Optimisation of Agricultural Water Use: A Decision Support System for ...

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

Optimisation of Agricultural Water Use

i

Decision Support System

Table of Contents

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


ii


Table of Contents

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


45

iii

46


Table of Contents

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


iv


Table of Contents

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


v

82


Table of Contents

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


vi


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


vii


List of Tables

Table (6.3):

the existing crop pattern

91

Level of similarity between the crop patterns resulted scenarios

94


viii


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

Optimisation of Agriculture Water Use

ix


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

Optimisation of Agriculture Water Use

x


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


xi


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


xii


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


xiii


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.


xiv


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.


xv


Abstract

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.


xvi


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.


xvii


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. Optimisation of Agricultural Water Use

xviii


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.


xix


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.


xx


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


xxi


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


xxii


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.


xxiii


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.


xxiv


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.


1


Chapter I


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


2


Chapter I


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.


3


Chapter I


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)


4


Chapter I


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.


5


Chapter I


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


6


Chapter I


(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.


7


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,


8


Chapter II


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

9


Chapter II


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


10


Chapter II


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). Optimisation of Agricultural Water Use

11


Chapter II


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. Optimisation of Agricultural Water Use

12


Chapter II


- 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.


13


Chapter II


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.


14


Chapter II

-


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.


15


Chapter II


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 Optimisation of Agricultural Water Use

16


Chapter II


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.


17


Chapter II


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


18


Chapter II


-

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


19


Chapter II


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.


20


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 Optimisation of Agricultural Water Use

21


Chapter III


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,


22


Chapter III


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).


23


Chapter III


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 Optimisation of Agricultural Water Use

24


Chapter III


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


25


Chapter III


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 Optimisation of Agricultural Water Use

26


Chapter III


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 Optimisation of Agricultural Water Use

27


Chapter III


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


28


Chapter III


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).


29


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

30


Chapter IV


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

31


Chapter IV


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

32


Chapter IV


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).


33


Chapter IV


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).


34


Chapter IV


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.


35


Chapter IV


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).


36


Chapter IV


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).


37


Chapter IV


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)


38


Chapter IV


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


39


Chapter IV


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.


40


Chapter IV


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)


41


Chapter IV


-

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: Optimisation of Agricultural Water Use

42


Chapter IV


-

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

43


Chapter IV


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


44


Chapter IV


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


45


Chapter IV


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 ). Optimisation of Agricultural Water Use

46


Chapter IV


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


47


Chapter IV


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:


48


Chapter IV


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 Optimisation of Agricultural Water Use

49


Chapter IV


Fig (4.5): Main structure of SWAP 2.0. Source: (Kroes et al.1999): Optimisation of Agricultural Water Use

50


Chapter IV


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

51


Chapter IV


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 Optimisation of Agricultural Water Use

52


Chapter IV


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 Optimisation of Agricultural Water Use

53


Chapter IV


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


54


Chapter IV


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).


55


Chapter V

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

56


Chapter V


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


57


Chapter V


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. Optimisation of Agricultural Water Use

58


Chapter V


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 Optimisation of Agricultural Water Use

59


Chapter V


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. Optimisation of Agricultural Water Use

60


Chapter V


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 Optimisation of Agricultural Water Use

61


Chapter V


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)


62


Chapter V

F4 =


)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.


63


Chapter V


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


64


Chapter V


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:


65


Chapter V

116


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


66


Chapter V


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.


67


Chapter V


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: Optimisation of Agricultural Water Use

68


Chapter V

H16

H 28

i =1 I16

WWF i × WD ijd × A ij ≤ MWW

for dry year

j =1 I28

i =1


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


69


Chapter V


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)


70


Chapter V


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


71


Chapter V


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 Optimisation of Agricultural Water Use

72


Chapter V


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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Chapter V


For each zone J and for wet year: i =28

~

~ ~

WDiw ×WWFi × Ai × TA ≥

i =1

j =16 i =28

j =1 i =1

~ ~

~

WDiw ×WWFi × Aij × AGAj ~ × EWF

For each J and for dry year : i =28

WDid ×WWFi × Ai ×TA ≥

i =1

j =16 i =28

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). Optimisation of Agricultural Water Use

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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 Optimisation of Agricultural Water Use

75

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


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

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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


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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


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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


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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]


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


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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

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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.


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75

Million.

55 35 15 No equity

50

60

70

80

90

Percentage equity in profit Groundwater [m³] Salinity [kg]


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


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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]


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

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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]


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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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

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-

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


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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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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:

Level

of Similarity

= 1 −

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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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 Optimisation of Agricultural Water Use

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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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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


96


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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-


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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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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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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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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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' Optimisation of Agricultural Water Use

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organisations.


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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List of References

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Bogardi I., Bardossy A., Duckstein L (1983): Regional Management of an aquifer for mining under fuzzy environmental objectives, Water resources research, Vol.19, No.6, pp 1394-1402. Brimberg J., Oron G., Merhrez A. (1993): A model for the development of marginal water sources in arid zones: The case of the Negev desert, Isreal, Water resources research, Vol.29, No.9, pp 3059-3067. Brouwer C., Heibloem M (1986): IRRIGATION WATER MANAGEMENT, Training manual no. 3, FAO, Rome. Butler I.W., Riha, S. J. (1989): GAPS, A General Purpose Simulation Model of the Soil-PlantAtmosphere System, Version 1.1 User's Manual, Ithaca, NY, Cornell University Department of Agronomy. CAMP (2000): Coastal Aquifer management plan, PWA, USAID, Gaza, Palestine. Carlos A.C., Van Veldhuin D.A., Lamont G.B.(2002): Evolutionary Algorithms for Solving MultiObjective Problem, Academic / Plenum Publishers, New York, ISBN 0-306-46762-3. Cohon H.L. (1978): Multiobjective Programming and planning, Academic Press INC , United State of America, ISBN: 0-12-178350-2. Deconinck (2002): Israeli water policy in a regional context of conflict: prospects for sustainable development for Israelis and Palestinians?, on: http://waternet.rug.ac.be/waterpolicy.htm. Doorenbos J., Kassam A.H. (1979), (reprint 1986): Yield response to water, FAO irrigation and drainage paper 33, FAO, Rome. Doorenbos J., Pruitt W.O. (1977): Crop water requirements, FAO irrigation and drainage paper 24, FAO, Rome. Eschenauer H.A., Koski J., Osyczka A. (1986): Multicriteria Design Optimisation, Procedures and Applications, Springer-Verlag, New York. FAO (1996): Population change natural resources environment linkages in the Arab states region, FAO, Rome. FAO (1997): Irrigation in the near east region in Figures, ISBN 92-5-103969-0. Optimisation of Agricultural Water Use

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FAO (2003): Unlocking the water potential of agriculture, FAO land and water development division publication, Rome. Fourer R., Gay D.M., Kernighan B. W (1993): AMPL. A Modelling Language for Mathematical Programming, Scientific Press. ISBN: 0-78950-701-3. Getachew B., Peralta R. C., Hughes T. C. (1999): Simulation/optimisation modelling for water resources planning and management, Journal of water resources planning and management, may/June. Haruvy N. (2000): Water commissioner committee on the use of treated wastewater (effluent) in irrigation, general and economic perspectives regarding crops, groundwater, soil, and pricing, Institute of soils, water and environmental sciences, agriculture research organisation, the Volcani center, Isreal, March. Jenkins M. W., Lund J. R. (2000): Integrated yield and shortage management under multiple uncertainties, Journal of water resources planning and management, Vol.126, No.5. Juan J.A., Tarjuelo J.M., Valiente M. G. (1996): Model for optimal cropping patterns within the farm based on crop water production functions and irrigation uniformity, I: Development of a decision model, Agricultural Water Management 31, pp 115-143. Kroes J.G., Van Dam J.C., Huygen J.C., Vervoort R.W., (1999): User's guide of SWAP version 2.0, Report 81, Dept. Water Resources, Wageningen Agriculture University. Technical Document 53, DLO Wind Staring Centre, Wageningen. Langniß O., Trieb F., Bandi A., Geyer D., Giesecke J., Bardossy A., Hartmann G., Marx. W. (1998): Innovations for the promotions of renewable energies, European Parliament, Directorate General for Research. Mahmoud M. I. H. (1999): Multiobjective optimal water resources management for a large river Basin, Journal of Systems Science and Systems Engineering, Vol.8, No.2, pp. 244-252. Meinzen, R.S and Rosegrant, M.W. (2001): Overview, 2020 Focus 9 (Overcoming Water Scarcity and Quality Constraints. On: www.ifpri.cgiar.org/2020/focus/focus09/focus09_01.htm Optimisation of Agricultural Water Use

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Mohamed A.S. (2001): Water Demand Management: Approach, Experience and Application to Egypt, Ph.D. Dissertation, DUP Science, 2001, ISBN: 90-407-2185-8. Monteith J.L. (1976): Vegetation and the Atmosphere, Volume 2, New York, Academic Press. MOPIC (1998): Master Plan for Sewerage and Storm Water Drainage in Gaza Governorates, Gaza, Palestine. Ouda O.K (1999): The potential use of treated wastewater in the Gaza District, M.Sc. Thesis D.E.W. 59, IHE, Delft, The Netherlands. Palestinian ministry of agriculture MOA. (1997): Agriculture practice survey in the Gaza Governorates, (unpublished data), Gaza, Palestine. PCBS (1997): The first population and hosing Census, Gaza, Palestine. PCBS (2000): Agriculture Statistics Various Data 1998, Gaza, Palestine. PCBS (2000a ): Agriculture Statistics Various Data 1999, Gaza, Palestine. PCBS (2001): Agriculture Statistics Various Data 2000, Gaza, Palestine,. Prasad B.N, Sinha A.K and Rai R.A. (2001): Crop planning of a model watershed of Ranchi using linear programming technique- A case study, Journal of Research (BAU). 13(1). PWA (2000): National Water Plan, Palestinian National Authority, Palestinian Water Authority, Gaza, Palestine. Raju K.S, Kumar D. N. (1999): Multicriterion decision making in irrigation planning, Agricultural Systems, 62, pp 117-129. Salameh E, Daoud R. (2000): Analysis and review of the prospects of implementing a differential irrigation water pricing/tariff system in the Jordan rift valley, IDRC, CRDI Canada. Sbeih M. Y. (2001): Proper management as well as laws and by laws needed toward effluent treated wastewater reuse in Palestine, Regional conference in water demand management conservation and pollution control, Amman, Jordan, 7-10 October.


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Appendix (I): Database and Mmultiobjective Model

Appendix (I) Database and Multiobjective model

Part A: Database

Part B: Multiobjective model


109



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



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


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



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


112



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


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



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


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



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


Sub-regional zones khanwg khanky 469 1080 304 731 314 850 115

rafahbl 68 40 54

rafahdb 383 268 299

rafahky 849 593 669



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



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


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



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.


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# 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


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



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


120

Farmer's acceptance % 62 62 62 65 65 65 65 65



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


121



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 Optimisation of Agricultural Water Use

122



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]; Optimisation of Agricultural Water Use

123



# 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


124



subject to waterd: sum {c in CROP, z in ZONE} Area[c,z] * wdemandd[c,z] - sum { z in ZONE } availwwater[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; Optimisation of Agricultural Water Use

125



# 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] =10000000; ; # Maximum salinity load decision variable subject to salintyd1 : sum { c in CROP,z in ZONE} SIrrigd[c,z] * Area[c,z]=0.5;


126


Appendix (II) Decision Support Charts

Appendix (II) Decision support charts


127



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. Optimisation of Agricultural Water Use

128



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. Optimisation of Agricultural Water Use

129


Million..


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]


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]


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 Optimisation of Agricultural Water Use

130



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 $ ]


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


131



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$]


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


132


Million.


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]


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]


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.


133



75 65

Million.

55 45 35 25 15 No equity

50

60

70

80

90

Percentage equity in groundwater use Groundwater [m³] Salinity [kg]


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]


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]


Irrigation [m³]

Decision support chart for percentage spatial equity in access to wastewater for wet year.


134



75 65

Million.

55 45 35 25 15 No equity

50

60 70 Percentage equity in wastewater use

Groundwater [m³] Salinity [kg]


80

90

Irrigation [m³]

Decision support chart for percentage spatial equity in access to wastewater for dry year.


135


Appendix (III): Scenarios Results

Appendix (III) Scenarios


136



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


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



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


138



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.


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



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




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



140



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.


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



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




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


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


Wet year Zone gazabh gazabl gazawg khanbh khanbl khandb khanky khanwg middbl midddb middwg northbh northbl rafahbl rafahdb rafahky Total


142



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



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


Wet year Zone gazabh gazabl gazawg khanbh khanbl khandb khanky khanwg middbl midddb middwg northbh northbl rafahbl rafahdb rafahky Total


144



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.


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



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




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


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



146



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


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



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


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




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


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


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

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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

-

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.

-

Omar K. Ouda (2000) "Potential Reuse of Treated Wastewater in the Gaza District", Water and Environmental Journal, Jerusalem, Palestine.

-

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

-

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.


150
