ECONOMICS OF CONJUNCTIVE WATER MANAGEMENT
UNDER CROP SALINITY TOLERANCE CONSTRAINTS
ISMAIL HIRSI
B.Sc. (Honours) Agriculture M.Sc. (Honours)) Agricultural Economics
A thesis submitted for the degree of Doctor of Philosophy in Environmental Sciences
International Centre of Water for Food Security Faculty of Science, School of Environmental Sciences
Charles Sturt University
ii
February 2008
iii
CERTIFICATE OF AUTHORSHIP
HD7 CERTIFICATE OF AUTHORSHIP OF THESIS &
AGREEMENT FOR THE RETENTION & USE OF THE THESIS
DOCTORAL AND MASTER BY RESEARCH APPLICANTS
To be completed by the student for submission with each of the bound copies of the thesis submitted for examination to the Centre of Research & Graduate Training. For duplication purpose, please TYPE or PRINT on this form in BLACK PEN ONLY. Please keep a copy for your own records.
I Mr. Ismail Hirsi S/O Farah Hirsi
Hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma at Charles Sturt University or any other educational institution, except where due acknowledgment is made in the thesis. Any contribution made to the research by colleagues with whom I have worked at Charles Sturt University or elsewhere during my candidature is fully acknowledged. I agree that the thesis be accessible for the purpose of study and research in accordance with the normal conditions established by the University Librarian for the care, loan and reproduction of the thesis.*
Signature Date
* Subject to confidentiality provisions as approved by the University
iv
ACKNOWLEDGEMENT
In the Name of Allah, the Merciful, the Compassionate All praises and thanks be to Allah (S.W.T), we praise Him, seek His aid, forgiveness, and His protection against our evil-self and wrong doings. My deep sense of gratitude is due to Allah (S.W.T), Who enabled me to complete this study. The efforts made with trust in Allah (S.W.T) and faith in His Prophet (may the blessings and peace of Allah be upon him) always bear fruit. May Allah (S.W.T) accept this humble effort as a reflection to His sayings: “Say: “Have you ever considered that if all the water you have, sink down in the ground, who then can bring you the clear-flowing water?”. (Sûrah 67, verse 30). “Mentioning (speaking of) the favours of Allah is (a show of) gratefulness. Leaving it (the favour) is ingratitude. Whoever does not thank (for) the little will not thank the much. And he who does not thank the people does not thank Allah...”(On the authority of Nu’man Ibn Basheer. Hadith No. 5325 in Al Jami’ Assaghir). With feelings of great pleasure and deep sense of profound gratitude, my acknowledgement also goes to my supervisor Professor Shahbaz Khan for his intelligible guidance and moral support during the course of this study. The benefits from many helpful discussions with Dr. Tom Nordblom, Dr. Richard Culas, and the members of International Centre of Water for Food Security, Charles Sturt University, Wagga Wagga must not be left unacknowledged. I am greatly indebted to the Australian Cooperative Research Centre for Irrigation Future (CRC IF). I am sincerely thankful for the cooperation from the officials of the Coleambally Irrigation Cooperative Limited (CICL), Coleambally, and the hospitality offered by the members of staff of the CICL during my field study tour to Coleambally Irrigation Area. Thanks are due to my wife, children, brothers, sister and other family members for their unreserved love, benevolent prayers, and sacrifices in sustaining my efforts during the studies. Sincere thanks are due to all my friends from Somalia, Pakistan, Australia, Egypt, Indonesia, China, Palestine, Oman, India, Bangladesh, Iran, Saudi Arabia, Lebanon, Djibouti, Ethiopia, and Ghana. I am indebted to all the members of the Islamic Students’ Association Riverina of Charles Sturt University during my study period. It was an unforgettable experience to see these companions as a profound reflection of the following verse of Al-Qur’an: “O mankind! We created you from a single (pair) of a male and a female and made you into nations and tribes that you may know each other (not that you may despise each other). Verily the most honoured of you in the sight of Allah is (he who is) the most righteous of you. And Allah has full knowledge and is well acquainted (with all things)” (Sûrah 49, verse 13). My greatest and ultimate gratitude is due to Allah (S.W.T), the Creator of the heavens and the earth. May He forgive my failings and weaknesses, strengthen and enliven my faith in Him and endow me with knowledge and wisdom, Aameen!
v
Ismail Hirsi
vi
ABSTRACT
The negative effect associated with soil salinisation has been an issue of
irrigated agriculture for centuries. The agricultural impacts associated with
excess soil salinity levels cause decrease in crop yield and other off farm
impacts such as damages to infrastructure and build environment. The main
goal of this dissertation was to study the economics of conjunctive water
management under crop salinity tolerance constraints. The specific
objectives were to: determine the possibilities of increasing gross margins
by taking optimal mix of crops under crop salinity tolerance constraints;
develop a hydrologic economic model and employ different mathematical
optimisation techniques using GAMS environment to determine the ways of
best use of conjunctive water for irrigation; and estimate and compare the
cost of irrigation and the resulting gross margins from using surface water,
groundwater and conjunctive water use with respect to optimal crop mix
under crop salinity tolerance constraints.
This study extended previous work on SWAGMAN Farm models, which
are a range of models of salt and water balance at the plant, farm and
catchment scale. However, this study integrated the Mass and Hoffmann
Model accounting for crop-soil groundwater salinity interactions in the
standard SWAGMAN Farm version. This was the key conceptual
contribution of this dissertation and an advance into the standard
SWAGMAN Farm model. It involved mixed integer programming in
GAMS (General Algebraic Modeling System) environment to model the
nonlinearities in the Mass and Hoffmann equation. This advance enables a
more scientific and accurate assessment of the impact of salinity on crop
vii
yield via-a-vis land and water management strategies to enhance
productivity and environmental sustainability in an economic decision
making environment. The nonlinearities and crop yield response to salinity
as defined by Mass and Hoffman cannot be captured by conventional
biophysical modelling techniques alone due to complex relationship.
The model was successfully validated on selected farms in two mature
irrigation areas in Pakistan and Australia. The overall model result show
that the yield and profitability response to salinity and groundwater depth
varies across farms within the same irrigation system. For a given level of
canal water allocation the gross margin per ha is lowest with the
groundwater use only, and highest for the canal water use only for current
salinity levels in the studied systems. The lowest economic returns under
groundwater use only mean those crop yields are adversely impacted due to
higher salinity of groundwater, lowering economic returns. In limiting
salinity areas, mixing of canal water with groundwater to achieve a specific
target salinity level enables farmer to achieve higher economic return per ha
and enhance total return from available water resources. This has
implications for allocating more canal water to saline environments such as
the tail ends, better groundwater mapping, and for public investments and
subsidy in groundwater use.
viii
TABLE OF CONTENTS
1 INTRODUCTION...................................................................................................... 1
1.1 BACKGROUND ..................................................................................................... 1
1.1.1 Global issues.................................................................................................. 4
1.1.2 Conjunctive water management in an Australian context.............................. 7
1.1.3 Conjunctive water management in a Pakistani context ................................. 8
1.2 PROBLEM STATEMENT......................................................................................... 9
1.2.1 Research question ........................................................................................ 10
1.2.2 Research objectives...................................................................................... 10
1.3 STRUCTURE OF THE THESIS................................................................................ 11
2 LITERATURE REVIEW........................................................................................ 13
2.1 INTRODUCTION.................................................................................................. 13
2.2 CONJUNCTIVE WATER MANAGEMENT................................................................ 16
2.2.1 At the irrigation system level ....................................................................... 17
2.2.2 At the farm level........................................................................................... 18
2.2.3 Key messages ............................................................................................... 20
2.3 HYDROLOGIC-ECONOMIC MODELS FOR WATER MANAGEMENT.......................... 20
2.3.1 At the irrigation system level ....................................................................... 21
2.3.2 At the farm level........................................................................................... 24
2.3.3 Observations on the hydrologic-economic models ...................................... 28
2.4 IRRIGATION WATER AND SOIL SALINISATION..................................................... 30
2.4.1 Economic loss due to soil salinity ................................................................ 32
2.4.2 Crop salinity tolerance constraints.............................................................. 34
2.4.3 Understanding the impacts of irrigation water salinity ............................... 36
2.4.4 Lesson learned ............................................................................................. 38
2.5 SUMMARY ......................................................................................................... 38
3 METHODOLOGY................................................................................................... 40
3.1 SWAGMAN FARM MODEL............................................................................... 40
3.2 MODELING OBJECTIVE FUNCTION...................................................................... 43
3.3 MODELING CONSTRAINTS.................................................................................. 46
3.3.1 Constraints on area of a landuse ................................................................. 46
3.3.2 Constraints on water allocation................................................................... 48
3.3.3 Constraints on root zone salinity ................................................................. 48
ix
3.3.4 Constraints on pumping from shallow watertable aquifer........................... 56
3.3.5 Constraints on net recharge......................................................................... 60
3.4 SUMMARY ......................................................................................................... 60
4 CONJUNCTIVE WATER MANAGEMENT AT THE FARM LEVEL: CASE
STUDIES IN AUSTRALIA .............................................................................................. 62
4.1 DESCRIPTION OF THE STUDY AREA .................................................................... 62
4.2 CASE STUDIES IN CONJUNCTIVE WATER MANAGEMENT..................................... 67
4.3 MODELING RESULTS AND DISCUSSION............................................................... 70
4.3.1 CIA Groundwater management zone 1........................................................ 70
4.3.2 CIA Groundwater management zone 2 & 3................................................. 72
4.3.3 CIA Groundwater management zone 4........................................................ 74
4.3.4 CIA Groundwater management zone 5........................................................ 76
4.4 GROUNDWATER SALINITY IMPACTS................................................................... 78
4.5 SUMMARY ......................................................................................................... 83
5 CONJUNCTIVE WATER MANAGEMENT AT THE FARM LEVEL: CASE
STUDIES IN PAKISTAN ................................................................................................. 84
5.1 DESCRIPTION OF THE STUDY AREA .................................................................... 84
5.2 CASE STUDIES IN CONJUNCTIVE WATER MANAGEMENT..................................... 88
5.3 MODELING RESULTS AND DISCUSSION............................................................... 91
5.3.1 Upper Rechna Doab .................................................................................... 91
5.3.2 Middle Rechna Doab ................................................................................... 92
5.3.3 Lower Rechna Doab .................................................................................... 93
5.4 GROUNDWATER SALINITY IMPACTS................................................................... 94
5.5 SUMMARY ......................................................................................................... 98
6 CONJUNCTIVE WATER MANAGEMENT AT THE IRRIGATION SYSTEM
LEVEL.............................................................................................................................. 100
6.1 CASE STUDY IN AUSTRALIA ............................................................................ 100
6.1.1 Surface water resources............................................................................. 100
6.1.2 Groundwater resources ............................................................................. 102
6.2 COST OF PUMPING IN AUSTRALIA.................................................................... 107
6.2.1 Input data for calculating the cost of pumping .......................................... 109
6.2.2 Net present values for diesel and electric pumps....................................... 110
6.3 COST OF PUMPING IN PAKISTAN ...................................................................... 111
x
6.4 COST OF CONJUNCTIVE WATER MANAGEMENT FOR A RANGE OF WATER USE
SCENARIOS..................................................................................................................... 113
6.4.1 Case study in Australia .............................................................................. 114
6.4.2 Case study in Pakistan ............................................................................... 119
6.5 SUMMARY ....................................................................................................... 125
7 SUMMARY AND CONCLUSIONS..................................................................... 127
7.1 OVERVIEW OF THE KEY ISSUES ........................................................................ 127
7.2 SUMMARY OF THE RESEARCH OBJECTIVES AND METHODOLOGY ..................... 128
7.3 AUSTRALIAN PROSPECTIVE ON CONJUNCTIVE WATER MANAGEMENT ............. 130
7.4 PAKISTANI PROSPECTIVE ON CONJUNCTIVE WATER MANAGEMENT ................. 132
7.5 COMBINED PROSPECTIVE ON CONJUNCTIVE WATER MANAGEMENT................. 134
7.6 A POSSIBLE WAY FORWARD............................................................................. 135
REFERENCES ................................................................................................................ 136
APPENDIX I .................................................................................................................... 164
APPENDIX II .................................................................................................................. 176
xi
LIST OF FIGURES
FIGURE 2.1 RELATIONSHIP BETWEEN RELATIVE PLANT YIELD AND SOIL ROOT ZONE
SALINITY (MASS AND HOFFMAN, 1977). ......................................................... 35 FIGURE 4.1 THE COLEAMBALLY IRRIGATION AREA. ......................................................... 63 FIGURE 4.2 MONTHLY RAINFALL FIGURES DURING 2006-07 (AER 2007). ........................ 64 FIGURE 4.3 MONTHLY EVAPOTRANSPIRATION FIGURES DURING 2006-07 (AER 2007). .... 65 FIGURE 4.4 CIA GROUNDWATER MANAGEMENT ZONES (KHAN ET AL., 2008)................... 69 FIGURE 4.5 FARM 1 - TOTAL GROSS MARGIN FOR VARIOUS WATER ALLOCATION LEVELS
AND WATER MANAGEMENT SYSTEMS.............................................................. 71 FIGURE 4.6 FARM 6 - TOTAL GROSS MARGIN FOR VARIOUS WATER ALLOCATION LEVELS
AND WATER MANAGEMENT SYSTEMS.............................................................. 73 FIGURE 4.7 FARM 9 - TOTAL GROSS MARGIN FOR VARIOUS WATER ALLOCATION LEVELS
AND WATER MANAGEMENT SYSTEMS. ............................................................. 76 FIGURE 4.8 FARM 11 - TOTAL GROSS MARGIN FOR VARIOUS WATER ALLOCATION LEVELS
AND WATER MANAGEMENT SYSTEMS.............................................................. 77 FIGURE 4.9 COMPOSITE SALINITY OF CONJUNCTIVE USE ................................................... 82 FIGURE 5.1 LOCATION MAP OF RECHNA DOAB IRRIGATION SYSTEM................................ 85 FIGURE 5.2 GROUNDWATER SALINITY IN RECHNA DOAB (ΜS/CM)................................... 88 FIGURE 5.3 TOTAL GROSS MARGIN FOR VARIOUS WATER ALLOCATION LEVELS AND WATER
MANAGEMENT SYSTEM IN UPPER RECHNA DOAB............................................ 92 FIGURE 5.4 TOTAL GROSS MARGIN FOR VARIOUS WATER ALLOCATION LEVEL AND WATER
MANAGEMENT SYSTEMS IN THE MIDDLE RECHNA DOAB................................. 93 FIGURE 5.5 TOTAL GROSS MARGIN FOR VARIOUS WATER ALLOCATION LEVEL AND WATER
MANAGEMENT SYSTEMS IN THE LOWER RECHNA DOAB.................................. 94 FIGURE 5.6 EFFECT OF MIXING RATIO OF SURFACE WATER AND GROUNDWATER ON THE
GROSS MARGINS IN UPPER RECHNA DOAB. ..................................................... 95 FIGURE 5.7 EFFECT OF MIXING RATIO OF SURFACE WATER AND GROUNDWATER ON THE
GROSS MARGINS IN MIDDLE RECHNA DOAB .................................................... 97 FIGURE 5.8 EFFECT OF MIXING RATIO OF SURFACE WATER AND GROUNDWATER ON THE
GROSS MARGINS IN LOWER RECHNA DOAB..................................................... 98 FIGURE 6.1 ANNUAL GENERAL SECURITY ALLOCATIONS SINCE 1982/83 (AER 2007)..... 101 FIGURE 6.2 ANNUAL DIVERSION AND LICENSED ENTITLEMENT (AER 2007). .................. 101 FIGURE 6.3 LOCATION MAP OF GROUNDWATER BORES IN COLEAMBALLY IRRIGATION
AREA (CICL, 2006). ..................................................................................... 103 FIGURE 6.4 GROUNDWATER USAGE IN COLEAMBALLY IRRIGATION AREA (AER 2007).. 106 FIGURE 6.5 ANNUAL COST OFF PUMPING ELECTRIC. ....................................................... 112 FIGURE 6.6 ANNUAL COST OF PUMPING DIESEL............................................................... 113 FIGURE 6.7 ANNUAL COST OF PUMPING TRACTOR........................................................... 113 FIGURE 6.8 MIXING RATIO OF SURFACE WATER AND GROUNDWATER FOR THE FARM 1. . 116
xii
FIGURE 6.9 LAND USE IN FARM 1 .................................................................................... 116 FIGURE 6.10 MIXING RATIO OF SURFACE WATER AND GROUNDWATER FOR THE FARM 6.. 117 FIGURE 6.11 MIXING RATIO OF SURFACE WATER AND GROUNDWATER FOR THE FARM 9. . 118 FIGURE 6.12 MIXING RATIO OF SURFACE WATER AND GROUNDWATER TO INDIVIDUAL FARM.
...................................................................................................................... 119 FIGURE 6.13 MIXING RATIO OF SURFACE AND GROUNDWATER FOR THE UPPER RECHNA
DOAB ............................................................................................................ 122 FIGURE 6.14 MIXING RATIO OF SURFACE AND GROUNDWATER FOR THE MIDDLE RECHNA
DOAB ............................................................................................................ 123 FIGURE 6.15 MIXING RATIO OF SURFACE AND GROUNDWATER FOR THE LOWER RECHNA
DOAB ............................................................................................................ 123 FIGURE 6.16 MIXING RATIO OF SURFACE WATER AND GROUNDWATER TO INDIVIDUAL FARM
AREA. ............................................................................................................ 124
xiii
LIST OF TABLES
TABLE 2.1 SALT TOLERANCE LEVELS OF GRAINS (ANZECC AND ARMCANZ, 2000). . 36 TABLE 3.1 LANDUSES CONSIDERED IN THE MODEL FOR A FARM. (MADDEN AND
PRATHAPAR, 1999; JEHANGIR AND KHAN, 2003)............................................ 45 TABLE 3.2 SOIL TYPES CONSIDERED IN THE MODEL FOR A FARM. (MADDEN AND
PRATHAPAR, 1999; JEHANGIR AND KHAN, 2003)............................................ 45 TABLE 4.1 AREAS (HA) OF EACH CROP TYPE IRRIGATED IN CIA, AND THE KERARBURY
CHANNEL AND OUTFALL DISTRICT, AND EACH CROP’S RELATIVE PERCENTAGE
TO TOTAL IRRIGATED AREA 2004 (COLEAMBALLY IRRIGATION CO-OPERATIVE
LIMITED 2005). ............................................................................................... 65 TABLE 4.2 AREAS (HA) OF EACH CROP TYPE IRRIGATED IN CIA, AND THE KERARBURY
CHANNEL AND OUTFALL DISTRICT, AND EACH CROP’S RELATIVE PERCENTAGE
TO TOTAL IRRIGATED AREA 2005 (COLEAMBALLY IRRIGATION CO-OPERATIVE
LIMITED 2006). ............................................................................................... 66 TABLE 4.3 A COMPARATIVE OVERVIEW OF THE MODELLED FARMS.................................. 70 TABLE 4.4 SCENARIO 1..................................................................................................... 79 TABLE 4.5 SCENARIO 2..................................................................................................... 79 TABLE 4.6 SCENARIO 3..................................................................................................... 80 TABLE 5.1 AREA UNDER MAJOR CROPS GROWN ON FARMS ACROSS IRRIGATION SUB-
DIVISIONS (HA)................................................................................................ 86 TABLE 5.2 NUMBER OF GROWING DAYS FOR PARTICULAR LAND USES. ............................ 87 TABLE 5.3 A COMPARATIVE OVERVIEW OF THE MODELLED FARMS IN PAKISTAN............. 89 TABLE 5.4 COMPOSITE EC OF CONJUNCTIVE WATER MANAGEMENT. ............................... 95 TABLE 5.5 COMPOSITE EC OF CONJUNCTIVE WATER MANAGEMENT. ............................... 96 TABLE 5.6 COMPOSITE EC OF CONJUNCTIVE WATER MANAGEMENT. ............................... 97 TABLE 6.1 SALINITY OF GROUNDWATER EXTRACTED FROM COLBORE- 2004/07 (CICL,
2005-2007) ................................................................................................... 103 TABLE 6.2 MONTHLY GROUNDWATER EXTRACTIONS FROM COLBORE 1994/95 TO
2006/07......................................................................................................... 104 TABLE 6.3 INPUT DATA USED FOR CALCULATING CAPITAL AND OPERATING COSTS IN AN
AUSTRALIAN CONTEXT (AFTER ROBINSON, 2002). ....................................... 109 TABLE 6.4 NET PRESENT VALUES OF DEEP GROUNDWATER BORES................................ 110
xiv
ACRONYMS AND ABBREVIATIONS
ANZECC Australian and New Zealand Environment and Conservation Council
ARMCANZ Agriculture and Resource Management Council of Australia and New Zealand
CIA Coleambally Irrigation Area
CICL Coleambally Irrigation Cooperative Limited
ColBore CICL augmentation bore (Bore no. 39406)
CSIRO Commonwealth Science and Industry Research Organisation
CSU Charles Sturt University
CW Canal Water
CWM Conjunctive water management
DNR NSW Department of Natural Resources (now DWE)
DPI NSW Department of Primary Industry
dS/m Deci Siemens per metre
EC Electric conductivity (a standard term used to refer to salinity as measured by micro Siemens per centimetre, µS/cm, at 25oC)*
GAMS General Algebraic Modeling System
GWSP Water Sharing Plan for the Lower Murrumbidgee Groundwater Sources
LCC Lower Chenab Canal
LWMP Land and Water Management Plan
LRDIS Lower Rechna Doab Irrigation System
MDB Murray-Darling Basin
MDBC Murray-Darling Basin Commission
MENA Middle East and North Africa
Mha Million hectare
ML Meggalitre
MRDIS Middle Rechna Doab Irrigation System
NPV Net Present Values
NSW New South Wales
SAR Sodium absorption ratio
SWAGMAN Salt Water And Groundwater Management
TGM Total Gross Margin
TW Tubewell
UCC Upper Chenab Canal
URDIS Upper Rechna Doab Irrigation System
µS/cm Micro Siemens per centimetre; the standard unit used to measure salinity (see EC)
WSP Water Sharing Plan
1
CHAPTER ONE
1 Introduction
This chapter explains key issues in using the term “Conjunctive Water
Management – the joint management of groundwater and surface water”,
and expands this definition to capture its relationship in association to: (i)
conjunctive use, (ii) link between groundwater and surface water, (iii)
irrigation system management, and (iv) interpretation and implications in
water governance. Global issues of groundwater use and its key challenges
are also discussed in brief. In the context of conjunctive water management
in Australia and Pakistan, research question is defined and research
objectives are formulated to address this research question. Thesis structure
is also presented in this chapter.
1.1 Background
Surface water is an increasingly scarce commodity, particularly in arid and
semi-arid regions of the world (Falkenmark, 1986). In these regions,
groundwater (being used alone or in conjunction with limited surface water
supplies) has become an unprecedented reality to fill the gap between
demand and supply of consumptive and environmental users (Ward et al.,
1996, 2006, 2007; Chermak et al., 2005; Ding, 2005). In practice, the
conjunctive water management can be envisioned at the farm and irrigation
system levels (Houk et al., 2005; Karlberg et al., 2006; Schoups et al.,
2006). Although the scope and scale of conjunctive water management
2
differ in these two levels, but it helps improve the overall availability and
reliability of water (Hussain et al., 2004; Shah and Singh, 2004; Peterson
and Ding, 2005; Sekar and Randhir, 2007; Syaukat and Fox, 2004).
Generally, the term ‘conjunctive water management’ refers to the joint
management of groundwater and surface water (Murray-Rust and Velde,
1994). This section introduces and expands on the definition of ‘conjunctive
water management’ to capture the concept of managing groundwater and
surface water as a single resource. It recognizes the hydrological and
agronomic link between surface and groundwater and juxtaposes the issues
from the management and governance perspective in the context of mature
large scale irrigation system across two diverse settings in Australia and
Pakistan. Within this context, key issues in the use of the term are associated
with its relationship to:
o Conjunctive use,
o Link between groundwater and surface water,
o Irrigation system management, and
o Interpretation and implications in water governance.
Conjunctive use: It is necessary to distinguish between conjunctive use (of
groundwater and surface water) and conjunctive water management because
conjunctive use has emerged as an on-farm practice based on institutions
which do not recognise hydraulic link between groundwater and surface
water. The term ‘conjunctive use’ refers to the practice of using multiple
resources for individual outcomes, while ‘conjunctive water management’
refers to the management of water resources for public welfare (Murray-
Rust, 2002; Merritt et al., 2005).
3
Link between groundwater and surface water: The “conjunctive water
management” refers explicitly to management across hydraulically
connected groundwater and surface water systems (Sahuquillo and Lluria,
2003; Sharda et al., 2006; Sheng, 2005). This definition leaves unclear the
capacity to use the term in reference to realising public goals where there
may be no measurable natural connectivity, such as may be the case in
developing aquifer storage and recovery management options.
Irrigation system management: In this context ‘conjunctive water
management’ refers to managing and accounting for aquifer recharge as a
tool to realise efficiencies across the complementarities of groundwater and
surface water (Blomquist et al., 2004; Diodato and Ceccarelli, 2006). The
reference to complementarities implies utilisation of natural efficiencies
whether they are associated with the link or not.
Interpretation and implications in water governance: The term ‘conjunctive
water management’ has different meanings and interpretation in water
governance literature. In settings with legally enforceable private property
rights in surface and groundwater, the use of both surface and groundwater
for private benefit are sanctioned by the law, and the water is defined a
public good (Colby, 1988; Orr and Colby, 2004). Else the private use of
groundwater is permissible within limits but there are no formal institutional
or legal rules to enforce the property rights (Meinzen-Dick, 1996; Bennett,
2005). Still in other settings where groundwater may be the only resource
available for human needs such as drinking and subsistence production, it
may be regarded as a basic human right and equal access for all may be
sanctioned by local norms and customs (Laamrani et al., 2000).
4
1.1.1 Global issues
There was a rapid growth in groundwater use especially since 1950.
Shallow wells and manual lifting devices were in use since the millennia. It
was used mainly for domestic needs and livestock (Shat et al., 2003).
Groundwater wells played little role in agriculture of most ancient
civilisations which grew in river valleys (Mosse, 1997). Studies on
individual ancient wells in the Middle East and North Africa (MENA)
regions speak to the limited scale of its use (Giordano, 2006).
The belt stretching from Spain to Persia to the Punjab was an exception
where wells supported an agrarian society during the medieval era (Hunt
and Hunt, 1976). For example, the Persian Wheel revolutionised the
irrigation agriculture in Mughal India (Mosse, 1997). In British India wells
accounted for about 1/3rd of irrigated land even in 1903 when irrigated was
limited to only 14 percent of cropped area (Shah et al 2003).
With the introduction of the tubewell and diesel and electric pumps in
1970’s, groundwater use soared. Despite this massive growth in
groundwater use in agriculture, global groundwater use is a quarter of total
global water withdrawals. Yet its contribution to agricultural production,
food security and poverty reduction is huge. Nearly half the world’s
population relies on groundwater as a drinking water supply (Shah et al
2006; Qadir et al., 2007; Barber, 2007). Irrigated agriculture remains the
major user of groundwater. The groundwater abstracted for agriculture is
generally of a high quality and often good for human use.
5
There is intense competition for high quality water (Kim, 1999). The
demand is huge for shallow groundwater that can be easily accessed at low
cost by irrigators and rural communities for sustaining their livelihoods and
food security (Hussain and Hanjra, 2002; Hussain and Hanjra, 2003;
Hussain et al., 2004b). Over abstraction of groundwater is often noticeable
in major groundwater basins, and this presents a complex challenge for
sustainable resource management (Hussain et al, 2004a). The groundwater
use in agriculture is high and increasing in developing countries, and the
associated challenges of sustainable management are the greatest both
because of the importance of the resource for sustaining livelihoods and
generally poor or often lacking laws and policies to protect its over use
(Shah et al. 2003; Chowdary et al., 2005; Gomann et al., 2005). The key
challenges include:
o Over exploitation of the resource beyond sustainable recharge
limits,
o Deterioration in quality through over use, and pollution from
agriculture and domestic and industrial uses,
o Arsenic poisoning of groundwater,
o Fall in watertable and inefficient use of energy for pumping,
o Use of poor quality groundwater for irrigation and human use and
associated impacts on productivity and human health,
o Instances of land subsidence and salt intrusion,
o Increase in salts in the root zone and impaired drainage due to
falling watertable, and
o Potential for social instability.
6
There is a general lack of data on groundwater use. Data on the impact of
agricultural groundwater use on food security and ecological systems are
rare (Nickum, 2003). Groundwater is becoming increasingly important for
agriculture in many parts of the world. For instance, the first wave of
groundwater irrigation began in the US, Spain, Italy and Mexico in early
parts of 1900’s (Garrido et al., 2006; Goesch et al., 2007; Llamas and
MartAnez-Santos, 2005a, 2005b; MacKay, 2006; Narayan et al., 2007). In
South Asia, parts of the North China plains, and of the MENA regions
groundwater use has now nearly peaked, while such revolution is not in
sight in much for the sub-Saharan Africa.
Over the years, conjunctive water management has emerged as a common
wisdom for ensuring plausible consumptive and environmental gains in
irrigated agricultural areas; particularly across parts of Central America,
South America, North America, the Middle East, South Asia, Central Asia
and Australia (O’Mara, 1988; Shah et al., 2003). During the periods of
limited surface water supplies, individual farmers make decisions of using
groundwater (alone or conjunctively with surface water) at farm level
(Qureshi et al., 2004); whereas at irrigation system level, a group of water
users take collective actions to manage the underlying aquifer for ensuring
the sustainable use of available groundwater resource (Pulido-Velazquez et
al., 2004, 2006; Griffin, 2006).
Surface water and groundwater typically have a natural hydrologic
connection, and conjunctive water management tries to utilize this
connection to use the already existing water resources more efficiently but
with convenience (Dudley and Fulton, 2006; Marques, et al., 2005, 2006;
7
Hafi, 2003, 2006). In irrigated agricultural areas when quantity or quality of
the primary source of water is of concern (Zhu et al., 2003; Watanabe et al.,
2006); conjunctive water management allows an individual (at farm level)
or a group of water users (at irrigation system level) to sustain (or increase)
crop production or productivity by being able to substitute or supplement
the primary source of water (i.e., surface water) with groundwater. While
conjunctive water management may prove successful for an individual or a
group of water users to cope with immediate changes and shortages, it is
also possible for conjunctive water users to deplete and/or deteriorate the
groundwater aquifer, and to harm other groundwater users who are not
involve in conjunctive water management but are reliant on the same
groundwater aquifer.
1.1.2 Conjunctive water management in an Australian context
Competition of surface water is growing, within and between consumptive
and environmental uses, while its resources are generally limited in
Australia (Elmahdi et al., 2006; Khan, 2007; Khan and Abbas, 2007; Khan
et al., 2006). Groundwater may help fulfilling the gap between supplies and
demands (Hafeez et al., 2007; Khan, 2007b). The allocation of groundwater
entitlements in Australia (that is, the volume of groundwater that irrigators
are entitled to extract in a given year) is based on annual groundwater
sustainable yield, with extractions restricted to long term average recharge
adjusted for discharge to dependent ecosystems. While groundwater
extractions (actual use) in the Murray Darling Basin were only half the
8
groundwater sustainable yield in 2000-01 (1250 gigalitres), increasing
demand for irrigation water combined with restrictions on access to surface
water are likely to lead to the activation of licences that are currently unused
or partially used within groundwater systems (Qureshi et al., 2006).
The goal of water resource management is to maximise the net social
benefits from water use (Khan et al., 2008; Oelmann , 2007). These benefits
from water use are the private and external benefits derived from water use
less any private and external costs. As is the case for many natural
resources, there are external costs associated with groundwater use
(Gonzalez et al., 2006). For example, over pumping may lead to land
subsidence, loss of habitat or ecological diversity, or increased groundwater
contamination through the inability of the resource to dilute and assimilate
contaminants. In the absence of price signals that reflect the external costs
of groundwater use, irrigators will have little or no incentive to reduce
consumption (Serra et al., 2006). As a result governments may intervene to
ensure that at least some of these costs are accounted for by irrigators. Thus,
for ensuring the sustainability of conjunctive water use of surface and
groundwater is an irrigated area, there is a need to understand the economics
of different promising conjunctive water management opportunities.
1.1.3 Conjunctive water management in a Pakistani context
Pakistan is fortunate enough because its soils, topography and climate are
generally suitable for farming but its agriculture sector faces the problem of
scarcity of the irrigation water. The designed cropping intensity of the
9
irrigation system was pitched low, in the order of 60-70 percent at the start,
but now the cropping intensity is more than 120 percent, indicating the
increased water demand (Jehangir et al. 2003). This paucity of irrigation
supplies has forced the farmers to use the groundwater to augment their
surface supplies. In most cases, farmers are using groundwater in
conjunction with limited surface water supplies on their farms.
In Pakistan, the literature review shows that all of the previous studies
conducted in the arena of water management reported the management
problems leading to the inefficiencies in irrigation application and reduction
in crop productivity, (Kijne and Velde 1991,2006; Mustafa 1991; Siddiq
1994 and Prathaper et al., 1994). Few of the studies took into consideration
the impact of waterlogging and salinity on productivity at farm level
(Prathaper et al., 1997; Traintafilis et al., 2004; Pannell and Ewing, 2006;
Steppuhn et al., 2005; Hajkowicz et al., 2002, 2005a, 2005b; and Young
Meyer et al., 1996; Sakkhati and Chawala 2002; Feng et al., 2005; John,
2005). None of these studies have taken into consideration the alternate
modes of irrigation and farmer returns under conjunctive water
management.
1.2 Problem statement
The negative effect associated with soil salinisation has been an issue of
irrigated agriculture for centuries. The soil salinity problem exists when the
build up of salts in a crops root zone is significant enough that a loss in crop
yield results. Although, waterlogged and saline soils are found naturally,
10
irrigated areas these salts typically originate from either a saline high
watertable or from salts in the applied water. The agricultural impacts
associated with excess soil salinity levels will be derived from the
corresponding decrease in crop yield. Additional plant symptoms associated
with high salinity levels are similar in appearance to those of drought, such
as wilting (Ayers and Westcot 1985; Brumbelow and Georgakakos 2007).
Conjunctive water use may help to improve water security, sustain
agricultural growth, and achieve higher economic returns; but due to the
increased salinity of irrigation water, long-term environmental sustainability
of irrigated agriculture may prove too questionable if conjunctive water use
is not managed appropriately. Proper accounting of crop salinity tolerance
constraints can help maximise benefits with lower environmental footprints
of agriculture from conjunctive water management under limited water
supplies both at the farm and irrigation system levels.
1.2.1 Research question
The main research question to be answered though this research is:
What is the role of crop salinity tolerance constraints to determine the
promising options of conjunctive water management practices for
irrigation purposes which would result in maximum gross margin
while meeting environmental requirements?
1.2.2 Research objectives
The objectives of this research are to:
11
o Determine the possibilities of increasing gross margins by taking
optimal mix of crops under crop salinity tolerance constraints,
o Develop a hydrologic economic model and employ different
mathematical optimisation techniques using the GAMS
environment to determine the ways of best use of conjunctive
water for irrigation,
o Estimate and compare the cost of irrigation and the resulting gross
margins from using surface water, groundwater and conjunctive
water use with respect to optimal crop mix under crop salinity
tolerance constraints, and
o Propose different policy interventions to maximise the socio-
economic and environmental benefits from conjunctive water
management.
1.3 Structure of the thesis
Chapter 1 introduces the conjunctive water management issues at global
level as well as in the context of irrigated agriculture situations in Australia
and Pakistan. Keeping in view the conjunctive water management issues
both at the farm and irrigation system levels, research question and
objectives are also outlined in this chapter. Chapter 2 outlines the lesson
learned from the literature reviewed related to hydrologic-economic
modeling of conjunctive water management in irrigated agricultural areas.
Chapter 3 describes different components of the conjunctive water
management model developed in this research. Modeling testing results are
12
also presented in this chapter. Chapter 4 and 5 present the modeling results
regarding the economics of conjunctive water management under crop
salinity tolerance constraints at the farm level, respectively. Separate case
studies are presented in this chapter for the selected irrigated agricultural
areas from Australia and Pakistan. Chapter 6 introduces the case study of
conjunctive water management at irrigation system level in Coleambally
Irrigation Area, Australia. However, this chapter also presents the cost of
conjunctive water management for a range of water use scenarios for the
case study areas in Australia and Pakistan. Chapter 7 presents conclusions
from this research and a possible way forward to improve the conjunctive
water management understanding and how to further adopt the modeling
tool developed under this research for the benefit of wider farming
community dependent on conjunctive water use in Australia, Pakistan and
elsewhere around the world.
13
CHAPTER TWO
2 Literature Review
This chapter reviews the practices, problems and prospects of conjunctive
water management at both the farm and irrigation system levels in various
irrigated agricultural areas around the world. However, main emphasis is
given to present an overview of the studies presenting hydrologic-economic
modeling of conjunctive water management in irrigated agricultural areas.
2.1 Introduction
Surface water is an increasingly scarce commodity, particularly in arid and
semi-arid regions of the world that cover about one-third of the total globe
land mass. In these regions, groundwater has become an unprecedented
reality, in these regions, to fill the gap between demand and supply of
consumptive and environmental users. Over the years, conjunctive use of
surface water and groundwater has emerged as a common wisdom for
ensuring plausible consumptive and environmental gains in irrigated
agricultural areas; particularly across parts of Central America, South
America, North America, the Middle East, South Asia, Central Asia and
Australia (O’Mara, 1988; Shah et al., 2003).
In practice, the conjunctive water use can be envisioned at farm level where
individuals make decisions of using groundwater to supplement limited
surface water supplies (Qureshi et al., 2004), and at irrigation system level
where water users take collective actions to replenish aquifer storage during
14
high rainfall periods for aquifer recovery to use groundwater during low
rainfall periods (Pulido-Velazquez et al., 2007). Although the scope and
scale differ in these two levels, but both are using surface water and
groundwater together to improve the overall availability and reliability of
water.
Usually, the surface water use has a benefit on the groundwater resource
through recharge (which can replenish aquifer storage, and in most cases
improve groundwater quality), or an adverse effect through contamination
(if the surface water is of poor quality). The groundwater use usually has a
benefit on the surface water resource through baseflow (which sustains
stream flows in low rainfall years, and in some cases improve stream flows
quality), or an adverse effect through stream flows depletion (if the
groundwater pumping induce the increase in seepage from the stream).
More often than not, the bulk of the groundwater use was developed after
most of the surface water use had been established, and if there is an
imbalance between benefits and adverse effects, it tends to favour the
groundwater users at the expense of the surface water supply. It is often the
case that users of one resource are not fully aware of the costs and benefits
of users of other water resource.
Surface water and groundwater typically have a natural hydrologic
connection, and conjunctive water use tries to utilize this connection to use
the already existing water resources more efficiently but with convenience
(Dudley and Fulton, 2006). In irrigated agricultural areas when quantity or
quality of the primary source of water is of concern; conjunctive water use
allows an individual (at farm level) or a group of water users (at irrigation
15
system level) to sustain (or increase) crop production or productivity by
being able to substitute or supplement the primary source of water (i.e.,
surface water) with groundwater. While conjunctive water use may prove
successful for an individual or a group of water users to cope with
immediate changes and shortages, it is also possible for conjunctive water
users to deplete and/or deteriorate the groundwater aquifer, and to harm
other groundwater users who are not involve in conjunctive water use but
are reliant on the same groundwater aquifer.
Conjunctive water management, on the other hand, is the management of
different water resources to create conducive environment for conjunctive
water use by an individual and a group of water users simultaneously, so
that wider ranging goals of equity, production and protection of different
water resources can be accomplished (Murray-Rust, 2002; Marrett, 2005). It
engages the principles of conjunctive water use, where surface water and
groundwater are used in combination to improve water availability,
reliability and convenience at farm level or irrigation system level; and can
be done with and without interventions from some external organisation(s).
Institutional constraints, environmental concerns, economic considerations,
and the socio-political climate are also important when implementing
conjunctive water management policies at both the farm and irrigation
system levels.
There clearly is no “one-size-fits-all” approach to conjunctive water
management, but it should include components like monitoring the status of
underlying aquifer at both the farm and irrigation system levels, evaluation
of the monitoring data to develop (or verify) management objectives, and
16
use of monitoring data to establish and enforce the management policies
(Lecina et al., 2005; Theodossiou and Latinopoulos, 2006). Primarily,
conjunctive water management should occur at farm levels where the unique
set of conditions is well understood and where interested water users can
participate and remain informed (Bredehoeft and Young, 1983). Monitoring,
the status of underlying aquifer at both the farm and irrigation system levels,
can help validating the conjunctive water management practices and policies
that are being implemented at these two levels (Shah et al., 2003). An
integrated approach can address these issues by considering these varied
dimensions of conjunctive water management to enhance overall
production, environmental achievements and social benefits for all (Khan et
al., 2007; Khan and Tariq, 2005; Fraiture, 2006).
2.2 Conjunctive water management
Conjunctive water management is often suggested as a means of taking
short-term actions that may come at some cost, in order that the water
supply will be more sustainable and/or more reliable in the long-term. Just
as surface reservoirs were built at some cost in order to improve the
availability of surface water supplies; conjunctive water management
usually includes some use of aquifer storage as a key in the long-term
management of water supplies. The most common objectives are to
physically increase water supplies, to increase supply reliability, or to
improve the flexibility in supply allocation.
17
For conjunctive water management, the cyclic nature of aquifer storage and
recovery is a critical operational consideration. Depending upon the water
cycle processes, three modes of operations can be categorised: short cycle1
annual cycle2 and long cycle3. Usually, long cycle approach is more efficient
and productive when the underlying aquifer is highly permeable, and is
practiced at the irrigation system level. Short cycle and possibly annual
cycle approaches are more appropriate when underlying aquifer is less
permeable, and are practiced at the farm level.
2.2.1 At the irrigation system level
There is no one standard set of issues or needs that motivates conjunctive
water management; this concept can take many forms and have many
objectives at the irrigation system level.
First, a large irrigation system exists where the surface water and
groundwater resources are jointly managed and/or regulated at the irrigation
system level by the public or private institution. In this case, conjunctive
water management typically involve: (i) the recharge of surface water into
the underlying groundwater aquifer and resulting in increase of aquifer
storage, (ii) the groundwater pumping as a supplement to surface water
1 Short cycle may spread over a course of days, weeks or perhaps months. This approach has been used to service peak daily and maximum monthly water demands in some areas. 2 In an annual cyclic approach, surface water is stored during the months when surface water supplies become available, and then recovered during periods of peak demand. In this approach, the aquifer storage and recovery are typically done with the same year to sustain a balance. The approach may or may not be coupled with the operation of surface water resources. 3 In a long cycle approach, the aquifer system is typically recharged during years of abundant surface water availability and recovery is done is a year or consecutive years of drought when there is a surface water shortage.
18
supply or to augment stream flow, and/or (iii) the substitution of one type of
water supply for another to make use of additional water in the future (e.g.,
surplus surface water may be supplied to a user who then foregoes the use
of groundwater in effect the groundwater is left in the underlying aquifer for
future use).
Second, a large irrigation system exists, which is managed and/or regulated
by the public or private institutions; however, there is no legal system in
place for conjunctive water management, and/or there is no public or private
institution with such responsibility. In this case, the aquifer storage is a
coincidental outcome of a surface water irrigation system. An example of
such irrigation systems is Indus Basin Plains in Pakistan.
Third, a large irrigation system exists, which is managed and/or regulated by
the public or private institution; but the management of groundwater is less
extensive and/or engaged in by a different institution. In this case, the
aquifer storage is also a coincidental outcome of a surface water irrigation
system. Examples of such irrigation systems are Coleambally, Murray and
Murrumbidgee irrigation areas in Australia.
2.2.2 At the farm level
Similar to the irrigation system level, there is also no one standard set of
issues or needs that motivates conjunctive water management; this concept
can take many forms and have many objectives at the farm level.
Firstly, a large irrigation system exists, which is managed and/or regulated
by the public or private institution. There are risks associated with uncertain
19
surface water supplies and their fluctuations, especially during low rainfall
years. The underlying aquifer has potential for pumping of groundwater that
is suitable for irrigation purposes. Surface water users, at the farm level,
look for conjunctive water use to increase the reliability of their supplies for
irrigation purposes. The groundwater resource is free (except for physical
supply costs) and is subjected to minimal or no regulations, especially that
limit pumping quantity. Users of groundwater may see no individual
benefits from any efforts to manage the aquifer, unless there are obvious
problems from watertable drawdown. Their motivation for participating for
conjunctive water management often comes from some external regulatory
pressure.
Secondly, a large irrigation system exists, which is managed and/or
regulated by the public or private institution. However, the underlying
aquifer has potential for groundwater pumping but its quality is marginally
suitable for irrigation. Not only, there are risks associated with uncertain
surface water supplies and their fluctuations, but groundwater quality is also
an issue. Surface water users, at the farm level, look for conjunctive water
use to increase the reliability of their supplies, as well as, to make the
quality of water suitable for irrigation purposes. The groundwater resource
is free (except for physical supply costs) and is subjected to minimal or no
regulations, especially that limit pumping quantity and quality. Users of
groundwater may see no individual benefits from any efforts to manage the
aquifer, unless there are obvious problems from watertable drawdown and
groundwater quality impacts. Their motivation for participating for
20
conjunctive water management also comes from some external regulatory
pressure.
2.2.3 Key messages
The key messages from the literature on conjunctive water management at
both the farm and irrigation systems levels, include, but may not be limited
to:
o Primarily, conjunctive water management should occur at farm
levels where the unique set of conditions is well understood and
where interested water users can participate and remain informed,
o Monitoring the status of underlying aquifer at both the farm and
irrigation system levels can help validate the conjunctive water
management practices and policies that are being implemented at
these two levels, and
o There is a need for analytical tools to: (i) quantify the impacts of
conjunctive water management at both the farm and irrigation
system levels, (ii) identify the consequences, particularly
pertaining to the hydrologic-economic aspects, of the specific
practices and policies that are proposed (or being implemented),
and (iii) compare these to the consequences of a future in which
there is no conjunctive water management.
2.3 Hydrologic-economic models for water management
In the past, decision-makers generally ignored the economic considerations
involved in water allocation, water use and water management (Krawczyk
21
and Tidball, 2006; Peterson et al., 2005; Wang et al., 2007). As water
scarcity increases and new sources of supply (e.g. groundwater) become
increasingly costly, decision-makers (including individuals at the farm level
as well as the managers at the irrigation system level) are beginning to
incorporate economic consideration into their decision making process.
Often, the balance between irrigation profitability and water resource
management has been stated as a policy goal but not defined in any
quantitative way (Acreman, 2005; Hellegers, 2006; Holmes et al., 2005;
Janssen and van Ittersum, 2007). This section presents an overview of the
hydrologic-economic modeling studies aimed at conjunctive water
management at the farm and irrigation system levels in various irrigated
agricultural areas around the world.
2.3.1 At the irrigation system level
Conjunctive water use plays an important hydrologic-economic role at
irrigation system level, as it reduces risks associated with uncertain surface
water supplies and their fluctuations. In other words, groundwater brings
stability in water supplies to meet the demands of consumptive and
environmental users. The economic value of the stabilisation role of
groundwater has significant implications for employing, managing and
promoting conjunctive water use, both in developing and developed
countries (Burt, 1964; Dains and Pawar, 1987; Tsur, 1993; FAO, 1994;
Meinzen-Dick, 1996; Hernandez-Mora, et al., 2001). During the drought
years, economic impacts can be minimal on irrigated agriculture if farmers
22
are able to switch from unreliable surface supplies to conjunctive water use
(Gleick and Nash, 1991). In fact, the stabilisation role, associated with the
flexibility of groundwater supplies, can boost agricultural productivity as it
will allow intensification and diversification of agricultural production in
otherwise inflexible surface-irrigation schemes. The stabilisation role of
groundwater is even important, through higher yields, in normal water years
(Tsur, 1990).
Lefkoff (1990) constructed a model of an irrigated, saline stream aquifer
system to simulate economic, agronomic, and hydrologic process. The
model is used to examine the effect of crop-mixing strategies on long term
profits. The hydrologic component of the model, which uses regression
equations to simulate salt transport, was verified with a method-of-
characteristics solution. The model was built on assumptions that simplify
the complex interactions between physical processes and human activity
which occur in the Arkansas Valley.
Different approaches have been used to understand hydrologic-economic
role of conjunctive water use at irrigation system level. (Provencher and
Burt, 1994) used the stochastic approaches while evaluating conjunctive
water management for three interrelated aquifers in California. The
conventional stochastic approach becomes infeasible when dealing with
large spatial dimension; however, this was not the case with the use of
Monte Carlo and Taylor series approximations. Results indicated that both
approaches perform well, providing almost identical estimates of the
optimal pumping policy for maximising hydrologic-economic benefits from
the three interrelated aquifers in California. The Taylor series approximation
23
was found to be particularly promising to decision-makers, because it is
user-friendly and is much less computer-intensive as compared to Monto
Carlo approach; all that is required is a software package (like GAMS)
capable of solving a set of nonlinear equations.
Belaineh et al. (1999) presented a linear programming-based
simulation/optimisation model, which integrates linear reservoir operation
rules along with the detailed stream aquifer system flows, conjunctive use of
surface and groundwater and delivery to water users via branching canals.
Groundwater flow was simulated using the MODFLOW program, which
solves the quasi three-dimensional groundwater flow equations.
Cai (2003) developed an integrated hydrologic-agronomic-economic model
in the context of a river basin in which irrigation is the dominant water use
and irrigation-induced salinity presents a major environmental problem. The
model is applied to problems of water management in the Syr Darya River
basin in Central Asia, providing environmental and economic information
regarding reservoir operations, infrastructure improvements, economic
incentives, and economic evaluation of irrigation water use.
Mohan and Jothiprakash (2003) used a combined optimisation and hydraulic
simulation modeling approach to estimate the optimal pumping policy for
maximising hydrologic-economic benefits from an irrigation area in India.
A linear programming model was used for optimisation of cropping
patterns, surface water releases and groundwater pumping schedules. A
hydraulic simulation model was used to evaluate the optimisation results
using long-term stream flows under periods of deficit, surplus and average
surface water deliveries from the reservoir. The results of the combined
24
optimisation and hydraulic simulation modeling were used to define the
alternate priority-based policies with respect to the cropping pattern,
irrigation intensity, net economic benefits, and area of rice crop. From
various alternative priority policies considered in the study, it was found
that the rice crop area and groundwater availability determines the irrigation
intensity and net economic benefits from the conjunctive water use (Bechini
et al., 2007; deVoil et al., 2006; Buysse et al., 2007; Humphreys et al.,
2006).
Balancing hydrologic, economic, environmental, and socio-political aspects
in conjunctive water management involves complex issues; tradeoffs
between economic and environmental dimensions are particularly complex.
For instance, an individual farmer optimising economic efficiency may
impact groundwater aquifer through changes in salinity and thereby impose
costs on other users who have no way of impacting individual farmer’s
behaviour or production practices impacted by his actions and management
decisions on groundwater uses (Triantafilis et al. 2004; Weersink and
Wossink, 2005). An integrated approach can address these issues by
considering these varied dimensions of conjunctive water use and
management of groundwater to enhance overall production and social
benefits for all (Khan, 2007; Xevi and Khan, 2005; Ball et al., 2005; David
et al., 2005; Krol et al., 2006; Ringler and Cai, 2006).
2.3.2 At the farm level
In 1996, the United States Bureau of Reclamation (USBR) began requiring
irrigation districts in the Central Valley Project of California to adopt
25
volumetric pricing for irrigation water as a Best Management Practice
(USBR, 1998). However, conjunctive water use systems, where surface
water supplies are managed jointly with groundwater resources, present a
unique challenge for adopting volumetric pricing for irrigation water since
the water pricing structure must accommodate the different attributes of
each water source, like their availability and end-use value. In the study by
(Schuck and Green 2002; Hardisty and Ozdemiroglu, 2005), water price
reflects the scarcity value of water during drought periods and the value of
aquifer recharge in wet periods; water pricing that linked to these two
important features were missing from earlier studies (Brown and McGuire,
1967; Martin and Kulakowski, 1991; Hewitt and Hanemann, 1995; Knapp
and Olson, 1995; Brill et al., 1997; Merritt, 2005).
While examining volumetric pricing for irrigation water as the primary
allocation tool for a conjunctive water use in an irrigation district in Kern
County, California, (Schuck and Green, 2002) employed Dynamic
Stochastic Programming under General Algebraic Modeling System
(GAMS) environment to determine the optimal water usage for different
levels of surface water supplies, aquifer levels and financial reserves.
Results suggest that moderate inter-seasonal variations in volumetric pricing
for irrigation water can conserve both water and energy. Additionally,
conjunctive water use reduces price variability and limits the impacts of
groundwater overdraft.
Each day, in India hundreds of thousands of farmers in canal, tank, and
other surface irrigation systems combine surface water with groundwater to
meet crop demand. They do so in an individual manner, uncontrolled by any
26
scheme or basin-level entity. Overexploitation of groundwater and intensive
irrigation in major canal commands has posed serious problems for
groundwater managers in India. It has been reported by Singh and Singh
(2002) that in many parts of the country the watertable is declining at the
rate of 1-2 m/year. At the same time in some canal commands, the
watertable rise is as high as 1 m/year. Deterioration in groundwater quality
by various causes is another serious issue. Summed together, all these issues
are expected to reduce the fresh water availability for irrigation, domestic
and industrial uses. If this trend continues unchecked, India is going to face
a major water crisis in the near future. Realizing this, the Government of
India has initiated several protective and legislative measures to overcome
the groundwater management-related problems but, due to the lack of
awareness and political and administrative will, none of the measures has
made any significant impact (Brown et al., 2006a).
In western and southern India, where rainfall is the main source of water,
tanks are the preferred water storage structures as they are simple to make
and easy to maintain. The tank system comprises: the small earthen dam, a
water spread area, sluice gates, surplus weirs and, most importantly, the
catchments and command areas. Often, these tanks are interlinked in chains
along a watershed making the whole system an ecologically sustainable way
for the effective utilisation of rainfall. The linked tanks in a watershed are
called tank cascades. Tanks are usually of two types: first are small dugout
tanks mainly used for providing drinking water and hence the sanitation of
these bodies is immaculately maintained. Second are the larger water tanks
maintained for multiple uses such as irrigation, provision of water for
27
livestock, washing of clothes, cleaning of utensils and for taking baths. In
addition, all tanks play an important role in maintaining the rural ecosystem
and for providing a haven for birds, animal and plants. However, these tank
systems are now in a state of decline, groundwater wells are widely thought
of as enemies of these tanks systems (Shah et al., 2003; Brown, et al.,
2006b).
In southern India, where tank irrigation system accounts for about one-third
of the area irrigated for rice cropping, (Palanisami and Easter, 1991)
investigated the hydro-economic interaction between tank and groundwater
being extracted from the neighbouring well. Rice yield from farms with
supplemental groundwater irrigation has been observed to be high. Timely
availability of supplemental irrigation when tank irrigation system is
exhausted has resulted in higher marginal returns to groundwater well
irrigation. However, these marginal returns vary from irrigation to irrigation
and from year to year; as the amount of groundwater pumping for
supplemental irrigation is dependent upon the storage in the tanks from
irrigation to irrigation and from year to year. (Ranganathan and Palanisami,
2004) investigated the value of groundwater that would stabilize rice
production under conjunctive management of groundwater resource and
tank irrigation system. It was suggested that the inclusion of stabilisation
value of the groundwater in hydrological economic analysis would help to
more accurately indicating benefits due to supplemental groundwater
irrigation in the tank irrigation system command area.
In regions with primary salinity, conjunctive use of surface and groundwater
presents unique challenges and opportunities. In such places, the objective
28
of conjunctive management is to maintain both water and salt balances. In
this situation, system managers require great control and precision in canal
water deliveries to different parts of the command to maintain an optimal
ratio of fresh and saline water for irrigation (Murray-Rust and Vander Velde
1992; Ahmed et al., 2007; Dehghanisanij et al., 2006; Dagar et al., 2006;
Flowers et al., 2005; Su et al., 2005). In many systems, it makes sense to
divide the command areas into surface water irrigation zones and
groundwater irrigation zones, depending on the aquifer characteristics and
water quality parameters. In others, providing recharge structures within a
surface system is often a useful component of a rehabilitation and
modernisation package. It is a risky business and requires a sound
conceptual model of the fate of the salts mobilised, if it is not to cause more
problems than it solves.
2.3.3 Observations on the hydrologic-economic models
A number of hydrologic-economic models are now available to: (i) quantify
the impacts of conjunctive water management at both the farm and irrigation
system levels, (ii) identify the consequences, particularly pertaining to the
hydrologic-economic aspects, of the specific practices and policies that are
proposed (or being implemented), and (iii) compare these to the
consequences of a future in which there is no conjunctive water
management. There are three broad categories of the hydrologic-economic
models exist that deal with conjunctive water management: (i) quantitative
models, (ii) decision-support models and (iii) scenario analysis models.
29
Quantitative models help examine the conjunctive water management
practices and policies that aim to increase water use efficiency and achieve
water resource and economic sustainability at both the farm and irrigation
system levels. Mostly, the relationships analysed may be within the
groundwater system (e.g., drawdown and/or aquifer quality deterioration
from pumping), within the surface water system (e.g., changes in surface
water delivery efficiencies), or the inter-connections between the two water
systems (e.g., effects on the aquifer storage and baseflow). Typically,
quantitative modeling means some type of hydrologic modeling; however,
some studies extend the quantification to relationships between the
economic outputs and the conjunctive water management practices and
policies.
Decision-support models are used to help identify the consequences,
particularly pertaining to the hydrologic-economic aspects, of the specific
practices and policies that are proposed (or being implemented), and
compare these to the consequences of a future in which there is no
conjunctive water management. Typically, decision-support modeling
means some type of optimisation modeling (Bazzani et al., 2005a; GoMez-
LimoN and MartiNez, 2006). Thus, some objective or set of objectives are
established (e.g., a goal of minimizing water shortages or of maximizing
farm profit), and the conditions that impact the objective(s) are specified in
accordance with the alternatives being assessed. Algorithms are then
developed that can test various combinations of conditions and compare
them as to how they perform in achieving the objective(s).
30
Scenario analysis models, which combine quantitative and decision-support
models, have some type of quantitative model embedded inside a platform
that allows varied combinations of input assumptions to be simulated. The
outcomes of these models help display the quantitative outcomes as affected
by the various conjunctive water management practices and policies. Thus,
these outcomes provide a foundation for discussions among the
stakeholders, involved in the conjunctive water management, so that
decisions regarding the selection of a specific practice and/or policy are
based on judgment, not merely based on the quantitative (or decision-
support) modeling results (Bazzani et al 2005b; Bartolini et al., 2007).
In the context of conjunctive water management in irrigated agricultural
areas, there is a need to identify a kind of scenario analysis model that can
be used to: (i) provide farmers with a tool to simulate and assess various
farm cropping scenarios in terms of economic return and environmental
effects, (ii) determine environmentally optimal irrigation intensity and
encourage water use efficiency through water and salinity auditing in an
integrated manner, and (iii) assist irrigation authorities (public and private)
for developing policies to achieve improved economic and natural resource
sustainability.
2.4 Irrigation water and soil salinisation
The negative impacts associated with soil salinisation have been an issue of
irrigated agriculture for centuries. A soil salinity problem exists when the
build up of salts in a crops root zone is significant enough that a loss in crop
31
yield results (Ayers and Westcot, 1985; Ashraf et al., 2006; Corwin et al.,
2007; Darwish et al., 2005; Houk et al., 2006; Lehtonen et al., 2007; Jalota
et al., 2007). Although, waterlogged and saline soils are found naturally,
irrigated areas these salts typically originate from either a saline high
watertable or from salts in the applied water.
The four primary reasons that irrigation causes salinisation include seepage
from poorly lined canals and reservoirs, excessive water application,
inadequate provision of drainage, and inadequate application of water to
leach away salts (Nordblom et al., 2006; Barrow, 1991; Ayars et al., 2006;
Schwabe et al., 2006; Saeed and Ashraf, 2005). As a result of excessive
seepage and deep percolation from over irrigation, water enters the aquifer,
typically saline, and decreases the watertable depth.
In general, when the watertable is within approximately 2 meters of the soil
surface, salts can rise to the surface through capillary action and render the
land unsuitable for agricultural production (Wichelns, 1999; Janmaat, 2005).
In 1999, the study area had an average watertable depth of only 2.1 meters
below the surface, with approximately 25% of the region’s watertable depth
to be less than 1.5 meters (Gates et al., 2002). These shallow watertable
depths are likely to be the significant cause of high soil salinity levels in the
study area.
Sharma, (2005a, 2005b) conducted field experiments on a sand loam soil to
evaluate the effects of conjunctive use of saline drainage water (7.2 – 9.8
dS/m; SAR = 8.4 – 13.5) and non-saline canal water (0.3 – 0.4 dS/m; SAR =
0.6 – 0.8) in different models on soil salinity build-up, growth and yield of
sunflower and succeeding sorghum. It showed that with the use of high
32
salinity drainage water in conjunction with non-saline canal water for post-
plant irrigations, good yields of sunflower could be obtained without any
serious soil degradation.
Several studies have been published within the crop sciences literature
examining the effects of either waterlogging or soil salinity on crop growth,
however there is little research available describing the combined effects. In
addition, few studies are available concerning the specific methods and
techniques for quantifying the overall economic costs of salinity and
waterlogging on agricultural production.
2.4.1 Economic loss due to soil salinity
Ghassemi et al. (1995) estimated the worldwide loss to farm income due to
soil salinisation in irrigated areas to exceed $11 billion a year. This figure
was found by multiplying the estimated worldwide quantity of salt-affected
land within irrigated agriculture of 112 million acres by a constant estimate
of income loss of $101/acre, as identified by (Dregne et al., 1991). Although
this figure is often referenced, it does little more than indicate that
significant losses are occurring. Additional research estimating the losses
within specific regions of the world is limited, no other study identified has
captured the total costs of both salinity and waterlogging in as much detail
as this study.
Miles (1977) estimated the total losses associated with 200,000 acres of
cropland within the Arkansas Basin that was being irrigated with highly
saline water. These lands were identified as croplands being irrigated with
33
Class C4 water, the U.S. Salinity Laboratory’s highest classification for
salinity hazard. The study identified a different crop distribution to occur
within these highly saline areas.
Grieve et al. (1986) estimated the total economic losses resulting from
waterlogging and soil salinisation of lands used for dairying and winter
cereal production within two irrigated areas of New South Wales, Australia.
They found the total losses on approximately 161,000 acres of land used for
winter cereals and dairy pasture to be approximately $9.2 million dollars, or
approximately $57/acre. This study simply accounted for the estimated area
of each district that was classified as waterlogged and applied a constant
yield deficit to these areas to estimate the losses. One of the main limitations
of this approach was that it failed to account for the degree of waterlogging
on crop yield. The total quantity of land affected by soil salinity was
estimated by extrapolation of survey data collected from the study area.
Production loss coefficients were calculated by summing the various crop
yield functions over the respective soil salinity frequency distributions. The
study then utilised an additive relationship between the waterlogging and
soil salinity impacts. They found that the losses from waterlogging
significantly outweighed the losses from soil salinity. This research did not
attempt to estimate the total agricultural impacts, instead it only focused
upon the dairying and winter cereal industries. An additional limitation of
the study is the disregard of how these losses would change from year to
year.
Jones and Marshall (1992) expanded upon the work of Grieve et al. (1986)
by including all of the primary crops produced, analysed the problem over
34
time, and allowed crop mixes to vary over time. They modelled the impacts
over a thirty-year time period assuming that soil salinity levels would
continue to increase at an average annual rate of .05 dS/m. This assumption
oversimplifies the soil salinisation process, ignoring distributional changes
and other annual fluctuations The study concluded that the annual reduction
in net farm income associated with waterlogging and soil salinity across the
region of approximately 84,720 acres to be $1 7 million (approx $200/acre)
Along with Grieve et al. (1986), both studies showed the losses from
waterlogging to be significantly higher than those from soil salinity.
2.4.2 Crop salinity tolerance constraints
One of the most important relationships that must be understood to estimate
the economic cost of salinity is the relationship between soil salinity levels
and crop yield. The agricultural impacts associated with excess soil salinity
levels will be derived from the corresponding decrease in crop yield.
Additional plant symptoms associated with high salinity levels are similar in
appearance to those of drought, such as wilting (Ayers and Westcot, 1985).
Many studies have been conducted to estimate the relationship between soil
salinity levels and crop yield.
Maas and Hoffman (1977) published an extensive review of the research
examining these relationships (Figure 2.1). They concluded that in general
crops will be unaffected by salinity up to a threshold at which time yield
will begin to decrease linearly as soil salinity levels increase. Soil salinity is
measured using the electrical conductivity of the soil extract (ECe) and is
35
reported in decisiemens per meter (dS/m). (Mass, 1996) presents a list of 84
crops identified into our qualitative group according to their sensitive to
salinity.
This type of two-piece relationship between relative crop yield (Yr) and
ECe has provided reasonable good fits for crop yield (Tanji, 1990; Mehari et
al., 2006). The estimated percentage of potential yield under soil salinities
exceeding the threshold for each crop can be described mathematically as
follows:
( )aECeb100Yr −−=
where, b is the slope of the yield salinity curve, ECe is the electrical
conductivity of the soil extract at root depth, and a is the salinity threshold
level at which crop yields begins to be effected.
Figure 2.1 Relationship between relative plant yield and soil root zone salinity (Mass and Hoffman, 1977).
36
Table 2.1 Salt tolerance levels of grains (ANZECC AND ARMCANZ, 2000).
Crops Salinity threshold, a Slope, b
Rice 3 12.2
Soybean 5 20
Maize 1.7 12
Sunflower 5.5 25
Fababean 1.55 9.6
Wheat 6 7.1
Barley 8 5
Lucerne 2.5 4
Annual pasture 2 12
The thresholds and slope parameters presented by (Maas and Grattan, 1999)
are used in this study to reflect the responsiveness of the relevant crops to
salinity stressing (Table 2.1). In this table, the response of different crops to
soil salinity varies greatly. The most salinity sensitive crop in the study area
is fababean production, which is consistent with information collected from
area producers who referred to fababean as being the “indicator crop” for
soil salinity.
2.4.3 Understanding the impacts of irrigation water salinity
Sahoo et al. (2006) developed a linear programming and fuzzy optimisation
models for planning and management of available land-water-crop system
of Mahandi-Kathajodi delta in eastern India. The models were used to
optimise the economic return, production and labour utilisation, and to
search the related cropping pattern constraints. The study shows that the
linear programming based management models the capability for optimal
37
land-water-crop system planning in case of single criteria decision systems,
while the fuzzy rule based management models can still be used to deal with
multiple criteria management decisions systems.
Khare et al. (2006) developed a simple economic-engineering optimisation
model to explore the possibilities of conjunctive use of surface and
groundwater using linear programming with various hydrological and
management constraints, and to arrive at an optimal cropping pattern for
optimal use of water resources for maximisation of net benefit. It was
indicated that conjunctive use options are feasible and can be easily
implemented in the area, which would enhance the overall benefits from
cropping activities.
Yadav (2007) conducted a field study on a loam-sand saline soil during
1999-2001. This involved assessment of effects of conjunctive use of saline
water with good quality water on five fodder crop rotations: oat-sorghum,
rye grass-sorghum, Egyptian clover-sorghum, Persian clover-sorghum and
Indian clover-sorghum and certain soil properties associated with it. Rye
grass, oat, sorghum and Persian clover were comparatively more tolerant to
saline conditions the Egyptian/Indian clover. The slight adverse effect on
infiltration rate and water dispersible clay with continuous use of increasing
quantities of marginal quality water warrants for continuous monitoring of
these parameters. However, such adverse impacts on production and soil
health could be minimised by using saline and good water in cyclic mode of
conjunctive use.
Kaur (2007) demonstrated the application of a decision-support system for
recommending best conjunctive water use plans for a, rice-wheat growing,
38
salt effected farmer’s field in Gurgaon district of Haryan (India).Cyclic
application of canal water (CW) and tube well (TW) and blending of 50%
CW with 50% TW emerged as the most suitable conjunctive water use
strategies for growing rice crop on the test salt-affected farm.
2.4.4 Lesson learned
The yield reduction function as affect by the average root zone salinity of
(Mass and Hoffman, 1977) is useful to include the role of crop salinity
tolerance constraints to determine promising options for conjunctive water
management for irrigation purposes which would result in maximum
economic return while meeting soil salinity and irrigation efficiency
constraints.
2.5 Summary
Based on the literature reviewed regarding: (i) the practices, problems and
prospects of conjunctive water management, (ii) hydrologic-economic
modeling of conjunctive water management, and (iii) linkages between
irrigation water and soil salinisation at both the farm and irrigation system
levels in various irrigated agricultural areas around the world, the key
messages include, but may not be limited to:
o Primarily, conjunctive water management should occur at farm
levels where the unique set of conditions is well understood and
where interested water users can participate and remain informed,
o Monitoring the status of underlying aquifer at both the farm and
irrigation system levels can help validate the conjunctive water
39
management practices and policies that are being implemented at
these two levels, and
o There is a need for analytical tools to: (i) quantify the impacts of
conjunctive water management at both the farm and irrigation
system levels, (ii) identify the consequences, particularly
pertaining to the hydrologic-economic aspects, of the specific
practices and policies that are proposed (or being implemented),
and (iii) compare these to the consequences of a future in which
there is no conjunctive water management.
o The yield reduction function as affected by the average root zone
salinity (Mass and Hoffman, 1977) is useful to include as a
constraint while determine options for conjunctive water
management at the farm and irrigation system levels.
40
CHAPTER THREE
3 Methodology
This chapter introduces the standard version of SWAGMAN Farm model,
which has been written in GAMS and utilizes Mixed Integer Non Linear
Programming solvers such as DICOPT (Discrete and Continuous
OPTimser), to find optimum cropping patterns for given soils, climatic,
irrigation and hydrological conditions. This study extends previous work of
SWAGMAN series models, and develops a customised version of the
SWAGMAN Farm model, which integrates the Mass and Hoffmann
equation in the standard SWAGMAN version and includes the modeling
constraints on area, water allocation, root zone, watertable and net recharge.
3.1 SWAGMAN Farm model
Modeling dynamic salt-water interactions at farm level is a complex task
due to conceptual and analytical issues as well as the lack of reliable and
accurate scientific data required to calibrate the model. Over the past
decades, new software tools have been developed to help promote rational
land and water management options and provide a means to monitor change
in water use efficiency and enhance environmental quality by maintaining
salt and groundwater at natural levels. One of the innovative tools is a state
of the art farm level hydrological economic model called SWAGMAN
Farm-Salt Water And Groundwater MANagement.
41
SWAGMAN Farm can capture economic and environmental tradeoffs in
adopting different land and water management options and help to decide
sustainable irrigation intensities. Regional groundwater investigations,
surface-groundwater interaction models of the irrigation regions and the
SWAGMAN Farm model, developed by CSIRO Land and Water (Khan et
al., 2008), are strategic developments in natural resource management which
are serving as the backbone for strategies such as improving water use
efficiency, reducing net recharge to groundwater and monitoring changes in
environmental conditions on a spatial basis.
Some leading irrigation areas in Australia have structured their farm
production and environmental management plan around on-farm net
recharge management using SWAGMAN Farm. The model accounts for
spatial dynamics in groundwater management zones, to help balance water
and salt loadings closer to the natural equilibria, while enhancing farm
profits. Farmers gain through sustained profits; environment and third
parties gain through better environmental quality and averted damages to the
natural systems and built infrastructure.
SWAGMAN Farm can be applied at the farm level, or alternatively
upscaled to system level, to calibrate salt and water balance under
alternative land and water management systems. SWAGMAN Farm is
lumped water and salt balance model which integrates agronomic, climatic,
irrigation, hydrogeological and economic aspects of irrigated agriculture
under shallow watertable conditions at a farm scale. The model has a PC
based and web based user-interface to help input data and visualise results.
42
This model has been used to develop management options such as the net
recharge management to control shallow watertable condition which focuses
on managing the net recharge beneath the root zone in relation to the
vertical and lateral regional groundwater flow. In SWAGMAN Farm model,
the lumped estimates of the water and salt balance components for the
cropping and fallow periods are computed for a range of irrigated crops such
as rice, soybean, maize, sunflower, fababean, canola, wheat, barley, hay
lucerne, grazed lucerne, annual pasture, perennial pasture as well as dryland
wheat and uncropped areas, for different irrigation, soil, climatic and
hydrogeological conditions. The water and salt balance computations for
each of the crops are derived using the results of detailed monitoring by a
number of researcher (Humphreys et al., 2006). The total gross margin for a
given farm area in a subdivision is optimised by using the SWAGMAN
Farm model.
The SWAGMAN Farm model has been written in GAMS -General
Algebraic Modeling Systems (GAMS Corporation, 1999) and utilizes
Mixed Integer Non Linear Programming solvers such as DICOPT (DIscrete
and Continuous OPTimser) to find optimum cropping patterns for given
soil, climatic, irrigation and hydrogeological conditions. The convergence
and appropriateness of optimisation routines is checked using the sensitivity
analysis techniques for a range of shallow watertable situations.
This study uses a customised version of the SWAGMAN Farm model,
which integrates the Mass and Hoffmann Model into the standard
SWAGMAN Farm version (Khan et al., 2008). This is the key conceptual
contribution of this study and an advance into the existing SWAGMAN
43
Farm model. It involved mixed integer programming to account for
associated with non-linearitreis of the Mass and Hoffmann equation. This
advance enables a more scientific and accurate assessment of the impact of
salinity on crop yield via-a-vis land and water management strategies to
enhance productivity and environmental sustainability.
3.2 Modeling objective function
The objective function of the model is to maximise total gross margin of the
farm using conjunctive water management practices while meeting
environmental requirements:
( )( )
∑⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
⎭⎬⎫
⎩⎨⎧ +
−
=s,c
S,C
S,C
C
s,c
GWPRICE*GWIRRNSWPRICE*SWIRRN
GMLW
XTGM
(1)
Note: In this model, pumping costs is not included.
Where,
TGM Total gross margin ($)
C Landuses considered in a farm (Table 3.1)
S Soil types considered in a farm (Table 3.2)
XC,S Area of a landuse C on soil type S (ha)
GMLWC Gross margin of a landuse C either given or calculated
($/ha)
In case of conjunctive use of surface water and
groundwater, this parameter is calculated using yield
reduction function of Mass and Hoffman (1977), as given
44
below:
GMLWC = PRICEC * YACTUALC – VCOSTSC
Where,
PRICEC Crop price ($/t)
YIELDC Crop yield when root zone salinity is
below crop salinity threshold level (t/ha)
VCOSTSC Variable costs for a crop C ($/ha)
YACTUALC Crop yield as affected by the changes in
root zone salinity (t/ha)
( ){ }100/b*aNSALT1*YIELDYACTUAL CC −−=
Where,
NSALT New average root zone salinity (dS/m)
b Slope of the yield salinity curve for a crop
C
a Average root zone salinity threshold level
of a crop C (dS/m)
SWIRRNC,S Irrigation with surface water of a landuse C on soil type S
(ML/ha)
SWPRICE Price of surface water ($/ML)
GWIRRNC,S Irrigation with groundwater pumped from shallow
watertable aquifer of a landuse C on soil type S (ML/ha)
GWPRICE Price of groundwater ($/ML)
The objective function is solved using an integer programming solver,
GAMS-OSL (Brooke et al., 1988), subject to the following constraints:
45
o Area of a landuse
o Water allocation
o Root zone salinity
o Pumping from shallow watertable aquifer
o Net recharge
Table 3.1 Landuses considered in the model for a farm. (Madden and Prathapar, 1999; Jehangir and Khan, 2003)
For Australian Conditions For Pakistani Conditions Model code Description Model code Description RICE Rice RICE Rice MAIZE Maize MAIZE Maize FABABEAN Fababean SUGARCANE Sugarcane WHEAT Wheat WHEAT Wheat DWHEAT Dryland Wheat COTTON Cotton LUCERNE Lucerne KFODDER Kharif Fodder PPASTURE Perennial Pasture RFODDER Rabi Fodder FALLOW Fallow FALLOW Fallow SOYBEAN Soybeans SUNFLOWER Sunflower CANOLA Canola BARLEY Barley HLUCERNE Lucerne for Hay APASTURE Annual Pasture DAPASTURE Dryland Annual
Pasture
Table 3.2 Soil types considered in the model for a farm. (Madden and Prathapar, 1999; Jehangir and Khan, 2003)
For Australian Conditions For Pakistani Conditions
Model code Description Model code
Description
SMC Self Mulching Clay SCY Silt, Silt Loam, Silty Clay and Silty Clay
46
For Australian Conditions For Pakistani Conditions
Model code Description Model code
Description
Loam NSMC Non Self Mulching
Clay CLM Clay and Clay Loam
TRBE Transitional Red Brown Earth
SLM Silt Loam
RBE Red Brown Earth LM Loam and Loamy Sand
SANDS Other Sandier Soils SDLM Fine Sandy Loam, Sandy Loam, Sandy Clay and Loam
3.3 Modeling constraints
3.3.1 Constraints on area of a landuse
The following constraints, on area of a landuse, are considered:
o Total area of all landuses (C) on all soil types (S) must match the
total area of the farm (AREA).
o Landuses on a soil type cannot exceed total area of the soil type.
o Area of a landuse (XCC) cannot exceed maximum allowable area
(PMXA). This constraint on maximum allowable area (PMXA) is
set to reflect real world considerations, such as enterprise
diversification, crop rotations, market demand and restrictions set
by the natural resource managers.
o Similarly, area of a landuse (XCC) must be greater than minimum
required area (PMNA). This constraint on minimum required area
(PMNA) is used to force landuses into the solution set so that
simulation can be performed.
47
o RICE is not to be grown on sandier soils (SANDS and RBE for
Australian conditions, and LM and SDLM for Pakistani
conditions).
Area of a landuse (XCC) in the optimal solution cannot be less than a
minimum required area (PMNA). The following two auxiliary binary
functions ensure that any landuse area in the solution set is viable (defined
as more than 20 ha for Australian conditions and 0.25 ha for Pakistani
conditions):
CC Y*AREA20XC ≤+− (2)
( )CC Y1*AREAXC −≤ (3)
Where,
XCC Area of a landuse (ha)
AREA Area of the farm (ha)
YC Binary variable (0,1)
As an illustration of how the above two equations work, suppose XCC for a
landuse enters the solution vector. XCC is constrained to be positive and
since YC can take on values of either zero or one, Equation 3 above has no
choice but to put YC equal to zero. Then, if YC is zero, Equation 2 will have
no choice other than to make XCC at least equal to 20. On the other hand, if
XCC is not in the solution vector, the above two equations are automatically
satisfied, and the rest of the solution proceeds as normal.
48
3.3.2 Constraints on water allocation
The irrigation applied to crops in a given year cannot be more than the water
allocation that is assigned to a farm:
∑=S,C
S,CS,C IRRN*XWALL
(4)
Where,
WALL Water allocation for a farm (ML)
XC,S Area of a landuse C on soil type S (ha)
IRRNC,S Total irrigation water (ML/ha)
In case of surface water use only, IRRNC,S = SWIRRNC,S
In case of conjunctive use of surface water and
groundwater,
IRRNC,S = SWIRRNC,S + GWIRRNC,S
3.3.3 Constraints on root zone salinity
The model allows the user to specify the maximum allowable annual rise in
root zone salinity (ASRISE) because of annual change in root zone salinity
(DELSALT) under conjunctive water management practices while meeting
environmental requirements:
ASRISEDELSALT ≤
Where,
DELSALT Change in root zone salinity (dS/m)
ASRISE Maximum allowable annual rise in root zone salinity
(dS/m)
49
Change in root zone salinity
The annual change in root zone salinity is estimated as under:
( )( ) 640/SOILWATER
*10000*AREA*DWT/1000*TSIN
DELSALT⎟⎟⎟
⎠
⎞
⎜⎜⎜
⎝
⎛=
(5)
Where,
DELSALT Change in root zone salinity (dS/m)
TSIN Net salt movement into the root zone (t)
DWT Depth to the watertable on a farm (m)
AREA Area of the farm (ha)
SOILWATER Average volumetric soil water content of the farm (-)
Net salt movement into the root zone
The annual net salt movement into the root zone is estimated as under:
( )( ) ⎥
⎦
⎤⎢⎣
⎡++
−+++=
SDSALT2DDSALT1DDSALTSRAINSCUFLOWCSALTSIRRN
TSIN
(6)
Where,
TSIN Net salt movement into the root zone (t)
SIRRN Salt brought into root zone by irrigation water (t)
CSALT Salt brought into root zone during cropping (t)
SCUFLOW Salt brought into root zone by capillary upflow during
fallow (t)
SRAIN Salt brought onto the farm by rain water (t)
DDSALT1 Salt removed with leaching water during cropping (t)
50
DDSALT2 Salt removed with leaching water during fallow (t)
SDSALT Salt removed by surface drainage during cropping (t)
SIRRN -salt brought into root zone by irrigation water, is estimated as
under:
( )( )∑
⎭⎬⎫
⎩⎨⎧ +
=S,C S,C
S,CS,C GWIRRN*GWIRRN
SWIRRN*SWIRRNX*SFACTSIRRN
(7)
Where,
SIRRN Salt brought into root zone by irrigation water (t)
SFACT 0.64 – a factor for converting salt concentration from dS/m
to t/ML
XC,S Area of a landuse C on soil type S (ha)
SWIRRNC,S Irrigation with surface water of a landuse C on soil type S
(ML/ha)
SWIRRN Salt concentration of surface water (dS/m)
GWIRRNC,S Irrigation with groundwater pumped from shallow
watertable aquifer of a landuse C on soil type S (ML/ha)
GWIRRN Salt concentration of groundwater pumped from shallow
watertable aquifer (dS/m)
CSALT -salt brought into root zone during cropping, is estimated as under:
( )∑ −=S,C
S.CS,CS,C DIFR1*CGWATER*CUCB*X*SFACTCSALT (8)
Where,
CSALT Salt brought into root zone during cropping (t)
51
SFACT 0.64 – a factor for converting salt concentration from dS/m to
t/ML
XC,S Area of a landuse C on soil type S (ha)
CUCBC,S Capillary up`flow for a landuse C on soil type S during cropping
(ML/ha)
This parameter is estimated as under:
SCS,C CFLOW*GPDCUCB =
Where,
GPD Growing period for each crop (d)
CFLOWS Capillary upflow corresponding to soil
type S and depth to the watertable on a
farm during cropping (ML/ha/d)
CGWATER Salt concentration of capillary upflow from shallow groundwater
aquifer at the farm (dS/m)
DIFRC,S Factor which allows for salt leaching during cropping due to
excess irrigation water and rain water for a landuse C on soil type
S.
If CUBCC,S ≤ 0, then DIFRC,S = 0
If CUBCC,S > 0, then
( )( ) S,CS,CS,CS,CS,C CUCB/WAVAIL*LFRACWEXCESSDIFR −=
If DIFRC,S ≤ 0, DIFRC,S = 0, and if DIFRC,S ≥ 1, DIFRC,S = 1
Where,
WEXCESSC,S Water in excess of actual
evapotranspiration (ML/ha)
52
LFRACC,S Leaching fraction for a landuse C on
soil type S during cropping (-)
WAVAILC,S Total water available for a landuse C on
soil type S during cropping (ML/ha)
Note: In this model, groundwater pumping is considered from shallow
watertable aquifer; therefore, GWIRRN is set equal to CGWATER.
SCUFLOW -salt brought into root zone by capillary upflow during fallow,
is estimated as under:
∑=S,C
S,CS,C CGWATER*BUCB*X*SFACTSCUFLOW
(9)
Where,
SCUFLOW Salt brought into root zone by capillary upflow during
fallow (t)
SFACT 0.64 – a factor for converting salt concentration from dS/m
to t/ML
XC,S Area of a landuse C on soil type S (ha)
BUCBC,S Capillary upflow for a landuse C on soil type S during
fallow (ML/ha)
This parameter is estimated as under:
SCS,C BFLOW*BPERIODBUCB =
Where,
BPERIODC Fallow period after each crop (d)
BFLOWS Capillary upflow corresponding to soil
53
type S and depth to the watertable on a
farm during fallow (ML/ha/d)
CGWATER Salt concentration of capillary upflow from shallow
groundwater aquifer at the farm (dS/m)
SRAIN -salt brought onto the farm by rain water, is estimated as under:
( )SFACT*CRAIN*AREA*RAINSRAIN = (10)
Where,
SRAIN Salt brought onto the farm by rain water (t)
RAIN Annual amount of rain water (ML)
AREA Area of the farm (ha)
CRAIN Concentration of rain water (dS/m)
SFACT 0.64 – a factor for converting salt concentration from dS/m
to t/ML
DDSALT1 and DDSALT2 -salt removed with leaching water during
cropping and fallow, respectively, are estimated as under:
∑=S,C
S,CS,CS,C CDWATER*VOLLF*X*SFACT1DDSALT
∑=S,C
S,CS,CS,C CDWATER*BRAIN*X*SFACT2DDSALT
(11)
Where,
DDSALT1 Salt removed with leaching water during cropping (t)
DDSALT2 Salt removed with leaching water during fallow (t)
SFACT 0.64 – a factor for converting salt concentration from dS/m
54
to t/ML
VOLLFC,S Water available for leaching of salt for a landuse C on soil
type S during cropping (ML/ha)
This parameter is estimated as under:
S,CS,CS,C AVAILLF*WAVAILVOLLF =
Where,
WAVAILC,S Total water available for a landuse C on
soil type S during cropping (ML/ha)
AVAILLFC,S Excess water fraction (water in excess
of actual evapotranspiration divided by
total water available for a landuse C on
soil type S during cropping (-)
BRAINC,S Rainfall for a landuse C on soil type S during fallow
(ML/ha)
CDWATERC,S Salt concentration of leaching water to shallow
groundwater aquifer for a landuse C on soil type S (dS/m)
SDSALT -salt removed by surface drainage during cropping, are estimated
as under:
∑ ⎟⎟⎠
⎞⎜⎜⎝
⎛=
S,C S,CS,C
S,CS,C
DSALINITY*PERDRAIN*IRRN*X
*SFACTSDSALT (12)
Where,
SDSALT Salt removed by surface drainage during cropping (t)
SFACT 0.64 – a factor for converting salt concentration from
55
dS/m to t/ML
XC,S Area of a landuse C on soil type S (ha)
IRRNC,S Total irrigation water (ML/ha)
PERDRAINC,S Fraction of total irrigation water that is surface drained for
a landuse C on soil type S during cropping (-)
DSALINITYC,S Salt concentration of surface drainage for a landuse C on
soil type S during cropping (dS/m)
Note: If a farm has a recycling system, SDSALT can be set as zero.
Salinity of leaching water
The salt concentration of leaching water to shallow groundwater aquifer is
estimated under the following conditions:
If water in excess of actual evapotranspiration (WEXCESS) is greater than
or equal to leaching fraction for a landuse C on soil type S during cropping
(LFRAC), then
S,CS,CS,C LFRAC/CWAVAILCDWATER = (13a)
Where,
CDWATERC,S Salt concentration of leaching water to shallow
groundwater aquifer for a landuse C on soil type S during
cropping (dS/m)
CWAVAILC,S Salt concentration of total water available for a landuse C
on soil type S during cropping (dS/m)
LFRACC,S Leaching fraction for a landuse C on soil type S during
cropping (-)
56
If water in excess of actual evapotranspiration is less than leaching fraction
for a landuse C on soil type S during cropping, then the salinity of leaching
water is adjusted in proportion to leaching fraction and amount of total
water available for a landuse C on soil type S. This condition allows salt
build up in the root zone due to inadequate amount of leaching water; and
the resulting equation is given as under:
S,CS,CS,C AVAILLF*CWAVAILCDWATER = (13b)
Where,
CDWATERC,S Salt concentration of leaching water to shallow
groundwater aquifer for a landuse C on soil type S during
cropping (dS/m)
CWAVAILC,S Salt concentration of total water available for a landuse C
on soil type S during cropping (dS/m)
AVAILLFC,S Excess water fraction (water in excess of actual
evapotranspiration divided by total water available for a
landuse C on soil type S during cropping (-)
3.3.4 Constraints on pumping from shallow watertable aquifer
Pumping from shallow watertable aquifer is an option in the model;
however, the model can be run in two modes: pumping or no pumping.
Using pumping constraints, the model calculates the new depth to
watertable. However, in case of no pumping, the model calculates the
volume of groundwater that is required to be pumped to maintain initial
depth to watertable (i.e., to obtain zero net recharge).
57
Under both the modelling mode: pumping or no pumping, the new depth to
water is estimated as under:
WTDWTNDWT ∆−= (14)
Where,
NDWT New depth to watertable (m)
DWT Initial depth to watertable (m)
∆WT Change in depth to watertable (m)
This parameter is estimated as under:
( )( )( ) 10/
AREA*SOILWATERTHETAS/PUMPNRECH
WT⎭⎬⎫
⎩⎨⎧
+−−
=∆
Where,
NRECH Net recharge at the farm (ML)
PUMP In pumping mode, it represents the volume of
pumped groundwater from shallow watertable
aquifer (ML), and
In no pumping mode, it represents the volume
of groundwater that is required to be pumped
(ML) to maintain initial depth to watertable
(i.e., to obtain zero net recharge)
THETAS Weighted average of saturated volumetric soil
water content of the farm (-)
SOILWATER Average volumetric soil water content of the
farm (-)
AREA Total area of the farm (ha)
58
Net recharge at the farm
The amount of net recharge to shallow groundwater aquifer is estimated as
under:
( )AREA*LEAKAGERECHBRECHGNRECH −+= (15)
Where,
NRECH Net recharge at the farm (ML)
RECHG Recharge under a landuse C on soil type S during cropping
(ML)
RECHB Recharge under a landuse C on soil type S during fallow
(ML)
LEAKAGE Amount of groundwater moved to deeper aquifer layers
(ML/ha)
AREA Total area of the farm (ha)
RECHG – recharge under a landuse C on soil type S during cropping is
estimated as under:
∑=S,C
S,CS,C WEXCESS*XRECHG
if WEXCESS ≥ 0 (16a)
∑=S,C
S,CS,C CUCB*XRECHG
if WEXCESS < 0 (16b)
Where,
RECHG Recharge under a landuse C on soil type S during cropping
(ML)
XC,S Area of a landuse C on soil type S (ha)
CUCBC,S Capillary upflow for a landuse C on soil type S during
59
cropping (ML/ha)
WEXCESSC,S Water in excess of actual evapotranspiration (ML/ha)
RECHB – recharge under a landuse C on soil type S during fallow is
estimated as under:
∑ −=S,C
2RECHF1RECHFRECHB
(17)
Where,
RECHB Recharge under a landuse C on soil type S during fallow
(ML)
RECHF1 Rain water during fallow (ML)
This parameter is estimated as under:
( )∑=S,C
S,C BFRAIN*BRAIN*X1RECHF
Where,
RECHF1 Rain water during fallow (ML)
XC,S Area of a landuse C on soil type S (ha)
BRAIN Rain water at the farm during fallow
(ML/ha)
BFRAIN 0.4 – a factor for adjusting rain water at
the farm during fallow (-)
RECHF2 Capillary upflow during fallow (ML)
This parameter is estimated as under:
∑=S,C
S,CS,C BUCB*X2RECHF
Where,
60
RECHF2 Capillary upflow during fallow (ML)
XC,S Area of a landuse C on soil type S (ha)
BUCBC,S Capillary upflow for a landuse C on soil
type S during fallow (ML/ha)
3.3.5 Constraints on net recharge
The amount of net recharge to shallow groundwater aquifer is constrained
by the maximum allowable annual change in depth to watertable, which is
set by the user:
ADWTWT ≤∆
Where,
∆WT Change in depth to watertable (m)
ADWT Maximum allowable annual rise in depth to watertable (m)
3.4 Summary
This study extends previous work of SWAGMAN series models, and
develops a customised version of the SWAGMAN Farm model, which
integrates the Mass and Hoffmann equation in the standard SWAGMAN
version and includes the modeling constraints on area of a landuse, water
allocation, root zone salinity, watertable changes, pumping from shallow
watertable aquifer, and net recharge. This customised version of the
SWAGMAN Farm model enables a more scientific and accurate assessment
of the impact of salinity on crop yield via-a-vis land and water management
strategies to enhance productivity and environmental sustainability.
61
It can be used to: (i) provide farmers with a tool to simulate and assess
various farm cropping scenarios in terms of economic return and
environmental effects, (ii) determine environmentally optimal irrigation
intensity and encourage water use efficiency through water and salinity
auditing in an integrated manner, and (iii) assist irrigation authorities (public
and private) for developing policies to achieve improved economic and
natural resource sustainability.
62
CHAPTER FOUR
4 Conjunctive Water Management at the Farm Level: Case
Studies in Australia
This chapter captures the heterogeneity in the basic resource characteristics
across farms, and modeling its impact on crop mix, yield, and gross returns
from conjunctive water management at farm level for the selected irrigated
agricultural areas from Australia. Depending upon the quality and aquifer
yields, the decision of installing deeper or shallower groundwater bore is
made. For instance, if deep aquifers have better quality (less saline) water
than the shallow aquifers and are high yielding, a deep bore becomes an
attractive option for farmers who want to supplement their existing
irrigation allocation, even though the capital cost is high. The chapter
presents the comparison of crop gross margins at the same farm under
various simulation scenarios to capture the impact of change in water
allocation and related salinity levels on the gross margin.
4.1 Description of the study area
The Coleambally Irrigation area was selected for this research study in
Australia (Figure 4.1). This irrigation area is located in western New South
Wales. It covers about 80,000 ha of irrigated land, practicing conjunctive
use of surface and groundwater for broadacre agriculture. Rice is the
principal crop. Other crops include winter cereals, wheat, maize, soybean,
hay lucerne, canola and barley etc. Irrigation farms within this irrigation
63
area are not identical. They vary in basic resource characteristics such as
size of landholding, mix of soil types, depth to watertable (that is current
impact of salinity and waterlogging), rice quota held (related to property
size and soil type). There will also be variation between farms based on
individual landholder preferences, farming technologies and other factors.
The focus, in these sections, is therefore on capturing the heterogeneity in
the basic resource characteristics across farms, and modeling its impact on
crop mix, yield, and gross returns. In order to capture the impacts of
variation in resource characteristics a pragmatic trade-off was made between
the complexity and difficulty in assembling information across more than
300 farm units in the Coleambally Irrigation area and adequately estimating
the scale impacts on productivity and gross margin.
Figure 4.1 The Coleambally Irrigation Area.
64
In Coleambally Irrigation Area, total average rainfall lies between 400 – 450
mm/year, which can generally be described as a semi-arid climate. Total
rainfall for 2006/07 was 239.5 mm. This figure is 156.9 mm or 40 percent
less than the long term average of 396.4 mm. As displayed in Figure 4.2, in
9 out of 12 months the rainfall was lower than the long term average. Only
in the months of July 2006, February and April 2007 the monthly rainfall
was greater than the long term average. Monthly evapotranspiration figures
for 2006/07 are represented in Figure 4.3. Based on data from CSIRO
Griffith, the total evapotranspiration of 2182 mm is 341mm higher or 18.5
percent higher than the long term average of 1841 mm. Almost every month
of the year, the monthly evapotranspiration exceeded the long term average.
0
5
10
15
20
25
30
35
40
45
50
Jul-0
6
Aug
-06
Sep-
06
Oct
-06
Nov-
06
Dec-
06
Jan-
07
Feb-
07
Mar
-07
Apr
-07
May
-07
Jun-
07
Rain
fall
(mm
)
2006/07 rainfall Long Term Average
Figure 4.2 Monthly rainfall figures during 2006-07 (AER 2007).
65
0
50
100
150
200
250
300
350
Jul-0
5
Aug
-05
Sep
-05
Oct
-05
Nov
-05
Dec
-05
Jan-
06
Feb-
06
Mar
-06
Apr
-06
May
-06
Jun-
06
Evap
otra
nspi
ratio
n (m
m)
2006/07 Long-term average
Figure 4.3 Monthly evapotranspiration figures during 2006-07 (AER 2007).
Table 4.1 and Table 4.2 outline the different types of land uses and their
respective areas in the CIA over the period of the research.
Table 4.1 Areas (ha) of each crop type irrigated in CIA, and the Kerarbury Channel and Outfall District, and each crop’s relative percentage to total irrigated area 2004 (Coleambally Irrigation Co-Operative Limited 2005).
Crop CIA Proportion CIA Proportion Crop (ha) of CIA (ML) of Delivery Water Crop Area (%) Use (%) (ML) Wheat 18451 29.60% 44519 19.43% 2.2 Rice 6985 11.88% 104195 45.46% 12.9 Pasture 8871 18.77% 25046 11.16% 2.0 Barley 5281 8.51% 6916 2.99% 1.2 Oats 2638 4.63% 3899 1.69% 1.2 Canola 2441 3.91% 2929 1.31% 1.1 Triticale 1992 2.98% 3338 1.44% 1.6 Corn 1965 5.36% 13009 7.43% 4.7 Fallow 1891 2.82% 1957 0.85% 1.0 Summer pasture 1566 2.29% 3260 1.44% 2.1 Soybean 1285 2.18% 5357 2.32% 3.6 Sorghum 988 1.46% 1516 0.67% 1.5 Winter pasture 592 1.16% 1331 0.59% 1.7 Lucerne 557 0.90% 1430 0.62% 2.3 Sunflower 295 0.68% 970 0.42% 2.1 Millet 272 0.58% 333 0.14% 0.8
66
Maize 153 0.22% 1421 0.61% 9.3 Forest 137 0.20% 490 0.21% 3.6 Fababeans 98 0.40% 200 0.19% 1.6 Grapes 78 0.17% 238 0.12% 2.3 Lupins 64 0.09% 0 0.00% 0.0 Other 61 0.09% 0 0.00% 0.0 Prunes 58 0.20% 66 0.03% 0.5 Olives 49 0.07% 83 0.04% 1.7 Stock - dams 39 0.09% 186 0.48% 18.9 Potatoes 28 0.04% 233 0.10% 8.2 Peas 25 0.04% 0 0.00% 0.0 Azuki beans 22 0.03% 40 0.02% 1.8 Fodder 12 0.16% 0 0.00% 0.0 Onions 11 0.02% 34 0.01% 3.1 Pumpkins 11 0.02% 90 0.04% 8.5 Clover 7 0.26% 15 0.01% 0.1 Green manure 3 0.00% 29 0.01% 8.5 Tomatoes 3 0.17% 0 0.18% 3.8 Total 56934 100.00% 223129 100.00% 3.92
Table 4.2 Areas (ha) of each crop type irrigated in CIA, and the Kerarbury Channel and Outfall District, and each crop’s relative percentage to total irrigated area 2005 (Coleambally Irrigation Co-Operative Limited 2006).
Crop CIA Proportion CIA Proportion Crop (ha) of CIA (ML) of Delivery Water Crop Area (%) Use (%) (ML) Rice 16,831 25.54% 215853 62.80% 12.2 Pasture 13,403 21.88% 28192 8.69% 2.0 Wheat 12,850 19.28% 27629 8.36% 2.2 Barley 6,864 10.25% 7081 2.20% 1.1 Corn/Maize 2,026 4.68% 16460 6.96% 7.4 Oats 2,298 3.30% 2688 0.77% 1.2 Soybeans 1,946 2.98% 9453 2.91% 4.8 Canola 1,482 2.48% 1303 0.89% 1.8 Triticale 1,350 1.91% 1638 0.47% 1.2 Sorghum 1,003 1.47% 2940 0.88% 3.0 Miscellaneous 705 1.14% 1236 0.35% 1.5 Fababeans 465 0.94% 522 0.33% 1.8 Other 332 0.81% 200 0.06% 0.3 Lucerne 459 0.75% 2135 0.82% 5.4 Fallow 474 0.67% 655 0.19% 1.4 Sunflower 381 0.54% 1363 0.39% 3.6 Grapes 83 0.23% 71 0.05% 1.1 Winter pasture 40 0.20% 96 0.11% 2.7 Azuki beans 20 0.17% 107 0.03% 0.9 Prunes 15 0.16% 83 0.02% 0.7
67
Forest 114 0.16% 497 0.14% 4.4 Stock - dams 35 0.15% 323 1.35% 44.8 Peas 96 0.14% 10 0.00% 0.1 Potatoes 54 0.08% 312 0.09% 5.8 Olives 25 0.04% 43 0.01% 1.7 Green manure 15 0.02% 0 0.00% 0.0 Onions 13 0.02% 31 0.01% 2.4 Vegetables 5 0.01% 0 0.00% 0.0 Garlic 0 0.00% 0 0.00% 0.0 Not defined 0 0.00% 1192 0.96% 0.0 Citrus 1 0.00% 23 0.01% 0.0 Clover 45 0.00% 143 0.04% 0.0 Millet 148 0.00% 112 0.03% 0.0 Summer pasture 60 0.00% 316 0.09% 0.0 Total 63,638 100% 322703 100% 5.07
Over the research period wheat was the most dominant land use with more
than 20287 ha sown each year (30 percent and 19 percent of the total
irrigated landscape in years 2005 and 2006 respectively). In the 2004/2005
irrigation season, rice was the dominant summer crop with a total area of
8142 ha. Rice grown during the 2005/2006 irrigation season increased to
18025 ha. For the entire growing area, wheat accounted for 20287 ha during
2004/2005 and 13610 ha during 2005/2006. For both irrigation seasons, rice
remained the main water user, with total deliveries equaling 45.46% and
62.80% in the 2004/2005 and 2005/2006 seasons respectively.
4.2 Case studies in conjunctive water management
Earlier studies (Khan et al., 2007) show that groundwater characteristics
vary across the five zones shown in (Figure 4.4). Therefore, this study
selected one representative farm from each zone. Farm 1 was selected from
zone 1. It is likely that zones 2 and 3 will be combined in the initial
68
assessment; therefore from these two zones a single Farm 6 was selected.
Farm 9 and farm 11 were selected from for zone 4 and 5 respectively.
To evaluate the impacts of allowable rise in salt concentration, leakage rate,
groundwater depth and salinity concentration in groundwater comparison
were made at each farm level for (a) surface water only, (b) groundwater
only and (c) conjunctive use of surface and groundwater (Appendix I). For
this purpose only one parameter was allowed to change for various model
runs for each system of water management. For each case the base model
was run followed by ten runs. For each run on the same farm the level of
groundwater was allowed to vary which has impact on composite salinity
level and therefore the gross margin. The total gross margin per hectare was
calculated for the base run and the ten runs.
69
Figure 4.4 CIA groundwater management zones (Khan et al., 2008).
Comparison was made for gross margin at the same farm under various runs
to capture the impact of change in water allocation and related salinity level
on the gross margin. The same method was used for comparisons across the
farms. Table 4.3 gives a comparative view of the basic characteristics and
parameters across the farms modeled. The selected farm differ in size but
per hectare water allocation is the same all other parameter the same except
the initial depth of watertable and concentration of salinity in the
groundwater. This variation does allow us to evaluate the impact of changes
in watertable and salinity on crop yield, crop choices and overall gross
margin.
70
Table 4.3 A comparative overview of the modelled farms.
Description Farm 1 Farm 6 Farm 9 Farm 11
Rainfall (mm) 346 346 346 346
Allowable rise in groundwater level (m) 0.1 0.1 0.1 0.1
Allowable rise in salt concentration. (dS/m) 2.25 2.25 2.25 2.25
Price of surface water ($/ML) 14.97 14.97 14.97 14.97
Price of ground water ($/ML) 40 40 40 40
Area of farm (ha) 267 225 221 339
Leakage (mm/year) 20 0.2 20 20
Initial depth to watertable (m) 1.0 0.6 3.5 1.0
Initial average root zone salinity (dS/m) 1.5 1.5 1.5 1.5
Concentration of surface water (dS/m) 0.14 0.14 0.14 0.14
Concentration of groundwater (dS/m) 0.7 0.7 0.7 0.7
Concentration of watertable in (dS/m) 2.8 4 1.3 2.2
Concentration of rain water (dS/m) 0.01 0.01 0.01 0.01
Rainfall recycling yes yes yes yes
Pumping from shallow watertable aquifer yes yes yes yes
4.3 Modeling results and discussion
4.3.1 CIA Groundwater management zone 1
The modelling result for various water allocation levels for farm 1, located
in CIA groundwater management zone 1, are given in Figure 4.5. The
highest total gross margin is achieved for the base run in case of surface
water use only ($ 745/ha). The total gross margin for all other on farm 1 are
lower for lower water allocation level and reach lowest value ($ 227/ha)
when water allocation level is 10%. For groundwater use only per hectare
gross margin is $ 464 for full allocation level of 100% and it falls to $ 210
for 10% allocation level. For conjunctive use of surface and groundwater,
71
the base run gives total gross margin of $ 604/ha. This value falls in
between the surface water only and groundwater only base runs. Likewise
total gross margin per hectare for each run in the case of conjunctive water
use are lower than the canal water use only but higher than groundwater use
only. This is an expected outcome as discuss earlier.
0
100
200
300
400
500
600
700
800
0 200 400 600 800 1000 1200 1400 1600
Total water allocation (ML)
Tota
l gro
ss m
argi
n ($
/ha)
Surface water Groundwater Conjunctive
Figure 4.5 Farm 1 - Total gross margin for various water allocation levels and water management systems
Overall results in figure above show that:
o Total gross margin increases as total water allocation increases
o The increase in total gross margin is higher at lower allocation
levels
o The increase in total gross margin is relatively lower at higher
allocation levels
o Surface water offers the highest gross margin followed by the
conjunctive use of surface and groundwater
o The groundwater only offers the lowest gross margin per ha.
72
The analysis at farm 1 suggests that conjunctive use of surface water and
groundwater offers higher total gross margin than groundwater alone.
However, I cannot generalise this result based on analysis from only one
farm. Therefore the analysis is also done for farm 6.
4.3.2 CIA Groundwater management zone 2 & 3
The modelling result for various water allocation levels for farm 6, located
in CIA groundwater management zone 2 & 3, are given in Figure 4.6. Farm
6 differs from farm 1 in three key respects: area of the farm is lower;
leakage rate is far lower, initial depth watertable is lower; concentration of
salts in watertable is higher. A higher concentration and shallower
watertable means that yield should be lower and total gross margin may fall.
In terms of total gross margin surface water use only should have highest
gross margin than groundwater use only while conjunctive use should have
higher gross margin than groundwater use only. Likewise for lower water
allocation the total gross margin should be lower for lower water allocation
levels and the same should hold for each system of water management on
farm 6. My modelling result supports these expectations (Figure 4.6).
The result show that total gross margin are highest for the base case ($
716/ha). The total gross margin falls as surface water allocation level falls.
For groundwater use only the total gross margin per ha is highest for the
base case and it does not change until the allocation level falls to 50%
beyond that level the total gross margin per ha begins to fall and reaches
lowest amount $223 per ha for lowest allocation level of 10%. For
73
conjunctive use only on farm 6 the base case run gives the highest gross
margin per ha of $519. For lower levels of total water allocation even with
conjunctive use the gross margins are lower than full allocation level. These
results are as expected by the theory therefore show that my model is robust.
The result for farm 6 shows that:
o total gross margin is highest for surface water use only and it
continues to rise for higher allocation level
o the total gross margin is lowest for groundwater use only and it
increases more slowly for higher allocation levels
o the total gross margin for conjunctive use of surface water falls in
middle but is higher for higher level of total water allocation.
0
100
200
300
400
500
600
700
800
0 200 400 600 800 1000 1200 1400
Total water allocation (ML)
Tota
l gro
ss m
argi
n ($
/ha)
Surface water Groundwater Conjunctive
Figure 4.6 Farm 6 - Total gross margin for various water allocation levels and water management systems
In terms of comparisons across farm1 and farm 6 the results show that:
o total gross margin for surface water only are higher at farm 1 than
farm 6 and this also true for each of the model run on two farms.
74
o for groundwater use only the total gross margin is high on farm
than farm 6 and same is true for each scenario
o for conjunctive use of surface and groundwater the total gross
margin is higher at farm 1 than farm 6 and this is also the case for
each model run.
The above result are quite likely because farm 1 has relatively deeper
watertable and lower groundwater salinity than farm 6, such that crop yield
and gross margin should be lower at farm 6. What is more important is that
this is true for all three water management system on two farms and each
scenario run across the farms. Again the soundness and consistency of the
model is confirmed from these farm level analyses. As noted earlier the
depth of watertable is greater on farm 9 than farm 6 but concentration of
salinity in the watertable is lower. This suggest that watertable level and
salinity environment are better in the root zone on farm 9 than farm 6 thus
farm 9 has better crop growing environment which means that it should
have higher gross margin per ha than farm 6.
4.3.3 CIA Groundwater management zone 4
The modelling result for various water allocation levels for farm 9, located
in CIA groundwater management zone 4, are given in Figure 4.7. The
analysis of total gross margin for farm 9 shows that per ha gross margin is
higher for surface water use only, for groundwater use only and for
conjunctive use of surface and groundwater this shows that the model can
successfully pick the difference in watertable and groundwater salinity and
shows its effect on total gross margin.
75
The overall results in terms of total gross margin for the base case and
various model runs for each system of water management show the same
pattern on farm 9 as on farm 6 but have lower values. The full explanation
of these result is thus not required here. The result for farm 9 shows that:
o Total gross margin is highest for surface water use and it continues
to rise for higher allocation level
o The total gross margin is lowest for groundwater use only and it
also continues to rise for higher allocation levels
o the total gross margin for conjunctive use of surface water falls in
middle but is higher for higher level of total water allocation and
continues as well.
The continuous rise in total gross margin for higher water allocation level
means that when watertable is deeper and shallow groundwater salinity is
lower the total gross margin continues to rise with higher water allocation
levels. This means that when growing condition are more suitable the
availability of more water can generate higher gross margin through higher
yield and possibly through higher return crops.
76
0
100
200
300
400
500
600
700
800
0 200 400 600 800 1000 1200 1400
Total water allocation (ML)
Tota
l gro
ss m
argi
n ($
/ha)
Surface water Groundwater Conjunctive
Figure 4.7 Farm 9 - Total gross margin for various water allocation levels and water management systems.
4.3.4 CIA Groundwater management zone 5
The modelling result for various water allocation levels for farm 11, located
in CIA groundwater management zone 5, are given in Figure 4.8. As
mention earlier farm 11 has same level of initial watertable level depth as
farm 1. All other factors and parameters are the same; the only difference is
that the concentration of salinity and watertable is higher in farm 11 than
farm 6. This means that crop yield should be lower on farm 11 than farm 1
such that total gross margin per ha must be lower. The modelling result in
farm 11 supports this. This suggests that the model is able to pick the impact
of higher shallow groundwater salinity on yield and gross returns. The
modelling results in terms of gross margin are similar to the one on farm 1
and therefore there is no need to explain them in full here.
77
0
100
200
300
400
500
600
700
800
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Total water allocation (ML)
Tota
l gro
ss m
argi
n ($
/ha)
Surface water Groundwater Conjunctive
Figure 4.8 Farm 11 - Total gross margin for various water allocation levels and water management systems
The result shows that:
o groundwater use only has lowest gross margin per ha
o conjunctive use of surface and groundwater has highest gross
margin than groundwater only
o canal water use have the highest gross margin per ha, and
o gross margin per ha increases with increase in water allocation
level for all three water management systems.
The result suggest that conjunctive use of surface and groundwater offers
higher return per ha than groundwater alone, but higher groundwater
allocation are desirable only when surface water is available for conjunctive
use the overall total gross margin.
78
4.4 Groundwater salinity impacts
Preliminary data analysis from the CIA show that in areas with shallow
watertable < 2m the salinity of groundwater should be < 5 dS/m (5000 EC).
These results are based on potential shallow groundwater pumping of 1
ML/ha /year, surface water irrigation salinity of 0.2 dS/m (2000 EC), target/
composite salinity for conjunctive use at 0.8 dS/m (8000 EC) and total
irrigation application of 8 ML/ha/year. However, if 0.5 dS/m (500 EC) is
taken as the combined salinity then groundwater up to 3.5 dS/m (3500 EC)
may only be used. I tested this finding further by using three composite
salinity levels which are 0.42 dS/m, 0.72 dS/m and 0.92 dS/m. In addition I
also assess the trends in watertable and salinity over time to evaluate
suitability of the current groundwater use for shallow groundwater pumping
and conjunctive use.
This hypothesis was tested using data for farm 1. For this purpose three
scenarios were estimated. In the first scenario (Table 4.4), groundwater EC
was 2.66 dS/m and surface water EC was 0.14 dS/m. The total water
allocation was 1400 ML. The composite EC was 0.42 dS/m. Various model
runs for this scenario were generated by adjusting proportion of canal water
and groundwater use such that the same target salinity level was achieved
and the combine water used from both sources were equal to total water
allocation 1400 ML. The scenario 2 (Table 4.5) differs from scenario 1 in
that the groundwater EC 5.3 dS/m and target salinity was 0.72 dS/m, other
thing being equal as in scenario 1. The scenario 3 (Table 4.6) has even
higher level of groundwater salinity and target EC level of 7.1 dS/m and
0.92 dS/m, with other things remaining the same. For each scenario, the
79
mixing ratios of canal water : groundwater was changed from 1:8 to 8:1 to
help understand the impact of changes in conjunctive water quality on the
gross margin.
Table 4.4 Scenario 1 Ratio Surface water Groundwater) Surface water Groundwater Conjunctive Composite TGM
EC EC EC
(dS/m) (dS/m (ML) (ML) (ML) (dS/m) ($/ha)
8:1 0.14 2.66 1244 156 1400 0.42 655
7:1 0.14 2.38 1225 175 1400 0.42 653
6:1 0.14 2.10 1200 200 1400 0.42 651
5:1 0.14 1.82 1167 233 1400 0.42 648
4:1 0.14 1.54 1120 280 1400 0.42 644
3:1 0.14 1.26 1050 350 1400 0.42 637
2:1 0.14 0.98 933 467 1400 0.42 626
1:1 0.14 0.70 700 700 1400 0.42 604
1:2 0.14 0.56 467 933 1400 0.42 582
1:3 0.14 0.51 350 1050 1400 0.42 572
1:4 0.14 0.49 280 1120 1400 0.42 565
1:5 0.14 0.48 233 1167 1400 0.42 559
1:6 0.14 0.47 200 1200 1400 0.42 557
1:7 0.14 0.46 175 1225 1400 0.42 555
1:8 0.14 0.46 156 1244 1400 0.42 552
Table 4.5 Scenario 2 Ratio Surface water Groundwater) Surface water Groundwater Conjunctive Composite TGM
EC EC EC
(dS/m) (dS/m (ML) (ML) (ML) (dS/m) ($/ha)
8:1 0.14 5.36 1244 156 1400 0.72 574
7:1 0.14 4.78 1225 175 1400 0.72 573
6:1 0.14 4.20 1200 200 1400 0.72 571
5:1 0.14 3.62 1167 233 1400 0.72 568
4:1 0.14 3.04 1120 280 1400 0.72 563
3:1 0.14 2.46 1050 350 1400 0.72 557
2:1 0.14 1.88 933 467 1400 0.72 544
1:1 0.14 1.30 700 700 1400 0.72 524
1:2 0.14 1.01 467 933 1400 0.72 502
1:3 0.14 0.91 350 1050 1400 0.72 492
1:4 0.14 0.87 280 1120 1400 0.72 483
1:5 0.14 0.84 233 1167 1400 0.72 479
1:6 0.14 0.82 200 1200 1400 0.72 476
1:7 0.14 0.80 175 1225 1400 0.72 475
1:8 0.14 0.79 156 1244 1400 0.72 474
80
Table 4.6 Scenario 3 Ratio Surface water Groundwater) Surface water Groundwater Conjunctive Composite TGM
EC EC EC
(dS/m) (dS/m (ML) (ML) (ML) (dS/m) ($/ha)
8:1 0.14 7.16 1244 156 1400 0.92 522
7:1 0.14 6.38 1225 175 1400 0.92 503
6:1 0.14 5.60 1200 200 1400 0.92 519
5:1 0.14 4.82 1167 233 1400 0.92 516
4:1 0.14 4.04 1120 280 1400 0.92 511
3:1 0.14 3.26 1050 350 1400 0.92 505
2:1 0.14 2.48 933 467 1400 0.92 491
1:1 0.14 1.70 700 700 1400 0.92 472
1:2 0.14 1.31 467 933 1400 0.92 450
1:3 0.14 1.18 350 1050 1400 0.92 439
1:4 0.14 1.12 280 1120 1400 0.92 432
1:5 0.14 1.08 233 1167 1400 0.92 427
1:6 0.14 1.05 200 1200 1400 0.92 425
1:7 0.14 1.03 175 1225 1400 0.92 424
1:8 0.14 1.02 156 1244 1400 0.92 422
The result show that for the first scenario the per ha gross margin increases
continuously as the canal water ratio increases from 1:1 to 8:1, where as
gross margin falls as the mixing ratio of groundwater increases from 1:1 to
1:8. This suggest that even for the same composite salinity levels the higher
gross margins are achievable by mixing higher salinity groundwater with
increasing amount canal water (reading from right to left from 1:1 ratio ). If
more canal water is not available for mixing, higher groundwater EC levels
would reduce gross margin. It also shows that even when groundwater EC
falls, a higher ratio of groundwater would reduce gross margin (reading
from left to right from 1:1 ratio).
This result suggests that with rising groundwater salinity level the mixing of
canal water in appropriate proportion to keep the target salinity with
desirable range can help increase gross margin. Alternatively even if better
quality groundwater become available such that groundwater salinity
81
continues to fall, the higher use of groundwater reduce gross margin even if
composite EC is the same. This suggest that the availability of canal water
in term of mixing ratios the salinity level of groundwater have key
significance for conjunctive management of surface and groundwater
resources.
For the second scenario which has higher groundwater EC and target EC the
gross margin are lower than scenario one and this is true for each mixing
ratio as well as all the ratios. For scenario three which has even high level of
groundwater EC and target salinity, the total gross margin are further lower
and this is true for all the mixing ratios. This suggests that:
o as groundwater EC increases lower gross margin are achieved.
o as target EC increases lower gross margin are achieved
The overall results for various composite salinity levels and groundwater
mixing ratios are given in Figure 4.9. These results show that:
o total gross margin per ha is higher for lower composite salinity
level of 0.42 dS/m
o total gross margin per ha is lower for higher composite salinity
level of 0.72 dS/m
o total gross margin per ha is lowest for highest composite salinity
level of 0.92 dS/m
And
o higher is the mixing ratio of surface water, higher is the gross
margin
o higher is the mixing of groundwater lower is the gross margin
82
o the gross margin begins to fall sharply once the canal water ratio is
below 2:1
Total Water Allocation 1400 ML
400
450
500
550
600
650
700
8:1 7:1 6:1 5:1 4:1 3:1 2:1 1:1 1:2 1:3 1:4 1:5 1:6 1:7 1:8
Conjunctive use ratio (Surface water : Ground water)
Tota
l gro
ss m
argi
n ($
/ha)
Composite salinity 0.42 dS/m Composite salinity 0.72 dS/m Composite salinity 0.92 dS/m
Figure 4.9 Composite salinity of conjunctive use
This result suggests that the canal water mixing ratio of at least 2:1 is highly
desirable for higher gross margin. The lower canal water mixing ratios
reduces gross margin. In terms of conjunctive use of surface and
groundwater these result suggest that in areas with poor quality groundwater
the gross margin would be lower if the farmer use more groundwater and
practice irrigated agriculture under condition of surface water scarcity.
Reliable supply of surface water and support measures for appropriate
utilisation of groundwater are therefore essential for a profitable agriculture
and improved salinity management.
83
4.5 Summary
Total gross margin per ha increases as total water allocation increases. The
increase in total gross margin is higher at lower allocation levels. The
increase in total gross margin is relatively lower at higher allocation levels.
Surface water offers the highest gross margin followed by the conjunctive
use of surface and groundwater. The groundwater only offers the lowest
gross margin per ha. The analyses at the farm level suggest that conjunctive
use of surface water and groundwater offers higher total gross margin than
groundwater alone, but higher groundwater allocation are desirable only
when surface water is available for conjunctive use.
The overall modelling results for the CIA show that groundwater depth
poses a significant constraint to crop yield and profits. The most profitable
crops can not profitably be grown under shallow groundwater tables,
particularly where groundwater salinity is also high. Shallow watertable and
high groundwater salinity are the least helpful combination of biophysical
conditions for profitable agriculture.
On the other hand deep watertable and low salinity offer the best production
environment for a profitable agriculture. Well drained soils with appropriate
groundwater depth can still be suitable for crop agriculture despite high
salinity levels. The availability of surface water can help in making use of
the saline groundwater through mixing to achieve suitable target salinity
level which may otherwise not be possible with groundwater use only.
84
CHAPTER FIVE
5 Conjunctive Water Management at the Farm Level: Case
Studies in Pakistan
This chapter captures the heterogeneity in the basic resource characteristics
across farms, and modeling its impact on crop mix, yield, and gross returns
from conjunctive water management at farm level for the selected irrigated
agricultural areas from Pakistan. The chapter presents the comparison of
crop gross margins at the same farm under various simulation scenarios to
capture the impact of change in water allocation and related salinity levels
on the gross margin.
5.1 Description of the study area
These farm level case studies were conducted in: (i) Sheikhupura sub-
division of the Upper Rechna Doab Irrigation System (URDIS), (ii)
Buchiana sub-division of the Middle Rechna Doab Irrigation system
(MRDIS), and (iii) Bhagat sub-division of the Lower Rechna Doab
Irrigation System (LRDIS). These parts are quite different to each other in
terms of cropping pattern, groundwater quality, soil texture, climatic
conditions, etc. The Rechna Doab is located between River Ravi and River
Chenab in the Upper Indus Basin of Pakistan (Figure 5.1).
The Rechna Doab has a gross command area of 2.98 million hectare (Mha)
of which 2.39 Mha is irrigated. It is served by three separate irrigation
systems, which are all linked to a common groundwater resource. The Doab
85
offers a good opportunity to look at the impact of salinity on crop
productivity and farm profits at scales ranging from field, to farm, to
irrigation system, to the unit of the Doab itself. The integrating factor is the
groundwater system, due to the great extent of groundwater extraction by
tubewells, recharge from irrigation and the bounding rivers, and the effects
on natural and induced salinity.
Figure 5.1 Location map of Rechna Doab Irrigation System
In Rechna Doab, cropping system was seasonal instead of annual. Crops
area sown in Rabi (Winter) and Kharif (Summer) seasons. Major crops in
Rechna Doab during Kharif season were rice, cotton, maize and fodder
while in Rabi season major crops were wheat and Rabi fodder. Sugarcane
was annual crop so it was used as such in Model. To convert seasonal crops
to annual cropping system, all possible combinations of crops were made by
taking each crop from Rabi and Kharif. Cropping systems used in this
model were classified as Rice-Wheat, Sugarcane, Maize-Wheat, Cotton-
86
Wheat, Kharif Fodder-Wheat, Cotton-Rabi Fodder, Rice-Rabi Fodder and
Fallow. The total area under major crops on farms in the sub-divisions
across Rechna Doab is given in (Table 5.1)
Table 5.1 Area under major crops grown on farms across irrigation sub-divisions (ha)
Subdivision Rice-wheat
Cotton-Wheat
Sugar-cane
Maize-Wheat
Kharif Fodder-Wheat
Rabi Fodder-Cotton
Rabi Fodder-Rice
Fallow
Malhi 6492 0 0 8470 0 4992 0 13741 Sadhoke 22596 170 0 15400 0 3596 75 26168 Shahdara 9148 192 0 15148 0 4548 2295 10948 Muridke 25546 0 0 6970 0 9546 0 30677 Gujranwala 35518 286 0 17776 0 20418 1108 17117 Nokhar 28463 1389 0 8104 0 8463 0 34329 Naushera 12875 0 0 13863 0 10875 0 29806 Sheikhupura 9039 409 0 15070 0 6039 0 15894 Sikhanwala 10500 0 0 5903 0 8500 0 8352 Chuharkana 19214 1807 0 8863 0 19214 0 20318 Sagar 29010 0 0 25365 0 10835 5075 26663 Sangla 4936 7049 0 7798 0 4736 0 15084 Mohlan 25097 4369 0 17646 0 18097 2238 21317 Mangtanwal 25685 6670 0 13540 0 4665 2535 9816 Paccadala 20610 6804 0 13261 0 7610 0 22136 Buchiana 4813 11154 0 13098 0 14813 1046 19232 Uqbana 324 16695 4788 29004 4788 324 13479 28836 Kotkhudayar 5690 3715 495 17820 3650 14600 0 4756 Aminpur 0 13301 12059 12496 12059 0 18698 8660 Tandlianwala 9568 19857 7398 25213 7398 9568 3262 1947 Kanya 11596 11868 0 10953 0 9596 0 12101 Tarkhani 0 12528 0 21086 0 0 15728 17726 Veryam 0 25675 17019 19433 9019 0 3746 19836 Wer 0 23248 0 19975 0 0 0 20453 Sultanpur 14579 9692 5720 3530 6720 1579 0 2635 Bhagat 7854 6870 20603 9140 5603 7854 2969 14274 Dhaular 12731 3839 9199 9551 7199 12731 0 10710 Haveli 10774 11370 29910 18540 10775 5385 0 4475
There were some exceptions about the area where vegetables, oil seed and
millets were grown. Some of the above combinations are effected by soil
type because some Kharif crops were sensitive to soil like rice show good
87
growth in finer soils while light soils are good for cotton. Rabi crops like
wheat and barseem (Rabi Fodder) can survive on wide range of soils. The
model uses the growth period of crops. Regarding the growing period of
crops, (Table 5.2) provides a summary of number of growing days for
specific cropping patterns.
Table 5.2 Number of growing days for particular land uses. Land use Growing Season Growing
period (Days) Bare season Bare periods
(days) Wheat-Rice Jun-Mid May 359 May 15 Sugarcane Annual 365 None None Wheat-Maize Nov-Sept 319 Oct 46 Wheat-Cotton Full Year 365 None None Wheat-Kharif Fodder Nov-Sept 319 Oct 46 Rabi Fodder-Cotton Nov-May 212 Jun-Oct 153 Rabi Fodder-Rice Full Year 365 None None Wheat Nov – Mid May 192 June – Oct 173 Fallow Seasonal 365 None None
Figure 5.2 presents groundwater salinity in Rechna Doab. There are two
distinct zones in the Rechna Doab: (i) the Upper zone with low salinity and
underlain with good quality groundwater; and (ii) the Lower zone with
higher salinity and poor quality groundwater.
88
3150000 3200000 3250000 3300000 3350000 3400000
750000
800000
850000
900000
950000
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
12000
13000
14000
15000
16000
Figure 5.2 Groundwater salinity in Rechna Doab (µS/cm)
5.2 Case studies in conjunctive water management
In the Rechna Doab, surface water allocation is done by weekly rotational
system called Warabandi (Chaudhry and Shah, 2003). The canal water is
normally delivered by turns which start from head of water course and end
at tail. Water supply is thus rotational and proportional but not volumetric.
The way system operates the maximum amount water delivered is about 2/3
of the water allowance. This means that farmer operate under condition of
deficit irrigation. This is why conjunctive use of groundwater is so
important in the system.
Groundwater is widely used in the Doab particularly in the upper and the
middle reaches where groundwater is of good quality. Conjunctive use of
surface water and groundwater is common on medium and large farmers;
small farmers may practice conjunctive use but to a lower extent because of
89
their smaller farm size, high cost of groundwater and infrastructure
constraint as water courses are basically designed for canal water delivery.
Farmers lift groundwater generally using shallow bores or skimming well
technologies. (Qureshi et al., 2004; Kahlown et al., 2005, 2007; Kirsch and
Characklis, 2006). Diesel pump electric engine or tractor may be use for
lifting water.
In order to capture variation in resource allocation across the Rechna Doab,
three representative farms were selected from a data set of actual farms
within the system. One farm each from Upper, Middle and Lower Rechna
Doab was selected (Appendix II). The three representative farms reflect the
variation in soil types and cropping pattern across the system. Small
adjustments were made to individual farms in order to reflect the mix of
different soils and variability in groundwater salinity. This was required for
extrapolation purposes as soil type and salinity plays a significant role on
crop mix and recharge amounts. The physical characteristics for each
representative farm and modelling parameters for salinity sensitivity
analysis are given in (Table 5.3).
Table 5.3 A comparative overview of the modelled farms in Pakistan.
Upper Middle Lower Rainfall (mm) 600 360 211 Allowable rise in groundwater level (m) 0.1 0.1 0.1 Allowable rise in salt concentration. (dS/m) 2.25 2.25 2.25 Price of surface water ($ ML-1) 0.46 0.24 0.3 Price of groundwater ($ ML-1) 3.32 3.42 3.3 Area of farm (ha) 10 10 10 Leakage (mm/year) 20 20 20 Initial depth to watertable (m) 1.5 1.65 1.75 Initial average root zone salinity (dS/m) 1.5 1.5 1.5 Concentration of surface water (dS/m) 0.14 0.45 0.75
90
Concentration of groundwater (dS/m) 0.7 2.5 2 Concentration of watertable in (dS/m) 1.5 5.5 7.5 Surface water allocation per farm (ML) 70 70 70 Groundwater allocation per farm (ML) 0 0 0 Concentration of rain water (dS/m) 0.01 0.01 0.01 Rainfall recycling yes yes yes Pumping from shallow watertable aquifer yes yes yes
The three farms represent the variation in groundwater depth, salinity
concentration of groundwater and concentration of soils in watertable and
concentration of soils in the Upper, Middle and Lower reaches of the
system. For instance, initial watertable depth is 1.5, 1.65, and 1.75 meter in
upper, middle and lower system respectively the concentration of salts in
surface water is 0.14, 0.45 and 0.75 dS/m reflecting that the salt
concentration rises as water moves down the system. The concentration of
salt in groundwater is 0.7, 2.5, and 2 dS/m in the three farm respectively.
The concentration of salts in watertable is 1.5, 5.5 and 7.5 dS/m
respectively.
For Upper Rechna Doab the selected representative farm has a shallower
initial watertable depth and lower concentration of salts surface and
groundwater than the farm selected from the middle and lower part of the
system. For the selected from the middle of the system the values of these
parameters are in the middle ranges compared to farm 1 and farm 3. The
representative farm selected from middle Rechna Doab has high level of
groundwater salinity and surface water salinity and higher concentration of
salt in watertable and a bit more depth of watertable. For the lower Rechna
Doab all salinity related parameter are worse than the representative farm as
explain earlier.
91
5.3 Modeling results and discussion
5.3.1 Upper Rechna Doab
The modelling result for various water allocation levels and water
management system in Upper Rechna Doab is given in Figure 5.3. It shows
that:
o surface water has the highest gross margin per ha
o groundwater has lowest gross margin per ha
o the total gross margin per ha for conjunctive use is in middle
And
o the total gross margin rises as total water allocation increases
o rise in total gross margin slows down for allocation level beyond
60 ML.
These result suggest that farmer with good access to surface water can take
advantage of the groundwater for conjunctive use to earn highest gross
margin.
92
0
50
100
150
200
250
300
0 20 40 60 80Total water allocation (ML)
Gro
ss m
argi
n ($
/ha)
Surface water Groundwater Conjunctive
Figure 5.3 Total gross margin for various water allocation levels and water management system in upper Rechna Doab
5.3.2 Middle Rechna Doab
The modelling result for various water allocation levels and water
management system in Middle Rechna Doab is given in Figure 5.4. It shows
that:
o surface water has highest gross margin per ha
o groundwater has lowest gross margin per ha
o the total gross margin per ha for conjunctive use is in middle
And
o the total gross margin rises as total water allocation increases
o rise in total gross margin begins to fall for allocation level beyond
60 ML.
These result suggest that farmer with good access to surface water can take
advantage of the groundwater for conjunctive use to earn highest gross
93
margin. For water allocation levels beyond 60% the use of groundwater or
conjunctive use does not increase gross margin. This means that their upper
limit for groundwater use is reached.
0
50
100
150
200
250
300
0 20 40 60 80Total water allocation (ML)
Gro
ss m
argi
n ($
/ha)
Surface water Groundwater Conjunctive
Figure 5.4 Total gross margin for various water allocation level and water management systems in the middle Rechna Doab
5.3.3 Lower Rechna Doab
The modelling result for various water allocation levels and water
management system in Middle Rechna Doab is given in Figure 5.5. For the
lower Rechna Doab all salinity related parameter are worse than the
representative farms the upper Rechna Doab areas as explain earlier.
Therefore, the expected gross margin per ha for same allocation level will
be lower. This study result confirms this expectation. Further the result
show that total gross margin per ha is lower for groundwater use only than
surface water use only where as conjunctive use gives a gross margin higher
than groundwater use only.
94
0
50
100
150
200
250
0 20 40 60 80Total water allocation (ML)
Gro
ss m
argi
n ($
/ha)
Surface water Groundwater Conjunctive
Figure 5.5 Total gross margin for various water allocation level and water management systems in the lower Rechna Doab.
5.4 Groundwater salinity impacts
For upper Rechna Doab, farmer can practice conjunctive use to mix canal
water with groundwater in different mixing ratios (Table 5.4). If the farmer
is able to achieve the same composite groundwater salinity, would gross
margin per ha be the same for different mixing ratio? This study result
shows this is not the case (Figure 5.6). Appropriate mixing ratio is required
for good conjunctive use management. When irrigation water is available,
groundwater with high salinity could be used. As irrigation water available
becomes lower such that one moves from left to right on the curve only
lower salinity groundwater could be used. The mixing ratio 1:1 gives a total
gross margin $ 252 /ha. As mixing ratio increases to 2:1 and 3:1 total gross
margin per ha increases due to more use of canal water. As mixing ration
95
changes 1:2 to 1:3 the total gross margin falls because of higher used of
groundwater, even if the target salinity is the same.
This suggests that the gross margin would increase due the dilution effect
and would decrease due to the concentration effect. This is a very strong
conclusion. We suggest that farmer with lowest supply of canal water will
achieve a lower gross margin than may be possible when more canal water
is available and appropriate mixing ratio could achieve. It also means that
where canal water supply is short such as at tail ends and groundwater is a
poor quality the return from conjunctive use will be lower.
Table 5.4 Composite EC of conjunctive water management. Ratio Surface water Groundwater) Surface water Groundwater Conjunctive Composite TGM
EC EC EC
(dS/m) (dS/m (ML) (ML) (ML) (dS/m) ($/ha)
3:1 0.14 1.26 53 18 70 0.420 257
2:1 0.14 0.98 47 23 70 0.420 255
1:1 0.14 0.70 35 35 70 0.420 252
1:2 0.14 0.56 23 47 70 0.420 248
1:3 0.14 0.51 18 53 70 0.420 236
Conjunctive w ater -Electric
225
230
235
240
245
250
255
260
3:1 2:1 1:1 1:2 1:3
Mixing ratio (SW:GW)
Gro
ss M
argi
n ($
/ha)
Figure 5.6 Effect of mixing ratio of surface water and groundwater on the gross margins in upper Rechna Doab.
96
For middle Rechna Doab, farmer can practice conjunctive use to mix canal
water with groundwater in different mixing ratios (Table 5.5). The upper
limit to groundwater use is also clear from the analysis of composite salinity
level and associated gross margin per ha as shown below.
Table 5.5 Composite EC of conjunctive water management. Ratio Surface water Groundwater) Surface water Groundwater Conjunctive Composite TGM
EC EC EC
(dS/m) (dS/m (ML) (ML) (ML) (dS/m) ($/ha)
3:1 0.45 4.55 53 18 70 1.475 225
2:1 0.45 3.53 47 23 70 1.475 223
1:1 0.45 2.50 35 35 70 1.475 220
1:2 0.45 1.99 23 47 70 1.475 216
1:3 0.45 1.82 18 53 70 1.475 214
My result show that when the mixing ratio 1:1 gives a total gross margin of
$ 220 /ha. As mixing ratio increases to 2:1 and 3:1 total gross margin per ha
increases due to more use of canal water. As mixing ration changes 1:2 to
1:3 the total gross margin falls because of higher use of groundwater, even
if the target salinity is the same.
The analysis of irrigation water available and groundwater salinity shown
below suggest that when water supplies are lower only low salinity
groundwater could be used (Figure 5.7). The optimum level of groundwater
salinity for this farm is 2.50 dS/m. For salinity higher than this more canal
water is required for conjunctive use. For highest level canal water
availability it may be possible to use groundwater with salinity level up to 5
dS/m.
97
Conjunctive water -Diesel
208210212214216218220222224226
3:1 2:1 1:1 1:2 1:3
Mixing ratio (SW:GW)
Gro
ss M
argi
n ($
/ha)
Figure 5.7 Effect of mixing ratio of surface water and groundwater on the gross margins in middle Rechna Doab
For lower Rechna Doab, farmer can practice conjunctive use to mix canal
water with groundwater in different mixing ratios (Table 5.6). The results
for the mixing ratios shown below are similar to the previous two farms
although gross margin are lower. This study result show that the mixing
ratio 1:1 gives a total gross margin $ 201 /ha. As mixing ratios increases to
2:1 and 3:1 total gross margin per ha increases due to more use of canal
water. As mixing ratio changes from 1:2 to 1:3 the total gross margin falls
because of higher used of groundwater, even if the target salinity is the
same.
Table 5.6 Composite EC of conjunctive water management. Ratio Surface water Groundwater) Surface water Groundwater Conjunctive Composite TGM
EC EC EC
(dS/m) (dS/m (ML) (ML) (ML) (dS/m) ($/ha)
3:1 0.75 3.25 53 18 70 1.375 206
2:1 0.75 2.63 47 23 70 1.375 204
1:1 0.75 2.00 35 35 70 1.375 201
1:2 0.75 1.69 23 47 70 1.375 197
1:3 0.75 1.58 18 53 70 1.375 196
98
This study results again show that when irrigation water availability is low
only low salinity groundwater could be use for conjunctive use. For
instance, for lower farm the desirable salinity limit is to 2 dS/m, as shown in
Figure 5.8. Beyond this level the gross margin would fall sharply. If more
irrigation become available it may be possible to use this saline groundwater
to achieve same target salinity. This may expand production but will reduce
per ha gross margin.
Conjunctive water -Diesel
190192194196198200202204206208
3:1 2:1 1:1 1:2 1:3
Mixing ratio (SW:GW)
Gro
ss M
argi
n ($
/ha)
Figure 5.8 Effect of mixing ratio of surface water and groundwater on the gross margins in Lower Rechna Doab.
5.5 Summary
In order to capture variation in resource allocation across the Rechna Doab,
three representative farms were selected from a data set of actual farms
within the system. One farm was selected from Upper, Middle and Lower
reaches of the Rechna Doab. These three representative farms reflect the
variation in soil types and cropping pattern across the system. Small
adjustments were made to individual farms in order to reflect the mix of
99
different soils and variability in groundwater salinity. This was required for
extrapolation purposes as soil type and salinity plays a significant role on
crop mix and recharge amounts.
For Upper Rechna Doab the selected representative farm has a shallower
initial watertable depth and lower concentration of salts surface and
groundwater than the farm selected from the middle and lower part of the
system. The representative farm selected from middle Rechna Doab has
high level of groundwater salinity and surface water salinity and higher
concentration of salt in watertable and a bit more depth of watertable. This
suggests that the gross margin per ha would be lower than the previous farm
from the upper Rechna Doab. The comparison of total gross margin across
the upper and lower Rechna Doab shows total gross margin are in fact lower
in the middle Rechna Doab farm and this is the case for all levels of surface
water supply as estimated by the model runs. A comparison of surface water
only and groundwater only shows that total gross margin per ha is lower for
groundwater only case.
For the lower Rechna Doab all salinity related parameter are worse than the
representative farm as explain earlier. Therefore, the expected gross margin
per ha for same allocation level will be lower. My result confirms this
expectation. Further the result show that total gross margin per ha is lower
for groundwater use only than surface water use only where as conjunctive
use gives a gross margin higher than groundwater use only.
100
CHAPTER SIX
6 Conjunctive Water Management at the Irrigation System
Level
This chapter first introduces the case study of conjunctive water
management at irrigation system level in Coleambally Irrigation Area using
a ColBore (community bore) and other deep groundwater bores (farmers’
bores). To help select the type of pumping, the cost of pumping is described
for electric and diesel pumps by using a procedure of discounting all the
costs (capital and variable) over the life of tubewell, taking into account the
opportunity cost of investment. This chapter concludes with the cost of
conjunctive water management for a range of water use scenarios for the
case study areas in Australia and Pakistan.
6.1 Case study in Australia
This section presents an overview of the surface water and groundwater
resources in Coleambally Irrigation Area.
6.1.1 Surface water resources
Maximum annual general security allocations since 1982-83 are shown in
Figure 6.1. Since 1994/95 there has been a continual downward trend in
allocations. Reduced allocations over the past ten years have adversely
affected landholders’ capabilities to invest in LMWP options. In 2006/07
214,113.3ML was diverted by CICL. This was 60 percent less than the
101
benchmarked average of 551,477 ML. Annual diversions, shown in Figure
6.2, continued to show a declining trend.
Figure 6.1 Annual general security allocations since 1982/83 (AER 2007).
Figure 6.2 Annual diversion and licensed entitlement (AER 2007).
102
6.1.2 Groundwater resources
During the 1990s, the Murray Darling Basin (MDB) Governments,
including NSW, were encouraging the development and use of groundwater
in the southern MDB on the understanding that it was an underutilised
resource (MDBC, 1998). In the Murrumbidgee Valley, this was evident by
State groundwater allocation announcements between 1991 and 1996 of
150% of annual entitlement (Lawson, 1996). During this time, the NSW
Government was also issuing conjunctive conditions on groundwater
licences as a default (Fullagar et al., 2006, 2007)
The existence of good quality deep groundwater under the CIA had already
been established, not least as the basis of town water supply. It was
proposed the pumping deep aquifers would induce downward leakage. The
associated potential to reduce the shallow groundwater mound in the CIA
that had been created by rice flooding was attractive to both the NSW
Government of the time, and the CIA community.
To test this proposition, a deep bore was constructed in 1988 in the centre of
the CIA, on the intersection of Channels 9 and 9b (Lawson and van der
Lelij, 1992). The location of the ColBore is indicated by a black star in
Figure 6.3. In terms of salt mobilisation, the groundwater from the bore of
about 650 µS/cm is shandied into channel water typically 100-200 µS/cm
(Table 6.1). Table 6.2 presents the monthly groundwater extractions from
ColBore 1994/95 to 2006/07. ColBore originally augmented only Channel
9b flows, with downstream farms subsequently incurring the salt cost of an
activity undertaken for public good.
103
Figure 6.3 Location map of groundwater bores in Coleambally Irrigation Area (CICL, 2006).
Table 6.1 Salinity of groundwater extracted from ColBore- 2004/07 (CICL, 2005-2007)
2006/07 2005/06 2004/05
Average Average Average
Salinity Salinity Salinity
(µS/cm) (µS/cm) (µS/cm)
August 420
September 670
October 612 671
November 574 452
December 578 609 698
January 562 631 698
February 515 669 615
March 507 484
April 551 631
May 579
Average 554.3 606.4 617.5
Pumping tests conducted between 1990 and 1992 showed her drawdown in
the Calivil was evident within a 12 km radius of the bore, and a decline in
watertables was also evident (Lawson and van der Lelij, 1992). However,
104
dry climatic conditions of this period saw watertables decline in control
areas also, and it was not possible to distinguish whether the additional
decline of 0.4-0.5m around ColBore to the bore was appropriately attributed
to the bore or to changes in surrounding land management practices.
Table 6.2 Monthly groundwater extractions from ColBore 1994/95 to 2006/07.
06/07 05/06 04/05 02/03 01/02 00/01 99/00 98/99 97/98
(ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML) (ML)
August 300 0 0 445 0 0 0 0 0
September 161 0 311 608 0 0 94 525 0
October 522 320 0 563 252 217 771 628 0
November 392 392 41 559 686 722 683 609 0
December 562 633 435 603 739 596 681 612 0
January 527 473 451 533 696 722 694 595 147
February 469 502 255 87 475 691 156 538 419
March 393 0 545 263 541 772 688 572 578
April 290 0 362 584 0 742 342 476 539
May 0 0 198 259 0 644 197 653 660
Transfers 0 2500 4417 0 0 0 0 0 500
Total 3616 4820 7015 4504 3389 5106 4306 5208 2843
At the time ColBore was installed, it was recognised as a necessary but only
short term measure to address the watertable issue. Concerns were that if
downward leakage was induced, it would ultimately reduce the quality of
the deep aquifer due to the higher salinity of shallow aquifers. The actual
impact of the bore on the watertables remains unclear. Enever (1999)
concluded there was no shallow watertable response that could be attributed
to Calivil drawdown. However, subsequent modelling has suggested the
response may not be evident until 15 or more years have passed (Prasad et
al., 2001). More recently, the NSW Government has interpreted Calivil
recovery from seasonal fluctuations in head as indicative of connectivity
105
with the shallow aquifers (Kumar, 2002; Akbar, et al., 2004; Jackson, et al.,
2006; Khan et al., 2005.; SKM, 2006).
Clear interpretation of connectivity is made difficult by the fluctuation of
shallow watertables in response to climate and land management. (Khan et
al. 2000) observed that higher rice production to the north of the CIA
corresponded with lower watertables and higher groundwater pumping.
Their studies suggested connectivity between the Shepparton and Calivil
was greater in this area, and around prior streams. Lawson (1996; and van
der Lelij, 1992; Evans, 2007) suggests this facilitated a downward flow
from the Shepparton to the Calivil. The impact on the Shepparton of
pumping from the Calivil appears to be spatially variable.
The shallow Shepparton sediments were deposited by a series of prior
streams over several million years. Below the Shepparton formation (20 to
60 meters thick), the Calivil aquifer systems often extends to depths greater
than 150 meters. Water movement through the deep aquifers is generally
from east to west except in the area with major groundwater pumping
around Darlington Point. Recharge to the deep aquifers is mainly from the
Murrumbidgee River downstream of Narrandera and from the irrigation
areas. The salinity increases from east to west, but is generally low. Deep
groundwater with low salinity levels (<0.005 µS/cm) occurs over a large
area extending between Narrandera and Hay. The shallow Shepparton
aquifer is often very saline especially under the irrigation areas where
salinity levels can be high e.g. 0.02-0.12 µS/cm. Deep bore yields may
exceed 400 L/s from depths of 90 to 250 meters.
106
The total metered groundwater usage for the past seven seasons is presented
in Figure 6.4. For the 2006/07 season, total groundwater usage for CICL’s
operational area was 103,015 ML. This has been the highest recorded
groundwater usage within CICL’s area. The groundwater usage mainly
depends on availability and cost of surface water (in the temporary water
trade market) and diesel prices. During 2006/07 diesel price remained high
but water prices were much higher making pumping groundwater a better
economic option. In 2006/07 the surface water supplied by the CICL was
lowest and groundwater pumped was highest, therefore this year
groundwater as a proportion of surface water increased to 57 percent.
Figure 6.4 Groundwater usage in Coleambally Irrigation Area (AER 2007).
107
6.2 Cost of pumping in Australia
There are two types of groundwater pumping systems, bores and spearpoint.
Bores are used to pump groundwater from aquifers to supplement surface
water supplies whereas spearpoint systems pump groundwater from shallow
watertables and are mainly used for salinity and waterlogging control but
can also supplement irrigation supply depending on the quality of the
groundwater. The selection of appropriate groundwater pumping system
depends on it capital cost and variable cost or financial viability. The cost of
setting up a pumping system can vary significantly between farms
depending on the pumping system's intended purpose, hydrological
conditions, groundwater quality, location to electrical power and disposal
options. The siting, design, materials and construction method used in
installing a bore are other factors that also influence cost but also have an
impact on the quantity and quality of water obtained (Robinson, 2002).
Different types of groundwater pumps consist of specific configurations,
some of which limit their use in certain situations. In saline areas shallow
groundwater pumping using spearpoint system is on-farm subsurface
drainage option to reclaim salt effected soil. The system is used to pump
shallow groundwater from subsurface formations within upper Shapperton
layer. The depth of pumping generally varies between 5-10 m and pumping
volume varies 0.5 to 4 ML/day.
Groundwater pumping is done during the irrigation season to lower the
shallow watertable. Due to the improved drainage and leaching,
waterlogging and soil salinity may decline and crop yields improve. Where
the groundwater is of good quality, the potential exist for mixing this water
108
with surface water to use for irrigation. The level of conjunctive use of
groundwater each will depend on the salinity of groundwater, surface water
allocation and rainfall.
Shallow bores are used for pumping from shallow watertables for salinity
control and irrigation depending on the salinity of the groundwater whereas
deep bores are mainly installed for irrigation supply. A shallow bore is used
for pumping groundwater within Shepparton formation (10-30 m). Shallow
bore are low yielding bores (1.25 ML/day) compare to deep bore up to (10
ML/day).
A deep bore is used to pump groundwater from deep aquifers within the
Calivil and Renmark layers (approximately 150 to 300 metres). Most deep
bore are equipped with turbine pumps. The pumping of deep bore is
generally higher. For example, within the CIA, the Calivil layer consists of
50 to 70% sands, with an average hydraulic conductivity of 12 m/day and
the Renmark layer consists of 30 to 50% sands, with an average hydraulic
conductivity of 7 m/day (Khan et al, 2000).
As these deep aquifers generally have better quality (less saline) water than
the shallow aquifers and are high yielding, a deep bore becomes an
attractive option for farmers who want to supplement their existing
irrigation allocation, even though the capital cost is high. The typical costs
for a groundwater bore are given in Table 6.3. Total capital cost of electric
tubewell is higher than diesel. The total variable costs of electric tubewells
are lower than diesel engine.
109
6.2.1 Input data for calculating the cost of pumping
Table 6.3 presents the detail of input data used for calculating capital and
operating costs in an Australian context (after Robinson, 2002).
Characteristics of a groundwater bore, capital cost and variable costs for
both the electric and diesel pumps is presented to calculate the cost of
pumping in Australia.
Table 6.3 Input data used for calculating capital and operating costs in an Australian context (after Robinson, 2002).
Characteristics of a groundwater bore
Bore Type 20" x 16" bore 20" x 16" bore 12" x 9" bore 12" x 9" bore
Bore Depth (m) 220 220 140 140
Pumping Head (m) 45 45 30 30
Bore Yield (ML/day) 25 25 10 10
Pumping Days 70 70 70 70
Engine Type Electric - 185kw
Diesel - Mitsibishi 190kw
Electric - 75 Diesel - Perkins 70kw
Pump Type Everflow Vertical Turbine Pump 350 FHH - 3 stage
Everflow Vertical Turbine Pump 350 FHH - 3 stage
Everflow Vertical Turbine Pump 250 FHH - 3 stage
Everflow Vertical Turbine Pump 250 FHH - 3 stage
CAPITAL COSTS
Pump (gear box, fittings, meter, installation) 49664 57432 24276 28684
Motor (protection and fittings) 15227 31646 11350 21452
Electricity Connection (1km line + sub-station) 40000 28000
Fuel tank (10000 litres) 2500 2500
Bore drilling 51170 51170 22398 22398
Bore casing (steel) and development 80096 80096 31697 31697
TOTAL $236,157 $222,844 $117,721 $106,731
VARIABLE COSTS
Diesel Engine ($/hr) litre/hr $/litre ($/hr) ($/hr) ($/hr)
*Diesel: 25 Ml/day bore 57.63 0.682 39.30
*Diesel: 10 Ml/day bore 15.4 0.682
Major overhaul (% motor value / 15 years) 20% 0.17
Minor overhaul (% motor value / 5 years) 5% 0.13
Oil (litres/year) 119 3 0.21
Filters (no./year) 14 10 0.08
Pump maintenance (% pump value / 30 years) 5% 0.04
Total Variable costs for Diesel Engine 39.94
110
Electric Engine ($/hr) kWh c/kWh^
**Electricity: 25 Ml/day bore 216.12 13.3 28.74
**Electricity: 10 Ml/day bore 57.63 13.3 7.66
Maintenance - switchgear and bearing ($/yr) 300 0.18 0.18
Maintenance - rewind allowance ($/yr) 200 0.12 0.12
Pump maintenance (% pump value / 30 years) 5% 0.04 0.02
Total Variable costs for electric engine 29.08 7.96
* Pump efficiency = 74%, Derating = 80%
** Pump efficiency = 74%, Derating = 80% (electric), Derating = 75% (diesel)
^ 38% peak rate and 62% off-peak rate
6.2.2 Net present values for diesel and electric pumps
It is difficult to determine which pump set should be use for conjunctive
water management. Net present value criteria were used to solve the issue.
This is overcome by doing a cost analysis. This is a process of discounting
all the costs (capital and variable) over the life of tubewell, taking into
account the opportunity cost of investment.
Table 6.4 presents the net present values for diesel and electric pumps
calculated by using the data provided in Table 6.3. The lower the NPV; the
better the groundwater pumping systems would be. The annuity value of
electric pumps is lower than the diesel ones, which means that electric
pumpsets are cheaper option for conjunctive use management. The costs of
diesel pumps will increase further once the subsidy on diesel is fully
accounted into the net present value of future costs.
Table 6.4 Net Present Values of deep groundwater bores.
DR 10% Electric Diesel 1 0.9091 264111 240101 261777 237979 2 0.8264 27951 23100 38928 32172 3 0.7513 27951 21000 38928 29247 4 0.6830 27951 19091 38928 26589 5 0.6209 27951 17355 40010 24843 6 0.5645 27951 15778 38928 21974 7 0.5132 27951 14343 38928 19976 8 0.4665 27951 13039 38928 18160 9 0.4241 27951 11854 40010 16968 10 0.3855 27951 10776 38928 15009 11 0.3505 27951 9797 38928 13644
111
12 0.3186 27951 8906 38928 12404 13 0.2897 27951 8096 40010 11590 14 0.2633 27951 7360 38928 10251 15 0.2394 27951 6691 43256 10355 16 0.2176 27951 6083 17 0.1978 27951 5530 18 0.1799 27951 5027 19 0.1635 27951 4570 20 0.1486 27951 4155 21 0.1351 27951 3777 22 0.1228 27951 3434 23 0.1117 27951 3122 24 0.1015 27951 2838 25 0.0923 27951 2580 NPV $468,404 $501,161 Annuity $51,603 $65,890
6.3 Cost of pumping in Pakistan
The cost of conjunctive use management varies for mixing ratio to achieve
the same target salinity. The higher groundwater mixing ratio in general
means higher cost. However, the total annual cost of groundwater pumping
depends on a range of factors including annual operational cost, capital cost,
replacement and repair cost, groundwater pumpage ( in ML per year).
The cost also depends on hours of operation, generally the cost being lower
the long the hours for the operation and higher for lower hours of operation.
Likewise the cost will vary by source of energy in terms of electric diesel or
tractor driven pumps.
The typical pumping cost for electric, diesel and tractor driven tubewells for
various hours of operation and annual groundwater pumping rate are given
in Figure 6.5, Figure 6.6 and Figure 6.7, respectively. A comparison of
figures shows that:
o the annual cost of electric pumping is lower than diesel, while
tractor pumping is the most expensive.
112
o annual cost increases with the increase in annual groundwater
pumping rate (ML/year).
o the annual cost is highest when daily hours of operation are lowest
(4 hours/day) where as the annual cost is lowest when the daily
operation are highest (12 hours/day).
These results suggest that the choice of pumping technology in term of
electric or diesel and the hours of operation and annual groundwater
withdrawal influence total annual cost of pumping. Use of electric pump
and longest hours of operation are lower cost option for the same annual
pumping.
This also implies that where groundwater is of good quality and electricity
is subsidised the farmer has incentive to extract more groundwater to reduce
average annual total cost. This may lead to over abstraction of groundwater
with adverse impacts on groundwater quality.
Electric
0200400600800
1000120014001600
0 50 100 150 200
Annual Groundwater pumping (ML)
Ann
ual C
ost (
$)
12 hours 8 hours 4 hours of operation per day
Figure 6.5 Annual Cost off pumping Electric.
113
Diesel
0200400600800
1000120014001600
0 50 100 150 200
Annual Groundwater pumping (ML)
Ann
ual C
ost (
$)
12 hours 8 hours 4 hours of operation per day
Figure 6.6 Annual cost of pumping Diesel.
Tractor
0200400600800
100012001400160018002000
0 50 100 150 200
Annual Groundwater pumping (ML)
Ann
ual C
ost (
$)
12 hours 8 hours 4 hours of operation per day
Figure 6.7 Annual cost of pumping Tractor.
6.4 Cost of conjunctive water management for a range of
water use scenarios
This section evaluated the costs of conjunctive water use for a range of
water use scenarios. Each scenario is represented by a mixing ratio such that
114
various scenarios represent various mixing ratios with each mixing ratio
giving the same level of target salinity.
6.4.1 Case study in Australia
The analysis is done for each selected farm in the CIA to help assess the
impact of changes in groundwater salinity and groundwater depth on the
costs of conjunctive management. For each scenario the same combinations
of canal water and groundwater were used to generate various model runs to
help understand the impact on cost of conjunctive water use, and the impact
of changes in groundwater EC on the costs. The mixing ratios of canal
water:groundwater was changed from 1:1 to 1:8 to 8:1 as shown in (Figure
6.8). The mixing ratio of 1:1 was used as the base case scenario for each
farm for comparing the cost of conjunctive use for various mixing ratios,
each achieving the same level of target salinity.
The result show that compared to the base case scenario the cost decreases
continuously as the canal water ratio increases from 1:1 to 8:1, where as cost
increases as the mixing ratio of groundwater increases from 1:1 to 1:8. This
suggest that even for the same target salinity levels the lower cost
conjunctive use is achievable by mixing higher salinity groundwater with
increasing amount of canal water (reading from right to left from 1:1 ratio ).
If more canal water is not available for mixing with higher EC groundwater,
it would increase the cost of conjunctive use. It also shows that even when
groundwater EC falls a higher ratio of groundwater would increase the cost
of conjunctive use (reading from left to right from 1:1 ratio). This is true for
all three crops shown. In terms of individual crops the costs are not the
115
same. Soybean has the lowest cost, sunflower has the highest, where as
barley is in the middle.
This result suggests that with rising groundwater salinity level the mixing of
canal water in appropriate proportion to keep the target salinity with
desirable range can help decrease the cost of conjunctive use. Alternatively
even if better groundwater quality water become available such that
groundwater salinity continues to fall, the higher use of groundwater
increase the cost of conjunctive use even if composite EC is the same. This
suggest that the availability of canal water in term of mixing ratio, and the
salinity level of groundwater have key significance for costs of conjunctive
management of surface and groundwater resources.
In aggregate terms for all crops (marked as Farm 1) the cost of conjunctive
use decreases with higher mixing ratios of canal water (moving from right
to left from 1:1 ratio). For higher mixing ratios of groundwater the reverse is
true. It must be noticed that for higher mixing ratios of groundwater the
gross margin per hectare are also lower. Higher cost and lower margin
therefore suggest that farmer have lower incentive to use groundwater
beyond a certain level. This suggests that:
o as groundwater use increases the cost of conjunctive use increases.
o the cost of conjunctive use is lower for higher canal water use .
116
0
10000
20000
30000
40000
50000
60000
8:1
7:1
6:1
5:1
4:1
3:1
2:1
1:1
1:2
1:3
1:4
1:5
1:6
1:7
1:8
Mixing Ratio of SW and GW
Cos
t of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
SoybeanSunflowerBarleyFarm 1
Figure 6.8 Mixing ratio of Surface water and groundwater for the Farm 1.
The crops and land use shown was chosen by the model run for each
scenario. For example, sunflower crop has highest area of the crop and uses
highest water so that sunflower become the one who has the highest cost of
conjunctive water use of surface and groundwater, while soybean has the
lowest cost of water use as shown in (Figure 6.9).
52.67
110104.33
0
20
40
60
80
100
120
SOYBEAN SUNFLOWER BARLEY
Are
a ( h
a)
Figure 6.9 Land use in Farm 1
117
The costs of conjunctive use are not the same for Farm 6, as excepted, rather
they are higher. However the costs have the same general trend for the
higher mixing ratio of canal water as well as the higher mixing ratio of
groundwater. The cost falls with increase in canal water use. The cost
increases with increase in the mixing ration of groundwater. That is, as the
mixing ratio of surface and groundwater increases from left to right the cost
of conjunctive increases while from right to left the cost of conjunctive
water use decreases as more canal water is used. In terms of individual crops
the costs are not the same. Soybean has the lowest cost, sunflower has the
highest, where as barley is in the middle.
In aggregate terms for all crops (marked as Farm 6, Farm 9 and Farm 11)
the cost of conjunctive use decreases with higher mixing ratio of canal water
(moving from right to left from 1:1 ratio) while it increases for higher
mixing ratios of groundwater (Figure 6.10, Figure 6.11 and Figure 6.12).
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
8:1
7:1
6:1
5:1
4:1
3:1
2:1
1:1
1:2
1:3
1:4
1:5
1:6
1:7
1:8
Mixing Ratio of SW and GW
Cost
of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
SOYBEANSuflowerBarleyFarm 6
Figure 6.10 Mixing ratio of Surface water and groundwater for the Farm 6
118
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
8:1
7:1
6:1
5:1
4:1
3:1
2:1
1:1
1:2
1:3
1:4
1:5
1:6
1:7
1:8
Mixing Ratio of SW and GW
Cost
of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
SoybeanSunflowerBarleyFarm 9
Figure 6.11 Mixing ratio of Surface water and groundwater for the Farm 9.
For Farm 9, the trends in costs for various mixing ratios as well as total cost
are the same. Full explanation is thus not needed. Again soybean has the
lowest cost, sunflower has the highest, where as barley is in the middle. The
attributes of farm 11 in terms of groundwater salinity, surface water salinity,
composite salinity are the same; the only difference is shallower
groundwater depth (2.8 vs 2.4 m) such that trend and the cost of conjunctive
water management would be similar to farm 1. The analysis showed this
was the case. Therefore, the analysis of farm 11 is not reported here. A
comparison of results for all three farms in the CIA shows that:
o The costs decrease as the mixing ratio of canal water increases
o The costs increase as the mixing ratio of groundwater increases
o Farm 1 has the highest cost, Farm 9 has the lowest and Farm 6 is in
the middle.
119
The ranking of the farms in terms of costs of conjunctive use are as
expected. The overall results show that by mixing canal water and surface in
appropriate combination farmer can reduce the cost of conjunctive
management and increase total return per ha. If mixing ratios are not
appropriate either the cost will be higher or total gross margin from crop
will be lower (Figure 6.12).
20000
25000
30000
35000
40000
45000
50000
55000
8:1
7:1
6:1
5:1
4:1
3:1
2:1
1:1
1:2
1:3
1:4
1:5
1:6
1:7
1:8
Mixing Ratio of SW and GW
Cos
t of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
Farm 1Farm 6Farm 9
Figure 6.12 Mixing ratio of surface water and groundwater to individual farm.
6.4.2 Case study in Pakistan
This case study evaluated the costs of conjunctive water use for a range of
water use scenarios. Each scenario is represented by a mixing ratio such that
various scenarios represent various mixing ratios. The analysis is done for
each selected farm in the Upper, Middle and Lower parts of the sub-basin,
to help assess the impact of changes in salinity and groundwater depth on
the costs of conjunctive management.
120
For each scenario different combinations of canal water and groundwater
were used to achieve the same target salinity level so as to help understand
the impact of changes in mixing ratios on the cost of conjunctive water use.
The mixing ratios of canal water:groundwater was changed from 1:1 to 1:8
to 8:1 as shown. Farmer can practice conjunctive use to mix canal water
with groundwater in different mixing ratios, achieving the same level of
target salinity but paying different cost for conjunctive use.
If the farmer is able to achieve the same composite salinity, would cost of
conjunctive use per ha be the same for different mixing ratio? This study
result show this is not the case. The mixing ratio 1:1 gives a cost of
conjunctive use at $132/ha. As mixing ratio increases to 1:2 and 1:3, the
cost of conjunctive use per farm increases due to more use of groundwater.
As mixing ratio changes from 2:1 to 3:1 the cost falls because of higher use
of surface water, even if the target salinity is the same.
The farm selected from the upper, middle and lower reaches of the system
differ in several key details in terms of conjunctive use. First electric
tubewell is used for Upper farm, where as diesel tubewells are used for
Middle and Lower farm which have higher cost due to the expensive fuel.
The target salinity level is the lower in Upper farm and some what higher in
Middle farm and highest in the Lower farm. Surface water salinity is lower
for the Upper farm, higher for the Middle farm but highest for the Lower
farm. Groundwater salinity level have a similar ranking. Watertable depth
differ widely and is quite shallow in the Upper farm (1.5 m) than Middle
(5.5 m) and Lower farm (7.5 m). This means that Upper farm has better
characteristics than Middle and Lower farm which would help to reduce the
121
cost. The expected cost should be lowest for the Upper farm especially for
higher mixing of canal water. The expected cost should be higher for
Middle farm because poorer quality groundwater allows more use of
groundwater which adds the cost. For the Lower farm all attributes are
worse such that there only be limited use of groundwater which would help
to reduce the cost. The expected cost of conjunctive management should be
lowest for the Lower farm. To achieve same target salinity level higher
canal mixing ratio would be needed for the Lower and Middle farms
reducing the cost of conjunctive use.
This suggests that the cost would decrease due to the increase of surface
water effect and would increase due to the high groundwater use. The
modelling results support this expectation. For Upper farm rice ranks
highest in terms of the cost followed by sugarcane and wheat in declining
order (Figure 6.13). For the Middle farm rice is not grown such that
sugarcane ranks at top follow by maize, cotton and wheat; fodder ranks at
the bottom (Figure 6.14). The same is true for the Lower farm, both in terms
of crops and their rankings (Figure 6.15). In terms of total cost of
conjunctive use the ranking show that Upper farm has the highest cost for
1:1 base case ratio.
The Middle farm has the lowest total cost where as the Lower farm ranks in
the middle (Figure 6.16). For higher mixing ratios of canal water total cost
of conjunctive use falls for the Lower farm but increase for the Upper farm
such that their relative ranking changes. For higher mixing ratio of
groundwater the reverse is true: the total cost is lower for the Lower farm
but higher for the Upper farm. This is expected as explained before. Higher
122
mixing ratio of groundwater for the Upper farm results in higher cost such
that the relative ranking of the upper and lower farm change. The middle on
the other hand stays in middle in term of ranking. These results strongly
support priori expectation and thus show this study the model is
theoretically and conceptually robust.
0
20
40
60
80
100
120
140
160
180
200
3:1
2:1
1:1
1:2
1:3
Mixing Ratio of SW and GW
Cos
t of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
RICE
SUGARCANE
MAIZE
WHEAT
KFODDER
RFODDER
compinate
Figure 6.13 Mixing ratio of surface and groundwater for the Upper Rechna Doab
123
0
20
40
60
80
100
120
140
160
180
200
3:1
2:1
1:1
1:2
1:3
Mixing Ratio of SW and GW
Cos
t of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
SUGARCANE
MAIZE
COTTON
WHEAT
KFODDER
RFODDER
compinate
Figure 6.14 Mixing ratio of surface and groundwater for the Middle Rechna Doab
0
20
40
60
80
100
120
140
160
180
3:1
2:1
1:1
1:2
1:3
Mixing Ratio of SW and GW
Cos
t of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
SUGARCANE
MAIZE
COTTON
WHEAT
KFODDER
RFODDER
compinate
Figure 6.15 Mixing ratio of surface and groundwater for the Lower Rechna Doab
124
60
80
100
120
140
160
180
3:1
2:1
1:1
1:2
1:3
Mixing Ratio of SW and GW
Cos
t of C
onju
nctiv
e W
ater
Man
agem
ent (
$)
Lower Rechna DoabMiddle Rechna DoabUpper Rechna Doab
Figure 6.16 Mixing ratio of surface water and groundwater to individual farm area.
Generally, the cost of conjunctive use falls when canal water mixing ratio
increase. The cost of conjunctive use increases with increase in groundwater
mixing ratio. This is a very strong conclusion which suggests that farmer
with lowest supply of canal water will have a higher cost of conjunctive use
than may be possible when appropriate mixing ratio is achieved and more
canal water is available. It also means that where canal water supply is short
such as at tail ends and groundwater has a poor quality the cost from
conjunctive use will be higher. High water requiring crops rank towards the
top in terms of the costs. Whereas crops such as winter and summer fodder
have the lowest costs both because they required less water and are irrigated
only when necessary. When rainfall occurs the fodder can grow almost
without any irrigation and this is the obvious reason for the lowest cost in
the case of fodder.
125
6.5 Summary
This chapter evaluated the feasibility of using groundwater of varying
quality by mixing it with canal water in different ratios and assessed its
impact on gross margin per ha and the cost of conjunctive use of surface
water and groundwater when mixed in different ratios to achieve the same
target salinity levels. The salinity of groundwater and its depth impact crop
yield and gross margin. Conjunctive use is feasible over a range of mixing
ratios particularly with increasing use of canal water. With decreasing use of
canal water conjunctive use is possible but increasingly costly.
The crop choices under conjunctive are influenced by the target salinity
level as well salinity and depth of groundwater. The crops requiring more
water rank higher in terms of the total cost of conjunctive use than crops
requiring less water or infrequent irrigation. The total annual cost of
conjunctive use especially pumpsets varies by type of technology, annual
water use and daily hours of operation of the pumpsets. Generally costs are
higher for diesel than electric pump. Total annual cost increases with
increase in annual volume water pump. The unit cost is higher for fewer
pumping hours but lower for longer pumping hours.
The conjunctive use of surface and groundwater is an economically
attractive and financially feasible option. Conjunctive use should be
practiced with the knowledge of quality of groundwater and its depth and
the quality of the surface water and volume of surface water available;
without such knowledge inappropriate conjunctive use management can
126
result in lost productivity and increase operating costs, reducing profits from
farming.
127
CHAPTER SEVEN
7 Summary and Conclusions
This chapter discusses different aspects of the proposed policy interventions
to maximise the socio-economic and environmental benefits from
conjunctive water management.
7.1 Overview of the key issues
Irrigation has supported civilisations since millennia. The negative effect
associated with soil salinisation has been an issue of irrigated agriculture for
centuries. A soil salinity problem exists when the build up of salts in the
crops root zone is significant enough that a loss in crop yield results.
Although, waterlogged and saline soils are found naturally, in irrigated areas
these salts typically originate from either a saline high watertable or from
salts in the applied water. The agricultural impacts associated with excess
soil salinity levels cause decrease in crop yield. Higher salinity level also
cause indirect off farm impacts such as damages to infrastructure and build
environment etc.
Conjunctive water use helps to improve water security, sustain agricultural
growth, and achieve higher economic returns; but due to the increased
salinity of irrigation water, long-term environmental sustainability of
irrigated agriculture may prove questionable if conjunctive water use is not
managed appropriately. Proper accounting of crop salinity tolerance
constraints can help maximise benefits with lower environmental impact of
128
agriculture from conjunctive water management under limited water
supplies both at the farm and irrigation system levels.
7.2 Summary of the research objectives and methodology
The main goal of this dissertation was to study the economics of conjunctive
water management under crop salinity tolerance constraints, using following
objectives.
o Determine the possibilities of increasing profitability by taking
optimal mix of crops under crop salinity tolerance constraints
o Develop a hydrologic economic model and employ different
mathematical optimisation techniques using GAMS environment
to determine the ways of best use of conjunctive water for
irrigation
o Estimate and compare the cost of irrigation and the resulting gross
margins from using surface water, groundwater and conjunctive
water use with respect to optimal crop mix under crop salinity
tolerance constraints and
o Propose different policy interventions to maximise the socio-
economic and environmental benefits from conjunctive water
management.
Different approaches have been used to understand hydrologic-economic
role of conjunctive water use at farm level and irrigation system level. These
include the stochastic approach. The conventional stochastic approach
becomes infeasible when dealing with large spatial dimension; however,
129
this was not the case with the use of Monte Carlo and Taylor series
approximations. The Taylor series approximation is found to be particularly
promising to decision-makers, because it is user-friendly and is much less
computer-intensive as compared to Monto Carlo approach; all that is
required is a software package (like GAMS) capable of solving a set of
nonlinear equations.
This study extends previous work of SWAGMAN series models, which are
lumped models of salt and water balance at the farm and catchment scale.
However, this study uses a customised version of the SWAGMAN Farm
model, which integrates the Mass and Hoffmann equation in the standard
version of the SWAGMAN Farm model. This is the key conceptual
contribution of this study and an advance into the SWAGMAN Farm model.
It involved mixed integer programming to model the nonlinearities in the
Mass and Hoffmann equation. This advance enables a more scientific and
accurate assessment of the impact of salinity on crop yield via-a-vis land
and water management strategies to enhance productivity and environmental
sustainability. The optimisation and integrated hydrologic–agronomic-
economic modeling approach employ mixed integer nonlinear programming
under General Algebraic Modeling System environment. The nonlinearities
and crop yield response to salinity as defined by Mass and Hoffman cannot
be capture by conventional modelling techniques due to complex
relationship.
The model was developed and successfully validated in selected farms in
two mature irrigation areas in Pakistan and Australia. The overall model
result show that the yield response to salinity and groundwater depth varies
130
across farms within the same irrigation system. The result support the
conceptual framework used by the model and shows that the model is
theoretically consistent and robust under a range of conjunctive use
situation.
7.3 Australian prospective on conjunctive water
management
For the Coleambally irrigation area the selected farms differed basically in
terms of initial depth of watertable and salinity concentration; all other
parameters and variables were the same such that the model run for each
farm capture the effect of changes in watertable depth and salinity on crop
choices and yield and hence gross margin per ha.
The modeling result showed that for a given level of water allocation the
gross margin per ha is lowest with the groundwater use only, and highest for
the canal water use only. The lowest gross margin for groundwater use only
means that crop yield is adversely impacted due to higher salinity of
groundwater lowering gross margin. The mixing of canal water with
groundwater enables the farmer to achieve higher gross margin per ha and
thus further increase total return from farming. Put alternatively this means
that higher salinity of groundwater constraint crop production. However,
this constraint can be overcome by mixing saline groundwater with good
quality surface water to achieve a certain target salinity level.
Various mixing ratios of groundwater and surface water would thus mean a
specific target salinity level. Other things remaining the same the gross
131
margin per ha would be lower for the higher target salinity level. This study
result show that compared to the base case where canal water and
groundwater mix into 1:1 ratio, per ha gross margin increase as the
proportion of canal water in conjunctive use increases. The gross margin per
ha decrease as the proportion of groundwater conjunctive increase. This lead
the conclusion that conjunctive use of surface and groundwater increase
gross margin by keeping target salinity in desirable range.
In terms of conjunctive use of surface and groundwater these result suggest
that in areas with poor quality groundwater the gross margin would be lower
if the farmer use more groundwater and practice irrigated agriculture under
condition of surface water scarcity. Reliable supply of surface water and
support measure for appropriate utilisation of groundwater are therefore
essential for a profitable agriculture and improved salinity management.
The overall modelling results for CIA show that groundwater depth poses a
significant constraint to crop yield and profits. The most profitable crops
can not profitably be grown under shallow groundwater tables, particularly
where groundwater salinity is also high. Shallow watertable and high
groundwater salinity are the least helpful combination of biophysical
conditions for profitable agriculture. On the other hand deep watertable and
low salinity offer the best production environment for a profitable
agriculture. Well drained soils with appropriate groundwater depth can still
be suitable for crop agriculture despite high salinity levels. The availability
of surface water can help in making use of the saline groundwater through
mixing to achieve suitable target salinity level which may otherwise not be
possible with groundwater use only.
132
7.4 Pakistani prospective on conjunctive water management
For validating the model in Rechna Doab, three representative farms were
selected; one farm each from Upper, Middle and Lower Rechna Doab. The
three farms differed in terms of the variation in groundwater depth, salinity
concentration of groundwater and concentration of salts in the watertable,
surface water and target salinity. The overall modelling results for Rechna
Doab show that:
o surface water offers the highest gross margin
o poor quality groundwater limits production and reduced gross
margin
o watertable depth is important for determining gross margin both in
case of irrigation with surface or groundwater or conjunctive use
o appropriate mixing ratios of surface water: groundwater can be
used but higher groundwater share in the mixing ratio almost
always gives lower gross margin
o when canal water supplies are in short supply only low salinity
groundwater can be used
o when canal water supplies are beyond medium levels it is possible
to use slightly higher salinity groundwater although it often leads
to lower gross margin per ha.
The overall result suggest that information on volume and quality of surface
water, watertable depth, groundwater salinity and responsiveness of crops to
root zone salinity and watertable depth is critical for optimising profits; and
133
the over use of poor quality groundwater will not only reduce profit but may
also have cost for the environment which is not evaluated here.
The overall modelling results for selected farms in both systems in Australia
and Pakistan show that groundwater salinity and depth poses a significant
constraint to crop yield and profits. Even the most profitable crops can not
profitably be grown under shallow groundwater tables, particularly where
groundwater salinity is also high. Shallow watertable and high groundwater
salinity are the least helpful combination of biophysical conditions for
profitable agriculture. On the other hand deep watertable and low salinity
offer the best production environment for a profitable agriculture. Well
drained soils with appropriate groundwater depth can still be suitable for
crop agriculture despite high salinity levels. The availability of surface
water can help in making use of the saline groundwater through mixing to
achieve suitable target salinity level which may otherwise not be possible
with groundwater use only.
The policy implications of these results are clear: by mixing canal water and
surface in appropriate combination farmer can reduce the cost of
conjunctive management and increase total return per ha. If mixing ratios
are inappropriate either the irrigation cost will be higher or total gross
margin from crop will be lower or the damage to the environment will
presumably increase, although the latter is not directly estimated by the
model.
134
7.5 Combined prospective on conjunctive water
management
The case study irrigation system in Australia and Pakistan present two
contrasting groundwater governance systems although conjunctive use is
practiced in both. Australia has a sound water policy and institutional
system to achieve the conjunctive use of surface and groundwater by
defining and implementing conjunctive water allocation rules. For instance,
total seasonal water allocation comprise of surface water allocation plus
groundwater pumping limits. Similarly area limits on certain crop are
applied for restricting maximum area of the crop for particular farm to help
minimise the damage to the environment. Such restrictions are generally
based on the depth and salinity of the groundwater. The trade in seasonal
water allocation is allowed subject to certain condition on fulfilling
environmental regulation such that large transfers out of irrigation district
do not cause significant change in salinity.
In the Rechna Doab by contrast the use of groundwater is unregulated.
There are no limits on the amount of groundwater that could be extracted.
Water rights in groundwater are not defined and there is no institutional
mechanism to limit over extraction of groundwater. The allocation of
groundwater to is to everyone according to his power and in particular the
abstraction is by everyone according to the power of extraction devices and
the ability to pay for the fixed and variable cost.
Private markets in groundwater work without any regard to the environment
or changes in salinity dynamics. Overextraction often leads to falling
groundwater level and impaired drainage. Over irrigation particularly with
135
poor groundwater often cause salinisation and alkalinisation of soil,
reducing productivity. The two cases therefore represent two constructing
water governance regime. The model works in both and provides consistent
estimates of the return and cost of conjunctive water management.
7.6 A possible way forward
The model can be helpful for making farm level decision and salinity and
crop choice as well as system level decision on allowable groundwater
discharges and zoning of groundwater for irrigation. The model can also
guide governance and management of poor and good quality management
zones. It can also aid in reallocating more surface water from the good
quality groundwater management zones to poor quality water zones such as
the tail ends. Two extensions of model are possible, first to estimate the
marginal productivity of water and its marginal value and second to assess
the environmental management costs and benefits of sustainable
groundwater management. It is recommended to take these aspects in future
studies both in Australia and Pakistan.
136
References
Acreman, M. 2005. Linking science and decision-making: features and
experience from environmental river flow setting. Environmental
Modelling and Software, 20(2), 99-109.
Ahmed, B. A. O., Yamamoto, T., Rasiah, V., Inoue, M., Anyoji, H. 2007.
The Impact of Saline Water Irrigation Management Options in a Dune
Sand on Available Soil Water and Its Salinity. Agricultural Water
Management, 88(1-3), 63-72.
Akbar, S., Khan, S., Hirsi, I. 2004. Saving Irrigation Channel Losses. Paper
presented at the Australian National Committee on Irrigation and
Drainage (ANCID) Conference meeting. South Australia. Oct 10-13.
Ashraf, M., Saeed, M. 2006. Effect of improved cultural practices on crop
yield and soil salinity under relatively saline groundwater
applications. Irrigation and Drainage Systems, 20(1), 111-124.
Ayars, J. E., Christen, E. W., Hornbuckle, J. W. 2006. Controlled drainage
for improved water management in arid regions irrigated agriculture.
Agricultural Water Management, 86(1-2), 128-139.
Ayers, R. S., Westcot, D. W. 1985. Water quality agriculture. FAO
Irrigation and Drainage Paper 29 Rev. 1, Roma, 174
Ball, M., Calaminus, B., Kerdoncuff, P., Rentz, O. 2005. Techno-economic
databases in the context of integrated assessment modelling.
Environmental Modelling & Software, 20(9), 1189-1193.
Barber, C. 2007. Augmentation of Groundwater Resources through Aquifer
Storage and Recovery (ASR) Method. In Groundwater (pp. 112-127).
137
Barrow, C. J. 1991. Land Degradation - Developments and Breakdown of
Terrestrial Environments. Cambridge: Cambridge University Press.
Bartolini, F., Bazzani, G. M., Gallerani, V., Raggi, M., Viaggi, D. 2007. The
impact of water and agriculture policy scenarios on irrigated farming
systems in Italy: An analysis based on farm level multi-attribute linear
programming models. Agricultural Systems, 93(1-3), 90-114.
Bazzani, G. M. 2005. A Decision Support for an Integrated Multi-Scale
Analysis of Irrigation: DSIIR. Journal of Environmental Management,
77(4), 301-314.
Bazzani, G. M., Di Pasquale, S., Gallerani, V., Morganti, S., Raggi, M.,
Viaggi, D. 2005. The sustainability of irrigated agricultural systems
under the Water Framework Directive: first results. Environmental
Modelling & Software, 20(2), 165-175.
Bechini, L., Stockle, C. O. 2007. Integration of a Cropping Systems
Simulation Model and a Relational Database for Simple Farm-Scale
Analyses. Agron J, 99(5), 1226-1237.
Belaineh, G., Peralta, R.C., Hughes, T. C. 1999. Simulation/Optimization
Modeling for Water Resources Management. Journal of Water
Resources Planning and Management, 125(3), 154-161
Bennett, J. 2005. Australasian environmental economics: contributions,
conflicts and 'cop-outs'. The Australian Journal of Agricultural and
Resource Economics, 49(3), 243-261.
Blomquist, W, E. Schlager, and T. Heikkila. 2004. Common Waters,
Diverging Streams: Linking Institutions and Water Management in
138
Arizona, California, and Colorado. Washington, D.C. Resources for
the Future Press.
Bredehoeft, J.D., R.A. Young, 1983. Conjunctive use of groundwater and
surface water for irrigated agriculture: Risk aversion. Water Resources
Research, Volume 19, Number 5: 1111-1121.
Brill, E., Hochman, E., Zilberman, D. 1997. Allocation and pricing at the
water district level. American Journal Agricultural Economics, 79,
952-963.
Brown, C., Rogers, P. 2006. Effect of Forecast-Based Pricing on Irrigated
Agriculture: a Simulation. Journal of Water Resources Planning and
Management, 132(6), 403-413.
Brown, C., Rogers, P., Lall, U. 2006. Demand Management of Groundwater
With Monsoon Forecasting. Agricultural Systems, 90(1-3), 293-311.
Brown, G., McGuire, C. B. 1967. A Socially Optimum Pricing Policy for a
Public Water Agency. Water Resources Research, 3(1).
Brumbelow, K., Georgakakos, A. 2007. Determining Crop-Water
Production Functions Using Yield-Irrigation Gradient Algorithms.
Agricultural Water Management, 87(2), 151-161.
Burt, O.R., 1964. The economics of conjunctive use of ground and surface
water. Hilgardia, 36: 31-111.
Buysse, J., Van Huylenbroeck, G., Lauwers, L. 2007. Normative, positive
and econometric mathematical programming as tools for incorporation
of multifunctionality in agricultural policy modelling. Agriculture,
Ecosystems and Environment, 120(1), 70-81.
139
Cai, X., Daene C., McKinney, A., Leon S. Lasdon. 2003. Integrated
Hydrologic-Agronomic-Economic Model for River Basin
Management. Journal of Water Resources Planning and Management.
Chaudhry, A., Shah, F. 2003. Conjunctive use of surface and groundwater
resources under alternative institutional mechanisms (pp. Department
of Agriculture and Resource Economics, University of Connecticut,
Storrs, USA).
Chermak, J. M., Patrick, R. H., Brookshire, D. S. 2005. Economics of
Transboundary Aquifer Management. Ground Water, 43(5), 731-736.
Chowdary, V. M., Rao, N. H., Sarma, P. B. S. 2005. Decision Support
Framework for Assessment of Non-Point-Source Pollution of
Groundwater in Large Irrigation Projects. Agricultural Water
Management, 75(3), 194-225.
Christen, E. W., Prasad, A., Khan, S. 2001. Spatial Analysis of shallow
groundwater pumping for salinity control and potential conjunctive
use- A case study of the Coleambally Irrigation Area.
Colby, B.G., 1988. Economic Impacts of Water Law - State Law and Water
Market Development in the Southwest. Natural Resources Journal. 28,
721.
Coleambally Irrigation Cooperative Limited (CICL), 2006d unpublished,
Coleambally Irrigation Cooperative Limited’s Water Smart Australia
Project, successful proposal for funding via the National Water
Commission.
Commission., M.-D. B. 2006. Evaluation of the connectivity between
surface water and groundwater in the Murray-Darling Basin.
140
Corwin, D. L., Rhoades, J. D., Simunek, J. 2007. Leaching requirement for
soil salinity control: Steady-state versus transient models. Agricultural
Water Management, 90(3), 165-180.
Dagar, J. C., Tomar, O. S., Kumar, Y., Bhagwan, H., Yadav, R. K., Tyagi,
N. K. 2006. Performance of some under-explored crops under saline
irrigation in a semiarid climate in Northwest India. Land Degradation
& Development, 17(3), 285-299.
Dains, S.R., Pawar, J.R. 1987. Economic returns to irrigation in India. New
Delhi, Report prepared by SDR Research Groups Inc. for the US
Agency for International Development
Darwish, T., Atallah, T., El Moujabber, M., Khatib, N. 2005. Salinity
evolution and crop response to secondary soil salinity in two agro-
climatic zones in Lebanon. Agricultural Water Management, 78(1-2),
152-164.
David, A., Thilak, M., John, Q. 2005. Modelling basin level allocation of
water in the Murray Darling Basin in a world of uncertainty (pp.
University of Queensland.).
Dehghanisanij, H., Agassi, M., Anyoji, H., Yamamoto, T., Inoue, M., Eneji,
A. E. 2006. Improvement of Saline Water Use Under Drip Irrigation
System. Agricultural Water Management, 85(3), 233-242.
deVoil, P., Rossing, W. A. H., Hammer, G. L. 2006. Exploring profit -
Sustainability trade-offs in cropping systems using evolutionary
algorithms: Integrative Modelling of Climatic, Terrestrial and Fluvial
Systems. Environmental Modelling & Software, 21(9), 1368-1374.
141
Ding, Y. 2005. The choices of irrigation technologies and groundwater
conservation in the Kansas High Plains: A dynamic analysis. Kansas
State University.
Diodato, N., Ceccarelli, M. 2006. Computational Uncertainty Analysis of
Groundwater Recharge in Catchment. Ecological Informatics, 1(4),
377-389.
Dregne, H.E., 1991. Arid land degradation: a result of mismanagement.
Geotimes 36 (6), 19–21.
Dridi, C., Khanna, M. 2005. Irrigation Technology Adoption and Gains
From Water Trading Under Asymmetric Information. American
Journal of Agricultural Economics, 87(2), 289-301.
Dudley, T., and Allan Fulton. 2006. Conjunctive Water Management: What
is it? Why Consider It? What are the Challenges? Retrieved 17, 2007,
from http://www.glenncountywater.org
Elmahdi, A., H, M., Khan, S., 2006. Using a system dynamics approach to
model sustainability indicator for the irrigation system-Australia.
Natural Resource Modelling Journal. 19, 465-481.
Enever, D, 1999, Coleambally Irrigation Area groundwater simulation
model: Model calibration and analysis of scenario results,
Consultancy Report 99-6, CSIRO Land and Water, Griffith. CICL
library reference: R863.
Evans, D. R. 2007. The Impact of Groundwater Use on Australia’s Rivers:
Land & Water Australia.
Falkenmark, M., 1986. Freshwater—Time for a Modified Approach.
Ambio. 15, 192-200.
142
Feng, Z. Z., Wang, X. K., Feng, Z. W. 2005. Soil N and salinity leaching
after the autumn irrigation and its impact on groundwater in Hetao
Irrigation District, China. Agricultural Water Management, 71(2),
131-143.
Flowers, T. J., Ragab, R., Malash, N., Gawad, G. A., Cuartero, J., Arslan, A.
2005. Sustainable strategies for irrigation in salt-prone Mediterranean:
SALTMED. Agricultural Water Management, 78(1-2), 3-14.
Fraiture, C. d. 2006. Integrated water and food analysis at the global and
basin level. An application of WATERSIM. Water Resour Manage.
Fullagar, I, Brodie, R, Sundaram, B, Hostetler, S, Baker, P, 2006, Managing
connected surface water and groundwater resources, brochure in
Science for Decision Makers series, Bureau of Rural Sciences,
Canberra
Fullagar, I. 2007. Translating conjunctive water management from concept
to practice in mature irrigation systems. Charles Sturt University,
Wagga Wagga.
Garrido, A., Martínez-Santos, P., Llamas, M. 2006. Groundwater irrigation
and its implications for water policy in semiarid countries: the Spanish
experience. Hydrogeology Journal, 14(3), 340-349.
Gates, T.K., Burkhalter, J.P., Labadie, J.W., Valliant, J.C., Broner, I., 2002.
Monitoring and modeling flow and salt transport in a salinity-
threatened irrigated valley. J. Irrigat. Drainage Eng. 128 (2), 87–99.
Ghassemi, F., A. J. Jakeman., H. A. Nix.1995. Salinisation of land and
water resources. Centre for Resource and Environmental Studies. The
Australian National University. Canberra ACT 0200. Australia
143
Giordano, M., 2006. Agricultural groundwater use and rural livelihoods in
sub-Saharan Africa: A first-cut assessment. Hydrogeology Journal. 14,
310 - 318.
Gleick, P. H., Nash, L. 1991. The societal and environmental costs of the
continuing California drought. Berkeley, California.: Pacific Institute
for Studies in Development, Environment, and Security,.
Goesch, T., Hone, S. and Gooday, P. 2007. Groundwater Management:
Efficiency and Sustainability, : ABARE Research Report 07.8,
Canberra, March.
Gomann, H., Kreins, P., Kunkel, R., Wendland, F. 2005. Model based
impact analysis of policy options aiming at reducing diffuse pollution
by agriculture--a case study for the river Ems and a sub-catchment of
the Rhine: Policies and Tools for Sustainable Water Management in
the European Union. Environmental Modelling & Software, 20(2),
261-271.
GóMez-LimóN, J. A., MartíNez, Y. 2006. Multi-Criteria Modelling of
Irrigation Water Market at Basin Level: a Spanish Case Study.
European Journal of Operational Research, 173(1), 313-336.
Gonzalez-Alvarez, Y., Keeler, A. G., Mullen, J. D. 2006. Farm-Level
Irrigation and the Marginal Cost of Water Use: Evidence From
Georgia. Journal of Environmental Management, 80(4), 311-317.
Grieve, A.M., Dunford, E., Marston, D., Martin, R.E., Slavich, P.,1986.
Effects of waterlogging and soil salinity on irrigated agriculture in the
Murray Valley: a review. Aust. J. Exp. Agric. 26, 761–777.
144
Griffin, R. C. 2006. Achieving Water Use Efficiency in Irrigation Districts.
Journal of Water Resources Planning and Management, 132(6), 434-
442.
Hafeez, M.M., Bouman, B.A.M., Giesen, N.V.d., Mushtaq, S., Vlek, P.,
Khan, S., 2007. Water reuse and cost-benefit of pumping at different
spatial levels in a rice irrigation system in UPRIIS, Philippines.
Physics and Chemistry of the Earth.
doi:10.1016/j.pce.2007.1004.1011.
Hafi, A. 2003. Conjunctive use of Groundwater and Surface Water in the
Burdekin Delta Area. Economic Record, 79, 52-62.
Hafi, A. 2006. Conjunctive Water Management Economic Tools for
Evaluating Alternative Policy and Management Options (No. 06.20):
ABARE.
Hajkowicz, S., Perraud, J. M., Dawes, W., DeRose, R. 2005. The strategic
landscape investment model: a tool for mapping optimal
environmental expenditure. Environmental Modelling and Software,
20(10), 1251-1262.
Hajkowicz, S., Young, M. 2005. Costing Yield Loss From Acidity, Sodicity
and Dryland Salinity to Australian Agriculture. Land Degradation &
Development, 16(5), 417-433.
Hajkowicz, S., Young, M. D. 2002. An Economic Analysis of Revegetation
for Dryland Salinity Control on the Lower Eyre Peninsula in South
Australia. Land Degradation & Development, 13(5), 417-428.
Hanley, N., Colombo, S., Tinch, D., Black, A., Aftab, A. 2006. Estimating
the benefits of water quality improvements under the Water
145
Framework Directive: are benefits transferable? Eur Rev Agric Econ,
33(3), 391-413.
Hardisty, P. E., Ozdemiroglu, E. 2005. The economics of groundwater
remediation and protection. Boca Raton, FL: CRC Press.
Hellegers, P. 2006. The Role of Economics in Irrigation Water
Management. Irrigation and Drainage, 55(2), 157-163.
Hernández-Mora N, L. M., Martinez-Cortina L (Ed.). 2001. Misconceptions
in aquifer over-exploitation. Implications for water policy in Southern
Europe: Kluwer Academic Publishers.
Hewitt, J. A., Hanemann, W. M. 1995. A discrete/continuous choice
approach to residential water demand under block rate pricing. Land
Economics, 71(2), 173-192.
Holmes, M. G. R., Young, A. R., Goodwin, T. H., Grew, R. 2005. A
Catchment-Based Water Resource Decision-Support Tool for the
United Kingdom. Environmental Modelling and Software, 20(2), 197-
202.
Houk, E., Frasier, M., Schuck, E. 2006. The agricultural impacts of
irrigation induced waterlogging and soil salinity in the Arkansas
Basin. Agricultural Water Management, 85(1-2), 175-183.
Houk, E., Frasier, M., Schuck, E., 2005. Irrigation technology decisions in
the presence of waterlogging and soil salinity. Global Business and
Economics Review. 7, 343-352.
Humphreys, E., Lewin, L. G., Khan, S., Beecher, H. G., Lacy, J. M.,
Thompson, J. A., et al. 2006. Integration of Approaches to Increasing
146
Water Use Efficiency in Rice-Based Systems in Southeast Australia.
Field Crops Research, 97(1), 19-33.
Hunt, R.C., Hunt, E., 1976. Canal Irrigation and Local Social Organization.
Current Anthropology. 17, 389-411.
Hussain, I., Hanjra, M.A. 2002. Agricultural water and poverty:
Understanding linkages and identifying interventions. Second South
Asia Water Forum, 14-16 December 2002 – Proceedings, vol.2.
pp.537-570., Islamabad, Pakistan, IWMI.
Hussain, I., Hanjra, M.A., 2003. Does irrigation water matter for rural
poverty alleviation? evidence from South and South-East Asia. Water
Policy. 5, 429-442.
Hussain, I., Mudasser, M., Hanjra, M.A., Amrasinghe, U., Molden, D.,
2004a. Improving wheat productivity in Pakistan: econometric
analysis using panel data from Chaj in the upper Indus Basin. Water
International. 29, 189-200.
Hussain, I., Regassa, N., Madar, S., 2004b. Water for Food Security for the
Poor. Water Poverty Theme: A Collection of Thematic Papers, Asian
Development Bank, Manilla, The Philippines.
Isbell RF, Reeve R, Hutton JT 1983 Salt and sodicity. In ‘Soils, an
Australian viewpoint’. pp. 107–117. (CSIRO Publishing: Melbourne)
Jackson, T., Watt, J., Hirsi, I., Khan, S. 2006. The effect of surface-
groundwater interactions on energy consumption in irrigation systems:
hydrologic & economic perspectives. Paper presented at the
Australian National Committee on Irrigation and Drainage (ANCID)
Conference meeting. Darwin, Northern Territory. Oct 15-18
147
Jalota, S. K., Sood, A., Vitale, J. D., Srinivasan, R. 2007. Simulated Crop
Yields Response to Irrigation Water and Economic Analysis:
Increasing Irrigated Water Use Efficiency in the Indian Punjab. Agron
J, 99(4), 1073-1084.
Janmaat, J. 2005. Water Applications and Pigouvian Taxes to Control
Irrigation-Induced Soil Degradation. Journal of Development
Economics, 76(1), 209-230.
Janssen, S., van Ittersum, M. K. 2007. Assessing farm innovations and
responses to policies: A review of bio-economic farm models.
Agricultural Systems, 94(3), 622-636.
Jehangir, W. A., Hugh Turral, Khan, Shahbaz. 2003. Net Gains from
Conjunctive Use of Surface and Ground Water. The Pakistan
Development Review, 42(4), 823-838.
Jehangir, W. A., Khan, S. 2003. Cropping Patterns for Conjunctive Water
Management in the Rechna Doab: Optimization Modelling to Protect
Groundwater Resources: IWMI Research Report.
John, M. A. 2005. Minimal models and agroecological policy at the regional
scale: An application to salinity problems in southeastern Australia.
Regional Environmental Change, 5(1), 1-17.
Jones, R., Marshall, G., 1992. Land salinisation, waterlogging and the
agricultural benefits of a surface drainage scheme in Benerembah
irrigation district. Rev. Market. Agric. Econ. 60 (2), 173–189.
Kahlown, M. A., Kemper, W. D. 2005. Reducing Water Losses From
Channels Using Linings: Costs and Benefits in Pakistan. Agricultural
Water Management, 74(1), 57-76.
148
Kahlown, M. A., Raoof, A., Zubair, M., Kemper, W. D. 2007. Water use
efficiency and economic feasibility of growing rice and wheat with
sprinkler irrigation in the Indus Basin of Pakistan. Agricultural Water
Management, 87(3), 292-298.
Karlberg, L., Ben-Gal, A., Jansson, P. E., Shani, U. 2006. Modelling
Transpiration and Growth in Salinity-Stressed Tomato Under
Different Climatic Conditions. Ecological Modelling, 190(1-2), 15-40.
Kaur, R., Paul, M., Malik, R. 2007. Impact Assessment and
Recommendation of Alternative Conjunctive Water Use Strategies for
Salt Affected Agricultural Lands through a Field Scale Decision
Support System – A Case Study. Environ Monit Assess, 129, 257–
270.
Kendy, E. 1999. Science into policy : water in the public realm :
proceedings, specialty conference, June 30-July 2, 1999, Bozeman,
Montana. Herndon, Va.: The Association.
Khan, S, Short, L, Best, L. 2000. Groundwater Dynamics in the
Coleambally Irrigation Area, a consultancy report to CSIRO Land
Water, Griffith
Khan, S., 2007a. Equivalent cropping area and whole farm water balance
approaches to reduce net recharge to shallow saline groundwater from
rice based cropping systems. Paddy and Water Environment. 5, 143-
152.
Khan, S., 2007b. Frontiers irrigation investment and management. Farm
Policy Journal. 4, 39 - 57.
149
Khan, S., Abbas, A., 2007. Upscaling water savings from farm to irrigation
system level using GIS-based agro-hydrological modelling. Irrigation
and Drainage. 56, 29-42.
Khan, S., Abbas, A., Blackwell, J., Gabriel, H. F., Ahmad, A. 2007.
Hydrogeological assessment of serial biological concentration of salts
to manage saline drainage. Agricultural Water Management, 92(1-2),
64-72.
Khan, S., Akbar, S., Rana, T., Abbas, A., Robinson, D., Paydar, Z.,
Dassanayke, D., Hirsi, I., Blackwell, J., Xevi, E., Carmichael, A.
2005. Off-and-on farm savings of irrigation water. Murrumbidgee
Valley water efficiency feasibility project: Water for a Healthy
Country Flagship report. Canberra: CSIRO
Khan, S., Akbar, S., Rana, Y., Abbas, A., Robinson, D., Dassanayake, D.,
Hirsi, I., Blackwell, J., Xevi, E., Carmichael, A. 2004. Hydrologic
economic ranking of water saving options Murrumbidgee
Valley.Report to Pratt Water - Water Efficiency Feasibility Project.
Khan, S., Connel, N., Rana, T., Xevi, E. 2008. Hydrologic-Economic Model
for Managing Irrigation Intensity in Irrigation Areas under Watertable
and Soil Salinity Targets. Environmental Modeling & Assessment,
13(1), 115-120.
Khan, S., Rana, T., Hanjra, M.A., 2008. A cross disciplinary framework for
linking farms with regional groundwater and salinity management
targets. Agricultural Water Management. 95, 35-47.
Khan, S., Tariq, R., Yuanlai, C., Blackwell, J., 2006. Can irrigation be
sustainable? Agricultural Water Management. 80, 87-99.
150
Khan, S., Tariq, R. 2005. Tradeable recharge credits in Coleambally
Irrigation Area: Report 6 Biophysical modelling for linking farms
with regional net recharge targets.
Khan, S., Xevi, E., O’Connell, N., Madden, J., Czhou, F. 2005. A Farm
Scale Hydrologic Economic Optimisation Model To Manage
Waterlogging And Salinity In Irrigation Areas.
Khanna, M., Isik, M., Winter-Nelson, A. 2000. Investment in Site-Specific
Crop Management Under Uncertainty: Implications for Nitrogen
Pollution Control and Environmental Policy. Agricultural Economics,
24(1), 9-21.
Khare, D., Jat, M. K., Ediwahyunan. 2006. Assessment of conjunctive use
planning options: A case study of Sapon irrigation command area of
Indonesia. Journal of Hydrology, 328(3-4), 764-777.
Kijne, J. W. 2006. Abiotic Stress and Water Scarcity: Identifying and
Resolving Conflicts From Plant Level to Global Level. Field Crops
Research, 97(1), 3-18.
Kijne, J. W., E. J. Velde 1991 Secondary Salinity in Pakistan Harvest of
Neglect. IIMI Review 5:1, 15–16.
Kim, C.S., Sandretto, C., Lee, D., 1999. Controlling groundwater quality
with endogenous regulatory instruments. Natural Resource Modeling.
12, 249-272.
Kirsch, B. R., Characklis, G. W. 2006. Equilibrium Approach to Integrating
Regional Surface Water Treatment and Limited Groundwater
Pumping Capacity. Journal of Water Resources Planning and
Management, 132(6), 443-453.
151
Knapp, K. C., L.J. Olson. 1995. The Economics of Conjunctive
Groundwater Management with Stochastic Surface Supplies. . Journal
of Environmental Economics and Management, 28, 340-356.
Koundouri, P., Nauges, C., Tzouvelekas, V. 2006. Technology Adoption
Under Production Uncertainty: Theory and Application to Irrigation
Technology. American Journal of Agricultural Economics, 88(3), 657-
670.
Krawczyk, J. B., Tidball, M. 2006. A Discrete-Time Dynamic Game of
Seasonal Water Allocation. Journal of Optimization Theory and
Applications, 128(2), 411-429.
Krol, M., Jaeger, A., Bronstert, A., GüNtner, A. 2006. Integrated Modelling
of Climate, Water, Soil, Agricultural and Socio-Economic Processes:
a General Introduction of the Methodology and Some Exemplary
Results From the Semi-Arid North-East of Brazil. Journal of
Hydrology, 328(3-4), 417-431.
Kumar, P. B., 2002, Review of Groundwater Use and Groundwater Level
Behaviour in the Lower Murrumbidgee Groundwater Management
Area 002, Groundwater Status Report No. 6, NSW Department of
Land and Water.
Laamrani, H., Khallaayoune, K., Laghroubi, M., Abdelafid, T., Boelee, E.,
Watts, S.J., Gryseels, B., 2000. Domestic use of irrigation water: The
Metfia in Central Morocco. Water International. 25, 410-418.
Lawson, S, 1996, Review of Groundwater Use and Water Level Behaviour
in the Lower Murrumbidgee Valley, Groundwater Status Report
152
Number 4, Technical Report No. 96/01, NSW Department of Land
and Water Resources.
Lawson, S., van der Lelij, A, 1992, Coleambally Deep Bore Project:
Analysis of long term pumping test (Summary Report), Department of
Water Resources, NSW Government.
Lecina, S., Playán, E., Isidoro, D., Dechmi, F., Causapé, J., Faci, J. M. 2005.
Irrigation evaluation and simulation at the Irrigation District V of
Bardenas (Spain). Agricultural Water Management, 73(3), 223-245.
Lefkoff, L. J., Gorelick, S. M. 1990b. Simulating physical processes and
economic behavior in saline, irrigated agriculture: Model
development. Water Resour. Res.,, 7(26), 1359-1369.
Lehtonen, H., BäRlund, I., Tattari, S., Hilden, M. 2007. Combining
Dynamic Economic Analysis and Environmental Impact Modelling:
Addressing Uncertainty and Complexity of Agricultural Development.
Environmental Modelling and Software, 22(5), 710-718.
Llamas, M. R., MartÃnez-Santos, P. 2005. Intensive Groundwater Use:
Silent Revolution and Potential Source of Social Conflicts. Journal of
Water Resources Planning & Management, pp. 337-341.
Llamas, M. R., Martinez-Santos, P. 2005. The Silent Revolution of
Intensive Ground Water Use: Pros and Cons. Ground Water, pp. 161-
161.
Maas, E.V., Grattan, S.R., 1999. Crop Yield as Affected by Salinity.
Agricultural Drainage, Agronomy Monograph no. 38, vol. 13–54.
ASA, Madison, WI.
153
Maas, E.V., Hoffman, G.J., 1977. Crop salt tolerance—current assessment.
J. Irrigat. Drainage Eng. 103 (IR2), 115–134
MacKay, H. 2006. Protection and management of groundwater-dependent
ecosystems: emerging challenges and potential approaches for policy
and management. Australian Journal of Botany, 54(2), 231-237.
Madden, J. C., Prathapar, S. A. 1999. Net Recharge Management -
SWAGMAN Farm: Concepts and application to the Coleambally
Irrigation Area: CSIRO Land and Water, Griffith, Consultancy Report
99-11. March 1999.
Marques, G. F., Lund, J. R., Howitt, R. E. 2005 Modeling irrigated
agricultural production and water use decisions under water supply
uncertainty. Water Resources Research, 41.
Marques, G. F., Lund, J. R., Leu, M. R., Jenkins, M., Howitt, R., Harter, T.,
et al. 2006. Economically Driven Simulation of Regional Water
Systems: Friant-Kern, California. Journal of Water Resources
Planning and Management, 132(6).
Martin, W. E., Kulakowski, S. 1991. Water price as a policy variable in
managing urbanwater use: Tucson, Arizona. Water Resources
Research, 27, 157–166.
Mehari, A., Schultz, B., Depeweg, H. 2006. Salinity impact assessment on
crop yield for Wadi Laba spate irrigation system in Eritrea.
Agricultural Water Management, 85(1-2), 27-37.
Meinzen-Dick, R., Mendoza, M. 1996. Alternative Water allocation
mechanisms Indian and international experiences. Economic and
Political Weekly, , 31.
154
Meinzen-Dick, R.S., 1996. Groundwater markets in Pakistan: participation
and productivity. Reserach Report 105, IFPRI,
Merrett, S. 2005. The price of water : studies in water resource economics
and management. London: IWA Publishing.
Merritt, W. S., Croke, B. F. W., Jakeman, A. J. 2005. Sensitivity testing of a
model for exploring water resources utilisation and management
options. Environmental Modelling and Software, 20(8), 1013-1030.
Merz, S. K. 2006. Towards a National Framework for Managing the
Impacts of Groundwater and Surface Water Interaction in Australia.
Australian Government, Canberra.
Meyer, W.S, Godwin, D. C, White, R. 1996 SWAGMAN Destiny: A tool to
project productivity change due to salinity, water logging and
irrigation management. Proceedings of the 8th Australian Agronomy
Conference, Toowoomba, 1996.
Miles, D.L., 1977. Salinity in the Arkansas Valley of Colorado. Interagency
Agreement Report EPA-IAG-D4-0544. Environmental Protection
Agency, Denver, Colorado
Mohan, S., V. Jothiprakash, 2003. Development of priority based policies
for conjunctive use of surface and ground water. Water International,
Volume 28, Number 2: 254-267.
Mosse, D., 1997. The Symbolic Making of a Common Property Resource:
History, Ecology and Locality in a Tank-irrigated Landscape in South
India. Development and Change. 28, 467-504.
Murray Darling Basin Commission (MDBC), 1998, Groundwater – a
resource for the future, MDBC, Canberra
155
Murray Rust, H., E. Vander Velde. 1992. “Conjunctive Use of Canal and
Groundwater in Punjab, Pakistan: Management and Policy Options.”
Advancements in IIMI’s Research 1992. A Selection of papers
presented at the Internal Program Review. Colombo, SriLanka:
International Irrigation Management Institute.
Murray-Rust, D.H., Velde, E.J.V., 1994. Conjunctive use of canal and
groundwater in Punjab, Pakistan: management and policy options.
Irrigation and Drainage Systems. 8, 208-231.
Murray-Rust, H., 2002.. Conjunctive water use and conjunctive water
management. In: Qureshi et al., (eds.), Proceeding of the International
Conjunctive Water Use Workshop, 16-17 April 2002, Lahore,
Pakistan: International Water Management Institute.
Mustafa, U. 1991 Economic impact of land degradation (Waterlogging and
salt effected soils) on Rice Production in Pakistan's Punjab.
Unpublished Ph.D. dissertation. University of Philippines, Las Banos.
Narayan, K. A., Schleeberger, C., Bristow, K. L. 2007. Modelling seawater
intrusion in the Burdekin Delta Irrigation Area, North Queensland,
Australia. Agricultural Water Management, 89(3), 217-228.
Nickum, J. E. 2003. Irrigated Area Figures as Bureaucratic Construction of
Knowledge: The Case of China. International Journal of Water
Resources Development, 19, 249-262.
Nordblom, T., Hume, I., Bathgate, A., Reynolds, M. 2006. Mathematical
Optimisation of Drainage and Economic Land Use for Target Water
and Salt Yields. Australian Journal of Agricultural and Resource
Economics, 50(3), 381-402.
156
O’Mara, G.T. (ed.), 1988. Efficiency in irrigation - the conjunctive use of
surface and groundwater resources. A World Bank Symposium, the
International Bank for Reconstruction and Development / the World
Bank, Washington D.C., USA. 196p.
O'Connell, N and Khan, S 1999 Water use efficiency at a farm scale -
SWAGMAN farm approach in Rice Water Use Efficiency Workshop;
CRC Australia.
Oelmann, M. 2007. Griffin, R. C.: Water Resource Economics. The
Analysis of Scarcity, Policies, and Projects. Journal of Economics,
91(2), 203-207.
Orr, P., Colby, B., 2004. Groundwater management institutions to protect
riparian habitat. Water Resources Research. 40, W12S03.
Palanisami, K., Easter, K. W. 1991. Hydro-economic interaction between
tank and well water. Indian Journal of Agricultural Economics, 46(2),
174–179.
Pannell, D. J., Ewing, M. A. 2006. Managing secondary dryland salinity:
Options and challenges. Agricultural Water Management, 80(1-3), 41-
56.
Peterson, D., Dwyer, G., Appels, D., Fry, J. 2005. Water Trade in the
Southern Murray-Darling Basin. Economic Record, 81, S115-S127.
Peterson, J. M., Ding, Y. 2005. Economic Adjustments to Groundwater
Depletion in the High Plains: Do Water-Saving Irrigation Systems
Save Water? American Journal of Agricultural Economics, 87(1),
147-159.
157
Prasad, A, Christen, EW, Khan, S, 2001, The potential role for deep
groundwater pumping in the control of irrigation induced salinity in
the Riverine Plain: a case study of the Coleambally Irrigation Area,
Technical Report 1/01, CSIRO Land and Water, Griffith.
Prathapar S A, Meyer W S, Jain S., Vander Lelij, 1994 SWAGSIM A soil
water and groundwater simulation model. CSIRO Division of Water
Resources, Report No. 94/3, Australia.
Prathapar S A, Meyer, W. S, Madden, J. C., Alociljá E 1997 SWAGMAN
Options: A hierarchical multicriteria framework to identify profitable
land uses that minimize water table rise and salinization. Applied
Mathematics and Computation 1997; 83(2/3): 217-40.
Provencher, B., Burt, O. 1994. Approximating the Optimal Groundwater
Pumping Policy in a Multiaquifer Stochastic Conjunctive Use Setting.
Water Resource Research, 30(3), 833-843.
Pulido-Velazquez, D., Sahuquillo, A., Andreu, J., Pulido-Velazquez, M.
2007. A general methodology to simulate groundwater flow of
unconfined aquifers with a reduced computational cost. Journal of
Hydrology, 338(1-2), 42-56.
Pulido-VeláZquez, M., Andreu, J., Sahuquillo, A. 2006. Economic
Optimization of Conjunctive Use of Surface Water and Groundwater
at the Basin Scale. Journal of Water Resources Planning and
Management, 132(6), 454-467.
Pulido-Velazquez, M., M.W. Jenkins, J.R. Lund, 2004. Economic values for
conjunctive use and water banking in southern California. Water
Resources Research 40, W03401, DOI: 10.1029/2003WR002626.
158
Qadir, M., Sharma, B. R., Bruggeman, A., Choukr-Allah, R., Karajeh, F.
2007. Non-conventional water resources and opportunities for water
augmentation to achieve food security in water scarce countries.
Agricultural Water Management, 87(1), 2-22.
Qureshi, A.S., M.N. Asghar, S. Ahmad, I, Masih, 2004. Sustaining crop
production in saline groundwater areas: A case study from Pakistani
Punjab. Australian Journal of Agricultural Research, 55(4): 421-431.
Qureshi, M. E., Qureshi, S. E., Goesch, T. I. M., Hafi, A. 2006. Preliminary
economic assessment of groundwater extraction rules. Economic
Papers, 25(1), 41-67.
Ranganathan, C. R., Palanisami, K. 2004. Modeling Economics of
Conjunctive Surface and Groundwater Irrigation Systems. Irrigation
and Drainage Systems, 18(2), 127-143.
Ringler, C., Cai, X. 2006. Valuing Fisheries and Wetlands Using Integrated
Economic-Hydrologic Modeling-Mekong River Basin. Journal of
Water Resources Planning and Management, 132(6), 480-487.
Saeed, M. M., Ashraf, M. 2005. Feasible Design and Operational Guidelines
for Skimming Wells in the Indus Basin, Pakistan. Agricultural Water
Management, 74(3), 165-188.
Sahoo, B., Lohani, A., Sahu, R. 2006. Fuzzy Multiobjective and Linear
Programming Based Management Models for Optimal Land-Water-
Crop System Planning. Water Resources Management, 20(6), 931-
948.
Sahuquillo A, Lluria M, 2003. Conjunctive use as potential solution for
stressed aquifers: social constraints. In: Llamas R and Custodio E
159
(eds) Intensive use of groundwater: Challenges and opportunities.
A.A.Balkema Publishers.
Sakthivadivel. R., Chawala, A.S. 2002 Innovations in conjunctive water
management: Artificial recharge in Madhya Ganga Canal Project.
International Water Management Institute, IWMI-TATA Water Policy
Research Program, Annual Partners Meeting.
Schoups, G., C. L. Addams, J. L. Minjares, and S. M. Gorelick. 2006
Sustainable conjunctive water management in irrigated agriculture:
Model formulation and application to the Yaqui Valley, Mexico.
Water Resour. Res., , 42.
Schuck, E. C., Green, G. P. 2002. Supply-Based Water Pricing in a
Conjunctive Use System: Implications for Resource and Energy Use.
Resource and Energy Economics, 24(3), 175-192.
Schwabe, K. A., Kan, I., Knapp, K. C. 2006. Drainwater Management for
Salinity Mitigation in Irrigated Agriculture. American Journal of
Agricultural Economics, 88(1), 133-149.
Sekar, I., Randhir, T. O. 2007. Spatial assessment of conjunctive water
harvesting potential in watershed systems. Journal of Hydrology,
334(1-2), 39-52.
Serra, T., Goodwin, B. K., Gil, J. M., Mancuso, A. 2006. Non-parametric
Modelling of Spatial Price Relationships. Journal of Agricultural
Economics, 57(3), 501-522.
Shah, T., O.P. Singh, A. Mukherji, 2006. Some aspects of South Asia’s
groundwater irrigation economy: analyses from a survey in India,
160
Pakistan, Nepal Terai and Bangladesh. Hydrogeology Journal, 14:
286-309.
Shah, T., Roy, A.D., Qureshi, A.S., Wang, J., 2003. Sustaining Asia's
groundwater boom: an overview of issues and evidence. Natural
Resources Forum. 27, 130-141.
Shah, T., Singh, O.P., 2004. Irrigation development and rural poverty in
Gujarat, India: a disaggregated analysis. Water International. 29, 167-
177.
Sharda, V. N., Kurothe, R. S., Sena, D. R., Pande, V. C., Tiwari, S. P. 2006.
Estimation of groundwater recharge from water storage structures in a
semi-arid climate of India. Journal of Hydrology, 329(1-2), 224-243.
Sharma, B. R., Minhas, P. S. 2005. Strategies for managing saline/alkali
waters for sustainable agricultural production in South Asia.
Agricultural Water Management, 78(1-2), 136-151.
Sharma, D. P., Singh, K. N., Kumbhare, P. S. 2005. Response of sunflower
to conjunctive use of saline drainage water and non-saline canal water
irrigation. Archives of Agronomy and Soil Science, 51, 91-100.
Sheng, Z. 2005. An aquifer storage and recovery system with reclaimed
wastewater to preserve native groundwater resources in El Paso,
Texas. Journal of Environmental Management, 75(4), 367-377.
Siddiq, A. 1994 Sustainability of Indus Basin: Impact of tertiary salinity on
wheat productivity, damage assessment and future public policy.,
Ph.D. Dissertation, University of Illinois, Urbana-Champaign.
161
Singh, D.K., A.K. Singh, 2002. Groundwater situation in India: problems
and perspective. International Journal of Water Resources
Development, Volume 18, Number 4: 563 – 580.
Steppuhn, H., van Genuchten, M. T., Grieve, C. M. 2005. Root-Zone
Salinity: I. Selecting a Product-Yield Index and Response Function for
Crop Tolerance. Crop Sci, 45(1), 209-220.
Su, N., Bethune, M., Mann, L., Heuperman, A. 2005. Simulating water and
salt movement in tile-drained fields irrigated with saline water under a
Serial Biological Concentration management scenario. Agricultural
Water Management, 78(3), 165-180.
Syaukat, Y., Fox, G. C. 2004. Conjunctive Surface and Ground Water
Management in the Jakarta Region, Indonesia. Journal of the
American Water Resources Association, 40(1), 241-250.
Tanji, K.K., 1990. Agricultural Salinity Assessment and Management.
American Society of Civil Engineers, New York.
Theodossiou, N., Latinopoulos, P. 2006. Evaluation and optimisation of
groundwater observation networks using the Kriging methodology.
Environmental Modelling & Software, 21(7), 991-1000.
Triantafilis, J., Odeh, I. O. A., Warr, B., Ahmed, M. F. 2004. Mapping of
Salinity Risk in the Lower Namoi Valley Using Non-Linear Kriging
Methods. Agricultural Water Management, 69(3), 203-231.
Tsur, Y., 1990. The stabilization role of groundwater when surface water
supplies are uncertain: the implications for groundwater development.
Water Resource Research, 26(5): 811-818.
162
Tsur, Y., 1993. The economics of conjunctive ground and surface water
irrigation systems: basic principles and empirical evidence from
southern California. Staff Paper P93-15, Department of Agricultural
and Applied Economics, University of Minnesota: 22p.
Tyagi, N. K., K. C. Tyagi., N. N. Pillai., L.S. Willardson. 1993. Decision
support for irrigation system improvement in saline environment.
Agricultural Water Management, 23, 285-301.
Wang, L., Fang, L., Hipel, K. W. 2007. Mathematical Programming
Approaches for Modeling Water Rights Allocation. Journal of Water
Resources Planning and Management, 133(1), 50-59.
Ward, F. A., Booker, J. F., Michelsen, A. M. 2006. Integrated Economic,
Hydrologic, and Institutional Analysis of Policy Responses to
Mitigate Drought Impacts in Rio Grande Basin. Journal of Water
Resources Planning and Management, 132(6), 488-502.
Ward, F. A., Michelsen, A. M., Demouche, L. 2007. Barriers to Water
Conservation in the Rio Grande Basin. Jawra Journal of the American
Water Resources Association, 43(1), 237-253.
Ward, F.A., Roach, B.A., Henderson, J.E., 1996. The economic value of
water in recreation: Evidence from the California drought. Water
Resources Research. 32, 1075-1082.
Watanabe, M., Adams, R. M., Wu, J. 2006. Economics of Environmental
Management in a Spatially Heterogeneous River Basin. American
Journal of Agricultural Economics, 88(3), 617-631.
163
Weersink, A., Wossink, A. 2005. Lessons from agri-environmental policies
in other countries for dealing with salinity in Australia. Aust. J. Exp.
Agric., 45(11), 1481-1493.
Wichelns, D. 1999. Economic efficiency and irrigation water policy with an
example from Egypt. International Journal of Water Resources
Development 15 (4): 543–556.
Xevi, E., Khan, S. 2005. A Multi-Objective Optimisation Approach to
Water Management. Journal of Environmental Management, 77(4),
269-277
YADAV, R. K., SINGH, S. P., LAL, D., KUMAR, A. 2007. Fodder
production and soil health with conjunctive use of saline and good
quality water in ustipsamments of a semi-arid region. Land
degradation & development, 18, 153–161.
Zhu, Z., Giordano, M., Cai X. and Molden, D. 2003 forthcoming. Water
Accounting in the Yellow River Basin. In: Proceedings of the 1st
International Yellow River Forum, 12-15 May 2003, Zhengzhou,
China.
164
Appendix I
Modeling set up for Farm 1 in CIA: Surface water only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 267
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.0
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 2.8
Surface water allocation per farm (ML) 1400 1260 1120 980 840 700 560 420 280 140 0
Groundwater allocation per farm (ML) 0.0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
165
Modeling set up for Farm 1 in CIA: Groundwater only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 267
Leakage (mm year-1) 20
Initial depth to watertable (m) 1
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 2.8
Surface water allocation per farm (ML) 0
Groundwater allocation per farm (ML) 1400 1260 1120 980 840 700 560 420 280 140 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
166
Modeling set up for Farm 1 in CIA: Conjunctive use only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 267
Leakage (mm year-1) 20
Initial depth to watertable (m) 1
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 2.8
Surface water allocation per farm (ML) 700 630 560 490 420 350 280 210 140 70 0
Groundwater allocation per farm (ML) 700 630 560 490 420 350 280 210 140 70 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
167
Modeling set up for Farm 6 in CIA: Surface water only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm)r 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 225
Leakage (mm year-1) 0.2
Initial depth to watertable (m) 0.6
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 4
Surface water allocation per farm (ML) 1180 1062 944 826 708 590 472 354 236 118 0
Groundwater allocation per farm (ML) 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
168
Modeling set up for Farm 6 in CIA: Groundwater only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Weather medium
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 225
Leakage (mm year-1) 0.2
Initial depth to watertable (m) 0.6
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 4
Surface water allocation per farm (ML) 0
Groundwater allocation per farm (ML) 1180 1062 944 826 708 590 472 354 236 118 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
169
Modeling set up for Farm 6 in CIA: Conjunctive use only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 225
Leakage (mm year-1) 0.2
Initial depth to watertable (m) 0.6
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 4
Surface water allocation per farm (ML) 590 531 472 413 354 295 236 177 118 59 0
Groundwater allocation per farm (ML) 590 531 472 413 354 295 236 177 118 59 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
170
Modeling set up for Farm 9 in CIA: Surface water only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm)r 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 221
Leakage (mm year-1) 20
Initial depth to watertable (m) 3.5
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 1.3
Surface water allocation per farm (ML) 1159 1043 927 811 695 580 464 348 232 116 0
Groundwater allocation per farm (ML) 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
171
Modeling set up for Farm 9 in CIA: Groundwater only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm)r 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 221
Leakage (mm year-1) 20
Initial depth to watertable (m) 3.5
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 1.3
Surface water allocation per farm (ML) 0
Groundwater allocation per farm (ML) 1159 1043 927 811 695 580 464 348 232 116 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
172
Modeling set up for Farm 9 in CIA: Conjunctive use only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 221
Leakage (mm year-1) 20
Initial depth to watertable (m) 3.5
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 1.3
Surface water allocation per farm (ML) 580 522 464 406 348 290 232 174 116 58 0
Groundwater allocation per farm (ML) 580 522 464 406 348 290 232 174 116 58 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
173
Modeling set up for Farm 11 in CIA: Surface water only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 339
Leakage (mm year-1) 20
Initial depth to watertable (m) 1
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 2.2
Surface water allocation per farm (ML) 1778 1600 1422 1244 1067 889 711 533 356 178 0
Groundwater allocation per farm (ML) 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
174
Modeling set up for Farm 11 in CIA: Groundwater only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 339
Leakage (mm year-1) 20
Initial depth to watertable (m) 1
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 2.2
Surface water allocation per farm (ML) 0
Groundwater allocation per farm (ML) 1778 1600 1422 1244 1067 889 711 533 356 178 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
175
Modeling set up for Farm 11 in CIA: Conjunctive use only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 346
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 14.97
Price of groundwater ($ ML-1) 40
Area of farm (ha) 339
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.0
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 2.2
Surface water allocation per farm (ML) 889 800 711 622 533 444 356 267 178 89 0
Groundwater allocation per farm (ML) 889 800 711 622 533 444 356 267 178 89 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
176
Appendix II
Modeling set up for Upper Rechna Doab: Surface water only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 600
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.46
Price of groundwater ($ ML-1) 3.32
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.5
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 1.5
Surface water allocation per farm (ML) 70 63 56 49 42 35 28 21 14 7 0
Groundwater allocation per farm (ML) 0
Concentration of rain water dS m-1) 0.01
Rainfall recycling yes
Pumping yes
177
Modeling set up for Upper Rechna Doab: Groundwater only - Electric
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 600
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.46
Price of groundwater ($ ML-1) 3.32
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.5
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 1.5
Surface water allocation per farm (ML) 0
Groundwater allocation per farm (ML) 70 63 56 49 42 35 28 21 14 7 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
178
Modeling set up for Upper Rechna Doab: Conjunctive use only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 600
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.46
Price of groundwater ($ ML-1) 3.32
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.5
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.14
Concentration of groundwater (dS m-1) 0.7
Concentration of watertable in (dS m-1) 1.5
Surface water allocation per farm (ML) 35 31.5 28 24.5 21 17.5 14 10.5 7 3.5 0
Groundwater allocation per farm (ML) 35 31.5 28 24.5 21 17.5 14 10.5 7 3.5 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
179
Modeling set up for Middle Rechna Doab: Surface water only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 360
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.24
Price of groundwater ($ ML-1) 3.42
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.65
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.45
Concentration of groundwater (dS m-1) 2.5
Concentration of watertable in (dS m-1) 5.5
Surface water allocation per farm (ML) 70 63 56 49 42 35 28 21 14 7 0
Groundwater allocation per farm (ML) 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
180
Modeling set up for Middle Rechna Doab: Groundwater only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 360
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.24
Price of groundwater ($ ML-1) 3.42
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.65
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.45
Concentration of groundwater (dS m-1) 2.5
Concentration of watertable in (dS m-1) 5.5
Surface water allocation per farm (ML) 0
Groundwater allocation per farm (ML) 70 63 56 49 42 35 28 21 14 7 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
181
Modeling set up for Middle Rechna Doab: Conjunctive use only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 360
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.24
Price of groundwater ($ ML-1) 3.42
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.65
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.45
Concentration of groundwater (dS m-1) 2.5
Concentration of watertable in (dS m-1) 5.5
Surface water allocation per farm (ML) 35 31.50 28.00 24.50 21.00 17.50 14.00 10.50 7.00 3.50 0
Groundwater allocation per farm (ML) 35 31.50 28.00 24.50 21.00 17.50 14.00 10.50 7.00 3.50 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
182
Modeling set up for Lower Rechna Doab: Surface water only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 211
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.30
Price of groundwater ($ ML-1) 3.30
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.75
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.75
Concentration of groundwater (dS m-1) 2
Concentration of watertable in (dS m-1) 7.5
Surface water allocation per farm (ML) 70 63 56 49 42 35 28 21 14 7 0
Groundwater allocation per farm (ML) 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling yes
Pumping yes
183
Modeling set up for Lower Rechna Doab: Groundwater only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Raifall (mm) 211
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.30
Price of groundwater ($ ML-1) 3.30
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.75
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.75
Concentration of groundwater (dS m-1) 2
Concentration of watertable in (dS m-1) 7.5
Surface water allocation per farm (ML) 0
Groundwater allocation per farm (ML) 70 63 56 49 42 35 28 21 14 7 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling 1
Pumping 2
184
Modeling set up for Lower Rechna Doab: Conjunctive use only
Base Run Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10
Rainfall (mm) 211
Allowable rise in groundwater level (m) 0.1
Allowable rise in salt concentration. (dS m-1) 2.25
Price of surface water ($ ML-1) 0.30
Price of groundwater ($ ML-1) 3.30
Area of farm (ha) 10
Leakage (mm year-1) 20
Initial depth to watertable (m) 1.75
Initial average root zone salinity (dS m-1) 1.5
Concentration of surface water (dS m-1) 0.75
Concentration of groundwater (dS m-1) 2
Concentration of watertable in (dS m-1) 7.5
Surface water allocation per farm (ML) 35 31.50 28.00 24.50 21.00 17.50 14.00 10.50 7.00 3.50 0
Groundwater allocation per farm (ML) 35 31.50 28.00 24.50 21.00 17.50 14.00 10.50 7.00 3.50 0
Concentration of rain water (dS m-1) 0.01
Rainfall recycling 1
Pumping 2
185
