Optimisation of Agricultural Water Use:
A Decision Support System for
the Gaza Strip
Von der Fakultät Bau-und Umweltingenieurwissenschaften der
Universität Stuttgart zur Erlangung der Würde eines DoktorIngenieurs (Dr.-Ing) genehmigte Abhandlung
Von
Omar Khalil Ouda
aus Palästina
Hauptberichter: Prof. Dr.-Ing. Dr. rer. nat. András Bárdossy
Mitberichter: Prof. Dr.sc.agr. Stephan Dabbert
Tag der mündlichen Prüfung: 27.10.2003
Institut für Wasserbau der Universität Stuttgart
2003
Optimisation of Agricultural Water Use: A Decision Support
System for the Gaza Strip
Cip - Titelaufnahme der Deutschen Bibliothek
Ouda, Omar:
Optimisation of Agricultural Water Use: A Decision Support System for
the Gaza Strip-/von Omar Ouda. Institut für Wasserbau, Universität
Stuttgart.- Stuttgart: Ins. Für Wasserbau, 2003
(Mitteilungen/ Institut für Wasserbau, Universität Stuttgart: H. 125)
Zugl.: Stuttgart, Uni., Diss., 2003
ISBN 3-933761-28-X
Gegen Vervielfältigung und Übersetzung bestehen keine Einwände, es
wird lediglich um Quellenangabe gebeten
Herausgegeben 2003 vom Eigenverlag des Instituts für Wasserbau
Druck: Sprint-Druck, Stuttgart
Preface
In many regions of the world water scarcity is becoming a severe problem. The
limited amount of available water and the fast increase of the population leads to
more and more frequent water shortages. Only careful planning and management
can help to reduce the severity of this problem.
In this work Mr. Omar Ouda investigated the water management problems in a
politically highly interesting area – the Gaza Strip. Agriculture is one of the major
water consumers in this densely populated area. Unconventional water uses, such as
the use of treated wastewater for irrigation have to be considered to improve the
water situation. Mr. Omar Ouda developed a multiobjective model for the optimization
of agricultural water use. His model finds optimal crop patterns considering
economical and environmental goals and constraints. The work shows that
agricultural water use can be reduced substantially without any severe economical
and environmental consequences.
The stay of Mr. Omar Ouda in Germany was supported by the DAAD – we gratefully
acknowledge this support. He participated in the newly established PhD program of
the University of Stuttgart ENWAT (Environment Water) and is the first candidate
who successful finished his work.
Stuttgart, Nov. 4, 2003
Prof. Dr.-Ing. András Bárdossy
Dedicated to:
My beloved wife Dalia
My daughters Basma and Miar
Acknowledgements
I wish to express a deep and sincere gratitude to my advisors, Professor András Bárdossy and
Professor Stephan Dabbert. I highly appreciate Professor Bárdossy's guidance, support and
valuable advices throughout my research period and the inspiring discussions I had with
Professor Dabbert whenever we met.
I would like to thank the German Academic Exchange Service (DAAD) for granting me the
financial support to complete my research.
Special thanks are due to Dr. Erwin Zehe, Yeshewatesfa Hundecha, and Fridjof Schmidt, who
offered me help and support, whenever I was in need of it. I want to thank all colleagues at
the Institute for Hydraulic Engineering for the perfect atmosphere, which I have enjoyed
during my stay at the institute.
My deep appreciation goes to the different Palestinian ministries and institutions for their cooperation and support throughout the research period.
Finally, I would like to express my deep respect and appreciation to my parents, brothers,
sister and relatives for their support and encouragement. But most of all, special thanks are
due to my wife, Dalia for her help, support and patience, not only during my PhD study, but
also throughout our lives.
Stuttgart, October 2003
Omar Ouda
Table of Contents
TABLE OF CONTENTS
TABLE OF CONTENT
i
LIST OF TABLES
vii
LIST OF FIGURES
ix
LIST OF ABBREVIATIONS
xi
ABSTRACT
xiii
1 INTRODUCTION AND RESEARCH METHDOLOGY
1
1.1 Introduction
1
1.2 Study objective
2
1.3 Methodology
3
1.3.1 Data requirements and formulation of IMDSUT database
5
1.3.1.1 Socio-economic information
5
1.3.1.2 Environmental and biophysical information
5
1.3.2 Formulation of multiobjective model
6
1.3.3 Decision-making algorithm
7
1.4 Organisation of the study
7
2 WATER RESOUCES MANAGEMENT IN ARID AND SEMI ARID AREAS
8
2.1 Introduction
8
2.2 Water shortage as a global problem
8
2.3 Regional water resources management activities
9
2.3.1 Egypt water resources management activities
9
2.3.2 Jordan water resources management activities
10
2.3.3 Isreal water resources management activities
12
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2.4 Water resources management activities in the Gaza Strip
13
2.4.1 Water balance in the Gaza Strip
13
2.4.2 Palestinian water resources management policy
14
2.4.3 Major water resources projects
15
2.4.4 Gaza water resources in the literature
16
2.5 Water for crops
17
25.1 Crop water requirement
18
25.2 Irrigation techniques
19
3 MULTIOBJECTIVE MODELLING
21
3.1 Introduction
21
3.2 History of multiobjective modelling
21
3.3 Mathematical programming definitions
22
3.4 Single vs. Multiobjective optimisation
22
3.5 Multiobjective model formulation methods
23
3.5.1 Weighting method
23
3.5.2 Constraint method
24
3.6 Multiobjective modelling applications
24
3.6.1 Multiobjective modelling for water resources management and planning
24
3.6.2 Multiobjective modelling for agricultural water use planning
27
4 STUDY AREA AND DATABASE
30
4.1 Study area
30
4.1.1 Location
30
4.1.2 Historical view
30
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4.1.3 Administration
31
4.1.4 Demography
32
4.1.5 Climate
32
4.1.6 Water resources
33
4.1.6.1 Surface water
33
4.1.6.2 Groundwater
34
4.1.7 Water demand
35
4.1.8 Agricultural sector
36
4.1.9 Soil
37
4.1.10 Economic situation
38
4.1.11 Land ownership
39
4.1.12 Land use
39
4.2 Database formulation
39
4.2.1 Socio-economic database
40
4.2.1.1 Allocation of target crop types
40
4.2.1.2 Prediction of available treated wastewater
40
4.2.1.3 Prediction of local crops product demand
42
4.2.1.4 Crops return value
43
4.2.1.5 Crops cultivation cost
43
4.2.1.6 Available agriculture area and maximum area
43
4.2.1.7 Level of farmer's acceptance for treated wastewater use
44
4.2.2 Biophysical database
45
4.3 Soil-Water-Atmosphere and Plant model (SWAP2.0)
4.3.1 Sub-models and routines
46
4.3.1.1 Soil water flow sub-model
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iii
46
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4.3.1.2 Soil heat flow sub-model
47
4.3.1.3 Solute transport sub-model
48
4.3.1.4 Irrigation and drainage
48
4.3.1.5 Simple crop model
48
4.3.2 Model structure
49
4.3.3 Application methodology
51
4.3.4 Model results
53
4.4 Conclusion
55
5 MULTIOBJECTIVE OPTIMISATION MODEL
56
5.1 Introduction
56
5.2 Multiobjective model conceptual framework
56
5.3 Programming language
60
5.4 Mathematical formulation of the multiobjective optimisation model
60
5.4.1 Single objective models objective functions
60
5.4.1.1 Maximisation of net profit
60
5.4.1.2 Maximisation of water use effectiveness
61
5.4.1.3 Maximisation of irrigated treated wastewater quantity
62
5.4.1.4 Minimisation of groundwater quantity
62
5.4.1.5 Minimisation of salinity load
63
5.4.2 Constraints for single and multiobjective models
63
5.4.2.1 Available agriculture area
64
5.4.2.2 To not exceed available treated wastewater quantity
64
5.4.2.3 To not exceed available groundwater quantity
64
5.4.2.4 To not exceed allocated maximum area for each crop
65
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5.4.2.5 To satisfy crops product local demand
65
5.4.2.6 To not allocate area for treated wastewater use in each zone more
than the level of farmer's acceptance to irrigate by treated wastewater in
this zone
66
5.4.3 Objective function of the multiobjective model
66
5.4.4 Multiobjective model decision parameter constraints formulations
67
5.4.4.1 Maximum allowable use of groundwater
68
5.4.4.2 Maximum allowable use of treated wastewater
68
5.4.4.3 Expected changes in farmers acceptance
69
5.4.4.4 Percentage coverage of crop products local demand
69
5.4.4.5 Minimum water use effectiveness
70
5.4.4.6 Maximum allowable salinity load
71
5.4.4.7 Spatial equity in access to profit
71
5.4.4.8 Spatial equity in access to groundwater
72
5.4.4.9 Spatial equity in access to treated wastewater
73
5.5 Multiobjective optimisation algorithm evaluation
74
6 INTEGRATED DECISION SUPPORT TOOL ( IMDSUT) PREFORMANCE
ANALYSIS
79
6.1 Evaluation of the decision parameters
79
6.1.1 Weights for the objectives
79
6.1.2 Allocation of maximum quantity for groundwater, treated wastewater, and
salinity load
82
6.1.2.1 Allocation of maximum groundwater quantity
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6.1.3 Spatial equity in right of access to profit, groundwater, and treated
wastewater resources
85
6.1.4 Percentage coverage of local products demand
86
6.1.5 Changes of farmer's acceptance to use treated wastewater
87
6.2 Formulation of scenarios
88
6.2.1 Analysis of scenarios results
90
6.3 IMDSUT sensitivity to changes crop return values
94
7 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
97
7.1 Summary
97
7.2 Conclusions
100
7.2.1 IMDSUT sensitivity analysis
101
7.3 Recommendations
102
LIST OF REFERENCES
104
APPENDIX (I) Database and Multiobjective model
109
APPENDIX (II) Decision support charts
127
APPENDIX (III) Scenarios
136
APPENDIX (IV) CURRICULUM VITAE
150
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List of Tables
LIST OF TABLES
Table
Title
Page
Table (2.1):
Estimated water balance in the Gaza Strip
14
Table (4.1):
Mean municipal and industrial water quality in the Gaza Strip
35
Table (4.2):
Soil types texture in the Gaza Strip
38
Table (4.3):
The contributions of different economic sectors to Gaza GDP
38
Table (4.4):
The Gaza Strip land ownership distribution
39
Table (4.5):
The Gaza Strip land use distribution
39
Table (4.6):
Wastewater treatment plants characteristics in the Gaza Strip
41
Table (4.7):
Treated wastewater quantity generated in each sub-regional area for
the year 2025
Table (4.8):
42
Estimated available treated wastewater in each sub-regional zone
for the year 2025
42
Table (4.9):
Gaza crops area, demand, cultivation costs, and returns values
44
Table (4.10):
Percentage of farmer's who accepted to use treated wastewater for
irrigation in each zone
45
Table (5.1):
Decision parameters for the multiobjective mode
75
Table (5.2):
Main outputs of the multiobjective model, the five single objective
models and the existing crop pattern
Table (5.3):
Table (6.1):
76
Differences in the main outputs of the five single objective models and the
exist crop pattern in percentage of the multiobjective model solution
76
Decision parameter values for the proposed five development
90
scenarios
Table (6.2):
Main outputs of the different proposed development scenario and
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List of Tables
Table (6.3):
the existing crop pattern
91
Level of similarity between the crop patterns resulted scenarios
94
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List of Figures and Maps
LIST OF FIGURES AND MAPS
Figure
Fig (1.1):
Title
Page
Structure of the integrated multiobjective decision support system tool
(IMDSUT)
4
Fig (4.1):
The Gaza Strip population projection
32
Fig (4.2):
Spatial rainfall distribution in the Gaza Strip
33
Fig (4.3):
Water demand projection in the Gaza Strip
36
Fig (4.4):
Projected water shortage in the Gaza Strip
36
Fig (4.5):
Main structure of Swap 2.0
50
Fig (4.6):
SWAP model application methodology
52
Fig (4.7):
Water demand of Eggplant and Valencia for wet and dry conditions in
each sub-regional zone
Fig (4 8):
54
Simulated yields for Eggplant and Valencia for wet and dry
meteorological conditions in each sub-regional zone
54
Fig (4 9):
Water demand for different crops in two sub-regional zones
55
Fig (5.1):
Conceptual formulation of the multiobjective optimisation model
59
Fig (5.2):
Standardised comparison of the performance of multiobjective model
and maximisation of profit, maximisation of water use effectiveness,
and minimisation of groundwater single objective models
Fig (5.3):
76
Standardised comparison of the performance of multiobjective model
and minimisation of salinity load, maximisation of wastewater use
single objectives models and existing crop pattern
77
Fig (5.4):
Optimum crop pattern based on multiobjective model
77
Fig (5.5):
Exiting crop pattern
78
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List of Figures and Maps
Fig (6.1):
Decision support chart for groundwater weight factor showing its
influence to the different socio-economical and environmental aspect
of agriculture water use
Fig (6.2):
81
Decision support chart for profit weight factor showing its influence to
the different socio-economical and environmental aspect of agriculture
water use
Fig (6.3):
81
Decision support chart for allocation of maximum groundwater under
dry year conditions
83
Fig (6.4):
Groundwater marginal value
84
Fig (6.5):
Decision support chart for allocation of spatial equity of right of access
to profit
Fig (6.6):
86
Decision support chart for allocation of percentage coverage of crop
product local demands
Fig (6.7):
87
Decision support chart for changes to the farmers acceptance decision
variable under dry year condition
Fig (6.8):
88
Main outputs of the different proposed development scenarios and the
existing crop pattern
92
Fig (6.9):
Crop pattern for the economy scenario
93
Fig (6.10):
Influence of changes in crops return values at agriculture system profit
96
Fig (6.11):
Influence of changes in crops return values at resulted crop patterns
96
LIST OF MAPS
Map
Title
Page
Map (4.1):
The Gaza Strip location
30
Map (4.2):
The Gaza Strip soil type
37
Map (4.3):
The Gaza Strip sub-regional zones
51
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List of Abbreviations
LIST OF ABBREVIATIONS
Ave
Average
CAMP
Coastal Aquifer Management Project
DOP
Palestinian- Israeli Declaration of Principles
FAO
Food and Agriculture Organisation of the United Nations
FIGP
Fuzzy Integer Goal Programming
GDP
Gross Domestic Product
IGP
Integer Goal Programming
IMDSUT
Integrated Multiobjective Decision Support System Tool
M
Million
Max
Maximum
MEff
Maximum Water Use Effectiveness
MGW
Minimum Groundwater Quantity
Min
Minimum
MOA
Ministry of Agriculture
MOPIC
Ministry of Panning and International Co-operation
MR
Maximum Reuse Quantity
MSL
Minimum Salinity Load
MTP
Maximum Net Profit
NDI
National Disposable Income
PA
Palestinian Authority
PCPS
Palestinian Central Bureau of Statistics
PLO
Palestinians Liberation Organisation
PWA
Palestinian Water Authority
SDP
Stochastic Dynamic Programming
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List of Abbreviations
SWAP
Soil-Water-Atmosphere and Plant Model
UN
United Nations
US$
United States Dollar
WHO
World Health Organisation of the United Nations
AMPL
A Modelling Language For Mathematical Programming
GAPS
General Atmosphere- Plant -Soil Simulator
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Abstract
ABSTRACT
Water scarcity is a problem in arid and semi-arid areas.
Water resource management
strategies implemented in these areas have a supply oriented measures. These measures result in an
overuse of the natural water resources and highly affect the water availability for future generations.
The Gaza Strip, as the study area, is located in an arid to semi-arid region. This area faces a
complicated water shortage problem, whereby the existing water demand exceeds the supply
capacity of natural water resources. This problem is expected to increase rapidly due to high
population growth and the potential economical development. Presently, irrigated agriculture is the
largest water consumer in the Gaza Strip. However, management of agriculture water use has
received little attention from the parties concerned. Therefore, there is a need for research towards
enhancing the effectiveness of agricultural water use. This will contribute highly to alleviating the
water shortage problem in arid and semi-arid areas generally and in Gaza Strip specifically.
The agricultural water system situation is complicated and inter-related, whilst at the same
time different socio-economic, biophysical, and environmental aspects control the water use
effectiveness. Multiobjective planning offers the possibility to integrate all these aspects.
An Integrated Multiobjective Decision Support System Tool (IMDSUT) has been
formulated. This tool has the capacity to optimise the agricultural water use on a regional scale
under consideration of the different agricultural water use controlling aspects. IMDSUT consists of
four main parts: an intensive database, a Soil-Water-Atmosphere and Plant model (SWAP 2.0), a
multiobjective optimisation model, and a decision-making algorithm.
The multiobjective optimisation model aimed to allocate an optimum crop pattern in each
sub-regional zone that satisfies the model constraints and decision parameter constraints and
optimises the performance of the objective function for both wet and dry meteorological conditions
had been formulated. The allocated crop pattern gives the optimum compromising values for five
contradicting objectives namely, to maximise the net profit, to maximise water use effectiveness
Optimisation of Agricultural Water Use
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Abstract
US$/m3, to maximise irrigated quantity of treated wastewater, to minimise the irrigated
groundwater quantity, and to minimise salinity load.
IMDSUT has been applied to the study area. The model results have been compared to the
existing crop pattern and with the results of other five single objective models. The model showed
advantages over the other single objective models and the existing crop pattern.
More insight into the model was gained by the development of five potential scenarios.
Each scenario presented a possible set of priorities for the decision-makers. The model proved its
ability to achieve each scenario goal and to consider singularly the different combination of
decision parameters. Out of this, the model could be successfully used for the optimisation of
agricultural water use in arid and semi-arid areas.
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Abstract
Zusammenfassung
Problembeschreibung
Die Wasserknappheit wird ein immer dringlicheres Problem in den ariden und semiariden Gebieten
der Erde. Der Wasserbedarf in der Landwirtschaft übersteigt die Kapazitäten der Wasserversorgung
trotz nachhaltiger Bewirtschaftung der Wasserreserven bei weitem. Der landwirtschaftliche Sektor
nimmt
in diesen Gebieten meist
einen Anteil von 90%
ein.
Die Strategie der
Wasserbewirtschaftung ist hier bei meist am Wasserdargebot orientiert. Dieser Ansatz führt jedoch
häufig zu einer Überbeanspruchung der natürlichen Wasserressourcen und gefährdet in hohem
Maße das Wasserdargebot für zukünftige Generationen.
Dieses Problem soll anhand des Gaza-Streifens, der in einer Region mit aridem bis semiaridem
Klima liegt, näher untersucht werden. Das Gebiet leidet unter akuter Wasserknappheit, wobei
jährlich etwa 20 Mio. m3 Wasser fehlen. Es wird befürchtet, dass dieses Defizit weiter zunehmen
wird, vor allem bedingt durch das hohe Bevölkerungswachstum der Region von jährlich etwa 3,2
%. Das Grundwasser ist hier die einzige natürliche Wasserressource.
Die Bewässerung
landwirtschaftlicher Flächen ist mit einem Anteil von 65% der derzeit größte Wasserverbraucher im
Gaza-Streifen. Bisher wurde die landwirtschaftliche Bewässerung von den Beteiligten jedoch nur
wenig beachtet. Dabei treten vor allem folgende Probleme auf:
Der Wasserpreis ist mit rund 0,40 US$/m3 sehr niedrig im Vergleich zu den
Opportunitätskosten von 1,0 US$/m3 (Kosten der Entsalzung).
Dies steht in direktem
Widerspruch zu den Dubliner Prinzipien (Nr.4) von 1992. Darin heißt es: “Wasser hat einen
ökonomischen Wert in all seinen konkurrierenden Nutzungen und sollte als ein Wirtschaftsgut
betrachtet werden.“
Die Art der angebauten Kulturen wird hauptsächlich durch die Interessen der Landwirte
bestimmt, d.h.
ohne jegliche planerische Hilfen.
Diese Art der landwirtschaftlichen
Bewässerung hat negative sozioökonomische und ökologische Folgen.
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Eine weitere Wasserressource, die im Untersuchungsgebiet verfügbar wäre, ist das behandelte
Abwasser . Es wurde im Gaza-Streifen jedoch seither noch nie zur Bewässerung verwendet.
Zusammenfassend lässt sich sagen, dass durch eine Verbesserung der Effektivität der
landwirtschaftlichen Bewässerung auf Grundlage integrierter Prinzipien der Wasserwirtschaft stark
dazu beigetragen könnte, die Wasserknappheit zu lindern. Dies gilt generell für aride und semiaride
Gebiete und insbesondere für den Gaza-Streifen.
Die landwirtschaftliche Wassernutzung ist von Natur aus kompliziert und steht mit anderen
Belangen in einer Wechselbeziehung, wobei verschiedene sozioökonomische und ökologische
Aspekte auf den Wert des Wassers in diesem Sektor einwirken.
Die mehrdimensionale
Planungsmodellierung bietet nun eine Möglichkeit, sämtliche Aspekte auf objektive Art und Weise
zu integrieren.
Untersuchungsziel
Die allgemeine Zielsetzung dieser Arbeit ist es, ein integriertes Planungsinstrument zur
Entscheidungsfindung zu entwickeln, das auf mehrdimensionalen Optimierungsmethoden basiert
und die Möglichkeit bietet, die Wassernutzung in der Landwirtschaft in ariden und semiariden
Gebieten zu verbessern. Durch dieses Planungsinstrument soll folgendes berücksichtigt werden
können:
-
Eine optimale Verteilung der Anbaukulturen, welche den besten Kompromiss zwischen den
folgenden fünf gegensätzlichen Zielen liefert: maximaler Netto-Ertrag, maximaler Wasserpreis
in US$/m3, maximale Bewässerung durch behandeltes Abwasser, minimale Nutzung von
Grundwasser für die Bewässerung und minimaler Salzeintrag in die Ackerflächen.
-
Sozioökonomische und ökologische Auswirkungen der landwirtschaftlichen Wassernutzung.
-
Die räumliche und zeitliche Variabilität des Wasserbedarfs und des Ernteertrags der
Anbaupflanzen.
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Abstract
-
Biophysikalische und meteorologische Schwankungen im Untersuchungsgebiet. Der Einbezug
der Entscheidungsträger in den Planungsprozess, umgesetzt durch die Möglichkeit, die
einzelnen Zielsetzungen zu gewichten und Zielwerte für eine Reihe von Entscheidungsvariablen
vorzugeben. Die Entscheidungsparameter beinhalten dabei die Aspekte, welche den größten
Einfluss auf die sozioökonomischen und ökologischen Belange der landwirtschaftlichen
Bewässerung haben.
-
Die Darstellung von Konflikten zwischen den verschiedenen Zielsetzungen.
Untersuchungsmethodik
Um die vorgestellte Zielsetzung zu verwirklichen, wurde ein integriertes, mehrdimensionales
Planungsinstrument zur Entscheidungsfindung (IMDSUT) formuliert.
Das Simulationsmodell
IMDSUT besteht aus vier Teilen, wie aus Abbildung (I) ersichtlich ist: eine umfassende Datenbank,
dem Modell für Boden, Wasser, Atmosphäre und Pflanzen (SWAP 2.0), dem mehrdimensionalen
Optimierungsmodell und einem Algorithmus zur Entscheidungsfindung.
Die IMDSUT-Datenbank wiederum besteht aus zwei Teilen: der sozioökonomischen Datenbank
und der ökologischen und biophysikalischen Datenbank. Die sozioökonomische Datenbank enthält
lokale Informationen, die die verschiedenen Einflussfaktoren auf dem Agrarsektor – wie z.B. den
Ernteerlös oder die lokale Produktnachfrage – beschreiben. Diese Informationen wurden bis zum
Planungsziel – dem Jahr 2025 – vorhergesagt, was jedoch einen hohen Unsicherheitsfaktor mit sich
bringt. Um diese Unsicherheit zu reduzieren, wurden verschiedene Maßnahmen getroffen. Das
SWAP-Modell für Boden, Wasser, Atmosphäre und Pflanzen wurde verwendet, um die
ökologischen und biophysikalischen Parameter abzuschätzen.
Diese Datenbank beinhaltet
Informationen über den Wasserbedarf der 28 verschiedenen Anbaupflanzen, deren Ernteertrag und
den Salzeintrag in das Ackerland als Folge der Bewässerung.
Die Anbaufläche dieser
Anbaupflanzen deckt dabei rund 90% der bewässerten Anbaufläche im Gaza-Streifen ab.
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Abstract
Meteorologische Daten
Bodenkennwerte
Modell für Boden
Wasser, Atmosphäre
und Pflanzen (SWAP)
Eigenschaften
der Anbaupflanzen
Ernteertrag
Salzeintrag
Wasserqualität
Bewässerungsbedarf
5 Modelle zur
Optimierung
einzelner Zielwerte
Optima einzelner
Zielwerte
Mehrdimensionales
Optimierungsmodell
Anbaufläche
Sozioökonomische Faktoren
Tabellen zur Entscheidungshilfe
Entscheidungsträger
Zielwerte für die Entscheidugsparameter
und Gewichtungen für die Zielsetzungen
SWAP 2.0
Mehrdimensionales
Optimierungsmodell
Optimale Verteilungen der
Anbaukulturen
Datenbank
Optimierungsmodell
Entscheidungsfindung
Abbildung (I): Ablaufschema des Integrierten mehrdimensionalen Planungsinstrumentes zur
Entscheidungsfindung (IMDSUT), bestehend aus vier integrierten Teilschritten: einer umfassenden
Datenbank, dem Modell für Boden, Wasser, Atmosphäre und Pflanzen (SWAP 2.0), dem
mehrdimensionalen Optimierungsmodell und einem Algorithmus zur Entscheidungsfindung.
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Decision Support System
Abstract
Um die räumliche Variabilität der Bodenkennwerte und Regenintensitäten zu berücksichtigen,
wurde das Untersuchungsgebiet in 16 Unterzonen eingeteilt.
Das SWAP-Modell wurde auf die 28 Anbaupflanzen in den einzelnen Zonen angewendet. Die
Resultate der Modellrechnung wurden mit existierenden Literaturwerten verglichen und von den
zuständigen Behörden der palästinensischen Gebiete bewertet.
Das mehrdimensionale Optimierungsmodell zielt darauf ab, eine Verteilung der Anbaukulturen
zu bestimmen, die den bestmöglichen Kompromiss für die zuvor genannten fünf Zielsetzungen auf
regionaler Ebene darstellt.
Der herkömmliche Ansatz, eine mehrdimensionale Zielwertfunktion zu formulieren, besteht darin,
einen Hauptzielwert zu benennen und die anderen Zielwerte als Randbedingungen anzusetzen.
Dieser Ansatz bringt jedoch Probleme verschiedenster Art mit sich. Aus diesem Grund wurde die
Zielfunktion des IMDSUT basierend auf der Normalisierungsmethode formuliert.
Die
verschiedenen Zielwerte werden dabei normalisiert, indem sie auf das jeweilige Optimum, welches
mit Hilfe der Modelle zur Optimierung einzelner Zielwerte bestimmt wurde, bezogen werden.
Hieraus resultiert eine Zielwertfunktion, die alle fünf gegensätzlichen Zielwerte beinhaltet. Um
eine Einbindung der Betroffenen in den Vorgang der Entscheidungsfindung zu ermöglichen, wurde
ein spezieller Algorithmus entwickelt.
Entscheidungsträger, den Tabellen zur
Optimierungsmodells.
mehrdimensionale
Er basiert auf einer Integration der Interessen der
Entscheidungshilfe und
des mehrdimensionalen
Die Tabellen zur Entscheidungshilfe wurden erstellt, indem das
Modell
sowohl
mit
variierten
Randbedingungen
Entscheidungsparameter, als auch auf unterschiedliche zugehörige
angewendet wurde.
für
die
Gewichtungsfaktoren
Die Aufgabe der Entscheidungsträger besteht darin, Zielwerte für die
Parameter festzulegen und Gewichtungen für die verschiedenen Zielsetzungen zu definieren.
Optimisation of Agricultural Water Use
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Decision Support System
Abstract
Ergebnisse und Diskussion
Das aufgestellte Modell IMDSUT wurde auf das Untersuchungsgebiet angewendet.
Die
Ergebnisse wurden mit der bestehenden Verteilung der Anbaukulturen sowie fünf Modellen zur
Optimierung einzelner Zielwerte verglichen. Dabei wiesen die Modellergebnisse deutliche Vorteile
gegenüber der bestehenden Verteilung der Anbaukulturen auf.
Es wurden Tabellen zur
Entscheidungshilfe für die Randbedingungen der einzelnen Entscheidungsparameter erstellt und
analysiert. Die Erstellung der Tabellen ermöglichte die Bewertung der Leistung des Modells und
die Abschätzung der Empfindlichkeit im Bezug auf die einzelnen Parameter.
Zum besseren
Verständnis des Modells wurden fünf potentielle Entwicklungsszenarien erstellt. Jede Variante
umfasst eine mögliche Prioritätenabfolge seitens der Entscheidungsträger. Die Formulierung dieser
Varianten zielt darauf ab, die Empfindlichkeit des Modells besser zu verstehen und in der Lage zu
sein, verschiedene Kombinationen von Randbedingungen der Entscheidungsparameter zu
betrachten. Die wichtigsten Ergebnisse der fünf Szenarien sind in Abbildung (II) dargestellt.
bestehende Situation
Szenario
M aximum freedom
Umwelt
Grundwasser
Abwasser
Wirtschaftlichkeit
0
10
20
30
40
50
60
70
80
90
100
M illionen
Grundwasser [Mm³]
Abwasser [Mm³]
Wassernutz [Mm³]
Salzgehalt [Mkg]
Profit [M.US$]
Abbildung (II): Darstellung der Ergebnisse für die untersuchten Entwicklungsszenarien im Vergleich zur
bestehenden Verteilung der Anbaukulturen.
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Decision Support System
Abstract
Folgerungen
Anhand der vorliegenden Studie kann folgendes geschlossen werden:
Die IMDSUT Datenbank ist sehr umfangreich. Die Erstellung einer soliden Datenbank wurde
durch die begrenzte Verfügbarkeit
von Informationen und historischen Daten im
Untersuchungsgebiet und die große Anzahl an berücksichtigten Anbaupflanzen erschwert.
Hinzu kommen mögliche Unsicherheiten bzgl. der Richtigkeit der Informationen. Deshalb
wurden verschiedene Maßnahmen und Methoden ergriffen, um die Genauigkeit der
Informationen im Laufe der Erstellung der Datenbank zu erhöhen.
Die Verwendung des Modells SWAP 2.0 ermöglicht es, den Einfluss biophysikalischer
Schwankungen auf den Wasserbedarf und den Ertrag der Anbaupflanzen angemessen zu
berücksichtigen.
IMDSUT ermöglicht den Entscheidungsträgern, ihre Interessen einzubringen und eine
Reihenfolge für ihre Prioritäten festzulegen, indem sie Zielwerte für eine Vielzahl von
Entscheidungsvariablen festlegen. Das Planungsinstrument berücksichtigt sämtliche Vorgaben
und Interessen und entwickelt eine optimale Verteilung der Anbaukulturen, die diesen genügt.
IMDSUT zeigt erhebliche Vorteile gegenüber Modellen zur Optimierung einzelner Zielwerte
und gegenüber den bestehenden Verteilungen der Anbaukulturen.
Eingehende Analyse des IMDSUT
Die Belastbarkeit und das Leistungsverhalten des IMDSUT als Planungsinstrument zur
Entscheidungsfindung in der landwirtschaftlichen Wassernutzung wurden bewertet und analysiert.
Die
Empfindlichkeit
des
Planungsinstruments
gegenüber
Veränderungen
der
Entscheidungsparameter kann wie folgt beschrieben werden:
♦ IMDSUT besitzt eine sehr geringe Empfindlichkeit gegenüber sehr niedrigen oder sehr hohen
Gewichtungsfaktoren. Die Randbedingungen des Modells und die Kompromisse zwischen den
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Decision Support System
Abstract
verschiedenen Zielsetzungen haben dabei den größten Einfluss auf die Reduzierung der
Empfindlichkeit des Modells.
♦
Um der lokalen Nachfrage nach Anbaupflanzen zu genügen, sollten der landwirtschaftlichen
Bewässerung jährlich mindestens 14 Mio. m3 Grundwasser zugeteilt werden.
geringfügige Erhöhung dieser Menge führt zu erheblichem Profitzuwachs.
Schon eine
Eine weitere
Erhöhung jedoch ändert nicht viel an der Gesamtbilanz. Der resultierende Wasserpreis liegt
deutlich über den Opportunitätskosten, so dass es vernünftig erscheint, mehr Grundwasser für
die Bewässerung zur Verfügung zu stellen.
♦ Die räumliche Ausgeglichenheit hinsichtlich Profitstreben, Grundwasser und behandeltem
Abwasser bringt ökonomische und ökologische Kosten mit sich. Folglich ist es Aufgabe der
Entscheidungsträger, das erwünschte Niveau an Ausgeglichenheit festzulegen.
Gemäß der
Analyse des IMDSUT ist die Rentabilität des Agrarsektors bei einem hohen Niveau sehr
empfindlich, während es bei einem hohen Niveau an räumlicher Gleichverteilung hinsichtlich
Zugang zu Grundwasser und behandeltem Abwasser sehr viel unempfindlicher reagiert.
♦ Die
eigenständige
Deckung
des
Nahrungsmittelbedarfs
ist
unter
Fachleuten
der
Wasserwirtschaft eine umstrittene Strategie. Sie findet aber noch immer starken Zuspruch bei
den Entscheidungsträgern, was vor allem in der instabilen politischen Lage begründet ist. Sie
verursacht sowohl ökonomische, als auch ökologische Kosten. Die Modellergebnisse sprechen
eindeutig gegen diese Strategie.
♦ IMDSUT ist sehr empfindlich gegenüber Veränderungen der lokalen Nachfrage an
landwirtschaftlichen Produkten. Aus diesem Grund ist es von großer Bedeutung, die Nachfrage
möglichst genau abzuschätzen.
♦ Die Bereitschaft der Landwirte, behandeltes Abwasser zu verwenden, stellt einen sehr wichtigen
Gesichtspunkt dar. IMDSUT zeigt, dass eine Erhöhung der Akzeptanz die Ausgabewerte des
Modells nur geringfügig beeinflusst, eine Verringerung jedoch eine starke Beeinträchtigung der
Optimisation of Agricultural Water Use
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Decision Support System
Abstract
landwirtschaftlichen Produktion bewirkt.
Die Berücksichtigung dieser Parameter ist somit
unerlässlich.
♦ Die Empfindlichkeit des IMDSUT gegenüber Schwankungen beim Ernteertrag liegen im
annehmbaren Bereich.
Dies liegt darin begründet, dass der Ernteertrag auf zwei der fünf
Zielsetzungen (Profit, Wasserpreis) Einfluss nimmt, welche die Zielwertfunktion des IMDSUTModells beschreiben.
Empfehlungen
Anhand der Studie wird zu weiteren Verbesserung empfohlen:
Der landwirtschaftlichen Bewässerung sollte insbesondere in ariden und semiariden Regionen
mehr Aufmerksamkeit geschenkt werden.
Die in der Studie vorgeschlagene Methodik und das Planungsinstrument stellen einen
geeigneten Ausgangspunkt in Richtung einer effizienten landwirtschaftlichen Bewässerung dar.
Bei der Umsetzung des IMDSUT-Modells sollten folgende Empfehlungen unbedingt
berücksichtigt werden:
♦ Die Anwendung des vorgeschlagenen Planungsinstruments für die landwirtschaftliche
Bewässerung sollte in enger Zusammenarbeit der zuständigen Regierungsbehörden und der
betroffenen Sozial- und Landwirtschaftsverbände erfolgen, um die jeweiligen Interessen und
Prioritäten berücksichtigen zu können und somit zum optimalen Planungsergebnis zu gelangen.
♦ Eine kontinuierliche Überarbeitung und Prüfung der Inhalte der Datenbanken ist sehr zu
empfehlen.
Die Aktualisierung sollte im sozioökonomischen Bereich auf Grundlage der
statistischen Jahresdaten und im ökologischen und biophysikalischen Bereich erfolgen. Durch
die Überarbeitung könnte die Unsicherheit des Modells in hohem Maße verringert werden,
wodurch die Verlässlichkeit zunimmt.
Optimisation of Agricultural Water Use
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Decision Support System
Abstract
♦ Es wird empfohlen, den Ernteertrag jährlich zu überprüfen und das Modell im Falle von
Veränderungen erneut anzuwenden, damit die Verteilung der Anbaukulturen gemäß den
Modellergebnissen angepasst werden kann.
♦ Anzubauende Obstbäume sollten im ersten Jahr der Modellanwendung bestimmt werden. Für
andere Anbaupflanzen ist das Modell jedes Jahr direkt nach der Überarbeitung der Datenbanken
anzuwenden. Nach dem ersten Jahr sind spezielle Baumsorten beizubehalten, die Optimierung
wird nur auf die verbleibende Anbaufläche angewendet.
♦ Das räumliche Profitstreben sowie der Zugang zu Grundwasser und Abwasser sollte in hohem
Maße berücksichtigt werden. Auf diese Weise wird die Umsetzung des Entwicklungsplanes
erleichtert, da sie die Bereitschaft der Landwirte, den Plan zu akzeptieren, steigert.
♦ Den Landwirten müssen wirtschaftliche Anreize geboten werden, z.B. durch Ermöglichung des
Exports ihrer Produkte oder durch Reduzierung des Wasserpreises. Anreize dieser Art werden
die Akzeptanz des Planes seitens der Landwirte ebenfalls erhöhen.
♦ Die Erarbeitung einer Methode zur Festlegung eines Wasserpreises für die landwirtschaftliche
Bewässerung, der einen angemessenen Anteil für behandeltes Abwasser beinhaltet, wird die
Bedenken der Landwirte im Hinblick auf die Verwendung von behandeltem Abwasser
verringern.
♦ Die Verwendung von Wasserzählern ist unerlässlich und sollte unverzüglich verwirklicht
werden.
Optimisation of Agricultural Water Use
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Decision Support System
Chapter I
Introduction and Research Methodology
1. INTRODUCTION AND RESEARCH METHODOLOGY
1.1 Introduction
Water is a scarce natural resource in arid and semi-arid areas. High population growth, the
improperly managed expansion of irrigated agriculture, and the potential improvement in the
standard of living increases highly the water demand in these areas. Presently, about 70 percent of
water in the world and over 90 percent in low-income developing countries is used for irrigation
(Meinzen-Dick et al. 2001).
The Gaza Strip as study area faces a crucial water shortage problem. The present annual water
shortage is estimated to be about 20 Mm3. This shortage is expected to increase further due to the
high population growth of 3.2% per year and the economic growth. Groundwater is the unique
local water source in the Gaza Strip, where less than 10% of all groundwater has a water quality
that is suitable for domestic use based on WHO standards. This is due to its high salinity and
nitrate levels (CAMP, 2000). Presently the agricultural sector is the largest water consumer in the
Gaza Strip. It consumes about 65% of the total water supply. Inspite of this, very few attempts
have been made to improve the effectiveness of agricultural water use in the Gaza Strip. The
existing agricultural water system in the Gaza Strip has the following problems:
-
It has a very low water use effectiveness of about 0.4 US$/m3 in comparison with a water
opportunity cost of about 1.0 US$/m3 (Desalination cost). This contradicts completely the spirit
of the well known 1992 Dublin Principles, No. 4 , which states "Water has an economic value
in all it's competing uses and should be recognised as an economic good".
-
The crop pattern is mainly determined by farmers' prerogative without any planning. This
practice has negatively affected the socio-economic and environmental outcomes through
agricultural water use.
-
Treated wastewater has never been used for irrigation in the Gaza Strip; inspite of the fact that
treated wastewater could cover a substantial part of irrigation water demand.
Optimisation of Agricultural Water Use
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Chapter I
Introduction and Research Methodology
The problems concerning agricultural water use face most arid and semi-arid areas.
So it is
important to formulate a planning methodology that would be able to handle these problems.
Planning for agricultural water use has potential economic, social, environmental and political
short and long-term effects. The economic effect can be evaluated by the agricultural sector
contribution to the national economy. The social effect is identified by the farmer's average income
and by the affordability of agriculture products in the local markets. Fresh water demand, treated
wastewater demand and potential salinity load in the agriculture land are the main environmental
concerns, due to their long-term effects. Political decision-makers interests play a central role in
setting general planning objectives and attaching priorities to different controlling aspects of
agricultural water use.
Crop water requirements and crop yields are highly dependent on the biophysical variabilities
in soil properties, crop characteristics, and meteorological conditions.
This fact identifies the
biophysical extent of the agricultural water use planning.
This situation shows that planning for agricultural water use is complicated and
multidisciplinary in nature, having different mutual, and to some extent, naturally contradicting
objectives.
A robust planning tool should have the ability to consider the different potential
objectives and to evaluate the trade-offs among these objectives. The multiobjective optimisation
technique ought to be the most suitable approach to this purpose.
1.2 Study objective
The overall objective of this study is to formulate an "integrated decision support system tool"
based on the multiobjective optimisation technique. This tool should have the capacity to optimise
the agricultural water use on a regional scale for arid and semi-arid areas. The Gaza strip will be
considered as a study area. The tool should have the capacity to account for:
-
to allocate an optimum crop pattern that gives optimum compromise values for the following
competing five objectives on a regional scale: maximise the net profit, maximise water use
Optimisation of Agricultural Water Use
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Decision Support System
Chapter I
Introduction and Research Methodology
effectiveness (US$/m3), maximise irrigated quantity of treated wastewater, minimise the
supplied groundwater quantity, and minimise salinity load.
-
to show the trade-offs between the different objectives.
-
to consider the socio-economic and environmental aspects of agriculture water use.
-
to consider the spatial and temporal variabilities in crops water requirements and crops yield.
-
to give the decision-makers the possibility to contribute to the planning process by attaching a
preference to each objective and by allocating target values for a set of decision parameters.
These decision parameters include the aspects that most influence the socio-economic and
environmental impacts of agricultural water use. Farther the model should consider the local
meteorological and soil conditions.
1.3 Methodology
The well-known water shortage problem and its far-reaching socio-economic, environmental,
and health consequences in the study area, have been the main motivations for the author to start
with this study. At an early stage of this research, an intensive and comprehensive evaluation of all
previous studies, which have handled the water shortage problem in the Gaza Strip, had been made.
It has been found that inspite of the fact that irrigated agriculture consumed more than 65% of total
water consumption in the study area, it has gained little attention.
At this stage the general
objective had become clear, which was to investigate and to formulate a modern technique that is
able to optimise the agricultural water use in the study area, but how? To do so, an integrated
multiobjective decision support system tool (IMDSUT) has been designed.
The tool has the
capacity to optimise the agricultural water use on a regional scale while considering of the socioeconomic, environmental, and biophysical variabilities of agricultural water use.
IMDSUT
components and the interaction between them are presented in Fig (1.1). IMDSUT consists of four
parts: intensive database, soil water plant and atmosphere model (SWAP 2.0), multiobjective
optimisation model, and decision-making algorithm.
Optimisation of Agricultural Water Use
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Decision Support System
Chapter I
Introduction and Research Methodology
Daily
meteorological
data
Soil
characteristics
Soil, Water
Atmosphere, Plant
Model (SWAP)
Crop
characteristics
Crops yield
Salinity load
Water quality
Irrigation demand
5 Single Objective
Optimisation
models
Single objective
optimum values
Multiobjective
Optimisation model
Land
area
Socio-economic
data
Decision support charts
Decision
Makers
Target decision parameter values
and objectives weight factors
SWAP 2.0
Multiobjective
Optimisation model
Database
Optimisation
model
Decision-making
Optimum Crop Pattern
Fig (1.1): Structure of the integrated multiobjective decision support system tool (IMDSUT)
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Chapter I
Introduction and Research Methodology
1.3.1 Data requirements and formulation of IMDSUT database
Water resource management and planning requires a substantial amount of data from
different sectors. Such data is very crucial in order to formulate a robust and comprehensive water
resource decision support system tool. Furthermore, it is generally time dependent, so it requires a
continuous renewal. As a result, the formulation of a comprehensive database is the most important
and difficult part, since its accuracy will highly influence the model results.
A brief explanation of the two groups of this database, namely, socio-economic, and
environmental and biophysical is given below.
1.3.1.1 Socio-economic information
Socio-economic data is time dependent. To lessen the affects of time dependency, a long data
set is needed. For this study, there was only three years data sequence from 1997 to 2000 available,
mainly due to three decades of Israeli occupation. Based on these three years, the values for socioeconomic parameters up to the year 2025 were forecasted. The year 2025 has been allocated as
target year for planning. The selection of a long-range target year reflects the fact that crop pattern
alteration is a long-range process.
1.3.1.2 Environmental and biophysical information
The environmental and biophysical information part includes data that covers crops water
requirement, crops yield, and salinity load due to irrigation. To account for the biophysical and
meteorological variabilities in the study area, the Gaza Strip has been subdivided into 16 subregional zones based on soil type and the spatial distribution of rainfall intensity. Then a SoilWater-Atmosphere and Plant simulation model (SWAP 2.0) has been used to simulate crops water
requirement, crops relative yield, and salinity load due to irrigation for the different crops under dry
and wet meteorological conditions.
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Decision Support System
Chapter I
Introduction and Research Methodology
It is important to notice that, the SWAP model requires substantial amount of input data such as
daily meteorological data, soil hydraulic properties and crop characteristics.
This data was
collected from different resources (ex. van Dam et al. 1997, SYS ,1993, Allen et al. 1998,
Doorenbos et al. 1979, and Doorenbos et al. 1977), evaluated and finally used in the model
simulation.
1.3.2 Formulation of a multiobjective optimisation model
A multiobjective optimisation model that aims to determine the optimum crop pattern in each
sub-regional zone that satisfies the model constraints and decision parameters constraints and
optimises the performance of the objective function for both wet and dry meteorological conditions
has been formulated. The allocated crop pattern gives the optimum compromise values for the
following contradicting five objectives on a regional scale: maximise the net profit, maximise water
use effectiveness (US$/m3), maximise irrigated quantity of treated wastewater, minimise the
irrigated groundwater quantity, and minimise salinity load.
The objective function has been
formulated based on a normalised value technique.
The model also includes a set of decision parameter constraints. These decision parameter
constraints will allow the decision-makers to specify their interest. The decision-makers will also
have the possibility to attach a weight factor value for each objective. These weight factors will
show the importance of each objective from a decision-makers point of view. Five single objective
models have been used to allocate an optimum value for each objective. The optimum values from
these objectives have been used to formulate objective function of the multiobjective model.
1.3.3 Decision-making algorithm
To facilitate the decision-makers contribution and involvement in the decision making process
a decision-making algorithm has been created. It is based on the integration between decisionmakers interest, decision support charts and the multiobjective optimisation model as shown in Fig
Optimisation of Agricultural Water Use
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Decision Support System
Chapter I
Introduction and Research Methodology
(1.1). The decision support charts have been created through applying the multiobjective model
under different decision parameter values and under different attached weight factors for the model
objectives.
1.4 Organisation of the Study
The study is organised as follows.
Chapter 2: Presents a review of the water resources management in arid and semi-arid areas, where
more emphasis has been given to the Gaza Strip and its neighbouring countries.
Chapter 3: Presents a review of multiobjective modelling and previous multiobjective applications
in water resource management that forms the background of this study.
Chapter 4: The first part of this chapter includes a detailed description of the study area, while the
second part covers the IMDSUT database formulation methodology and main findings.
Chapter 5: Describes the formulated multiobjective model in detail.
Chapter 6: Here, IMDSUT capacity and performance as a decision support system tool for
agricultural water use planning has been evaluated and analysed. In the first part, an evaluation of
the different decision parameter has been made and decision support charts for each parameter has
been presented. In the second part, five potential development scenarios have been presented and
evaluated in order to gain more insight into the tool's capacity.
Chapter 7: This chapter summarises the problem, objectives, methodology, and main findings. The
remaining sections present the study conclusions and recommendations.
Optimisation of Agricultural Water Use
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Decision Support System
Chapter II
Water Resources Management in Arid and Semi-Arid areas
2. WATER RESOURCES MANAGEMENT IN ARID AND SEMI-ARID
AREAS
2.1 Introduction
Management of a water resources system must be responsive to the physical system itself, to
the socio-economic system that generates water demand, and to the political system that make
planning decisions. The physical system is characterised by hydrologic, biological, and chemical
complexities while socio-economic and political systems introduce those complexities that always
seem to arise when humans are involved (Cohon, 1978). Water resources management strategy can
be evaluated by its ability to integrate the different systems in a manner that optimises the outputs
of each system.
Water resources management strategy is a coherent combination of measures. Water resources
management measures can be supply oriented, demand oriented. Supply oriented measures are
mostly technical such as construction of reservoirs, and water supply networks. Demand oriented
measures can be grouped into four categories: Technical measures (that includes the previous
mentioned supply oriented measures, and measures aim to increase the system effectiveness from
technical perspectives), financial implementation incentives, legal regulations, and institutional
arrangements. In implementation, measures of different categories are often combined together
such as irrigation water metering, as a financial and technical incentive, with policy to protect the
meters, as a legal incentive (Mohamed, 2001).
2.2 Water shortage as a global problem
Many of the developing countries are facing water scarcity problems. Water scarcity may
highly limit the economical development of these areas. The global withdrawal of fresh water has
increased by more than a factor of four from 1940 to 1990. Increases in irrigation and to a lesser
extent industrial water use, have been the largest sources of this growing demand (World resources,
Optimisation of Agricultural Water Use
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Decision Support System
Chapter II
Water Resources Management in Arid and Semi-Arid areas
1996-1997). Over the past thirty years, the irrigated area has expanded by about 1.6% a year and it
is expected to increase by 0.6 % a year during the next thirty years. In 2000, about 69% of the
global fresh water resources were consumed by agriculture. In the Middle East and North Africa ,
31% of arable land was irrigated (FAO, 2003), and about 91% of the water withdrawal was directed
toward agriculture (FAO, 1997). The United Nations World Water Development Report reported
that the estimated water lost in irrigation is about 55% of total irrigation supply (UN, 2003). This
highlights the need to improve the water use effectiveness and to manage agricultural water use in a
modern and integrated approach. The following countries, which are mostly located in the Middle
East region withdraw more than 90% of their renewable water resources: West Bank and Gaza
Strip, Bahrain, Barbados, Egypt, Israel, Jordan, Kuwait, Libyan, Malta, Oman, Qatar, Saudi Arabia,
Turkmenistan, United Arab Emirates, Uzbekistan, Yemen (FAO, 2003). It is important to notice
that, most of these countries are located in politically unstable areas. As a result, the potential for
conflicts because of water is very high.
2.3 Regional water resources management activities
The previous paragraph presented the global extent of the water shortage problem. In this
part, more insight into the problem at regional scale will be presented. To do so, water resources
management activities in Egypt, Jordan, and Israel, which bounds Palestine, will be summarised.
2.3.1 Egypt water resources management activities
Egypt is located in the northeastern corner of Africa, with a total area of about 1 Mkm2. It is
bordered in the north by the Mediterranean sea, in the east by Gaza Strip, Israel and the Red Sea, in
the south by Sudan and in the west by Libya (FAO, 1997). Egypt's government has managed its
water resources since the early sixties through the construction of Aswan High Dam, which plays
the role of a reliable central reserve of water for the Egyptian economy. Since the beginning of this
scheme, the dam has prevented the disastrous consequences of a severe drought period as well as
Optimisation of Agricultural Water Use
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Decision Support System
Chapter II
Water Resources Management in Arid and Semi-Arid areas
potential large damages due to flooding (Langniß et al,1998). The irrigated agriculture has been
confined to be approximately 3.15% of the total land area. The most important area of all economic
activity is the river oasis of the Nile. Agriculture accounted for 17% of Egypt's GDP and provided
employment to 38% of the labour force. The Nile is the main water resource in Egypt. The Nile
accounts for 90% of the total renewable fresh water supply of about 62.5 Billion m3/year. The total
water demand was about 70 Billion m3/year for the year 2000. Agricultural demand accounts for
85% of the total need (FAO, 1997). Sustained crop pattern in the old lands of the Nile valley and
delta characterises by high water demand. Rice and sugarcane in 1992 were consuming about 2530% of the irrigation water supply. These crops seem profitable from the farmer's perspective, but
from a socio-economical perspective they are very expensive due to their high irrigation demand.
To improve this situation, different regulations have been implemented to change crop pattern,
upgrade irrigation system effectiveness, rationalise water consumption, and improve the drainage
networks to allow for better water recycling. These measures have resulted in improving
agricultural production and slowly shifted crop pattern towards less water consumption and high
quality crops grown on newly reclaimed land (Langniß O. et al. 1998).
2.3.2 Jordan water resources management activities
Jordan lies to the east of Jordan river and it is bordered in the north by Syria, in the north
east by Iraq, in the south by Saudi Arabia, in the far south west by the Gulf of Aqaba, and in the
west by West Bank and Israel. The existing cultivable land is estimated to be about 4.3% of total
area of the country (381740 ha). About 56% of the cultivable area was used for cultivation in 1992.
In the same year, agriculture activity accounted for 6% of the Jordan's GDP, and 12% of its export
earning, and 10% of its labour force (FAO, 1997).
Jordan's largest source for external surface water is the Yarmouk river on the Syrian border.
Originally, the annual flow of the Yarmouk River was estimated at about 400 Mm3 (of which Israel
withdraws about 100 Mm3).
Total flow is now much lower as a result of upstream Syrian
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development works, which were done in the1980's. The Yarmouk River accounts for 40% of
Jordan's surface water resource. It is the main source for the King Abdullah canal and is considered
to be the backbone of development in the Jordan valley. Other major water basins include the
riversides Wadis, Zarqa, Mujb, the Dead Sea, Hasa, and Wadi Araba. Interior Jordanian surface
water resource are estimated to about 400 Mm3 /year. Jordan's groundwater is distributed among 12
major basins. The safe yield renewable groundwater resource is estimated at 275 Mm3/year. Most
of it is presently exploited at maximum capacity or even beyond the safe yield threshold (FAO,
1997). Wastewater production was estimated at 232 Mm3/year in 1993 and the quantity of reused
treated wastewater reached 50 Mm3/year. The reuse of treated wastewater in Jordan is the highest
level in the world. The treated wastewater in the country is returned to the King Tall dam, where it
is mixed with the surface flow and used in the pressurised irrigation distribution system along the
Jordan valley. It is of importance to mention that reused wastewater is an essential element of
Jordan's water strategy (FAO, 1997). Total annual water demand was estimated at 984 Mm3 in
1993. Irrigation has been reported in Jordan for a long time, since 1958, However, the government
of Jordan has started to pay more attention to irrigation projects. The government decided to divert
part of the Yarmouk river water and constructed the East Ghor canal. In addition to canal and dam
construction, the government started to drill wells for irrigation purposes.
This situation has
allowed the development of irrigation over a large area. Surface water and groundwater resources
in Jordan have been extensively developed. The total quantity of reused treated wastewater is
expected to reach 237 Mm3 /year by 2020 (FAO, 1997). The government also plans to improve
water use effectiveness through conversion from surface to a pressurised irrigation network. The
demand management techniques have gained recently more attention in Jordan. In this direction,
Salameh published a study, which aimed to allocate an irrigation water-pricing system in the
Jordanian valley.
In this study, the authors have tried to devise a water tariff function that
accounted for environmental and socio-economical aspects of water use in agriculture (Salameh E.
et al. 2000).
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2.3.3 Israel water resources management activities
Very limited information is available about its water resources because it considers water as a
"matter of national security". Annual water resources in Israel have been estimated to be about
2,000 Mm3 and agricultural water use accounts approximately for 1,300 Mm3 (65%). Current
projections for water use in Israel until 2020 assume that treated wastewater will comprise an
increasing component of water use for agriculture up to 46% in 2020. The area of land irrigated
with treated wastewater is rising continuously. It increased from about 5,100 ha in the year 1975 to
36,300 ha in 1994. Currently, about one third of the effluents is treated at a territory level, and
about 50% by means of secondary or near secondary treatment. The rest is disposed of (Haruvy,
2000).
Despite irrigated agriculture is consumes about 65% of water demand in Israel, it
contributes only 2% of total Israeli GDP. The average Israeli per capita gross water consumption is
about 321m3/year, while the average Palestinian per capita consumption is 35 m3/year (Deconinck,
2002). In August 2001, in a cabinet meeting of the Israeli government, the long-term water policy
plan up to year 2020 has been approved. The main points of this water policy plan can be
summarised as follows:
- Maintain the long-term level per capita domestic consumption at 130 m3
- The plan estimated that the population of Israel in 2020 would be around 8.6 M capita. The
total water requirements to meet the per capita need will be about 1120 Mm3
- Another important assumption is the preservation of agricultural production in Israel. To
preserve the agriculture in 2020 at its present scale an amount of 530 Mm3 of high quality
water and about 630 Mm3 of treated wastewater would be required every year.
- Natural water resources can not meet the total demand. Therefore, there is a need to utilise
non-conventional water resources (seawater desalination, treated wastewater).
- The plan also mentions the Palestinian population in the occupied territories. The water
supply to the population of the Gaza Strip is presumed to be exempt from the Israeli
national system.
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- Finally the plan stressed the implementation of an integrated approach to manage the water
industry in Israel.
For more information about the plan, the reader is kindly referred to (Deconinck, 2002).
From the previous paragraphs, the following can be concluded.
-
All the three countries considered are facing a water shortage problem. This shows the regional
extent of the problem.
-
Despite high agricultural water consumption, the agriculture sector contributes a small
percentage to the total GDP of each country.
-
Water management activities in all countries are based on supply oriented measures and only
Egypt has made moves towards demand oriented measures especially in the new reclaimed
agriculture land.
-
Jordan and Israel are using treated wastewater for irrigation on large scale. Their experiences in
this field could present great technical and managerial benefits to the Palestinian water
management authorities.
2.4 Water resources management activities in the Gaza Strip
2.4.1 Water balance in the Gaza Strip
The present water demand in the Gaza Strip exceeds the sustainable supply of the local
groundwater aquifer. The water balance components are presented in table (2.1). The table shows
that the agriculture sector is the highest water consumer.
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Table (2.1): Estimated water balance in the Gaza Strip
Inflow [Mm3/year]
Rainfall recharge
Lateral inflow
Min.
40
20
Max.
45
35
Saltwater Intrusion
Water System leaks
Wastewater Return Flows
Other Recharge(1)
Irrigation Return Flows
Loss of Aquifer Storage
Totals
Net Balance
10
10
10.5
3.5
20
2.1
116
-25,9
15
15
10.5
3.5
25
3.2
152
-17,8
Outflow [Mm3/year]
Min.
Municipal Abstraction 47
80
Agricultural
abstraction
5
Mekroat Abstraction
10
Discharge to the sea
142
Max.
47
100
8
15
170
Source: (CAMP, 2000). 1= Includes recharge from WWTPs in Jabalia and Wadi Gaza
2.4.2 Palestinian water resources management policy
The Gaza Strip has been under Israeli occupation since 1967. During this period, the Gaza Strip
water sector has been completely ignored and the natural water resources have been completely
depleted.
In September 1993, the Palestinian Liberation Organisation (PLO) and the Israeli
government signed in Washington the declaration of principles on interim self-government
arrangement (Oslo agreement). This agreement has given the Palestinians the possibility to form a
national authority (Oslo, 1993).
By-law No. 2, 1996 concerning the establishment of the
Palestinian Water Authority (PWA), "PWA must participate in preparing and detailing of regional
water plans, and the supervision and inspection of individual water projects and the preparation of a
national water plan" (PWA, 2000). Based on this law, the PWA has prepared the national water
policy, which contains the following items:
-
Pursue Palestinian interests in connection with obtaining rights to water resources shared with
other countries
-
All sources of water are public property.
-
Water has a unique value for human survival and health and all citizens have the right of access
to water of good quality for personal consumption at costs they can afford.
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Water Resources Management in Arid and Semi-Arid areas
Water supply must be based on sustainable development of all available and feasible water
resources.
-
Industrial and agricultural development and investment must be mutually compatible and
optimally integrated with the available resources and based on sustainable development.
-
The development of Palestinian water resources must be co-ordinated on the national level and
carried out at appropriate local level.
-
One responsible body should carry out the national water sector management; responsibility for
policy regulation being separated from the service delivery functions.
-
Conservation and optimal utilisation of water resources is of particular importance.
-
Protection and pollution control of water resources should be ensured. "The polluter pays"
principle will be enforced in order to guarantee environmental protection.
-
The government will co-operate with regional and extra-regional parties in programmes and
projects in order to promote the optimum utilisation of water resources, to identify and develop
new and additional supplies and to collect and share relevant information and data.
Significantly, water has been given in the policy an economical, environmental and social value.
The policy presents a general legal and managerial framework for water management activities in
Palestine.
2.4.3 Major water resources projects
This complicated water shortage problem in the Gaza Strip has attracted many internationally
funded projects in the last few years. These projects aimed mostly to solve the problem through
improving the deteriorated infrastructure. The largest projects were:
-
Coastal Aquifer Management Project (CAMP) funded by United State of America.
-
Master plan for sewerage and storm water drainage in the Gaza Governorates funded by France.
-
Sewerage development plan in the area of Khan Yunis funded by Japan.
-
Water and sewage project in Northern Gaza funded by Sweden.
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The most relevant project to this study is the CAMP project. CAMP was the largest water
resources management project that has been ever implemented in the Gaza Strip. The total project
budget was about 20 MUS$. It has started in the year 1999, due to the second Intifada it has been
stopped in the year 2001. The analytical part of the project was completed by May 2000. The
principle task of the project was to prepare an integrated aquifer management plan, whose
implementation will provide adequate water supply for the Gaza Strip and sustain the aquifer for the
future (CAMP, 2000). The overall goal of the project can be subdivided into three goals
-
Sustain the aquifer by reducing pumping and augmenting water in circulation.
-
Supply potable water to an increasing population by supplemental supply, treatment and
distribution.
-
Provide alternative water supply for irrigation to sustain the agriculture sector.
The project handled the agricultural water use as part of comprehensive aquifer management plan.
So a quantitative analysis and projection of the potential irrigation demand and treated wastewater
use had to be made. However, the project did not investigate in detail the agriculture water usage
and potential methods to improve its effectiveness.
2.4.4 Gaza water resources in the literature
Very few publications have dealt with the Gaza Strip water resources. Yaqubi et al. (2002)
evaluated the integrated water resources management plan, which had been prepared by CAMP
project (Yaqubi et al. 2002). The authors found that the total implementation cost of plan will be
about 1.5 Billion US$ and the plan could only be implemented if the conditions of sustainability
were fully considered. These conditions for sustainability are mainly the economical cost recovery
and the set up of a proper managerial and legal framework.
Assaf (2001) evaluated the existing and the planned desalination facilities in the Gaza Strip
by assessing its socio-economic and environmental impacts. He considered the existing saline
groundwater and seawater desalination facilities and mapped out the existing water demand and
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identified different potential sources of supply. He concluded that, the implementation of a large
scale seawater desalination plant to cover all domestic water demand is an urgent need and should
be implemented now inspite off the existing Intifada (Assaf, 2001).
Sbeih (2001) tried to formulate proper management measures as well as laws needed
towards effluent treated wastewater reuse in Palestine. The author stressed the importance of
finding proper wastewater treatment facilities that have low treatment cost and high effluent quality
and on the need to change crop pattern toward crops suitable for treated wastewater (Sbeih, 2001).
Al- Dadah (2001) reported that nearly 95% of the cultivated vegitables in the Gaza Strip are
irrigated by drip or sprinkler irrigation. The author stressed the need to establish water-pricing
system to encourage water conservation and penalise abusive water use. He also stressed the
importance of crop pattern development planning (Al- Dadah, 2001).
Out of the previous paragraph the following can be concluded:
-
The agricultural water use in the Gaza Strip has received a very little attention, inspite of the
fact that it is the largest water consumer.
-
The principles of integrated water resources management and the socio-economical and
environmental values of water use form an important part of the Palestinian Water National
Policy. However, no single act has been made towards the formulation of a methodology for
agricultural water use based on this principal.
2.5 Water for crops
All crops need water to grow. The most well known source of water for plant growth is
rainwater. There is important question, which comes to mind: what to do if there is too little
rainwater? If there is too little rain, water must be supplied from other sources, therefore irrigation
is needed. The amount of irrigation water needed for crop growth depends not only on the amount
of water already available from rainfall, but also on the total amount of water needed at different
stages of crop growth.
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With respect to the need for irrigation water, a distinction can be made among three climatic
situations:
-
Humid climates: more than 1200 mm of rain per year. The amount of rainfall is sufficient to
cover the water needs of the various crops. Excess water may cause problems for plant growth
and thus drainage is required.
-
Sub-humid and semi-arid climates: between 400 and 1200 mm of rain per year. The amount of
rainfall is important but often not sufficient to cover the water needs of the crops. Crop
production in the dry season is only possible with irrigation, while crop production in the rainy
season may be possible but unreliable: yields will be less than optimal.
-
Semi-arid, arid and desert climates: less than 400 mm of rain per year. Reliable crop production
based on rainfall is not possible; irrigation is thus essential (FAO 1986).
Out of the previous paragraph, irrigation is needed in arid and semi-arid areas where water is
scarce in these areas. Therefore, efficient irrigation is crucial. Good knowledge of crops water
requirements, scheduling of irrigation water and the use of modern irrigation techniques will highly
improve the irrigation efficiency.
2.5.1 Crop water requirement
Allocation of crop water requirement should consider the different influencing aspects such
as: crop properties, meteorological condition, soil characteristics, irrigation water quality, and
irrigation method. The study on crop water requirement allocation still attracts researchers due to
its major affects on the efficiency of agricultural water use. The literature presents different
methods and simulation models for crop water requirement allocation, for example:
-
Blaney- Criddle method
-
Penman-Monteith method
-
Pan evaporation method.
-
CROPWAT, Irrigation simulation model
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-
SWAP Soil- Water-Atmosphere-Plant Simulator
-
GAPS General Atmosphere- Plant -Soil simulator
The accuracy of each method and model depends on the availability of input information, crop type,
and the geographical location of the study area. The evaluation and comparison between the
different methods and models is out of the scope of this dissertation.
SWAP model is presented in chapter five. For more information about the different methods and
models the reader should refer to Allen R.G. et al. 1998, Butler et al. 1989, SYS et al. 1991, Van
Dam et al. 1997.
2.5.2 Irrigation techniques
Water is applied to the field by three main irrigation techniques: surface irrigation, sprinkler
irrigation, and drip irrigation.
Surface irrigation (furrows, borders, or basin) have on farm application efficiencies as low
as 40-60%, depending on land topography and soil texture. Surface irrigation is the oldest existing
technique and has the lowest investment cost. Sprinkler on farm application efficiency ranges from
60-70% depending upon wind speed, air temperature, and sprinkler type.
Drip irrigation
characterised by directly localising water (through emitters) to crop, rate of water application can be
kept very low and frequency of irrigation can be well controlled, thus deep percolation losses,
evaporation, and surface run-off losses are minimised. Therefore water application efficiency can
reach up to 95%.
Techanical evaluation of different irrigation techniques is out of the scope of this research.
However, it is important to mention that:
-
Improve the application efficiency of irrigation is of extreme important. This can be done
through allocating crop water requirement properly and using an efficient irrigation technique.
-
Techanical development in this field is very high, therefore, it is of extreme important to follow
up the new technologies and achievements. This will offer the possibility to continuously
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improve the irrigation efficiency.
-
Through out this research, SWAP model has been used to calculate crop water requirements,
and sprinkler irrigation has been considered as the principal irrigation technique.
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Chapter III
Multiobjective Modelling
3. MULTIOBJECTIVE MODELLING
3.1 Introduction
Each day of our lives is filled with multiobjective problems. For example, on my way to the
office should I take the car or the bus? Well, the bus is cheaper, but the car is more convenient,
particularly since I could stop at the store on my way home from work. The bus is more energy
efficient, but I can listen to the radio in the car. There are probably other attributes or objectives in
addition to cost, convenience, energy consumption, and comfort that might be considered in the
choice between the car and the bus.
Most projects, designs, and planning problems are characterised by a large number of
alternative potential solutions. The common purpose of analysis is to choose the best trade-offs
among the different solution. In engineering, it is often a problem to formulate a design in which
there are several criteria or design objectives. If the objectives are opposing in nature, then the
problem becomes that of finding the best possible compromise. An optimum design problem must
then be solved based on multiobjective approach. As an example, an engineer is given the task to
design a beam with minimum deformation and weight. This is a multiobjective problem, again with
two opposing objectives. That is, an increase in weight would cause a reduction in deformation.
3.2 History of multiobjective modelling
Multiobjective modelling is applicable to a wide range of problems in both the private and
public sectors. Multiobjective has been implemented to solve different scientific and engineering
problems since many years now. After Eschenauer et al. (1986), Leibniz G.W. (1646-1716) and
Euler L. (1707-1783) used infinitesimal calculus to find the extreme values of functions. This made
it possible for pioneers to study various new fields of mechanics. Bernoulli J. (1655-1705),
Bernoulli D. (1700-1782), and Sir Isaac Newton (1643-1727) used these methods to lead them to
their findings; Newton in minimising the resistance of a revolving body and the Bernoulli's in
isoperimetric problems. de Lagrange J.L. (1736-1813) and. Hamilton W.R (1805-1865) developed
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the several theorems, which serve as the basis for the solution of all optimum design problems.
Later, function approximations were developed by Lord Rayleigh (1842-1919), Ritz W. (18781909), Galerkin B.G. (1871-1945) and others to solve complicated time-consuming functions,
because they could be approximated relatively accurately.
A French-Italian economist named
Pareto V. (1848-1923) first developed the principle of multiobjective optimisation for use in
economics. His theories became collectively known as Pareto's optimality concept (Eschenauer et
al. 1986).
3.3 Mathematical programming definitions
Mathematical programming addresses optimisation problems which posses a specific structure:
Maximise or minimise an objective function subject to a set of constraints, which defines feasibility.
The objective function and the constraints are mathematical functions of decision variables and
parameters. Decision variables are the system aspects, which can be controlled, while parameters
are givens quantities that can not be controlled. A collection of values for each of the decision
variables is called a solution, while the feasible solution is the one, which satisfies the constraints.
The role of the objective function is to provide basis for the evaluation of the feasible solutions.
The feasible solution, which gives the best value of the objective function, is called optimal solution
(Cohon, 1978).
3.4 Single vs. Multiobjective optimisation
Many real-life decision-making problems need to achieve several objectives. The main goal of
single objective optimisation is to find the optimum solution, which corresponds to single objective
function. This optimisation type provides the decision-makers with insights into the nature of the
problem, but usually can not provide different alternative solutions that trade different objectives
against each other. On the contrary, in a multiobjective optimisation with conflicting objectives,
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Multiobjective Modelling
there is no single optimal solution. The interaction among different objectives comes with a set of
compromised solutions, which presents a trade-off between the different objectives.
Many objectives consideration in the design or planning process provides three major
improvements to the decision-making process (Cohon, 1978).
A wider range of alternatives is usually identified, when a multiobjective methodology is
employed.
Consideration of multiple objectives promotes more appropriate roles for the participants in the
planning and decision-making processes, who generates alternative solutions, and decisionmakers who use the solutions generated by the analyst to make informed decisions.
Models of a problem will be more realistic if many objectives are considered.
3.5 Multiobjective model formulation methods
Mutliobjective model formulation methods are highly related to the problem characteristics. In
the literature, different model have been presented with different formulation methods such as
constraints method, weighting method, distance-based method, the noninferior set estimation
method. In the following paragraphs the weighting methods and the constraints method will be
presented.
3.5.1 Weighting method
After allocation of the different objectives by the decision-makers, this method takes each
objective function and multiplies it by a weighting coefficient. The weighting coefficient should be
able to convert the different objectives units to cost unit. The weighting coefficients should be
specified beforehand by the decision-makers. The modified functions are then added together to
obtain a single objective function, which can easily be solved using any single objective method
(Cohon, 1978).
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Multiobjective Modelling
3.5.2 Constraint method
The constraint method operates by optimising one objective while all the others are constrained
to some values. The constrained values should be specified by the decision-makers beforehand
(Cohon, 1978).
These methods have the following disadvantages.
The weighting coefficients and the constrained values must be specified beforehand. This will
pre-define the ranges of potential solutions.
There is no clear criterion that exists to allocate the principal objective.
3.6 Multiobjective modelling applications
Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Multiobjective management and
planning models have very wide ranges of application possibilities for both public and private
sectors from natural resources management to management of large-scale private companies. This
comes with a wide variety of multiobjective models. In the following parts an overview of the most
recent and important studies that have used multiobjective planning techniques to handle the water
resources management problems will be presented. In the second part a review of the different
studies that have handled the agricultural water use, based on multiobjective techniques will be
presented.
3.6.1 Multiobjective modelling for water resources management and planning
Bogardi et al. (1983) reported on a dynamic multiobjective methodology for the
management of a multipurpose regional aquifer. The proposed methodology aimed to provide a
multiobjective planning model for managing simultaneously a regional aquifer and a mineral
extraction scheme under water hazard for a bauxite mining case study in western Hungary. The
proposed model included dynamic features and provided an explicit trade-off between the economic
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Multiobjective Modelling
objectives and fuzzy environmental objectives. The economic objective reflected mining benefit
and costs. The environmental objectives refereed to the preservation of environmental function of
the aquifer, which was the recharge of thermal springs (Bogardi et al., 1983). The authors have been
very successful in introducing the environmental objectives in fuzzy terms. This was due to the fact
that long-range social impacts of environmental degradation are difficult to measure or to assess and
no consensus can be reached on numerical indicators corresponding to a sound environment, which
is not an unusual occurrence.
After Bogardi et al. (1983), applications of fuzzy composite
programming have been reported in different multiobjective water resources planning and
management studies (etc: Bardossy et al. 1985, Bardossy, 1988, Bardossy et al. 1989, Sutardi et al.
1995).
Brimberg et al. (1993) reported a management model for the development of marginal water
sources (saline groundwater, treated wastewater, and rainfall harvesting) in the Negev desert, Israel
based on linear programming technique. The model objective was this: to minimise the operational
and capital costs of water supply in the whole Negev desert area, while simultaneously allocating a
conventional regional supply in a best way among a set of local sites. An estimation of utilisation
cost for each type of marginal water was made for each zone. The authors formulated a set of
constraints aiming to satisfy demand, water quality requirements, available capital and not exceed
the storage capacity for each site (Brimberg et al., 1993). It is important to notice that the authors
handled a very important issue and presented a good preliminary optimisation model for the
utilisation of marginal water resources in arid areas. This model considered the minimisation of
financial cost as a principal objective in its constraint method based formulation.
This
consideration limited the model's capacity to integrate the numerous important environmental
biophysical and social aspects of marginal water resources allocation planning.
A Multiobjective water resources investment-planning model has been reported by Sutardi
et al. (1994).
An integration of stochastic dynamic programming (SDP) and integer goal
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Multiobjective Modelling
programming (IGP) modelling approach has been proposed by the author to deal with the problem
of multiobjective multicriteria sequential decision-making under budgetary and socio-technical
uncertainties. Application of SDP model yielded primarily an optimal investment planning policy.
IGP model determined the economic return of each investment decision level together with its
associated project portfolio based on goals and criteria preferences (Sutardi et al. 1994). The
authors elicited the scenarios of future budget availability and subjective inputs from a group of
decision-makers by collective opinion techniques. The principal author extended his work by
applying Fuzzy Integer Goal Programming (FIGP) to determine the optimal return for each level of
possible funding decision through selecting, scheduling, and budgeting the potential project in each
scheduling horizon of the SDP model (Sutardi et al. 1995). The authors demonstrated that the two
models would analyse the problem of budget uncertainty for irrigation development in Indonesia for
a long-term development horizon. Their models also handled the problem of economical and socioeconomic uncertainty.
The main problem of their work was that they did not consider the
environmental and meteorological uncertainty in the agriculture projects and its potential
consequences in the agriculture system.
A Multiobjective model aimed to determine an optimal water reservoirs operation for large
river basins to meet a multipurpose water demand has been reported by Mahmoud (1999). The
author used the constraint method to formulate his multiobjective model.
The model's four
objectives were: the maximisation of annual municipal water supply (principal objective), ice
prevention, annual irrigation water supply, and upstream and down stream hydropower generation
(Mahmoud, 1999). The author was able to integrate the different aspects of water resources
management in his optimisation model, but the following major problems caused some weakness in
his work. Firstly, the economical aspect has not been considered in his model. Secondly, the
author did not identify clearly the role of decision-makers.
Getachew et al. (1999) reported a simulation/optimisation model that integrates linear
reservoir decision rules, detailed simulations of stream/aquifer system flows, conjunctive use of
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Multiobjective Modelling
surface and groundwater, and delivery via branching canals to water users for water supply
optimisation. The model was prepared for an area characterised by shallow groundwater level and
very high seepage and infiltration rates (Getachew et al. 1999). The author applied the model to
hypothetical study area under different development scenarios. This proposed model was very
interesting from the theoretical point of view, but it was unsuitable to be applied to real life
problems.
Jenkins et al. (2000) presented a model that integrated urban water supply reliability analysis
with shortage management options such as dry year option, and spot market water transfer and long
- short term yield simulation to probabilistic shortage management optimisation (Jenkins et al.
2000).
3.6.2 Multiobjective modelling for agricultural water use planning
The use of constraint method for crop pattern planning in watershed has been reported by
Banker et al. (1997). The authors detailed a multiobjective model where the maximisation of profit
was the principal objective. To not exceed the acceptable soil loss limit and to cultivate more than
40% of the cultivable agriculture area formed the constrained objectives (Banker et al. 1997). The
model was applied to Aagadgaon watershed in India. The authors ignored many influences and
importance aspects of agricultural water use planning (such as available water, environmental
aspects and spatial variabilities on crops water requirement and crops yield) in the formulation of
their model.
Raju et al.(1999) formulated a multi-criterion decision-making method for irrigation
planning. The authors prepared three single objective linear models to account for maximum net
profit, agricultural production and labour employment for the Sri Ram Sagar project in India. The
outputs of these models have been used as criterion for lower and higher bounds of a multiobjective
model.
The multiobjective model has been formulated based on constraint method with
maximisation of net profit as the principal objective function. Different combinations of agricultural
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Multiobjective Modelling
production and labour employment objective values have been formulated based on a close
consultation with decision-makers to create 37 different development scenarios. Cluster analysis
and two multi-criteria evaluation methods were used to come up with an optimal scenario (Raju et
al. 1999). The reported method presented an interesting approach for irrigation planning on a
regional scale. A problem, perhaps, with the proposed approach is that it accounted only for the
economical aspect of irrigation planning and ignored completely the environmental aspects of
agricultural water planning.
Prasad's et al. (2001) reported model set about to develop a crop pattern under constraints of
limited water resources based on a liner programming technique for the Ranchi basin, India. Three
single objectives models (maximisation of net profit, maximisation of cultivated area, and
maximisation of labour employment) subjected to the same set of constraints were formulated. The
results of the three models were then compared with the existing crop pattern.
The authors
concluded that maximisation of net profit model resulted in a crop pattern that superseded the other
two models (Prasad et al. 2001). However, the proposed model did not account for various other
important aspects on its crop pattern allocation and it is far from being a good planning tool.
Out of the previous paragraphs, the following remarks summarise in all previous reported
studies:
-
Maximisation of profit has been the main objective for most of the reported studies.
-
Water quantity has been rarely considered as a problem.
-
The environmental aspects of water use have been given a second priority. This indicates that a
little attention has been given to the principle of sustainability and environment conservation.
-
Arid and semi-arid areas have been gained second-rate attention in multiobjective water
resource management and planning.
-
Crop water requirements and crop yields are highly dependent on soil and meteorological
conditions. Naturally, oil and meteorological conditions vary spatially. This will result in
Optimisation of Agricultural Water Use
28
Decision Support System
Chapter III
Multiobjective Modelling
spatial variations in crop water requirements and crop yields. These variations have been never
considered in any of the previously reported studies.
-
Only one single case has considered the use of treated wastewater in their model
-
Allowing the decision makers at early planning stage to set target values for important decision
parameters and to give them the possibility to evaluate the impact of their contributions has
been reported in very few cases.
-
Most of the reported works for multiobjective optimisation of agricultural water use are based
on a constraint method with its previously mentioned disadvantages (see Sec. 3.5.2).
Optimisation of Agricultural Water Use
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Decision Support System
Chapter IV
Study Area and Database
4. STUDY AREA AND DATABASE
This chapter consists of three main parts. In the first part, an overview of the study area will be
presented. In the second part, the database formulation methodology will be presented. In the third
part, soil water atmosphere and plant model (SWAP 2.0) theoretical background and application
methodology will be described.
4.1 Study area
4.1.1 Location
Palestine is located at the
southeastern
Mediterranean
edge
of
the
between
the
Longitudes 34.5o to 35.5o East and
the Latitudes 29.5o to 33.5o North as
shown in Map (4.1). The Gaza Strip
is situated on the southeastern coast
of Palestine. The area is bounded by
the Mediterranean in the West, the
1948 cease-fire line in the North and
East, and by Egypt in the South.
The total area of Gaza Strip is 365
Km2. It is approximately 40 Km
Map (4.1): The Gaza Strip location
long and the width varies from 8 Km in the North to 14 Km in the South
4.1.2 Historical View
Over the last hundred years the Gaza Strip was under Turkish, British, Egyptian, and Israeli
rule. At the beginning of the twentieth-century, the entire region was an integrated part of the
Turkish Ottoman Empire. After World War I, the Ottoman Empire collapsed. Hereafter, the
Optimisation of Agricultural Water Use
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Study Area and Database
British ruled Palestine under the British Mandate. In 1948, the Mandate expired and Israel declared
it as an independent Jewish State within 78% of Palestine. Hundreds of thousands of Palestinians at
that time fled their homes to the neighboring countries and amongst other to the Gaza Strip and
West Bank, where they are still living in refugee camps up to this day.
After the armistice
agreement between Israel and Egypt in 1949, the Gaza Strip became a political entity controlled by
Egypt until the year 1967. Following the second major Arab-Israeli war in 1967, Israel gained
control over the remaining Palestinian territories, West Bank and Gaza Strip as well as the Sinai of
Egypt and the Golan Heights of Syria. This forced hundreds of thousands of Palestinians to flee the
Palestinian Territories and to resettle in the refugee camps established since 1948 in Syria, Lebanon,
and Jordan.
In December 1987, the first Palestinian Uprising (Intifada) began. After four years of
struggles, in 1991, all parties involved in the Middle East conflict entered peace negotiations,
culminated in Madrid Peace Conference. After signing of the Palestinian- Israeli Declaration of
Principles (DOP) in Oslo in 1993 and its implementation agreements. The West Bank and Gaza
Strip constituted a new Palestinian entity, which was expected to enable the Palestinian people to
live in better human and environmental living conditions. The breakdown of the peace negotiations
has led to problems and difficulties. The second Intifada started in September 2000. Now, the
overwhelming aim is to accelerate the peace process in order to achieve a better future for the
Palestinian people.
4.1.3 Administration
Gaza Strip consists of five Governorates: Northen, Gaza, Middle, Khan Yunis, and Rafah.
Each Governorate consists of Villages, Camps, and Cities.
The municipalities or the village
councils are responsible for all public services in its administration area. Both the Palestinian Water
Authority (PWA) and Ministry of Planning and International Co-operation (MOPIC) coordinate
between the different municipalities and village councils regarding water and sanitation work.
Optimisation of Agricultural Water Use
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4.1.4 Demography
According to 1997 census finding, the Gaza Strip population is about 1.023 Million with an
annual growth rate of 3.2%. The average population density is almost 2297 Capita/km2. The
population density in Gaza refugee camps ranges from 29,000 to 100,000 Capita/km2 (Palestinian
Central Bureau of Statistics (PCBS), 1997).
MOPIC investigated three potential scenarios for population forecasts in the Gaza Strip:
low, medium, and high. These projections have been based on population characteristics including
age structure, migration, and birth, and death rates. MOPIC used the PCBS figures for the year
1997 as a base year. MOPIC adapted the medium scenario as the most realistic scenario and used it
for all development plans. Fig (4.1) shows the projected population in the Gaza Strip according to
the adapted scenario.
Population [M. Capita]
3,0
2,5
2,0
1,5
1,0
0,5
0,0
1997
2005
2010
2015
2020
2025
Year
Fig (4.1): The Gaza Strip population projection
4.1.5 Climate
The Gaza Strip has an arid to semi-arid Mediterranean climate. The southern part is almost
arid while the northern is semi-arid to moderately humid climate. Rainfall occurs only in the winter
season from October to the end of April. The rainfall intensity in the Gaza Strip is characterized by
high spatial variation, where an average 13 years ranges from about 475mm/year in the North to
about 256 mm/year in the South. The following Fig (4.2) presents Minimum, Maximum and
Average values for 13 years records in Gaza strip. The average annual temperature in the Gaza
Strip ranges from 19 Co to 21 Co. The maximum value occurs in August and ranges from 26 Co to
Optimisation of Agricultural Water Use
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Decision Support System
Chapter IV
Study Area and Database
28 Co. The minimum occurs in January, and ranges from 12 Co to 14 Co. The average annual
relative humidity is around 65% reaching its peak value of about 87% in August and September.
The average annual potential evaporation is about 1200 to 1400 mm (MOPIC, 1997).
900
800
700
[mm/year]
600
Min.
Max.
Ave.
500
400
300
200
100
0
North
Gaza
Middle
K. Yunis
Rafah
Location (North to South)
Fig (4.2): Spatial rainfall distribution in the Gaza Strip.
4.1.6 Water resources
4.1.6.1 Surface water
The surface water system in the Gaza Strip consists of Wadis.
Wadis are ephemeral
streams, characterized by flash floods occurring after heavy rainfall. During most of the time, the
Wadis are completely dry. The major Wadi in Gaza Strip is Wadi Gaza, which originates in the
Negev Desert. Its catchment area is about 3500 Km2 . The estimated average annual flow of Wadi
Gaza is 20 to 30 Mm3. Dry periods without any significant runoff are experienced as well. When
surface runoff occurs, it lasts for a limited number of days. There are other two small and
insignificant Wadis in the Gaza Strip: Wadi El- Salqa in the south flows to the sea and Wadi Beit
Hanon in the north, flows partially to the sea and partially into Israel. Presently, surface water
resources are not used in the Gaza Strip (Ouda, 1999).
Optimisation of Agricultural Water Use
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4.1.6.2 Groundwater
Groundwater is the only sustainable water resource in the Gaza Strip. Its annual recharge
rate varies between 45 Million m3 /year to 60 Million m3 /year. This variation is dependent upon the
annual variation in rainfall quantity. The coastal aquifer of the Gaza Strip is part of a regional
groundwater system, which stretches from the coastal areas of Sinai (Egypt) in the south to Haifa in
the north. The coastal aquifer is 10-15 km wide, and it's thickness ranges from 0 m in the east to
about 200 m at the coastline. The coastal aquifer consists primarily of Pleistocene age Kurkar
Group deposits including calcareous and silty sandstones, silts clays, unconsolidated sands, and
conglomerates. Within Gaza Strip, the total thickness of the Kurkar fold is about 100 m at the shore
in the south, and about 200 m near Gaza City. At its eastern border, the saturated thickness is about
60-70 m in north, and only few meters in the south near Rafah. Local, parched water conditions
exist throughout the Gaza Strip due to the presence of shallow clays (CAMP, 2000). Few pump
tests have been made in Gaza Strip. The pump tests show that the aquifer transmissivity ranges
between 700 and 5,000 m2 /day. Corresponding values of hydraulic conductivity (K) are mostly
within a relatively narrow range of about 20-80 m/day. Most of the wells that have been tested are
municipal wells spread across more than one sub-aquifer.
Hence, little is known about any
differences in hydraulic properties between these sub-aquifers. Specific yield values are estimated
to be about 15-30 percent whilst specific storativity is about 10-4 (CAMP, 2000).
The major documented water quality problems in the Gaza Strip are the elevated salinity and nitrate
concentrations in the aquifer. The WHO drinking waters standards for chloride (250 mg/L) and for
nitrate (50 mg/L) are exceeded in many areas as shown in table (4.1).
Salinity highly affects the usability of water for irrigation and domestic water supply. It is
estimated that less than 10 percent of Gaza's aquifer water has water quality that complies with
WHO drinking water standard. The areas with good water quality are primarily in the north and
along the coastal sand dune areas of the Mawasi (southwest).
Optimisation of Agricultural Water Use
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Table (4.1): Main municipal and industrial water quality in the Gaza Strip
Water quality
North
NO3. [mg/l]
WHO value =50
Range
Mean
12-280
101.1
Cl [mg/l]
WHO value =250
Range
Mean
42-470
129
Gaza
Middle
Khan Younis
Rafah
27-224
17-95
29-380
17-230
30-802
65-1015
54-1582
46-1136
111.6
49.6
201
90.05
381
442
740
364
Source:(CAMP, 2000)
Based on the existing information, the main chloride sources are:
-
Seawater intrusion. Several shallow agricultural and municipal wells, primarily in coastal areas,
have been abandoned in the past 10 years due to seawater intrusion.
-
Lateral inflow of brackish water from Israel in the middle and southern areas of the Gaza Strip.
-
Presence of natural deep brines at the base of the coastal aquifer.
Most of the municipal wells in Gaza show nitrate levels in excess to the WHO drinking water
standard of 50 mg/L. The most affected areas are the urban centers, where nitrate concentrations
are increasing, in some cases rapidly, at a rate up to 10mg/L per year. The main sources of nitrates
are fertilizers and domestic sewage effluent. The quantities of sewage that annually infiltrate to the
groundwater through ceepits and septic tanks are significant. It is about 12 Mm3 /year.
4.1.7 Water demand
Population growth, the social welfare, and the expected changes in agricultural and
industrial water demand will shape the water demand in the future. The population of the Gaza
Strip will increase by more than one million in the next 20 years ( see Fig (4.1). Fig (4.3) presents
the projected water demand per sector based on CAMP project estimation. Fig (4.4) presents the
projected water shortage "deficit between demand and sustainable water resources capacity " up to
year 2020. This Figure has been prepared under the assumption of an average annual sustainable
water resource of about 132 Mm3.
Optimisation of Agricultural Water Use
35
Decision Support System
Chapter IV
Study Area and Database
300
[Mm³]
250
200
150
100
50
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
20
00
0
Year
Domestic and Industrial
Agriculture
Total demand
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
140
120
100
80
60
40
20
0
20
00
[Mm³]
Fig (4.3): Water demand projection in the Gaza Strip. Source: (CAMP, 2000) modified
Year
Fig (4.4): Projected water shortage in the Gaza Strip. Source: (CAMP, 2000) modified
In order to achieve a sustainable water situation, the water shortage has to be reduced. In
this study, the potential of a reduction in agricultural sector is considered
4.1.8 Agricultural sector
Agriculture is the most important economic sector in the Gaza Strip, but its contribution to
the GDP has decreased from 32% in the early seventies to 25% in the early nineties. The level of
investment in agricultural activities is relatively low due to marketing and product transportation
problems, which come about from the frequent border closures by Israel.
The importance of fruit trees, particularly citrus fruits, has diminished from 63% of total
agricultural output in 1970 to 25% in 1995. In comparison, substantial increase in vegetables
contribution to the agricultural output have been recorded for the last years (MOPIC, 1996).
Optimisation of Agricultural Water Use
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Study Area and Database
The total agricultural area in the Gaza Strip is about 16650 hectares. The irrigated area is
about 10,800 hectares, in addition to about 1000 hectares of greenhouses. The number of farms is
estimated to be between 15,000 and 20,000 (MOA, 1998). The average area per farm is estimated
to be between 0.8 to 1.1 hectares.
The percentage of workers in all agricultural activities during the year 2000 in the Gaza
Strip amounted to about 10%. 57 % of them worked in family owned farms (PCBS, 2000).
Presently, water is considered as a "free good" for farmer's, being without any type of
metering or pricing. The farmer's pay only the water abstraction cost which is less than 0.05
US$/m3.
4.1.9 Soil
Six different soil types can be distinguished in the Gaza Strip as shown in map (4.2),
(MOPIC, 1997). Most of agricultural activity is situated on five soil types, whereas the sixth soil
type (Loess soil) is located mainly in
N
industrial and domestic area. Table
W
(4.2) shows the texture of the soil
E
S
types in the Gaza Strip and the
percentage of the total area covered by
each type. The soil code in the table
has been proposed from the author, in
Soil Types :
Dark brown / reddish brown (bh)
Loessal sandy soil (db)
Sandy loess soil (wg)
Sandy loess soil over loess (ky)
Sandy regosols (bl)
Loess soils
order to simply distinguish between
the different soil during database
formulation.
Map (4.2): The Gaza Strip soil type (MOPIC, 1997, modified).
Optimisation of Agricultural Water Use
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Study Area and Database
Table (4.2): Soil texture in the Gaza Strip.
Soil type
Soil code
Portic Calcaric Arenosol
bl
(Sandy regosol)
Calcaric Cambisol
ky
(Sandy loess soil over
loess)
Arenic Calcic Luvisol
db
(Loessial sandy soil)
Hypocalcic Calcisol
bh
(Dark brown/ reddish bron
clay loam)
Calcaric Cambisol
wg
(Sandy loess soil)
Source: (Goris et al. 2001) modified.
Clay
[%]
8.5
Silt
[%]
1.8
Sand
[%]
89.9
Soil
texture
Sand
Area
[% ]
31.6
17.5
16.3
66.2
Sandy loam
15.9
18
25
57
Sandy loam
23
25.3
12.8
61.9
Sandy clay
loam
20.5
23.2
20.3
56.6
Sandy clay
loam
9
4.1.10 Economic situation
It is difficult to present an accurate and realistic picture of economic development over the
past decade. Data is generally unreliable and often conflicting. In fact, few basic statistical data
from the past exists. The annual growth rates of Gross Domestic Product (GDP) and National
Disposable Income (NDI) in the Gaza Strip in the period from 1970 onward show that a reasonable
growth was attained over the seventies and in the recent years. Gazes working abroad (mainly
Israel) earned 30% of the GDP. The GDP in 1992 was about 800 Million US dollar, 15% out of it,
due to transfers from Palestinian living abroad. The annual per capita income in 1992 was 1,260
US$ (MOPIC, 1996).
The principle economic sectors in the Gaza Strip are agriculture,
construction, industry, trades and services. Their contribution to the GDP is shown in Table (4.3).
Table (4.3): The contributions of different economic sectors to Gaza GDP.
Sector
Agriculture
Construction
Industry
Service and trade
1992
26%
19%
10%
45%
1993
25%
21%
10%
44%
1994
25%
22%
8%
45%
Source: (MOPIC, 1996)
Optimisation of Agricultural Water Use
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4.1.11 Land ownership
Most of the Gaza Strip land is owned by the private sector. The following table (4.4) shows
the land ownership in the Gaza Strip.
Table (4.4): The Gaza Strip land ownership distribution.
Land Ownership Type
Governmental land
Occupied Israeli Settlements and yellowa area
Private land
Wagf land: owned by Ministry of Islamic Affair
Bier El - Saba'a landb
Total
Area
(hectare)
5300
5700
18540
760
6200
36500
Percentage
14.5
15.6
50.8
2.1
17
100
Source: (MOPIC, 1998). a: Yellow area is the border area and security area, b: Bier El - Saba'a land is a
governmental land that, has been taken by private sector without a legal agreement.
4.1.12 Land use
The present distribution of land use is given in table (4.5). It shows that about 15.6% of the
total area is occupied by Israeli settlements. The final condition of these Settlements is still under
negotiation with the Israeli government as part of the on-going peace process. The Agriculture area
covers about 45.7% of the total area, this shows the importance of the agriculture sector to the
national economy.
Table (4.5): The Gaza Strip land use distribution.
Type of land use
Area
[hectare]
5750
5700
16700
8350
36500
Built up area
Israeli Settlement and yellow area
Agriculture area
Unused land
Total
Percentage
15.8
15.6
45.7
22.9
100
Source: (MOPIC, 1998).
4.2 Database formulation
The formulated database includes the different parameters that influence agricultural water
use. These parameters can be classified according to their calculation methodology into two types:
socio-economic parameters and biophysical parameters. In the remaining part of this chapter, the
Optimisation of Agricultural Water Use
39
Decision Support System
Chapter IV
Study Area and Database
calculation methodology and the main findings for the different parameters will be presented. First
the socio-economic parameters will be presented, then the biophysical parameters.
4.2.1 Socio-economic information
The socio-economic database contains information about the projected treated wastewater
quantity, projected local crops product demand, crops return value, crops cultivation cost, existing
crop pattern, and level of farmer's acceptance for treated wastewater use in the study area. As
mentioned earlier, year 2025 has been specified as the target-planning year. The socio-economic
parameters have been estimated for the target year. The projection of socio-economic data is
characterised by high uncertainty. This uncertainty results from the following important aspects:
-
The long-range unstable political situation in the study area limits the available information
about socio-economic development in the study area.
-
The expected high variabilities in crop prices and cultivation cost due to the economical open
market strategy, which has been adopted by the Palestinian Authority (PA).
These uncertainty sources have been considered throughout the preparation of the socio-economic
database.
4.2.1.1 Allocation of target crop types
The selection of target corps has been based on the existing crop pattern. 20 crops were
selected, which cover about 90% of the total irrigated area; including 7 types of trees that are
suitable for treated wastewater irrigation. The remaining 13 crop types are mainly vegetables, of
which 8 are cultivated in more than one cultivation season. Thus, the total number of considered
crops is 28. The target crops type is shown in table (4.9), page 44.
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4.2.1.2 Predication of available treated wastewater
The Gaza Strip has only a partial sewage system at present, whereby less than 50% of
population is served by a sewer. The remaining percentage is depending on septic tanks or ceepits
for wastewater disposal. The existing sewer system serves a greater percentage of the northern
cities and communities than in the rural south. Presently, there are three major treatment plants in
the Gaza Strip. They are generally overloaded and mismanaged. Table (4.6) shows the general
characteristics of the existing wastewater treatment plants.
Table (4.6): Wastewater treatment plants characteristics in the Gaza Strip.
Location
Beit Lahia
Treatment
Quantity
method
[m3/day]
Stabilization ponds 8000-10000
and aerated lagoons
Gaza
Anaerobic ponds 40,000 – 45,000
followed with biotowers
Rafah
One aerated lagoon
3000 - 40000
Final disposal
Remarks
Surrounding
sand dunes
The treatment plants is
overloaded
and
mismanagement
75% to the sea The treatment plant
and
25% rehabilitated two years
infiltrated to the ago for 32,000 m3/day.
ground aquifer
To the sea
The
plant
is
overloaded
and
mismanagement
The Palestinian Authority is planning to improve the sewage system in the Gaza Strip. It
proposes to build three new Wastewater Treatment Plants, one in the North to serve the Northern
Governorate, one in the Middle to serve Gaza and Middle Governorate, and the third in Khan Yunis
to serve Khan Yunis and Rafah Governorate. The preliminary studies for the three treatment plants
are already finished. Throughout this study, the proposed wastewater treatment plants will be
considered as the treated wastewater sources.
The estimation of the available treated wastewater for the year 2025 was made in
consistence with PA plans for wastewater sector development. Treated wastewater projection has
been based on the following information:
-
MOPIC adapted population projection for each sub-regional area
-
MOPIC proposed average domestic water demand (litre per capita per day)
Optimisation of Agricultural Water Use
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-
The expected coverage of the sewer system proposed by MOPIC
-
The expected sewer system collection and treatment effectiveness proposed by MOPIC
Based on their assumptions, the quantity of treated wastewater for the year 2025 in each subregional area has been predicted as shown in Table (4.7). It is important to mention that, the
prediction process has been based completely on MOPIC estimation, in order to reduce estimation
uncertainty.
Table (4.7): Treated wastewater quantity generated in each sub-regional area for the year 2025.
Location
North
Gaza
Middle
K. Yunis
Rafah
Total
Population
Water
demand
Total water
Sewer
demand
coverage
Thousand
Capita
446
894
360
488
299
2487
l/C.day*
Mm3/year
150
150
150
150
150
150
24.4
48.9
19.7
26.7
16.4
136.2
Wastewater
production
%
Collection and
treatment
effectiveness
%
90
90
90
90
90
90
80
80
80
80
80
80
17.6
35.2
14.2
19.2
11.8
98.0
Mm3/year
Sources: (MOPIC, 1998, CAMP, 2000) modified. L/C.day = litre per capita per day
Finally, the available treated wastewater for each sub area has been distributed to each zone
according to the zone area as shown in Table (4.8).
Table (4.8): Estimated available treated wastewater in each sub-regional zone for the year 2025.
Zone
Gazabh
Gazabl
Gazawg
Khanbh
Total
Mm3/year
11.33
12.18
1.59
0.68
Zone Mm3/year
Khanbl
4.42
Khandb
5.16
Khanky
5.83
Khanwg
1.01
Zone
Mm3/year
Middlebl
5.54
Middledb
8.82
Middle wg
10.00
Northbh
6.08
98.0
Zone
Northbl
Rafahbl
Rafahdb
Rafahky
Mm3/year
11.51
2.46
6.87
4.62
4.2.1.3 Prediction of local crops product demand
The projection of total local crops product demands is highly uncertain due to the following
main three factors: population projection uncertainty, supply demand relation and its effects on crop
prices, and food consumption culture, which could be change in consistence with changing in the
standard of living. To reduce the projection uncertainty of local crop products demands, the
projection has been based on the following:
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-
MOPIC adapted population for the year 2025 has been used.
-
Crops local product demands have been calculated based on a three year long statistical records
for monthly household consumption, in order to consider the local food consumption culture.
The records for the years 1998,1999 and 2000, were used (PCBS, 2000, PCBS, 2000a, PCBS,
2001).
-
Maximum allowable area for each crop has been specified. This measure aims to reduce the
possible variabilities in crops market prices. The projected local crops product demand is as
shown in table (4.9), page 44.
4.2.1.4 Crops return value
The crops return values are variable in nature. In order to reasonably estimate the crops
return values, the estimation has been based on three years average in farm prices (PCBS, 2000,
PCBS, 2000a, PCBS, 2001). Also the previous mentioned pre-condition, which restricts the
allowable cultivable area for each crop, will affect greatly the potential variability in crops return
values. The estimated crop price is shown in table (4.9), page 44.
4.2.1.5 Crops cultivation cost
The crops cultivation costs have been calculated by conducting personal interviews carried
by undergraduate students from Islamic University of Gaza with farmers and agriculture engineers
in the study area, where the actual cultivation cost for each crop has been allocated. For trees, the
initial cultivation costs are economically distributed over the trees average life. Table (4.9) shows
the cultivation costs for each crop.
4.2.1.6 Available Agriculture area and maximum area
The cultivable agriculture area in each zone has been allocated to be equivalent to the
exiting cultivated area. The maximum allowable crop area has been defined to be two times the
Optimisation of Agricultural Water Use
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existing area for trees and 5 times the existing area for vegetables. This limitation aims to: firstly,
as has been mentioned earlier, reduce the potential variability in corps returns values. Secondly, a
to limit the crop pattern alteration investment cost.
Table (4.9): Gaza crops area, demand, cultivation costs, and returns values.
Crop
Cabbage
Cauliflower a
Cauliflower sp
Citrus others*
Cucumber sp
Cucumber s
Eggplant w
Eggplant a
Guava*
Grapefruits
Jew's melon
Lemon*
Olive*
Onion
Pepper a
Pepper sp
Potato w
Potato s
Shamoti*
Squash sp
Squash s
Strawberry
Sweetpotato
Tomato sp
Tomato s
Valencia*
Watermelon w
Watermelon s
Area
hectare
208.7
115.1
115.1
339.4
293
290
115.1
106.1
450
130
168.6
336.6
2689
40
83.1
83.2
557.3
557.3
501.2
180.2
180.1
166.4
295
180.4
180.5
1930
133.6
133.6
Demand 2025
ton/year
7462
2724
2761
6559
13805
13805
8581
8208
5193
1530
6443
8757
11193
1865
2239
2239
22236
22274
5873
2089
2127
485
2283
30221
30221
5873
13431
13431
Return Value
US$/ton
323
420
420
339
412
412
309
309
377
145
468
473
1250
502
489
489
242
242
263
408
408
1500
273
367
367
158
160
160
Cultivation cost
US$/hectare
3320
3500
3500
1500
3480
3480
5400
5400
1500
1500
1330
1500
1500
4350
5400
5400
3200
3200
1500
5500
3400
24270
3200
3280
3280
1500
6100
4000
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
4.2.1.7 Level of farmer's acceptance for treated wastewater use
Farmer's acceptance to use treated wastewater is an essential aspect for the success of any
wastewater reuse project. So it is advisable to be considered at an early planning stage.
A
questionnaire conducted by Ouda (1999) showed a spatial variation in the level of acceptance. This
was highly related to the spatial variation in groundwater quality. A farmer who has high water
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quality has less interest in using treated wastewater. The level of farmer's acceptance to used
treated wastewater for crop irrigation in their farms is as shown in Table (4.10).
Table (4.10): Percentage of farmer's who accepted to use treated wastewater for irrigation in each zone.
Sub-regional Zone Gazabh Gazabl Gazawg Khanbh Khanbl Khandb Khanky Khanwg
%acceptance
62
62
62
65
65
65
65
65
Sub-regional Zone Middlebl Middledb Middlwg North bh Northbl Rafahbl Rafahdb Rafahky
%acceptance
65
65
65
46
46
72
72
72
Source : (Ouda, 1999)
4.2.2 Biophysical database
The biophysical database includes information, which cover the crops water requirements,
crops yield, and salinity load due to irrigation under different combinations of soil and
meteorological conditions. Presently, there are no direct records available about the crops water
requirements in Gaza Strip. Al- Dadah (2001) reported that crops water allocation in Gaza Strip
needs a review. So that, the amount of irrigation should be based on soil conditions, type of crops,
climate, agricultural practices, and growing season (Al- Dadah, 2001). The spatial and temporal
variations in rainfall intensity and the spatial variation in soil characteristics have initiated the needs
for a capable tool. This tool should be able to consider all these variations and to allocate crops
water requirements, and crops yield for the 28 targeted crops under different combinations of soil
and meteorological conditions. Soil-Water-Atmosphere and Plant model (SWAP2.0) has been
chosen for this purpose. In the following part, a general description of the SWAP model will be
presented. After that, the model application and result evaluation methodology will be described.
4.3 Soil-Water-Atmosphere and Plant model (SWAP2.0)
The Soil Water Atmosphere and Plant Model (SWAP) aims to simulate water, solute and
heat transport in the soil atmosphere-plant environment. The program includes detailed sub models
on soil water flow, solute transport, soil heat flow, soil evaporation, plant transpiration and crop
growth, all operating from diurnal to seasonal cycles. Earlier version of the program were
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developed by Feddes et al. (1978), Belmans et al. (1983), Wessling et al. (1991), Kabat et al. (1992)
and Van den Broek et al. (1994), and Van Dam et al. (1997). In the following paragraphs,
descriptions of the most important and relevenat sub-models will be presented. For more
information about the model, the reader is kindly requested to refer to Van Dam et al. (1997) and
Kroes et al. (1999).
4.3.1 Sub-models and routines
4.3.1.1 Soil water flow sub-model
Flow of soil water results from the spatial differences of the soil water potential. The model
implements Darcy's equation to calculate the quantity of soil water fluxes. For one-dimensional
vertical flow, Darcy's equation can be written as:
q = − K ( h)
∂(h + z )
∂z
Where q is soil water flux density (positive upward) (cm/day), K is hydraulic conductivity (cm/day),
h is soil water pressure head (cm) and z is the vertical co-ordinate (cm), taken positively upward.
Water balance considerations of an infinitely small soil volume result in the continuity equation for
soil water:
∂θ
∂q
= − − S (h)
∂t
∂z
Where θ is volumetric water content (cm3 / cm3), t is time (d) and S is soil water extraction rate by
plant roots (cm3 / cm3 .day). The combination of the two equations results in Richards' equation:
∂θ
∂h
= C ( h) =
∂t
∂t
∂h
+1
∂z
∂z
∂ K ( h)
− S (h)
where C is the water capacity (d θ / dh) (cm-1 ).
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Richards' equation has a clear physical basis at a scale where the soil can be considered as a
continuum of soil, air and water. SWAP solves Richards' equation numerically, subject to specified
initial and boundary conditions and with known relations between θ , h and K. These relationships,
which are generally called the soil hydraulic functions, can be measured directly in the soil, or
might be estimated from basic soil data by applying Pedotransfer function. The soil hydraulic
functions are described by analytical expressions of Van Genuchten and Mualem or by tabular
values.
Root water extraction at various depths in the root zones calculated from potential
transpiration, root length density and possible reductions due to wet, dry or saline conditions (Van
Dam et al.1997).
4.3.1.2 Soil heat flow sub-model
Soil temperature may affect the surface energy balance, soil hydraulic properties,
decomposition rate of solutes, and growth rate of roots.
SWAP version 2.0 uses the soil
temperatures only to adjust the solute decomposition rate. Combination of the general soil heat flux
equation and the equation for conservation of energy yields the differential equation for transient
soil heat flow:
∂T ∂
∂T
Cheat
=
λheat
∂t ∂z
∂z
where: Cheat is the soil heat capacity (J/cm-3 oC -1 ), T is the soil temperature (oC),
λheat
is the
thermal conductivity (Jcm-1 oC -1 d-1)
This equation is solved either analytically or numerically. In the analytical solution uniform thermal
conductivity and soil heat capacity are assumed, and at the soil surface a sinusoidal temperature
wave is adopted. In the numerical solution the thermal conductivity and the soil heat capacity are
calculated from the soil composition and the volume fractions of water and air as described by De
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Vries (1975). At the soil surface the daily average temperature is used as a boundary condition (Van
Dam et al. 1997).
4.3.1.3 Solute transport sub-model
SWAP simulates convection, diffusion and dispersion, non-linear adsorption, first order
decomposition and root uptake of solutes. This permits the simulation of ordinary pesticide and salt
transport, including the effect of salinity on crop growth. The model SWAP simulates the residence
time of solutes in the saturated zone analogous to mixed reservoirs. In this way, solute transport
from soil surface to surface water can be derived (Van Dam et al. 1997).
4.3.1.4 Irrigation and drainage
Irrigation may be prescribed at fixed times or scheduled according to a number of criteria.
The scheduling option allows the evaluation of alternative application strategies.
The timing
criteria includes allowable depletion of readily available water in the root, allowable daily stress,
and critical pressure head or water content at a certain depth. Field drainage can be calculated with
a linear flux-groundwater level relationship, with a tabular flux-groundwater relationship, or with
drainage equations of Hooghoudt and Ernst. The use of drainage equations allows the design or
evaluations of drainage systems (Van Dam et al. 1997).
4.3.1.5 Simple crop model
SWAP contains three crop growth routines: Detailed model (WOFOST), the same model
attuned to simulate grass growth, the simple crop growth model. The simple crop growth model is
useful when crop growth does not need to be simulated or when crop growth input data are
insufficient (Van Dam et al. 1997). In this study, the simple crop growth model will be used. The
simple model does not calculate the crop potential or actual yield. However, the user may define
yield response factors for various growing stages. For each growing stage k the actual yield Yak
(kg/ha) relative to potential yield Yp,k (kg/ha) during this growing stage is calculated by:
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1−
Ya,k
Yp,K
= Ky,k 1−
T a,k
Tp,K
where: Ka,k is the yield response factor of growing stage k, and Tp, k and Ta, k are the potential and
actual transpiration, respectively during the growing period k.
4.3.2 Model structure
Fig (4.5) presents the main structure of the SWAP model. Simulation and sub-run control
parameters are initialised at the start of the simulation. The simulation starts for each sub-run with
the potential crop production of the first day. Potential crop production is defined as the total dry
matter production of a green crop surface that, during its entire growth period, is optimally supplied
with water and nutrients, and grows without interference from weeds, pests or diseases.
The
production level is essentially determined by the prevailing weather conditions. To get an estimate
of the potential production, the complete period of the sub-run is calculated (block A). Once
potential crop production is determined, the simulation of water-limited crop growth starts with an
initialisation of sub-models for Timing and Soil. Optionally the Irrigation sub-model is initialised.
Next the simulation starts the day at 00.00 hour with the intake of meteorological data after which
the sub-model Soil solves the discreet equations for water flow, solute transport and heat flow
(block B). These calculations are performed with a time step, which will be decreased, maintained
or increased according to numerical conditions for the solution of water flow and solute transport
equations.
Within the sub-model soil the top, lateral and bottom boundary conditions are
determined first, after which the sink term of root water extraction is calculated. With boundary
conditions and sink terms known, the Richard’s equation is solved, resulting in values for pressure
heads and moisture contents for the next time step. Soil temperatures are then determined by
solving the heat flow equation. Parameters for hysteresis are updated and the daily water fluxes are
integrated. If interaction with the surface water system is required (extended drainage), the various
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Fig (4.5): Main structure of SWAP 2.0. Source: (Kroes et al.1999):
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surface water flows are calculated. Also during each time step the solute transport equation is
solved using the actual soil water fluxes. The sub-model Soil is called for each time step until the
end of the day. Once the end of a day is reached and the calculations with the sub-model Soil are
finished, the actual crop growth rates are determined and its corresponding state variables are
integrated. After updates of some parameters the next day of simulations starts. Once the last day
of a simulation sub-run is reached the sub-model Soil is terminated and once the end of the last subrun is reached the complete simulation ends (Kroes et al.1999).
4.3.3 Application methodology
The SWAP model has been
N
used
to
calculate
crops
water
W
E
S
requirements, crops yield and salinity
load due to irrigation. SWAP model
Sub-regional zones :
North bl
North bh
Gaza bl
Gaza bh
Gaza wg
Middle bl
Middle db
Middle wg
Khan bh
Khan bl
Khan db
Khan ky
Khan wg
Rafah bl
Rafah db
Rafah ky
application methodology consists of
three main steps.
Firstly the target
crops have been selected as has been
described earlier. Secondly, Gaza
Strip has been subdivided into 16 subregional zones. The zones have been
allocated according to the spatial
Map (4.3): The Gaza Strip sub-regional zones location.
distribution of soil types and rainfall intensity. Loess soil were neglected due to the fact that it is
located mainly in industrial and domestic area. Map (4.3) shows the sub-regional zones location.
The zone names consist of two parts, the first marks the region and the second marks the soil code.
The soil code has been named by author in order to simplify the distiguish between the different soil
types (see page 37). Thirdly, the different model input parameters have been collected. The model
has been applied for all targeted crops in each zone and under both wet and dry meteorological
conditions. The model application methodology is as shown in Fig (4.6). The Figure shows that
Optimisation of Agricultural Water Use
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the model requires a substantial amount of input data. The collection of accurate and consistence
input data is the most difficult part in the model application. This is mainly due to the previous
mentioned limitation of local data and the wide variety of needed data types. Parts of the data have
been collected from local sources and the remaining part has been collected from different
literature. The locally collected data includes daily meteorological information, water quality, and
irrigation methodology. The required plants characteristics such as leaf area index, root depths,
crop factors have been collected from different sources in the literature (Van Dam et a, 1997; SYS,
1993; Allen et al. 1998; Doorenbos et al. 1979; and Doorenbos et al. 1977).
General information
Location
- Time variables for
simulation (start,
end, and duration)
- Simulation process
which should be
considered.
- etc
Salinity characteristics:
- Salinity concentration
in irrigation water and
rainfall
- Salinity dispersion
length
- Relative roots uptake
rate
- etc.
Irrigation
- Irrigation method
- Solute
concentration
- Active time
criteria
- Active depth
criteria
- etc
Plant characteristics
- Length of crop cycle
- Leaf area index
- Crop factor
- Crop root depth
- Yield response factor
- Water and salt stress
Reponses function
- etc.
Swap 2.0
Simulation
Model
Soil characteristics
- Soil layers
- Soil textures
- Initial
groundwater level
- Mualem and Van
Genuchten
parameters
- etc.
Daily meteorological
data
- Daily global
radiation
- Rainfall
- Min. and Max.
temperature
- Humidity
- Wind speed
- rainfall
Model outputs:
- Hydrological water balance
- Irrigation demand and scheduling
- Salinity balance
- Crop relative yield
- etc.
Fig (4.6): SWAP model application methodology
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The soil texture data has been based on a study made by Goris et al.(2001). A pedo transfer
function has been used to account for the SWAP required Mualem and Van Genuchten parameters.
The model has been applied to 28 crops in 16 sub-regional zones and for wet and dry years. This
makes in total 896 runs.
4.3.4 Model Results
The SWAP 2.0 results for the Valencia (citrus) as perennial crop and for Eggplant as
seasonal crop are presented in Fig (4.7) and Fig (4.8) as an example. The figures show high
variations in crop water requirements and yields among the different zones and for the different soil
and meteorological conditions. Hence, there is a substantial space for optimising the spatial crop
pattern. It is important to notice that, due to the substantial amount of inputted data, which are
subjective and the possible uncertainty in the model methodology, a degree of uncertainty is
expected in the model results. The verification of model results is only possible by conducting
long-range field experiments. Conducting field experiments would be out of the scope of this
research. In order to evaluate the performance of the model, two actions have been implemented.
Firstly, the model results for the different crops have been forwarded to relevant people in the study
area, i.e. Palestinian Ministry of Agriculture and Palestinian Water Authority agriculture and water
resources engineers. They have found that, the model results are reasonable. Secondly, a literature
review has been made to compare the literature values of crop water requirements and the model
result values.
For example, Monteith J.L. (1976) has reported the works of Davis and Grass
(1966), Davis et al. (1969) and Bingham et al.(1971). They have conducted a long-term experiment
in order to allocate the water relations of citrus in California. They found that the mean annual
evaporation from vegetation and soil over a year was 720 mm/year. Monteith J.L. (1976) also
presented the work of Kalma (1970).
He has adapted a water balance technique for orange
plantation in Israel. He found that the mean annual evaporation over two year period of about 840
mm. Al- Dadah (2001) has reported that, the yearly average irrigation demand of citrus in Gaza is
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about 1000 mm. Based on their figures, it is clear that the model results for citrus are within the
literature reported range. It is lower than the figure given by Al- Dadah (2001). This is mainly due
to the fact that Al- Dadah (2001) has given a figure based on surface irrigation techniques, which
rafahky
rafahdb
rafahbl
northbl
northbh
middwg
midddb
middbl
khanwg
khanky
khandb
khanbl
khanbh
gazawg
gazabl
10000
9000
8000
7000
6000
5000
4000
3000
2000
gazabh
Irrigation demand .
[m³/hectare]
has much less irrigation effectiveness than sprinkler irrigation.
Zones
Eggplant Wet
Eggplant dry
Valencia wet
Valencia dry
Eggplant Wet
Zones
Eggplant dry
Valencia wet
rafahky
rafahdb
rafahbl
northbl
northbh
middwg
midddb
middbl
khanwg
khanky
khandb
khanbl
khanbh
gazawg
gazabl
80
70
60
50
40
30
20
10
0
gazabh
Yield [ton/hectare].
Fig (4.7): Water demand of Eggplant and Valencia for wet and dry conditions in each sub-regional zone.
Valencia dry
Fig (4 8): Simulated yields for Eggplant and Valencia for wet and dry meteorological conditions in each subregional zone
Optimisation of Agricultural Water Use
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Fig (4.9) below shows the differences in crop water requirement among different crops in
different sub-regional zones.
The difference between some crops can be as much as 5000
m3/hectare.year as can be seen for the difference between cabbage and lemon irrigation demand.
t
pl
an
Eg
g
Sq
ua
ch
St
ra
w
be
ry
Ca
bb
ag
e
cu
cu
m
be
r
m
at
o
To
o
Po
tat
Ol
iv
e
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
Le
m
on
Irrigation demand [m³/hectare.year].
Hence, this fact adds additional need for optimising the spatial and local crop pattern.
C ro p
N o rth b h
K hanbl
Fig (4.9): Water demand for different crops in two sub-regional zones.
4.4 Conclusion
The construction of a complete database for agricultural water use is a very complicated
process. This is mainly due to the different types of information that needed to be included in the
database, which range from socio-economic to biophysical and environmental information and the
potential uncertainties in these data. The author has tried to counteract the uncertainties of model
by as many means as possible. However, a continuous revision and updating of the model-input
data is recommended. The database is presented in Appendix (I).
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Multiobjective Optimisation Model
5. MULTIOBJECTIVE OPTIMISATION MODEL
5.1 Introduction
Planning for agricultural water use has many naturally inter-related and sometimes contradicting
socio-economic and environmental objectives. Those cause difficulties to agricultural water use
planners. The traditional method to solve this problem in the literature was to form a single
objective model that is able to create an optimum solution based on a single principal objective, and
to consider the other objectives as constraints. This approach has been reported in many studies in
different areas, for example: Juan et al. (1996), Banker et al. (1997), Mahmoud (1999), and Prasad
et al. (2001). The second existing method is the weighting method, where weighting coefficients
are attached to each objective in order to form a lump single objective function.
The main
drawback of this method is to weight each objective accordingly. Most previous agricultural water
use planning studies assumed that crop water requirements and crop yield have a constant value for
the whole watershed area. They have rarely considered the potential variations in crop water
requirements and crop yield as a result of spatial variation in soil and meteorological condition.
To tackle these drawbacks, a multiobjective optimisation model has been formulated. The
model forms the core of the IMDSUT decision support system tool.
In the remaining sections of this chapter, the conceptual framework of the multiobjective model
will be described. Then afterwards mathematical formulation of the model will be presented.
Finally the model results will be presented.
5.2 Multiobjective model conceptual framework
The model aims to determine optimum crop pattern in each sub-regional zone that maximises
the model objective function for both wet and dry meteorological conditions.
The model
conceptual framework is sketched in Fig (5.1). Firstly, five single objective models have been
formulated and implemented.
These models aim to: the maximisation of total net profit, the
maximisation of water use effectiveness (US$/m3), the maximisation of irrigated treated wastewater
Optimisation of Agricultural Water Use
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Multiobjective Optimisation Model
quantity, the minimisation of irrigated groundwater quantity, and the minimisation of salinity load
resulted from irrigation. These models will come out with five optimum values for the different
objectives. Secondly, these optimum values have been used to formulate the multiobjective model
objective function.
The formulated multiobjective model objective function accounts
simultaneously for the five previous mentioned objectives. A normalised value technique has been
used to formulate the multiobjective model objective function.
Normalised value technique
standardises the different objectives by dividing it by the optimum value obtained from the single
objective models.
The formulated multiobjective model can determine a single optimum crop pattern for wet and
dry year meteorological conditions that compromises the five contradicting objectives. At the same
time satisfies model constraints and decision variable constraints.
Crop price, crop cultivation costs, local crop demand, available groundwater, available treated
wastewater, level of farmer’s acceptance for treated wastewater use in each zone, groundwater
price, treated wastewater price, crops water requirement and crops yield in each zone and for each
meteorological conditions have been used as input parameters.
The constraints for both
multiobjective model and single objective models are:
-
The crop pattern area should not exceed the available agriculture area in each sub-regional
zone.
-
The treated wastewater demand should not exceed the available treated wastewater quantity in
each sub-regional zone.
-
The ground water demand should not exceed available groundwater in the whole area.
-
The total cultivated area for each crop should be less than or equal to the allocated maximum
area for each crop
-
The crop pattern should have the capacity to satisfy crop product local demand.
-
The percentage of area for treated wastewater use out of the total area should be less than or
equal to the percentage of farmer's who accept to use treated wastewater in their farms, from
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Multiobjective Optimisation Model
the total number of farmer's in each zone (level of farmer's acceptance to used treated
wastewater).
It is important to notice that the last two constraints are only applied to the single objective
models and they are considered as decision parameter constraints in the multiobjective model in
order to facilitate the decision-makers involvement in the decision making process.
A set of decision parameter constraints has been formulated. These parameters will allow the
decision-makers to contribute at an early planning stage by setting target values for important
aspects in agricultural water use that may have substantial socio-economic and environmental
consequences. The decision parameter constraints are:
-
Maximum allowable groundwater quantity that can be used for irrigation.
-
Maximum allowable treated wastewater quantity that can be used for irrigation
-
Expected changes in farmer's acceptance to use treated wastewater for irrigation in their
farms.
-
Percentage coverage of crops product local demand for each crop.
-
Minimum allowable water use effectiveness US$/m3.
-
Maximum allowable salinity load that acceptable to impose in the agriculture land due to
irrigation.
-
Level of spatial equity among farmer's profit. This simply means, the allocations of minimum
profit per hectare that each farmer has to gain as a percentage of average profit over the whole
area.
-
Level of spatial equity in access to the groundwater.
-
Level of spatial equity in access to wastewater.
In the following parts of this chapter the decision parameter constraints will be presented in
more details. In addition to decision parameter constraints, the decision-makers will have the
possibility to attach a weight factor for each objective. The introduction of an objective weight
factor was aimed to help the decision-makers prioritise their planning.
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Socio-economic database:
- Existing crop pattern
- Agriculture area
- Treated wastewater quantity
- Groundwater quantity
- Local crops product demand
- Crops return value
- Crops cultivation cost
- Farmer's acceptance to use
treated wastewater
- Maximum allowable area for
each crop
- Groundwater price
- Treated wastewater price
Biophysical and
environmental database:
- Crops water requirements
- Crops yield
- Salinity load per crops
Single objective models:
- Maximisation of net profit
- Maximisation of water use
efficiency
- Maximisation of irrigated
treated wastewater quantity
- Minimisation of irrigated
groundwater quantity
- Minimisation of salinity load
Single objective model
optimum values:
- Maximum net profit
- Maximum water use
effectiveness
- Maximum treated wastewater
- Minimum groundwater use
Models constraints:
- To not exceed:
• Available agriculture area
• Available groundwater
• Available treated wastewater
• Maximum area for each crop
• Level of farmer acceptance*
- to satisfy crops product local
demand*
* applied only for single objective
Decision Parameter
Constraints:
- Minimum allowable water use
effectiveness
- Maximum allowable
groundwater quantity
- Maximum allowable treated
wastewater quantity
- Maximum allowable salinity
load
- Percentage coverage of local
crops product demand
- Expected changes in farmer's
acceptance for reuse.
- Spatial equity in:
• Profit
• Access to groundwater
• Access to treated wastewater
models
Objectives weighting Factors
Multiobjective
Model
Optimum crop pattern and the corresponding
profit, water use effectiveness, groundwater
demand, treated wastewater demand and
salinity load based on the decision-makers
attached decision parameters and weight
factors.
Fig (5.1): Conceptual formulation of the multiobjective optimisation model
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5.3 Programming language
A Modelling Language For Mathematical Programming (AMPL), has been used as
programming platform for the multiobjective model and IMDSUT decision support system tool.
AMPL offers an interactive command environment for setting up and solving mathematical
programming problems.
AMPL offers the possibility to formulate wide different types of
mathematical models ranging from simple linear programming models to complicated highly nonlinear models. For more information about AMPL the reader should refer to Fourer et al. (1993).
5.4 Mathematical formulation of the multiobjective optimisation model
In the following paragraphs the mathematical formulation of the multiobjective optimisation
model will be introduced. Firstly the mathematical formulation of the single objective models
objective functions will be described. Secondly single objective models and multiobjective model
constraints will be presented. Thirdly, the mathematical formulation of the multiobjective model
objective function will be presented.
Finally the mathematical formulation of the decision
parameter constraints will be described.
5.4.1 Single objective models objective functions
As has been described in the conceptual model, five single objective models have been
formulated and implemented. This is in order to determine an optimum value for each objective.
These values will be used in the formulation of the multiobjective model objective function.
5.4.1.1 Maximisation of net profit
This model objective is to allocate a crop pattern that generates the maximum profit from
irrigated agriculture in the Gaza Strip for both wet and dry years, whilst satisfying the model
constraints. The model will allocate the crop pattern that obtains the maximum profit.
The
mathematical formulation of the model objective function is as shown below.
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MTP = max. (F1)
In which:
MTP = maximum net profit that can be gained from irrigated agriculture (US$/year).
F1 =
16 28
16 28
Ri × Aij × (Yijd + Yijw ) − 2 ×
j =1 i =1
16 28
GWC × Aij × (WD
CC i × Aij −
j =1 i =1
ijd
+ WD
ijw
)
j =1 i =1
16 28
+
16 28
GWC × WWF
i
× Aij × (WD
ijd + WD
ijw ) −
j =1 i =1
WWC × WWF i × Aij × (WD
ijd + WD
ijw )
j =1 i=1
i = crop index
j = zone index
Ri = economical return value for each crop (US$/ton)
Yijd = crop yield in each zone for dry year (ton/hectare)
Yijw = crop yield in each zone for wet year (ton/hectare)
Aij = crop area in each sub-regional zone (hectare)
CCi = crop cultivation cost (US$/hectare)
GWC = groundwater price (US$/m3)
WWC = wastewater price (US$/m3)
WDijd = crop water requirements for each crop in each sub-regional zone and for dry year
(m3/hectare.year)
WDijw = crop water requirements for each crop in each sub-regional zone and for wet year
(m3/hectare.year)
WWF = wastewater factor, which equal 1 for crops that suitable to be irrigated by treated
wastewater base on WHO recommendation and 0 for the other crops
5.4.1.2 Maximisation of water use effectiveness
This model objective is to allocate a crop pattern that enables maximum water use
effectiveness (US$/m3) from irrigated agriculture in the Gaza Strip for both wet and dry years
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whilst satisfying the model constraints. The mathematical formulation of the model objective
function is as shown below.
MEff = max. (F2)
In which:
MEff = maximum water use effectiveness (US$/m3)
#
!
F 2 = !" F1 ÷
#
! 16 28
!
'
" '
$ $
$
& & $
(WDijw + WDijd ) × Aij %
%
j =1 i =1
5.4.1.3 Maximisation of irrigated treated wastewater quantity
Here, the model objective is to allocate a crop pattern that maximise the quantity of treated
wastewater that can be used for irrigation in the Gaza Strip for both wet and dry years whilst
satisfying the model constraints. The mathematical formulation of the model objective function is
as shown below.
MR = max. (F3)
In which: MR = maximum quantity of treated wastewater used (m3/year)
F3 =
(16 (28
(WD ijw + WD ijd ) × Aij × WWF i
j =1 i =1
5.4.1.4 Minimisation of groundwater quantity
This model aims to allocate a crop pattern that minimise the groundwater demand for
irrigation purpose in the Gaza Strip under both wet and dry years in order to satisfy the model
constraints. The mathematical formulation of the model objective function is as shown below.
MGW = min.(F4)
In which: MGW = minimum groundwater quantity (m3/year)
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F4 =
Multiobjective Optimisation Model
)16 )28
(WD ijw + WD ijd ) × Aij −
j =1 i =1
)16 )28
(WD ijw + WD ijd ) × Aij × WWF i
j =1 i =1
5.4.1.5 Minimisation of salinity load
This model aims to allocate a crop pattern, that minimises the salinity load that can result
from irrigation in the Gaza Strip for both wet and dry years whilst satisfying the model constraints.
Salinity loads will highly depend on the crop water requirements and the groundwater quality in
each sub-regional zone. So a crop cultivated in a zone with low water quality and high irrigation
demand will produce high salinity load. The mathematical formulation of the model objective
function is as shown below.
MSL = min (F5)
In which:
MGW = minimum salinity load (ton/year)
F5 =
*16 *28
( SLijw + SL ijd ) × Aij
j =1 i =1
SLd/w = salinity load in kg/hectare is the result of irrigation water quality for each crop in each zone
per ton/hectare and for dry and wet year.
5.4.2 Constraints for single and multiobjective models
The single objective models and the multiobjective model are subjected to a set of
constraints that should be satisfied in order to achieve the principle requirements of the people in
the study area and not to exceed natural existing conditions. The mathematical formulation of the
main five constraints will be presented.
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5.4.2.1 Available agriculture area (16 constraints for 16 Zones)
This constraint has been introduced in order to keep the total suggested cultivated area in
each sub regional zone equal to or less than the available cultivable area in that zone. So 16
constraints have been introduced each corresponding to one sub-regional zone. The constraint
mathematical formulation is as shown below:
+28
Aij ≤ AGAj
i =1
In which: AGAj = the available cultivable agriculture area in each sub-regional zone (hectare).
5.4.2.2 To not exceed available treated wastewater quantity (32 constraints for 16 zones 2
meteorological conditions)
This constraint aims to limit the total treated wastewater demand to be less or equal to
available treated wastewater in each sub-regional zone. 32 constraints have been introduced each
corresponding to one sub-regional zone under wet and dry years. The constraint mathematical
formulation is as shown below: These two equations are respectively for dry and wet year
conditions.
,28
WDijd × WWFi × Aij ≤ AWW j
i =1
-28
WDijw × WWFi × Aij ≤ AWW j
i =1
In which AWWj = Available treated wastewater in each zone (m3/year)
5.4.2.3 To not exceed available groundwater quantity (2 Constraints for dry and wet years)
This constraint aims to ensure that, the groundwater used for irrigation should be less than or
equal to the available ground water quantity. The two equations are respectively for dry and wet
yearly meteorological conditions. The constraint mathematical formulation is shown below
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16
28
.
j =1
16
WD ijd × A ij −
.
i =1
16
j =1
j =1
16
WD ijw × A ij −
/
i =1
/
WWF i × WD ijd × Aij ≤ AW
.
i =1
28
/
28
.
28
/
i =1
WWF i × WD ijw × Aij ≤ AW
j =1
In which: AW = available groundwater for irrigation in the Gaza Strip (m3/year)
It is important to notice that estimation of the available groundwater quantity that may used
for irrigation is a hard task. This is mainly due to the water shortage problem, which will cause
competition among different sectors and the potential to use non-conventional water resources such
as desalinated seawater for domestic water supply in the study area.
The use of the non-
conventional water resources depends mainly upon the development of the peace process, which is
hard to forecast.
5.4.2.4 To not exceed the allocated maximum area for each crop (28 constraints)
This constraint has been introduced in order to keep the model suggested cultivated area for
each crop equal to or less than a pre-specified maximum area. This maximum area is two times the
existing area for fruit trees and five times the existing area for vegetables.
The constraint
mathematical formulation is shown below:
016
Aij ≤ CMAi
j =1
In which: CMAi = the maximium allowable cultivation area for each crop (hectare)
5.4.2.5 To satisfy crops product local demand (56 constraints)
This constraint is applied only in the single objective models. The constraint aims to ensure
that the proposed crop pattern is able to produce enough crop products to at least satisfy local
demand for each crop. The total number of constraints is 56 to account for 28 crops for each wet
and dry year. The constraint mathematical formulation is shown below:
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116
Multiobjective Optimisation Model
Ai × Yijd ≥ DCi
for dry year , and
j =1
216
Ai × Yijw ≥ DCi
for wet year
j =1
In which: DCi = crop product local demand (ton/year)
5.4.2.6 To not allocate area for treated wastewater use in each zone more than the level of
farmer's acceptance to irrigate by treated wastewater in this zone (16 constraints)
This constraint is applied only in the single objective models. The total number of
constraints is 16. The constraint mathematical formulation is as shown below:
328
WWF
j
× Aij ≤ FAF j × AGA
j
i =1
In which: FAFj = levels of farmer's acceptance to irrigate by treated wastewater in each subregional zone (% of farmers)
5.4.3 Objective function of the multiobjective model
The objective function aims to derive an optimum crop pattern based on an optimally
compromise from five naturally contradicting objectives. The function's five main components are
shown below. Each component presents a single objective and it is multiplied by a corresponding
weighting factor. The first objective maximises net profit. The second maximises the water use
effectiveness. The third maximises treated wastewater use. The fourth minimises the ground water
use and as a result it has a negative sign. The fifth minimises the salinity load, and therefore also
with a negative sign.
A normalised value technique has been used to formulate an effective
objective function. By applying the normalised value technique each objective will be standardised
by its maximium or minimum value. The arithmetic sum of these standardised values will form the
multiobjective model objective function.
The theoretically optimum solution should have an
arithmetic sum value of one. That simply means the model has been able to allocate a crop pattern
that gives the optimum value for each single objective. This would be impossible due to the
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contradicting nature of the different objectives. For example, it is impossible to achieve maximium
profit while using minimum groundwater quantity. The mathematical formulation of the
multiobjective function is as fellow:
9
6
F1
F2
F3
F4
F5 4
MaxVa = max .8 PF × (
) + EF × (
) + RF × (
) − GF × (
) − SF× (
)5
MTP
MEff
MR
MGW
MSL
7
In which
PF= Profit weighting factor
EF = Water use effectiveness weighting factor
RF = Treated wastewater use weighting factor
GF = Groundwater use weighting factor
SF = Salinity load weighting factor
It is important to notice that MTP, MEff, MR, MGW, and MSL were obtained by the single
objective models and have constant values in the multiobjective model objective function.
5.4.4 Multiobjective model decision parameter constraints formulations
Planning for agricultural water use has substantial socio-economic and environmental
consequences. The decision parameter constraints cover the most important parameters that raise
most of these consequences. The values of these parameters are supposed to be set by the decisionmakers with the help of decision-making charts. The decision parameters have two main purposes.
Firstly, to facilitate the decision makers contribution to the planning. Secondly, to impose initial
boundary conditions on the potential solution. This will protect the model from producing
catastrophic solution such as a high groundwater demand or a very low water use effectiveness.
The formulation of decision parameter constraints will also offer the possibility to evaluate the
model sensitivity to these parameters.
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5.4.4.1 Maximum allowable use of groundwater
Groundwater is the only natural water source in the Gaza Strip. This decision parameter
will offer the decision-makers the possibility to allocate a maximium allowable quantity of
groundwater that can be used for irrigation. This quantity will depend upon the availability of water
resources and on the decision makers level of consideration to each potential socio-economic and
environmental consequences that may result from the provision of such groundwater. In the model,
groundwater has been treated as both objective and decision parameter constraint.
Treating
groundwater as decision parameter constraint aims, in addition to the previously mentioned reason,
to ensure that the model will not come out with a catastrophic solution such as high demand.
Therefore the decision parameter constraint forms an initial boundary condition for the model. The
mathematical formulation of the groundwater decision parameter constraint is:
?
= 16
=
> @
i =1
F
D 16
D
E G
i =1
28
16
WD ijd × Aij - @
@
28
@
j =1
i =1
j =1
28
16
28
G
j =1
WD ijw × A −
G
i =1
G
<
:
WWF i × WD ijd × A ij ; : ≤ MAGW
for dry year
C
A
WWF i × WD ijw × Aij B A ≤ MAGW
for wet year
j =1
In which: MAGW = maximum allowable quantity of groundwater that can be used for irrigation
(m3/year)
5.4.4.2 Maximum allowable use of treated wastewater
Treated wastewater is a potential non-conventional water resource in the Gaza Strip. This
decision parameter will give the decision-makers the possibility to specify the maximum treated
wastewater quantity that can be used for irrigation. This quantity depends mainly on the socioeconomic and environmental values and potential consequences of treated wastewater use. In the
model, treated wastewater has been handled as both objective and decision parameter constraint.
This is due to the same previously mentioned reasons in the groundwater case. The mathematical
formulation of the groundwater decision parameter constraint is:
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H16
H 28
i =1
I16
WWF i × WD ijd × A ij ≤ MWW
for dry year
j =1
I28
i =1
Multiobjective Optimisation Model
WWF i × WD
ijw
× A ij ≤ MWW for wet year
j =1
In which: MWW = maximum irrigated treated wastewater quantity in (m3 /year)
5.4.4.3 Expected changes in farmers acceptance
This decision parameter will give the decision-makers the possibility to evaluate the
influence of changing the level of farmer's acceptance in the agricultural system output and to
evaluate the model sensitivity to this important parameter. This evaluation would help them in
setting up a Farmer's awareness campaign, or any type of measures to improve the level of farmer's
acceptance and to allocate a financial budget for this purpose. The mathematical formulation of the
groundwater decision parameter constraint is:
J28
WWF
j
× Aij ≤ FAF j × TGA j × ECFA
j
i =1
In which: ECFA = expected change in farmer's acceptance for treated wastewater use in each zone
(% increase of farmers acceptance)
5.4.4.4 Percentage coverage of crop products local demand
This decision parameter constraint aims to offer the decision-makers the possibility to set
values for percentage local coverage of crop product demand. This is a very important a priori
decision because it might be more reasonable to import crops with very low profitability and very
high water consumption than to produce it locally. However certain autonomy of production is also
an important factor under politically unstable conditions. The mathematical formulation of the
groundwater decision parameter constraint is:
K16
For each crop
Ai × Yijd ≥ X i × DCi for dry year
j =1
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L16
Ai × Yijw ≥ X i × DCi for wet year
For each crop
j =1
In which: Xi is the decision parameter with value range between 0 for 0% local coverage and 1for
100% local coverage of product demand.
5.4.4.5 Minimum water use effectiveness (US$/m3)
This decision parameter will give decision-makers the possibility to specify the minimum
profit that should be gained by using each cubic meter of water in agriculture. This decision
parameter has important economical and environmental consequences. In a way that it will not be
economically reasonable to use water for agricultural purpose if the water use effectiveness is less
than the opportunity cost of water. In the model, water-using effectiveness has been also treated as
both objective and decision parameter constraint for the same previously mentioned reasons. The
mathematical formulation of this decision parameter is:
For wet year
S
Q M16 M28
Q
Q
Q
Q
R
Ri × Aij ×Yidw −
j =1 i =1
+
M16 M28
CCi × Aij −
j =1 i =1
M16 M28
M16 M28
P
N
GWC × Aij ×WDijw
N
j =1 i=1
GWC ×WWFi × Aij × WDijw −
j =1 i =1
M16 M28
N
WWC ×WWFi × Aij × WDijwO
N
N
≥ MWUEw ×
M16 M28
WDijw × Aij
j =1 i=1
j =1 i =1
For dry year
Z
X T16 T28
X
X
X
X
Y
Ri × Aij × Yijd −
j =1 i =1
+
T16 T28
T16 T28
CCi × Aij −
j =1 i =1
GWC × WWFi × Aij × WDijd −
j =1 i =1
T16 T28
W
GWC × Aij × WDijd
j =1 i =1
T16 T28
WWC × WWFi × Aij × WDijd V
U
U
U
≥ MWUEd ×
U
U
T16 T28
WDijd × Aij
j =1 i =1
j =1 i =1
In which:
MWUEw = minimum water use effectiveness for wet year (US$/m3/year)
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MWUEd = minimum water use effectiveness for dry year (US$/m3/year)
5.4.4.6 Maximum allowable salinity load (ton/year)
This decision parameter constraint allows the decision-makers to allocate a value for the
maximum salinity load resulting from irrigation that can be opposed on the agricultural land. This
load depends on the spatial distribution of crop pattern, since the salinity load from each crop is
spatially dependent. This results from the spatial variation in irrigation water quality and crop water
requirement. Salinity load is a very important environmental and economical factor in the sense
that high salinity load will increase the salt content in the agriculture soil and this will highly reduce
the land productivity. At the same time, putting a high restriction on the salinity load will result in a
reduction of the total profit. In the model, salinity has been treated as both objective and decision
parameter constraint for the same previously mentioned reasons. The mathematical formulation of
the salinity load decision parameter constraint is:
]
[
[
\
^
` ^
a16 a28
SL ijw × Aij _ ≤ MASL w For wet year
j =1 i =1
]
[
[
\
a16 a28
^
` ^
SLijd × Aij _ ≤ MASL d For dry year
j =1 i =1
In which:
MASLd = Maximum allowable salinity load for dry year (ton/year).
MASLw = Maximum allowable salinity load for wet year (ton/year).
5.4.4.7 Spatial equity in access to profit (US$/hectare)
The spatial equity in access to profit is a very important decision parameter, where it offers
the decision-makers the possibility to equally distribute the profit per hectare to the farmers. This is
met through setting a minimum value out of the average profit, that each farmer has to gain. Spatial
equity in profit distribution among the farmers will highly improve the implementation of an
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optimal crop pattern. This decision parameter will offer the possibility for the decision-makers to
set a pre-specified value for the level of spatial equity distribution. This value may range between
one, which means all farmers will have the same profit, to zero which means there is no constraint
regarding profit distribution among the farmers. The mathematical formulation of this decision
parameter constraint is as follows:
For each zone and for dry year.
g g
e e i =28
e
f f h
i =28
d
d
b
b
b
Ri × Ai ×Yid − h CCi × Ai c ×TAc ≥
i =1
g
e
e
f
i =1
g
e j =16 i =28
e
h
f h
d
j =16 i =28
Ri × Aij ×Yijd − h
j =1 i =1
h
j =1 i =1
d
b
b
b
CCi × Aij c × AGAj c b × EQF
For each zone and for wet yea.
n n
l l i =28
l
m m o
i =28
k
k
i
i
i
Ri × Ai ×Yiw − o CCi × Ai j ×TAj ≥
i =1
i =1
n
l
l
m
n
l j =16i =28
l
o
m o
k
j =16 i =28
Ri × Aij ×Yijw − o
j =1 i =1
o
j =1 i =1
k
i
i
i
CCi × Aij j × AGAj j i × EQF
In which:
TA is total available agriculture area (hectare)
EQF is spatial equity factor with a value ranging from 0 to1
5.4.4.8 Spatial equity in access to groundwater (m3/hectare)
The spatial equity in access to ground water resources is an important decision parameter c,
where it offers the decision-makers the possibility to spatially allocate the right of access to
groundwater in cubic meter per hectare among farmers. Access to groundwater is very important to
farmers, since it will allow the farmers to cultivate vegetables, which has the highest financial
return. Considering spatial equity in access to groundwater coupled with spatial equity in access to
profit will highly improve the implementation possibility of the optimal crop pattern. The spatial
equity value may range between one, which means all farmers in all zones will have the same
quantity of groundwater, to zero which means there are no constraints regarding to the access to
groundwater among the farmers and it will depend on the crop allocation. The consideration of
spatial equity in access to groundwater will have socio-economic and environmental consequences.
These consequences should be highly considered by the decision-makers. A decision support chart
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has been prepared in order to help the decision-makers in evaluating the consequences of their
decision. The mathematical formulation of this decision parameter constraint is as follows:
For each zone and for wet year:
u u
s s i =28
s
t t v
r
r
i = 28
p
p
p
WDiw × Ai − v WDiw ×WWFi × Ai q ×TAq ≥
i =1
u
s
s
t
u
s j =16i =28
s
v
t v
WDiw × Aij − v
i =1
j =1 i =1
v
j =1 i =1
r
r
j =16i = 28
p
p
p
WDiw ×WWFi × Aij q × AGAj q p × EGF
For each zone and for dry year :
| |
z z i = 28
z
{ { }
y
i =28
y
w
w
w
WDid × Ai − } WDid ×WWFi × Ai x ×TAx ≥
i =1
i =1
|
z
z
{
|
z j =16i =28
z
}
{ }
j =16i =28
WDid × Aij − }
j =1 i =1
}
j =1 i =1
y
y
w
w
w
WDid ×WWFi × Aij x × AGAj x w × EGF
In which:
EGF is spatial equity factor in access to groundwater with a value ranging from 0 to1
5.4.4.9 Spatial equity in access to treated wastewater (m3/hectare)
The spatial equity in access to treated wastewater is an important decision parameter from
two aspects. Firstly, the farmer's interest in using treated wastewater will be very low due to the
low financial output of fruit trees, which are suitable for treated wastewater use based on WHO
recommendation. Secondly, it is of main interest for decision-makers to use as much as possible of
treated wastewater in irrigation, in order to keep groundwater for domestic purpose. Therefore it is
of extreme importance to as much as possible equally distribute the treated wastewater among
farmers to reduce the level of potential conflict between farmers and decision-makers and this will
improve the implementation possibility of the optimum crop pattern. The spatial distribution of
treated wastewater will have also economical consequences, which should be considered by the
decision-makers.
A decision support chart will be prepared to help the decision-makers in
evaluating the consequences of their decision.
The mathematical formulation of this decision
parameter constraint is as follows:
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For each zone J and for wet year:
ƒ ƒ
  i =28

‚ ‚ „
€
€
~
~
~
WDiw ×WWFi × Ai  × TA ≥
ƒ


‚
i =1
ƒ
 j =16 i =28

„
‚ „
j =1 i =1
€
€
~
~
~
WDiw ×WWFi × Aij  × AGAj  ~ × EWF
For each J and for dry year :
Š Š
ˆ ˆ i =28
ˆ
‰ ‰ ‹
‡
‡
…
…
…
WDid ×WWFi × Ai † ×TA† ≥
i =1
Š
ˆ
ˆ
‰
Š
ˆ j =16 i =28
ˆ
‹
‰ ‹
j =1 i =1
‡
‡
…
…
…
WDid ×WWFi × Aij † × AGAj † … × EWF
In which:
EWF is the spatial equity factor in access to treated wastewater with a value ranging from 0 to 1
5.5 Multiobjective optimisation results
The model has been implemented for the study area with decision parameters constraints values
as shown in table (5.1). These values give the highest degree of freedom to the model. A high
value is given for the percentage coverage of local product demand decision parameter constraints,
which means that any resulted crop pattern have to 100% satisfy the local products demand and that
is in consistence with the constraints in single objective models. Table (5.2) below presents the
result summary for the multiobjective model and the five single objective models in addition to the
existing crop pattern under wet year condition.
It is important to mention that, groundwater
demand, treated wastewater, and crops yield for the existing crop pattern have been calculated
based on SWAP model result, which highly improves the irrigation efficiency use in comparison
with the existing practices in the study area. The table shows that the multiobjective model has a
much better performance than the existing crop pattern and is able to allocate a compromise among
the different objectives. For example, in comparison to the maximisation of profit model, with only
a reduction of 9 % in profit, the multiobjective model has been able to find a crop pattern that
increases water use effectiveness by 4%, increase wastewater use by 5%, reduce irrigation demand
by 14%, reduces groundwater demand by 43%, and reduce salinity load by 25% as shown in Table
(5.3).
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To have a more clear vision of the result Fig (5.2) and Fig (5.3) have been prepared. They show
the performance of the different models and the existing crop pattern regarding the five main
economic and environmental parameters (profit, water use effectiveness, groundwater demand,
wastewater demand, and salinity load). The different parameters have been standardised by it is
corresponding optimal value. So a model output value near to one means that it is close to it's
optimum. The figures clearly show the advantages of the multiobjective model over the other
models and the existing crop pattern. Fig (5.4) presents the optimum crop pattern, which results
from the multiobjective model. The figure shows the crop types distribution in each zone, which
can be implemented to achieve the optimum objective values. Fig (5.5) presents the existing crop
pattern. A quick comparison between the existing crop pattern to the model produced crop pattern
shows the following: each sub-regional zone in the existing crop pattern is cultivated with a wide
range of crops. While in the model produced crop pattern consists off much smaller range of crops.
This is mainly due to the fact that existing crop pattern are allocated by the farmer's without any
means of planning.
As a conclusion, the proposed multiobjective model has proved to be superior over the
existing crop pattern and has been able to account for the different socio-economic and
environmental aspects of agricultural water use. More details about the model performance and the
role of the decision parameters and weight factors will be presented in the next chapter.
Table (5.1): Decision parameters for the multiobjective model.
Decision parameters
Value
Percentage coverage of local product demands
Minimum quantity of treated wastewater use
Maximum quantity of groundwater use
Minimum water use effectiveness
Maximum allowable salinity load
Expected change in farmers acceptance for reuse
Profit spatial equity factor
Spatial equity in access to groundwater
Spatial equity in access to treated wastewater
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Dry year
Wet year
100
10 Mm3
60 Mm3
0
8 Mkg
0
0
0
0
100
10 Mm3
60 Mm3
0
8 Mkg
0
0
0
0
Decision Support System
Chapter V
Multiobjective Optimisation Model
Table (5.2): Main outputs of the multiobjective model, the five single objective models and the existing crop
pattern.
Model
Groundwater Wastewater Irrigation
Mm3
Mm3
Mm3
17.27
25.92
43.18
Multiobjective
24.63
24.64
49.27
Max. profit
14.33
38.16
52.48
Max. Wastewater
20.58
20.20
40.78
max W.U.eff.
12.90
35.38
48.28
min groundwater
17.90
20.69
38.59
min salinity
b
51.17
36.66
51.17
Exist crop pattern
Salinity
M.kg
47.6
59.5
70.1
47.9
63.7
36.6
67.5
Profit W.U.effa.
M. US$ US$/m3
78.59
1.82
85.85
1.74
50.58
0.96
79.19
1.94
51.69
1.07
53.70
1.39
55.64
1.08
a:W.U.eff = water use effectiveness . b: Presently there is no wastewater use, this value is equal to the potential quantity
of treated wastewater that can be irrigated in the existing crop pattern.
Table (5.3): Differences in the main outputs of the five single objective models and the exist crop pattern in
percentage of the multiobjective model solution
Groundwater Wastewater Irrigation
Salinity
Profit W.U.effa.
Mm3
Mm3
Mm3
Thousand ton M. US$ US$/m3
0
0
0
0
0
0
Multiobjective
-43
-5
-14
-25
9
-4
Max. profit
17
47
-22
-47
-36
-47
Max. Wastewater
-19
-22
6
0
1
7
max W.U.eff.
-25
37
-12
34
-34
-41
min groundwater
-4
-20
11
23
-32
-24
min salinity
16
41
-18
-42
-29
-41
Exist crop pattern
Model
groundwater
2
1,5
1
Water Using efficiency
Wastewater
0,5
0
profit
Salinity
Multiobjective
max. Water ise effectiviness W.U.eff.
Max. profit
Min. groundwater
Fig (5.2): Standardised comparison of the performance of multiobjective model and maximisation of profit,
maximisation of water use effectiveness, and minimisation of groundwater single objective models.
Optimisation of Agricultural Water Use
76
Decision Support System
Chapter V
Multiobjective Optimisation Model
groundwater
2
1,5
1
Water Using efficiency
Wastewater
0,5
0
profit
Multiobjective
Salinity
Min. salinity
Max. wastewater
Exist
Fig (5.): Standardised comparison of the performance of multiobjective model and minimisation of salinity
load, maximisation of wastewater, single objective models, and existing crop pattern
N
W
E
S
Cab bage
Cau liflo_a
Cau liflo_s
Citr us_ oth
Cuc um b_s p
Cuc um b_s
Eggp lan t_w
Eqq_ pla nt_
Gua va*
Gra pefruit
Jew's_me lo
Lim on
Oliv e
Onion
Pepp er_a
Pepp er_sp
Pota to_ w
Pota to_ s
Sham oti
Squa sh _sp
Squa sh _s
Stra wbe rry
S._p ota to
Tom ato _sp
Tom ato _s
Vale ncia
W aterm _w
W aterm _s
Fig (5.4): Optimum crop pattern based on multiobjective model
Optimisation of Agricultural Water Use
77
Decision Support System
Chapter V
Multiobjective Optimisation Model
N
W
E
S
Cab bage
Cau liflo_a
Cau liflo_s
Citrus_oth
Cuc um b_s p
Cuc um b_s
Eggp lan t_w
Eqq_ pla nt_
Gua va*
Grapefruit
Jew's_ me lo
Lim on
Olive
Onio n
Pepp er_ a
Pepp er_ sp
Pota to_ w
Pota to_ s
Sham ot i
Squa sh _sp
Squa sh _s
Stra wbe rry
S._p ota to
Tom ato _sp
Tom ato _s
Vale nci a
W at erm _w
W at erm _s
Fig (5.5): Existing crop pattern
Optimisation of Agricultural Water Use
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Decision Support System
Chapter VI
IMDSUT Performance Analysis
6. INTEGRATED DECISION SUPPORT TOOL (IMDSUT) PREFORAMNCE
ANALYSIS
IMDSUT capacity and performance as a decision support tool for agricultural water use
planning will be evaluated and analysed throughout this chapter. In the first part, an evaluation of
the different decision parameters will be made and the model's sensitivity to these parameters will
be discussed. Decision support charts for each decision parameters will be presented and analysed.
In the second part, a set of scenarios will be formulated in order to get more insight into the tool
capacity and performance. The outputs of these scenarios will be evaluated and analysed.
6.1 Evaluation of the decision parameters
IMDSUT has 14 different decision parameters. They can be classified as following:
-
Five objective weight factors
-
Three decision parameters for the allocation of: maximum groundwater quantity, minimum
treated wastewater quantity, and maximum salinity load
-
Three decision parameters to specify the level of spatial equity in rights of access to: profit; to
groundwater; and to treated wastewater resources
-
One decision parameter to specify the percentage coverage of local crops product demand
-
One decision parameter to specify the potential changes of farmer's acceptance to use treated
wastewater
6.1.1 Weights for the objectives
Attaching values for the objective weight factors forms an integrated part of the decision
support system tool. The weight factors were formulated to allow the decision-makers to rank their
priorities concerning the different socio-economic and environmental influencing aspects in the
agricultural water use planning. Given a zero value to objective, this simply will result in complete
Optimisation of Agricultural Water Use
79
Decision Support System
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ignorance of that objective, on the other hand, given a high weighting factor, will result in this
objective superseding the others. The decision-makers are entitled to attach a weighting factor
value to each of the different five objectives, which form the multiobjective model objective
function as shown in Chapter V, page 67. In the following parts, the effects of attaching weight
factor values for the different objectives will be analysed and decision support charts, which have
been prepared in order to support the decision making process will be presented. The decision
support charts for weight factors have been prepared under the following condition:
-
A different weighting will be attached to the target objective, while the remaining objectives
will have an equal weighting of one.
-
A unique set of values for the decision parameter constraints has been used for the construction
of the different weight factor decision support charts. The set is presented in Table (5.1) in
chapter V, page 75.
The model has been run under a wide range of weight factor values starting from 0.01 up to
100 in order to formulate decision support charts for the different objective weight factors and to get
more insight into the model sensitivity to the objectives weight factors. The decision support charts
for weight factors and for all objectives are presented in Appendix (II). Fig (6.1) and Fig (6.2)
below show the effects of both the groundwater and profit weight factors on socio-economic and
environmental aspects of agricultural water use. From these charts and from those charts in
Appendix (II), the following can be noticed:
- The sensitivity of the model output is very low to either extreme high or low weight factor
values. One can notice this by observing the gentle gradient after a weight factor value of 2
and behind a weight factor value of 0.5 for most of the objectives as shown in the Fig (6.1).
The model constraints and the balance between the different inter-related and contradicting
objectives are the main influences, which reduce model sensitivity to high or low weight factor
values. This can be explained as follows:
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80
70
Million.
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Groundwater weight factor
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Fig (6.1): Decision support chart for groundwater weight factor showing its influence to the different socioeconomic and environmental aspects of agricultural water use
90
80
Million.
70
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Profit weight factor
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Fig (6.2): Decision support chart for profit weight factor showing its influence to the different socioeconomic and environmental aspects of agricultural water use
•
The constraint of "to not exceed the total agriculture area", and the constraint "to not exceed
the allowable maximum cultivated area for each crop", in addition to the constraint of
"satisfying local crops product demand" will bound the optimum value for each objective.
After achieving the optimum value, an increase or decrease in the weight factor will have a
very limited impact on the model outputs.
•
The different objectives are inter-related and sometimes contradicting. The basic principle of
the model is to find a means to compromise between these objectives. This balance will
highly reduce the sensitivity of the model to extreme weight factor values. For example,
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when attaching low weight factor for groundwater, the balance of powers in the objective
function between the maximum profit and maximum treated wastewater use will control the
model result. It is important to mention that crops cultivated by treated wastewater have low
return values and high irrigation demands.
•
The maximum profit is not highly related with the other objectives except water use
effectiveness objective. Therefore satisfaction of the constraints is the only limitation that
restricts this objective. From this, the model is relatively sensitive to attaching a high profit
weight factor value in comparison to other weight factors as shown in the steep gradient of
Fig (6.2).
6.1.2 Allocation of maximum quantity for groundwater , treated wastewater, and salinity
load
Deciding the maximum quantity of groundwater that can be used for irrigation and the
minimum wastewater quantity that should be used, in addition, the allocation of maximum salinity
load that can be accumulated in the soil due to irrigation are very important decision parameters.
Decision support charts, which aid the decision-makers in their thought process, have been made.
The preparation of these charts was done by implementing the decision support tools for a wide
range of potential values for each parameter. The decision support charts are presented for each
parameter in Appendix (II). In the following paragraph, the decision support charts for groundwater
will be analysed and discussed as an example.
6.1.2.1 Allocation of maximum groundwater quantity
Groundwater is a limited resource, so it is important to offer the decision-makers the
possibility to specify the maximum quantity of groundwater that can be used for irrigation. To do
so, a decision support chart for groundwater decision parameter has been prepared.
The
multiobjective model has been implemented for wide range of maximum allowable quantities of
Optimisation of Agricultural Water Use
82
Decision Support System
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IMDSUT Performance Analysis
groundwater.
Fig (6.3) below shows the effects at various levels of maximum groundwater
Million.
quantities allocation on socio-economic and environmental aspects of agricultural water use.
90
80
70
60
50
40
30
20
10
10
15
20
25
30
35
40
Groundwater [Mm³]
Wastewater [m³]
Irrigation [m³]
Salinity [kg]
Profit [US$]
Fig (6.3): Decision support chart for allocation of maximum groundwater under dry year conditions
The figure shows that a minimum groundwater quantity of about 14 Mm3 /year is needed to
satisfy local crop product demands. A small increase in this quantity comes with a substantial
increase in the profit. The maximum profit can be achieved by setting a groundwater quantity of
about 20 Mm3 /year. This high increase in profit results from the cultivation of high profitability
crops such as strawberries.
Mm3/year.
The profit value remains relatively stable up to the level of 35
At this stage the profit sharply declines as the model has been forced to use a
groundwater quantity more than it is optimum capacity. In order to satisfy this, the model allocates
crops, which have the highest irrigation demand, without any consideration to the other socioeconomic and environmental aspects. Biswas has reported that "the efficient economic allocation
of water is determined by that amount of water which, if allocated to each user in the basin, results
in the highest return for the amount of water available" (Biswas, 1996). Based on that the optimum
allocated groundwater quantity for irrigation should be about 20 Mm3/year.
Due to the limited water availability in the study area, each cubic meter of water has to have
a high return value that is at least equal to the opportunity cost of water. The model has been used
to determine the marginal value of groundwater that is used for irrigation. The marginal value of
Optimisation of Agricultural Water Use
83
Decision Support System
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IMDSUT Performance Analysis
groundwater is the amount of profit that can be gained by using an additional cubic meter of water.
Allocation of groundwater marginal value is an essential aspect since it will help the decisionmakers in allocating the maximum quantity of groundwater for irrigation proposes and in setting a
groundwater pricing strategy. The marginal value is characterised by that after achieving the
optimum marginal value, as more and more water is used, the marginal value declines because of
the law of diminishing marginal productivity (Biswas, 1996). Fig (6.4) below is consistence with
this law, where the marginal value of groundwater is increasing up to optimum value and again
starts to decline rapidly.
Marginal value [US$/m³]..
3 ,3
2 ,8
2 ,3
1 ,8
1 ,3
0 ,8
10
15
20
25
30
35
40
G ro u n dw ater [M m ³]
W et
D ry
Fig (6.4): Groundwater marginal value
As a conclusion, the two figures present a good insight to the sensitivity of the model and
may help the decision-makers in setting a value for the maximum groundwater quantity that can be
used for irrigation purposes.
Similar charts for treated wastewater quantity and salinity load
quantity, are presented in Appendix (II).
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6.1.3 Spatial equity in rights of access to profit, groundwater, and treated wastewater
resources
Spatial equity means that the farmers should have an equal right of access to the
groundwater and treated resources and to the profit in all sub-regional zones. The equity in access
is very important in order to facilitate the implementation of any crop pattern development plan
through enhancing the farmer's acceptance to this plan. It is fact that farmers would like to have
more profit, as a result they will ask for more groundwater rights and will have much less interest
for treated wastewater rights, due to the low profitability of crops irrigated by treated wastewater.
So it is important to find means to equally distribute these resources among farmers in order to gain
their support for the implementation of the crop pattern management plan.
Spatial equity has socio-economic and environmental cost, which should be considered
during the spatial equity level allocation. To support the decision making process and to evaluate
the influence of spatial equity in the different socio-economic and environmental aspects of
agricultural system, the model has been implemented for wide range of spatial equity factors and for
the three resources. Fig (6.5) below presents the decision support chart for spatial equity in profit
under dry condition. The decision support charts for other resources under wet and dry conditions
are presented in Appendix (II). The percentage equity in profit means that each farmer will gain at
least this percentage of profit out of the average profit per hectare. It is clear from the Fig (6.5) that
considering high percentage of spatial equity will result in a high reduction in the agricultural
system profitability and will slightly affect the other factors. So as a conclusion, the agricultural
system profitability is highly sensitive to the spatial equity in profit and it is the role of decisionmakers to allocate the needed level of equity.
Optimisation of Agricultural Water Use
85
Decision Support System
Chapter VI
IMDSUT Performance Analysis
75
Million.
55
35
15
No equity
50
60
70
80
90
Percentage equity in profit
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Fig (6.5): Decision support chart for allocation of spatial equity of right of access to profit
6.1.4 Percentage coverage of local products demand
Self-food sufficiency is a debatable strategy among water resources scientists. This strategy
still has strong support among decision-makers especially due to the unstable political situation in
the whole world and especially in the study area. The important question is how much the decisionmakers are ready to pay in order to implement such a strategy. To study the effects of this strategy,
percentage reduction factors in the local coverage of crops product demand have been implemented,
which range from 100% self-sufficiency to 50% self-sufficiency for all crop demands in order to
formulate a decision support chart. Fig (6.6) shows the effects of these reductions on the different
economic and environmental aspects of the agricultural water use for dry year condition. The wet
year decision support chart is presented in Appendix (II).
The improvements in profit and
environmental values are very clear. This is mainly due to the reduction of the cultivated areas for
some crops that are characterised by very low profitabilities and very high water demands, like
watermelon and fruit trees in general. These results support highly the standpoint against the
strategy of self-food sufficiency.
Far from the self-food sufficiency strategy, a good estimation of crop product demands is
very important and very sensitive aspect as shown in Fig (6.6). The diagram that shows a 10%
reduction in crop product local demands will add about 4 Million US$ to the total profit and will
Optimisation of Agricultural Water Use
86
Decision Support System
Chapter VI
IMDSUT Performance Analysis
reduce the groundwater demand by about 1 million m3. Therefore good estimation of local crop
Million.
products demand is a very crucial aspect in agricultural water system planning.
90
80
70
60
50
40
30
20
10
50
60
70
80
90
100
Percentage coverage of local crops product demand
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Fig (6.6): Decision support chart for allocation of percentage coverage of crops product local demands
6.1.5 Changes of farmer's acceptance to use treated wastewater
The degree of success of treated wastewater use projects is highly dependent on the level of
farmer's acceptance. This means the percentage of farmers out of the total numbers of farmers, who
are willing to irrigate their crops by treated wastewater. So it is important to consider the spatial
distribution of the level of farmers' acceptance at an early planning stage. The use of treated
wastewater use is essential in the study area since it is the main sustainable non-conventional water
resources that may cover part of the irrigation water demand. The existing distribution of level of
farmers' acceptance to use treated wastewater has been presented in chapter IV. This distribution
can be changed by different means such as implementation of farmers' awareness campaign. So it
is important for decision-makers to evaluate the agricultural system sensitivity towards changing the
level of farmer's acceptance. Based on that, a level can be set for potential changes in farmers'
acceptance and they can allocate financial budget for this purpose. A a decision support chart has
been prepared for this purpose as shown in Fig (6.7) for a dry year condition. The chart for wet
year condition is presented in Appendix (II). Out of the diagram, the reader can notice that, the
increase or reduction in level of farmers acceptance will slightly change the profit, but will
Optimisation of Agricultural Water Use
87
Decision Support System
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IMDSUT Performance Analysis
influence highly the treated wastewater demand. That means, by applying farmers' awareness
campaign, the decision-makers can increase the treated wastewater demand, decrease the
groundwater demand, whilst the profitability of the system is the same.
Million.
75
55
35
15
-30
-20
-10
0
10
20
30
Percentage changes in farmer's acceptance
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Fig (6.7): Decision support chart for changes to the farmers acceptance decision parameter under dry
year condition
6.2 Formulation of scenarios
The main goal of this study is to prepare a decision support system tool for agricultural
water management that considers the socio-economic and environmental consequences of
agricultural water use. The decision-makers role has been specified in the previous sections. This
role concentrates on attaching values for a set of decision parameters. The attaching of these values
will form different potential development scenarios. The combinations of decision parameter
values may result in infinite numbers of potential development scenarios. However, the author has
prepared five potential development scenarios. The formulation of these scenarios was aimed to
evaluate the tool ability to consider the different decision parameters and to understand the tradeoffs among the different objectives that form the multiobjective model objective function. Table
(6.1) below presents the five development scenarios and their corresponding decision parameter
values.
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88
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IMDSUT Performance Analysis
The first scenario is called the economy scenario.
This scenario aims to allocate an
optimum crop pattern that produces the best economic return. The scenario is characterised by the
following:
-
High weights for profit and water use effectiveness objectives
-
30% reduction of the local crop product demands coverage
-
70% spatial equity in access to profit
-
A minimum water use effectiveness of al least 1.7 US$/m3
-
High flexibility on the remaining decision parameters
The second scenario is called the wastewater scenario. This scenario aims to allocate an
optimum crop pattern that maximises the wastewater use in the study area.
The scenario is
characterised by the following:
-
High weight for treated wastewater objective
-
Minimum treated wastewater use of at least 35 Mm3
-
20% in farmer's acceptance for reuse
-
70% spatial equity in access to treated wastewater
-
High flexibility on the remaining decision parameters
The third scenario is called the groundwater scenario. This scenario aims to allocate an optimum
crop pattern that minimises the groundwater use in the study area. The scenario is characterised by
the following:
-
High weight for groundwater objective
-
Maximum groundwater use of 20 Mm3
-
70% spatial equity in access to groundwater
-
High flexibility on the remaining decision parameters.
The fourth scenario is called the environment scenario. This scenario aims to allocate an
optimum crop pattern that optimises the different environmental aspects of agricultural water use.
The scenario is characterised by the following:
Optimisation of Agricultural Water Use
89
Decision Support System
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IMDSUT Performance Analysis
-
High weights for groundwater, treated wastewater, and salinity load objectives
-
Maximum groundwater use of 20 Mm3
-
Minimum treated wastewater use of 35 Mm3
-
Maximum salinity load of 65 M.kg
-
High flexibility on the remaining decision parameters
Table (6.1): Decision parameter values for the proposed five development scenarios
Decision parameters
Profit weight factor
Water use effectiveness weight factor
Treated wastewater weight factor
Groundwater weight factor
Salinity load weight factor
Percentage coverage of local product demands
Minimum quantity of treated wastewater Mm3
Maximum quantity of groundwater use Mm3
Minimum water use effectiveness US$/ m3
Maximum allowable salinity load M.kg
Changes in farmers acceptance for reuse %
Spatial equity in profit
Spatial equity in access to groundwater
Spatial equity in access to treated wastewater
A*
B*
10
10
1
1
1
70
20
40
1.7
80
0
70
50
50
1
1
10
1
1
70
35
25
1.2
80
+20
50
50
70
Scenarios
C*
D*
1
1
1
10
1
70
30
20
1.2
80
0
50
70
50
1
1
10
10
10
70
35
20
1.2
65
0
50
50
50
E*
1
1
1
1
1
70
10
60
1
80
0
0
0
0
A: economy: B: Wastewater, C: Groundwater, D: Environment, E: maximum freedom
The fifth scenario is called the maximum freedom scenario. This scenario aims to allocate an
optimum crop pattern that optimises the agricultural water use under the following conditions:
-
All objectives have similar weights
-
High flexibility to be given to all decision parameters
6.2.1 Analysis of scenarios results
The proposed scenarios have been implemented in the IMDSUT. The scenario results for
wet year conditions are summarised in Table (6.2) and Fig (6.8). The optimum crop pattern for the
Optimisation of Agricultural Water Use
90
Decision Support System
Chapter VI
IMDSUT Performance Analysis
economy scenario is presented in Fig (6.9). The crop pattern and results of the different scenarios
are presented in Appendix (III).
Table (6.2): Main outputs of the different proposed development scenario and the existing crop pattern
Scenario
Economy
Wastewater
Groundwater
Environment
Maximum freedom
Exist crop pattern
Groundwater Wastewater Irrigation
Mm3
Mm3
Mm3
23.69
20.00
43.69
14.44
35.00
49.44
20.00
30.00
50.00
11.55
35.00
46.55
12.31
38.25
50.56
b
51.2
36.7
51.2
Salinity Profit W.U.effa.
M.kg M. US$ US$/m3
66.15
95.77
2.19
64.10
77.37
1.56
69.40
75.16
1.50
53.13
64.44
1.38
63.98
81.92
1.62
67.5
55.6
1.1
a:W.U.eff = water use effectiveness . b: Presently there is no wastewater use, this value is equal to the potential quantity
of treated wastewater that can be irrigated in the existing crop pattern.
Out of table (6.2) and Fig (6.8) the following can be noticed:
-
A quick review of the scenario performance and the existing crop pattern performance proves
the advantages of using the proposed IMDSUT tool for agricultural water use planning. The
existing crop pattern has the lowest profit, lowest water use effectiveness, highest irrigation
demand and second highest salinity load.
This situation urges the need to review the
agricultural water use in the study area.
-
For each scenario a crop pattern could be identified that optimises the objective of this scenario.
For example, the economy scenario has produced a crop pattern that has the maximum profit,
maximum water use effectiveness at the same time satisfying the constraints and decision
parameter constraints.
-
The combination of reduction in the percentage coverage of local crops product demand and
attaching high weights for profit and water use effectiveness objectives in the economy
scenario, has resulted in much higher profit, in comparison with the profit, which resulted from
considering each parameter separately. This is can be seen by comparing the profit in table
(6.2), Fig (6.2), and Fig (6.6).
-
A comparison between the outputs of the groundwater scenario and the environment scenario
shows an interesting phenomenon, which requires more analysis. The phenomenon is, inspite of
the fact that the groundwater scenario aims to minimise the groundwater demand, that the
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Decision Support System
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IMDSUT Performance Analysis
scenario has allocated a crop pattern, which has much higher groundwater demand than in the
environment scenario. The reasons behind this unexpected result are:
•
The attached value for the spatial equity for access to groundwater decision parameter in the
groundwater scenario is 70%, while for the environmental scenario is 50%. This has forced
the model to use more groundwater to satisfy this decision parameter constraint in the
groundwater scenario.
•
The model's basic hypothesis is to allocate an optimum compromise of the five objectives.
The compromising process and the trade-offs among the different objectives, is the potential
third reason for this phenomenon.
-
The maximum freedom scenario has been able to allocate crop pattern that balances between the
different objectives. As a result its outputs are within an average ranges in comparison with
different scenario. This proves the IMDSUT capacity to account for the different objectives.
Exist crop pattern
Scenario
Maximum freedom
Environment
Groundwater
Wastewater
Economy
0
10
20
30
40
50
60
70
80
90
100
Million
Groundwater [Mm³]
Salinity [Mkg]
Wastewater [Mm³]
Profit [M.US$]
Irrigation [Mm³]
Fig (6.8): Main outputs of the different proposed development scenarios and the existing crop pattern.
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N
W
E
S
Cabbage
Cauliflo_a
Cauliflo_s
Citrus_oth
Cucumb_sp
Cucumb_s
Eggplant_w
Eqq_plant_
Guava*
Grapefruit
Jew's_melo
Limon
Olive
Onion
Pepper_a
Pepper_sp
Potato_w
Potato_s
Shamoti
Squash_sp
Squash_s
Strawberry
S._potato
Tomato_sp
Tomato_s
Valencia
Waterm_w
Waterm_s
Fig (6.9): Crop pattern for the economy scenario
As has been mentioned, each scenario comes out with a crop pattern that optimises the
objective of the scenario. This will raise the question: how much dose each crop pattern differ from
the other scenarios crop patterns and from the existing crop pattern? Table (6.3) below summarises
the level of similarity in percentage between the different scenarios crop patterns and the existing
crop pattern. The level of similarity has been calculated based on the following formula:


Ž
Œ
Level
of Similarity
Ž
Œ
= Œ 1 −
Œ

‘ ‘ 
16
’
28
’
j =1 i =1
A 1 ij − A 2 ij
2 × Totalarea


Where :
A1ij = area for each crop in the first scenario in each sub-regional zone (hectare)
Optimisation of Agricultural Water Use
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Decision Support System
Chapter VI
IMDSUT Performance Analysis
A2ij = Area for each crop in the second scenario in each sub-regional zone (hectare)
Totalarea = total cultivated area (hectare).
Table (6.3) shows a low level of similarity between the different crop patterns and much
lower level of similarity between the existing crop pattern and the scenarios produced crop patterns.
The level of similarity is at its highest level between the maximum freedom scenarios crop pattern
and the other crop patterns. This is mainly due to the fact that maximum freedom scenarios equally
balancing between the different scenario objectives, therefore a higher similarity level is expected.
The table shows the needs to consider highly the objectives and the decision- maker's interests and
priorities in early planning stage. This is important, while different objectives and interests will
result in completely different crop pattern that is hard to change after starting with implementation.
Table (6.3): Level of similarity between the crop patterns resulted scenarios
Scenario
Economy
Groundwater
Wastewater
Environment
Maximum
freedom
Economy
Groundwater
Wastewater
Environment
Maximum
freedom
Existing crop
pattern
100 %
36 %
38%
32%
34%
36%
100%
41%
34%
51%
38%
41%
100%
36%
55%
32%
34%
36%
100%
47%
34%
51%
55%
47%
100%
Existing
crop
pattern
30%
35%
32%
39%
32%
30%
35%
32%
39%
32%
100%
6.3 IMDSUT sensitivity to changing in crop return values
The crops return values (prices) are variable in nature. They are depended on the relation
between supply and demand curves. The formulation of a mathematical relation that simulates the
variation in crop return values for the study area is impossible due to the following reasons. Firstly,
the unstable political situation, which means sudden and unexpected restrictions in product
transport or export. Secondly, very limited historical records are available that cover the crop return
values and crop demand in the study area. The estimation of crop return values has been made for
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IMDSUT Performance Analysis
the tool database based on three years in farms crop prices. The estimated crops return values are
shown in table (4.9), chapter V, page 42.
To gain more insight into the sensitivity of crop pattern to the return values, 20 randomly
generated crops return values were randomly generated of 10 scenarios with 5% changes in return
values and 10 scenarios with 10% changes in return values. Microsoft Excel was used to generate
an evenly distributed random number between 0-1 for each crop. For Crops with a random number
greater or equal to 0.5, their return values were increased, while the others were decreased.. The
generated crops return values scenarios are presented in Appendix (III).
The optimisation was carried out 20 times with the changed values for the maximum
freedom scenario. The results are presented in Appendix (III). Fig (6.10) below presents the
maximum, minimum and average profits corresponding to 5% and the 10% changes in crop return
value. Fig (6.11) below presents the average percentage changes in crop pattern due to changes in
crops return values. From the two figures and the tables in Appendix (II) the following can be
concluded:
-
The results of the model are not very sensitive to changes in return values. This is because of
the fact that, crops return values have influence on two objectives (profit, water use
effectiveness) out of five objectives, which form the basis of the IMDSUT model objective
function.
-
Changes in crop pattern are very clear. 10% changes in return value has resulted in 11%
changes in crop pattern. This is not a major problem, since more than 50% of the crops are
seasonal, which allows an annual adaptation.
-
It is recommended to review crop pattern as a function of the crop return values reqularly and to
apply the model for the new prices.
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Chapter VI
IMDSUT Performance Analysis
Profit [M.US$].
100
90
80
70
60
50
40
0%
5%
10%
Percentage changes in crop return value
Minimium
Average
Maximium
Exist
Average percentage changes..
from the optimum
Fig (6.10): Influence of changes in crops returns values on the profit
14
12
10
8
6
4
2
0
0%
5%
10%
Percentage random changes in crops return values
Fig (6.11): Influence of changes in crop return values crop patterns
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Decision Support System
Chapter VII
Summary, Conclusions, and Recommendations
7. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
First part of this chapter summarises the problem, objectives, methodology, and main findings.
The remaining sections present the study conclusions and recommendations.
7.1 Summary
Water shortage is becoming an increasing problem in arid and semi-arid areas. The water
demand is exceeding the sustainable water resources supply capacity. The agriculture sector is the
largest water consumer, whereby it consumes more than 90% of water supply in these areas. Water
resource management strategies in these areas are characterised by supply oriented measures.
These strategies result in an overuse of the natural water resources and highly affect water
availability for future generations.
The Gaza Strip is located in arid to semi-arid region. The area faces a complicated water shortage
problem characterises by a water balance deficit of about 20 Mm3/year. This quantity is expected to
increase rapidly. This is mainly due to high population growth of about 3.2%. Groundwater is the
only existing natural water source. Presently, irrigated agriculture is the largest water consumer in
the Gaza Strip, where it consumes more than 65% of water. However, management of agricultural
water use has received little attention from the parties concerned in the Gaza Strip. The existing
agricultural water system in the Gaza Strip has the following main problems:
- It has a very low water use effectiveness of about 0.4 US$/m3 in comparison with a water
opportunity cost of about 1.0 US$/m3 (Desalination cost). This contradicts completely the well
known 1992 Dublin Principles, No. 4, which states "Water has an economic value in all it's
competing uses and should be recognised as an economic good."
-
The crop pattern is mainly determined by farmers' decision without any planning. This practice
has negatively affected the socio-economic and environmental outcomes through agricultural
water use.
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Chapter VII
-
Summary, Conclusions, and Recommendations
Treated wastewater has never been used for irrigation in the Gaza Strip; inspite of the fact that
treated wastewater could cover a substantial part of irrigation water demand.
As a conclusion, enhancing the effectiveness of agricultural water use based on integrated water
resource management principle could highly contribute to alleviating the water shortage problem in
arid and semi-arid areas generally and in the Gaza Strip specifically. Agricultural water use is
naturally complicated and inter-related, where different socio-economic and environmental aspects
control the water use effectiveness in this sector. Multiobjective planning and modelling has
offered the possibility to integrate all these aspects in a good manner.
The overall objective of this study was to formulate an integrated decision support system tool
based on multiobjective optimisation techniques that has the capacity to optimise the agricultural
water use in a regional scale for arid and semi-arid areas. The tool had the capacity to account for:
-
An optimum crop pattern that gives the optimum compromise values for the following
contradicting five objectives in a regional scale: maximise the net profit, maximise water use
effectiveness US$/m3, maximise irrigated quantity of treated wastewater, minimise the irrigated
groundwater quantity, and minimise salinity load
-
The socio-economic and environmental aspects of agricultural water use
-
The spatial and temporal variabilities in crops water requirements and crops yield
-
Biophysical and meteorological variabilities in the study area
-
To give the decision-makers the possibilities to contribute to the planning process by attaching
a weight for each objective and by allocating target values for a set of decision parameters. The
decision parameters will include the most influencing aspects that effect the socio-economic
and environmental values of agricultural water use
-
To show the trade off between the different objectives
To achieve the stated objective an integrated multiobjective decision support system tool
(IMDSUT) had formulated. IMDSUT consisted of four main parts: an intensive database, a Soil-
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Chapter VII
Summary, Conclusions, and Recommendations
Water-Atmosphere and Plant model (SWAP 2.0), a multiobjective optimisation model, and a
decision-making algorithm.
The IMDSUT intensive database consists of two main parts: socio-economic database and
environmental and biophysical database. The socio-economic database contains local information
that covers the different influencing parameters in the agriculture sector, such as crop return values,
local crop product demands. The information has been predicted up to year 2025 as a targetplanning year. High level of uncertainty is expected in this type of information. Different measures
have been used to reduce this uncertainty. Soil-Water-Atmosphere and Plant model (SWAP) has
been used to account for the environmental and biophysical information. This database includes
information about crop water requirements, crops yields, and salinity load on cultivated land due
irrigation for 28 crops. The 28 crops existing cultivated area is about 90% of irrigated area in the
Gaza Strip. To consider the spatial variations in soil characteristics and rainfall intensity, the study
area has been sub-divided into 16 sub-regional zones. The SWAP model has been implemented for
the 28 crops in the 16 sub-regional zones. The model results have been compared with the existing
literature values for each crop and have been assessed by relevant authorities in the Palestinian
territories.
The model aims to allocate an optimum crop pattern. This crop pattern gives the optimum
compromise values for the previous mentioned five contradicting objectives on a regional scale.
The traditional formulation approach for multiobjective objective function is to specify a principal
single objective and to set the other objectives as objectives constraints. This approach had some
problems. To handle these problems, an IMDSUT objective function has been formulated based on
a normalised value technique. The normalised value technique standardises the different objectives
by dividing them by the optimum value obtained from the single objective models. This leads to a
single objective function that includes the five contradicting objectives.
To facilitate the decision-makers contribution and involvement in the decision making process,
a decision-making algorithm has been created. It is based on the integration between decisionOptimisation of Agricultural Water Use
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Chapter VII
Summary, Conclusions, and Recommendations
makers interest, decision support charts and the multiobjective optimisation model. The decision
support charts have been prepared by implementing the multiobjective model under different values
of decision parameter constraints and under different attached weight factors values for the model
objectives. The decision-makers involvement will be through setting target values for the decision
parameters and attaching weights to the different objectives.
The formulated model has been implemented in the study area. The model results have been
compared with the existing crop pattern and five single objective models.
The model results
showed advantages over the existing crop pattern and the five single objective models. A decision
support chart for each of the decision parameters has been prepared and analysed. The preparation
of decision support charts has offered the possibility to evaluate the model performance and to
assess its sensitivity toward the different parameters. More insight into the model was gained by
the development of five potential scenarios. Each scenario presented possible set of priorities. The
formulation of these scenarios has improved our understanding of the model sensitivity and ability
to consider different combination of decision parameters values.
7.2 Conclusions
Out of the study the following can be concluded:
“
IMDSUT has the capacity to account for the different socio-economic, environmental, and
biophysical aspects of agricultural water use. Based on using a normalised value technique, the
model accounts for five mutually and inter-related objectives in a way that the optimum
compromise values for each objective can be achieved by implementing the model allocated
crop pattern.
“
IMDSUT contains an intensive database. The preparation of a robust database has faced many
difficulties due to the limitation in information availability, historical records about the study
area, and the large number of crops under considerations. This situation comes with high
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Summary, Conclusions, and Recommendations
potential uncertainties in the information accuracy. Different measures and techniques have
been used to improve the information accuracy through out the database preparation process.
“
The use of SWAP 2.0 model has offered the possibility to properly account for the influence of
biophysical variabilities in crop water requirements and crop yields.
“
IMDSUT also allows the decision-makers to incorporate their interest and to rank their priorities
through setting target values for wide variety of decision parameters. The tool will account for
all these priorities and interests and will allocate an optimum crop pattern that satisfies these
priorities and interests.
“
IMDSUT has shown its advantages over other single objective models and over the exiting
crop pattern.
7.2.1 IMDSUT sensitivity analysis
IMDSUT capacity as a decision support tool for agricultural water use has been evaluated
and analysed. The tool sensitivity towards the different decision parameters is as follows:
♦ IMDSUT has low sensitivity to extreme low or high objective weight factors. The model
constraints and the trade-offs between the different objectives are the main forces that reduce
the model sensitivity.
♦ In order to satisfy crop product local demands, at least 14 Mm3 /year of groundwater should be
allocated for agricultural water use.
A small increase in this quantity will come with a
substantial increase in profit. A further increase, dose not lead to a greater increase in the profit.
The resulting water use effectiveness is much higher than opportunity cost, so it may be
judicious to allocate more groundwater for irrigation
♦ Spatial equity in right of access to profit, groundwater, and treated wastewater has economical
and environmental costs. So it is up to decision-makers to allocate the level of equity they
desire. Generally out of the IMDSUT analysis, the agricultural system profitability is very
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Summary, Conclusions, and Recommendations
sensitive to high level of spatial equity in access to profit, while it has much lesser sensitivity to
the spatial equity in access to groundwater and treated wastewater resources.
♦ Food self-sufficiency is a debatable strategy among water resources scientists. This strategy
still has strong support among decision-makers especially due to the unstable political situation.
This strategy has both economical and environmental cost. Out of the model results analysis,
the results highly support a standpoint against the strategy of complete self-food sufficiency.
♦ IMDSUT is very sensitive to any changes in the crops local product demand. So it is of extreme
important to estimate these demands properly.
♦ Farmer's acceptance to use treated wastewater is a very important aspect, as it may highly
facilitate the use of treated wastewater.
IMDSUT shows that, an improvement of this
acceptance will slightly affect the model outputs, but its reduction will highly impact the
agricultural system outputs negatively. So it is important to consider this parameter.
♦ IMDSUT sensitivity to changes in crops return values is at an acceptable level. This comes out
of the fact that, crops return values will have influences only on two objectives (profit, water use
effectiveness) out of the 5 objectives, which forms the IMDSUT model objectives function.
7.3 Recommendations
Out of the study, the following can be recommended:
“
Agricultural water use should receive more and more attention especially in arid and semi-arid
areas.
“
The study proposed methodology and tool is a good start point towards effectively manages the
agricultural water use.
“
The implementation of IMDSUT model should be done under the consideration of the following
important recommendations:
♦ Any application of the proposed IMDSUT tool for agricultural water use planning should be
based on a close co-operation between the different related governmental, social and farmers'
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Chapter VII
organisations.
Summary, Conclusions, and Recommendations
This would incorporate their interest and priorities and come out with an
optimum plan that could be implemented.
♦ A continuous revision and evaluation of database information is recommended. This revision
should be based on yearly statistical data for socio-economic information and through
conducting environmental and biophysical research in the study area. This revision process will
reduce highly the uncertainty and will increase the reliability of the model.
♦ It is recommended to review the crop return values in yearly basis and to implement the model
for the new return values to modify the crop pattern.
♦ The crop pattern for fruit trees should be implemented and should be fixed based on the first
year database. For other crops, a yearly run of the model should be made directly after the
database information revision process completed.
♦ The spatial equity in access to profit, groundwater, wastewater should be considered. This
would facilitate the implementation of a development plan by increasing the farmers'
willingness.
♦ Inventing a water pricing strategy for agricultural water use that contains a compatible price for
treated wastewater will increase highly the farmers' acceptance to use treated wastewater.
♦
Water metering is a need and should be implemented as soon as possible.
♦
Economic incentives have to be presented to the farmers. These incentives may include for
example, offering export possibilities for their products, and reduction in water price. These
types of incentives would improve the farmer's acceptance to the plan.
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Decision Support System
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Appendix (I): Database and Mmultiobjective Model
Appendix (I)
Database and Multiobjective model
Part A: Database
Part B: Multiobjective model
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Appendix (I): Database and Mmultiobjective Model
Part A: IMDSUT Database File
The database consists of two main parts, Environmental and biophysical data and socioeconomic data. The following table (I1) presents a general summary of the database
Parameter
Water demand for both wet and dry
Salinity load for wet and dry year
Crop yields for dry and wet year
Exiting crop area
Wastewater use factor
Type
Environment
Environment
Environment
Socio-economic
Socio-economic
No. of readings
896
896
896
448
448
Socio-economic
Socio-economic
Socio-economic
Socio-economic
Unit
m3 /hectare.year
kg/hectare.year
ton/hectare.year
Hectare/zone
1 for tree , o for
vegitables
ton/year
US$/ton
US$/hectare
hectare
Crops product local demand
Crops return values
Crops cultivation cost
Crops allowable maximum cultivation
area
Cultivable area in each sub regional
zone
Available treated wastewater quantity
in each sub-regional zone
Farmers acceptance for reuse
Socio-economic
hectare
16
Socio-economic
m3 /year
16
Socio-economic
%
16
28
28
28
28
A- Environmental and biophysical database
3
Water demand for wet year (m /hectare.year)
Param wdemandw:
Sub-regional zones
Crops
northbh northbl
gazabh
gazabl
gazawg
middbl
Cabbage
3106
3507
2587
3513
3328
3534
Cauliflower a
1150
1453
1150
1291
806
1631
Cauliflower sp
1235
1383
1299
1636
1561
1906
Citrus others*
3667
3959
3092
3823
3328
3857
Cucumber sp
2431
2438
1856
2447
1908
2925
Cucumber s
4160
7836
4144
5818
3949
6610
Eggplant w
5407
6523
5613
6460
6075
6465
Eggplant a
5821
7913
4653
7879
5925
8741
Guava*
4665
6982
4653
7000
4460
7912
Grapefruits
6613
7392
5821
6913
6251
7511
Jew's melon
5821
7913
4653
7879
5925
8741
Lemon*
3472
5255
3482
5253
4447
6118
Olive*
2054
2003
1495
1997
1785
2356
Onion
5821
7913
4653
7879
5925
8741
Pepper a
5867
6783
5652
7614
5591
7632
Pepper sp
6342
6783
5397
6784
6345
7686
Potato w
1326
1655
1327
2122
1826
2157
Potato s
2924
3461
2920
3459
3345
3455
Shamoti*
5821
7913
4653
7879
5925
8741
Squash sp
2318
3213
2164
3150
2785
3430
Squash s
2425
2441
1856
2440
1908
2451
Strawberry
3294
3594
3100
3884
3379
4381
Sweetpotato
6618
6829
5280
6512
5819
7351
Tomato sp
6198
6580
5477
6567
6031
6767
Tomato s
3265
3259
3002
3245
3392
3765
Optimisation of Agricultural Water Use
110
midddb
3435
1811
1559
3997
3997
5286
6002
7125
5715
7180
7125
4278
2536
7125
7083
6781
1644
2945
7125
3171
2678
3830
6589
6679
3654
middwg
3138
1253
1994
3954
3189
5397
6413
5924
5939
7220
5924
4423
2275
5924
6831
7481
1827
3361
5924
2830
2390
4023
7279
6538
3860
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Valencia*
5821
7913
4653
7879
5925
8741
7125
5924
Watermelon w
2242
3833
2236
3760
2881
3647
2640
2727
Watermelon s
4385
5108
4314
5062
4316
4865
5515
4829
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Sub-regional zones
Crops
khanbh khanbl khandb
khanwg
khanky
rafahbl
rafahdb
rafahky
Cabbage
3017
3527
3431
3134
3314
3503
3414
3101
Cauliflower a
1712
2009
1765
1994
1402
2006
1517
2091
Cauliflower sp
1795
1892
1894
2386
1815
1906
1897
1754
Citrus others*
3484
3847
3962
3879
3177
3855
3882
3756
Cucumber sp
2802
2849
2685
3190
2628
2847
2715
3099
Cucumber s
5076
6626
6088
6333
6002
6641
5951
5856
Eggplant w
5863
6440
5936
6309
5416
6945
6724
6072
Eggplant a
6959
8787
7138
7414
5793
8770
7152
7249
Guava*
5804
7898
5724
5904
5782
7870
5718
5789
Grapefruits
6972
7968
7547
7915
6193
7969
7218
6494
Jew's melon
6959
8787
7138
7414
5793
8770
7152
7249
Lemon*
4635
6113
4286
5909
4333
6134
5710
4344
Olive*
2392
2331
2539
3019
2570
2323
2557
2488
Onion
6959
8787
7138
7414
5793
8770
7152
7249
Pepper a
7328
8316
7981
7804
6192
8385
7841
7021
Pepper sp
6855
7610
7475
7337
5596
7615
6804
6900
Potato w
1911
2618
2358
1847
2339
2609
2353
2333
Potato s
2914
3470
2938
3320
2920
3472
2964
3552
Shamoti*
6959
8787
7138
7414
5793
8770
7152
7249
Squash sp
3065
3201
2927
3374
3214
3410
3070
3423
Squash s
2794
2845
2691
3191
2634
2833
2711
3092
Strawberry
4257
4694
4709
4685
4131
4708
4688
4457
Sweetpotato
7144
7416
7264
7798
5707
7356
6919
6543
Tomato sp
6321
6716
6615
6583
5421
7179
6515
6393
Tomato s
3281
3644
3647
3881
3270
3677
3646
3541
Valencia*
6959
8787
7138
7414
5793
8770
7152
7249
Watermelon w
3227
4321
3814
4012
3873
3523
3419
3361
Watermelon s
4686
4887
5150
4680
4170
4843
4582
4774
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Salinity load for wet year (kg/hectare.year)
param Sirrigw:
Sub-regional zones
Crops
northbh northbl
gazabh
gazabl
gazawg
middbl
Cabbage
230
52
573
259
741
955
Cauliflower a
85
22
256
95
180
441
Cauliflower sp
91
20
287
121
347
515
Citrus others*
267
58
676
277
733
1030
Cucumber sp
177
35
404
177
420
778
Cucumber s
309
116
922
430
881
1790
Eggplant w
399
96
1244
476
1350
1750
Eggplant a
427
115
1025
575
1310
2340
Guava*
344
103
1030
515
990
2140
Grapefruits
488
109
1288
509
1390
2030
Jew's melon
427
115
1025
575
1310
2340
Lemon*
258
78
775
389
992
1660
Olive*
151
29
328
146
394
633
Onion
427
115
1025
575
1310
2340
Optimisation of Agricultural Water Use
111
midddb
509
269
231
586
586
785
889
1050
846
1060
1050
636
374
1050
middwg
466
186
296
581
468
802
951
874
880
1070
874
658
335
874
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Pepper a
435
100
1260
563
1250
2070
1050
1020
Pepper sp
469
100
1196
500
1410
2080
1010
1110
Potato w
98
25
295
157
408
584
244
272
Potato s
216
51
647
255
745
933
436
499
Shamoti*
427
115
1025
575
1310
2340
1050
874
Squash sp
169
47
473
229
614
912
465
415
Squash s
176
35
404
177
420
651
392
350
Strawberry
247
54
697
291
760
1210
574
604
Sweetpotato
491
101
1175
485
1300
2000
979
1080
Tomato sp
451
95
1194
476
1330
1800
977
959
Tomato s
237
47
655
235
747
1000
534
566
Valencia*
427
115
1025
575
1310
2340
1050
874
Watermelon w
167
57
499
278
644
989
393
406
Watermelon s
325
76
959
374
962
1320
820
718
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Sub-regional zones
Crops
khanbh khanbl
khandb
khanwg
khanky
rafahbl
rafahdb
rafahky
Cabbage
371
69
407
387
1150
69
405
842
Cauliflower a
211
40
209
247
484
40
180
568
Cauliflower sp
221
37
224
295
626
37
225
475
Citrus others*
423
74
464
475
1090
75
455
1010
Cucumber sp
339
55
314
390
896
55
318
831
Cucumber s
626
130
723
784
2080
131
707
1590
Eggplant w
722
127
703
780
1870
137
797
1650
Eggplant a
852
171
842
911
1990
171
844
1960
Guava*
714
155
678
729
2000
154
677
1570
Grapefruits
857
156
893
978
2140
156
854
1760
Jew's melon
852
171
842
911
1990
171
844
1960
Lemon*
573
121
510
733
1500
121
679
1180
Olive*
293
46
299
371
883
45
301
672
Onion
852
171
842
911
1990
171
844
1960
Pepper a
905
164
949
967
2150
165
932
1910
Pepper sp
844
150
887
908
1930
150
807
1880
Potato w
236
52
280
228
810
51
279
635
Potato s
359
68
348
410
1010
68
351
965
Shamoti*
852
171
842
911
1990
171
844
1960
Squash sp
372
62
343
413
1100
66
360
919
Squash s
338
55
315
390
898
55
317
828
Strawberry
532
94
565
586
1450
94
563
1230
Sweetpotato
883
146
863
967
1980
145
823
1780
Tomato sp
766
130
774
805
1850
139
762
1710
Tomato s
397
70
426
474
1110
71
426
948
Valencia*
852
171
842
911
1990
171
844
1960
Watermelon w
400
85
454
498
1350
70
407
916
Watermelon s
579
96
612
580
1440
95
544
1300
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use
.
Yield for wet year (ton/hectare)
param yieldw:
Sub-regional zones
Crops
northbh northbl
gazabh
gazabl
gazawg
middbl
midddb
middwg
Cabbage
36
42
32
42
35
41
39
39
Cauliflower a
26
32
25
32
29
32
28
30
Cauliflower sp
25
31
26
31
29
31
30
30
Optimisation of Agricultural Water Use
112
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Citrus others*
23
26
22
27
24
26
25
24
Cucumber sp
33
37
31
37
34
37
35
34
Cucumber s
56
72
53
73
62
71
63
62
Eggplant w
47
65
35
66
45
63
56
55
Eggplant a
30
40
26
40
31
39
35
31
Guava*
26
43
28
43
25
36
38
37
Grapefruits
21
28
11
27
16
26
22
21
Jew's melon
25
34
22
34
26
34
30
26
Lemon*
4
5
4
5
4
5
4
4
Olive*
27
32
26
31
30
31
30
30
Onion
21
29
19
29
22
29
25
22
Pepper a
28
31
25
38
31
37
33
33
Pepper sp
21
28
11
28
16
27
24
21
Potato w
28
31
28
32
30
31
30
30
Potato s
28
32
26
32
28
31
30
29
Shamoti*
25
34
22
34
26
34
30
26
Squash sp
32
37
32
37
32
37
35
35
Squash s
28
31
27
31
29
31
30
29
Strawberry
26
33
24
34
29
32
28
29
Sweetpotato
23
27
16
26
20
26
24
23
Tomato sp
38
41
34
41
37
41
40
40
Tomato s
37
42
34
42
38
41
41
40
Valencia*
19
26
17
26
20
25
22
20
Watermelon w
39
55
37
54
43
53
46
45
Watermelon s
17
22
13
22
17
22
19
18
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Crops
khanbh
Cabbage
37
Cauliflower a
27
Cauliflower sp
28
Citrus others*
24
Cucumber sp
34
Cucumber s
50
Eggplant w
47
Eggplant a
27
Guava*
35
Grapefruits
20
Jew's melon
23
Lemon*
4
Olive*
27
Onion
19
Pepper a
29
Pepper sp
20
Potato w
28
Potato s
28
Shamoti*
23
Squash sp
33
Squash s
28
Strawberry
24
Sweetpotato
20
Tomato sp
37
Tomato s
37
Valencia*
17
khanbl
42
31
31
27
37
71
65
39
44
28
33
5
31
28
38
28
31
31
33
37
31
33
27
41
41
25
khandb
39
28
30
25
35
62
56
36
34
21
30
5
31
26
34
20
29
30
30
35
30
28
23
40
41
23
Optimisation of Agricultural Water Use
Sub-regional zones
khanwg
khanky
39
33
28
28
30
28
25
22
35
31
62
48
56
31
29
26
33
23
22
9
25
22
4
3
30
26
21
19
33
22
23
8
29
28
30
26
25
22
34
31
30
26
27
18
22
13
40
32
41
35
19
17
113
rafahbl
42
32
31
27
37
72
67
39
44
28
34
5
31
29
38
28
31
31
34
37
31
32
27
42
41
25
rafahdb
39
31
21
25
35
65
74
34
30
23
29
4
31
24
34
24
29
30
29
35
29
37
24
40
41
22
rafahky
35
28
28
23
33
55
40
28
30
13
24
4
28
20
28
13
29
27
24
33
28
23
17
36
38
18
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Watermelon w
35
54
47
44
41
55
50
48
Watermelon s
16
22
20
19
15
22
20
17
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Water demand for dry year (m3 /hectare.year)
Param wdemandd:
Sub-regional zones
Crops
northbh northbl
gazabh
gazabl
gazawg
middbl
midddb
middwg
Cabbage
3123
3094
3067
3512
3208
3466
3288
3851
Cauliflower a
1430
1467
1476
2009
2083
2250
2155
2444
Cauliflower sp
1844
2008
1796
1915
1764
2387
1845
2566
Citrus others*
3583
3623
3343
4189
3731
3780
3840
3711
Cucumber sp
3381
3781
3470
3792
3958
3767
3866
3922
Cucumber s
5343
6529
5521
6571
6161
6967
6678
6328
Eggplant w
6373
6212
5982
6959
6266
6994
6520
7318
Eggplant a
5798
8760
5797
8761
7400
8756
8578
7391
Guava*
5835
7906
5790
7864
5930
7884
7151
7403
Grapefruits
7201
7623
6158
7566
7286
7547
7399
7922
Jew's melon
5798
8760
5797
8761
7400
8756
8578
7391
Lemon*
4632
6149
4633
6137
5910
7009
5707
7382
Olive*
2875
2822
2949
2843
2942
2834
2967
3680
Onion
5798
8760
5797
8761
7400
8756
8578
7391
Pepper a
7633
8350
6219
8259
7114
8318
8450
8291
Pepper sp
7314
7203
5838
7733
6549
7719
7109
7360
Potato w
1319
1632
1905
2063
1826
2562
2980
2627
Potato s
2998
3078
2997
3022
3366
3065
3000
3356
Shamoti*
5798
8760
5797
8761
7400
8756
8578
7391
Squash sp
2395
2996
2926
3363
2731
3228
3108
3172
Squash s
3395
3308
2891
3310
3168
3764
3875
3921
Strawberry
4457
4704
3983
4702
4698
4902
4749
4973
Sweetpotato
7053
6909
6370
7525
7262
7788
7214
7574
Tomato sp
5781
6300
5319
7017
6159
6864
6729
6601
Tomato s
4339
4191
3976
4594
4663
4602
4262
4645
Valencia*
5798
8760
5797
8761
7400
8756
8578
7391
Watermelon w
3213
3197
2902
3706
3924
4285
3882
4269
Watermelon s
4913
5098
4490
4825
4446
5231
5644
5605
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Crops
khanbh
Cabbage
3508
Cauliflower a
2268
Cauliflower sp
2346
Citrus others*
3804
Cucumber sp
3330
Cucumber s
6659
Eggplant w
6393
Eggplant a
8103
Guava*
6960
Grapefruits
7355
Jew's melon
8103
Lemon*
5795
Olive*
2865
Onion
8103
khanbl
3459
2480
2844
4226
3752
7703
6732
9661
7876
7831
9661
6987
3311
9661
khandb
3234
2185
2573
3691
3870
7151
7294
8566
7133
7186
8566
5702
2964
8566
Optimisation of Agricultural Water Use
Sub-regional zones
khanwg
khanky
3796
3122
2460
2114
2538
2463
4330
3701
3918
3291
7207
5957
7004
6059
8864
7263
7401
5803
7650
5983
8864
7263
7384
5782
3679
3118
8864
7263
114
rafahbl
3460
2019
2754
4227
3766
7592
6733
8767
7859
7830
8767
7006
3330
8767
rafahdb
3235
2256
2525
3692
3880
7146
7296
8568
7132
7814
8568
5695
3674
8568
rafahky
3127
2184
2467
3481
3751
5834
5834
8704
7230
7111
8704
5773
2890
8704
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Pepper a
7946
8947
9110
8248
6953
9005
9047
7778
Pepper sp
7427
7358
7879
8105
6311
7360
7656
6796
Potato w
2380
2529
2947
2531
2188
2504
2187
2192
Potato s
3006
3038
3018
3352
2963
3039
2994
2988
Shamoti*
8103
9661
8566
8864
7263
8767
8568
8704
Squash sp
3147
3636
3152
3339
3422
3509
3356
3467
Squash s
3345
3754
3867
3928
3286
3743
3869
3751
Strawberry
5009
5420
5465
5391
4412
5432
5430
5043
Sweetpotato
7436
7404
7319
8288
5925
7896
7314
6952
Tomato sp
6176
7147
7046
6999
5857
6919
6657
6524
Tomato s
4258
4616
4264
4644
3819
4622
4978
4308
Valencia*
8103
9661
8566
8864
7263
8767
8568
8704
Watermelon w
3752
4309
4832
3682
4691
4431
4848
4842
Watermelon s
5197
5265
5611
5260
4749
5266
5559
4672
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Salinity load for dry year (kg/hectare.year)
param Sirrigd:
Sub-regional zones
Crops
northbh northbl
gazabh
gazabl
gazawg
middbl
midddb
middwg
Cabbage
231
46
680
259
714
937
487
571
Cauliflower a
106
22
328
148
464
609
319
363
Cauliflower sp
136
30
397
141
392
645
273
381
Citrus others*
261
53
730
304
822
1010
563
545
Cucumber sp
246
55
758
275
872
1000
566
576
Cucumber s
396
96
1230
485
1370
1890
991
940
Eggplant w
471
92
1330
513
1390
1890
966
1090
Eggplant a
426
128
1280
639
1640
2340
1270
1090
Guava*
431
116
1280
579
1320
2130
1060
1100
Grapefruits
531
112
1360
557
1620
2040
1100
1170
Jew's melon
426
128
1280
639
1640
2340
1270
1090
Lemon*
344
91
1030
454
1320
1900
849
1100
Olive*
211
41
650
208
652
762
437
544
Onion
426
128
1280
639
1640
2340
1270
1090
Pepper a
566
123
1380
611
1590
2250
1260
1230
Pepper sp
541
106
1290
571
1460
2090
1050
1090
Potato w
98
24
423
152
407
692
442
390
Potato s
222
45
664
223
750
828
444
498
Shamoti*
426
128
1280
639
1640
2340
1270
1090
Squash sp
174
44
639
244
601
859
455
466
Squash s
247
48
631
240
697
1000
567
576
Strawberry
334
71
896
353
1060
1350
712
746
Sweetpotato
523
102
1420
557
1620
2110
1070
1130
Tomato sp
420
91
1160
508
1360
1830
985
969
Tomato s
316
61
867
333
1030
1220
623
682
Valencia*
426
128
1280
639
1640
2340
1270
1090
Watermelon w
239
47
646
274
876
1160
577
635
Watermelon s
364
75
998
356
991
1420
839
833
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Crops
khanbh
Cabbage
432
Cauliflower a
279
Cauliflower sp
289
khanbl
68
49
56
khandb
383
259
305
Optimisation of Agricultural Water Use
Sub-regional zones
khanwg
khanky
469
1080
304
731
314
850
115
rafahbl
68
40
54
rafahdb
383
268
299
rafahky
849
593
669
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Citrus others*
462
82
432
530
1270
82
433
934
Cucumber sp
404
73
453
479
1120
73
454
1010
Cucumber s
821
152
849
892
2060
149
848
1750
Eggplant w
787
132
865
866
2090
132
865
1580
Eggplant a
992
188
1010
1090
2500
171
1010
2350
Guava*
857
155
845
914
2010
154
845
1970
Grapefruits
904
154
851
945
2070
154
925
1930
Jew's melon
992
188
1010
1090
2500
171
1010
2350
Lemon*
717
138
678
915
2010
138
677
1570
Olive*
351
65
349
453
1070
65
434
780
Onion
992
188
1010
1090
2500
171
1010
2350
Pepper a
981
176
1080
1020
2410
177
1080
2120
Pepper sp
915
145
935
1000
2180
145
908
1850
Potato w
293
50
350
313
756
49
259
596
Potato s
370
60
358
414
1020
60
355
812
Shamoti*
992
188
1010
1090
2500
171
1010
2350
Squash sp
382
70
369
409
1170
68
393
930
Squash s
406
73
453
481
1120
72
453
1010
Strawberry
626
108
656
674
1540
109
652
1390
Sweetpotato
919
146
870
1030
2060
156
870
1890
Tomato sp
748
138
825
856
2000
134
779
1750
Tomato s
516
89
499
568
1300
89
583
1150
Valencia*
992
188
1010
1090
2500
171
1010
2350
Watermelon w
464
85
575
456
1630
87
577
1320
Watermelon s
642
104
667
651
1650
104
661
1270
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Yield for dry year (ton/hectare)
param yieldd:
Crops
northbh northbl
Cabbage
37
42
Cauliflower a
29
32
Cauliflower sp
29
31
Citrus others*
24
27
Cucumber sp
34
37
Cucumber s
58
71
Eggplant w
46
68
Eggplant a
27
41
Guava*
33
38
Grapefruits
22
28
Jew's melon
23
35
Lemon*
4
5
Olive*
28
31
Onion
19
30
Pepper a
26
38
Pepper sp
20
28
Potato w
29
31
Potato s
28
31
Shamoti*
23
35
Squash sp
34
37
Squash s
28
31
Strawberry
26
32
Sweetpotato
23
27
Optimisation of Agricultural Water Use
gazabh
33
28
27
22
30
49
32
22
24
11
19
3
25
16
19
10
28
26
19
32
26
19
12
Sub-regional zones
gazabl
gazawg
41
37
32
29
31
28
27
24
37
32
72
60
67
44
40
24
41
30
27
15
34
21
5
3
31
28
29
18
38
28
28
15
31
29
32
28
34
21
37
34
31
27
32
24
27
15
116
middbl
41
31
31
26
37
70
64
39
42
26
33
5
30
28
37
27
30
31
33
37
31
32
26
midddb
40
30
28
25
35
66
57
34
36
22
29
4
30
24
33
22
28
30
29
35
29
27
23
middwg
38
28
29
25
34
62
53
30
34
21
25
4
29
21
32
21
28
29
25
35
29
27
21
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Tomato sp
38
42
34
42
37
41
39
39
Tomato s
37
42
34
42
37
41
41
40
Valencia*
17
26
14
26
16
25
22
19
Watermelon w
42
54
39
55
42
53
48
44
Watermelon s
17
21
12
23
17
21
19
17
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Crops
khanbh
Cabbage
36
Cauliflower a
29
Cauliflower sp
26
Citrus others*
24
Cucumber sp
34
Cucumber s
56
Eggplant w
46
Eggplant a
29
Guava*
24
Grapefruits
20
Jew's melon
24
Lemon*
4
Olive*
28
Onion
21
Pepper a
28
Pepper sp
18
Potato w
28
Potato s
28
Shamoti*
24
Squash sp
34
Squash s
29
Strawberry
23
Sweetpotato
20
Tomato sp
37
Tomato s
38
Valencia*
18
Watermelon w
41
Watermelon s
16
khanbl
41
32
30
26
37
70
65
39
44
27
34
5
30
29
37
28
30
31
34
37
31
33
27
41
42
25
55
22
khandb
40
30
28
25
35
65
57
36
39
23
30
5
30
26
32
23
29
29
30
35
29
27
24
40
41
23
49
19
Sub-regional zones
khanwg
khanky
39
32
29
27
29
24
25
21
35
29
59
45
55
29
32
25
35
22
24
9
27
22
4
3
30
25
23
18
31
18
24
8
29
28
30
25
27
22
35
31
29
25
27
14
20
11
39
32
40
34
20
16
46
40
18
13
rafahbl
41
32
31
26
37
71
65
39
44
27
34
5
30
29
38
28
30
31
34
37
31
32
26
41
42
25
54
22
rafahdb
40
29
28
25
35
65
57
36
39
23
30
5
30
26
33
23
29
30
30
35
30
26
24
40
41
23
48
20
rafahky
35
29
26
23
32
5!
40
30
23
13
25
4
27
21
22
12
28
27
25
33
27
18
14
35
36
19
44
17
khanky
8.7
8.9
20.9
2.9
270.1
23.5
23.5
1.5
1.5
khanwg
1.5
1.5
3.6
0.5
46.7
4.1
4.1
0.3
0.3
B- Socio-economical data base
Exist Area (hectare)
Crops
gazabh
Cabbage
17.9
Cauliflower a
31.4
Cauliflower sp 499.4
Citrus others*
98.4
Cucumber sp
597.2
Cucumber s
5
Eggplant w
5
Eggplant a
8.8
Guava*
8.8
gazabl
9.4
16.4
261.3
51.5
312.5
2.6
2.6
4.6
4.6
gazawg
1.2
2.1
33.8
6.7
40.4
0.3
0.3
0.6
0.6
Optimisation of Agricultural Water Use
param exist:
Sub-regional zones
khanbh
khanbl
1
6.6
1
6.7
2.4
15.8
0.3
2.2
31.7
204.3
2.8
17.8
2.8
17.8
0.2
1.2
0.2
1.2
117
khandb
7.7
7.9
18.5
2.5
239.2
20.8
20.8
1.4
1.4
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Grapefruits
3.1
1.6
0.2
0.5
3.4
3.9
4.4
0.8
Jew's melon
18.2
9.5
1.2
0.6
3.7
4.4
4.9
0.9
Lemon*
10.7
5.6
0.7
0.1
0.3
0.4
0.4
0.1
Olive*
56.8
29.7
3.8
0
0
0
0
0
Onion
10.7
5.6
0.7
0.1
0.4
0.4
0.5
0.1
Pepper a
9.4
4.9
0.6
9.4
60.6
71
80.1
13.9
Pepper sp
9.4
4.9
0.6
9.4
60.6
71
80.1
13.9
Potato w
12.6
6.6
0.9
0.4
2.3
2.7
3.1
0.5
Potato s
0
0
0
12.7
81.8
95.7
108.1
18.7
Shamoti*
8.8
4.6
0.6
0.2
1.2
1.4
1.5
0.3
Squash sp
0
0
0
3.1
20.1
23.6
26.6
4.6
Squash s
31.4
16.4
2.1
1
6.7
7.9
8.9
1.5
Strawberry
0
0
0
0.1
0.6
0.7
0.8
0.1
Sweetpotato
17.9
9.4
1.2
1
6.6
7.7
8.7
1.5
Tomato sp
19.5
10.2
1.3
0
0
0
0
0
Tomato s
25.3
13.2
1.7
0.4
2.8
3.3
3.8
0.6
Valencia*
71.3
37.3
4.8
0.6
3.9
4.5
5.1
0.9
Watermelon w 16.7
8.7
1.1
0.5
3
3.6
4
0.7
Watermelon s
16.7
8.7
1.1
0.5
3
3.5
4
0.7
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Sub-regional zones
Crops
middbl midddb middwg:= northbh
northbl
rafahbl
rafahdb
rafahky
Cabbage
8.4
13.4
15.7
5.9
9.8
12.8
35.9
24.2
Cauliflower a
0.9
1.4
1.7
2.2
3.6
16.7
46.7
31.4
Cauliflower sp 109.1
173.8
202.7
173.7
287.1
22.5
63
42.4
Citrus others*
6.8
10.8
12.5
51.7
85.5
0.8
2.1
1.4
Cucumber sp
121.3
193.3
225.4
38.5
63.7
53.7
150.1
101
Cucumber s
1.6
2.6
3
2.6
4.4
7.5
20.9
14.1
Eggplant w
1.6
2.6
3
2.6
4.4
7.5
20.9
14.1
Eggplant a
15.1
24
28
1.8
3.1
4.3
12.1
8.1
Guava*
15.1
24
28
1.8
3.1
4.3
12.1
8.1
Grapefruits
1
1.6
1.9
2.8
4.7
1.8
4.9
3.3
Jew's melon
20.2
32.2
37.6
8
13.3
9.5
26.6
17.9
Lemon*
0.5
0.8
0.9
3.2
5.3
0
0
0
Olive*
0
0
0
154.9
256
0
0
0
Onion
0.5
0.8
1
3.2
5.3
0
0
0
Pepper a
4.5
7.2
8.3
43.4
71.6
30.4
84.9
57.1
Pepper sp
4.5
7.2
8.3
43.4
71.6
30.4
84.9
57.1
Potato w
5.8
9.3
10.9
11.3
18.7
3.7
10.3
7
Potato s
11.2
17.9
20.9
12.4
20.6
8.8
24.6
16.6
Shamoti*
15.1
24
28
1.8
3.1
4.3
12.1
8.1
Squash sp
0.4
0.7
0.8
0.1
0.2
37.8
105.8
71.2
Squash s
0.9
1.4
1.7
2.1
3.6
16.7
46.7
31.4
Strawberry
0
0
0
0
161.1
0.5
1.5
1
Sweetpotato
8.4
13.4
15.7
6
9.8
12.8
35.9
24.2
Tomato sp
7.6
12.2
14.2
27.3
45.2
5.5
15.3
10.3
Tomato s
10.6
16.9
19.7
11.3
18.8
0.2
0.7
0.5
Valencia*
13.3
21.1
24.6
54.9
90.8
1.1
3.1
2.1
Watermelon w
3.7
5.9
6.9
3.2
5.3
3.5
9.8
6.6
Watermelon s
3.7
5.9
6.9
3.2
5.3
3.5
9.8
6.6
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Optimisation of Agricultural Water Use
118
Decision Support System
Appendix (I): Database and Mmultiobjective Model
# Wastewater use factor
param wwdemand:
Sub-regional zones
Crops
northbh northbl
gazabh
gazabl
gazawg
middbl
midddb
middwg
Cabbage
0
0
0
0
0
0
0
0
Cauliflower a
0
0
0
0
0
0
0
0
Cauliflower sp
0
0
0
0
0
0
0
0
Citrus others*
0
0
0
0
0
0
0
0
Cucumber sp
0
0
0
0
0
0
0
0
Cucumber s
0
0
0
0
0
0
0
0
Eggplant w
0
0
0
0
0
0
0
0
Eggplant a
1
1
1
1
1
1
1
1
Guava*
1
1
1
1
1
1
1
1
Grapefruits
0
0
0
0
0
0
0
0
Jew's melon
1
1
1
1
1
1
1
1
Lemon*
1
1
1
1
1
1
1
1
Olive*
0
0
0
0
0
0
0
0
Onion
1
1
1
1
1
1
1
1
Pepper a
0
0
0
0
0
0
0
0
Pepper sp
0
0
0
0
0
0
0
0
Potato w
0
0
0
0
0
0
0
0
Potato s
0
0
0
0
0
0
0
0
Shamoti*
1
1
1
1
1
1
1
1
Squash sp
0
0
0
0
0
0
0
0
Squash s
0
0
0
0
0
0
0
0
Strawberry
0
0
0
0
0
0
0
0
Sweetpotato
0
0
0
0
0
0
0
0
Tomato sp
0
0
0
0
0
0
0
0
Tomato s
0
0
0
0
0
0
0
0
Valencia*
1
1
1
1
1
1
1
1
Watermelon w
0
0
0
0
0
0
0
0
Watermelon s
0
0
0
0
0
0
0
0
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Crops
khanbh
Cabbage
0
Cauliflower a
0
Cauliflower sp
0
Citrus others*
0
Cucumber sp
0
Cucumber s
0
Eggplant w
0
Eggplant a
1
Guava*
1
Grapefruits
0
Jew's melon
1
Lemon*
1
Olive*
0
Onion
1
Pepper a
0
Pepper sp
0
Potato w
0
Potato s
0
khanbl
0
0
0
0
0
0
0
1
1
0
1
1
0
1
0
0
0
0
khandb
0
0
0
0
0
0
0
1
1
0
1
1
0
1
0
0
0
0
Optimisation of Agricultural Water Use
Sub-regional zones
khanwg
khanky
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
1
1
1
1
0
0
1
1
0
0
0
0
0
0
0
0
119
rafahbl
0
0
0
0
0
0
0
1
1
0
1
1
0
1
0
0
0
0
rafahdb
0
0
0
0
0
0
0
1
1
0
1
1
0
1
0
0
0
0
rafahky
0
0
0
0
0
0
0
1
1
0
1
1
0
1
0
0
0
0
Decision Support System
Appendix (I): Database and Mmultiobjective Model
Shamoti*
1
1
1
1
1
1
1
1
Squash sp
0
0
0
0
0
0
0
0
Squash s
0
0
0
0
0
0
0
0
Strawberry
0
0
0
0
0
0
0
0
Sweetpotato
0
0
0
0
0
0
0
0
Tomato sp
0
0
0
0
0
0
0
0
Tomato s
0
0
0
0
0
0
0
0
Valencia*
1
1
1
1
1
1
1
1
Watermelon w
0
0
0
0
0
0
0
0
Watermelon s
0
0
0
0
0
0
0
0
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
parameter Crop demand return value
Cultivation cost
Maximum Area
Crops
Ton/year
US$/ton
US$/hectare
hectare
Cabbage
7462
322.5
3320
901
Cauliflower a
2750
420.3
3500
904
Cauliflower sp
2750
420.3
3500
3859.9
Citrus others*
1
411.8
3480
673.3
Cucumber sp
1
411.8
3480
5378.4
Cucumber s
7720;
308.8;
5400
669
Eggplant w
7720
308.8
5400
669
Eggplant a
1529.7
144.5
1500
574.5
Guava*
5193.5
377
1500
574.5
Grapefruits
5282
468.3
1330
200
Jew's melon
8756.5
472.5
1500
1044
Lemon*
11193
1250
1500
145
Olive*
1866
502.3
4350
1002.5
Onion
6559
338.8
1500
146
Pepper a
1820
489
5400
2787.5
Pepper sp
1820
489
5400
2787.5
Potato w
22236.5
241.5
3200
530
Potato s
22236.5
241.5
3200
900
Shamoti*
5872.5
262.5
1500
574.5
Squash sp
2126
407.5
5500
1476.5
Squash s
2126
407.5
3400
903.5
Strawberry
485
1500
24270
833
Sweetpotato
2284
272.8
3200
901.5
Tomato sp
1
367.3
3280
905
Tomato s
1
367.3
3280
259.6
Valencia*
5872.5
157.5
1500
1697.75
Watermelon w
13432
160.3
6100
416.5
Watermelon s
13432
160.3
4000
416
a= autumn; sp= Spring; s= summer, w = winter. * Trees crop with a potential for treated wastewater use.
Parameter Cultivable area Wastewater quantity
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
Hectare
16200
8500
1100
900
5400
6300
7100
1300
m3
6076090
11513910
11334580
12177650
1592460
5540160
8818760
9996380
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Farmer's
acceptance
%
62
62
62
65
65
65
65
65
Decision Support System
Appendix (I): Database and Mmultiobjective Model
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
4000
6300
7300
6800
12800
3100
8500
5700
682850
4423680
5158490
1009430
5826500
2456780
6865620
4616660
65
65
65
46
46
72
72
72
Part B: Multiobjective model
# Final multiobjective optimisation model based on normalises value approach for wet and
dry combination
set CROP;
set ZONE;
# crop type
# area Sub-regional zones
# Parameter and variables
param FAF {ZONE} >= 0; # Farmer acceptance for reuse
param profit {CROP} >= 0; # profit from each crop
param cult {CROP} >= 0;
# cultivation cost
param gwprice >=0; # groundwater price
param wwprice >= 0; # wastewater price
param minsld >=0; # minimum salinity load dry
param minslw >=0; # minimum salinity load dry
param maxpd >=0; # maximum profit dry
param maxpw >=0; # maximum profit wet
param maxeffd >= 0; # maximum water efficiency dry
param maxeffw >= 0 ; #maximum water efficiency wet
param maxrd >= 0 ; #maximum reuse dry
param maxrw>=0; # maximum reuse wet
param mingw >=0 ; # minimum groundwater wet
param mingd >= 0; # minimum groundwater dry
param wfp >=0 ; #weighting factor profit
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param wfr >= 0; # weighting factor reuse
param wfeff >=0; # weighting factor eff
param wfg >= 0; # weighting factor groundwater
param wfs >= 0; # weighting factor salinity load
param demand {CROP} >= 0;
# crop market demand
param availarea {ZONE} >= 0;
# total available area in each zone
param exist {CROP,ZONE} >= 0;
# existing cropping pattern
param yieldd {CROP,ZONE} >= 0; # crop yield dry year
param yieldw{CROP,ZONE} >= 0; # crop yield wet year
param wdemandd {CROP,ZONE} >= 0;
# crop water demand dry year
param wdemandw {CROP,ZONE} >= 0;
# crop water demand wet year
param wwdemand {CROP,ZONE} >= 0;
# wastewater factor WHO
param availwater >= 0;
# available ground water
param availwwater {ZONE} >= 0;
# available wastewater
param SIrrigd {CROP,ZONE}; # salt accumulation due to irrigation
param SIrrigw {CROP,ZONE}; # salt accumulation due to irrigation
param maxy {CROP}; # maximum possible crop yield
var Area {c in CROP, z in ZONE} >= 0;
# crop area
var twdemandd { z in ZONE} = sum { c in CROP} wwdemand[c,z] * wdemandd[c,z] * Area[c,z];
var twdemandw { z in ZONE} = sum { c in CROP} wwdemand[c,z] * wdemandw[c,z] * Area[c,z];
# treated wastewater demand
var gwdemandd { z in ZONE} = sum { c in CROP} wdemandd[c,z] * Area[c,z] - sum { c in
CROP} wwdemand[c,z] * wdemandd[c,z] * Area[c,z];
var gwdemandw { z in ZONE} = sum { c in CROP} wdemandw[c,z] * Area[c,z] - sum { c in
CROP} wwdemand[c,z] * wdemandw[c,z] * Area[c,z];
# groundwater demand
var irrigationd { z in ZONE} = sum { c in CROP} wdemandd[c,z] * Area[c,z] ;
var irrigationw { z in ZONE} = sum { c in CROP} wdemandw[c,z] * Area[c,z] ;
# total irrigated water
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var sirrigd {z in ZONE} = sum {c in CROP} SIrrigd[c,z] * Area[c,z];
var sirrigw {z in ZONE} = sum {c in CROP} SIrrigw[c,z] * Area[c,z];
# Accumulation of salt due to irrigation
var sirrigld= sum {z in ZONE}sirrigd[z]; # total salinity load dry
var sirriglw= sum {z in ZONE}sirrigw[z]; # total salinity load wet
var wcostd {z in ZONE}= sum { c in CROP} wdemandd[c,z] * Area[c,z]*gwprice;
var wcostw {z in ZONE}= sum { c in CROP} wdemandw[c,z] * Area[c,z]*gwprice;
# groundwater cost
var wwcostw {z in ZONE}= sum { c in CROP} wdemandw[c,z] * wwdemand[c,z]
*Area[c,z]*wwprice;
var wwcostd {z in ZONE}= sum { c in CROP} wdemandd[c,z] * wwdemand[c,z]
*Area[c,z]*wwprice;
var cultcost {z in ZONE} =sum {c in CROP} cult[c] * Area[c,z];
var returnd {z in ZONE} =sum {c in CROP} profit[c] * Area[c,z] * yieldd[c,z];
var returnw {z in ZONE} =sum {c in CROP} profit[c] * Area[c,z] * yieldw[c,z];
var profitwwd {z in ZONE} = sum {c in CROP} profit[c] * Area[c,z] * yieldd[c,z]
*wwdemand[c,z]- sum {c in CROP} cult[c] * Area[c,z]*wwdemand[c,z];
var profitwww {z in ZONE} = sum {c in CROP} profit[c] * Area[c,z] * yieldw[c,z]
*wwdemand[c,z]- sum {c in CROP} cult[c] * Area[c,z]*wwdemand[c,z];
# wastewater use profit
var profitw =sum {z in ZONE} returnw[z] - sum {z in ZONE} cultcost[z]-sum {z in ZONE}
wcostw[z] -sum {z in ZONE} wwcostw[z];
var profitd =sum {z in ZONE} returnd[z]- sum {z in ZONE} cultcost[z] -sum {z in ZONE}
wcostd[z]-sum {z in ZONE}wwcostd[z];
var effd =((sum {c in CROP, z in ZONE}profit[c] * Area[c,z] * yieldd[c,z] -sum {c in CROP, z in
ZONE} cult[c] * Area[c,z])/sum {c in CROP, z in ZONE} wdemandd[c,z] * Area[c,z]);
var effw =((sum {c in CROP, z in ZONE}profit[c] * Area[c,z] * yieldw[c,z] -sum {c in CROP, z in
ZONE} cult[c] *Area[c,z])/sum {c in CROP, z in ZONE} wdemandw[c,z] * Area[c,z]);
var twdemandtd =sum {z in ZONE}twdemandd[z];
var twdemandtw = sum {z in ZONE}twdemandw[z];
var gwdemandtd =sum {z in ZONE}gwdemandd[z];
var gwdemandtw =sum {z in ZONE}gwdemandw[z];
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# spatial equity variables profit
var mdp {z in ZONE} >=0;
var mwp {z in ZONE} >=0;
var equityfpw = profitw/sum {z in ZONE}availarea[z];
var equityfpd = profitd/sum {z in ZONE}availarea[z];
# spatial equity variables groundwater
var mdg {z in ZONE} >=0;
var mwg {z in ZONE} >=0;
var equityfgw = gwdemandtw/sum {z in ZONE}availarea[z];
var equityfgd = gwdemandtd/sum {z in ZONE}availarea[z];
# spatial equity variables wastewater
var mdw {z in ZONE} >=0;
var mww {z in ZONE} >=0;
var equityfww = twdemandtw/sum {z in ZONE}availarea[z];
var equityfwd = twdemandtd/sum {z in ZONE}availarea[z];
# wastwater use equity factor preparation
var demcd {c in CROP} = (sum {z in ZONE} yieldd[c,z] * Area[c,z])/demand[c];
var demcw {c in CROP} = (sum {z in ZONE} yieldw[c,z] * Area[c,z])/demand[c];
# local crop demand
# objective function:
maximize total_profit : ((profitw/maxpw)*wfp +(profitd/maxpd)*wfp +(effw/maxeffw)*wfeff +
(effd/maxeffd)*wfeff +(twdemandtd/maxrd)*wfr +(twdemandtw/maxrw)*wfr (gwdemandtd/mingd)*wfg - (gwdemandtw/mingw)*wfg-(sirrigld/minsld)*wfs (sirriglw/minslw)*wfs);
# Model constraints:
# Area constraints
subject to totalarea { z in ZONE} : sum {c in CROP} Area[c,z] = availarea[z];
# available groundwater
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subject to waterd: sum {c in CROP, z in ZONE} Area[c,z] * wdemandd[c,z] - sum { z in ZONE }
availwwater[z] <= availwater;
subject to waterw: sum {c in CROP, z in ZONE} Area[c,z] * wdemandw[c,z] - sum { z in ZONE }
availwwater[z] <= availwater;
# total wastewater demand less than the available wastewater
subject to wastewaterd {z in ZONE}: sum {c in CROP} wwdemand[c,z] * wdemandd[c,z] *
Area[c,z] <= availwwater[z];
subject to wastewaterw {z in ZONE}: sum {c in CROP} wwdemand[c,z] * wdemandw[c,z] *
Area[c,z] <= availwwater[z];
# Crop maximum area constraint
subject to change1 {c in CROP} : maxa[c] >= sum {z in ZONE} Area [c,z];
# Decision Parameter constraints:
# Farmer acceptance
subject to farmeracept { z in ZONE} : sum {c in CROP} wwdemand[c,z]*Area[c,z] <=
FAF[z]*availarea[z];
# Equity in access to profit
subject to eqprw {z in ZONE} : ((sum {c in CROP} profit[c] * Area[c,z]* yieldw[c,z]- sum {c in
CROP} cult[c] *Area[c,z]-sum {c in CROP} wdemandw[c,z] * wwdemand[c,z]
*Area[c,z]*wwprice-sum { c in CROP} wdemandw[c,z] * Area[c,z]*gwprice)/(sum {c in CROP}
Area[c,z]+1)) > = mwp[z]*equityfpw;
subject to eqprd {z in ZONE}: ((sum {c in CROP} profit[c] * Area[c,z]* yieldd[c,z]- sum {c in
CROP} cult[c] *Area[c,z]-sum {c in CROP} wdemandd[c,z] * wwdemand[c,z]
*Area[c,z]*wwprice-sum { c in CROP} wdemandd[c,z] * Area[c,z]*gwprice)/(sum {c in CROP}
Area[c,z]+1)) >=mdp[z]*equityfpd;
subject to equitypw {z in ZONE}: mwp[z] >=0;
subject to equitypd {z in ZONE}: mdp[z] >=0;
# Equity in access to groundwater
subject to eqgww {z in ZONE} : ((sum {c in CROP} Area[c,z]* wdemandw[c,z] -sum { c in
CROP} wdemandw[c,z] *wwdemand[c,z]*Area[c,z])/(sum {c in CROP} Area[c,z]+1)) >=
mwg[z]*equityfgw;
subject to eqgrd {z in ZONE}: ((sum { c in CROP} wdemandd[c,z] *Area[c,z]-sum { c in CROP}
wdemandd[c,z] * wwdemand[c,z]*Area[c,z])/(sum {c in CROP} Area[c,z]+1))
>=mdg[z]*equityfgd;
subject to equitygw {z in ZONE}: mwg[z] >=0;
subject to fequitygd {z in ZONE}: mdg[z] >=0;
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# Equity in access to wastewater
subject to eqwww {z in ZONE} : (( sum { c in CROP} wdemandw[c,z] * wwdemand[c,z]
*Area[c,z])/(sum {c in CROP} Area[c,z]+1)) >= mww[z]*equityfww;
subject to eqwrd {z in ZONE}: ((sum { c in CROP} wdemandd[c,z] * wwdemand[c,z] * Area[c,z])
/(sum {c in CROP} Area[c,z]+1)) >=mdw[z]*equityfwd;
subject to equityww {z in ZONE}: mww[z] >=0;
subject to fequitywd {z in ZONE}: mdw[z] >=0;
# local crop product demand coverage decision variable
subject to Demandd {c in CROP} : sum {z in ZONE} yieldd[c,z] * Area[c,z] >= PCF*demand[c];
subject to Demandw {c in CROP} : sum {z in ZONE} yieldw[c,z] * Area[c,z] >= PCF*demand[c];
# Maximum groundwater use decision variable
subject to waterd1 : sum { c in CROP,z in ZONE} wdemandd[c,z] * Area[c,z] - sum { c in CROP,
z in ZONE} wwdemand[c,z] * wdemandd[c,z] * Area[c,z] <=400000000;
subject to waterw1: sum { c in CROP,z in ZONE} wdemandw[c,z] * Area[c,z] - sum { c in
CROP,z in ZONE} wwdemand[c,z] * wdemandw[c,z] * Area[c,z]<=40000000;
# Minimum wastewater use decision variable
subject to wwaterd1 : sum { c in CROP,z in ZONE} wwdemand[c,z] * wdemandd[c,z] * Area[c,z]
>=10000000; ;
subject to wwaterw1: sum { c in CROP,z in ZONE} wwdemand[c,z] * wdemandw[c,z] * Area[c,z]
>=10000000; ;
# Maximum salinity load decision variable
subject to salintyd1 : sum { c in CROP,z in ZONE} SIrrigd[c,z] * Area[c,z]<=9000000;
subject to salintyw1: sum { c in CROP,z in ZONE} SIrrigw[c,z]* Area[c,z]<=9000000;
# minimum water using efficiency decision variable
subject to efd: (((sum {c in CROP, z in ZONE}profit[c] * Area[c,z] * yieldd[c,z] -sum {c in CROP,
z in ZONE} cult[c] * Area[c,z])/sum {c in CROP, z in ZONE} wdemandd[c,z] * Area[c,z])) >=0.5;
subject to efw: (((sum {c in CROP, z in ZONE}profit[c] * Area[c,z] * yieldw[c,z] -sum {c in
CROP, z in ZONE} cult[c] * Area[c,z])/sum {c in CROP, z in ZONE} wdemandw[c,z] *
Area[c,z]))>=0.5;
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Decision Support System
Appendix (II) Decision Support Charts
Appendix (II)
Decision support charts
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Decision Support System
Appendix (II) Decision Support Charts
80
70
Million.
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Groundwater weight factor
Groundwater [m³]
Wastewater [m³]
Salinity [kg]
Profit [US$]
Irrigation [m³]
Million.
Decision support chart for groundwater weight factor shows its Influences in the different socioeconomic and environmental aspect of the agricultural water use under wet year condition.
90
80
70
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Profit weight factor
Groundwater [m³]
Wastewater [m³]
Salinity [kg]
Profit [US$]
Irrigation [m³]
Decision support chart for profit weight factor shows its Influences in the different socio-economic
and environmental aspect of the agricultural water use under wet year condition.
80
70
Million.
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Salinity load weight factor
Groundwater [m³]
Wastewater [m³]
Salinity [kg]
Profit [US$]
Irrigation [m³]
Decision support chart for salinity load weight factor shows its Influences in the different socioeconomic and environmental aspect of the agricultural water use under wet year condition.
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Decision Support System
Appendix (II) Decision Support Charts
80
70
Million.
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Salinity load weight factor
Groundwater [m³]
Wastewater [m³]
Salinity [kg]
Profit [US$]
Irrigation [m³]
Decision support chart for salinity load weight factor shows its Influences in the different socioeconomic and environmental aspect of the agricultural water use under dry year condition.
80
70
Million.
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Water use effectiveness weight factor
Groundwater [m³]
Wastewater [m³]
Salinity [kg]
Profit [US$]
Irrigation [m³]
Decision support chart for water use effectiveness weight factor shows its Influences in the different
socio-economic and environmental aspect of the agricultural water use under wet year condition.
80
70
Million.
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Water use effectiveness weight factor
Groundwater [m³]
Wastewater [m³]
Salinity [kg]
Profit [US$]
Irrigation [m³]
Decision support chart for water use effectiveness weight factor shows its Influences in the different
socio-economic and environmental aspect of the agricultural water use under dry year condition.
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Decision Support System
Million..
Appendix (II) Decision Support Charts
80
70
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Treated wastewater weight factor
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Million..
Decision support chart for treated wastewater weight factor shows its Influences in the different socioeconomic and environmental aspect of the agricultural water use under wet year condition.
80
70
60
50
40
30
20
10
0,01
0,10
1,00
10,00
100,00
Treated wastewater weight factor
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Million.
Decision support chart for treated wastewater weight factor shows its Influences in the different socioeconomic and environmental aspect of the agricultural water use under dry year condition.
90
80
70
60
50
40
30
20
10
10
15
20
25
30
35
40
Groundwater [Mm³]
Wastewater [m³]
Irrigation [m³]
Salinity [kg]
Profit [US$]
Decision support chart for allocation of maximum groundwater under wet year conditions
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90
80
Million..
70
60
50
40
30
20
10
22
27
32
37
42
47
Treated Wastewater [Mm³]
Groundwater [m³]
Salinity [kg]
Irrigation [m³]
Profit [US$]
Decision support chart for allocation of minimum treated waster under wet year conditions
90
80
Million.
70
60
50
40
30
20
10
22
27
32
37
T re a t e d w a s te w a t e r [M m ³]
G ro u n d w a te r [ m ³]
S a li n ity [ k g ]
42
47
Irrig a tio n [m ³]
P ro fi t [ U S $ ]
Marginal value [US$/m³]..
Decision support chart for allocation of minimum treated wastewater under dry year conditions
1,3
1,2
1,1
1
0,9
0,8
22
27
32
37
42
47
Treated wastewater [Mm³]
Wet
Dry
Treated wastewater marginal value
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90
80
Million.
70
60
50
40
30
20
10
40
45
50
55
60
65
70
75
80
85
Salinity load [Mkg]
Groundwater [m³]
Wastewater [m³]
Irrigation [m³]
Profit [US$]
Decision support chart for allocation of maximum salinity load under wet year conditions
90
80
Million.
70
60
50
40
30
20
10
40
45
50
55
60
65
70
75
80
85
Salinity load [Mkg]
Groundwater [m³]
Wastewater [m³]
Irrigation [m³]
Profit [US$]
Marginal value [US$/m³]..
Decision support chart for allocation of maximum salinity load under dry year conditions
2
1,8
1,6
1,4
1,2
1
0,8
40
45
50
55
60
65
70
75
80
85
Salinity load [Mkg]
Dry
Wet
Salinity load marginal value
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Million.
Appendix (II) Decision Support Charts
90
80
70
60
50
40
30
20
10
50
60
70
80
90
100
Percentage coverage of local crops product demand
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Decision support chart for percentage coverage of local crops product demand under wet year
conditions
Million.
75
55
35
15
-30
-20
-10
0
10
20
30
Percentage changes in farmer's acceptance
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Decision support chart for percentage changes in farmer's acceptance for reuse under dry year
conditions
75
65
Million
55
45
35
25
15
No equity
50
60
70
80
90
Percentage equity in groundwater use
Groundwater m3
Wastewater m3
Irrigation m3
Salinity kg
Profit US$
Decision support chart for percentage spatial equity in access to groundwater for wet year.
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Appendix (II) Decision Support Charts
75
65
Million.
55
45
35
25
15
No equity
50
60
70
80
90
Percentage equity in groundwater use
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Decision support chart for percentage spatial equity in access to groundwater for dry year.
Million:
75
55
35
15
No equity
50
60
70
80
90
Percentage equity in profit
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Decision support chart for percentage spatial equity in access to profit for wet year.
75
65
Million.
55
45
35
25
15
No equity
50
60
70
80
90
Percentage equity in wastewater use
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
Irrigation [m³]
Decision support chart for percentage spatial equity in access to wastewater for wet year.
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Appendix (II) Decision Support Charts
75
65
Million.
55
45
35
25
15
No equity
50
60
70
Percentage equity in wastewater use
Groundwater [m³]
Salinity [kg]
Wastewater [m³]
Profit [US$]
80
90
Irrigation [m³]
Decision support chart for percentage spatial equity in access to wastewater for dry year.
Optimisation of Agricultural Water Use
135
Decision Support System
Appendix (III): Scenarios Results
Appendix (III)
Scenarios
Optimisation of Agricultural Water Use
136
Decision Support System
Appendix (III): Scenarios Results
A: economic scenario
Crop pattern
Sub-regional zones
Crops
gazabh gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
Cabbage
0.0
0.0
0.0
4.0
0.0
0.0
0.0
35.5
Cauliflower a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cauliflower sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Citrus others*
0.0
0.0
18.3
0.0
0.0
0.0
70.1
0.0
Cucumber sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cucumber s
52.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant w
0.0
0.0
91.7
0.0
0.0
0.0
0.0
0.0
Eggplant a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Guava*
3.6
0.0
0.0
0.0
83.3
0.0
0.0
21.8
Grapefruits
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Jew's melon
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Lemon*
232.9
0.0
0.0
0.0
0.0
0.0
154.6
0.0
Olive*
143.2
49.4
0.0
19.2
0.0
409.5
0.0
0.0
Onion
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pepper a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pepper sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Potato w
555.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Potato s
0.0
0.0
0.0
28.8
0.0
48.2
0.0
72.7
Shamoti*
0.0
0.0
0.0
0.0
0.0
0.0
186.9
0.0
Squash sp
46.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash s
57.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
0.0
717.3
0.0
0.0
98.6
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
235.3
0.0
Tomato s
528.4
0.0
0.0
38.1
0.0
172.4
13.2
0.0
Valencia*
0.0
83.3
0.0
0.0
0.0
0.0
49.9
0.0
Watermelon w
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon s
0.0
0.0
0.0
0.0
358.1
0.0
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Crops
Cabbage
Cauliflower a
Cauliflower sp
Citrus others*
Cucumber sp
Cucumber s
Eggplant w
Eggplant a
Guava*
Grapefruits
Jew's melon
Lemon*
Olive*
Onion
Pepper a
Pepper sp
Potato w
Potato s
middbl midddb
0.0
0.0
0.0
0.0
0.0
0.0
107.1
0.0
0.0
0.0
0.0
0.0
0.0
220.5
0.0
0.0
0.0
409.5
27.5
0.0
142.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
34.4
0.0
27.3
0.0
0.0
0.0
0.0
0.0
middwg
0.0
0.0
0.0
0.0
0.0
0.0
133.9
0.0
333.9
0.0
0.0
0.0
140.6
0.0
0.0
0.0
0.0
121.6
Optimisation of Agricultural Water Use
Sub-regional zones
northbh
northbl
rafahbl
0.0
0.0
90.1
122.2
42.2
0.0
0.0
574.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
92.8
0.0
0.0
128.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
47.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
312.8
252.9
0.0
0.0
200.0
0.0
0.0
0.0
0.0
0.0
19.2
0.0
0.0
0.0
0.0
23.8
0.0
102.7
137
rafahdb
0.0
0.0
0.0
0.0
0.0
0.0
0.0
530.0
0.0
0.0
0.0
0.0
143.2
0.0
0.0
0.0
0.0
126.9
rafahky
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
88.0
310.0
0.0
0.0
0.0
0.0
0.0
Decision Support System
Appendix (III): Scenarios Results
Shamoti*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
0.0
0.0
0.0
0.0
17.1
0.0
0.0
0.0
Sweetpotato
61.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20.1
Tomato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
152.0
Valencia*
0.0
0.0
0.0
0.0
0.0
0.0
49.9
0.0
Watermelon w
0.0
0.0
0.0
0.0
174.1
0.0
0.0
0.0
Watermelon s
0.0
0.0
0.0
0.0
0.0
69.3
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Main result for the economy scenario
Dry year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Wet year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Groundwater
m3
3642530
3372880
564694
262511
2419740
880217
1428460
378615
2049370
1472500
1255530
1296480
2267090
988594
4246800
785756
27311767
Wastewater
m3
2034730
1032780
135744
111204
656161
2334970
3351870
160995
1178170
2928330
3509770
1448890
1555180
376737
1243020
2555070
24613621
irrigation
m3
5677260
4405660
700438
373715
3075900
3215190
4780330
539610
3227540
4400830
4765300
2745370
3822260
1365330
5489820
3340820
51925373
Salinity
kg
1248940
328860
155653
45762.9
60803.1
380881
1641470
66650.2
869268
652586
708391
203181
56549.7
26874.5
650837
903284
7999991
profit
Water use effectiveness
M.US$
US$/ m3
10162490
1.79
17472900
3.97
1287611
1.84
609000
1.63
3574210
1.16
4158420
1.29
4663010
0.98
877418
1.63
3400787
1.05
8246750
1.87
6574500
1.38
4823910
1.76
10573680
2.77
2023191
1.48
7766380
1.41
3695490
1.11
89909747
1.73
Groundwater
m3
2627620
2786110
361950
220723
2212810
770025
1318620
352773
1992860
1165560
1131440
980256
2152570
1007630
3939840
666542
23687329
Wastewater
m3
1599310
915661
108687
88943.7
657994
1755120
2673470
128430
1176150
2340290
2604880
1086040
1329070
377264
1174550
1984140
20000000
irrigation
m3
4226930
3701770
470637
309666
2870810
2525140
3992090
481203
3169010
3505860
3736320
2066300
3481640
1384890
5114380
2650680
43687326
Salinity
M.kg
929754
275845
104779
37910.9
56558.2
299022
1368300
59422.3
854610
519530
554422
153065
51775.3
27162.6
606297
716585
6615038
profit
Water use effectiveness
M.US$
US$/ m3
10721990
2.54
19624900
5.30
1369081
2.91
596289
1.93
3574210
1.25
4170050
1.65
4699500
1.18
861008
1.79
3437067
1.08
8351250
2.38
6952130
1.86
4500960
2.18
10727780
3.08
2052271
1.48
10361780
2.03
3772960
1.42
95773226
2.19
Optimisation of Agricultural Water Use
138
Decision Support System
Appendix (III): Scenarios Results
B: Wastewater scenario
Crop pattern
Sub-regional zones
Crops
gazabh gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
Cabbage
0.0
0.0
0.0
0.0
0.0
124.0
0.0
0.0
Cauliflower a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cauliflower sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Citrus others*
652.3
289.7
0.0
0.0
0.0
0.0
0.0
101.4
Cucumber sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cucumber s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant w
0.0
0.0
64.3
0.0
0.0
0.0
0.0
0.0
Eggplant a
0.0
0.0
0.0
0.0
0.0
14.6
0.0
0.0
Guava*
0.0
0.0
1.1
0.0
0.0
0.0
0.0
0.0
Grapefruits
0.0
0.0
44.6
0.0
0.0
0.0
0.0
0.0
Jew's melon
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.6
Lemon*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Olive*
552.9
0.0
0.0
70.2
0.0
491.4
0.0
0.0
Onion
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pepper a
0.0
0.0
0.0
0.0
22.8
0.0
0.0
0.0
Pepper sp
0.0
0.0
0.0
0.0
45.5
0.0
0.0
0.0
Potato w
14.9
0.0
0.0
0.0
0.0
0.0
260.5
22.0
Potato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Shamoti*
0.0
0.0
0.0
0.0
244.7
0.0
449.5
0.0
Squash sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash s
57.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
0.0
560.3
0.0
0.0
0.0
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato s
342.6
0.0
0.0
19.8
0.0
0.0
0.0
0.0
Valencia*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon w
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon s
0.0
0.0
0.0
0.0
227.0
0.0
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Crops
Cabbage
Cauliflower a
Cauliflower sp
Citrus others*
Cucumber sp
Cucumber s
Eggplant w
Eggplant a
Guava*
Grapefruits
Jew's melon
Lemon*
Olive*
Onion
Pepper a
Pepper sp
Potato w
Potato s
middbl midddb
0.0
0.0
0.0
0.0
62.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
215.4
158.9
43.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
352.6
middwg
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
569.4
0.0
13.4
0.0
63.8
0.0
Optimisation of Agricultural Water Use
Sub-regional zones
northbh
northbl
rafahbl
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
106.8
0.0
0.0
0.0
0.0
0.0
145.0
0.0
0.0
26.6
0.0
0.0
0.0
0.0
0.0
138.8
0.0
0.0
0.0
0.0
0.0
0.0
126.9
0.0
0.0
563.7
109.6
129.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
175.2
0.0
86.5
0.0
0.0
139
rafahdb
10.0
0.0
0.0
547.5
0.0
0.0
0.0
80.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
25.5
rafahky
0.0
74.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
413.6
0.0
0.0
0.0
0.0
66.7
Decision Support System
Appendix (III): Scenarios Results
Shamoti*
0.0
118.5
0.0
0.0
0.0
0.0
0.0
0.0
Squash sp
0.0
0.0
0.0
46.5
0.0
0.0
0.0
0.0
Squash s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
79.0
0.0
0.0
0.0
193.7
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
59.2
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato s
0.0
0.0
83.4
0.0
0.0
0.0
0.0
0.0
Valencia*
0.0
0.0
0.0
0.0
0.0
0.0
186.9
0.0
Watermelon w
0.0
0.0
0.0
0.0
161.4
0.0
0.0
15.6
Watermelon s
0.0
0.0
0.0
0.0
0.0
200.4
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Main result for the wastewater scenario
Dry year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Wet year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Groundwater
m3
1555990
2634700
396262
84308.4
1734030
507589
569982
106034
650373
1057910
666369
1003280
3089230
1055120
693470
457607
16262254.4
Wastewater
m3
6343380
2537750
336482
406809
2363870
2801960
3264690
898810
1509470
1923130
4203310
2030180
4937700
961173
6292340
2387870
43198924
irrigation
m3
7899360
5172450
732743
491117
4097910
3309550
3834670
1004840
2159850
2981040
4869680
3033460
8026940
2016290
6985810
2845480
59461190
Salinity
kg
1743960
382894
162693
60550.2
80220.5
393300
1320680
123632
586834
441942
724623
223471
117940
39585.6
823956
773712
7999993
profit
Water use effectiveness
US$
US$/ m3
7421220
0.94
15708300
3.04
942621
1.29
457113
0.93
2375990
0.58
3700270
1.12
2849510
0.74
786962
0.78
3956110
1.83
2706780
0.91
3309550
0.68
5055630
1.67
15976850
1.99
1501988
0.74
5575250
0.80
2234080
0.79
74558224
1.25
Groundwater
m3
1154480
2176340
253991
64963.8
1645300
512159
609318
92709.2
549963
1038520
530263
824045
2946900
970366
648621
419332
14437271
Wastewater
m3
4960670
2282270
269106
325377
2150020
2106140
2603930
751780
1317590
1523930
2518460
1719730
4460280
961501
5252430
1796800
35000014
irrigation
m3
6115150
4458610
523096
390341
3795320
2618300
3213250
844489
1867550
2562450
3048720
2543770
7407180
1931870
5901050
2216130
49437276
Salinity
M.kg
1349100
329615
116167
48085.2
74198.6
311352
1105510
103826
508038
379213
452927
187270
108670
37782.3
696710
601936
6410400
profit
Water use effectiveness
US$
US$/ m3
8798820
1.44
17389300
3.90
1025471
1.96
449840
1.15
2322910
0.61
3655770
1.40
2849510
0.89
712098
0.84
4004080
2.14
2737890
1.07
3346920
1.10
4647630
1.83
16026950
2.16
1501988
0.78
5592550
0.95
2306320
1.04
77368047
1.56
Optimisation of Agricultural Water Use
140
Decision Support System
Appendix (III): Scenarios Results
C: Groundwater scenario
Crop pattern
Sub-regional zones
Crops
gazabh gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
Cabbage
0.0
0.0
45.7
0.0
0.0
139.8
296.1
0.0
Cauliflower a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
32.6
Cauliflower sp 254.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Citrus others*
0.0
0.0
64.3
0.0
0.0
0.0
413.9
84.5
Cucumber sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cucumber s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant w
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Guava*
0.0
214.8
0.0
0.0
246.2
0.0
0.0
0.0
Grapefruits
11.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Jew's melon
0.0
0.0
0.0
0.0
0.0
37.2
0.0
12.9
Lemon*
0.0
207.8
0.0
0.0
0.0
0.0
0.0
0.0
Olive*
738.4
0.0
0.0
45.4
0.0
409.5
0.0
0.0
Onion
0.0
0.0
0.0
0.0
0.0
43.5
0.0
0.0
Pepper a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pepper sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Potato w
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Potato s
0.0
0.0
0.0
0.0
286.4
0.0
0.0
0.0
Shamoti*
216.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash s
8.9
0.0
0.0
44.6
0.0
0.0
0.0
0.0
Strawberry
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
7.4
0.0
0.0
0.0
Tomato sp
352.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Valencia*
38.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon w
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon s
0.0
427.4
0.0
0.0
0.0
0.0
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Crops
Cabbage
Cauliflower a
Cauliflower sp
Citrus others*
Cucumber sp
Cucumber s
Eggplant w
Eggplant a
Guava*
Grapefruits
Jew's melon
Lemon*
Olive*
Onion
Pepper a
Pepper sp
Potato w
Potato s
middbl midddb
0.0
237.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
342.2
0.0
0.0
95.0
7.4
201.3
0.0
58.7
0.0
0.0
0.0
34.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
middwg
0.0
33.8
0.0
0.0
146.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
474.5
0.0
0.0
32.1
0.0
1.1
Optimisation of Agricultural Water Use
Sub-regional zones
northbh
northbl
rafahbl
0.0
123.5
0.0
0.0
0.0
0.0
0.0
128.4
191.8
0.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
506.7
0.0
0.0
83.1
0.0
0.0
0.0
96.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
264.2
0.0
288.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
21.4
309.2
0.0
0.0
0.0
0.0
0.0
141
rafahdb
179.9
0.0
0.0
294.2
0.0
0.0
0.0
0.0
0.0
22.8
0.0
0.0
0.0
0.0
0.0
0.0
238.2
0.0
rafahky
0.0
0.0
0.0
312.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
11.3
0.0
0.0
0.0
0.0
246.6
Decision Support System
Appendix (III): Scenarios Results
Shamoti*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash sp
0.0
0.0
42.5
0.0
0.0
0.0
0.0
0.0
Squash s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
10.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
60.8
0.0
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
20.6
0.0
0.0
0.0
0.0
Tomato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Valencia*
0.0
42.4
0.0
0.0
0.0
0.0
114.8
0.0
Watermelon w
0.0
0.0
0.0
0.0
174.1
0.0
0.0
0.0
Watermelon s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Main result for the groundwater scenario
Dry year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Groundwater
m3
2275470
2062120
146675
149078
924971
848362
924525
181346
1055070
837472
1003520
955627
4951580
544658
1103110
736695
18700279
Wastewater
m3
4963030
3509860
475659
263282
1939010
2334970
3005920
749008
2174050
2810900
3502760
1341740
2314260
760934
3699940
2782160
36627483
irrigation
m3
7238500
5571980
622335
412360
2863980
3183330
3930450
930354
3229120
3648370
4506280
2297360
7265840
1305590
4803050
3518860
55327759
Salinity
kg
1598840
409311
138062
50669.5
56426.2
378022
1354490
114522
868093
540669
669773
170385
106793
25684
566769
951486
7999995
profit
Water use effectiveness
US$
US$/ m3
7873620
1.09
5891010
1.06
689355
1.11
534160
1.30
4973060
1.74
4101930
1.29
3976060
1.01
942770
1.01
4834581
1.50
6560310
1.80
3482410
0.77
2596950
1.13
16627350
2.29
3546432
2.72
5108410
1.06
2610860
0.74
74349268
1.34
Groundwater
m3
2239030
2163410
152162
124521
1048780
870819
981382
179803
1022500
870819
1009020
939921
5799590
547620
1174870
875750
19999997
Wastewater
m3
3808830
3140980
380849
210580
1944430
1755120
2397540
626483
2118750
2257880
2098710
1007060
2090490
761999
3088460
2311840
30000001
irrigation
m3
6047870
5304380
533011
335101
2993210
2625940
3378920
806286
3141250
3128700
3107740
1946980
7890090
1309620
4263330
3187590
50000018
Salinity
M.kg
1334370
389957
118084
41096.7
58717.3
311966
1164150
99202.3
845378
463046
460903
144264
116307
25791
503808
863063
6940103
profit
Water use effectiveness
US$
US$/ m3
8674620
1.43
5984480
1.13
746975
1.40
515999
1.54
4973060
1.66
4043880
1.54
4211780
1.25
887150
1.10
4929711
1.57
6741570
2.15
3436480
1.11
2522900
1.30
16609950
2.11
3546432
2.71
4826340
1.13
2505110
0.79
75156437
1.50
Wet year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Optimisation of Agricultural Water Use
142
Decision Support System
Appendix (III): Scenarios Results
D: Environment scenario
Crop pattern
Zones
Crops
gazabh gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
Cabbage
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cauliflower a
191.8
48.1
41.8
0.0
0.0
0.0
0.0
0.0
Cauliflower sp
0.0
0.0
0.0
0.0
0.0
0.0
450.8
0.0
Citrus others*
0.0
368.9
0.0
0.0
0.0
0.0
0.0
0.0
Cucumber sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cucumber s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant w
0.0
75.1
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Guava*
0.0
0.0
68.2
58.5
0.0
409.5
71.5
84.5
Grapefruits
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Jew's melon
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Lemon*
0.0
0.0
0.0
0.0
351.0
0.0
0.0
0.0
Olive*
960.1
0.0
0.0
0.0
0.0
0.0
187.8
0.0
Onion
0.0
42.1
0.0
0.0
0.0
0.0
0.0
0.0
Pepper a
0.0
0.0
0.0
0.0
17.0
0.0
0.0
0.0
Pepper sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Potato w
409.4
0.0
0.0
0.0
0.0
0.0
0.0
45.5
Potato s
0.0
25.4
0.0
31.5
0.0
220.5
0.0
0.0
Shamoti*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash s
0.0
48.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Valencia*
0.0
158.1
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon w 58.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon s
0.0
84.3
0.0
0.0
172.0
0.0
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use
Crops
middbl midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Cabbage
0.0
0.0
0.0
0.0
124.4
0.0
0.0
0.0
Cauliflower a
43.1
209.3
0.0
0.0
0.0
0.0
0.0
40.4
Cauliflower sp 123.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Citrus others*
0.0
0.0
266.7
94.4
489.7
0.0
0.0
0.0
Cucumber sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cucumber s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant w
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant a
0.0
0.0
0.0
0.0
83.1
0.0
0.0
0.0
Guava*
0.0
0.0
207.8
0.0
0.0
0.0
0.0
0.0
Grapefruits
0.0
0.0
0.0
39.7
0.0
0.0
0.0
0.0
Jew's melon
0.0
0.0
0.0
0.0
132.1
0.0
0.0
0.0
Lemon*
0.0
0.0
0.0
0.0
99.1
223.2
0.0
0.0
Olive*
233.2
409.5
0.0
0.0
0.0
0.0
612.0
410.4
Onion
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pepper a
0.0
0.0
0.0
0.0
20.2
0.0
0.0
0.0
Pepper sp
0.0
0.0
0.0
0.0
45.5
0.0
0.0
0.0
Potato w
0.0
0.0
213.0
367.2
0.0
0.0
0.0
119.2
Potato s
0.0
11.2
0.0
0.0
0.0
0.0
238.0
0.0
Shamoti*
0.0
0.0
0.0
178.7
0.0
0.0
0.0
0.0
Optimisation of Agricultural Water Use
143
Decision Support System
Appendix (III): Scenarios Results
Squash sp
0.0
0.0
42.5
0.0
0.0
0.0
0.0
0.0
Squash s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
0.0
0.0
0.0
0.0
10.6
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
59.2
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Valencia*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon w
0.0
0.0
0.0
0.0
131.7
0.0
0.0
0.0
Watermelon s
0.0
0.0
0.0
0.0
84.3
86.8
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use
Main result for the environmental scenario
Dry year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Groundwater
m3
1294700
1347560
73735
94689
1057710
665469
952957
115160
381307
419726
694372
484337
3714790
457089
712572
360945
12827118
Wastewater
m3
4448240
4617050
404426
407160
3391010
2920960
1500290
625384
1634190
2337020
3509520
1813610
5157890
1956790
3485340
2369240
40578120
irrigation
m3
5742940
5964610
478161
501849
4448720
3586430
2453250
740545
2015500
2756740
4203900
2297950
8872680
2413880
4197910
2730180
53405245
Salinity
kg
1276160
435902
106410
61789
86869
424966
850547
91474
546157
409772
622160
169238
130149
47194.4
498814
742397
6499999
profit
US$
5132890
6207810
1014660
553761
5246780
6245300
4445740
1161290
2697392
3209250
4980790
2769000
11283680
3209820
3869710
2160960
64188833
Water use effectiveness
US$/ m3
0.89
1.04
2.12
1.10
1.18
1.74
1.81
1.57
1.34
1.16
1.18
1.20
1.27
1.33
0.92
0.79
1.20
Groundwater
m3
923679
1231290
65249.8
91791
981963
647829
631999
84038.5
283982
359243
509446
486907
3778780
420372
705432
348972
11550973
Wastewater
m3
3343140
4152230
304172
339534
3084240
2343980
1226730
498888
1426450
1751840
2814060
1820810
4659170
1957460
3494520
1782780
35000004
irrigation
m3
4266820
5383530
369422
431325
4066200
2991810
1858730
582926
1710430
2111080
3323500
2307720
8437950
2377840
4199950
2131750
46550983
Salinity
M.kg
949205
393778
82023
53077
79321
35437
642733
71974
463809
313668
491536
169551
123600
46413
499086
579159
5313309
profit
Water use effectiveness
US$
US$/ m3
6233600
1.46
6217480
1.15
903670
2.45
796361
1.85
5089240
1.25
5526640
1.85
4662150
2.51
1097570
1.88
2749392
1.61
3385210
1.60
5409070
1.63
2855320
1.24
10975180
1.30
3209820
1.35
3104710
0.74
2223690
1.04
64439103
1.38
Wet year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Optimisation of Agricultural Water Use
144
Decision Support System
Appendix (III): Scenarios Results
E: Maximum freedom scenario
Crop pattern
Sub-regional zones
Crops
gazabh gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
Cabbage
0.0
0.0
0.0
0.0
0.0
0.0
0.0
33.4
Cauliflower a
74.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cauliflower sp 193.1
0.0
0.0
0.0
0.0
0.0
248.5
0.0
Citrus others*
0.0
116.7
68.2
0.0
0.0
409.5
0.0
81.9
Cucumber sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cucumber s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Eggplant w
0.0
0.0
41.8
0.0
0.0
0.0
0.0
0.0
Eggplant a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Guava*
0.0
325.8
0.0
0.0
351.0
0.0
0.0
0.0
Grapefruits
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.7
Jew's melon
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Lemon*
0.0
84.5
0.0
0.0
0.0
0.0
0.0
0.0
Olive*
946.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Onion
52.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pepper a
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pepper sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Potato w
239.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Potato s
0.0
0.0
0.0
31.5
0.0
220.5
0.0
12.1
Shamoti*
0.0
0.0
0.0
58.5
0.0
0.0
461.5
0.0
Squash sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Squash s
57.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
0.0
277.5
0.0
0.0
0.0
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Valencia*
57.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon w
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Watermelon s
0.0
45.5
0.0
0.0
189.0
0.0
0.0
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Crops
Cabbage
Cauliflower a
Cauliflower sp
Citrus others*
Cucumber sp
Cucumber s
Eggplant w
Eggplant a
Guava*
Grapefruits
Jew's melon
Lemon*
Olive*
Onion
Pepper a
Pepper sp
Potato w
Potato s
middbl midddb
0.0
0.0
0.0
0.0
0.0
0.0
0.0
409.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
260.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
105.2
middwg
0.0
0.0
132.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
474.5
0.0
39.8
0.0
0.0
0.0
Optimisation of Agricultural Water Use
Sub-regional zones
northbh
northbl
rafahbl
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
50.2
0.0
0.0
0.0
69.2
0.0
0.0
0.0
223.2
26.6
0.0
0.0
0.0
132.1
0.0
0.0
588.8
0.0
221.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
45.5
0.0
317.0
0.0
0.0
0.0
0.0
0.0
145
rafahdb
0.0
0.0
0.0
612.0
0.0
0.0
0.0
12.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
159.1
rafahky
112.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8.9
0.0
0.0
227.8
0.0
0.0
0.0
0.0
0.0
Decision Support System
Appendix (III): Scenarios Results
Shamoti*
0.0
0.0
0.0
64.6
0.0
0.0
0.0
0.0
Squash sp
0.0
0.0
42.5
0.0
0.0
0.0
0.0
0.0
Squash s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Strawberry
94.5
0.0
0.0
0.0
385.2
0.0
0.0
0.0
Sweetpotato
0.0
0.0
0.0
0.0
59.2
0.0
0.0
0.0
Tomato sp
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tomato s
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Valencia*
0.0
0.0
0.0
0.0
0.0
0.0
0.0
173.7
Watermelon w
0.0
115.3
40.3
0.0
0.0
0.0
0.0
47.6
Watermelon s
45.5
0.0
0.0
0.0
0.0
86.8
66.7
0.0
a= autumn; sp= Spring; s= summer. w = winter. * Trees crop with a potential for treated wastewater use.
Main result for the maximum freedom scenario
Dry year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Wet year
Zone
gazabh
gazabl
gazawg
khanbh
khanbl
khandb
khanky
khanwg
middbl
midddb
middwg
northbh
northbl
rafahbl
rafahdb
rafahky
Total
Groundwater
m3
1192830
1524340
257530
94712
995085
665469
525329
167363
701244
763227
961667
686510
3985530
457089
936157
580729
14494811
Wastewater
m3
4720780
4324800
504680
474025
2764480
3507780
3351870
749008
1822340
3512690
3502760
1555220
5157890
1754130
5243620
2904470
45850543
irrigation
m3
5913610
5849140
762210
568738
3759560
4173250
3877200
916371
2523580
4275920
4464430
2241730
9143420
2211220
6179770
3485200
60345349
Salinity
kg
1312910
431360
169114
69690
74061
492534
1335400
112781
686184
633307
664546
166039
134738
43400
729244
944690
7999998
profit
Water use effectiveness
US$
US$/ m3
6040140
1.02
13320730
2.28
862360
1.13
393086
0.69
5206420
1.38
3831630
0.92
3923170
1.01
881667
0.96
3449070
1.37
3324750
0.78
3578590
0.80
2966610
1.32
21254700
2.32
3326550
1.50
5212690
0.84
2019540
0.58
79591703
1.32
Groundwater
m3
819719
1308110
165068
91808
923643
647829
348397
144840
635354
614195
668623
629324
3525130
420372
859252
507305
12308969
Wastewater
m3
3565120
3865850
404085
407101
2772200
2923010
2673470
626483
1590680
2917690
2098710
1300250
4659170
1756580
4377020
2313260
38250679
irrigation
m3
4384840
5173970
569153
498909
3695840
3570840
3021870
771323
2226030
3531880
2767340
1929580
8184310
2176960
5236280
2820560
50559685
Salinity
M.kg
974329
381245
126168
61152.4
72549
421533
1038660
94865
606003
521154
411543
142703
120083
42618.8
618384
764615
6397606
profit
Water use effectiveness
US$
US$/ m3
7025600
1.60
14391630
2.78
980600
1.72
377730
0.76
5206420
1.41
3884880
1.09
4027610
1.33
825057
1.07
3456360
1.55
3426510
0.97
3716220
1.34
2904490
1.51
21490100
2.63
3326550
1.53
4862140
0.93
2020150
0.72
81922047
1.62
Optimisation of Agricultural Water Use
146
Decision Support System
Appendix (III): Scenarios Results
Crops return values scenarios
Crops
Cabbage
Cauliflower a
Cauliflower sp
Citrus others*
Cucumber sp
Cucumber s
Eggplant w
Eggplant a
Guava*
Grapefruits
Jew's melon
Lemon*
Olive*
Onion
Pepper a
Pepper sp
Potato w
Potato s
Shamoti*
Squash sp
Squash s
Strawberry
Sweetpotato
Tomato sp
Tomato s
Valencia*
Watermelon w
Watermelon s
Crops
Cabbage
Cauliflower a
Cauliflower sp
Citrus others*
Cucumber sp
Cucumber s
Eggplant w
Eggplant a
Guava*
Grapefruits
Jew's melon
Lemon*
Olive*
Onion
Pepper a
Pepper sp
5% crops return values changes scenarios
2
3
4
5
6
7
Crop return value US$/ton
323 339 339
306
339
306
339
306
420 399 441
399
399
399
399
441
420 441 441
399
399
399
399
399
339 356 322
322
356
356
356
322
412 391 432
391
432
391
391
432
412 391 432
391
432
432
391
432
309 293 293
324
324
324
293
293
309 324 293
293
293
324
324
324
377 396 396
396
358
396
396
358
145 152 152
137
152
137
152
152
468 445 445
492
492
445
445
445
473 449 449
449
449
449
449
449
1250 1188 1188 1313
1188
1313 1313 1313
502 527 527
527
527
477
527
527
489 465 513
513
465
513
513
513
489 465 465
513
513
465
465
513
242 254 254
254
254
254
254
229
242 254 229
229
229
254
254
254
263 249 276
276
276
276
276
276
408 428 387
428
428
387
387
428
408 387 387
387
428
428
387
428
1500 1575 1425 1575
1575
1575 1425 1425
273 286 286
259
286
259
286
286
367 386 349
349
386
386
386
349
367 386 349
386
349
386
386
386
158 165 150
165
150
150
165
150
160 168 152
168
168
168
168
152
160 152 152
168
168
168
152
168
exist
exist
493
498
497
477
484
485
500
499
479
474
486
473
478
491
490
489
1
10% crops return values changes scenarios
Crop return value US$/ton
1
2
3
4
5
6
290 355
355
355
290
355
462 462
378
462
462
378
378 462
378
462
378
462
373 373
305
373
305
305
453 453
371
453
453
371
453 371
371
371
371
453
340 340
340
278
340
278
340 340
340
340
340
340
415 339
415
339
339
339
130 130
159
159
159
159
515 421
421
421
421
515
520 520
425
425
425
425
1125 1125 1125
1125
1375
1375
452 452
553
553
452
452
440 440
440
538
538
440
538 440
538
440
440
538
Optimisation of Agricultural Water Use
147
7
290
378
462
373
371
453
340
340
339
159
515
425
1375
452
440
440
8
9
10
306
441
441
322
432
391
324
324
396
137
492
449
1188
477
465
465
229
254
276
387
387
1425
286
386
386
165
168
168
306
399
399
322
391
432
293
324
396
137
492
496
1188
477
465
465
229
254
276
428
387
1425
259
386
386
150
152
152
306
399
441
322
391
391
293
324
358
152
492
496
1188
477
465
513
254
229
249
387
387
1425
259
386
386
165
168
168
8
290
462
462
373
371
453
278
278
339
130
515
520
1125
452
538
440
9
10
355 355
462 462
462 462
305 373
371 453
453 371
278 340
278 278
415 415
130 159
421 421
425 520
1125 1375
553 452
440 440
440 538
Decision Support System
Appendix (III): Scenarios Results
Potato w
Potato s
Shamoti*
Squash sp
Squash s
Strawberry
Sweetpotato
Tomato sp
Tomato s
Valencia*
Watermelon w
Watermelon s
480
481
475
495
496
494
492
483
482
476
488
487
266 266
217 266
289 289
448 367
367 448
1350 1350
246 246
331 404
331 404
142 173
144 144
144 176
266
217
236
448
367
1350
246
331
404
142
176
144
266
266
289
448
448
1650
246
404
331
142
176
144
217
217
289
448
367
1350
300
404
331
142
176
144
266
266
236
448
367
1350
300
404
331
142
176
176
266
266
289
448
367
1650
300
404
404
142
176
176
266
217
236
367
367
1350
300
404
331
142
176
144
266 266
266 266
289 289
367 448
448 367
1350 1650
300 246
404 404
404 331
142 173
176 144
144 144
5% changes in crops return values main results for wet year
Scenario groundwater wastewater salinity
Mm3
Mm3
M.kg
1
12.53
38.23
64.23
2
12.00
38.17
63.79
3
12.51
38.04
63.90
4
12.52
38.24
64.09
5
12.51
38.28
64.03
6
12.02
38.13
63.60
7
11.95
38.33
63.88
8
11.95
38.29
63.89
9
12.07
38.10
63.73
10
11.99
38.28
63.90
Average
12.20
38.21
63.90
profit
M.US$
85.95
78.29
85.28
84.17
86.81
80.80
77.32
78.50
79.00
77.59
81.37
Water use effectiveness Crop pattern changes
US$/ m3
%
1.69
95.45
1.56
88.34
1.69
82.45
1.66
97.20
1.71
93.13
1.61
92.13
1.54
90.31
1.56
90.56
1.57
80.08
1.54
94.65
1.61
90.43
5% changes in crops return values main results for dry year
Scenario groundwater wastewater salinity
Mm3
Mm3
M.kg
1
14.73
45.77
80.00
2
14.23
45.87
80.00
3
14.78
45.75
80.00
4
14.73
45.81
80.00
5
14.77
45.81
80.00
6
14.35
45.82
80.00
7
14.15
45.97
80.00
8
14.13
45.99
80.00
9
14.34
45.80
80.00
10
14.20
45.94
80.00
Average
14.44
45.85
80.00
Optimisation of Agricultural Water Use
profit
M.US$
83.52
75.94
82.72
81.76
84.16
78.35
74.88
76.25
76.41
75.32
78.93
148
Water use effectiveness Crop pattern changes
US$/ m3
%
1.38
95.45
1.26
88.34
1.37
82.45
1.35
97.20
1.39
93.13
1.30
92.13
1.25
90.31
1.27
90.56
1.27
80.08
1.25
94.65
1.31
90.43
Decision Support System
Appendix (III): Scenarios Results
10% changes in crops return values main results for wet year
Scenario groundwater wastewater salinity
Mm3
Mm3
M.kg
1
11.78
38.39
63.96
2
11.43
38.39
63.97
3
11.95
38.21
63.60
4
12.50
38.27
64.21
5
11.78
38.39
64.05
6
11.89
38.34
63.53
7
12.51
38.24
64.19
8
11.38
38.39
64.02
9
11.19
38.42
63.79
10
12.51
38.24
64.15
Average
11.89
38.33
63.95
profit
M.US$
78.84
76.55
74.33
86.86
73.06
75.52
89.19
74.32
73.47
93.68
79.58
Water use effectiveness Crop pattern changes
US$/ m3
%
1.57
90.30
1.54
90.30
1.48
90.60
1.71
90.50
1.46
77.20
1.50
90.20
1.76
89.90
1.49
89.30
1.48
73.20
1.85
91.60
1.58
87.31
10% changes in crops return values main results for dry year
Scenario groundwater wastewater salinity
Mm3
Mm3
M.kg
1
13.61
46.11
80.00
2
13.10
46.11
80.00
3
14.26
45.92
80.00
4
14.77
45.76
80.00
5
13.58
46.11
80.00
6
14.19
46.00
80.00
7
14.77
45.76
80.00
8
13.10
46.11
80.00
9
12.84
46.13
80.00
10
14.77
45.78
80.00
Average
13.90
45.98
80.00
Optimisation of Agricultural Water Use
profit
M.US$
76.90
74.76
72.13
84.37
70.52
73.01
86.57
72.52
71.39
90.97
77.31
149
Water use effectiveness Crop pattern changes
US$/ m3
%
1.29
90.30
1.26
90.30
1.20
90.60
1.39
90.50
1.18
77.20
1.21
90.20
1.43
89.90
1.22
89.30
1.21
73.20
1.50
91.60
1.29
87.31
Decision Support System
Appendix (IV)
Curriculum Vitae
CURRICULUM VITAE
Name
Date of Birth
Sex
Nationality
Education :
April 2001 to Present
Omar Khalil Ouda
21/4/1972
Male
Palestinian
- Ph.D. student at Stuttgart University, Germany.
1997 - 1999
- M.Sc. Degree in Water and Environmental Resources
Management.
IHE, Delft, The Netherlands
1990 - 1995
-
B.Sc. Degree in Civil Engineering
U.A.E. University, Al Ain, U.A.E
1990
-
High school degree
Zaid al Awal School
Al Ain, UAE
Work Experience
1999-2000
-
Lecture, Civil Engineering Department, Islamic
University of Gaza, Palestine.
1995-1997
-
Publications
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Civil Engineer,
Palestine
Team Engineering Group, Gaza,
Omar K. Ouda and Mohamed R. Al-Agha (2000),
"Treated Wastewater Use in Gaza District: The
Question of Public Acceptance", Fifth International
Water technology Conference, Alexandria, Egypt 35th March.
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Omar K. Ouda (2000) "Potential Reuse of Treated
Wastewater in the Gaza District", Water and
Environmental Journal, Jerusalem, Palestine.
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Ouda O. and Bardossy A. (2003), "Multiobjective
Model to Optimise The Treated Wastewater Uses in
Gaza Strip", II International Conference on Efficient
Use and Management of Urban Water Supply,
Tenerife Canary Islands, Spain. 2-4th April
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Ouda O. and Bardossy A. (2003) "Multiobjective
model to optimise the economical value of
agriculture water use in Gaza Strip", EGS-AGUEUG Joint Assembly, Poster presentation. Nice,
France, 06 - 11th April.
Optimisation of Agricultural Water Use
150
Decision Support System