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DEVELOPMENT OF A REFERENCE MODE FOR
CHARACTERISATION OF SALINITY PROBLEM IN THE
MURRAY DARLING BASIN
Naeem Khan, Alan McLucas and Keith Linard
University College, University of New South Wales
School o f Aerospace, Civil and Mechanical Engineering
Australian Defence Force Academy
Northcott Drive, Canberra, ACT 2600, Australia
+61 2 62688328/ +61 2 62688337
n.khan@ adfa.edu.au
ABSTRACT
Reference modes are the patterns of dynamic behaviour produced by feedback structures
linking variables considered key to a specific problem. Identifying reference modes can be a
challenge when data is scanty or available from a variety of sources and presented at
different levels of aggregation. Lack of unequivocal reference modes can lead to ambiguity
and conflict among stakeholders. This paper describes an attempt to identify and specify
reference modes for the problem of dryland salinity. The method suggested by Saeed (2002)
was applied. Dryland salinity in the Murray Darling Basin of Australia is used as a case
study. The extent of the salinity problem in the Murray Darling Basin is described. Sources
and availability of data for key salinity parameters are then evaluated. Insights gained from
application of Saeed’s method are discussed. Shortcomings of the method can be reduced
through extensive and close involvement of stakeholders right from earliest stages when
attempting to identify the preliminary model boundary.
Keywords: System dynamics modelling, reference modes, problem articulation, dryland
salinity, Murray Darling Basin, Australia
1.0 INTRODUCTION
Effective policy interventions deliver sustained and desirable changes to system of purposeful
human activity. In problem solving approaches such as that proffered by Kepner and Tregoe
(1981), problems are considered to be deviations from the pre-existing conditions. To
improve our understanding of the reasons for deviation, problems need to be characterized.
This characterization includes articulation of the problem according to the available modes of
understanding. Problem articulation refers to initial characterization of the problem in terms
of time horizon, stakeholders perceptions of the problem, observable symptoms, the
perceived causes of the problem, and factors affecting it (Saeed 2001; 2002). Problem
characterization is usually done through a combination of the discussions with the client
team, archival research, data collection, interviews and direct observation or participation
(Saeed 1998; Sterman 2000). Two of valuable processes in problem articulation are
establishing reference modes and explicitly setting the time-horizon. Saeed (1998) suggests
that half of the understanding about the problem is achieved through the learning processes
involved in the development of the reference modes. Facilitating such understanding is
fundamental to the system dynamics discipline.
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In this research task, the concept of reference mode of behaviour as it applies to the dryland
salinity was investigated. Reference modes were developed from the time series data as well
as highly aggregated descriptive data. The 20 step method developed by Saeed (2001, 2002)
for building reference mode was followed in an attempt to define reference modes for dryland
salinity which has become a serious problem for farmers, local, State and Federal
governments and environmental authorities. Such problems are present in numerous regions
around the world including Sudan, Ethiopia, India and Pakistan. Available data was assessed
in an attempt to build reference modes to guide subsequent development of the system
dynamics models. Gaps in the quantitative and qualitative data were identified.
This paper presents the results of an investigation into development of reference modes for
the dryland salinity. The description starts with revisiting of the concept of reference modes,
its significance, usage and factors to be considered while developing reference modes. Then
Saeed’s method for developing reference mode is described that is followed by a discussion
of the results of this application. Near the end, the insights gained from this research are
summarised.
2.0 CONCEPT OF REFERENCE MODES REVISITED
In system dynamics, the term ‘reference mode’ is used to denote a pattern of graphs that
present the idealised or actual behaviour of different variables over time. The other terms
used include behaviour over time (BOT) graph, reference behaviour or reference conditions.
These terms fundamentally refer to the same thing that is behaviour over time and specifying
patterns that characterise those changes.
The concept of reference modes is not new being used in a variety of disciplines and for a
variety of purposes. The disciplines in which it has been used include basic sciences, social
sciences and management sciences. In chemistry laboratories equipments (atomic absorption
spectrophotometers, colorimeters etc) are calibrated against standard solution concentrations.
These standard solutions, with known concentrations, work as reference solutions for
calibrating the equipment. In physics laboratories, instruments are calibrated against the
standards provided by the Standards Institute. The measurements held at the Standards
Institute act as the reference for calibration of equipment (weights and weighing balances,
measuring devices etc). Within management sciences, widely used approaches for problem
analysis (for example.Kepner and Tregoe (1981)) define a problem as a deviation from the
“SHOULD BE” conditions. Here the “SHOULD BE” conditions provide the reference
conditions for comparison of the present conditions and for characterization of the problems.
In control theory, it is assumed that there exists a mathematical model describing the
dynamical behaviour of the underlying process, which can be changed from existing to the
desired behaviour (Ozbay 1999). In this case, the existing behaviour can act as reference
behaviour to identify the desired behaviour.
Although different disciplines used the concept of reference mode, they use only the past or
the known behaviour of a variable that depends upon observations made in the past. In
System Dynamics modelling, the models are calibrated against the recent observed behaviour
of the system. The behaviour is represented by a pattern arising from the combination and
interaction of variables in sets of feedback structures.
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The System Dynamics Modelling literature has made a marked contribution to the
development of the reference modes concept. Whilst the processes of building reference
modes start from time series data, the reference mode is more than a graph of the time series.
Saeed (2002) termed it as an abstract concept that represents a fabric of trends and shows
how different variables change with respect to each other over time. In particular, a time
series graph simply represents correlation. The developer of the reference modes is
presenting dynamic hypothesis(Helsinki School of Economics 1981; Oliva 1996; Maliapen
2000; Sterman 2000; Raimondi 2001) of the causality based on empirical data and local
knowledge.
While developing reference modes a number of factors need to be considered:
e Time Scale relevant to the decision maker but sufficient to encompass longer term
trends (for example, a balance between the reality of the 3 year election time cycle
and the reality of the long term economic and environmental cycles);
e Logical boundaries (for example, are there 0% and 100% limits that will eventually
bound observed local exponential growth )
e Valid scales of measurement for the parameter (that is, ordinal, interval scales, not
nominal)
e Reference modes may be developed from:
e Data-sets, particularly where the problem space is clearly bounded and data
can be collected with high levels of the relilability and specificity.
e Technical Characteristics for example error rates of machines increase as a
result of wear over time such as suggested by Ford and Sterman (1998)
e Expert knowledge
e Inference based on the surrogate data sets, such as may be substituted for
incomplete quantitative or qualitative data.
The reference modes are used for two purposes: first learning about the problem and its
definition; and second for building confidence in the model through testing the hypothesised
causality. Development of reference mode is a learning process that leads the effort of the
modeller through to identification of model variables.
3.0 METHODOLOGY
To accomplish this research task, a methodology consisting of learning cycles approach has
been adopted. This method provides a step-by-step approach towards development of
reference modes. A brief review of methodology is given in the following paragraphs.
Over a decade, Saeed (2001; 2002; 2002) has developed a technique for ‘problem slicing’
and development of reference modes in a 20 step method (Figures 1,2 &3). His method is
based on the Kolb’s (1984)’ model of experiential learning that includes leaming cycle:
feeling, watching, thinking and doing. Figure 1 shows the Broad framework of the
Saeed(2002)’s method. This method describes a method involving five learning cycles. Each
learning cycle consists of four steps. The method starts with the examination of available
time series data. During the process, each leaming cycle yields an intermediate product. The
intermediate products of this learning process are domain boundary, preliminary system
boundary, preliminary model boundary and model boundary. At the end of the learning cycle
5, a reference mode is produced. This reference mode consists of a graph showing a pattern
of the past behaviour as well as likely future behaviour of the variables.
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Each step of this method is described in the Figures 2 and 3 along-with the learning cycle it
pertains to. The detailed method is illustrated in the section 5 while describing the results of
the application of this methodology.
Data for identifying reference modes for the proposed dryland salinity model was acquired
from a variety of sources. Datasets came in different formats having been prepared for
Domain
Boundary
Preliminary
System Boundary
Preliminary
Model Boundary
Model
Boundary
Reference Mode
Figure 1 Broad Framework of Saeed’s Method
disparate customers for a variety of reasons, using different methods and at different times.
The climate data was acquired from the Bureau of Meteorology, Australia. Temperature and
rainfall data exists in the form of a time series data-set. Some of the data acquired was in the
form of printed graphs. Data was generated from these graphs by using the software
Grapher™, The water resources data was acquired from the different published material.
Most of these were in hardcopy and an Excel’™ dataset was generated through Grapher™.
The agricultural, land use and socio-economic data was acquired through the Agstat, a
database in Microsoft™ Access held by the Australian Bureau of Statistics. The gaps in data
were addressed through direct contact with the local agencies. The land clearing data was
acquired through the Australian Greenhouse Office. The quality of the data lies with the
source of the data and integrity of the methods of collection. The original datasets have been
aggregated at a variety of levels according to their original purpose. They also differ in their
precision. None was collected specifically for this research activity. Alternate sources of data
have not been discussed.
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Leaming Cycle 1
2.
examine problem description
2
identification of key variables
sanpshots of current situation aN
4
Select a subgroup from the past trends
that best represents problem history
OUTPUT
Domain Boundary|
3
Collect and plot time series data
Leaming Cycle 2
“ 5
examine multiple set of complex
historical time series
6
Decompose each set of
omplex patterns into simpler parts
Leaming Cycle 3
raph various components of
decomposed patterns
/
~
Select a subgroup of patterns
representing the behavious of interest
and discard remaining subgroups
OUTPUT
Preliminary
system boundary
(PSB)
—
examine selected group of patterns in PSB
9
10
ggregate at the desired level
\
12
Assemble graphed historical pattern
into fabric of model variables
graph inferred behaviour of
aggregated and abstract variables
11
OUTPUT
Preliminary
model bounda
(PMB)
Figure 2 Method for Development of Reference Modes (Saeed 2001, 2002),
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Cycles 1-3
Leaming Cycle 4
13
Examine Selected group patterns in PMB
f
14
infer behavior of stocks
missing in the data
~
16
Assemble historical patterns
in the decomposed variables
into fabric of model variables
—
15
graph behavior of the
additional variable conceived
Leaming Cycle 5
17
Examine past behavior of variables
in the extended model boundary
/
18
make intelligent projections
of the future behaviour of model
variables in the extended boundary
~
20
review past and inferred
future trendsof the model and
policy variables as a fabric and
ensure logical consistency
\e
A
19
Graph inferred future trends for the variables
in the extended model boundary
Figure 3 Method for Development of Reference Modes (Saeed 2002, 2001), Cycles 4.& 5
4.0 MURRAY DARLING BASIN: A BRIEF INTRODUCTION
Murray Darling Basin (MDB) is situated in the southeast of Australia. It covers 1,061 km?
and consists of the tributaries of the Murray and Darling Rivers. The greater portion of the
Basin is formed of extensive plains and low undulating areas mostly below 200m above sea
level. It has range of climatic conditions and natural environments from the rainforests of the
cool and humid eastern uplands to the temperate Mallee country of the southeast, the inland
subtropical areas of the far north to the hot, dry semi-arid and arid areas of the far westem
plains. It extends through three states. Figure 4 shows the extent of the Murray Darling Basin.
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OUTPUT
model boundary
(MB)
OUTPUT
Reference Mode
1.9 million people live in the Basin. Agriculture is the dominant activity of the Basin.
Agriculture is practiced both on the irrigated areas as well as dryland areas. The total area of
the crops and pastures irrigated in the MDB is 1,472,241 ha. Agricultural land use consists of
crops, pastures and grasses. The total area devoted to crops is 7,137,303 ha. The main crops
grown include wheat, barley, rice, oilseeds, cotton and number of horticultural commodities.
Commercial agriculture is undertaken on the 51,672 farms. Dryland salinity is a major issue
that is threatening productivity, livelihood and infrastructure. The other major environmental
issues (MDBC 2004) in the basin include sustainable communities, effective management,
‘
State boundary
River network
2B Irrigation area
Winter grazing
Rangelands
‘SOUTH
Summer grazing
Wheat/Sheep belt _{-™
é
-
VICTORIA MELBOURNE @
Figure 4 Key Features of the Murray Darling Basin (MDBC 2002)
communication, conflicting values, land capacity, nutrient and sediment export, water
allocation, competing demands for water, changed water-flow pattems, using water
efficiently and water reliability.
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5.0 RESULTS AND DISCUSSION
5.1 Learning Cycle -1
This learning cycle consists of four steps and results in delineation of the domain boundary of
the salinity problem. Each step is described below:
5.1.1 Step-1 Problem Description: Snapshots of Current Situation
In the following paragraphs, different snapshots of salinity in the Murray Darling Basin are
described as cited in the literature. The term ‘snapshot’ has been used consistent with its
dictionary meaning. In dictionary sense the term refers to:
e “A short description of a small amount of information that gives you an idea of what
something is like”. (online Oxford English dictionary)
e Anisolated observation.(Houghton 2000)
e Animpression or view of something brief or transitory (Merrium- Webster 2003)
The snapshots described below include the past and current status of salinity in the basin and
the literature perceived causes of salinity. These snapshots have been described as an isolated
observation and may or may not have relationships with the previous or the next snapshot.
5.1.1.1 Salinity Data snapshots
There are several concerns about the data. These concerns include that a) the trends that data
shows are based on the risk associated with the rise of the water-table (NLWRA 2001) rather
than actual salinity, b) the new advancement in data collection methods has identified the salt
affected lands that actually existed before but were not identified due to the limitations of the
methods used for data collection at that time. Other than implementation, the paucity of data
can also restrict identification of the appropriate remediation measures. The initiative of
salinity and land use mapping is quite recent and there are expected considerable delays in
generation of consolidated data-sets. The following excerpts from literature depict the status
and the quality of existing salinity data.
e “The paucity of data available to accurately characterize the state of degradation of
Australia's irrigated and dryland areas is a major restriction on the implementation of
appropriate and priority remediation programs” (Evans, Newman et al. 1996)
e “Even where the data is available (e.g., in Victoria, Southwest Australia), the
forecasted groundwater levels to 2020 and 2050 are based on straight-line projection
of recent trends in groundwater level. Due to inadequacies in current methods,
accurate groundwater surfaces cannot be developed with the existing distributed data”
(NLWRA 2000)
5.1.1.2 History of Salinization in the Murray Darling Basin
Land salinity in the MDB has a long history. When Sturt encountered Darling River in 1829,
the flow was low and the water was too salty to drink (Crabb 1997). Salinity was perceived as
a threat to the profitability of production systems when recharge from large scale irrigated
production system caused water-table to rise and reach within two meters of the land surface.
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The problem first emerged in the South Australian Murray, then in the Murrumbidgee,
Curlwaa, Wakool Irrigation areas (Figure 5) and reach dryland areas (Crabb 1997). And
currently, it is considered a risk to the sustainability of agricultural production systems (both
dryland land as well as irrigated), river health, water quality, biodiversity and urban
infrastructure.
5.1.1.3 Extent of Salinity in the Murray Darling Basin
In the Murray Darling Basin’s salt affected lands include 300,000 hectares of dryland and
96000 hectares of irrigated land. 560,000 hectares of irrigated land has watertable within 2
meters of land surface (MDBMC 1999). Around five million tonnes of salts are mobilised
every year. Out of this, two million tonnes are transported to sea through rivers while three
million tonnes are retained in the land and redistributed to other areas (MDBC 1999). The
prediction has been made that within the Murray Darling Drainage Division, the area affected
by the dryland salinization will in areas from 2000 (in 1998) square kilometers to 10,000
Square Kilometers by the year 2010 (MDBC 1997). The following excerpts from literature
also demonstrate the extent of salinity in the Basin:
e “In NSW, between 120,000 and 174,000 hectares of land are estimated to be affected
by dryland salinity” (Prime Minister's Science, Engineering and Innovation Council -
PMSEIC 1999; MDBC 1997).
e “Highly saline watertable are rising by half a meter a year in Wagga
Wagga’ (Standing Committee on Environment and Heritage, 2000)
e “If we continue to use our land the way we do now, by 2050 the area of affected land
1890s
Along SA Murray
1920's
Murrumbidgee
1970s
Dryland Salinity
we
1950s
Wakool IA
Curlwaa IA
Figure 5 Chronological occurrence of Salinity (left) and dryland salinity (right) shown in
brown patches in the Murray Darling Basin of Australia
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in the NSW part of the Murray-Darling Basin could increase to 2-4 million hectares.
Irrigation salinity is estimated to affect 320,000 ha, or 15% of irrigated land. About
70-80% of irrigated land in NSW is threatened by rising watertables and associated
salinity problems” (EPA, 1997).
e “By 2040, 1.3 million hectares of irrigated land in the Murray Darling Basin are
expected to be salinized or water logged- due to rising water tables” (Murray Darling
Basin Groundwater Working Group, 1996).
e “For salinization to occur, it is necessary to have both an increase in water reaching
the groundwater system and a source of salt to remobilize to ground surface. Basin
suffers massive imbalances between rainfall and evaporation resulting in
concentration of salt in the landscape. Through its geological history, the sediments of
the Basin accumulated hundreds of millions of tonnes of salts. Most of these salts lie
below the surface. The upward movement of the watertable brings the salt that lie
below the surface to the surface” (Evans, Newman et al. 1996)
Salinity is increasing exponentially and it is affecting other areas and production systems like
dryland and urban areas. In irrigated areas, the greatest rate of rise is occurring in the
Murrumbidgee Irrigation Area, Coleambally Irrigation Area, Berriquin-Denimean-Deniboota
Irrigation Area and Goulbum Murray Irrigation Area where Watertables are rising at about
10-50 cm/year (BRS 1999). In Shepparton area the watertable has reached within one meter
below the ground surface. Keyworth (1996) expects all irrigated areas in the southern Murray
Darling Basin to have watertable within two meters of the land surface by the year 2010.
5.1.1.4 Climate
Climate affects salinity in multiple ways. First, fluctuations in rainfall can affect the recharge
and discharge of ground water. An increase in evapo-transpiration from soil surface can
enhance the transport of salts towards surface and ultimate formation of the salt crust.
Climate change can impact water resources in the MDB in two ways:
e Significant reductions in stream flow in the MDB,
0 0 to 20% reduction by 2030 (Jones et al. cited in (Howden, Hartle et al.
2003)and
0 10 to 35% reduction by 2050 (Amell 1999 cited in (Howden, Hartle et al.
2003).
e “increase in water demand due to increased evaporation rates, lower rainfall.
Frequency of general and high security water allocations for environmental flows not
being met will increase” (Howden, Hartle et al. 2003)
Based on their analysis, Howden, Hartle et al. (2003) stressed on finding ways to integrate the
effects of the climate change into policies and practices.
5.1.1.5 land use
The landscape in the Murray Darling Basin has been changing and will continue to change.
The major human induced impacts had been settlement and land clearing for agricultural
urban and industrial uses. Agriculture is one of the major sectors for land use change. (Crabb
1997).
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e In Australia 20% of the total land area is under forest. The major ecosystems are
shrubland, savanna and grasslands. It covers the 88% of the ecosystems in Australia.
The cropland and crop/ natural vegetation mosaic covers 6% of the area. A major
expansion in agricultural development during 1950s to 1980 had been due to
extensive clearing and increase in cultivated area. (World Resources Institute 2003)
e Land clearing started in Australia many years ago and it is still continuing. The term
land clearing refers to removal of the natural cover (e.g. forest) from the land for
alternative uses. Within various studies conducted by the Australian government,
different terms have been used for land clearing. In the “Land Clearing a Social
History” (AGO 2000), the term land clearing is used with the above meaning but in
the “Carbon Accounting System” (AGO 2000) the terms land conversion and re-
clearing have been used. Land conversion refers to the first time clearing of the forest
while re-clearing refers to the clearing of the re-growth and conversion to an alternate
landuse. The current motivators for land clearing include land availability, clearing
controls, environmental and social influences, financial and Institutional incentives,
agricultural research and development, and market forces (AGO 2000).
e Graetz, Wilson et al. (1995) assessed that 1,029,640 sq km have been thinned and
cleared within intensive landuse zones and most of this is in the Murray Darling
Basin. One of the causes of land clearing was conditional purchases. For example
from 1860's to 1960's leases and conditional purchases were issued on the proviso
that a certain percentage of tree cover was to be removed each year (BRS 2000).
¢ “The native vegetation regimes evolved to make the best use of available rainfall
while avoiding the salts. All vegetation pumps water from the soils and transpire a
component to the atmosphere (through the evapo-transpiration process). Any change
in vegetation density or type (e.g., a change in vegetation’s water pumping
capabilities) will alter the volume of water reaching the saturated zone below.
Clearing of native vegetation disturbed the current balance.” (Evans, Newman et al.
1996)
5.1.1.6 River Water Diversions and Salt Carrying Capacity of Rivers
The diversions from the Murray Darling River have increased. The amount of water presently
taken from rivers is not ecologically sustainable and a new balance between the
environmental requirements and the consumptive use will have to be struck (Toyne 1995
cited in Crabb 1997:53) These river extraction have increase multi-fold over the last five
decades. For example, In 1960 the diversions from the Barwon Darling and the New South
Wales and the Queensland tributaries were 50,000 ML while in 1990-91 they were 1.4
million ML. Moreover, the increase in diversion has been primarily due of the cotton industry
and the use by the growers of large on farm water storages (Crabb 1997). The following
excerpts from literature depict the picture:
“The continuing saga of the extraction of massive amounts of irrigation water from inland
rivers to satisfy the escalating demands of the irrigation industry is Australia’s most serious,
and ultimately most disastrous water related issue” (White 2000).
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“The impacts of land clearing and management of the Murray Darling Basin Waters by
construction of dams and canals have lessened the variations in flow and salinity. However,
the exploitation of the waters has reduced the capacity of the rivers to carry salt to the sea
without prejudice to water users in the downstream reaches and has delivered a far higher salt
load to the river systems. This has occurred through saline water drainage directly to the
rivers and through increased groundwater flows” (Evans, Newman et al. 1996)
5.1.1.6 Delays
Delays in the groundwater system’ s response to disturbances exacerbate our understanding of
the dynamics mechanisms and exactly how they contribute to dryland salinity as it is evident
from the following excerpt:
“the groundwater system responded very slowly to these massive disturbances, so the full
consequences of the human impacts have begun to be felt only after decades, or even a
century. In fact, we know that the incipient degradation processes will often continue for
centuries to come. We have now released a time-bomb with slow fuse” (Evans, Newman et
al. 1996)
5.1.2 Step-2 Key Variables
In this learning cycle, the following variables have been identified from the problem
description described above:
e Agricultural land (hectares).
Land clearing (hectares).
Climate:
Rainfall (mm/year).
Evapo-transpiration (mm/year).
Temperature.
Stream-flow (mean annual stream flow).
Stream salinity.
Salt affected Rivers.
Value of Agricultural production.
5.1.3 Step-3 Collect and plot time series data
In this step, the data was acquired from different secondary sources that include Bureau of
Meteorology, the Murray darling Basin Commission, National Land and Water Resources
Audit, Green house office, Australian Statistical Bureau and published literature., the data
about important variables is presented below in the form of graphs:
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— 5 Year Mean
=
a
°
é
a
Temperature Anomalies (°C)
Departures from
1961-90 mean
1.5 +
1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Figure 6 Mean Annual Temperature Anomalies for Australia (BOM 2008)
2.0 | ——— Maximum Temperature
Trond
‘Minimum Temperature
Temperature Anomaly (°C)
os lu L L n n n L m ul
Year
Figure 7 Annual Maximum and Minimum temperature anomalies (base 1961
‘to 1990) for the Southeast Australia (BOM 2003)
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Rainfall (mm)
8
—5 Year Mean
“00 ifn no AS Hi |
ntl
Ill
|
|
1990 2000
1900 1910 1920 1930 1940 1950 1960 1970
Year
1980
Figure 8 Anmual Rainfall for Australia (BOM 2003)
a
f=}
a
Mean Rainfall (mm)
Fs
i=)
i=)
is)
i=}
fs)
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Figure 9 Mean Anmual Rainfall in the South East Australia (BOM 2003)
o
a me
SAD
g = a Strategy 1988
= 00 os
é
200 ‘a0 5 ae Pa oe ES = Fe
° [ aa R eT —e ar paar
1920 ©1940. 1960 1980 2000 2020 2040 2060. 2080 2100
peat Figure 12 River Salinity in the NSW (MIDBC 1999)
Figure 10 River Salinity at Morgan (MIDBC 1999) 12000
35000 smn
10000
30000 000
seco
_ 28000 To
7g 2
< ‘6000
‘a |
so00
8 3
§ 15000 =
& 3000
10000 ‘ono, ‘ir arg
en
1000 So me
Neon 11088
o T T T ¥. T T
s920/al iowa 080/84 sa80/64 a8TO/TL sawo/AL s920/08
o T 7 7 7 ; : T
Figure 13 Total Diversions in the Murray Darling Basin
(excluding Queensland) Source: (Crabb 1997)
1920 1930 1940 1950 1960 1970 1980 1990
Figure 11 Storage Capacity in the Murray Darling Basin (Crabb 1997)
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bol extensive cleaing aT TS
ww2
droughts
°
1860 18701880 ~—«130~«1900=« a1. 18201880184 8HD190 «STO 1aB0 10m)
Year of reporting
Figure 14 Historical trends of agricultural industry development, converted
to dry sheep equivalents (DSE) with major influences (NLWRA 2001)
Australian farm incomes (in 2000-01 dollars)
18 250
Terms of trade (Index: 1996-97 = 100)
6
mM 200
180
Zw
Boa
100
6
50
2
0 °
SSO OP Sf OO OS PS PS oS
SP
Figure 15 Australian Farm Incomes (AGO 2000)
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POPP MS
Year of reporting
Figure 16 Examples of change in landuse intensity index (NLWRA 2001)
Figure 17 Time-graph of the land dearing (x Toco! hectares) from 1988 to
1998 (AGO 2002)
5.1.4 STEP 4 Domain Boundary
All the variables identified in the step-2 and plotted in the step three are considered important
and have been retained within the problem domain. At this stage the following variable are
included in the problem domain:
Agricultural land (hectares).
e Land clearing (hectares).
e Climate:
0 Rainfall (mm/year).
o Evapo-transpiration (mm/year).
o Temperature.
Stream-flow (mean annual stream flow).
Stream salinity.
Salt affected Rivers.
Value of Agricultural production.
5.2 LEARNING CYCLE-2
In this learning cycle, the changing patterns of the variables identified in the preceding
learning cycle are analysed. Complexities of the patterns have been further simplified to aid
analysis. This learning cycle consists of four steps. First, the variables identified in the
learning cycle-1 have been re-examined from the perspective of their relationship with
salinity. The nature of the most data suggests that the further decomposition of the patterns
into simpler patterns through Fourier series analysis is difficult. However the general trends
amongst the data have been simplified visually and plotted against time for comparison with
each other. The output of this learning cycle is the preliminary system boundary.
5.2.1 STEP 5-Examination of Variables
The variables identified in the Section 5.1.4 have direct logical links with salinity. In the
following paragraphs, these relationships are discussed.
Climate is the main driver that determines the water fluxes between atmosphere and the land.
The parameters considered here are i) temperature, ii) rainfall/precipitation, and iii) evapo-
transpiration. Temperature affects the wether and seasonal patterns. It affects both the evapo-
transpiration and precipitation. Water provides medium for the movement of salt through the
biosphere. Under the forces of evaporation, salts in the groundwater move upwards through
soil capillaries towards the soil surface. At the soil/ground surface, water evaporates and
leaves the salt crust. Climate is very important model variable and must be included in the
model.
Land clearing replaces the vegetation with clear land and affects the net evapo-transpiration
and ultimate water balance in the region. Land clearing had been a very significant event in
the Murray Darling Basin and must be included in the model. Generally land clearing has
been done for the purpose of agricultural development.
Agricultural land hereby refers to land under crops or kept for agricultural purposes like
grazing/animal husbandry. It provides a base for the growth of plants/crops. The root system,
nutrient use pattern and root-shoot ratio determines the water-uptake by crops. Moreover,
Khan N 16
crops vary in their tolerance to salts. This is a variable that cannot be left out from a viable
model of salinity.
Stream salinity is an indicator of catchment health, that is, how much salts are leaking or
being exported from the land to the water. Moreover, water taken from streams for irrigation
purpose depends upon the salinity of streams. It can be included in the model as an indicator
of catchment health.
Value of agricultural production/farm incomes indicates the economic viability of agricultural
land. Salinity affects the land productivity and hence crop yields and it provides a good
indicator of land problems.
5.2.2 STEPS 6 and 7
Steps 6 and 7 have been combined, as the processes of simplification and the processes of
graphing are not different in this case. The graphed pattems are shown in the Figure 16
below.
Rainfall
Cleared Land
Irrigated Land
ae Agricultural land
River Diversions
P|
Now Time —»
Farm Income
Figure 18 Pattern of Behaviour of Different Variables Overtime
5.2.3 STEP-8
In This learning cycle, data about different variables was simplified and graphed. Time series
data about the evapo-transpiration over the Basin was not available and will be addressed in
the following learning cycles. The temperature is indirectly reflected in the evapo-
Khan N 17
transpiration, therefore, it has not been included in the system boundary. However upon
analysis, the following variables have been found to constitute the system boundary:
e Rainfall,
Evapo-transpiration,
Agricultural land,
Cleared Land,
Irrigated land,
River diversions, and
Farm income
5.3 LEARNING CYCLE 3
5.3.1 STEP-9 Examine selected group of patterns
Data presented in the leaming cycle 1 and 2 has been collected form the secondary sources
and represents state of those parameters at different geographical levels. For example Figures
6 to 9 present climatic data at the national level as well as at the eastern Australia level. The
Figures 7 and 9 present temperature anomalies and mean annual rainfall at the regional level.
Some information is at the Basin scale for example Figure 10 presents salinity at Morgan that
represents the salts collected in water at the downstream of the Basin. In Figure 12, average
salinity forecasts are also on the individual river Basin scale but these rivers represent a major
part of the Murray Darling Basin. Time graphs shown in the Figure 17, land clearing are at
the state level. Storage capacities represented in the Figure 11 are at the state level. River
diversions shown in the Figure 13 is summed up at the Basin level while Queensland has
been excluded.
The problem of data collection at the basin level is in reported studies for example Australian
Bureau of Statistics do not take Murray Darling Basin as one category, therefore, there are
problems in the aggregation of data.
5.3.2 STEP-10 Aggregate at the desired level
The concept of aggregation has been used in two perspectives. First it is used for combining
the data from different geographical regions. Second, it is used for combining different
variables to create new variables at a different level of system. In the following paragraphs,
first the problems in geographical aggregation are discussed and thereafter follow the
problems in variables aggregation.
At the Basin level, parts of all the four states (not the whole state) are involved. To work at
the Basin level, data from all states will be required but the state figures cannot be used as
none the whole state is part of the Basin.
Climate information represents three geographical focuses, i) at the national scale and ii) at
the regional scale. The climate change phenomenon is occurring over the whole continent and
it is no different from the Murray Darling Basin. Therefore the trends shown over the eastern
Australia have been adopted. A gricultural production figures presented in the Learning cycle-
1 represents the national figures. Over last 100 years the major development of agriculture
had been in the Murray Darling Basin. Major Water reservoirs have been built and more land
had been brought under cultivation as well as the area under irrigation has increased.
Therefore the agricultural development curve may be relatively steeper in the Murray Darling
Khan N 18
Basin than the overall Australia figures. Landuse intensity shown in Figure-16 is at the
catchment scale. Multiple landuse intensity curves have been shown in different areas of the
MDB, however, these don’t represent the whole MDB figure. However, these figures indicate
an increasing trend in the landuse intensity. The only results available are from the studies for
specific projects. Therefore, these are the figures that will have to be taken into account while
looking at the landuse intensity. Multiple time series shown in the Figure 17 for land clearing
provide data at the state level. Figure 11 shows already aggregated data of water storages.
The three variables, irrigated land, agricultural land, and cleared land are the types of the
landuses instead types of land. Land remains same but uses change. Therefore the three
variables are aggregated to give to constitute a variable, total land. All those transformation
of landuses remain within the total land.
5.3.3 STEP-11 Graph inferred behaviour of the aggregated and abstract variables
T otal land
Cleared Land
land under forest/bush
Agricultural land
Now Time —>
Figure 19 Aggregated T otal land
5.3.4 STEP-12 Assemble graphed historical pattern into fabric of model
variables
So far the following variables have been identified to be in model
e Total Land
o Land under forest/bush
0 Cleared land
o Agricultural land
e Rainfall
e Evapo-transpiration
e River Diversions
Khan N 19
5.4 LEARNING CYCLE 4
In this leaming cycle, the variables selected in the learning cycle -5 have been examined. On
the basis of this examination, variables missing in the examined data have been identified.
The graphs presented in the following sections represent our hypothesis about the overtime
behaviour of the missing variables. Our hypothesis is based on the ancillary data. The output
of this learning cycle is the boundary of salinity model.
5.4.1 STEP-13 Examine selected group patterns
The patterns expressed in the above learning cycles indicate that land clearing is increasing
while the total land is constant. River diversions have increased and farm incomes have a
general decreasing trend throughout Australia. However these variables present a partial
picture. The problem in focus is salinity and the data only does not lead to develop a mode of
behaviour for salinity problem. In the following sections, such variables that are missing in
data are identified and described.
5.4.2 STEP-14 infer stocks missing in the data
The following stocks seem missing in the data examined above. The term salinity refers to
the amount of salts in water. In other words it is a ratio of salts to water in a solution.
Therefore the variables describing the physical stocks need to be included in the list of main
variables:
Stock of salts: Salt stock is the main important variables as the slats are naturally a part of the
earth crust. The movement of water moves salts through different phases of the soil and water
and plants, e.g., Stock of Salts in Soil, Stock of Salts in Groundwater, Stock of Salts in
rainwater.
Stock of Water: In water cycle, water moves through atmosphere, it passes through land into
groundwater, rivers or sea. The snapshots described in the section-xxx indicate that overtime
the watertable has come up and has brought salts with it. It indicates an increasing trend in
the stock of the groundwater.
Depth to watertable, the generic model that explains salinity states that rise in the watertable
provides the medium for salts solubility and brings salts to the soil surface. Any model of
salinity should examine the depth of the watertable or the elevation of watertable. When the
watertable becomes within 2 meters from the soil surface, the yields of the crops start to
decrease depending upon the salt tolerance of the crop.
Other than that, the rates of profit, rates of gross and net rent will need to be included in the
specific modules conceming profitability of the farming enterprise. There exists a correlation
between the worsening terms of trade and declining value of production
Khan N 20
5.4.3 STEP-15 graph behaviour of the additional stocks missing in data
Total Salts
Salts on soil surface
Salts Dissolved
Salts undissolved
| Underground water
Now Time —»
Figure 20 Behaviour of Missing Stocks
5.4.4 STEP-16 Assemble historical patterns in the decomposed variables into fabric of
model variables
At this level, the following variables have been suggested to be included in the model
boundary:
e Climate
o Rainfall
o Evapotranspiration
e Total land
e Land under forest/bush
e Cleared land
o Agricultural land
Salts
Land surface salts
Undissolved salts
River Salts
5.5 LEARNING CYCLE 5
In this learning cycle, the past behaviour of the output of the learning cycle 4 has been
examined. Projections have been made into future based on logical relationships. Graphs of
Khan N 21
this behaviour are presented. The output of this analysis becomes the reference mode. It
contains the variables identified through data, variables missing in data (abstract) and the
future trend of the variables.
5.5.1 STEP 17 Examine past behaviour of the variables in the
The past behaviour of the variables has already been discussed. Here only the past behaviour
of the missing variables is discussed. The salt stock in land has not changed, the only
difference has been that its location has been changed. And its location has been influenced
by the stock of water. An increase in recharge has increased the ground water that has
resulted in elevation of watertable.
5.5.2 STEP-18 & 19 projections into future
Total Salts
Rainfall
Total Land
| ened an
ee under ForestiBush
Irigated Land
[| $b Catvated Land
River Diversions
fo on soil surface
Farm Income
Dissolved salts
AUN
Pe salts
Now Time —>
Figure 21 A Reference Mode for Salinity Problem
Khan N 22
5.5.3 STEP 20 Ensure logical consistency
The behaviour shown in the Figure 21 draws its logic from the salinity processes described in
literature (Section 5.1). These processes provide a basis for examining logical consistency
and may include:
e due to rise in the watertable, the salts in the subsoil become dissolved in the water and
move towards soil surface. At the soil surface, the water evaporates and leaves the salt
crust. Overtime, due to increase in groundwater recharge due to land clearing, the
watertable has increased its elevation and it is still rising at the 0.5 meters/year in
Waga Waga. With the overland flow of the rainwater, these salts wash out to the
rivers. Diversions from river for human consumption decrease the quantity of
available water downstream for dilution of these slats. Graph in Figure show an
increase in river diversion.
e River diversions are likely to maintain their current status as shown in Figure 21. It is
because of two main reasons i) the awareness about salinity problem has increased. It
has intu increased public pressure for environmental flows. Environmental groups
are major stakeholders, ii) the major irrigation schemes have already been built and
area has brought under irrigation. There are no planned irrigation schemes.
e The future projections shown in Figure 21 indicate an increase and then smoothening
in land clearing. The increase is based on the current status of land clearing. Land
clearing is still continuing in the Queensland and on the margins of the wheat-belt in
the New South Wales.
e The total salts content, available land and the climatic variables have been considered.
constant. Although the climate change is happening, it is a very long term process of
change. According o the Australian Bureau of Meteorology, there is a slight upward
trend in the rainfall but some authors have accrued it to the difference in measurement
and analysis techniques. For the purpose of this model, the rainfall trend has been
considered as the current.
6.0 INSIGHTS FROM APPLICATION OF SAEED’S METHOD
The application of Saeed (2001)’s method for characterisation of the salinity problem
provides several insights about the potential and limitations of this method. The method
provides a valuable means for focusing analysts’ attention to the main issues. The question of
starting “from where” in the fuzzy complex system is a prime one and Saeed’s method
provides a starting point. However, difficulties were encountered in the use of this method for
the salinity problem. These difficulties and the suggested ways to overcome these difficulties
are summarised in the following paragraphs. First the overall issues in application of this
method are described. Then the issues arising from the structure of the method are discussed.
6.1 Issues in overall process of developing reference modes
6.1.1 Sources of data
Saeed’s method relies on the published statistical data as the primary source of information to
start with and proceeds to final development of the reference modes. Use of other sources of
data are not envisaged in his framework. In situations where, the published statistical data is
not available, sources other than such numerical data will typically need to be considered.
Khan N 23
These sources may include the knowledge within the mental models of the systems
expert/players of day to day systems functioning or written descriptions (Forrester 1994; Ford
and Sterman 1998; Sterman 2000).
The method's application, described in the preceding pages, was strongly constrained by the
lack of consistent numerical data. Saeed’s approach would seem to imply a belief that the
system dynamics models can be derived from statistical time series data. Forrester has
consistently emphasized that key dynamical properties find their origin in system structure
and the policies that guide decisions (e.g., Forrester 1995). In general, Such information is
not amenable to statistical collection and must come from the altemative sources of
information, for example the mental models of the key role players.
6.1.2 Stakeholders involvement
The involvement of stakeholders in the problem articulation stage is important not only
because the stakeholders are:
i) a part of the problem space as they influence the current system’s behaviour through their
decisions,
ii) an integral part of learning cycles and
iii) knowledgeable people essential to activities directed at identifying missing variables,
missing data or explaining variations in quality of data..
The process of stakeholder involvement in the problem articulation stage also is important in
developing the foundation for the subsequent validation process. Ford and Sterman (1997)
stressed the inclusion of process knowledge of experts into system dynamics models. Without
their involvement, the procedure of problem articulation will remain incomplete. Without
their involvement in characterizing the modes of the problem behaviour the identification of
reference modes from patchy deficient statistical data becomes a painful process of looking
into a magic bowl. The shortcomings identified in this instance might be reduced through
extensive and close involvement of stakeholders right from the earliest stages when
attempting to identify the preliminary model boundary.
6.1.2 Aggregation of data
Saeed’s method has some limitations in its application to the salinity problem. These
limitations arise first because of the nature of the salinity problem and second because of the
nature of available statistical data. First the available statistical data is not enough to
characterize the modes of salinity behaviour based just on the time series data. The data that
is available is not classified according to the catchment. It is collected and classified
according to administrative boundaries (statistical units). Second, the concept of catchment
has different connotations. The surface water catchment does not necessarily coincide with
the groundwater catchments. The social catchment, that is the areas influencing, the adoption
of resource use practices does not coincide with these either. In those situations, the
aggregation of secondary data may not represent the reference behaviour of salinity problem.
Available statistical data is from localised studies in different periods in time. Consolidated
time series data is not available. Even the Australian Bureau of Statistics’ A gstats provides
data only from 1984 to 1997 with some parameters or data missing. In such situations
alternatives to the aggregate data may also be highlighted.
Khan N 24
6.1.2 All variables in reference mode or a few?
From Saeed’s paper it appears that all the variables in the model must be represented in the
reference mode as the process of model development and development of reference modes is
not different. Literature presents a different picture. Ford (1999:185) used only one variable
(Deer population) as a reference mode in his Kebab Deer herd while he used only a
descriptive reference mode (Ford 1999:226) while his models contained more than one
variables. In the company profit problem, Albin (1997) used only two variables (profit and
inventory) to designate reference modes of the problem while his model contained more than
two variables. He also used four variables to designate reference mode while his model
contained more than four variables in the Heroine crime system. This raises the question that
instead of having all variables in data, Would not be the only main variables enough to give a
reliable reference mode?
6.2 Structural issues
6.2.1 Main framework of the method
The learning cycle approach provides a phased approach for advancement in understanding
complex situations. This approach is in line with the reductionist problem solving approaches
( i.e., breaking problems into smaller and smaller sub-problems (Joel 2002)). At each stage
some data is discarded. The domain is reduced to preliminary system, the preliminary system,
in tum is reduced to preliminary model and reference mode (Figure 1). During the whole
process, both the model and the reference modes are the product of the same process and
same sources of data. Only addition of likely future pattern differentiates a reference mode
from the model. Each learning cycle leads to a next level of hierarchy in leaming. The
diagram representing the methodology (Figure 2 &3) does not explicitly show the linkages
between the output of each learning cycle and its place in the entire learning process.
Moreover, the process of developing reference needs to be further articulated. This 20 Step 5
stage learning cycle is NOT the articulation process. Articulation process is the agenda one
follows and the methods or tools one employs, in e.g., steps 2/3/4. This process should be
designed to foster learning.
6.2.2 Duplication
Step by step following methodology uncovers that there may exist some duplication, for
example, in step 5, the examination of domain boundary (STEP-4) is suggested. Domain
boundary consists of the graphs. The task of examination will involve referring (time and
again) to the graph of step-4. The examination of domain boundary can better be performed
at Step-4. This will result in unifying steps 4 and 5. The same is the case of steps 4 and 5,
steps 8 and 9, steps 12 and 13 and steps 16 and 17. It will reduce the steps from 20 to 16 and
will further clarify the approach by removing duplication and without losing logical
progression from domain to Reference Mode.
6.2.3 All Steps in series or transition to alternate steps possible
As presented in the method, it appears to be that all steps must be followed to reach a
reference mode. In some situations where the available data or the information provided may
require the use of alternate steps. Arrangements may be specified for smooth transition from
one learning cycle to next when (as presented the methodology looks like a series circuit
where every step must be followed) some steps cannot be performed due to the nature of
available information or available tools of analysis.
Khan N 25
6.2.3 Missing procedures
Some of the steps will need to be further elaborated that how these steps can be performed. A
methodology must show how the specified steps are to be performed (Jayaratna 1994). For
some steps it is mentioned in detail that how these steps can be performed like step 6
(leaming cycle 2) but for some steps for example step5,9,13 and 17 such procedures are not
mentioned. The review of these steps from the view of implementation/ practicality can lead
towards ease in implementation and natural flow of the methodology.
7.0 CONCLUSION
The study indicates that the Saeed’s method is a valuable starting point when identifying
variables that might be incorporated into the model. The method helps in identifying data
shortcomings. It helps keep the modeller well focused on the problem being addressed.
However, difficulties were encountered in application of this method. In part, the difficulties
stem from Saeed’s expectation that a sound statistical database will be available for key
variables. This was not the case in this problem nor, it is suggested, in the generality of
dynamic based problems. Variations in the level of aggregation of available data proved
problematic, as did a lack of involvement of the many opposite stakeholders in defining the
problem space. Whilst most appropriate when consistently aggregated data is available for a
specific problem space, Saeed’s method was found lacking here. The shortcomings in this
instance might be reduced through extensive and close involvement of stakeholders right
from the earliest stages when attempting to identify the preliminary model boundary. It is
based on the beliefs that stakeholders are i) a part of the problem space as they influence the
current system’s behaviour through their decisions, ii) an integral part of learning cycles and
iii) knowledgeable people essential to activities directed at identifying missing variables,
missing data or explaining variations in quality of data. By addressing these issues, a template
can be prepared based on the Saeed’s method that may help the modellers in problem
articulation and development of reference modes.
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