpa

100 Kpa

1500 Kpa

Farm2_S1,

0-10 cm 0.449 0.376 0.362 0.297 0.287 0.270 0.170 2.4E-06

Farm2_S1, 0.425 0.352 0.349 0.318 0.300 0.283 0.186 3.2E-06


20-30 cm

Farm2_S2,

0-10 cm 0.483 0.355 0.384 0.281 0.271 0.262 0.199 5.1E-06

Farm2_S2,

20-30 cm 0.436 0.351 0.359 0.308 0.292 0.276 0.168 5.1E-06

Farm2_S3,

0-10 cm 0.443 0.349 0.348 0.267 0.260 0.244 0.165 7.6E-06

Farm2_S3,

20-30 cm 0.406 0.346 0.334 0.310 0.286 0.272 0.168 7.6E-06

Lot13PGR_S1,

0-10 cm 0.482 0.421 0.392 0.268 0.248 0.234 0.111 8.8E-06

Lot13PGR_S1,

20-30 cm 0.435 0.402 0.347 0.290 0.258 0.241 0.094 1.2E-05

Lot13PGR_S2,

0-10 cm 0.484 0.385 0.380 0.247 0.238 0.222 0.113 6.0E-06

Lot13PGR_S2,

20-30 cm 0.417 0.361 0.312 0.238 0.222 0.202 0.084 9.6E-06

Lot13PGR_S3,

0-10 cm 0.503 0.441 0.406 0.334 0.324 0.303 0.198 7.4E-06

Lot13PGR_S3,

20-30 cm 0.461 0.416 0.365 0.329 0.313 0.292 0.188 1.1E-05


Table 12 Soil water retention parameters fitted with RECT based on MIR data for glasshouse soils.

Sample code, depth

van Genuchten parameters

α

(cm-1)

η

(-)

θr

(cm3/cm3)

θs

(cm3/cm3)

R2 SSE

Farm2_S1,

0-10 cm

0.05750 1.13448 0.1809 0.4488 0.991 0.00042

Farm2_S1,

20-30 cm

0.05693 1.15322 0.2110 0.4249 0.950 0.00161

Farm2_S2,

0-10 cm

0.13456 1.17825 0.1983 0.4826 0.962 0.00200

Farm2_S2,

20-30 cm

0.05233 1.17232 0.1956 0.4359 0.950 0.00205

Farm2_S3,

0-10 cm

0.07233 1.19682 0.1736 0.4428 0.986 0.00065

Farm2_S3,

20-30 cm

0.03580 1.17112 0.1938 0.4059 0.947 0.00174

Lot13PGR_S1,

0-10 cm

0.02015 1.32277 0.1230 0.4820 0.992 0.00076

Lot13PGR_S1,

20-30 cm

0.01389 1.30350 0.1237 0.4350 0.973 0.00206

Lot13PGR_S2,

0-10 cm

0.03399 1.29368 0.1250 0.4839 0.983 0.00153

Lot13PGR_S2,

20-30 cm

0.02593 1.29149 0.1079 0.4170 0.985 0.00110

Lot13PGR_S3,

0-10 cm

0.03658 1.20409 0.2105 0.5029 0.992 0.00045

Lot13PGR_S3,

20-30 cm

0.03358 1.19106 0.2058 0.4609 0.977 0.00105


2.3 Pedotransfer function predictions

2.3.1 Methodology

To generate hydraulic properties for the Northern Adelaide Plains irrigation project in a cost-

effective manner where direct measurements or MIR predictions are not available, pedotransfer

functions (PTF) that utilise either existing gridded basic soil properties (such as particle size)

or measured particle size data were used. Pedotransfer functions typically use relatively easy to

measure soil properties (particle size, bulk density, etc.) to predict either the water content at a

specific soil pressure head or moisture retention parameters of a specific model. The predictive

capacity of PTF generally increases when more input data is provided. Therefore, this study

will also explore different approaches that use different data types when generating soil

hydraulic properties through PTFs. The predictive capacity of the approaches will be tested

using a limited set of independent soil hydraulic data (water retention curve and saturated

hydraulic conductivity, Ks).

2.3.2 Input data from site specific soil cores

Soil water retention parameters were predicted with the Rosetta software (Schaap et al., 2001)

using input data (%sand, %silt, %clay, bulk density) from soil cores collected from 10 sites

during this project. Soil profiles were distributed across four Soil Groups: calcareous soils (3),

hard red brown soils (6), sand over clay soils (3), and deep uniform to gradational soils (1). Soil

cores could generally be collected only at shallow depths, usually down to 30 or 60 cm and

exceptionally to 90 cm. Because soil hydraulic properties have been obtained through direct

measurements on the same soil material, use of pedotransfer functions would likely yield soil

hydraulic properties that are inferior to the ones measured directly. The value of the

pedotransfer function predictions is more in providing estimates that can be tested versus the

direct measurements, and hence allow to validate the pedotransfer functions. This is important

as the pedotransfer function predictions will be used for i) soil depths were no direct

measurements are available (usually the deepest part of the soil profile), and ii) for the cracking

soils that were not sampled during this project. Predicted van Genuchten soil moisture

parameters and saturated hydraulic conductivity are shown in Table 13 to Table 16.


Table 13 Soil water retention parameters for deep uniform to gradational predicted with Rosetta (Schaap et al., 2001) using input parameters from site specific soil cores.

Soil sample Input parameters for PTFs van Genuchten parameters

Clay %

Silt %

Sand %

Bulk density

α

(cm-1)

n (-)

θr (cm3/cm3)

θs (cm3/cm3)

Ks (cm/day)

NAP-8,

0-10cm 9.57 21.35 69.08 1.641 0.0404 1.415 0.0393 0.346 40.78

NAP-8,

10-30cm 27.97 26.68 45.35 1.485 0.0154 1.389 0.0714 0.409 26.24


Table 14 Soil water retention parameters for hard red brown predicted with Rosetta (Schaap et al. 2001) using input parameters from site specific soil cores.


Clay %

Silt %

Sand %

Bulk density

α

(cm-1)

n (-)

θr (cm3/cm3)

θs (cm3/cm3)

Ks (cm/day)

NAP-10,

0-10cm

14.1 29.1 56.9 1.44 0.020 1.440 0.050 0.394 34.400

NAP-10,

10-30cm

15.6 28.7 55.6 1.66 0.019 1.439 0.052 0.392 25.080

NAP-12,

0-10cm

18.0 29.7 52.3 1.59 0.019 1.396 0.053 0.372 13.280

NAP-12,

10-30cm

16.9 28.8 54.3 1.31 0.023 1.353 0.048 0.356 11.890

NAP-13,

0-10cm

19.9 23.9 56.2 1.46 0.017 1.452 0.064 0.451 46.830

NAP-13,

10-30cm

20.0 23.8 56.2 1.50 0.025 1.309 0.052 0.356 9.770

NAP-13,

30-60cm

38.6 26.5 34.6 1.75 0.022 1.196 0.070 0.347 1.670

NAP-15,

0-10cm

41.1 27.4 32.0 1.48 0.019 1.221 0.075 0.362 2.110

NAP-15,

10-30cm

14.1 29.1 56.9 1.44 0.020 1.440 0.050 0.394 34.400

NAP-18,

0-10cm

26.0 20.2 53.7 1.53 0.019 1.364 0.068 0.408 14.930

NAP-18,

10-30cm

Rep1

24.9 20.2 55.0 1.54 0.021 1.333 0.063 0.388 11.730

NAP-20,

0-10cm

27.0 24.8 48.2 1.45 0.016 1.399 0.071 0.418 12.990

NAP-20,

10-30cm

27.5 24.5 48.3 1.25 0.016 1.402 0.072 0.421 13.600


Table 15 Soil water retention parameters for sand over clay soil predicted with Rosetta (Schaap et al. 2001) using input parameters from site specific soil cores.


Clay %

Silt %

Sand %

Bulk density

α (cm-1)

n (-)

θr (cm3/cm3)

θs (cm3/cm3)

Ks (cm/day)

NAP-9,

0-10cm 3.00 0.00 97.00 1.575 0.0296 3.697 0.0549 0.3637 832.38

NAP-9,

10-30cm 9.52 4.76 85.71 1.450 0.031 2.024 0.0562 0.4115 194.91

NAP-11,

0-10cm 13.19 1.01 85.80 1.698 0.0281 1.755 0.0571 0.3406 69.75

NAP-11,

10-30cm 27.78 0.00 72.22 1.535 0.0242 1.332 0.0734 0.404 31.48

NAP-11,

30-60cm

Rep1

26.39 0.72 72.89 1.621 0.0253 1.300 0.0679 0.3746 20.95

NAP-11,

60-90cm

Rep1

20.26 0.00 79.74 1.469 0.0239 1.567 0.0716 0.4194 71.35

NAP-14,

0-10cm 27.78 0.00 72.22 1.535 0.0304 4.498 0.0523 0.3502 1331.27

NAP-14,

10-30cm 0.00 0.00 100 1.613 0.0309 4.165 0.0502 0.318 1035.97

NAP-14,

30-60cm 0.00 0.00 100 1.754 0.0309 4.097 0.0498 0.31 962.35

NAP-14,

60-90cm 0.00 0.00 100 1.741 0.0309 4.118 0.0499 0.3126 986.26


Table 16 Soil water retention parameters for calcareous soil predicted with Rosetta (Schaap et al. 2001) using input parameters from site specific soil cores.


Clay %

Silt %

Sand %

Bulk density (g/cm3)

α (cm-1)

n (-)

θr (cm3/cm3)

θs (cm3/cm3)

Ks (cm/day)

NAP-16,

0-10 cm 17.2 19.3 63.5 1.414 0.023 1.44 0.057 0.418 40.78

NAP-16,

10-30 cm 25.1 16.3 58.6 1.428 0.021 1.38 0.070 0.427 26.24

NAP-17,

0-10cm 26.7 24.6 48.7 1.463 0.016 1.39 0.070 0.415 12.80

NAP-17,

10-30cm

Rep1

27.3 26.9 45.8 1.447 0.015 1.40 0.072 0.417 11.39

NAP-19,

0-10cm 15.5 19.8 64.7 1.328 0.024 1.44 0.057 0.438 61.97

NAP-19,

10-30cm

Rep1

14.4 15.7 69.9 1.354 0.027 1.48 0.055 0.434 74.14

2.3.3 Input data from regional database

Soil water retention parameters were predicted with Rosetta using input data (%sand, %silt,

%clay, bulk density) from the regional CSIRO database (Soil and Landscape Grid Digital Soil

Property Maps for South Australia2 [3” resolution - approx. 100m cell size]), The database was

interrogated by selecting input data for locations defined by 14 soil profiles that had previously

been studied and documented but for which no particle size or bulk data was available. Soil

profiles were distributed across five Soil Groups: calcareous soils (4), hard red brown soils (4),

cracking soils (1), sand over clay soils (3), and deep uniform to gradational soils (2). For each

soil, profiles of minimum, mean, and maximum particle size versus depth were determined.

These mean values were used to predict the van Genuchten soil water retention parameters

(Table 17 to Table 21). A full description of the regional soil data is available from Appendix 5.

2 Source reference: Liddicoat, Craig; Holmes, Karen; Maschmedt, David; Rowland, Jan; Searle, Ross; Odgers,

Nathan (2014): Soil and Landscape Grid Digital Soil Property Maps for South Australia (3" resolution). v3. CSIRO. Data Collection.http://doi.org/10.4225/08/5472DCCD081D2

http://doi.org/10.4225/08/5472DCCD081D2



Table 17 Soil water retention parameters for calcareous soil predicted with Rosetta (Schaap et al. 2001) using input parameters from database.

Soil horizon

Depth (cm)

Soil Group 1: Calcareous soil

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (cm-1)

n (-)

Ks (cm/day)

l (-)

Horizon 1 0-15 0.064 0.4355 0.0271 1.6225 98.86 0.5

Horizon 2 15-30 0.0701 0.425 0.0239 1.4595 49.33 0.5

Horizon 3 30-60 0.0748 0.4315 0.0236 1.355 35.68 0.5

Horizon 4 60-100 0.0753 0.4256 0.0235 1.3396 31.23 0.5

Horizon 5 100-200 0.0735 0.4087 0.0242 1.298 22.91 0.5


Soil horizon

Depth (cm)

Group 2: Hard red brown soil

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (cm-1)

n (-)

Ks (cm/day)

l (-)

Horizon 1 0-15 0.0696 0.4389 0.0233 1.4215 46.36 0.5

Horizon 2 15-30 0.0801 0.4373 0.023 1.3027 24.97 0.5

Horizon 3 30-60 0.0875 0.4439 0.0234 1.2684 18.8 0.5

Horizon 4 60-100 0.0833 0.4309 0.0238 1.2566 16.97 0.5

Horizon 5 100-200 0.0807 0.4112 0.0255 1.2196 12.82 0.5


Soil horizon

Depth (cm)

Soil Group 3: Cracking soil

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (cm-1)

n (-)

Ks (cm/day)

l (-)

Horizon 1 0-15 0.0875 0.4782 0.021 1.3448 32.06 0.5

Horizon 2 15-30 0.0944 0.4721 0.0225 1.281 22.41 0.5

Horizon 3 30-60 0.0962 0.4671 0.0226 1.2588 18.2 0.5

Horizon 4 60-100 0.0962 0.4671 0.0226 1.2588 18.2 0.5

Horizon 5 100-200 0.0896 0.4271 0.024 1.2155 11.48 0.5



Soil horizon

Depth (cm)

Soil Group 4: Sand over clay

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (1/cm)

n (-)

Ks (cm/day)

l (-)

Horizon 1 0-15 0.0567 0.4043 0.0305 2.3469 290.75 0.5

Horizon 2 15-30 0.063 0.3966 0.0264 1.6463 74.09 0.5

Horizon 3 30-60 0.0711 0.4015 0.0248 1.3078 24.65 0.5

Horizon 4 60-100 0.0722 0.3997 0.0257 1.2637 20.1 0.5

Horizon 5 100-200 0.0695 0.3812 0.0259 1.2511 16.02 0.5


Soil horizon

Depth (cm)

Soil Group 5: Deep gradational to uniform

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (1/cm)

n (-)

Ks (cm/day)

l (-)

Horizon 1 0-15 0.0601 0.4256 0.0276 1.6534 95.58 0.5

Horizon 2 15-30 0.0681 0.4167 0.0246 1.4332 42.66 0.5

Horizon 3 30-60 0.0783 0.4219 0.024 1.2804 22.06 0.5

Horizon 4 60-100 0.0788 0.42 0.0243 1.2687 20.39 0.5

Horizon 5 100-200 0.0735 0.4007 0.0254 1.2477 17.21 0.5

3 Validation of PTF

3.1 Methodology

The ability of the PTFs of Schaap et al. (2001) to reliably predict soil water retention parameters

was tested by comparing the predicted values with direct measurements. For this purpose we

used cores for which both input parameters for the pedotransfer function were known (particle

size and bulk density) and direct measurements of hydraulic properties were available.

3.2 Laboratory moisture retention data

Two data sets were used for this purpose: i) a database from Green (2010) involving two Soil

Groups (sand over clay soils and hard red brown soils) (Table 22), and ii) the database obtained


in the current project encompassing four Soil Groups (sand over clay soils, hard red brown

soils, deep uniform to gradational soils, and calcareous soils) (Table 13 to Table 16). Cross plots

for the four van Genuchten parameters are shown in Figure 19 while Figure 20 shows the cross

plot for saturated hydraulic conductivity. Pearson correlation coefficients for all parameters are

listed in Table 23. The general trend is that the α parameter has the poorest predictions while the

remaining parameters are reasonably well predicted.

Table 22 van Genuchten soil hydraulic parameters estimated with the RETC (Van Genuchten et al. 1991) program utilizing measured θ-h and K at -10kPa values. Soil group D (hard red brown texture contrast): site HX, SR, TR. Soil group G (sand over clay soils): site PGR (data source: Green, 2010).

θr

(cm3/cm3) θs

(cm3/cm3) α

(cm-1) n (-)

Ks (cm/day)

l (-)

Soil Group G – Sand over clay soils PGR1 0-10 0.0213 0.48562 0.0726 1.387 26.42 0.5

10-30 0.02355 0.41408 0.0844 1.306 5.74 0.5 30-50 0.08506 0.29924 0.0217 1.418 1.13 0.5 50-70 0.1252 0.3256 0.0525 1.162 1.47 0.5

PGR2 0-10 0.04738 0.36289 0.0307 1.604 31.67 0.5 010-30 0.11528 0.29965 0.0253 2.219 2.31 0.5 30-50 0.10292 0.26978 0.0214 1.906 1.87 0.5

Soil Group D – Hard red brown texture contrast HX1 0-10 0.11283 0.37227 0.0407 1.731 85.23 0.5

010-30 0.10706 0.29021 0.0216 1.626 1.33 0.5 30-50 0.19457 0.39005 0.0089 1.135 0.09 0.5

HX2 0-10 0.0865 0.38013 0.0269 1.672 23.39 0.5 010-30 0.07219 0.31194 0.0188 1.806 2.12 0.5 30-50 0.0547 0.3009 0.0282 1.044 7.61 0.5

SR1 0-10 0.13093 0.34124 0.026 1.447 2.88 0.5 010-30 0.06954 0.30038 0.0211 1.276 0.40 0.5 30-50 0.1503 0.33013 0.0117 1.401 0.04 0.5

SR2 0-10 0.14207 0.31235 0.0711 1.384 37.74 0.5 010-30 0.14464 0.33074 0.0248 1.285 0.72 0.5 30-50 0.25182 0.45953 0.0059 1.143 0.03 0.5

TR1 0-10 0.12737 0.31948 0.0372 1.687 4.59 0.5 010-30 0.1734 0.29973 0.0329 1.428 15.43 0.5 30-50 0.18846 0.36002 0.032 1.223 2.46 0.5

TR2 0-10 0.11133 0.31963 0.0125 1.306 0.15 0.5 010-30 0.0444 0.29 0.0093 1.115 0.21 0.5 30-50 0.15207 0.32962 0.0078 1.257 0.11 0.5


Figure 19 Validation of PTF using CSIRO’s laboratory data (this study) and Green’s field data (Green, 2010). Soil group A = calcareous soils; soil group D = hard red brown soils; soil group G = sand over clay soils; soil group M = deep gradational soils.

Table 23 Pearson correlation coefficient between estimated (pedotransfer function - PTF) and measured (laboratory) van Genuchten parameters.

Parameter Pearson correlation coefficient

All soil groups combined

Hard red brown soils

Sand over clay soils Calcareous soils

α (PTF) - α (lab) 0.324 0.411 0.429 0.270

n (PTF) - n (lab) 0.705 0.416 0.597 0.358

θr (PTF) - θr (lab) 0.738 0.822 0.910 0.707

θs (PTF) - θs (lab) 0.516 0.010 0.909 0.715

0 0.04 0.08 0.12α - PTF

0

0.04

0.08

0.12

α -

lab

1

1.25

1.5

1.75

2

2.25

2.5n

- lab

1 1.25 1.5 1.75 2 2.25 2.5n - PTF

0

0.05

0.1

0.15

0.2

0.25

θ r -

Lab

0 0.05 0.1 0.15 0.2 0.25θr - PTF

0.2

0.3

0.4

0.5

0.6

θ s -

lab

0.2 0.3 0.4 0.5 0.6θs - PTF

Green (CL031-soil Group D)Green (CL036-soil Group D)Green (CL035-soil Group D)Green (CL012-soil Group G)CSIRO (soil Group D)CSIRO (soil Group G) CSIRO (soil Group A)CSIRO (soil Group M)

1 2 3 4 5 6

1

2

3

4

5

6


Figure 20 Validation of PTF for KS using CSIRO’s laboratory data.

1E-007 1E-006 1E-005 1E-004 1E-003K

S (lab, m/s)

1E-007

1E-006

1E-005

1E-004

1E-003K S (P

TF, m

/s)

Hard red brown soilsDeep gradational soilsSand over clay soilsCalcareous soils1:1

Pearson correlation = 0.58 (all data)


4 Summary of hydraulic properties

Soil hydraulic parameters used in HYDRUS-1D simulations are summarised in Table 24 to Table

28. Three datasets were combined to have the best available information across the different

Soil Groups and soil depths. For the shallowest depths the directly measured values were used:

these provide the highest reliability as they do not depend on some prediction method using

related prediction variables. Where such data were not available, the MIR predictions were used

as they provide site specific data using auxiliary data. Finally, if neither direct measurements

nor MIR predictions were available, the pedotransfer function predictions based on the regional

data set were used.

Table 24 summary of van Genuchten soil hydraulic parameters. Orange colour = direct measurements on site specific soil cores; blue colour = predicted with MIR using site specific core material; grey colour = PTF predictions.

Soil depth (cm)

Soil Group 1: Calcareous soils

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (cm-1)

n (-)

Ks (cm/day)

l (-)

0-15 0.078 0.480 0.035 1.239 207.36 0.5

15-30 0.096 0.482 0.085 1.208 181.44 0.5

30-60 0.0758 0.485 0.2781 1.1639 146 0.5

60-100 0.0001 0.4810 0.2305 1.1382 267.79 0.5

100-200 0.0735 0.4087 0.0242 1.298 22.91 0.5


Table 25 Summary of van Genuchten soil hydraulic parameters. Orange colour = direct measurements on site specific soil cores; blue colour = RETC fitted to retention data predicted with MIR using site specific core material; grey colour = PTF predictions.

Soil depth (cm)

Soil Group 2: Hard red brown soils

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (cm-1)

n

(-)

Ks (cm/day)

l (-)

0-15 0.073 0.469 0.025 1.217 50.11 0.5

15-30 0.101 0.446 0.059 1.187 13.98 0.5

30-60 0.109 0.465 0.282 1.133 13.33* 0.5

60-100 0.1365 0.4588 0.0891 1.1216 269* 0.5

100-200 0.0807 0.4112 0.0255 1.2196 12.82 0.5

*Measured and MIR predicted Ksat is too high for these layers (432 and 269 respectively for 30-60 and 60-90 cm depths). Therefore, Ksat was estimated from average particle size and bulk density data using ROSETTA.

Table 26 Summary of van Genuchten soil hydraulic parameters. Grey colour = PTF predictions.

Soil depth (cm)

Soil Group 3: Cracking soils

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (1/cm)

n (-)

Ks (cm/day)

l (-)

0-15 0.0875 0.4782 0.021 1.3448 32.06 0.5

15-30 0.0944 0.4721 0.0225 1.281 22.41 0.5

30-60 0.0962 0.4671 0.0226 1.2588 18.2 0.5

60-100 0.0962 0.4671 0.0226 1.2588 18.2 0.5

100-200 0.0896 0.4271 0.024 1.2155 11.48 0.5

Table 27 summary of van Genuchten soil hydraulic parameters. Orange colour = direct measurements on site specific soil cores; grey colour = PTF predictions.

Soil depth (cm)

Soil Group 4: Sand over clay soils

ϴr (cm3/cm3)

ϴs (cm3/cm3)

Alpha (cm-1)

n (-)

Ks (cm/day)

l (-)

0-15 0.0587 0.4127 0.0171 1.7878 311.04 0.5

15-30 0.0820 0.4434 0.0371 1.4397 103.68 0.5

30-60 0.1008 0.3866 0.0243 1.4745 71.71 0.5

60-100 0.1097 0.4117 0.0250 1.7920 190.08 0.5

100-200 0.0695 0.3812 0.0259 1.2511 16.02 0.5


Table 28 summary of van Genuchten soil hydraulic parameters. Orange colour = direct measurements on site specific soil cores; blue colour = RETC fitted to retention data predicted with MIR using site specific core material; grey colour = PTF predictions.

Soil depth (cm)

Soil Group 5: Deep uniform to gradational soils

ϴr (cm3/cm3) ϴs (cm3/cm3)

Alpha (cm-1)

n (-)

Ks (cm/day)

l (-)

0-15 0.041 0.394 0.0018 1.580 39.05 0.5

15-30 0.126 0.473 0.0014 1.329 27.65 0.5

30-60 0.102 0.442 0.0219 1.1886 171 0.5

60-100 0.1139 0.4545 0.0184 1.1781 233 0.5

100-200 0.0735 0.4007 0.0254 1.2477 17.21 0.5


References

Green, G. 2010. Point and regional scale modelling of vadose zone water and salt fluxes in an

area of intensive horticulture. PhD Thesis. Flinders University: 187 pp.

Schaap, M., Leij, F. and van Genuchten M.Th. 2001. rosetta: a computer program for estimating

soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology

251: 163-176.

van Genuchten, M.T.h, Leij F.J., and Yates S.R. 1991. The RETC code for quantifying the

hydraulic functions of unsaturated soils. Report No. EPA/600/2-91/065. R. S. Kerr

Environmental Research Laboratory, U. S. Environmental Protection Agency, Ada, OK.

85 pp.

Youngs E.G. 2001. Hydraulic conductivity of saturated soils. Chapter 4. In Soil and

Environmental Analysis. Physical methods, revised and expanded. CRC Press.

Appendix 2 Soil chemical properties | 146

APPENDIX 2 Soil chemical properties

Contents 1 Methodology............................................................................................................. 147

1.1 Derivation of Gapon selectivity coefficients ............................................................. 147

1.2 Calculation of ionic strength ..................................................................................... 148

1.3 Calculation of activity coefficients ............................................................................ 148

1.4 Calculation of selectivity coefficients ....................................................................... 149

References ............................................................................................................................. 152


1 Methodology

1.1 Derivation of Gapon selectivity coefficients

Ion exchange reactions of four cations (Ca, Mg, Na, K) accounted for in Hydrus-1D are based

on the Gapon convention (Gapon, 1933; Simunek and Suarez, 1994):

0.5𝐶𝐶𝐶𝐶 + 𝑁𝑁𝐶𝐶 − 𝑋𝑋 ↔ 𝑁𝑁𝐶𝐶 + 𝐶𝐶𝐶𝐶0.5 − 𝑋𝑋 (1)

0.5𝐶𝐶𝐶𝐶 + 𝐾𝐾 − 𝑋𝑋 ↔ 𝐾𝐾 + 𝐶𝐶𝐶𝐶0.5 − 𝑋𝑋 (2)

0.5𝑀𝑀𝑀𝑀 + 𝐶𝐶𝐶𝐶0.5 − 𝑋𝑋 ↔ 0.5𝐶𝐶𝐶𝐶 + 𝑀𝑀𝑀𝑀0.5 − 𝑋𝑋 (3)

The Gapon selectivity or exchange coefficients for reactions (1-3) are defined as (Simunek

and Suarez, 1994):

𝐾𝐾𝐶𝐶𝐶𝐶/𝑁𝑁𝐶𝐶 = [𝐶𝐶𝐶𝐶−𝑋𝑋] [𝑁𝑁𝐶𝐶][𝑁𝑁𝐶𝐶−𝑋𝑋][𝐶𝐶𝐶𝐶]0.5 (4)

𝐾𝐾𝐶𝐶𝐶𝐶/𝐾𝐾 = [𝐶𝐶𝐶𝐶−𝑋𝑋] [𝐾𝐾][𝐾𝐾−𝑋𝑋][𝐶𝐶𝐶𝐶]0.5 (5)

𝐾𝐾𝑀𝑀𝑀𝑀/𝐶𝐶𝐶𝐶 = [𝑀𝑀𝑀𝑀−𝑋𝑋] [𝐶𝐶𝐶𝐶]0.5

[𝐶𝐶𝐶𝐶−𝑋𝑋][𝑀𝑀𝑀𝑀]0.5 (6)

Where [Na], [K], [Mg], and [Ca] are molal activities in solution (dimensionless), and [Na-X],

[K-X], [Mg-X], and [Ca-X] are adsorbed concentrations (mmolc/kg soil).

The molal activity [i] is related to the molal concentration mi by an activity coefficient which

corrects for non-ideal behaviour. For aqueous solutes, the relation is (Simunek et al., 1996):

[𝑖𝑖] = 𝛾𝛾𝑖𝑖𝑚𝑚𝑖𝑖/𝑚𝑚𝑖𝑖0 ≡ 𝛾𝛾𝑖𝑖𝑚𝑚𝑖𝑖 (7)

where [i] is the activity of ion i (dimensionless), γi is the activity coefficient (dimensionless),

mi is the molality (mol/kgH2O), mi0 is the standard state, i.e. 1 mol/kg H2O. The factor 1/mi

0 is

unity for all species and cancels in the practical enumeration of Equation (7) but causes the

activity to become dimensionless.

Activity coefficients γi for solutes were calculated using the Debye-Hückel theory. In this

theory, first the ionic strength, I, is defined which describes the number of electrical charges in

the solution (Appelo and Postma, 2005):

𝐼𝐼 = 12� ∑𝑚𝑚𝑖𝑖 𝑚𝑚𝑖𝑖,0� 𝑧𝑧𝑖𝑖2 (8)


where zi is the charge number of ion i, and mi is the molality of i (mol/kg H2O). Similarly to

the definition of activity, the ionic strength becomes dimensionless by division with the

standard state mi0 i.e. 1 mol/kg H2O. The ionic strength of freshwater is typically smaller than

0.02 while seawater has an ionic strength of about 0.7 (Appelo and Postma, 2005).

For an ionic strength I of up to about 0.5, the Davies equation is used to calculate activity

coefficients (Appelo and Postma, 2005):

𝑙𝑙𝑙𝑙𝑀𝑀10𝛾𝛾𝑖𝑖 = −𝐴𝐴𝑧𝑧𝑖𝑖2 �√𝐼𝐼

1+√𝐼𝐼− 0.3𝐼𝐼� (9)

where A is a temperature dependent coefficient equal to 0.5085 at 25 °C.

1.2 Calculation of ionic strength

First, ionic strength I was calculated for 9 soil samples across four soil groups using Eq. (8).

For nine analytes (K+, Na+, Ca2+, Mg2+, Br-, Cl-, F-, NO3-, SO4

2-) pore-water composition (for

details see Task 1 Report, Oliver et al., 2018) was obtained by extraction of soil solution from

soil samples at maximum water holding capacity, where the latter is defined as the water

content at -5 kPa (Jenkinson and Powlson, 1976; McLaughin et al., 1997). The extraction

solution had 4 mg/L chloride which is equal to the amount of chloride in rain water near

Adelaide (Crosbie et al., 2012). Table 2 provides measured solution composition in mmol/L and

the corresponding ionic strength based on Eq. (8). Because all ionic strength values were

sufficiently smaller than 0.5, the Davies equation (Eq. 9) was used to calculate the ion activity

coefficients.

1.3 Calculation of activity coefficients

Activity coefficients calculated with Eq. 7 are listed in Table 2. As shown in Figure 1, activity

coefficients decrease with increasing ionic strength and the rate of decrease is strongest for

divalent ions.


Figure 1 Activity coefficients as function of ionic strength.

1.4 Calculation of selectivity coefficients

Gapon selectivity or exchange coefficients were calculated using Eq. (4-6), with adsorbed

concentrations from Table 3. All calculated Gapon coefficients are shown in Table 3, with mean

values listed in Table 1. Green (2010) also derived Gapon coefficients for Sand over clay and

Hard red brown soil from the NAP. For Sand over clay, Gapon coefficients were in the range

1-20 for KCa/Na, 0.3-1.7 for KMg/Ca, and 0.03 – 1.5 for KCa/K. For Hard red brown, values were

in the range 1.8-12 for KCa/Na, 0.09-1.65 for KMg/Ca, and 0.14-2 for KCa/K.

Table 1 Mean values of Gapon selectivity or exchange coefficients Soil Group KCa/Na KCa/K KMg/Ca

Hard red brown 0.753 0.065 0.033

Deep uniform to gradational 1.313 0.025 0.013

Sand over clay 1.880 0.014 0.020

Calcareous 0.957 0.009 0.038

0 0.01 0.02 0.03Ionic Strength (-)

0.5

0.6

0.7

0.8

0.9

1

Activ

ity c

oeffi

cien

ts (-

)Ca, MgNa, KDavies model (divalent ion)Davies model (monovalent ion)


Table 2 Soil chemical solution composition, ionic strength (I), and activity coefficients (γi) (based on data from Task 1 Report, Oliver et al., 2018). Soil Code Major Soil Group Field

Replicate Sample Depth (cm)

Solution composition (mmol/L) Ionic Strength

(-)

Activity coefficients (-)

Ca2+ K+ Mg2+ Na+ F- Cl- Br- NO3- SO42- I Ca2+ K+ Mg2+ Na+

NAP 6 Hard red brown 1 10-30 0.093 0.275 0.333 2.573 0.143 0.740 0.001 0.077 0.060 0.003 0.801 0.946 0.801 0.946



CL014 Hard red brown 1 30-50 1.070 0.372 0.842 12.206 0.365 1.484 0.002 0.249 0.509 0.011 0.646 0.896 0.646 0.896


NAP 8 Deep uniform to gradational

1 10-30 0.236 0.135 0.381 6.945 0.086 1.119 0.002 0.008 0.084 0.005 0.738 0.927 0.738 0.927

NAP 8 Deep uniform to gradational

2 30-60 1.519 0.384 3.405 28.410 0.147 2.906 0.003 0.003 0.754 0.026 0.542 0.858 0.542 0.858

NAP 9 Sand over clay 2 30-60 2.206 0.069 0.870 3.005 0.007 0.018 0.000 0.079 0.219 0.008 0.685 0.910 0.685 0.910

NAP 19 Calcareous 1 30-60 1.912 0.053 1.157 9.906 0.017 7.482 0.011 0.825 0.248 0.012 0.640 0.894 0.640 0.894


Table 3 Adsorbed concentrations, solution composition, and Gapon coefficients (based on data from Task 1 Report, Oliver et al., 2018). Soil Code

Major Soil Group Field Replicate

Sample Depth (cm)

Adsorbed concentration (mmolc/kg=meq/kg) Solution composition (mol/L) KCa/Na KCa/K KMg/Ca

Ca-X Mg-X Na-X K-X Ca2+ K+ Mg2+ Na+

NAP 6 Hard red brown 1 10-30 61.15 46.67 24.80 13.42 9.26E-05 2.75E-04 3.33E-04 2.57E-03 0.697 0.138 0.004



CL014 Hard red brown 1 30-50 56.11 60.18 46.92 18.76 1.07E-03 3.72E-04 8.42E-04 1.22E-02 0.498 0.038 0.040


NAP 8 Deep uniform to gradational 1 10-30 49.01 16.66 13.80 17.24 2.36E-04 1.35E-04 3.81E-04 6.94E-03 1.734 0.027 0.004

NAP 8 Deep uniform to gradational 2 30-60 33.27 28.78 31.65 16.33 1.52E-03 3.84E-04 3.40E-03 2.84E-02 0.893 0.023 0.023

NAP 9 Sand over clay 2 30-60 80.19 21.36 3.00 9.02 2.21E-03 6.87E-05 8.70E-04 3.01E-03 1.880 0.014 0.020

NAP 19 Calcareous 1 30-60 51.77 35.32 13.71 7.74 1.91E-03 5.26E-05 1.16E-03 9.91E-03 0.957 0.009 0.038


References

Appelo C.A.J. and D. Postma. 2005. Geochemistry, Groundwater and Pollution A.A. Balkema

Publishers, Leiden, The Netherlands. 2nd Ed. (xviii + 649 pages). (ISBN 04 1536 428 0)

Crosbie RS, Morrow D, Cresswell RG, Leaney FW, Lamontagne S and Lefournour M (2012)

New insights into the chemical and isotopic composition of rainfall across Australia.

CSIRO Water for a Healthy Country Flagship, Australia.

Gapon, E.N., 1933. Theory of exchange adsorption V. J. Gen. Chem. (USSR) 3, 667–669,

Chem. Abstr. 28, 4516.

Green, G.P., 2010. Point and regional scale modelling of vadose zone water and salt fluxes in

an area of intensive horticulture. Thesis submitted to Flinders University, South Australia.

https://flex.flinders.edu.au/file/27320d77-7088-4d13-8bc5-9c2b1fc4a071/1/Thesis-

Green-2010.pdf.

Jenkinson, D.S., and. Powlson, D.S. 1976. The effects ofbiocidal treatments on metabolism in

soil - V. A method for measuring soil biomass. Soil Biology and Biochemistry. 8: 209-

213.

McLaughlin, M. J., Tiller, K. G., Smart, M. K., 1997. Speciation of cadmium in the soil

solution of saline/sodic soils and relationship with cadmium concentrations in potato

tubers (Solanum tubersum L.). Aust. J. Soil Res. 35: 183-98.

Oliver, D.P., Fruzangohar, M., Johnston, C., Ouzman, J., Barry, K., 2018, Sustainable

expansion of irrigated agriculture and horticulture in northern Adelaide corridor. Task 1:

Development and optimisation of modelling domain and impact assessment of irrigation

expansion on the receiving environment, Goyder Institute for Water Research Technical

Report Series No. 18/xx, Adelaide, South Australia.

Simunek, J., and Suarez, D. L. 1994. Major ion chemistry model for variably saturated porous

media. Water Resour. Res., 30(4), 1115-1133.

Simunek, J., Suarez, D. L., and Sejna, M. 1996. The UNSATCHEM software package for

simulating one-dimensional variably saturated water flow, heat transport, carbon dioxide

production and transpon, and solute transpon with major ion equilibrium and kinetic

chemistry, Version 2.0. Res. Rep. No. 141, U.S. Salinity Laboratory, USDA-ARS,

Riverside, Calif.

https://flex.flinders.edu.au/file/27320d77-7088-4d13-8bc5-9c2b1fc4a071/1/Thesis-Green-2010.pdf

https://flex.flinders.edu.au/file/27320d77-7088-4d13-8bc5-9c2b1fc4a071/1/Thesis-Green-2010.pdf

Appendix 3 Root water uptake parameters | 153

Appendix 3 Root water uptake parameters

Contents 1 Methodology .............................................................................................................154

2 Data ...........................................................................................................................154

References ..............................................................................................................................160


1 Methodology

Root water uptake parameters for use in Hydrus-1D were compiled from the literature. Crops

were then grouped into five categories: vegetative crops, root crops, fruit crops, grain crops,

and other vegetation. Simulations with HYDRUS-1D were carried out for wine grapes,

almonds, pistachios, pasture/legumes, onions, carrots, and potatoes. Greenhouse crops were

also simulated: tomato, cucumber, capsicum, and eggplant. Results from these crops may be

extrapolated to similar crops provided the root water uptake parameters and rooting depths are

similar.

2 Data

Root water uptake parameters are summarised into groups: vegetative crops, root crops, fruit

crops, grain crops, and other vegetation (Table 1). Bold faced crops are the ones for which

HYDRUS-1D simulations were carried out. Parameters for other crops are included to allow

extrapolation of simulation results. Rooting depths for crops used in the HYDRUS-1D

simulations are given in Table 2. Parameters describing salinity stress are provided in Table 3.

Table 1 Root water uptake parameters for common crops. h1: no water extraction at higher pressure heads; h2: h below which optimum water uptake starts; h3: h below which water uptake reduction starts at high Tpot; h4: h below which water uptake reduction starts at low Tpot; h5: wilting point, no water uptake at lower pressure heads. Crops simulated in this study are in bold face.

Crops h1 [cm] h2 [cm] h3 [cm] h4 [cm] h5 [cm] Reference

Vegetative crops

Alfalfa, legumes -15 -30 -1500 -1500 -8000 Taylor and Ashcroft 1972

Beans (snap & lima)

-15 -30 -750 -2000 -8000 “

Cabbage -15 -30 -600 -700 -8000 “

Canning Peas -15 -30 -200 -300 -8000 “

Celery -15 -30 -400 -600 -8000 “

Grass -15 -30 -300 -800 -8000 “

Pasture -10 -25 -200 -800 -8000 Wesseling et al. 1991

Lettuce -15 -30 -400 -600 -8000 Taylor and Ashcroft 1972


Tobacco -15 -30 -300 -800 -8000 “

Sugar Cane - tensiometers

-15 -30 -150 -500 -8000 “

Sugar Cane – blocks

-15 -30 -1000 -2000 -8000 “

Sweet Corn -15 -30 -500 -1000 -8000 “

Turfgrass -15 -30 -240 -360 -8000 “

Common chicory

-5 -10 -350 -250 -1250 Vandoorne et al. 2012

Root crops

Onions - early growth

-15 -30 -450 -550 -8000 “

Onions - bulbing time

-15 -30 -550 -650 -8000 “

Sugar Beets -15 -30 -400 -600 -8000 “

Sugar Beets -10 -25 -320 -600 -16000 Wesseling et al. 1991

Potatoes -15 -30 -300 -500 -8000 Taylor and Ashcroft 1972

Potatoes -10 -25 -320 -600 -16000 Wesseling et al. 1991

Carrots -15 -30 -550 -650 -8000 Taylor and Ashcroft 1972

Broccoli - early

-15 -30 -450 -550 -8000 “

Broccoli - after budding

-15 -30 -600 -700 -8000 “

Cauliflower -15 -30 -600 -700 -8000 “

Soybean NA NA -1000 -1000 -10000 Fujimaki et al. 2008

Fruit corps

Lemons

-15 -30 -400 -400 -8000 Taylor and Ashcroft 1972

Oranges

-15 -30 -200 -1000 -8000 “


Mandarin -10 -25 -200 -1000 -8000 Phogat et al. 2014

Deciduous Fruit -15 -30 -500 -800 -8000 Taylor and Ashcroft 1972

Almonds/

Pistachios

-10 -25 -500 -800 -8000 Phogat et al. 2012

Olives NA NA -400* -400* -25000** * Ferreira 2017; **Xiloyannis et

al. 1999

Olives 0 0 -4000 -4000 -20000 Rallo and Provenzano 2013

Avocados


Grapes - early seasons

-15 -30 -400 -500 -8000 “

Grapes - during maturity

-15 -30 -1000 -1000 -8000 “

Strawberries -15 -30 -200 -300 -8000 “

Cantaloupe (rockmelon)

-15 -30 -350 -450 -8000 “

Tomatoes/ cucumber/ eggplant/ capsicum

-15 -30 -800 -1500 -8000 “

Bananas -15 -30 -300 -1500 -8000 “

Grain crops

Corn -15 -30 -325 -600 -8000 Wesseling et al. 1991

Corn - vegetative period


Corn - during ripening

-15 -30 -8000 -12000 -24000 “

Small Grains - vegetative period

-15 -30 -400 -500 -24000 “


Small Grains - during ripening

-15 -30 8000 -12000 -24000 “

Wheat 0 -1 -500 -900 -16000 Wesseling et al. 1991

Other vegetation

Chinese Tamarisk (saltcedar)

-20 -30 -300 -1000 -8000 Moayyad 2001

Cottonwood -0.1 -2 -80 -250 -1500 Moayyad 2001

Table 2 Rooting depths for crops used in HYDRUS-1D simulations. Crop Rooting depth (cm) Reference

Wine grapes 100 Phogat et al., 2017

Almonds 100 Phogat et al., 2018

Pistachios 100 Allen et al., 1998

Pasture 100 Allen et al., 1998

Onions 60 Allen et al., 1998

Carrots 60 Allen et al., 1998

Potatoes 60 Allen et al., 1998


Table 3 Salt tolerance data for herbaceous crops (Maas and Hoffman, 1977).



References

Allen R.G., Pereira L.S., Raes D., Smith M. 1998. Crop evapotranspiration: Guidelines for

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Appendix 4 Soil property maps | 162

Appendix 4 Soil property maps Table of Contents

1 Methodology ............................................................................................................... 163

2 Data ............................................................................................................................. 165

References ............................................................................................................................... 170


1 Methodology

Soil attribute maps of particular interest were obtained from the GIS-based database Land and Soil Spatial Data for Southern Australia developed by the DWLBC’s, Soil and Land Program (Liddicoat et al., 2014). The sampling points from the NatureMaps were overlain onto the mapping data to generate the soil attribute maps and to extract the soil type classification of the sampling points. The soil attribute maps generated through this process include the toxicity of Aluminium and Boron, Salinity, depth to water table, deep drainage, and potential root zone depths for different crop types (Table 1 and Section 2). These maps provide useful background information for the further planning of expansion of irrigated agriculture in the Northern Adelaide Plains, and in the area north of the current study area between Gawler and Light River.

Table 1 Overview of soil property maps. Map #

Soil property map Features displayed Property at soil profile sites

1 Aluminium toxicity Proportion of land with potentially high or moderate aluminium toxicity. Moderate toxicity is 2-4 mg/kg extractable aluminium and high toxicity is more than 4 mg/kg extractable aluminium.

All 12 soil profile sites within the Goyder project focus area have negligible to minor toxicity.

2 Deep drainage Depth to impeding layer determines the deep drainage characteristics of the soils.

None of the 12 soil profile sites within the Goyder project focus area have an impeding layer within 100 cm of the surface.

3 Depth to toxic layer - Boron

Depth to boron concentrations exceeding 15 mg/kg. Boron concentrations exceeding 15 mg/kg are considered toxic.

Only 2 out of 12 soil sampling sites exceed the 15 mg/kg threshold within 25-50 cm of the surface. For all other sites the distance to this layer is at least 50 cm.

4 Depth to water table Maximum groundwater level maintained for at least two weeks per year.

For all 12 soil profiles the depth to groundwater is at least 200 cm for 70% of the landscape.

5 Dry saline land Indicative ECe (dS/m) in surface and subsurface soil. Compare with salinity threshold values (Table 3, Appendix 3)

Two out of 12 soil sampling sites have a moderately high ECe (ECe 2-4 dS/m at surface), all other sites have a moderately low ECe (ECe 4-8 dS/m at surface).

6 Potential root zone depth for crops

Potential rooting zone depth for sensitive perennial horticultural crops such as citrus and avocadoes (CA)

Ten out of 12 soil sampling sites have a potential rooting zone depth of 30-40cm; the remaining soil sites have a rooting zone depth of 20-30 cm.


Potential rooting zone depth for medium sensitive perennial horticultural crops (CB) including stone fruits, pome fruits and almonds

Eight out of 12 soil sampling sites have a potential rooting zone depth of 40-50cm; the remaining four soil sites have a rooting zone depth of 30-40 cm.


Potential rooting zone depth for hardy perennial horticultural

Nine out of 12 soil sampling sites have a potential rooting zone depth of 50-60cm; the remaining


crops (CC) such as grape vines and olives

three soil sites have a rooting zone depth of 30-40 cm


Potential rooting zone depth for root crops including potatoes, carrots and onions (CD)

Ten out of 12 soil sampling sites have a potential rooting zone depth of 30-40cm; the remaining two soil sites have a rooting zone depth of 20-30 cm


Potential rooting zone depth for above ground annual horticultural crops such as brassicas (CD)

Ten out of 12 soil sampling sites have a potential rooting zone depth of 30-40cm; the remaining two soil sites have a rooting zone depth of 20-30 cm


2 Data

Soil property maps for the focus are shown in Figure 1 to Figure 10.

Figure 1 Aluminium toxicity.

Figure 2 Deep drainage.


Figure 3 Depth to toxic layer - Boron.

Figure 4 Depth to water table.


Figure 5 Dry saline land.

Figure 6 Potential root zone depth for crops (CA: sensitive perennial horticultural crops).


Figure 7 Potential root zone depth for crops (CB: medium sensitive perennial horticultural crops).

Figure 8 Potential root zone depth for crops (CC: hardy perennial horticultural crops).


Figure 9 Potential root zone depth for crops (CD: root crops).

Figure 10 Potential root zone depth for different crops (CE: above ground annual horticultural crops).


References

Liddicoat, C., Holmes, K., Maschmedt, D., Rowland, J., Searle, R., Odgers, N. 2014. Soil and Landscape Grid Digital Soil Property Maps for South Australia (3" resolution). v3. CSIRO. Data Collection.http://doi.org/10.4225/08/5472DCCD081D2



The Goyder Institute for Water Research is a partnership between the South Australian Government through the

Department for Environment and Water, CSIRO, Flinders University, the University of Adelaide, the University of South

Australia, and the International Centre of Excellence in Water Resource Management.

Documents

Sustainable expansion of irrigated agriculture and ......Appendix 1 Soil hydraulic properties | 101 NAP-14, 30-60cm 0.371 0.312 0.215 0.113 0.098 0.087 0.064 NAP-14, 60-90cm 0.362