Using the SWAGMAN Destiny model and GIS to Evaluate

Using the SWAGMAN Destiny model and GIS to Evaluate Productivity and Groundwater

Recharge in the Coleambally Irrigation Area

Emmanuel Xevi, Yun Chen and Shahbaz Khan

September 2010

Technical Report No. 12/10

BETTER IRRIGATION BETTER ENVIRONMENT BETTER FUTURE

CRC for Irrigation Futures i

Using the SWAGMAN Destiny model and GIS to Evaluate Productivity and GroundwaterRecharge in the ColeamballyIrrigation Area

Emmanuel Xevi1, 2, Yun Chen1, 2 and Shahbaz Khan1, 2

1CSIRO Land and Water 2CRC for Irrigation Futures

CRC for Irrigation Futures

CRC for Irrigation Futures Technical Report 12/10

September 2010

ii CRC for Irrigation Futures

CRC IF Copyright Statement

© 2010 IF Technologies Pty Ltd. This work is copyright. It may be reproduced subject

to the inclusion of an acknowledgement of the source.

978 0 9808674 8 0 (pdf)

978 0 9808674 9 7 (print)

Important Disclaimer

The Cooperative Research Centre for Irrigation Futures 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, the Cooperative

Research Centre for Irrigation Futures (including its employees and consultants)

excludes all liability to any person for any consequences, including but not limited to all

losses, damages, costs, expenses and any other compensation, arising directly or

indirectly from using this publication (in part or in whole) and any information or

material contained in it.

The contents of this publication do not purport to represent the position of the project

partners1 in any way and are presented for the purpose of informing and stimulating

discussion for improved decision making regarding irrigation in Coleambally Irrigation

Area.

Acknowledgments

Remote sensing data for the actual land use for the 2004-05 growing season was

provided by Mohsin Hafeez, ICT center, CSU, Wagga Wagga.

1 The project partners are: CSIRO, CRC for Irrigation Futures, Coleambally Irrigation Company Ltd.,

CRC for Irrigation Futures iii

Executive Summary

Arable land degradation due to salinisation is an important issue in intensive irrigation

areas such as the Coleambally irrigation area. An increase in irrigation and rainfall

recharge to the watertable will lead to unacceptably high watertables, and salinisation.

This issue has been dealt with in a number of ways.

Irrigation water providers have provided incentives through their land and water

management plans to encourage farmers to manage their water use to improve

efficiency and reduce water losses. The net recharge management initiative is

an example.

Regulations have been enacted to limit land area grown to rice and to require EM31

surveys on farms. And efforts were made to encourage research to understand the

processes and mechanisms of soil salinisation that will lead to better on-farm

management of water and promote efficiency gains.

One of the outputs of this research has been to develop SWAGMAN2 Destiny, a

computer-based biological simulation model describing the bio-physical interaction

between crop/soil/atmosphere system under different management scenarios.3

Integrating biological simulation models into a GIS framework, as SWAGMAN-GIS

does, helps interpret and understand model results in the spatio-temporal context.

With this GIS framework, users can “see” model results using farm or regional maps.

The modelling unit (feature polygon) can represent a field (paddock), farm or the entire

region. For each modelling unit, different management options can be assigned to

evaluate the effect of crops, soil type, climate and groundwater level conditions on

various environmental factors such as water leaching through the rootzone or different

levels within the soil profile.

SWAGMAN-GIS was developed to integrate our current best understanding of

soil/crop/climate interaction processes (SWAGMAN Destiny model) into a GIS

framework. Productivity, water use efficiency and water discharge beyond the rootzone

in a spatio-temporal scale can all be analysed.

2 SWAGMAN� (Salt Water And Groundwater MANagement)

iv CRC for Irrigation Futures

The SWAGMAN Destiny model simulates the effect of land use, soil and climate on

watertables, salinity and crop productivity. The model uses Arc-GIS shape files of soil

and land use and simulates water balance and crop growth for each soil polygon unit in

the soil shape file. GIS functionality is integrated into the model using ESRI Arc-

objects to process spatial input data for the model in addition to post-processing of the

output. The output from the soil/crop/climate model is processed and displayed using

thematic mapping resources available in Arc-GIS. Specifically, this application makes

defining and modifying spatial crop, soil and management input parameters and post-

processing of simulation results easier.

Results show spatial distribution of crop yield, actual evapotranspiration, productivity,

water use efficiency discharge below the rootzone (1.5 m) for maize and rice. They

also show the effect of weather and soil on water use efficiency and productivity. Since

a large proportion of the Coleambally Irrigation Area has watertables shallower than 2

m, the discharge below the rootzone is potentially useful as an input to groundwater

modelling environments such as MODFLOW. The productivity and water use

efficiency maps can be used to establish potential hotspots that need further analysis

and intervention. While this methodology represents upscaling of a one-dimensional

model to a regional scale model, it does not consider horizontal interactions between

the computational units (polygons). For example, intensive irrigation activity in a

paddock might affect the groundwater level in an adjacent paddock with implications for

capillary rise, or runoff from one paddock may infiltrate in an adjacent paddock.

CRC for Irrigation Futures v

Contents

Acknowledgements.........................................................................................................iiExecutive Summary........................................................................................................iii1. Introduction ............................................................................................ ............1

1.1. Report Background ................................................................................................... 21.2. Aims ..........................................................................................................................21.3. Outline .......................................................................................................................3

2. The Study Region .................................................................................................. 4 2.1. Soils ..........................................................................................................................42.2. Land Use ...................................................................................................................62.3. Climate ......................................................................................................................82.4. Groundwater ............................................................................................................. 9

3. SWAGMAN-GIS Model ..................................................................................... 14 3.1. SWAGMAN Destiny Model ..................................................................................... 14

3.1.1. Crop Growth ...............................................................................................16 3.1.2. Soil Water Balance .................................................................................... 16 3.1.3. Model Testing and Validation .................................................................... 16

3.2. SWAGMAN-GIS Model Development .................................................................... 17 3.3. Database Development and Datasets .................................................................... 17 3.4. Model Outputs ......................................................................................................... 19

4. Application of SWAGMAN-GIS ....................................................................... 20 5. Results and Discussions ................................................................................ 22

5.1. Maize Simulations ................................................................................................... 22 5.2. Rice Actual Evapotranspiration ............................................................................... 29 5.3. Application to Actual Land Use (2004 – 2005) ....................................................... 38

6. Conclusion and Implication for Land and Water Management Plan ........... 40 References ............................................................................................................... 41 Appendix A ............................................................................................................... 43 Appendix B ............................................................................................................... 44

vi CRC for Irrigation Futures

List of Figures Figure 1. Location of Coleambally Irrigation Area ......................................................................... 4

Figure 2. Soil types in the Coleambally Irrigation Area. ................................................................ 6

Figure 3. Proportions of total irrigated area sown to various crops within operational area ......... 6

Figure 4. Rice area and total other crop area ............................................................................... 7

Figure 5. Distribution of rice areas and farms (2004 – 2005). ....................................................... 8

Figure 6. Long-term monthly averages of rain and potential evapotranspiration (ET) .................. 8

Figure 7. Annual total rain and potential evapotranspiration for Coleambally .............................. 9

Figure 8. Schematic of water balance components ...................................................................... 9

Figure 9. Conceptual diagram of Coleambally Irrigation Area .................................................... 10

Figure 10. Location of piezometers ............................................................................................. 11

Figure 11. Piezometric levels below ground surface in the upper Shepparton formation ........... 11

Figure 12. Piezometric levels below ground surface in the lower Shepparton formation ........... 12

Figure 13. DataFlow diagram of SWAGMAN-GIS model ........................................................... 14

Figure 14. SWAGMAN – Destiny model Inputs, processes and outputs. ................................... 15

Figure 15. User Interface of SWAGMAN GIS ............................................................................. 18

Figure 16. Maize Yield, ET, Productivity, Water Use Efficiency and Rootzone Discharge ......... 23




Figure 20. Rice Yield, ET, Productivity, Water Use Efficiency and Rootzone Discharge ........... 31




Figure 24. Soil type, actual landuse, Rootzone discharge and water use efficiency .................. 39

List of Tables Table 1. Area proportion of each soil group in the CIA ................................................................. 5

Table 2. Irrigated crop areas within CICL's operational area ........................................................ 7

Table 3. Summary of water table accessions in the CIA (ML) .................................................... 10

Table 4. Area of the CIA with groundwater level in various ranges for lower Shepparton. ........ 12

Table 5. Area of the CIA with groundwater level in various ranges for upper Shepparton. ........ 13

Table 6. Crop Data......................................................................................................................18

Table 7. Soil Profile Data for Mundiwa Clay Loam (SMC) .......................................................... 19

Table 8. Irrigation Schedule ........................................................................................................ 21

Table 9. Soil Profile Data for sand............................................................................................... 44

Table 10. Soil Profile Data for RBE. ............................................................................................ 44

Table 11. Soil Profile Data for Wunnamurra (TRBE). ................................................................. 45

Table 12. Soil Profile Data for Black Earth(NSMC). .................................................................... 45

CRC for Irrigation Futures 1

1. Introduction

Increasing competition among water users, ongoing drought and the likelihood of

decreased rainfall as a result of climate change continue to focus attention on water

availability in Australia. One consequence of this is that a concerted review of available

water resources and use is needed.

This review should include a systematic analysis of how to improve irrigation systems

to increase profitability and reduce adverse environmental effects. Any analysis must

incorporate the potential impact that climate change will have on the hydrological cycle

and consequently on food production. It also must be done on a whole-of-catchment

scale to adequately address water quantity and quality issues and to understand the

concepts and processes that underpin water availability, crop production and

environmental requirements. This might involve system-wide analysis of alternative

cropping patterns, productivity and water use efficiency, e.g. growing more winter crops

and less summer crops. One tool that could be used in any review is modelling.

Most of the crop productivity simulations that are based on crop and soil water balance

models are site-specific. The scale resolution of most crop models is a single point on

the earth’s surface. Regional estimates of crop productivity require either scaling the

model simulations from point estimates or scaling the data inputs from point

measurements and then doing the simulations. There are different approaches to

integrating GIS with simulation models, such as the embedding, loose coupling and

tight coupling methods.

Several attempts at integrating GIS into a biophysical model have been made.

Examples are: AEGIS/WIN (Agricultural and Environmental Geographic Information

System for Microsoft Windows) described in Hoogenboom et al. (1999), where the

CERES crop growth models and DSSAT framework were coupled to a GIS system;

Ascough II et al. (2004), who described AgSimGIS, an integrated GIS and Agricultural

System Modelling where the rootzone water quality model (RZWQM, Ahuja et al.,

2000) was coupled to a GIS framework; and Robertson et. al. (2005), who described a

modified version of AgET that was coupled to ArcGIS 9.0 to map estimated recharge

across the lake Warden Recovery Catchment (LWRC) on the south coast of Western

Australia.

In this paper, we present a framework that couples a comprehensive crop growth, soil

water and salt balance model to a GIS system. The tight coupling approach was used

to integrate GIS and the SWAGMAN Destiny model. In this approach the

2 CRC for Irrigation Futures

crop/soil/climate model and GIS functionality have a common user interface. The crop

model was compiled as a COM object and interacts directly with Arc-Objects for GIS

functionality. Data flow between the crop model and GIS framework is handled by the

common user interface that reads ascii output files produced by the crop model and

writes them to shape files for thematic mapping.

The model’s executable is invoked for each modelling unit, and the output is collated

and joined to the polygon attribute tables of the shape file representing modelling

domain. The user interface developed can be used to:

� Define crop, soil, weather and groundwater level conditions and management

options and assign them to modelling units (polygon features); and

� Create thematic maps of yield, actual crop evapotranspiration and rainfall.

1.1. Report Background This report details the development of a GIS-based soil, water and crop growth model

that can be used to evaluate the water balance and water productivity on a catchment

scale. The spatial recharge output from this model can be used as input into

groundwater models such as those developed using MODFLOW.

Most crop models use point scale simulations to evaluate yields on a per hectare basis

assuming uniformity of soils, nutrients and water application. Some of the models try to

upscale the results from point scale to a spatial scale by performing several simulations

and interpolating the results. Very few of the models begin by interpolating the model

inputs and using the generated spatial fields to simulate productivity and hydrological

variables. This report presents an application of SWAGMAN-GIS to the Coleambally

Irrigation Area (CIA) and the estimation of fluxes through different soil layers.

1.2. Aims The aims of the research were to provide:

• The ability to prepare crop/soil/climate and management scenarios on spatial

and temporal scales;

• The ability to perform simulations and present results on spatial and temporal

scales using thematic maps;

• Improved, whole-of-region presentation and understanding by developing

thematic maps for yield, ET, recharge (at different horizons, including rootzone)

and salinity levels; and

• Maps of recharge that can be used as input to groundwater models.


1.3. Outline The outline of this report is as follows;

� A description of the CIA and its climate;

� A description of the soil/crop/environment model, SWAGMAN Destiny;

� How the SWAGMAN- GIS was developed;

� An application of the developed model to the CIA and the results for two crops

(maize and rice); and

� Conclusions and implications for Land and Water Management plans (LWMPs).


2. The Study Region

The CIA is located in NSW and sources its irrigation water from the Murrumbidgee

River (Figure 1). The CIA is about 95,000 ha, and comprises over 500 farms with 359

rice licence holdings in the 2004-2005 season. Fields are large (up to 70 ha) and well

maintained, making them ideal for instrument validation. The main summer crops

grown are rice, maize and soybeans, while winter crops include wheat, barely, oats,

and canola.

Figure 1. Location of Coleambally Irrigation Area.

The effects of climate change projected for south eastern Australia include substantial

changes in rainfall and run off, both of which can reasonably be expected to affect

irrigation and rainfed agricultural practice and productivity. It is therefore important to

evaluate the spatial distribution of productivity and recharge to groundwater to assess

the environmental impact of changed conditions.

2.1. Soils

The five main soil groups recognised in the Riverine landscape are: sandhill soils (SS),

red-brown earths (RBE), transitional red-brown earths (TRBE), non self-mulching clays

(NSMC) and self-mulching clay soils (SMC) (Hughes 1999, van Dijk and

Talsma, 1965).


CIA soils are very varied, from sand patches in the northwest to transitional red-brown

earth in the south. Transitional red-brown earth dominates the area although a large

patch of self-mulching clay can be found in the northern parts of the region.

Clays. The hydraulic conductivity of self-mulching clays (up to 0.5 m deep) is around

30 mm/day, whereas the conductivity for deeper horizons (1.5 to 3 m) is relatively low

(0.5 to 1 mm/day). Hydraulic conductivity for shallow non-self-mulching (hard setting)

clays is around 4 mm/day.

Red-brown earths. This group consists of loamy or sandy surface horizons deeper

than 0.1 m that abruptly change to clay subsoils. The reported hydraulic conductivity

values for this soil group vary greatly between 58 mm/day to 1039 mm/day.

Transitional red-brown earths. These soils have hydraulic characteristics of clays

and red-brown earths, ranging from 0.026 to 10 mm/day between a depth of 0.2 and

0.6 m. The top clay layer is very shallow (0.08 to 0.1 m). The deeper profiles contain

lime and gypsum.

Sands over clay. These soils mainly consist of sandy top soils (0.1 to 0.6 m) with

dense sub clay soils. The surface layer hydraulic conductivity is greater than

100 mm/day.

Deep sandy soils. These soils are of aeolian origin and contain coarse sands to a

depth of 4 m. Their hydraulic conductivity values that can be greater than

1000 mm/day.

Figure 2 shows the soil map of the CIA and Table 1 shows the proportion of total area

for each soil type.

Table 1. Area proportion of each soil group in the CIA.

Soil Group Area (ha) Proportion (%)

NSMC 1201 1.1

SMC 17769 16.1

RBE 24392 22.2

TRBE 52692 47.9

Sand 13863 12.7

Other 77.9 0.1

Total 110095 100


Figure 2. Soil types in the Coleambally Irrigation Area.

2.2. Land Use

The main crops grown in the CIA are wheat, pasture and rice. Rice is grown on about

60 per cent of the total area. The distribution of crop areas is shown in Table 2 and

Figures 3 and 4 show the comparison of rice area to the area sown to all other crops.

Figure 5 shows the distribution of rice areas and farms for the 2004–2005 growing

season.

Figure 3. Proportions of total irrigated area sown to various crops within CICL’s operational area 2006/07 (Source: Coleambally Irrigation Co-operative Limited Annual Environment Report (AER) 2007).


Figure 4. Rice area and total other crop area (Source: Coleambally Irrigation Co-operative Limited Annual Environment Report (AER) 2007).

Table 2. Irrigated crop areas within Coleambally Irrigation Co-operative Limited CICL's operational area. (Source: CICL Annual Environment Report, 2007).

Crop

CIA Area

(ha)Kerarbury Area

(ha)WCC Area

(ha)Total Area

(ha)Proportion of total

Area (%) Wheat 11861.2 128 520 12509.2 24.8 Pasture 8838.7 100 1020 9958.7 19.7

Rice 7744.9 379 395 8518.9 16.9 Barley 5494.5 224 200 5918.5 11.7 Corn 2131 1210 0 3341 6.6 Oats 1676.2 0 3 1679.2 3.3

Triticale 1599 0 54 1653 3.3 Canola 1602 0 0 1602 3.2 Lucerne 703 0 60 763 1.5

Faberbeans 669 44 0 713 1.4 Sorghum 455 100 20 575 1.1 Soybeans 477.5 0 0 477.5 0.9

Fallow 402 0 0 402 0.8 Clover 400 0 0 400 0.8

Green manure 336 0 0 336 0.7 Stock - dams 159 0 128 287 0.6

Sunflower 171 110 0 281 0.6 Maize 184 0 0 184 0.4 Other 107.5 60 0 167.5 0.3

Grapes 87.3 60 0 147.3 0.3 Prunes 43 100 0 143 0.3 Forest 124 0 0 124 0.2 Peas 115 0 0 115 0.2

Potatoes 52 0 0 52 0.1 Lupins 36 0 0 36 0.1

Vegetables 34.3 0 0 34.3 0.1 Winter pasture 30 0 0 30 0.1

Millet 0 0 26 26 0.1 Onions 20 0 0 20 0.0 Olives 15 0 0 15 0.0

Miscellaneous 12 0 0 12 0.0 Azuki beans 1 0 0 1 0.0

Lab lab 1 0 0 1 0.0 Undefined 0 0 0 0 0.0

Total 45582.1 2515 2426 50523.1 100

0

10000

20000

30000

40000

50000

60000

70000

1997

/98

1998

/99

1999

/00

2000

/01

2001

/02

2002

/03

2003

/04

2004

/05

2005

/06

2006

/07

Tota

l cro

p ar

ea (h

a)

Rice Total Other Crop


Figure 5. Distribution of rice areas and farms (2004 – 2005).

2.3. Climate

Climate data from 1888 to 2007 was downloaded for Coleambally from the SILO PPD

website. The dataset contains maximum and minimum temperatures, solar radiation

and rainfall, but not wind speed and dew point temperatures. Because of this

SWAGMAN Destiny simulations used the Priestley-Taylor method to estimate potential

evapotranspiration rather than the modified Penman-Monteith method. Figure 6 shows

the long-term monthly averages of rainfall and potential evapotranspiration estimated

using the most recent FAO method reported in the PPD dataset. Figure 7 shows the

annual rainfall and evapotranspiration for Coleambally.

Figure 6. Long term monthly averages of rain and potential evapotranspiration (ET) using data from (1889 – 2007) (Source:SILO PPD).

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12

Rai

n/ET

(mm

)

Month

RainET


Figure 7. Annual total rain and potential evapotranspiration for Coleambally (Source: SILO PPD, 1889-2007).

2.4. Groundwater

Recharge to the unconfined shallow aquifer - the watertable - comes from saturated

flow after irrigation and rainfall. This is offset by discharge from the watertable through

evaporation, transpiration and leakage to the deeper aquifers. Table 3 shows a

summary of the estimated contributions of different sources to watertable recharge.

Coleambally Irrigation Cooperative Limited has a number of measures to reduce

recharge to the watertable as a way of reducing the spread of salinisation

and minimising land degradation, including whole farm planning and net

recharge management.

Figure 8 is a diagrammatic representation of the water balance components, including

recharge to groundwater. Christen et al. (2000) used piezometric data to determine

area-wide recharge estimates for the CIA. They concluded that net recharge always

occurred in summer while net discharge occurred in winter.

Figure 8. Schematic of water balance components.

0

200

400

600

800

1000

1200

1400

1600

1800

1880 1900 1920 1940 1960 1980 2000 2020

ET/R

ain

(mm

)

Year

Rain

ET


Table 3. Summary of water table accessions in the CIA (ML) (Source: http://www.colyirr.com.au/environment/lwmp/contents.asp?ID=1&part=2).

Accessions Outflows Balance

Rice crop accessions 44,000

Other crops on farm 15,000

Channel seepage 15,000

Aquifer lateral flow 60,000

Total 74,000 60,000 14,000

Figure 9. Conceptual diagram of Coleambally Irrigation Area showing the geological formations and major flow directions.

Figure 9 shows a 3-D conceptual diagram of the CIA showing the general directions of

subsurface flows through geological layers. The diagram was derived from the actual

geological layers at the site. A network of about 809 piezometers covers the CIA and

surrounds; Figure 10 shows their distribution. The piezometer screen is located in the

upper Shepparton aquifer (5 to 12 m deep) and lower Shepparton aquifer (12 to 35 m

deep). The depth to the groundwater pressure head is read twice yearly in

February/March and August/September. Figure 11 shows maps of the piezometric

levels in the upper Shepparton aquifer from 2004 to 2007. Figure 12 shows maps of

piezometric levels in the lower Shepparton aquifer from 2004 to 2007. Table 4 and

Table 5 show the proportion of the area with groundwater levels in various ranges from

1985 to 2007 in the lower and upper Shepparton aquifers.


Figure 10. Location of piezometers. (Source: Coleambally Irrigation Co-operative Limited Annual Environment Report (AER) 2007).

Figure 11. Piezometric levels (depth(m)) below ground surface in the upper Shepparton formation (2004–2007) (Source: CICL AER, 2007).


Figure 12. Piezometric levels (depth(m)) below ground surface in the lower Shepparton formation(12 -35m) (Source: Coleambally Irrigation Co-operative Limited Annual Environment Report (AER) 2007).

Table 4. Area of the CIA with groundwater level in various ranges for lower Shepparton (5-12m) 1986 to September 2007 Source: Coleambally Irrigation Co-operative Limited Annual Environment Report (AER) 2007.


Table 5. Area of the CIA with groundwater level in various ranges for upper Shepparton (12-35m) 1986 to September 2007 (Source: Coleambally Irrigation Co-operative Limited Annual Environment Report (AER) 2007).


3. SWAGMAN-GIS Model

SWAGMAN-GIS integrates a crop/soil/environment model into a GIS framework. The

existing SWAGMAN Destiny model is a standalone model with an application user

interface. The input/output routines of the model engine have been extensively

modified to integrate tightly into a GIS environment. Figure 13 shows a dataflow

diagram of the integrated model.

Figure 13. DataFlow diagram of SWAGMAN-GIS model.

3.1. SWAGMAN Destiny Model

Destiny is a point scale, soil water balance and crop growth simulation model with crop

growth affected by water, salt and aeration stress. This model encapsulates our best

understanding of the major controlling processes in this complex irrigated production

system. It also allows us to examine the consequences of a range of management

options aimed at influencing productivity and soil, salt and water management.

The model can quantify year-to-year variation in yield using long-term weather data

and thus can be used for risk assessment. In addition, it can be used to determine the

leaching fraction expressed as a percentage of total infiltration at different levels of the

soil profile. Figure 14 shows a conceptual diagram of the model input, processes

and output.


Figure 14. SWAGMAN – Destiny model Inputs, processes and outputs.

Projecting trends in growth and yield from a range of crops grown with different

irrigation practices in a variable climatic environment where the irrigated areas are

underlain with shallow saline watertables is demanding and complex. This task

requires some form of simulation model that can not only represent the key driving

processes but also analyse the long-term effect of management practices. The

principle aims are to predict change, evaluate trends and indicate the uncertainty range

in biophysical variables such as crop leaf area development, yield, salt distribution and

groundwater recharge.

The SWAGMAN� (Salt Water And Groundwater MANagement) series of models was

developed to achieve policy goals such as regional salinity balance (Meyer et al., 1996;

Khan et al., 2003).


3.1.1. Crop Growth

A common growth model is used to simulate the leaf area development, biomass

growth and root proliferation of the following crops: wheat, maize, soybean, sunflower,

rice, cotton, grape vines, eucalypt woodlots, deciduous fruit trees, lucerne and

grazed pastures.

The model simulates crop response to water deficit, waterlogging (soil aeration), soil

salinity and nitrogen (for wheat, maize, sunflower and cotton only). It assumes a

homogeneous crop stand with enough plants to realistically achieve the maximum

value of leaf area index that is given as an input to the model. While the model uses

some components of the comprehensive crop growth model, CERES (Singh et al.,

1989), it cannot simulate response to population or development of individual yield

components. The model uses a common suite of routines for every crop with species-

specific inputs to describe crop differences.

3.1.2. Soil Water Balance

The SWAGMAN Destiny model uses soil water balance as described in Ritchie (1985)

and Ritchie and Otter-Nacke (1985). Changes to the original model include an

infiltration routine (Broadbridge and White, 1987) and process descriptions to account

for impeded drainage. Also included are process descriptions for drainage of water into

tile drains, defining the location of the watertable and the effect of the underlying

groundwater pressure head.

Water and salt balance is estimated daily within a 5 m soil profile divided into fifteen

layers of varying depths. Layers are thin near the surface, where changes in water

content are fastest, and thick at the bottom of the profile, where water content changes

are slow. Most of the crops simulated take up water within the upper 2 m of soil.

Potential evaporation is estimated using a modified Penman-Monteith approach from

daily weather records using coefficients and methods described in Meyer et al. (1988).

At a minimum, the data requirements are solar radiation, maximum and minimum

temperatures. If wind speed and relative humidity data are not available, a Priestley-

Taylor method is used to estimate potential evaporation.

3.1.3. Model Testing and Validation

The model has been calibrated and tested for more than 10 years in conditions that are

very similar to those at Coleambally. Meyer et. al. (1996a, 1996b) used data from

intensive monitoring sites in Cohuna in Victoria to test SWAGMAN Destiny model for


maize, wheat and soybean growth. The simulated water content of the soil profile and

groundwater levels were also tested with measured data. They found a reasonable

agreement between simulated and measured data. Edraki et. al. (2003) used data

obtained from field experiments with maize silage crop in the Murray irrigation district of

southern NSW from late 1998 to early 2000 under different soil and groundwater

conditions to validate the SWAGMAN Destiny model. They found good agreement

between simulated soil profile water content, yield, LAI and observed values.

3.2. SWAGMAN-GIS Model Development

SWAGMAN GIS was developed using Microsoft Visual Studio .NET 2005 and the C#

language. GIS functionality is implemented through linkages to the COM callable

wrappers around ArcObject classes. ArcObject is the platform on which the ArcGIS

family of applications rely, providing data management, map presentation functionality.

Through COM interfaces exposed by these wrappers, MapControl and TOCControl can

be embedded in the application and will function the same way as they do in ArcView.

Shape files can be pre authored in ArcGIS desktop applications or other software.

Geodatabases were used to store all the crop specific data for the irrigation area in

addition to the area’s geofeature layers to facilitate minimum input from the user.

Figure 15 shows the main application screen using the MapControl and TOCControl of

the ArcObjects controls. Personal geodatabases were developed using Microsoft

Access and loaded with shape files in addition to soil, crop, weather and initial

groundwater levels, which are tied directly to feature attribute tables.

3.3. Database Development and Datasets

The data requirements for SWAGMAN-GIS include soil, climate, crop, initial

groundwater levels and miscellaneous structural parameters (e.g. simulation start time

and frequency of output). In addition, pre-authored Arc-GIS maps of soil and land use

are required. The thematic maps and data tables are stored in a personal

geodatabase so that joins and relationships between attribute tables can be

established. Table 6 and Table 7 show example data for crop and soil profile physical

characteristics. Tables 9, 10 and 11 in Appendix B show soil physical properties for

other soil types. Variable names are defined in Appendix A.


Figure 15. User Interface of SWAGMAN GIS.

Table 6. Crop Data.

CropID

CropName

Crop Type DDVeg DDMat DDNoIrrig

PeakLAI

TBase CropFactor

Par Conv Fac

PotYield

Summer

1 Maize GRAINS 1100 2000 100 8 8 0.85 3.5 15000 True 2 Rice RICE 1295 1895 100 10 8 1 3.05 15000 True 3 Vines FRUITDE

CID 2000 3500 50 5 5 0.75 3 40000 True

4 Wheat GRAINS 1300 2250 150 6 0 1.05 2.5 7000 False

SppAerFact

ISow Root DRate

Rt Vol Mass

Root Diam

Init Root Depth

Stock on Stock off Cutfile Salt Coeff1 SaltCoeff2

SaltCoeff3

1 300 0.2 7.5 0.035 8 0 0 None 1.7 0.12 9.11 290 0.1 10 0.01 15 0 0 None 3 0.12 11.11 245 0.05 10 0.03 8 0 0 None 1.5 0.1 111 135 0.15 10 0.025 8 0 0 None 1.7 0.12 9.1


Table 7. Soil Profile Data for Mundiwa Clay Loam (SMC).

ProfileID

Layer No Depth Cumdepth

LL DUL Sat Theta Init

Bulkdensity

Ks RWUCN Root Weight Index

2 1 2 2 0.08 0.21 0.4 0.168 1.3 10 1 12 2 5 7 0.09 0.22 0.42 0.176 1.4 9.4 1 12 3 8 15 0.18 0.35 0.43 0.28 1.4 5 1 12 4 11 26 0.27 0.39 0.43 0.312 1.4 3.5 1 0.82 5 14 40 0.27 0.35 0.4 0.28 1.5 3.5 0.9 0.52 6 17 57 0.27 0.38 0.4 0.304 1.5 3.3 0.9 0.32 7 20 77 0.27 0.38 0.4 0.304 1.5 3 0.8 0.252 8 23 100 0.26 0.36 0.39 0.288 1.6 3 0.6 0.12 9 25 125 0.26 0.36 0.39 0.288 1.6 2.6 0.4 0.12 10 25 150 0.26 0.36 0.39 0.288 1.6 2.5 0.2 0.12 11 50 200 0.27 0.37 0.39 0.296 1.6 2.5 0.1 02 12 50 250 0.27 0.37 0.4 0.296 1.6 2.5 0 02 13 75 325 0.27 0.37 0.4 0.296 1.6 2 0 02 14 75 400 0.27 0.37 0.4 0.296 1.6 0.5 0 02 15 100 500 0.27 0.37 0.4 0.296 1.6 0.1 0 0

3.4. Model Outputs

Model outputs include spatial mappings of yield, evapotranspiration (ET), leaching

fractions at various depths, rootzone leaching fraction, water use efficiency

and productivity.


4. Application of SWAGMAN-GIS

A SWAGMAN-GIS model was developed with data specific to CIA incorporated in the

geodatabase. Simulations were performed for four crops (maize, rice, vines and wheat)

for three different years. Weather data was sourced from SILO patched point datasets.

Simulations were carried out for the 2000-2001 growing period using the SILO patched

point4 data and the soil map in Figure 2. Additional simulations were conducted for the

years 2001-2002 to 2003-2004. Table 8 shows the hypothetical irrigation

schedule used.

Water use efficiency is defined as the amount water used by the plant to satisfy crop

water demand divided by the amount applied as irrigation plus effective rainfall.

(mm)Rainfall(mm)Irrigationfactorcrop(mm)iontranspirat-Evapo

(mm)AppliedWater(mm)use water Crop

��

��WUE (1)

Note. WUE Water use efficiency

Water productivity is defined as crop production per unit of applied water. Applied water

is defined as the sum of irrigation water applied and rainfall.

(ML/ha)AppliedWater(t/ha)ProductionCroptyProductivi � (2)

The rootzone is defined as the soil profile up to 150 cm deep.


Table 8. Irrigation Schedule.

Day number Amount (mm) 300 40.0

320 40.0

325 50.0

330 60.0

335 60.0

340 60.0

345 50.0

350 50.0

355 50.0

365 50.0

10 50.0

20 50.0

30 50.0

40 50.0

50 50.0

60 50.0


5. Results and Discussion

5.1. Maize Simulations

Actual maize evapotranspiration (ETa) ranged from 450 to 900 mm over the irrigation

area (Figure 16B). The highest value of ETa was in the self-mulching clay soils and the

lowest value in the sandy soil. The low available water (30 to 40 mm/m of the first 40

cm of soil depth for sand compared to 120 to 210 mm/m for red-brown earth) that

influenced growth and leaf area development has contributed to the low ETa

values for sand.

BA


Figure 16. Maize Yield (A), ET (B), Productivity, (C), Water Use Efficiency (D) and Rootzone Discharge (E) for 2000 – 2001.

DC

E


Maize yields (Figure 16A) range from 5 to 10 t/ha with the lowest yields occurring in

sand and the highest occurring in self-mulching clays. Actual evapotranspiration varies

between 460 mm in sand to 900 mm in self-mulching clays (Figure 16B). Maize water

use efficiency range from 48 to 90 per cent with the lowest occurring on sand and the

highest on self-mulching clays. Maize productivity ranged from 0.54 to 1.02 t/ML with

the lowest occurring on sand and the highest on self-mulching clay.

Figures 16, 17, 18, and 19 show simulation results for 2001 to 2005. They show

variations in weather patterns. The amount of water leaving the rootzone into deeper

layers varies from 1 to 4 cm a year on self-mulching clay, 2 to 12 cm on non self-

mulching clay, 2 to 16 cm on red-brown earth, 3 to 13 cm on transitional red-brown

earth and 50 to 65 cm on sand (Figures 17E, 18E and 19E). The lowest occurred on

the self-mulching clays and the highest on sand.

A B


Figure 17. Maize Yield (A), ET (B), Productivity (C), Water Use Efficiency (D) and Rootzone Discharge (E) for 2001 – 2002.

C D

E


A B

C D



E


A B

C D



5.2. Rice Actual Evapotranspiration

Rice crop water use is uniform, a result that was expected since the rice fields are

flooded. The value of actual crop evapotranspiration in this case is 1085 mm

(Figure 20A).

E


A B

C D


Figure 20. Rice Yield (A), ET (B), Productivity (C), Water Use Efficiency (D) and

Rootzone Discharge (E) for 2000 – 2001.

E


Figure 20 also shows rice yields, productivity, water use efficiency and rootzone

discharge for the period 2000-2001. Rice yield was uniform over all soil groups (13.7

t/ha) except for sand, where yields were slightly lower (13.6 t/ha). The deepest

drainage (85 cm) below the rootzone (150 cm) occurred in sand. The drainage below

the rootzone ranged from 1 to 85 cm.

Since the rootzone for rice is completely saturated, the local watertable is at the

surface and the recharge to watertable is determined by the saturated hydraulic

conductivity of the most limiting layer. In this case, the most limiting layer is the bottom

one, which had saturated conductivity of 0.7 cm/day. Rice water use efficiency varied

slightly across soil groups but was generally low at about 57 per cent. Figure 21 to

Figure 23 show similar results for the years 2001-2002, 2002-2003 and 2003-2004.

The differences in variables across different years reflect differences in weather

patterns. For example, rootzone discharge varied from 24 to 30 cm on non-self-

mulching clay, 12 to 15 cm on self-mulching clay, 18 to 22 cm for red-brown earth, 30

to 45 cm for transitional red-brown earth and 85 to 105 cm for sand.


A B

C D


Figure 21. Rice Yield (A), ET (B), Productivity (C), Water Use Efficiency (D) and Rootzone Discharge (E) for 2001 – 2002.

E


A B

DC



E


A B

C D



5.3. Application to Actual Land Use (2004–2005) The model was applied to actual land use during the 2004–2005 growing season. The

land use map was superimposed on the soil map and SWAGMAN–GIS was run on

each of the resulting land use and soil type units. Figure 24 shows the soil map, land

use map and the simulated rootzone discharge and water use efficiency maps. The

water use efficiency ranged from 20 per cent for fallow on transitional red-brown earth

to more than 100 per cent on Forest Horticulture on self-mulching clays. The rootzone

discharged varied from -0.5 cm (capillary rise) for fallow on self-mulching clays to 95

cm for rice on sand.

E


Figure 24. Soil type, actual landuse (2004 – 2005), Rootzone discharge and water use efficiency.


6. Conclusion and Implications for Land and Water Management Plan (LWMP).

Integrating a biophysical model with a GIS framework makes it easier to understand

the processes of water movement in the soil and the effect of land use and soil types.

This is because the model provides a way of evaluating the effect of land use changes

and climatic variation on water movement through the soil and on productivity and

water use efficiency on an irrigation district level. For example, the simulations show

that the combined effects of soil and weather produce a wide range of values for

rootzone drainage of about 1.0 to 65 cm for maize and 12 to 105 cm for rice. This result

has implications for water saving initiatives and water use efficiency plans in irrigation

areas.

The model can be used to show areas in an irrigation district that are prone to greater

accessions to the watertable and will help with reclassifying land and determining

suitability for a particular land use.

It can be used to delineate hotspot areas requiring more attention for water and crop

management. It can help with developing LWMPs as it can be used to bring together

whole farm planning and regional net recharge management to minimise land

degradation due to rising groundwater levels and resulting salinisation.

The model requires soil and land use maps, as well as information on groundwater

levels and the physical characteristics of representative soil profiles for each soil group

in the soil map. In addition, crop-specific information such as genetic coefficients

describing growth stages and root distribution is required.

In this report, simulations were conducted using available data and maps. The

SWAGMAN Destiny model has been tested as a point scale model and validated

thoroughly. Upscaling the model to a regional level will provide output to a reasonable

level of accuracy. The simulation results can be improved further by incorporating

routines for water exchange processes between computational units (polygons).


References

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Zone Water Quality Model. Water Resources Publications, Englewood, CO.

372 pp.

Ascough II, J.C., Green, T.R., Cipra, J.E., Ahuja, L.R., Ma, L. (2004). Space-time

modelling of agricultural landscape variability using agsimgis. Annual

Hydrology Days Conference Proceedings. March 10-12, 2004. Fort Collins,

CO. pp. 10-21.

Broadbridge, P and I., White. (1987). Time to ponding: comparison of analytic, quasi-

analytic and approximate predictions. Water Resources Res. 23:2302-

2310.

Christen E.W., A. Prasad and S. Khan (2000) Using piezometric data to determine

area-wide annual recharge. A case study for the Coleambally Irrigation

Area. CSIRO Land and Water Technical Report No 39-00

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Validation of SWAGMAN Farm and SWAGMAN Destiny models. CSIRO

Land and Water Technical Report 44/03. pp45.

Hoogenboom, G., P.W. Wilkens, and G.Y. Tsuji (eds). (1999). DSSAT v3, volume 4.

University of Hawaii, Honolulu, Hawaii.

Hughes, J.D. (1999). SOILpak for southern irrigators. NSW Agriculture.

Khan S., Xevi E., and Meyer W. S. (2003) Salt, Water and Groundwater Management

Models to Determine Sustainable Cropping Patterns in Shallow Saline

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titled Crop Production in Saline Environments. 325-340. Co-published

simultaneously in Crop Production in Saline Environments, Global and

Integrative Perspectives, Ed Sham S. Goyal, Surinder K. Sharma and D.

Williams, Haworth Press.

Maas, E.V. and G.J. Hoffman. (1977). Crop salt tolerance. Current assessment. J.

Irrig. And Drain. Div. ASCE 193(IR2):115-134.Meyer, W.S., R.J.G. White,

D.J. Smith, and B.D. Baer (1994) Monitoring a rice crop to validate the

CERES Rice Model. CSIRO Division of Water Resource Technical

Memorandum 94/13.


Meyer, W.S., D. Smith, and G. Shell. 1988. Estimating reference evaporation and crop

evapotranspiration from weather data and crop coefficients. An addendum

to AWRAC Research Project 84/162.

Meyer, W. S., S.A. Prathapar, Doug C. Godwin and Robert J.G. White (1996).

Assessing longevity and assisting management of irrigated areas using

SWAGMAN® models. Paper presented at IAA Conference, Australian

Solution, Adelaide, SA. May 1996.

Meyer, W.S, Godwin D.C. and White R.J.G. (1996a) SWAGMAN Destiny. A tool to

project productivity change due to salinity, waterlogging and irrigation

management. Proc. 8th Aust. Agron. Conf. Toowoomba Qld.

Meyer W.S., Godwin D.C. and White, R.J.G (1996b) SWAGMAN Destiny model

development. Ch8, p 34 – 44 in D.C. Poulton (ed), Resource potential of

shallow water table. Research study v2137 MDBC-NRMS.

Ritchie, J.T. (1985). A user-oriented model of the soil water balance in wheat. pp. 293-

305. In W. Day and R. K. Atkin (ed.) Wheat growth and modelling. Series A:

Life Sciences Vol. 86. Plenum Press, NY.

Ritchie, J.T. and S. Otter-Nacke. (1985). Testing and validating the CERES-Wheat

model in diverse environments. AgRISTART Publ. No. YM-15-00407,

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(ed.) Proc. of 1989 Summer Computer Simulation Conference. Soc.

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Appendix A

Glossary Bd - Moist bulk density of soil layer

DDMat - Day Degrees to Maturity

DDNoirrig - Day Degrees from planting until the first irrigation event.

DDVeg - Day Degrees for the vegetative phase

Isow - Julian day of sowing

DUL - Drainage upper limit soil water content for soil layer.

Ksmacro - Saturated hydraulic conductivity for macropore flow from layer.

LL - Lower limit of plant-extractable soil water for soil layer L.(cm/cm)

MaxRtDep - Represents the maximum depth of the root system of the crop.

ParConvFac - Efficiency of conversion of intercepted PAR into biomass.

PeakLAI - Peak Leaf area index

PotYield - potential yield (kg/ha).

Rld - Crop root length density in soil layer(cm root/cm**3 soil).

Rootdrate - Maximum rate of downward extension of root growth (cm/ degree C

day)..

RootLWratio - Root length:weight ratio(cm/g).

Rtdep - Current root depth of crop.

RunOn - Daily runoff from field.

Rwucon - Zero to unity scalar indicating relative rate of change in soil water

content due to root water uptake from layer

Salb - Bare soil albedo

Tbase - base temperature

Stockoff - Critical pasture biomass for removal of grazing animals (kg dm/ha).

Stockon - Critical pasture biomass for commencing grazing (kg dm/ha).

Sat - Saturated water content for layer .(cm/cm)


Appendix B

Table 9. Soil Profile Data for sand.

ProfileID Layer_No Depth Cum_depth LL DUL Sat Theta_Init Bulk_density Ks_Macro RWUCN WR

5 1 2 2 0.04 0.1 0.12 0.08 1.3 30 1 1

5 2 5 7 0.04 0.1 0.12 0.08 1.3 30 0.7 1 5 3 8 15 0.04 0.07 0.11 0.056 1.3 25 0.6 1

5 4 11 26 0.05 0.09 0.11 0.072 1.2 20 0.4 0.8

5 5 14 40 0.15 0.19 0.26 0.152 1.3 18 0.2 0.5 5 6 17 57 0.19 0.36 0.37 0.288 1.4 16 0.1 0.3

5 7 20 77 0.2 0.41 0.42 0.328 1.4 12 0 0.25

5 8 23 100 0.08 0.11 0.12 0.088 2 6 0 0.1 5 9 25 125 0.08 0.11 0.12 0.088 2 2 0 0.1

5 10 25 150 0.08 0.11 0.12 0.088 2 2 0 0.1

5 11 50 200 0.08 0.11 0.12 0.088 2 2 0 0 5 12 50 250 0.08 0.11 0.12 0.088 2 2 0 0

5 13 75 325 0.08 0.11 0.12 0.088 2 2 0 0

5 14 75 400 0.08 0.11 0.12 0.088 2 2 0 0 5 15 100 500 0.08 0.11 0.12 0.088 2 0.7 0 0

Table 10. Soil Profile Data for RBE.

ProfileID Layer_No Depth Cum_depth LL DUL Sat Theta_Init Bulk_density Ks_Macro RWUCN WR

10 1 2 2 0.06 0.18 0.2 0.144 1.3 20 1 1

10 2 5 7 0.06 0.18 0.2 0.144 1.3 18.8 1 1

10 3 8 15 0.08 0.29 0.44 0.232 1.3 10 1 1

10 4 11 26 0.11 0.31 0.38 0.248 1.5 10 1 0.8

10 5 14 40 0.23 0.38 0.4 0.304 1.5 7 0.9 0.6

10 6 17 57 0.27 0.4 0.41 0.32 1.5 7 0.9 0.3

10 7 20 77 0.25 0.4 0.41 0.32 1.5 6.6 0.8 0.25

10 8 23 100 0.24 0.4 0.41 0.32 1.4 6 0.6 0.1

10 9 25 125 0.27 0.37 0.4 0.296 1.4 6 0.4 0.1

10 10 25 150 0.31 0.35 0.39 0.28 1.5 5.2 0.2 0.1

10 11 50 200 0.31 0.35 0.38 0.28 1.6 5 0.1 0

10 12 50 250 0.31 0.35 0.38 0.28 1.6 5 0 0

10 13 75 325 0.31 0.35 0.38 0.28 1.6 2 0 0

10 14 75 400 0.31 0.35 0.38 0.28 1.6 0.5 0 0

10 15 100 500 0.31 0.35 0.38 0.28 1.6 0.15 0 0


Table 11. Soil Profile Data for Wunnamurra (TRBE).

ProfileID Layer_No Depth Cum_depth LL DUL Sat Theta_Init Bulk_density Ks_Macro RWUCN WR 26 1 2 2 0.17 0.4 0.45 0.3 1.3 10 1 1 26 2 5 7 0.19 0.4 0.45 0.3 1.3 9.4 1 1 26 3 8 15 0.22 0.42 0.46 0.3 1.3 5 1 1 26 4 11 26 0.23 0.44 0.46 0.32 1.3 3.5 1 0.8 26 5 14 40 0.25 0.44 0.45 0.36 1.4 3.5 0.9 0.5 26 6 17 57 0.28 0.46 0.47 0.36 1.4 3.3 0.9 0.3 26 7 20 77 0.28 0.46 0.48 0.36 1.3 3 0.8 0.25 26 8 23 100 0.28 0.46 0.49 0.36 1.3 3 0.7 0.1 26 9 25 125 0.28 0.46 0.49 0.36 1.3 2.6 0.5 0.1 26 10 25 150 0.28 0.46 0.49 0.36 1.2 2.5 0.3 0.1 26 11 50 200 0.28 0.46 0.49 0.36 1.2 2.5 0.2 0 26 12 50 250 0.28 0.46 0.49 0.36 1.2 2.5 0.1 0 26 13 75 325 0.28 0.46 0.49 0.36 1.2 2 0 0 26 14 75 400 0.28 0.46 0.49 0.36 1.2 0.5 0 0 26 15 100 500 0.28 0.46 0.49 0.36 1.2 0.25 0 0

Table 12. Soil Profile Data for Black Earth(NSMC).

ProfileID Layer_No Depth Cum_depth LL DUL Sat Theta_Init Bulk_density Ks_Macro RWUCN WR 6 1 2 2 0.27 0.4 0.45 0.32 1.3 10 1 1 6 2 5 7 0.29 0.4 0.45 0.32 1.3 9.4 1 1 6 3 8 15 0.32 0.42 0.46 0.336 1.3 5 1 1 6 4 11 26 0.33 0.44 0.46 0.352 1.3 3.5 1 0.8 6 5 14 40 0.32 0.44 0.45 0.352 1.4 3.5 0.9 0.5 6 6 17 57 0.38 0.46 0.47 0.368 1.4 3.3 0.9 0.3 6 7 20 77 0.37 0.47 0.48 0.376 1.3 3 0.8 0.25 6 8 23 100 0.37 0.46 0.47 0.368 1.3 3 0.7 0.1 6 9 25 125 0.39 0.47 0.48 0.376 1.3 2.6 0.5 0.1 6 10 25 150 0.38 0.46 0.49 0.368 1.2 2.5 0.3 0.1 6 11 50 200 0.38 0.46 0.49 0.368 1.2 2.5 0.2 0 6 12 50 250 0.38 0.46 0.49 0.368 1.2 2.5 0.1 0 6 13 75 325 0.38 0.46 0.49 0.368 1.2 2 0 0 6 14 75 400 0.38 0.46 0.49 0.368 1.2 2 0 0 6 15 100 500 0.38 0.46 0.49 0.368 1.2 0.2 0 0


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