CLIMATE CHANGE IMPACTS ON URBAN...

CLIMATE CHANGE IMPACTS ON URBAN WATER SECURITY: A CASE STUDY OF THE NADI/LAUTOKA

URBAN AREAS, VITI LEVU, FIJI

by

Linda B. K. Yuen

A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Climate Change

Copyright © 2013 by Linda B. K. Yuen

Pacific Centre for Environment and Sustainable Development

The University of the South Pacific

February, 2013

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ACKNOWLEDGEMENT

I would like to extend my utmost gratitude to Professors Beth Holland and Yahya Abawi

as well as Dr Simon White for their guidance, support and encouragement throughout

the duration of this research. Despite roadblocks along the way, I am thankful that they

did not give up on me.

I am also grateful for the scholarship awarded under the AusAID Climate Leaders

Program which allowed me to carry out this research.

This research would not have been possible without the access to data for my study site

through the Fiji Meteorological Service’s Climate Unit and Water Authority of Fiji,

especially their Hydrology Unit. For this, I would like to thank them. Valuable

assistance provided by Mr Paula Tawakece and the Hydrology team in Lautoka during

the field visits shall not go unacknowledged.

Special thanks also go to all my fellow student colleagues at the FSTE Postgraduate Lab

and PACE-SD’s ‘Jetty Lab’ who have been a constant source of support in our

collective quest for our respective higher degrees and who have also provide many light-

hearted moments during times of stress and despair. Through this experience, it would

be my honour to say that I have made many life-long friends.

Without the loving support and encouragement from my family, especially my dear

Mom, and friends, I may not have had the motivation and spirit to persevere and

complete this journey which I had chosen almost 2 years ago and for this, I would like to

sincerely thank them all.

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LIST OF ABBREVIATIONS

AOGCM Atmosphere-Ocean General Circulation Models

AOSIS Alliance of Small Island States

AR4 (IPCC) Fourth Assessment Report

AusAID Australian Agency for International Development

C-CAM Conformal-Cubic Atmospheric Model

CSIRO Commonwealth Scientific and Industrial Research Organisation

ENSO El Niño-Southern Oscillation

FMS Fiji Meteorological Service

FSC Fiji Sugar Corporation

GCM Global Climate Model

GDP Gross Domestic Product

GEF Global Environment Facility

GHG Greenhouse Gases

GIS Geographic Information System

ICOLD International Commission on Large Dams

IBA Important Bird Area

IPCC Intergovernmental Panel on Climate Change

IWRM Integrated Water Resources Management

LWRM Land and Water Resources Management (Fiji)

MRD Mineral Resources Department (Fiji)

NMS National Meteorological Service

NRW Non-Revenue Water

ODA (Japan) Overseas Development Assistance

PCCSP Pacific Climate Change Science Program

PICPP Pacific Islands Climate Prediction Project

PWD Public Works Department (PWD)

RSMC Regional Specialised Meteorological Centre

SPCZ South Pacific Convergence Zone

SPI Standardized Precipitation Index

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SST Sea Surface Temperature

USP University of the South Pacific

WAF Water Authority of Fiji

WMO World Meteorological Organization

WSD Water and Sewerage Department (Fiji)

WWC World Water Council

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TABLE OF CONTENTS

ACKNOWLEDGEMENT ............................................................................................................ iii

LIST OF ABBREVIATIONS ....................................................................................................... iv

LIST OF FIGURES ...................................................................................................................... ix

LIST OF TABLES ......................................................................................................................... x

ABSTRACT ................................................................................................................................... 1

1 CHAPTER 1: INTRODUCTION .......................................................................................... 2

1.1 Background .................................................................................................................... 2

1.2 Climate Projection in Small Island Countries ................................................................ 6

1.3 Research Focus and Objectives ...................................................................................... 8

1.4 Fiji’s Climate Regime .................................................................................................. 11

1.5 Fiji Climate Change Studies ........................................................................................ 15

1.6 Thesis Outline .............................................................................................................. 16

2 CHAPTER 2: LITERATURE REVIEW ............................................................................. 18

2.1 Study Site ..................................................................................................................... 18

2.2 Water Resources Authorities in Fiji ............................................................................. 20

2.3 Donor Aid in Water Management ................................................................................ 22

2.3.1 AusAID PICPP .................................................................................................... 22

2.3.2 GEF IWRM Project ............................................................................................. 22

2.3.3 EU Pacific HYCOS .............................................................................................. 22

2.4 Water Resources and Usage in the Nadi Basin ............................................................ 23

2.4.1 Surface Water ....................................................................................................... 24

2.4.2 Groundwater ........................................................................................................ 28

2.4.3 Rainwater ............................................................................................................. 31

2.5 The Water Balance Model ........................................................................................... 32

3 CHAPTER 3: METHODOLOGY ....................................................................................... 35

3.1 Vaturu Historical Rainfall Statistical Analysis ............................................................ 35

3.1.1 Quality Control .................................................................................................... 36

3.1.2 Monthly Mean, Minimum and Maximum Trends ............................................... 37

3.1.3 Decadal Trends .................................................................................................... 37

3.1.4 Annual Trends ...................................................................................................... 38

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3.1.5 Seasonal Trends ................................................................................................... 38

3.2 Rainfall Projections ...................................................................................................... 39

3.2.1 Annual Rainfall and Temperature ........................................................................ 41

3.2.2 Summer Rainfall and Temperature ...................................................................... 41

3.2.3 Winter Rainfall and Temperature......................................................................... 42

3.3 Vaturu Water Balance Model ...................................................................................... 42

3.3.1 Reliability under Climate Change Scenarios ....................................................... 45

3.3.2 Population Growth Scenarios ............................................................................... 46

3.3.3 Demand and Reliability with Population Growth Scenarios ............................... 47

3.3.4 Reliability with Combination of Climate Change and Population Growth.......... 48

3.3.5 Estimated Water Consumption ............................................................................ 48

3.3.6 Estimated Domestic Water Consumption ............................................................ 49

3.4 Vaturu Reservoir Historical Water Level Statistical Analysis ..................................... 49

3.4.1 Annual Mean Water Levels ................................................................................. 49

3.4.2 Monthly Mean Water Levels ............................................................................... 49

4 CHAPTER 4: RESULTS AND INTERPRETATION ....................................................... 50

4.1 Vaturu Rainfall Statistical Analyses ............................................................................ 50

4.1.1 Quality Control .................................................................................................... 50

4.1.2 Monthly Mean, Minimum and Maximum Trends ............................................... 51

4.1.3 Decadal Trends .................................................................................................... 53

4.1.4 Annual Rainfall Trends ........................................................................................ 54

4.1.5 Seasonal Trends ................................................................................................... 56


4.2.1 Annual Rainfall and Temperature ........................................................................ 59

4.2.2 Output – Summer Rainfall and Temperature ....................................................... 62

4.2.3 Output – Winter Rainfall and Temperature ......................................................... 64


4.3.1 Reliability under Climate Change Scenarios ....................................................... 67

4.3.2 Population Growth Scenarios ............................................................................... 70

4.3.3 Demand and Reliability with Population Growth Scenarios ............................... 70

4.3.4 Reliability with Combination of Climate Change and Population Growth.......... 72

4.3.5 Estimated Water Consumption ............................................................................ 74

4.3.6 Estimated Domestic Water Consumption ............................................................ 75

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4.4 Vaturu Reservoir Historical Water Level Statistical Analysis ..................................... 76

4.4.1 Annual Mean Water Levels ................................................................................. 76

4.4.2 Monthly Mean Water Levels ............................................................................... 77

5 CHAPTER 5: DISCUSSIONS ........................................................................................... 78

5.1 Vaturu Rainfall Statistical Analysis ............................................................................. 78



5.4 Historical Vaturu Reservoir Water Levels ................................................................... 80

5.5 Recent Improvements to Nadi/Lautoka Regional Water Supply ................................. 80

5.6 Solutions for Improving Water Security ...................................................................... 82

5.7 Challenges .................................................................................................................... 84

5.8 Limitations to the Study ............................................................................................... 85

5.8.1 Limitations to Data and the Model ...................................................................... 85

5.8.2 Limitations to Data Access .................................................................................. 87

5.8.3 Limitations to Study Site Access ......................................................................... 88

6 CHAPTER 6: CONCLUSION............................................................................................. 90

7 CHAPTER 7: RECOMMENDATIONS .............................................................................. 91

BIBLIOGRAPHY ........................................................................................................................ 93

APPENDIX 1 PCCSP CLIMATE FUTURES – FIJI ............................................................... 104

1.1 Annual Rainfall and Temperature .............................................................................. 104

1.2 Summer Rainfall and Temperature ............................................................................ 109

1.3 Winter Rainfall and Temperature .............................................................................. 118

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LIST OF FIGURES �

Figure 1-1: Diagram of a public water supply system. .................................................................. 6�

Figure 1-2: Satellite image of Vaturu, with yellow lines indicating its direction with relation to

Nadi and Lautoka . ................................................................................................................. 9�

Figure 1-3: Niño and La Niña events between 1950 and 1998 . .................................................. 14�

Figure 2-1: Nadi catchment rivers and topography. ..................................................................... 19�

Figure 2-2: Satellite image of Vaturu dam and reservoir. ............................................................ 25�

Figure 2-3: Vaturu catchment showing the four tributaries and hydrology stations. ................... 26�

Figure 2-4: A montage of headlines on water disruption in a daily newspaper. .......................... 27�

Figure 2-5: Vaturu rainfall station. .............................................................................................. 31�

Figure 4-1: Vaturu mean monthly rainfall (mm) (1982 – 2011). ................................................. 52�

Figure 4-2: Vaturu monthly mean, minimum and maximum rainfall (mm) trends (1982 – 2011).

............................................................................................................................................. 52�

Figure 4-3: Vaturu decadal mean monthly rainfall (mm) (1982-2011). ...................................... 53�

Figure 4-4: Vaturu annual total rainfall (mm) (1982-2011). ........................................................ 54�

Figure 4-5: Vaturu annual rainfall anomaly (%) (1982 – 2011). ................................................. 56�

Figure 4-6: Vaturu seasonal rainfall (mm) (1982 – 2011). .......................................................... 57�

Figure 4-7: Vaturu storage volume (ML) under current climate (Scenario 1). ............................ 67�

Figure 4-8: Vaturu storage volume (ML) under Scenario 12. ..................................................... 68�

Figure 4-9: Future water demand (ML/day) with population growth scenarios of 0.4%, 0.8% and

1.6%. .................................................................................................................................... 70�

Figure 4-10: Reliability of Vaturu reservoir with population growth. ......................................... 71�

Figure 4-11: Reliability of Vaturu reservoir under Scenario 11 with population growth. ........... 72�



Figure 4-14: Annual Vaturu reservoir water level (m) (1984 – 2011). ........................................ 76�

Figure 4-15: Vaturu reservoir mean monthly water level (m) (1984 – 2011). ............................. 77�

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LIST OF TABLES �

Table 4-1: Missing Data. .............................................................................................................. 50�

Table 4-2: Independent Samples Test. ......................................................................................... 50�

Table 4-3: Vaturu Mean Monthly Rainfall (mm) (1982 – 2011). ................................................ 51�

Table 4-4: Vaturu Decadal Annual Mean Rainfall (mm). ........................................................... 53�

Table 4-5: ANOVA of Provisional and Decadal Rainfall. .......................................................... 54�

Table 4-6: Extreme Annual Rainfall (mm) and Anomalies (mm and %). ................................... 55�

Table 4-7: Seasonal Rainfall Distribution. ................................................................................... 56�

Table 4-8: Extreme Winter Rainfall (mm and %). ....................................................................... 58�

Table 4-9: Extreme Summer Rainfall (mm and %). .................................................................... 58�

Table 4-10: Projected Most Likely Scenario for Annual Surface Temperature and Rainfall. ..... 59�

Table 4-11: Most Likely Fiji Climate Future – Annual Temperature and Rainfall. .................... 60�

Table 4-12: PCCSP - Annual Rainfall Projections. ..................................................................... 61�

Table 4-13: Projected Most Likely Scenario for Summer Surface Temperature and Rainfall. ... 62�

Table 4-14: Most Likely Fiji Climate Future – Summer Surface Temperature and Rainfall. ..... 63�

Table 4-15: Projected Most Likely Scenario – Winter Surface Temperature and Rainfall. ........ 64�

Table 4-16: Most Likely Fiji Climate Future – Winter Surface Temperature and Rainfall. ........ 65�

Table 4-17: PCCSP – Winter and Summer Rainfall Projections. ................................................ 66�

Table 4-18: Reliability and Probability of Reservoir Failure with Rainfall Change Effects. ...... 69�

Table 4-19: Nadi and Lautoka Projected Population Growth. ..................................................... 70�

Table 4-20: Estimated Water Consumption in Nadi and Lautoka. .............................................. 74�

Table 4-21: Daily Water Usage (L and %) for Typical Urban Residents in Fiji. ....................... 75�

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ABSTRACT�

Fiji’s abundant annual rainfall is unevenly distributed in time and space. Fiji’s changing

climate, compounded with rapid urban development and growth of the tourism industry

in the Pacific have added pressure to the public water supply systems rendering them

inadequate, to various degrees, to meet the population’s water needs. This study

incorporates a range of rainfall projections from the IPCC AR4 climate model runs to

evaluate the security of the Nadi/Lautoka public water supply, an urban centre with a

combined population of 94,504 people. The study evaluated the projected water budget

of the primary public water source for Nadi and Lautoka: Vaturu Reservoir.

The water budget builds on rainfall projections from the B1, A1B and A2 scenarios

obtained from the PCCSP Pacific Climate Futures online tool

(www.pacificclimatefutures.net) and a projected population growth rate of 0.8%

established during Fiji’s latest census in 2007. The 0.8% growth rate bracketed a low

growth rate of 0.4% and high growth rate of 1.6% per year. A modified version of the

PICPP’s Vaturu Water Balance Model was used to assess the effects of changing rainfall

and population on the reliability of the water supply system.

Population growth and the resulting water demand had a greater impact on water supply

than projected changes in rainfall. With no change in population, the effect of projected

rainfall resulted in water supply reliability of 100% or very close to it. By contrast, with

no change in projected rainfall, even the low population growth scenario of 0.4%

resulted in decreasing reliability of water supply throughout the next 80 years. For the

high population growth scenario of 1.6%, the Vaturu water supply met water demand

77% of the time by 2055 and only 43.6% of the time by 2090, assuming constant

reservoir capacity. This study demonstrates that for Fiji, a representative Pacific small

island country, population growth is a more important determinant of the reliability of a

key urban centre water supply than future rainfall.�

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1 CHAPTER�1:� INTRODUCTION��

1.1 Background�

The islands of the Pacific are especially vulnerable to the impacts of climate variability

and climate change due to their geographic isolation from the continental mainland,

small size, high population density and limited natural resources (Scott et al., 2003).

Small islands are among the first to be affected by changes in climatic parameters and

with extreme natural events becoming more frequent and intense (Bettencourt et al.,

2002). The need for urgent attention has prompted small island nations to stand together

during international climate change conferences and negotiations and call for immediate

action on climate change mitigation and assistance in adaptation (Alliance of Small

Island States, 2010, Alliance of Small Island States, 2012, Komai and Wasuka, 2011,

Ronneberg, 2012).

According to the 4th Assessment Report of the Intergovernmental Panel on Climate

Change or IPCC AR4 in short, there is now indisputable evidence from historical

climate data that global average air and sea surface temperatures have been increasing

(IPCC, 2007a). This warming has also resulted in accelerated rates of melting ice and

snow which in turn pour tremendous volumes of freshwater into the ocean and thus

raising global sea level. The global climate (temperature and radiation) drives the

hydrological cycle (Strahler and Strahler, 1999) and these long-term changes will cause

the hydrological cycle to operate differently and affect global precipitation seasonally

and geographically. For example, the IPCC documented more frequent and intense

droughts in some parts of the world (such as western United States and southern Africa)

and severe flooding in other parts (such as South-East Asia) (Pachauri and Reisinger,

2007).

Despite some uncertainties in modelling and the projection of the impacts of climate

change on the hydrological cycle, the result of multi-model ensemble predictions show

that there will be an overall increase in precipitation across the tropical Pacific (Bates et

al., 2008). For high volcanic islands such as Viti Levu, this would mean an increase in

rainfall and often also in flooding frequency and severity. For coral atolls such as

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Funafuti in Tuvalu and Tarawa in Kiribati, this would offer much relief to the frequent

short-term dry spells. The same climate projection can have different impacts on the

geographically scattered and geologically diverse islands of the Pacific region and

makes each country vulnerable in different ways.

The vulnerability and specific needs of small island nations to water resources issues is

well known within the region. Only recently has this been recognised by the

international community through the World Water Council (WWC) who designated a

discussion theme that concerns specifically the small islands during the Third World

Water Forum in Kyoto, Japan in 2003. World Water Day, celebrated annually on 22nd

March, is an international effort to create awareness in various aspects of water and

sanitation. The theme for 2011 was “Water for Cities: Responding to the Urban

Challenge” highlighting the recognition by the United Nations that the rapidly growing

urban population, industrialisation, conflicts, natural disasters and climate change and

variability are posing significant challenges to meeting the human right of having access

to safe basic water supply and sanitation (Pacific Applied Geoscience Commission,

2011b). This year – 2012, the World Water Day campaign focused on the increasing

concern of food security with the theme of “Water and Food Security”. Such visibility

underscores the need for immediate assistance to address water security problems in

small islands.

Different sectors of society describe the concept of ‘water security’ differently. One

definition of water security is the “sustainable access on a watershed basis to adequate

quantities of water, of acceptable quality, to ensure human and ecosystem health” (Dunn

and Bakker, 2009). It is also described as “the capacity of a population to ensure that

they continue to have access to potable water” (World Water Council, 2011). While

Dunn and Bakker’s definition encompasses the water needs of both people and the

ecosystem, the WWC’s main concern is safe drinking water for humans. My study

approaches water security with relation to the needs of human beings.

Fiji, like the rest of the Pacific region is faced with increasing problems of water

availability (excess and deficit), supply reliability and quality (Scott, et al., 2003,

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Duncan, 2011). Industrialised nations have the financial and technological capacity to

alleviate water insecurity; unfortunately the same capacity is not available for smaller

and developing nations. Fiji, in general, is fortunate to have abundant rainfall and means

to attenuate variability in rainfall through construction of dams. However, prolonged

droughts and possible impact of climate change will undoubtedly put pressure on the

security of water supply.

One way of managing the security of water supply in Fiji is through the use of dams. A

dam is a physical barrier which obstructs water flow and allows water to collect and be

stored in the lake or reservoir that forms behind the structure. Dams have been used for

thousands of years for managing water resources. The earliest known dam was built

around 3000 – 2800 B.C. in what is now the country of Jordan in the Middle East

(Henderson-Sellers, 1979). While technologies back then were primitive and confined to

a small-scale, dam construction has developed and in modern times are often large-scale

infrastructures that are sometimes met with criticisms from those living nearby who may

be affected. However, dams are an effective means to provide a stable water source to

the public and alleviate the pressures of seasonal rainfall deficits.

Countries and territories in the Pacific experience seasonal rainfall patterns. It is not

always possible to build dams to store excess rain that falls. Dams can only be

constructed where there is a significant surface water source (rivers) and suitable

geology. Construction of a dam could be quite expensive, and therefore still not

affordable for smaller economies despite having all the abundant surface water resources

and suitable natural conditions. Where it is possible to have a dam, they are usually used

to store river water as the primary source for public supply, especially for urban areas. It

is also well known that dams are commonly used to generate hydroelectricity, for

example, in Monasavu – Fiji, in Afulilo – Samoa and in Espiritu Santo - Vanuatu. In

Fiji, water supply dams such as the Vaturu and mini-dams are found in the Western

Division where dry spells are common. High annual rainfall in Suva compared to the

western side of Viti Levu maintains consistent river flows and allows public water

supply to source raw water directly from rivers without the need for a dam. There are,

however, weirs built at a few of the intakes (such as Savura) (Nath, 2008). Weirs are

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similar to dams but are much smaller and acts to regulate river flow rates rather than to

impound water for Suva’s water supply.

Public water supply systems typically use two types of reservoirs (Henderson-Sellers,

1979):

a) Storage reservoirs have bigger storage capacities than demand and are located

close to the water source and away from the population. Raw water from storage

reservoirs must first be treated before further distribution to the supply network.

The Vaturu reservoir is a good example of a storage reservoir.

b) Service or distribution reservoirs are much smaller and typically have capacity to

meet demand only for a day. Service reservoirs contain treated water and are

located close to the population. Since storage reservoirs and treatment plants are

typically far from the urban centres, service reservoirs ensure that there is

sufficient water pressure and available water for end users. The Nadi and

Lautoka water supply network contains many service reservoirs such as those

located at Black Rock, Votualevu and Mocambo.

A typical public water supply system with its typical components, including the

storage and service reservoirs described above, is shown in Figure 1-1, below.

Figure 1-1: Diagram of a public water supply system (modified from Queensland Environmental Protection Agency and Wide Water Bay Corporation (2004)).

1.2 Climate�Projection�in�Small�Island�Countries�

Atmosphere-Ocean General Circulation Models (AOGCMs) available to climate

research scientists have the capacity to produce, with good confidence, quantitative

projections of climate change (Randall et al., 2007). However, some climate variables

such as temperature are projected with better confidence than others such as

precipitation (Randall et al., 2007).

Sea levels near small islands in the Caribbean Sea, Indian Ocean and Northern and

Southern Pacific Oceans are projected to rise, although not uniformly and not without

varying degrees of uncertainty between the different models. A recent analysis of

changing sea levels in the Pacific confirmed that with mean annual increases ranging

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from 3.1 mm in Kiribati to 8.4 mm in Tonga, sea level rise is indeed occurring at a rate

3 to 4 times higher than that of the global mean (Aung et al., 2012). Temperature is

likely to increase during all seasons, but may lag from the global mean. Most models

project a likely increase in annual rainfall across the equatorial Pacific but with so little

landmass in this region, the AOGCMs are often unable to differentiate land from ocean

surfaces (Christensen et al., 2007).

High resolution projections remain a challenge over small islands (Lal et al., 2008). The

current best estimates are achieved by producing projections over ocean surfaces that in

most cases only crudely reflect the actual processes on land (Christensen et al., 2007).

Climatic processes such as the midsummer drought in the Caribbean and ocean-

atmosphere interaction in the Indian Ocean still need to be better understood before

regional projections can be effectively downscaled for small islands (Christensen et al.,

2007). The uncertainty surrounding future sea surface temperature (SST) changes and

how it may affect the origin, frequency and distribution of tropical cyclones is another

factor hindering development of high resolution projections for small islands.

Global climate models (GCMs) currently available can make projections on a horizontal

scale of 100 – 200 km however this is not adequate for estimation of hydrological

responses to climate parameters at the river basin scale1. To make global projections

more useful and meaningful on a regional or local scale, they need to be downscaled to

an island or a river basin level. Global models and projections can be downscaled either

dynamically, using meteorological models to simulate how global patterns affect local

physical conditions or statistically by using mathematical equations to relate variations

in global climate to those in the local climate (Lenart, 2008, Kirby and Edgar, 2009).

In the Pacific region, the first attempt to dynamically downscale global climate

projections to 8 km was done by Lal et al. (2008) for the Fiji Islands. The research

demonstrated the promising potential of using the Commonwealth Scientific and

Industrial Research Organisation’s (CSIRO) Conformal-Cubic Atmospheric Model (C-

�1�A�river�basin�is�a�land�area�that�is�drained�by�a�river�and�its�tributaries�and�projections�made�at�this�scale�allow�changes�in�physical�parameters�within�the�basin�itself�to�be�modelled.�

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CAM) in simulating past and future spatial and temporal distribution of minimum and

maximum temperatures and rainfall at a high resolution comparable to the actual

observed values.

IPCC’s global projections have also been statistically downscaled for the Hawaiian

Islands by Timm and Diaz (2009) who concluded that the islands’ wet winters

(November to April) will see precipitation decreasing by 5% to 10%, while that in the

dry summers (May to October) will increase by about 5% by 2100.

All of the Pacific Islands (excluding Australia and New Zealand), combined, despite

having 0.12% of the world’s people, only contribute to about 0.03% of CO2 emission

from fossil fuel consumption (Hay et al., 2003). If the Pacific were to suddenly become

carbon neutral, the impact on global greenhouse gas (GHG) concentrations would be

small. It is, therefore, imperative that industrialised nations and other major GHG

emitters reduce their emissions while simultaneously supporting small island states in

their adaptive measures. While it is evident that initial attempts have been made in

strengthening the Pacific region’s understanding of climate variability and change, there

remains much to be done.

1.3 Research�Focus�and�Objectives�

This project investigates the impact of changes in rainfall on the water balance of the

Vaturu reservoir in the Nadi Basin. Vaturu catchment is protected by the Fiji

government to provide public water supply to the Nadi and Lautoka Urban Areas.

General public access and any activity that could potentially pollute the water catchment

are prohibited under the Water Supply Act (1985). For conservation purposes, the

Vaturu reservoir and the whole of its catchment is included in the Koroyanitu National

Heritage Park (Masibalavu and Dutson, 2006). The conservation area has no legal status

but is recognized as an Important Bird Area (IBA) by BirdLife International in

association with the local communities that share ownership of the Park (Salati et al.,

1983).

The capacity of the Vaturu reservoir to provide a secure freshwater source for the public

supply system to the urban centres of Nadi Town and partially to Lautoka City is

assessed using a water balance approach. Nadi Town and Lautoka City are both located

on the main island of Viti Levu and in the Western Division of Fiji (Figure 1-2, below).

Figure 1-2: Satellite image of Vaturu, with yellow lines indicating its direction with relation to Nadi and Lautoka (Google Earth, 2011a, South Pacific Real Estate, 2011).

The western side of Viti Levu and other large islands in Fiji are relatively hot and dry

compared to the eastern side, which receives higher annual rainfall, due to orographic

influences. The western region is prone to dry spells when rainfall can be well below the

normal values. During severe drought episodes, as seen during 1997/98 and most

recently in 2010, it is common to experience rainfall deficit to the extent that public

water supply and natural ecosystems services may be compromised.

Projects supported by both the Fiji Government and external aid have been implemented

to address the issue of adaptation to the impacts of water shortage and excess. Donor

aided projects have also invested heavily in the installation of expensive and

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sophisticated equipment in the Navua and Rewa Rivers as part of flood forecasting and

warning systems (Holland, 2008, Finiasi, 2007). Both projects were funded by the

European Union. These systems have been reported to have helped warn residents in

affected areas to evacuate to higher ground and guided emergency services to areas that

required urgent assistance (Raicola, 2010, Staff Reporter 2009). The success of the flood

warning systems in the Navua and Rewa Rivers has encouraged government to support

the installation of similar systems for the Nadi and Ba Rivers (Wise 2010).

While natural water resources availability is influenced by the climate, water

consumption patterns are mainly influenced by other factors such as demand with

economic development and population growth and, to some extent, increasing

temperature.

This project aims to contribute to knowledge in water resources issues of the study area

by investigating the following specific objectives:

1. To further develop and test PICPP’s2 daily water balance model for the Vaturu

dam catchment;

2. To assess the reliability of supply from the Vaturu reservoir separately with

climate change and population growth;

3. To assess the impact of future climate change (2030, 2055 and 2090) on rainfall

patterns and their impacts on the water security of the Vaturu water supply; and

4. To develop demand management options to adapt to the risks of future supply

system failure

Flooding associated with heavy rainfall and tropical cyclones also compromise the water

supply infrastructure and riparian and delta communities; however, the impacts of

flooding are not a detailed part of this study.

� �

�2�The�Pacific�Islands�Climate�Prediction�Project�(PICPP)�is�funded�by�AusAID.�This�project�helps�build�capacity�in�seasonal�climate�forecasting�in�specific�climate�sensitive�sectors�in�its�10�participating�Pacific�island�countries.�

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1.4 Fiji’s�Climate�Regime��

Under the Koppen Climate Classification System, the whole of the Fiji group exhibits a

“tropical forest climate” (Af) where average monthly temperature is above 18°C, they

lack a winter season, annual rainfall exceeds evaporation and are moist with adequate

precipitation in all months (Strahler and Strahler, 1999).

Southeast Trade Winds are the prevailing winds throughout the group and interact with

orography to determine the rainfall regime of different parts of the country. Warm air

rises in the equatorial region and descends in the higher latitudes. The rotation of the

Earth causes the descending air to move back towards the equator from the southeast

direction in the southern hemisphere and creating the SE trade winds. The mountainous

interior of Viti Levu creates a ‘rain shadow’ effect on the western side of the island

whereby the bulk of the moisture in the air carried by the southeast trade winds across

the island fall as rain as it ascends the mountains (Strahler and Strahler, 1999). Nadi is

located on the leeward side of Viti Levu and exhibits this orographically-induced “rain

shadow effect”, receives only about two thirds of the amount of rain that Suva does.

Annual rainfall is 1810 mm in Nadi and 3040 mm in Suva. As much as 6000 mm of rain

falls in the mountainous interior of Viti Levu, annually. During the driest months, Nadi

may receive as little as only a third of the rainfall that Suva does during the same period

(Fiji Meteorological Service, 2010).

Fiji’s temperature range is small due to the buffering effect provided by the maritime

conditions. The warmest months are January-February and coolest, July-August, with

the variation in average temperature only around 2 – 4°C. There are two distinct seasons

in Fiji and these dictated predominately by precipitation more than any other climatic

parameter. Fiji’s seasonality is controlled largely by the movement of the South Pacific

Convergence Zone (SPCZ) throughout the year.

The SPCZ is where the two bands of wind resulting from persistent anticyclones; one in

the southwest Pacific near New Zealand moving east and the other from the southeast

Pacific moving west, meet. The SPCZ usually brings cloud and rain and depending on

seasonal weather patterns, can either create enhanced upper level convection bringing

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more rain or suppressed convection causing drier conditions (Strahler and Strahler,

1999). Movement in the position of the SPCZ influences daily weather and seasonal

climate in Fiji.

The dry season runs from May to October while the wet season, which also happens to

be the tropical cyclone season, extends from November to April.

An average of 8 to 11 cyclones pass through the Southwest Pacific region every season,

peaking around January to March (Fiji Meteorological Service, 2009). During the

2011/2012 season, 5 – 8 tropical cyclones were projected; which were below the average

numbers. Historical records of tropical cyclones since 1969 indicate a lower occurrence

during a La Niña season (with seven cyclones) compared to an El Niño season (with 10)

(Fiji Meteorological Service, 2011). Hazards associated with cyclones include torrential

rain, flash flooding, landslides, storm surges, heavy saltspray and lightning (Pacific

Applied Geoscience Commission, 2006). River and coastal flooding are sources of

major concern for people living in the riparian and delta regions of major river systems

on the two main islands in Fiji as flooding can occur with little warning and often causes

severe damage to public infrastructure and to the people, their health, livelihoods and

property.

Severe flooding events in January, February and March/April of 2012 caused

widespread damage to infrastructure and agriculture, particularly in the Western

Division. The sugar industry incurred losses of $27 million in January and at least $9

million from the two floods that followed (Baselala, 2012a). Damage caused to roads

and bridges in the worst affected areas cost the government $15 million in repairs (Malo,

2012). More than 12,000 people had to seek refuge in evacuation centres during the

March/April floods which also claimed seven lives (Chaudhary, 2012a). Tourists were

stranded in hotels and at the airport due to damaged roads and suspension of flights

during the most severe days of the flooding (Baselala, 2012b). The already struggling

water supply system also suffered damages of almost $19 million (including cost of

emergency water supply) (Chaudhary, 2012b).

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El Niño-Southern Oscillation (ENSO) events affect different geographic locations

differently. ENSO phases may not always behave the same way within the same country

and therefore the amount of rainfall and actual number of cyclones may be less or more

than what was expected at the beginning of the season. In Fiji, an El Niño typically

means anomalously dry conditions while La Niña brings excess rainfall (Fiji

Meteorological Service, 2010); however, the 2010/11 La Niña brought severe drought

for much of 2010, especially in the Western Division.

IPCC defines a drought as “the phenomenon that exists when precipitation is

significantly below normal recorded levels, causing serious hydrological imbalances that

often adversely affect land resources and production systems” (Intergovernmental Panel

on Climate Change, 2007). Different sectors have different definitions of what a

“drought” is but the 4 most commonly referred to types of drought are as follows

(Wilhite and Glantz, 1985):

i) Meteorological droughts occur when there is a prolonged period with less

than “normal” precipitation. The definition of a meteorological drought is

region specific because of the characteristic atmospheric dynamics of a

specific geographic region.

ii) Agricultural droughts arise when soil conditions start to become too dry to

support normal crop production and are caused by an extended period of

below average rainfall. The onset of an agricultural drought differs for

different crops that have different water demand requirements (Rasmussen et

al., 1993)

iii) Hydrological drought occurs when water reserves available in sources such

as aquifers, lakes and reservoirs fall below the statistical average.

Hydrological drought tends to show up more slowly because it involves

stored water that is used but not replenished. Like an agricultural drought,

this can be triggered by more than just rainfall deficit. Unsustainable

groundwater abstraction is one of the other major contributing factors toward

hydrological droughts.

iv) Socioeconomic drought occurs when weather-induced water deficit causes a

shortage of an economic good i.e. when the supply is not able to meet

demands.

All 4 types of drought occur in Fiji, where agriculture contributed 12% to national

income and supported 70% of the workforce in 2010, and this extreme climate event can

be especially devastating to the country.

The 1997/98 El Niño brought nation-wide dry conditions and was estimated to have cost

at least $130 million to different sectors of the Fiji economy (Rina, 2010). The 1997/98

and 2010/11 events are generally considered among the most severe to have affected Fiji

so far. Significant El Niño events were also experienced in Fiji in 1982/83, 1987 and

1999/2000 and La Niña events in 1971/72, 1975/76 and 1988/89. Figure 1-3, below,

shows the occurrence of El Niño and La Niña events between 1950 and 1998.

Figure 1-3: Niño and La Niña events between 1950 and 1998 (National Oceanic and Atmospheric Administration, 2012).

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1.5 Fiji�Climate�Change�Studies�

The Fiji Meteorological Service’s diligence in adhering to international climate

monitoring guidelines has produced some of the most complete climate data sets among

the small islands of the Pacific. This has allowed researchers (if they are granted access

to the data), exemplified by the three studies below, to carry out climate data analyses to

establish the presence or absence of long-term climate change patterns in Fiji.

Mataki et al. (2006) conducted an analysis to establish the baseline climatology of Viti

Levu using Nadi and Suva data. They found that Suva’s mean daily temperature had

risen at a significant rate of 0.25°C per decade and that of Nadi by about 0.07°C per

decade from 1961 to 2003. The authors, however, found no significant changes in trend

in mean annual rainfall at either of the sites. The same authors noted that ENSO had a

more intense effect on Nadi rainfall than it did for Suva (Mataki et al., 2006).

Using the Standardized Precipitation Index (SPI) method with rainfall from 1948 to

2008, Deo (2011) found that there was a significant negative trend in the changes of

monthly rainfall, especially for Nadi and Labasa leading him to conclude that the

frequency and intensity of droughts in Fiji was likely to increase in the future.

Kumar et al. (2012) found high inter-annual variability in Fiji’s rainfall, however, the

long-term trend did not appear to be significant. The authors also found no significant

differences in seasonal rainfall, but did highlight that the last 20 years were slightly

wetter than the long-term normal.

Both Mataki et al. (2006) and Kumar et al. (2012) concluded that there was no

significant long-term rainfall trend. On the contrary, Deo (2011) was adamant that a

significant negative trend existed. Kumar et al. (2012) speculated that part of Deo’s

analysis may have contained some calculation errors which created a false significant

trend. In addition, Deo (2011) used data extracted primarily from the Comprehensive

Pacific Rainfall Database or PACRAIN, while Mataki et al. (2006) and Kumar et al.

(2012) sourced data directly from FMS. The difference in data sources may also have

been a factor in Deo’s (2011) findings.

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1.6 Thesis�Outline�

I present the findings of a study designed to assess the impact of long-term climate

change on the public water supply security of a Pacific urban centre which uses a central

public water supply system. This study uses the Vaturu reservoir, which serves as a

primary water source for the urban centres of Nadi and Lautoka, as a case study.

The content of this thesis is presented in seven chapters as outlined below:

Chapter 1: Introduction

This present chapter provides a background to the research with its rationale for study

and objectives.

Chapter 2: Literature review

The current state of water resources in the study site will be reviewed and work will

centre on approaches to the use of water balances in different parts of the world. The

chapter will also describe rationale for applying this method to the selected study site.

Chapter 3: Methodology

The methods and procedures undertaken in this study are explained in this chapter.

Chapter 4: Results and interpretation

The results of data analysed using Microsoft Excel and SPSS Statistics softwares are

presented in graphical and tabulated forms and explained.

Chapter 5: Discussion of results

Discussion of graphs and tables presented in the previous chapter are discussed in this

section.

Chapter 6: Conclusion

Conclusions are drawn from relevant findings in the study and presented in this chapter.

Chapter 7: Recommendations

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Recommendations are provided in this chapter that may contribute to the improved

understanding and management of public water supply systems. Suggestions are also

offered for possible studies in the future that could enhance the knowledge in issues

concerning general water security. � �

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2 CHAPTER�2:� LITERATURE�REVIEW�

This chapter first introduces the study site giving information on its location, population

and economic activities. I then discuss the roles of the government agencies and give

examples of donor organisations and projects involved in water resources management

in Fiji. The availability and usage of surface water, groundwater and rainwater in the

study area are then discussed, followed finally by a description of the water balance

approach and its applications in water resources management.

2.1 Study�Site��

The general area of interest in this study includes the whole of the Nadi River Basin as

well as the city of Lautoka which is outside of the catchment boundary and about 20 km

north of Nadi Town. Nadi Basin is located in the Ba Province of the Western Division

on the main island of Viti Levu, Fiji. It is the smallest, by area, of the five main river

systems on the island. The boundaries of the catchment span between 17°40’S and

17°52’S latitude and 177°52’W and 177°40’W longitude and has an area of 517 km2.

Elevation of the catchment ranges from sea level at Nadi Bay to 670 m in the headwater

up in the Nausori Highlands. Three main tributaries – the Marakoa, Namosi and Nawaka

rivers join the Nadi River at different points but the main river itself is about 50 km long

(Figure 2-1, below).

Figure 2-1: Nadi catchment rivers and topography (Nadi Town Council, 2009).

The population of the Nadi urban area, comprising the central town and peri-urban areas,

grew 178% from 15,220 in 1986 to 42,284 in 2007 while Lautoka grew 33.7% from

39,057 to 52,220 (Fiji Islands Bureau of Statistics, 2008). This population growth since

the 1980s was driven by the expansion of the commercial and service industries which

were required to cater to growth in the tourism sector. The demand for a reliable

drinking water supply has been increasing rapidly for decades; however the rate of water

supply infrastructure upgrade has not kept up with consumer needs.

The main economic activities in Nadi are sugarcane farming and tourism. Sugarcane was

traditionally the most important agricultural product in the Nadi Basin and the whole of

Fiji. Sugar exports, at its peak in 1996, made up 37% of the country’s total exports for

the year and contributed 11% of the GDP (Fiji Sugar Corporation Limited, 2009). In

recent years, the expiration of native land leases on many cane farms has re-directed

farmers toward market or vegetable cropping resulting in a rapid decline of the sugar

industry in Fiji’s economy. In fact, sugar export has fallen to 26% of total exports and

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6% of GDP in 2005 (Fiji Sugar Corporation Limited, 2009) but sugar, while no longer

dominant, still remains an important source of income and employment for the Western

Division.

Nadi is the tourism hub of Fiji and has the highest density of hotels. The location of an

international airport in Nadi has been a major factor which has contributed to the rapid

development of the tourism sector in the town. There were close to 620,000 visitors to

Fiji in 2010 who contributed 25% of the year’s GDP (Travel Daily News Network,

2011). Active marketing campaigns aim to increase this to 1 million and demonstrates

the continued confidence in the growth of this sector in Fiji’s economy. The sugar and

tourism industries that are so vital to Nadi and depend so much on the climate are also

among the most susceptible to the impacts of climate variability events.

The most recent flooding events in the Western Division, described in Chapter 0,

demonstrates how climatic change and variability can put enormous pressure on the

Nadi and Lautoka’s water resources and supply, infrastructures, commercial and tourism

sectors and the growing population.�

2.2 Water�Resources�Authorities�in�Fiji�

The Water Authority of Fiji (WAF) is a commercial statutory body that was created

under the Public Enterprises Act (1996) to manage water supply and sewerage systems.

The Water Authority of Fiji Promulgation (2007) came into effect in January 2010 and

WAF has since assumed all the responsibilities, activities and operations of the former

Water and Sewerage Department (WSD) of the Ministry of Public Works. WAF

currently claims service coverage to over 800,000 people through 144,000 metered

connections (Ministry of Works Transport & Public Utilities, 2010), which is almost

94% of Fiji’s total population.

Several other government agencies also hold responsibility for various aspects of water

resources management in Fiji. WAF manages the urban water supply and the Mineral

Resources Department (MRD) is traditionally the agency managing groundwater

resources and facilitating the provision of water supply to rural communities. MRD also

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assesses applications for commercial groundwater abstraction and issues licences in

accordance with the Mining Act and Regulations (1978).

Urban drainage, flooding mitigation and irrigation works are being managed by the Land

and Water Resources Management (LWRM) whose operation is guided by the Streams

and Rivers Act (1985).

The Fiji Meteorological Service (FMS) serves Fiji as well as several nearby Pacific

Island countries in providing terrestrial, marine and aviation weather forecasts. FMS also

serves the special function of providing tropical cyclone warning services in the

Southwest Pacific in its capacity as a WMO-designated Regional Specialised

Meteorological Centre (RSMC). With relation to water resources, FMS is the most

reliable climate data collection government agency in Fiji. They are obliged to adhere to

WMO regulations and standard practices in their operations and thus the quality of the

data they collect and archive meets international standards.

The Hydrology Division of WAF, LWRM and the Fiji Sugar Corporation (FSC) also

have their own respective rainfall collection networks. FSC and FMS have closely

collaborated in research and data sharing. Unfortunately, other agencies treat data

ownership as a rather sensitive issue and are often reluctant to share the data that they

have collected. The reluctance to share data is a hindrance to research in water resources

and is unlikely to change until national or international water policies on data sharing are

in place.

The development of a national water policy has been underway for most of the last

decade. WAF is now legally mandated to monitor surface water resources and manage

the public water supply network. They could possibly lead the task of facilitating the

finalising and legalising of the national water policy. WAF is the central coordinating

agency but the responsibility for water resources monitoring and management remains

divided among several other agencies which adhere to their respective outdated

mandates that include small clauses regarding specific aspects of water resources

management. � �

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2.3 Donor�Aid�in�Water�Management�

Donor agencies have been instrumental in providing financial aid, technical expertise

and capacity development in water resources management in Fiji. The three examples

below highlight some of the aspects in which capacity in water resources management in

Fiji has been enhanced.

2.3.1 AusAID�PICPP�

The AusAID-funded Pacific Islands Climate Prediction Project (PICPP) is focusing on

the water supply issue for the Nadi. The PICPP’s Nadi project is investigating the use of

ENSO-based seasonal climate forecasts to help improve management of water resources

in the Vaturu reservoir (Pacific Islands Climate Prediction Project, 2008). The project

also aims to build capacity of FMS staff in using seasonal climate forecasting as a tool to

produce information to the water sector stakeholder and allow them to make informed

decisions during droughts.

2.3.2 GEF�IWRM�Project�

The Global Environment Facility (GEF) funded Integrated Water Resources

Management (IWRM) demonstration project in the Nadi Basin is one example of

external support in addressing water issues in the Pacific. For Fiji, this project aims to

apply the holistic IWRM approach and its principles in the monitoring and management

of different aspects of water resources in the whole basin. The IWRM project also

installed hydrological monitoring stations to collect data for better flood preparedness,

improved water resources monitoring for water supply and hydropower generation and

analyses of future climate and hydrological behaviour (Government of Fiji, 2007).

2.3.3 EU�Pacific�HYCOS�

The European Union also supported the Pacific Hydrological Cycle Observing System

(Pacific HYCOS) project in establishing monitoring programmes and developing and

building capacity in the 14 participating countries (Pacific HYCOS, 2010). While Nadi

Basin was not a selected site for Pacific HYCOS in Fiji, the project contributed to the

IWRM project’s activities the Nadi Basin through the design of its hydrological

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monitoring programme. In other Pacific HYCOS project countries notable achievements

were made in the establishment of monitoring capacity in high volcanic islands

including Papua New Guinea, Vanuatu and Solomon Islands for river flow monitoring

which allowed floods to be forecasted during periods of heavy rain and water shortage

during dry spells. In low-lying atolls including Marshall Islands, Kiribati and Tuvalu, the

Pacific HYCOS project’s contribution to groundwater resources assessment and

monitoring and rainwater monitoring and domestic rainwater harvesting systems

allowed availability and quality of water resources to be better understood especially

during impending dry spells (Pacific HYCOS, 2010).

Being Fiji’s leading organisation in water supply and hydrological monitoring, WAF

should be ideal agency to coordinate water resources management efforts; however, in

reality, this is not quite so. Duncan’s assessment of the state of water resources

management in the Pacific (2011) showed that for Fiji, a national water policy, water

legislation and an integrated water resources management (IWRM) strategy are still in

draft form. There is an inter-sectoral water coordination body, only as interim, and a

water-use efficiency plan does not exist yet. The fact that WAF was only established two

years before and is still building its capacity toward an effective management of the

public water supply is noted. The support given by donor organisations is also making a

significant contribution toward WAF achieving its mandated role in providing a

consistent and safe potable water supply to the public.

2.4 Water�Resources�and�Usage�in�the�Nadi�Basin�

Water resources available in the Nadi Basin include surface water, groundwater and

rainwater. The most widely utilised surface water source comes from the network of

rivers and creeks that constitute the Nadi River catchment system. There is a good

groundwater source in the Meguniyah which is an aquifer that was extensively studied in

the 1970s by MRD and while rain water harvesting systems do exist, they are quite

under-utilised (Gale and Booth, 1993).

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Water resources in the Nadi Basin are used for a whole range of activities including

domestic and community consumption in the urban and peri-urban areas, commercial

and retail properties, hotels and resorts, food processing and irrigation for crops. The

Nadi and Lautoka urban areas have grown significantly both in terms of population and

economic development in the past 20 years. With the increase in water demand in Nadi

and Lautoka, increased water storage capacity and efficiency in delivery of adequate

water supply to the consumers is essential.

The 3 freshwater sources used in the Nadi Basin are described in more detail in the

following sections.

2.4.1 Surface�Water��

Surface water is any natural water resource found as streams and springs and as

freshwater lagoons, lakes and swamps (Scott et al., 2003). Surface water is the primary

freshwater source for urban Nadi and Lautoka. The reticulated public water supply is

sourced from the Vaturu reservoir and treated at the Nagado Water Treatment Plant

before being diverted by two main lines to supply drinking water to Nadi and partially to

Lautoka. The water demand of Lautoka is further supplemented by two smaller

catchments in Buabua and Saru.

The Vaturu dam began construction in 1980 and operation in 1984, provides the main

source of water supply for the Nadi and Lautoka urban areas. The dam is located in the

headwaters of the Nadi River, about 30 km from Nadi and comprises a 56 m high rock-

filled embankment with an earth core (Figure 2-2). It commands a catchment area of

approximately 40 km2 or slightly less than 8% of the total area of the Nadi River

catchment. The four tributaries of Waidamu, Savunaba, Nukunuku and Wainivau Creeks

form four sub-catchments which drain into the Vaturu reservoir (Figure 2-3). Infrequent

hydrological monitoring is carried out in each of the tributaries when budget and staff

availability allows. Magodro (after the confluence of Nukunuku and Wainivau) and

Waidamu each also have a rain gauge on site. Apart from the rainfall station at Vaturu,

the other two raingauges have fallen into disrepair and have been producing poor quality

data since they have been installed about 30 years ago (personal communication, Mr

Paula Tawakece, WAF Hydrology Officer, 01/11/2011).

Figure 2-2: Satellite image of Vaturu dam and reservoir (Google Earth, 2011b).

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Figure 2-3: Vaturu catchment showing the four tributaries and hydrology stations (Google Earth, 2012, Tawakece, 2011).

The total storage volume of Vaturu reservoir is between 23.5 (Maunsell, 2004) and 27

Mm3 (Baulderstone, 2010). The supply system was designed for an initial population of

80,000 and claimed to have capacity to cater for an anticipated population of up to

187,000 by 2001 (Baulderstone, 2010). The combined populations of both the Lautoka

and Nadi urban areas had not reached 100,000 even by 2010. The 2007 Census

estimated that over 540,000 tourists would visit the country in 2009 (Fiji Islands Bureau

of Statistics, 2008). When tourist numbers are taken into account, it becomes quite

evident that the tourism industry has placed some serious stress on the system.

A preliminary study on the impacts of climate change on water supply from the Vaturu

dam showed that with a mid-range population forecast, the dam will be able to

adequately meet consumer water demand until 2040 (World Bank, 2000). The chronic

water supply disruptions being highlighted frequently by the media (Figure 2-4)

indicates that the system has exceeded its maximum supply capacity well before the 26�

�

timeframe estimated in that earlier World Bank study. As the tourism industry grows, it

is possible that the unfortunate practice of prioritising tourism water demand over the

demand of the general public in Nadi and Lautoka may arise.

Figure 2-4: A montage of headlines on water disruption in a daily newspaper (Chaudhary, 2011, Naivauwaqa, 2008, Nasiko, 2012a, Nasiko, 2012b, Staff Reporter, 2008a, Staff Reporter, 2008b, Staff Reporter, 2008c, Staff Reporter, 2011, Vosamana, 2011, Vosamana, 2012) .

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Population and economic growth will be the major stress factors on water security in

urban areas. The development of recent major tourism and private residential projects

already has and will certainly further increase demand on the supply system in the near

future. These pressures, when compounded by the uncertainty of climatic changes on

water resources availability, make Fiji’s biggest agriculture and tourism centres even

more vulnerable and warrants the need for research into possible solutions.

The high rate of non-revenue water (NRW) is partly to blame for the supply system’s

inconsistent water supply to consumers. NRW is “the difference between the volume of

water put into a water distribution system and the volume that is billed to customers”

(Frauendorfer and Liemberger, 2010) and is also known as ‘unaccounted-for-water’.

One estimate of NRW for Fiji is as high as 70% (Bridges, 2007) while another is as low

as 29% (World Bank, 2000). In a personal communication, yet another estimate was

provided with NRW as 40 – 50%, after pipe leakage control initiatives (personal

communication, Mr Paula Tawakece, WAF Hydrology Officer, 01/11/2011). The range

of estimates for NRW demonstrates the difficulty in accurately assessing the volume of

water loss through the supply network.

A country’s freshwater resource is measured as ‘total renewable water resources’ or

TRWR. With estimated per capita total renewable water resources ranging from 28.6 –

33.7 m3 (Gleick et al., 2008, Bridges, 2007) and withdrawal of only 0.3% (Food and

Agriculture Organization of the United Nations, 2012), resource availability is unlikely

to be one of the reasons behind Fiji’s water insecurity problems.

2.4.2 Groundwater��

Groundwater can be found underground within suitable rock types, as aquifers, and

within the pores of soil layers (Lerner et al., 2005). The entire Vaturu catchment (located

in the upper Nadi Basin) overlies part of the geological formation referred to as the

Koroimavua Andesitic Group in Rodda’s Geological Map of Viti Levu (Rodda, 1967).

Gale and Booth (1993) further classify the aquifer in this area as a ‘low-productivity

aquifer’ and little more is known about it due to the lack of hydrogeological data.

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The Meigunyah Aquifer, which lies to the northeast of Nadi Town, is an important

source of recharge for river systems in the lower Nadi basin. It is an extensive alluvial

aquifer underlying the Nadi River delta and covers an area of about 60 km2, with a

length of 11 km and width that varies from 4 to 7 km at different places. The aquifer

recharges mainly from direct rainfall (at a rate of about 11% of rainfall) and discharges

to rivers and creeks and to the sea from its coastal boundary (Gale and Booth, 1993).

The aquifer also provides an important source of groundwater, as a water supply, to the

peri-urban and rural populations living in the lower Nadi Basin (Pacific Applied

Geoscience Commission, 2007).

Preliminary hydrogeological investigations and geophysical work were undertaken in

the Lautoka area from 1976 to 1977. Three of the five boreholes that were drilled

produced passable yield which was not considered feasible as a public water supply

(Peach, 1988). There was later renewed interest in Lautoka, warranting its inclusion as

part of groundwater investigations for the growing Nadi/Lautoka urban areas.

The Nadi Groundwater Evaluation Project initiated early groundwater investigation in

Nadi (Peach, 1988). Little information on the status of the groundwater in Nadi was

available prior to this. The initial exercise accumulated preliminary baseline data which

paved the way for the design of a better structured groundwater investigation

programme. The programme was subsequently supported by the British Government

from 1978 to 1980 (Peach, 1988) and much of the hydrogeological information known

today, including studies of the Meigunyah Aquifer, is a result of those intensive

investigations.

A total of 75 boreholes were drilled, however, only two yielded enough groundwater to

allow them to be used as a public supply (Peach, 1988). Both have been abandoned since

the commissioning of the Vaturu dam in 1984.

More recently MRD has been conducting groundwater investigations in Nadi to evaluate

the potential to drill private production wells (Tadulala, 2011). Many boreholes have

indicated some groundwater potential but not enough for long term abstraction. There

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are at least two water bottling companies abstracting and selling bottled groundwater

from the Meigunyah Aquifer. The companies, Diamond Aqua and Aqua Pacific, sell

bottled water to both the local and export markets. Presumably, the abstraction licences

were granted on the basis of the favourable results that came out of MRD’s

investigations.

The Public Works Department (PWD) has a borehole subsidy scheme to assist

development of rural domestic water supplies. Once the borehole is operational, PWD

would have little or no further involvement and it becomes the community’s

responsibility for its operation and maintenance. The latter scheme benefited many

consumers in the northern and western regions of Viti Levu where droughts are common

occurrences. The number of new boreholes drilled under this scheme fell from 414 in

1999 to 230 in 2003 (Fiji Government Online, 2011), most likely due to the expansion

of the reticulated system in recent years.

There are also privately owned boreholes that have been drilled by private contractors

and commissioned with or without MRD or PWD’s knowledge. It is estimated that

between 20 to 30% of rural communities in Nadi and 10 to 20% in Nawaka and

Magodro use groundwater as a primary source of freshwater (Gale and Booth, 1993) and

it would not come as a surprise that some communities and individual families would

slip through the government’s radar in their quest for a reliable freshwater supply.

Fiji currently does not have any legislation to regulate and monitor the development of

wells and boreholes for groundwater usage (Pacific Applied Geoscience Commission,

2007). Domestic and commercial consumers (farmers, bottling companies, etc) are not

legally obliged to monitor and report to any authority on their volume of abstraction.

The European Union-funded Programme for Water Governance facilitated the

development of a draft National Water Policy for Fiji which makes provisions for

controlled abstraction of groundwater, however, since the project’s conclusion in 2006;

the Policy remains to date in a draft form (Pacific Applied Geoscience Commission,

2011a).

2.4.3 Rainwater��

The Nadi Basin experiences the same wet and dry seasons described in earlier sections.

The dry season runs from October to May while the wet season spans from November to

April. Vaturu is located in the interior highlands and therefore would naturally exhibit a

higher rainfall than the coastal region where Nadi and Lautoka are situated. A climate

station at the Vaturu dam compound (Figure 2-5) has been in operation since the dam

opened in 1982. FMS and WAF’s Hydrology Unit each keep separate records of the

same data collected from the Vaturu station and permission was granted for both sets of

data to be used for this study.

Figure 2-5: Vaturu rainfall station (Photograph by author).

Sugarcane farming, the largest agricultural activity in the catchment, is a rain-fed crop.

FMS produces a rainfall outlook specifically for the sugar industry to help farmers plan

the planting schedule for their crops.

According to Gale and Booth (1993), less than 1% of the rural population in the Nadi

and Nawaka and 1 – 10% in the Magodro capture rainwater as their primary source of

freshwater. An effective strategy would be to encourage rainwater collection as a

supplementary source of freshwater.

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There has not been a subsequent survey of rainwater utilization in the study area since

Gale and Booth (1993) and it is difficult to establish whether there has been a change in

trends since then. The author observed that many houses in the Nadi and Lautoka urban

centres are fitted with guttering and downpipes that merely drain straight into the ground

or into the municipal drainage network.

Water storage tanks are owner initiated and are not required by law in houses and

commercial or community buildings. With the high cost of purchasing rainwater tanks

and additional expenses associated with installing and maintaining the roof catchment

condition, guttering and piping network may be a deterrent to widespread utilisation.

Consumers’ reliance on the government to ‘rescue’ them from water shortage situations

may also contribute. Rainwater usage is embraced by many countries in the Pacific,

including Kiribati, Marshall Islands and Tuvalu, as a principal freshwater source (GWP

Consultants and Pacific Applied Geoscience Commission, 2007) and with careful

maintenance of the catchment system, there is no reason why this cannot be utilised in

Nadi and Lautoka or Fiji in general.

2.5 The�Water�Balance�Model�

Water balance/budgets account for the inputs and outputs of water in a natural water

system, e.g. an agricultural field, a river catchment or an island (United Nations

Educational Scientific and Cultural Organization, 1974). Water budgets can provide a

robust tool to help us better understand the components and movement of water in

watersheds.

For any arbitrary volume and during any period of time, the difference between total

input and output will be balanced by the change of water storage within the volume

(United Nations Educational Scientific and Cultural Organization, 1974). Therefore, the

general water balance equation is:

Change in storage (�S) = Inflow (I) – Outflow (O)

The use of a water-balance technique implies measurements of both storages and fluxes

(rates of flow) of water. More complex versions of the equation are available including

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up to 15 parameters that account for the amount of existing data, the purpose of the

computation, the type of water body concerned and its dimensions, its hydrologic and

hydrographic features and the duration for the balance time interval and the phase of the

hydrological regime (i.e. flood or low flow) (United Nations Educational Scientific and

Cultural Organization, 1974). An example of a complex model was used by Ojo (1969)

in his water balance study for West Africa, while Makhlouf and Michel (1994)

demonstrated the robustness of a simple model with only two parameters to examine

water balance for selected watersheds in France.

The water balance approach has been credibly applied in water resources management in

the Fena Valley Reservoir in Guam (Yeung, 2005) and in the Carneros Creek in

California, United States (Zlomke, 2003); and in catchment runoff modelling with

climate change effects in the Epitácio Pessoa catchment in Brazil (Galvíncio et al.,

2008). Water balance studies, such as the examples above, require resources such as

appropriate technical expertise, long-term observed data and a good monitoring

programme for hydrometeorological parameters. Unfortunately, most independent

countries in the Pacific cannot afford to carry out such studies without external aid. The

lack of local expertise and government support also hinder progress and prevent the

application of the water balance approach in water resources management.

This study of the water balance of the Nadi and Lautoka urban areas addresses the Nadi

catchment and is done at the smaller hydrological scale rather than the climatological

scale (Dyck, 1983). For islands in the humid tropical Pacific, where there is limited data

that can be used as input, simple methods are most appropriate and have been shown to

produce accurate results (Scotter et al., 1979, Balek, 1983, Sophocleous, 1991,

Makhlouf and Michel, 1994) under similar conditions.

The Vaturu catchment’s small size and uniformity in geology, topography and

vegetation justifies the use of a simple water balance with the following relationship:

�S= Q+P-E-O-L

where:

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�S is the change in storage in the Vaturu reservoir (in megalitres (ML))

Q is runoff into the Vaturu reservoir (in megalitres (ML))

P is direct precipitation over the reservoir’s surface (in millimetres (mm))

E is evaporation from the reservoir (in millimetres (mm))

O is outflow from the reservoir (in megalitres (ML)) and

L is loss from the reservoir and its catchment area (in megalitres (ML))

The variables in this equation will be further broken down into subcomponents in the

Methodology described in the next chapter.

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3 CHAPTER�3:� METHODOLOGY�

Chapter 3 details the methodology used to address the four objectives outlined in

Chapter 1.2. I first describe how the rainfall data was analysed to establish baseline

normal rainfall conditions for Vaturu. I will then describe how the Pacific Climate

Futures tool was used to produce climate scenarios for 2030, 2055 and 2090 under the

IPCC emission scenarios of B1, A1B and A2. Rainfall projections were then extracted

from the climate scenario outputs produced by the tool and used in the water balance

model. This chapter further describes how the model was used to establish the reliability

of the Vaturu reservoir under each of the 19 rainfall scenarios. Finally, the observed

historical Vaturu reservoir water levels were statistically analysed and compared with

historical rainfall to examine how rainfall affects reservoir water levels.

3.1 �Vaturu�Historical�Rainfall�Statistical�Analysis�

The two rainfall data sets used in this analysis came from FMS and WAF’s Hydrological

Unit for the Western Division. One rain gauge inside the Vaturu dam compound is

manually read at 9 am daily. The reading is recorded in an FMS-issued log book and

reported daily over the telephone to the Hydrology Office located in Lautoka where staff

also records the values in their own log books. There are two sets of records of daily

rainfall from the Vaturu dam station from the same equipment; however, FMS and

Hydrology carry out data entry, analyses and archiving independently. The Hydrology

Unit’s copy of the data is entered into a TIDEDA database. TIDEDA is a licensed

software package developed by New Zealand’s National Institute of Water and

Atmospheric Research (NIWA) for capturing, analyzing and archiving time series data

and is used by many countries in the Pacific for managing their national environmental

databases (National Institute of Water and Atmospheric Research, 2012). The main

software used to undertake data analyses in this study is Microsoft Excel (version

2007). The SPSS Statistics software is used for basic statistical testing. The TIDEDA

software itself is not used in this study because the data obtained from the database was

already extracted and converted into a spreadsheet format and is compatible with

Microsoft Excel.

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3.1.1 Quality�Control�

FMS provided monthly data for 29 years from 1982 to 2010 for the Vaturu rainfall

station. The dataset contained seven data values missing (coded as “Missing”) from the

348 months period. This gap of 2% is well within the World Meteorological

Organization’s (WMO) guidelines stipulating 80% completeness (World Meteorological

Organization, 2008).

WAF provided daily rainfall data for a 35-year period from 1977 to 2011. The data is

recorded from the same station but has been archived separately as described above.

When monthly totals were calculated from the daily values using pivot tables, a gap of

three missing months out of the 420 was found. This 0.71% gap is again well within

WMO’s guidelines. These missing values as well as those from the FMS data were

identified (Table 4-1) for the application of quality control.

Since the data is recorded manually, transmitted verbally, and is transferred several

times before being archived, several sources could have contributed to the small

percentage of error in both sets of data. Possible sources of error could have resulted

from malfunction in the instrument itself, from the observer (or the recorder), and during

data entry from the log books. Without access to the original rainfall log books from the

Vaturu station and the respective databases kept by FMS and WAF, it was not possible

to verify the specific sources of error.

An independent 2-sample t-test can be used to determine whether two sets of data are

statistically similar. This method was applied, using SPSS Statistics, to determine if

FMS and WAF’s datasets were similar to each other. Not surprisingly, the t-test

evaluation output in Table 4-2 showed no significant statistical difference between the

means of the two datasets. The gaps in the FMS data were filled with values of the

corresponding month from WAF’s TIDEDA dataset to produce an internally consistent

time series. Values for 2011 were not provided by FMS and were therefore extracted

from the TIDEDA dataset to produce a 30-year sequence that permitted calculation of

monthly average rainfall for Vaturu consistent with the specification of WMO Technical

Regulations (1988).

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Since the Vaturu data used was outside of the 30-year periods specified by WMO, the

30-year mean calculated here will be referred to as the “provisional normal”. The

merged 30-year data set of monthly rainfall was used for the rest of this statistical

rainfall analysis, results of which can be found in Chapter 4.1

WMO standards were followed where possible as they are the standards practiced by

most of the Pacific’s national meteorological services (NMS), including Fiji’s.

3.1.2 Monthly�Mean,�Minimum�and�Maximum�Trends�

The Excel data spreadsheet was arranged with the years as rows and months as columns.

In order to find the monthly mean, minimum and maximum statistics, the following

functions were used:

a) Minimum – MIN (lowest monthly rainfall in mm)

b) Mean – AVERAGE (normal monthly rainfall in mm)

c) Maximum – MAX (highest monthly rainfall in mm)

Since the months in the dataset are arranged column-wise, the column for each month

was selected as the array of data in the function. The results are plotted in two graphs –

one showing monthly mean rainfall (provisional normal) alone (Figure 4-1) and the

other showing the three trends of monthly mean, minimum and maximum rainfall in one

graph (Figure 4-2).

3.1.3 Decadal�Trends�

Decadal mean monthly trends were examined for significant inter-decadal change in

monthly rainfall by selecting the decadal ranges of data from 1982 – 1991, then 1992 –

2001 and finally 2002 – 2011. It was not possible to use standard decades (1981 – 1990,

1991 – 2000, etc) in this analysis because data recorded at the Vaturu rainfall station did

not start at such a year. Since rainfall records started in 1982 in Vaturu, a ‘decade’ in this

analysis was considered to be a consecutive 10-year period starting from that year.

The AVERAGE function was used to calculate mean monthly rainfall for each 10-year

period. The resulting distribution of monthly rainfall in these decades, and the

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provisional monthly normal were graphed in Figure 4-3. Annual mean rainfall for each

decade was then calculated by totalling the 12 mean monthly values for each 10-year

period. The annual rainfall anomaly for each decade was calculated by subtracting the

decadal mean annual rainfall from the provisional normal annual rainfall of 2,972.2 mm

and presented in Table 4-4. The statistical significance of this difference was determined

using the ANOVA tool in SPSS. The ANOVA output is presented in Table 4-5.

3.1.4 Annual�Trends�

To calculate the annual total rainfall, the row-wise data for each year was simply totaled

using the SUM function. Figure 4-4 shows the results produced.

To find the extremes, the monthly rainfall values were sorted in descending order with

high extremes (wettest) months/years located at the top of the data array and the low

extremes (driest) months/years at the bottom. The array was extracted and shown in

Table 4-6.

An anomaly is a departure from the mean value. Knowing how much each year’s rainfall

varies from the mean makes it possible to identify trends in long-term rainfall patterns.

Annual rainfall anomalies were calculated for each year using the following relationship:

Annual anomaly = annual rainfall – mean annual rainfall (2,972.2 mm)

The anomalies were converted to a percentage value of the mean annual rainfall and

plotted in Figure 4-5.

3.1.5 Seasonal�Trends�

The 6-month rainfall total for each season was calculated using the SUM function and

labeled as the starting month and year for that season. The function COUNT was used

to count the number of winters (30) and summers (29) used in the seasonal analysis.

Summer and winter totals were listed in separate columns to calculate a mean value for a

typical month in the respective seasons. The AVERAGE function was used. A summary

of the results can be found in Table 4-7 and a plot of the dry and wet seasonal total

rainfall in Figure 4-6.

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Seasonal rainfall anomalies were determined by subtracting each 6-month period’s total

from the respective seasonal mean already presented in Table 4-7. The winter and

summer seasonal anomalies were sorted to reveal the extremes and shown in Table 4-8

and Table 4-9, respectively.

3.2 � Rainfall�Projections� �

Rainfall projections were obtained from the Pacific Climate Change Science Program’s

(PCCSP) online Pacific Climate Futures Exploration Tool

(www.pacificclimatefutures.net). The tool provides future climate change projections for

14 Pacific island countries as well as East Timor using 18 out of the 24 global climate

models (GCM) that were used for IPCC’s AR4. These 18 GCMs have been selected

because they are considered to be relatively suitable for the Pacific region (Pacific

Climate Change Science Program, 2011).

The tool allows users from a wide range of backgrounds and levels of understanding in

climate change query and generates future projections through its three modes of usage –

basic, intermediate and advanced.

Three emission scenario options are available in the tool:

1. B1 – low emission scenario

2. A1B – medium emission scenario

3. A2 – high emission scenario

Three time period options are available in the tool:

� 2030

� 2055

� 2090

Ten climate variable options are available in the tool:

1. Maximum daily temperature (°C)

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2. Minimum daily temperature (°C)

3. Surface temperature (°C)

4. Rainfall (mm)

5. Heavy rainfall (mm)

6. Wind speed (m/s)

7. Strong wind (m/s)

8. Humidity (%)

9. Evaporation (mm/d)

10. Solar radiation (W/m2)

The Climate Futures tool displays projections for climate variables, except temperature,

by default as a percentage change relative to the 20-year period from 1981 to 2000. Only

in the Advanced mode is the option given to display projections in absolute values.

Eighteen season options are available in the tool:

1. January

2. February

3. March

4. April

5. May

6. June

7. July

8. August

9. September

10. October

11. November

12. December

13. March – May (MAM)

14. June – August (JJA)

15. September – November (SON)

16. December – February (DJF)

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17. November – April (NDJFMA)

18. May – October (MJJASO)

For the purpose of this study, the two climate variables of surface temperature (°C) and

rainfall (mm) were selected for the generation of future climate conditions. These two

variables were used because they are often the most obvious climate parameters to be

felt directly by the population. A projection output was obtained for each of the three

selected IPCC emission scenarios (B1, A1B and A2) with each of the available modeled

timeframe (2030, 2055 and 2090) on an annual scale and separately for the summer and

winter seasons. Summary tables for projected climate futures with percentage of models

in agreement are as provided in Chapter 4.2 on an annual, summer and winter basis.

Although temperature will have an effect on evaporation and hence the water balance

model, temperature projections produced using the Climate Futures tool was not used in

this study.

3.2.1 Annual�Rainfall�and�Temperature�

The variables of annual rainfall and annual surface temperature for the time frames of

2030, 2055 and 2090 under each of the B1, A1B and A2 scenarios were selected to

produce projected future climate scenarios. A total of nine climate projections were

produced using the intermediate mode of the Climate Futures Tool. After each

simulation, the tool produces a table showing the extent of climate change in the two

selected variables in categories. Colour codes show the degree of likelihood of each

possible climate future as determined by the number and percentage of models in

agreement with any particular projected change. The actual projected output for annual

rainfall and temperature can be found in Appendix 1.1 with a summary matrix presented

in Table 4-12.

3.2.2 Summer�Rainfall�and�Temperature�

The same procedure as in Chapter 3.2.1 was applied but using the season option of

November – April, that is, Fiji’s summer. Again nine combinations of the three

scenarios and three time periods generated nine climate change projections for the

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variables of rainfall and surface temperature during summer. The output can be found in

Appendix 1.2 with the most likely projections tabulated in Table 4-13 and a summary

matrix presented in Table 4.14.

3.2.3 Winter�Rainfall�and�Temperature�

The same procedure as in Chapter 3.2.1 was applied but using the season option of May

– October, that is, Fiji’s winter. Again nine combinations of the three scenarios and three

time periods generated nine climate change projections for the variables of rainfall and

surface temperature during winter. The output can be found in Appendix 1.3 with the

most likely projections tabulated in Table 4-15 and a summary matrix presented in Table

4-16. Table 4-14 and Table 4-16 were combined to produce Table 4-17 – a matrix of the

most likely rainfall scenarios with seasonal variation which will be used in the water

balance to simulate water levels under different rainfall scenarios.

3.3 Vaturu�Water�Balance�Model�

First, the original PICPP daily water balance model was examined and modification

made to suit this study. This procedure was undertaken to achieve the first objective of

the study.

The spreadsheet-based model was developed as part of the AusAID-funded PICPP

project as part of its implementation in Fiji to assess water supply availability in the

Nadi Basin. The project also uses seasonal climate forecasting to help improve water

management of the Vaturu reservoir.

In this study, a modified version of the model was used to generate a series of simulated

daily storage volumes under the 19 rainfall scenarios.

The simple water balance equation: �S= I – O is further broken down into the

components used in this model to derive the equation below:

�S= Q + P – Nr – Sc – Se – Sp – Ev – L – E

where Q = runoff or inflow (megalitres (ML)) (parameter 9)

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P = direct precipitation (mm) (parameter 10)

Nr = Nagado release (ML) (parameter 13)

Sc = scour release loss (ML) (12)

Se = seepage loss (ML) (parameter 15)

Sp = spill (ML) (parameter 14)

Ev = evaporation (mm) (parameter 4)

L = leakage loss (ML) (parameter 16) and

E = error (parameter 17)

All other parameters not used directly in the equation but included in the model and

listed below have been used to calculate the components of the equation above.

Parameters of the Vaturu Water Balance Model:

1. Historical reservoir rainfall (mm) – consist of actual observed daily rainfall

2. Rainfall scenario – numbered 1 – 19, representing the current rainfall and 18

rainfall scenarios generated using the PCCSP’s Climate Futures Exploration Tool

3. Water balance rainfall (mm) – the simulated daily rainfall (product of the

historical dam rainfall for the same day in the year and a scaling factor based on

the rainfall scenario used) which is used for calculations in the water balance

model

4. Daily evaporation (mm) – consist of actual observed daily evaporation

5. Reservoir storage volume (ML) – total volume of water held behind the dam for

any particular day

6. Change in storage volume (ML) – the difference in storage volume between the

current day and the day before.

7. Reservoir depth (m) – also referred to in dam operations as reservoir water level

or RWL , this is the height of the water level above mean sea level (AMSL)

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8. Reservoir surface area (km2) – the total surface area of the reservoir covered by

water

9. Inflow (ML) – the total volume of rainfall that runs off from the whole

catchment and flows into the reservoir.

10. Direct rainfall (mm) – the depth of rainfall that falls directly over the reservoir.

11. Scour operation – designated as “1” if the scour outlet is open that day and “0” if

it is closed. The model defines this status by setting a rule to assume an open

scour only if the water level exceeds FSL during the rainy season (November to

April)

12. Scour release (ML) – the amount of water released by the scour outlet in a day

(223.85 ML) if it was opened, i.e. if scour operation value = 1

13. Nagado release (ML) – the daily volume of water released to the Nagado Water

Treatment Plant (WTP) for public supply which is designated to be 85 ML/d

(this is the current production capacity) and constant throughout the period of

time that the water balance is run for.

14. Spill (ML) – the volume of that runs over the spillway at anytime that the dam is

above the full supply level (FSL) of 527 m.

15. Dam seepage (ML) – the product of the dam storage volume and a constant

seepage factor of 0.04% to present the daily volume lost through seepage through

the bottom and sides of the dam.

16. Pipe leakage (ML) – the estimated volume of water lost daily through leakages

anywhere in the piping network.

17. Error (ML) – Assumed to be -0.53ML and constant throughout the period of time

that the water balance is run for.

18. Water depth above spill threshold (m) – the depth of water flowing over the

spillway when FSL is exceeded.

19. Volume above FSL (ML) – the volume of water that spills over the spillway.

20. Days empty – the number of days where the reservoir has a storage volume of 0

21. Empty events – the number of blocks of consecutive days where the dam is

empty

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Modifications to the original model:

1. Rainfall scenarios are used (parameter 2)

2. A rule for scour operation is defined (parameters 11 and 12)

There are several assumptions of the model:

a) The reservoir is full at the beginning of the simulation period

b) The scour outlet is only open during the wet season (November to April) if the

dam is overflowing, i.e. above 527 m and closed at all other times.

c) The simplified rainfall-runoff relationship is set at a coefficient of 0.46

throughout the model, i.e. 46% of rainfall that falls over the Vaturu catchment

ends up as runoff into the reservoir.

The probability of failure of a reservoir is the proportion of time units out of the whole

duration of the period analysed when the reservoir is empty (McMahon and Mein, 1978)

and is given by the following relationship:

Pe = P/N

where P = number of occurrences of reservoir being empty and

N = total number of time units used in the analysis

The reliability of the reservoir is can be defined as:

Re = 1 – Pe

These 2 relationships were used to calculate the probability of failure and reliability of

the Vaturu water supply for different combinations of projected rainfall change and

population growth.

3.3.1 Reliability�under�Climate�Change�Scenarios�

The Vaturu Daily Water Balance Model was used to simulate the behavior of the

reservoir under the parameters and assumptions described above. The input field for

rainfall scenario was simply changed to the designated scenario number (1 – 19). Each

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rainfall scenario contained projected annual, summer or winter rainfall change for 2030,

2055 or 2090. When a scenario was entered into the model as an input parameter, the

model looked up (from Table 4-12 and Table 4-17) the projected monthly rainfall factor

for the corresponding month and applied the factor to the historical daily rainfall already

in the model. The model then computed a new time series of daily rainfall under that

particular rainfall scenario. A runoff coefficient of 0.46 (personal communication,

Yahya Abawi, 20/09/2011) was used to convert daily rainfall into daily inflow into the

dam. Using the simulated daily rainfall as input, the water balance model computed

daily storage volume (ML) (parameter 20) in the Vaturu reservoir. No adjustments to

historical temperatures were made as discussed before. A graph was then generated that

showed daily storage volumes in the Vaturu reservoir under the selected rainfall

scenario. A series of 19 graphs were generated and 2 examples (Scenarios 1 and 12) of

the output are presented in Figure 4-17 and Figure 4-18.

Using Excel’s COUNT function, of the number of days where the storage volume is 0

and the number of events (parameter 21) consisting of consecutive nil-storage days were

counted. This procedure was followed to produce the number of water supply failure

days and events under each of the 19 rainfall scenario. Using the equations above,

probability of failure and reliability of the supply (over 24 years consisting of 8752 time

units (days)) were then calculated for the 19 scenarios and tabulated in Table 4-18.

3.3.2 Population�Growth�Scenarios�

Fiji’s last population census, which was conducted in 2007, determined the national

growth rate between 2007 and 1996 to be 0.7% per year (Fiji Islands Bureau of

Statistics, 2008). An updated estimate made in 2011 (Central Intelligence Agency, 2011)

increase slightly to 0.8%. The 2007 census showed Nadi alone having a current

population growth rate of 2.8% which is a significant drop from the previous decade

where growth was assessed to be 7.1% (FIBS, 2008). With this marked decrease in

growth rate within 20 years, it was decided that 2.8% cannot be realistically used as a

long term rate to project future populations of Nadi. Thus, this study used the

conservative national rates to create the scenarios of 0.4% as low growth, the current

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national rate of 0.8% as moderate and 1.6% as high growth. The projected populations

for the years 2030, 2055 and 2090 were calculated using the Excel function

FVSCHEDULE and presented in Table 4-19. There were two function criteria for

FVSCHEDULE – the ‘principal’, which was designated to be the population of the

previous year and the ‘schedule’ designated as the growth rate.

3.3.3 Demand�and�Reliability�with�Population�Growth�Scenarios�

Future water demand was projected by simply extrapolating the current demand trend

using the ‘straight line projection method’ (PDH Engineer, 2005). The future trend was

determined by multiplying the future projected population obtained in the previous step

by the current per capita water demand which according to this method was assumed not

to change in the future.

The way in which water is released from the reservoir for water supply is known as the

release or operating rule and usually as much water that is needed by the consumers is

released (McMahon and Mein, 1978). For the Nadi/Lautoka supply, the Vaturu reservoir

releases the full supply daily to Nagado, in order to meet the water demands of these two

centres. Since the current water demand is 85 ML/day, which corresponds to the current

daily treatment and supply rate from the Nagado Water Treatment Plant, future demand

was calculated using this value and the same population growth rates as in Section 3.3.2.

The Excel function FVSCHEDULE was used to calculate the demand for each year

from 2011 to 2090, using these three population growth rates. The results for 2030, 2055

and 2090 were graphed (Figure 4-9).

The above projected demand at different population growth rates was entered in the

water balance model under Scenario 1. On each simulation, the model simulated for the

number of days where the reservoir would run dry with the effect of population changes

only and no climate change effect (since Scenario 1 represented current climate).

Furthermore, the probability of failure and corresponding reliability for the nine demand

scenarios of the Vaturu reservoir established above was determined using the

relationship Re = 1 – (P/N) presented in Chapter 3.3 above. The number of days where

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the Vaturu reservoir will run empty was graphed in Figure 4-9 and its corresponding

reliability in Figure 4-10.

3.3.4 Reliability�with�Combination�of�Climate�Change�and�Population�Growth�

The effect of projected rainfall changes with climate change combined with water

demand due to population growth was examined to see the impact both factors had on

the water security of Nadi and Lautoka. Three representative rainfall scenarios of the

nine that accounted for projected seasonal rainfall differences were combined with each

of the nine population growth water demand scenarios (2030, 2055 and 2090 at 0.4%,

0.8% and 1.6% growth rates) for this analysis to calculate future Vaturu reservoir

reliability. The rate of NRW from the previous analysis was reverted to the default 29%.

The 3 rainfall scenarios used were:

Scenarios 11: +7.3% mean annual rainfall in summer and -1.1% in winter

Scenarios 15: +0.6% mean annual rainfall in summer and +0.9% in winter

Scenarios 18: +1.6% mean annual rainfall in summer and -1.8% in winter

For this analysis, two input fields in the model were changed for each simulation –

rainfall scenario and water demand. The rainfall scenario field was first changed from

the default of Scenario 1 to Scenario 11 then each of the nine projected water demand

values were entered in the water demand input field. Each time the water demand input

was changed, Excel’s COUNT function counted the number of days where the

reservoir’s storage volume became zero. The resulting number of days where the Vaturu

supply failed was used to calculate reliability of Vaturu reservoir under Scenario 1 and

different population growth water demand. The same procedure was used to determine

reliability under Scenarios 15 and 19. Figure 4-11, Figure 4-12 and Figure 4-13

presented the reliability graphs for Scenarios 11, 15 and 19 respectively.

3.3.5 Estimated�Water�Consumption�

Daily urban water usage was difficult to determine since the Water Authority of Fiji

does not publicize this information widely. A best estimate of Nadi and Lautoka’s water

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consumption was derived using known population and 300 L per day estimated per

capita consumption for Fiji (World Bank, 2000) and through interviews with staff during

a recent visit to the WAF Hydrology Unit in Lautoka. Table 4-20 was the resulting

estimated breakdown of daily water usage by the different sectors. Another estimate that

gave Fiji a daily per capita consumption of 200 lpd (GWP Consultants and Pacific

Applied Geoscience Commission, 2007) was not used in this analysis because it was

considered to be a conservative estimate.

3.3.6 Estimated�Domestic�Water�Consumption�

There is very limited public information on the breakdown of domestic daily water

usage in Fiji. Therefore, an online water footprint calculator

(http://www.waterfootprintkemira.com/meter) (Kemira, 2012)� was used to calculate

daily per capita water consumption for Fiji based on typical domestic water use

activities. Although the estimates are only generalized values, it provides an idea on the

most water-demanding of daily activities. This breakdown is shown in Table 4-21.

3.4 Vaturu�Reservoir�Historical�Water�Level�Statistical�Analysis�

Observed daily water levels obtained directly from the Hydrology Unit cover the

duration of the dam’s operation since its opening till the end of 2011.

3.4.1 Annual�Mean�Water�Levels�

The mean, minimum (using the MIN function and maximum (using the MAX

functionW value for each year contained in the dataset were found and used to produce

the summary graph in Figure 4-14 to highlight the year-to-year changes in mean, high

and low water levels.

3.4.2 Monthly�Mean�Water�Levels�

The long-term mean monthly water level was calculated using the AVERAGEIF

function where all data points that met the function criteria of the specified month were

averaged and presented in Figure 4-15.

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4 CHAPTER�4:� �RESULTS�AND�INTERPRETATION�

4.1 Vaturu�Rainfall�Statistical�Analyses�

4.1.1 Quality�Control�

Table 4-1: Missing Data.

Year Month Data Source 1982 January FMS1991 November FMS1998 December FMS2010 January FMS2010 February FMS2010 May FMS2010 June FMS2010 September FMS

1987 November WAF TIDEDA2010 December WAF TIDEDA2011 November WAF TIDEDA

Data provided by FMS contained seven missing values while WAF’s data from the

TIDEDA database contained three. None of the data gaps coincided.

Table 4-2: Independent Samples Test.

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t dfSig. (2-tailed)

Mean Diff.

Std. Error Diff.

95% Confidence Interval of the Diff.

Lower Upper

Rain.mm Equalvariances assumed

.123 .726 .512 685 .609 9.62626 18.78895 -27.26459 46.51711

Equalvariances not assumed

.512 683.675 .609 9.62626 18.79298 -27.27264 46.52515

The T-test concludes that, since the p-value of 0.609 is greater that the confidence level

of 0.05, there is no statistical difference between the arithmetic means of the TIDEDA

and FMS datasets. TIDEDA data was, therefore, used to fill in gaps in the FMS data.

51��

4.1.2 Monthly�Mean,�Minimum�and�Maximum�Trends�

Table 4-3: Vaturu Mean Monthly Rainfall (mm) (1982 – 2011).�

Month Monthly Mean

(mm) % of Annual

Jan 533.6 18.0%Feb 463.4 15.6%Mar 544.9 18.3%Apr 256.4 8.6%

May 131.6 4.4%Jun 72.8 2.4%Jul 73.3 2.5%

Aug 99.0 3.3%Sep 87.3 2.9%Oct 139.5 4.7%

Nov 243.6 8.2%Dec 326.6 11.0%

Annual Mean 2,972.2

The wettest month in Vaturu was March with 544.9 mm rain and the driest occurred

three months later in June with 72.8 mm. The summer months from November to April

receive 79.7% of the mean annual rainfall and the remaining 20.1% from May to

October. More than half (51.9%) of Vaturu’s annual rain occurred in the three months

from January to March. The provisional normal annual rainfall for Vaturu is 2,972.2 mm

(Table 4-3).

0

100

200

300

400

500

600

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rainfall�(m

m)

Month

Vaturu�Mean�Monthly�Rainfall�(mm)�(1982�� 2011)�

�

Figure 4-1: Vaturu mean monthly rainfall (mm) (1982 – 2011).

The biggest difference between rainfalls in two consecutive months can be found in

March and April with April receiving less than half of the rainfall in March (Figure 4-1).

�200

0

200

400

600

800

1000

1200

1400

1600

1800


Rainfall�(m

m)

Month

Vaturu�Monthly�Rainfall�(mm)(1982�� 2011)�Summary

Mean Min Max

�

Figure 4-2: Vaturu monthly mean, minimum and maximum rainfall (mm) trends (1982 –

2011).

52��

In January, a high record 1,554.1 mm fell in Vaturu – more than half of the mean annual

total in just one month (Figure 4-2). The low extremes, by contrast, had little or rainfall

for the entire month.�

4.1.3 Decadal�Trends��

0

100

200

300

400

500

600

700


Rainfall�(m

m)

Month

Vaturu�Decadal�Monthly�Average�Rainfall��(mm)(1982�2011)

Normal

1982�1991

1992�2001

2002�2011

Figure 4-3: Vaturu decadal mean monthly rainfall (mm) (1982-2011).

A notable decrease in rainfall of about 150 mm (about 5% of annual rainfall) was seen

for March from 1982 – 1991 (red line) compared to 2002 – 2011 (purple line) in Figure

4-3. The wettest month in Vaturu during 1982 – 1991 was March but moved up to

January during the two decades that followed. Similarly, the driest month during 1982 –

1991 was July and moved up by a month to June for the next two decades.

Table 4-4: Vaturu Decadal Annual Mean Rainfall (mm). Decade Annual Rainfall (mm) Anomaly (%) 1982-1991 2,880.2 -3.11992-2001 2,998.5 0.9

2002-2011 3,000.5 1.0

1982-2011 2,972.2 -1.2

53��

The annual mean rainfall from 1982-1991 was slightly less (3.1%) than the normal

(Table 4-4). The two decades that followed both recorded a slightly higher (around 1%)

mean annual rainfall than the 30-year provisional normal.

Table 4-5: ANOVA of Provisional and Decadal Rainfall.

MonthRain

Sum of Squares df Mean Square F Sig.

Between Groups 800.831 3 266.944 .008 .999

Within Groups 1477469.599 44 33578.855

Total 1478270.430 47

An ANOVA of the provisional and decadal annual rainfall showed that they were

similar with no statistically significant differences at 95% confidence interval (Table

4-5).

4.1.4 Annual�Rainfall�Trends�

y�=�16.529x�� 30040R²�=�0.0384

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Rainfall�(m

m)

Year

Vaturu�Annual�Total�Rainfall�(mm)�(1982�2011)

Figure 4-4: Vaturu annual total rainfall (mm) (1982-2011).

54��

55��

The 30-year trend comprising annual total rainfall (mm) for Vaturu from 1982 to 2011

showed an annual increase of 16.5 mm/year (Figure 4-4), which was not a statistically

significant increase and in agreement with PCCSP’s evaluation for Fiji (2011).�

Table 4-6: Extreme Annual Rainfall (mm) and Anomalies (mm and %).

Year

Vaturu Total Annual Rainfall (mm) 1982-2011

Vaturu Annual Rainfall Anomaly (mm) 1982-2011

Vaturu Annual Rainfall Anomaly (%) 1982-2011

1999 4,393 1,420.8 47.8% 2000 4,389.5 1,417.3 47.7% 1989 4,253 1,280.8 43.1% 2008 3,933.7 961.5 32.4% 2009 3,832.9 860.7 29.0% … … … …2004 2,286.5 -685.7 -23.1% 1994 2,262.1 -710.1 -23.9% 1998 2,104.3 -867.9 -29.2% 1992 1,901 -1,071.2 -36.0% 1987 1,447.3 -1,524.9 -51.3%

Count 30 30 30 Min 1,447.3 -1,524.86 -51.3% Max 4,393 1,420.84 47.8% Mean 2,959.7 -12.4 -0.4% Range 2,945.7 2,945.7 99.1%

Annual rainfall anomalies were presented both as absolute values (mm) and as a

percentage of mean annual rainfall (2,972.2 mm) in Table 4-6, above. The wettest year

was 1999 with a total annual rainfall of 4393 mm. This translated to 1,420.8 mm or

47.8% more rain than an average year in Vaturu. 1999 and 2000 were the two wettest

years in the 1982 – 2011 record when Fiji was experiencing La Niña conditions during

these two years. By contrast, the driest year was 1987 when only 1,447.3 mm of rain fell

during the year translating to a deficit of 51.3% (less than half) compared to an average

year in Vaturu. A strong El Niño affected Fiji in 1987.

y�=�0.0056x�� 11.107R²�=�0.0384

�60%

�40%

�20%

0%

20%

40%

60%

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Rainfall�(m

m)

Year

Vaturu�Annual�Rainfall�Anomaly�(%)�(1982�2011)

Figure 4-5: Vaturu annual rainfall anomaly (%) (1982 – 2011).

The peak years, circled in red, registered positive anomalies of more than 40% while

some years of rainfall deficit recorded as little as only half of mean annual rainfall

(Figure 4-5).

4.1.5 Seasonal�Trends�

Table 4-7: Seasonal Rainfall Distribution.

Normal Total Seasonal

Rainfall (mm)

% of Annual

Total Normal Total Seasonal

Monthly Rainfall (mm)

Winter 591.1 20.1% 98.5

Summer 2,347.3 79.9% 391.2

In Vaturu, close to 80% of annual rainfall occurs during the summer months while the

remaining 20% falls during winter (Table 4-7). In comparison to Vaturu, summer in

Suva receives 62.2% annual rainfall and 76.6% in Nadi.

56��

y�=�6.0305x�� 11449R²�=�0.0545

y�=�12.584x�� 22770R²�=�0.0186

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1980 1985 1990 1995 2000 2005 2010 2015

Rainfall�(m

m)

Year

Vaturu�Seasonal�Rainfall��(mm)(1982�2011)

Winter�Total�Rainfall�(mm)

Summer�Total�Rainfall�(mm)

Linear�(Winter�Total�Rainfall�(mm))Linear�(Summer�Total�Rainfall�(mm))

Figure 4-6: Vaturu seasonal rainfall (mm) (1982 – 2011).

Both the summer and winter trends showed an overall increase in seasonal rainfall –

12.6 mm for summer and 6.0 mm for winter, but these increases were not significant

(Figure 4-6). PCCSP projected with moderate confidence that over the 21st century in

Fiji, summer rainfall is likely to increase while that in winter is likely to decrease (2011).

The historical trend seen in Vaturu for winter contradicted PCCSP’s projections for Fiji.

� �

57��

58��

Table 4-8: Extreme Winter Rainfall (mm and %).

Year Winter Total Rainfall (mm) % Anomaly

1987 48.6 -91.8%

1998 222.4 -62.4%

2010 307.1 -48.0%

1994 328 -44.5%

1992 372.5 -37.0%

.. … …

1996 831.8 40.7%

2002 848.3 43.5%

1989 917.6 55.2%

2011 934.4 58.1%

2000 1,055.1 78.5%

Winter of 1987 recorded an extreme deficit in rainfall with only 48.6 mm throughout the

entire 6-month period i.e. only about 8% of the normal winter rainfall (591.1 mm)

(Table 4-18). There has not been another winter since where there was such deficit on

the same magnitude. By contrast, winter of 2000 received 1,055.1 mm rainfall – 78.5%

higher rainfall than an average winter.

Table 4-9: Extreme Summer Rainfall (mm and %). Year Summer Total Rainfall (mm) % Anomaly 1991 986.6 -58.0% 1997 1,013.9 -56.8% 1986 1,144.5 -51.2% 2009 1,359.7 -42.1% 2004 1,490.3 -36.5% … … …2007 3,341.6 42.4% 1999 3,377.0 43.9% 2008 3,393.8 44.6% 1998 3,698.9 57.6% 1988 3,958.6 68.6%

59��

Summer of 1991 recorded a rainfall deficit of 58% (986.6 mm of the summer mean of

2,437.3 mm) for the whole 6-months (Table 4-9). In the high extreme, summer of 1988

received 3,958.6mm, which is 68.6% more rainfall than normal summers

4.2 Rainfall�Projections� �

Using PCCSP’s online Pacific Climate Futures Exploration tool, the following output

projects were produced. �

4.2.1 Annual�Rainfall�and�Temperature�

Table 4-10: Projected Most Likely Scenario for Annual Surface Temperature and

Rainfall.

Year

Emission

scenario Most likely climate scenario

% of models in

agreement

2030 B1 Warmer and little change in rainfall 41%

2030 A1B Warmer and little change in rainfall 55%

2030 A2 Warmer and little change in rainfall 60%


2055 A1B Warmer and little change in rainfall 44%

2055 A2 Warmer and little change in rainfall 46%


2090 A1B Hotter and little change in rainfall 41%

2090 A2 Hotter and little change in rainfall 35%

The most likely scenario (that is, the highest number of models in agreement) from the

above nine annual projections (Table 4-10) are summarized in the table below (Table 4-

11).

� �

60��

Table 4-11: Most Likely Fiji Climate Future – Annual Temperature and Rainfall. IPCC Scenarios B1 A1B A2

2030

Warmer (increase in annual mean temperature of 0.7°C) and no change in rainfall (7/17 models)

Warmer (increase in annual mean temperature of 0.7°C) and little change (annual mean decrease of 1%) in rainfall (10/18 models)

Warmer (increase in annual mean temperature of 0.7°C) and little change ( annual mean increase of 1%) in rainfall (9/15 models)

2055


Warmer (increase in annual mean temperature of 1.2°C) and little change (annual mean increase of 1%) in rainfall (8/18 models)

Warmer (increase in annual mean temperature of 1.3°C) and no change in rainfall (7/15 models)

2090


Hotter (increase in annual mean temperature of 2°C) and little change (annual mean increase of 1%) in rainfall (7/17 models)

Hotter (increase in annual mean temperature of 2.5°C) and little change (annual mean increase of 4%) in rainfall (9/14 models)

Since rainfall is the primary variable of interest in the water balance model in this study,

the rainfall projections from this tool were extracted and re-organised into a scenario

matrix. The projected changes in rainfall apply throughout the 12 months of the year.

However, there is large seasonal variation in rainfall in Fiji (for Vaturu – about 80% in

summer and 20% in winter) and the averaging of this variation over a year masks the

seasonal effects. The use of seasonal projections would be more effective.

� �

61��

Table 4-12: PCCSP - Annual Rainfall Projections.

2030 2055 2090

Current B1 A1B A2 B1 A1B A2 B1 A1B A2

Rainfall 1 2 3 4 5 6 7 8 9 10

January 1 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

February 2 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

March 3 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

April 4 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

May 5 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

June 6 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

July 7 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

August 8 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

September 9 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

October 10 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

November 11 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

December 12 1.00 1.00 0.99 1.01 0.99 1.01 1.00 0.99 1.01 1.04

This matrix of annual rainfall projections shows the most likely annual rainfall change

scenario for 2030, 2055 and 2090 under the IPCC emission scenarios B1, A1B and A2

(Table 4-12). Values greater than 1 denote projected rainfall increase while values less

than 1 represent a decrease. For example, in 2030 under B1 it was projected that there

would most likely be no change in annual rainfall and this was denoted by a rainfall

factor of 1. Under A1B for the same year, a decrease of 1% annual rainfall relative to the

baseline period of 1981 – 2000 was projected and represented by a factor of 0.99 in the

matrix. For 2090, under A2, an increase of 1% rainfall was projected to be most likely

and thus denoted by a factor 1.01. The numbers 1 to 10 along the top of the table

represent each of the different rainfall projections on an annual scale.

� �

62��

4.2.2 Output�–�Summer�Rainfall�and�Temperature�

Table 4-13: Projected Most Likely Scenario for Summer Surface Temperature and Rainfall.

Year Emission scenario

Most likely climate scenario

% of models in agreement

2030 B1 Warmer and wetter 29%

2030 A1BWarmer and little change in rainfall 50%

2030 A2Warmer and little change in rainfall 46%

2055 B1Warmer and little change in rainfall 47%


2055 A2 Warmer and wetter 40%


2090 A1BHotter and little change in rainfall 41%

2090 A2 Hotter and wetter 42%

The table above presents the most likely climate change scenario during summer and the

percentage of models in agreement. For example, for 2090 under A2 a future climate

that would be ‘hotter and wetter’ was projected with 42% of the models in agreement

and was therefore considered to be the ‘most likely’ climate future.� �

63��

Table 4-14: Most Likely Fiji Climate Future – Summer Surface Temperature and Rainfall.

IPCC Scenarios B1 A1B A2

2030

Warmer (increase in annual mean temperature of 0.7°C) and wetter (annual increase of 7.3% in rainfall) (5/17 models)

Warmer (increase in annual mean temperature of 0.7°C) and little change (annual mean increase of 1.2%) in rainfall (9/18 models)

Warmer (increase in annual mean temperature of 0.8°C) and little change ( annual mean increase of 0.8%) in rainfall (7/15 models)

2055


Warmer (increase in annual mean temperature of 1.3°C) and little change (annual mean decrease of 0.6%) in rainfall (7/18 models)

Warmer (increase in annual mean temperature of 1.3°C) and wetter (annual mean increase of 8.3% in rainfall) (6/15 models)

2090


Hotter (increase in annual mean temperature of 2.1°C) and little change (annual mean increase of 1.6%) in rainfall (7/17 models)

Hotter (increase in annual mean temperature of 2.7°C) and much wetter (annual mean increase of 19.4% in rainfall) (6/14 models)

Since temperature is excluded from this study of the Vaturu water balance, only the

projected rainfall change was extracted from this table and used to create the seasonal

rainfall scenario matrix (Table 4-14). Using the same example as before, under A2 in

2090, the most likely projected climate future would be hotter (increase in annual mean

temperature of 2.7°C) and wetter (annual mean increase of 19.4% in rainfall). The

projected rainfall change of 19.4% was then extracted and used to create the seasonal

rainfall projection matrix.�

� �

64��

4.2.3 Output�–�Winter�Rainfall�and�Temperature�

Table 4-15: Projected Most Likely Scenario – Winter Surface Temperature and Rainfall.

Year Emission scenario

Most likely climate scenario

% of models in agreement


2030 A1B Warmer and drier 33%


2055 B1 Warmer and drier 52%




2090 A1BHotter and little change in rainfall 35%

2090 A2 Hotter and drier 28%

The table above presents the most likely climate change scenario during winter and the

percentage of models in agreement. For example, for 2090 under A2 a future climate

that would be ‘hotter and drier’ was projected with 28% of the models in agreement.

Although 28% was not a majority agreement, it was the highest percentage of model in

agreement, therefore this was considered to be the ‘most likely’ climate future.�

� �

65��

Table 4-16: Most Likely Fiji Climate Future – Winter Surface Temperature and Rainfall.

IPCC Scenarios B1 A1B A2

2030


Warmer (increase in annual mean temperature of 0.7°C) and drier (annual mean decrease of 9.8% in rainfall) (6/18 models)

Warmer (increase in annual mean temperature of 0.8°C) and little change ( annual mean decrease of 0.4%) in rainfall (7/15 models)

2055

Warmer (increase in annual mean temperature of 0.9°C) and drier (annual mean decrease of 9.6%) in rainfall (9/17 models)



2090


Hotter (increase in annual mean temperature of 1.9°C) and little change (annual mean decrease of 1.8%) in rainfall (6/17 models)

Hotter (increase in annual mean temperature of 2.4°C) and drier (annual mean decrease of 10.3%) in rainfall (4/14 models)

Projected rainfall change was extracted from this table (Table 4-16) and used to create

the seasonal rainfall scenario matrix. Using the same example as before, under A2 in

2090, the most likely projected climate future would be hotter (increase in annual mean

temperature of 2.4°C) and drier (annual mean decrease of 10.3% in rainfall). The

projected rainfall change of 10.3% was then extracted and used to create the seasonal

rainfall projection matrix in the next step.�

� �

66��

Table 4-17: PCCSP – Winter and Summer Rainfall Projections.

2030 2055 2090

B1 A1B A2 B1 A1B A2 B1 A1B A2

Rainfall 11 12 13 14 15 16 17 18 19

January 1 1.073 1.012 1.008 1.02 1.006 1.083 1.014 1.016 1.194

February 2 1.073 1.012 1.008 1.02 1.006 1.083 1.014 1.016 1.194

March 3 1.073 1.012 1.008 1.02 1.006 1.083 1.014 1.016 1.194

April 4 1.073 1.012 1.008 1.02 1.006 1.083 1.014 1.016 1.194

May 5 0.989 0.902 0.996 0.904 1.009 0.997 0.996 0.982 0.897

June 6 0.989 0.902 0.996 0.904 1.009 0.997 0.996 0.982 0.897

July 7 0.989 0.902 0.996 0.904 1.009 0.997 0.996 0.982 0.897

August 8 0.989 0.902 0.996 0.904 1.009 0.997 0.996 0.982 0.897

September 9 0.989 0.902 0.996 0.904 1.009 0.997 0.996 0.982 0.897

October 10 0.989 0.902 0.996 0.904 1.009 0.997 0.996 0.982 0.897

November 11 1.073 1.012 1.008 1.02 1.006 1.083 1.014 1.016 1.194

December 12 1.073 1.012 1.008 1.02 1.006 1.083 1.014 1.016 1.194

Projections on a seasonal scale (winter from November to April and summer from May

to October) were obtained through the Advanced mode using the ensemble mean of the

scenario with the highest number of models in agreement. Similar to the table of annual

projections (Table 4-12), only rainfall projections were extracted and combined into a

matrix to show the rainfall for every month of the year under each of the three IPCC

scenarios (B1, A1B, A2) and the three projection time frames (2030, 2055, 2090).

Values greater than 1 denote projected rainfall increase while value less than 1 represent

decrease. For example, 2090 under B1 was projected with a majority of 47% models in

agreement (from Table 4-13) that it would be warmer (mean annual temperature

increase of 1.2°C) and have little change in rainfall (mean annual increase of 1.4%)

(from Table 4-14) during summer, and projected with a majority of 41% models in

agreement (Table 4-15) that it would be warmer (mean annual temperature increase of

1.9°C) and have little change in rainfall (mean annual decrease of 1.8%) (from Table 4-

16) during winter, therefore these two rainfall projections were transferred to the matrix

in Table 4-17 as 1.014 (101.4%) of baseline rainfall during summer and 0.996 (99.6%)

of winter rainfall respectively. These rainfall projections for summer and winter under

B1 were collectively named rainfall Scenario 8. The rainfall change factors for the other

18 scenarios were similar derived and numbered and were used in the Vaturu Water

Balance Model.


4.3.1 Reliability�under�Climate�Change�Scenarios�

0

5000

10000

15000

20000

25000

30000

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Dam�storage�volume�(M

L)

Date

Vaturu�Reservoir�Storage�Volume�(1984�2007)�under�Current�Climate�(Scenario�1)

Figure 4-7: Vaturu storage volume (ML) under current climate (Scenario 1).

Simulation of the model under current climate (Scenario 1) showed that the Vaturu

reservoir ran empty on only 1 day (20th November 1987)

67��

0

5000

10000

15000

20000

25000

30000

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Dam�storage�volume�(M

L)

Date

Scenario�12�Vaturu�Reservoir�Storage�Volume�(1984�2007)

Figure 4-8: Vaturu storage volume (ML) under Scenario 12.

Simulation of the model under Scenario 12 (increase of 1.2% mean annual rainfall in

summer and decrease of 9.8% in winter) showed that reservoir ran empty for seven

consecutive days (i.e. in the same single event) from 14 – 17 November 1987.

68��

69��

Table 4-18: Reliability and Probability of Reservoir Failure with Rainfall Change Effects.

Scenario

SummerRainfallChange (%)

WinterRainfallChange (%)

No. of 'Day Empty'

No. of Events Probability

of Failure Reliability1 0 0 1 1 0.000114 0.9998862 0 0 1 1 0.000114 0.9998863 -1.0 -1.0 4 1 0.000457 0.9995434 1.0 1.0 0 0 0 15 -1.0 -1.0 4 1 0.000457 0.9995436 1.0 1.0 0 0 0 17 0 0 1 1 0.000114 0.9998868 -1.0 -1 4 1 0.000457 0.9995439 1.0 1.0 0 0 0 1

10 4.0 4.0 0 0 0 111 7.3 -1.1 0 0 0 112 1.2 -9.8 7 1 0.0008 0.999213 0.8 -.04 0 0 0 114 2.0 -9.6 5 1 0.000571 0.99942915 -0.6 0.9 0 0 0 116 8.3 -0.3 0 0 0 117 1.4 -0.4 0 0 0 118 1.6 -1.8 0 0 0 119 19.4 -10.3 0 0 0 1

Projected rainfall changes under the different scenarios caused few supply failures over

a period of 24 years (8752 time units (days)). Probability of failure (Pe) occurred in 8 of

the 19 scenarios and each only for a single period consisting of 1 – 7 consecutive days.

The minimum duration of a 1-day failure was seen under Scenarios 1, 2 and 7 while the

maximum duration of a 7-day failure was seen under Scenario 12. The results indicated

that rainfall change alone did not have a significant effect on the reliability of the Vaturu

reservoir.

� �

4.3.2 Population�Growth�Scenarios�

Table 4-19: Nadi and Lautoka Projected Population Growth.

Population Growth Scenarios

Year 0.004 (L) 0.008 (M) 0.016 (H)

2011 94,504 94,504 94,504

2030 101,951 109,951 127,771

2055 112,651 134,188 190,009

2090 129,543 177,350 331,172

With a low growth rate of 0.4%, the Nadi and Lautoka areas are projected to have a

combined population of 129,543 by 2090. The population for the same year with a

moderate growth of 0.8% would become 177,350 and if a high growth scenario were to

eventuate, the population of the study area would grow to 331,172.

4.3.3 Demand�and�Reliability�with�Population�Growth�Scenarios��

85 91.7 101.3 116.58598.9

120.7

159.5

85

114.9

170.9

297.9

0

50

100

150

200

250

300

350

2011

2016

2021

2026

2031

2036

2041

2046

2051

2056

2061

2066

2071

2076

2081

2086

Deman

d�(M

L/da

y)

Year

Future�Water�Demand��(ML/day)�with�Population�Growth�under�Scenario�1

Demand�at�0.4%�(ML/day)Demand�at�0.8%�(ML/day)Demand�at�1.6%�(ML/day)

Figure 4-9: Future water demand (ML/day) with population growth scenarios of 0.4%, 0.8% and 1.6%.

Assuming current water demand of 85 ML/day and no change in per capita

consumption, future water demand was extrapolated using the annual population growth 70�

�

rates of 0.4% 0.8% and 1.6%. Under a low population growth of 0.4%, water demand

was projected to increase to 116.5 ML/day by 2090. Under a moderate rate of 0.8%,

Fiji’s current national population growth rate, demand was projected to increase to just

under 160 ML/day by 2090. At a high growth rate of 1.6%, the demand was projected

rise to almost 300 ML/day. These future water demand projections were simulated using

the effects of population growth only and, as represented by rainfall Scenario 1 (current

climate), with no climate change effects.

99.6% 98.4% 96.2%98.8%

95.3%

81.9%

100.0%96.4%

77.4%

43.6%

0

0.2

0.4

0.6

0.8

1

1.2

2000 2020 2040 2060 2080 2100

Reliability

Year

Reliability�of�Vaturu�Reservoir�with�Population�Growth�under�Scenario�1

Reliability�at�0.4%



�

Figure 4-10: Reliability of Vaturu reservoir with population growth.

As water demand due to population growth increased with time, the reliability of the

supply was projected to decrease. Under the low population growth scenario, the

reservoir was projected to be able to adequately meet the demand 99.6% of the time

falling to 96.2% in 2090, provided the population growth rate remained at 0.4%. If the

population grew at 0.8%, the reservoir met water demand 98.8% of the time in 2030 but

by 2090, projected demand was only met 81.9% of the time. Assuming the selected high

population growth scenario, while Vaturu reservoir can still supply water to everyone

96.4% of the time in 2030, this is projected to decrease to 43.6% in 2090 if the same

growth rate is maintained and capacity of the supply unchanged from today.

�

71��

4.3.4 Reliability�with�Combination�of�Climate�Change�and�Population�Growth�

100.0%

99.7% 98.9% 96.6%99.2% 96.0%

84.7%96.8%

80.4%

46.4%

0

0.2

0.4

0.6

0.8

1

1.2

2000 2020 2040 2060 2080 2100

Reliability

Year

Reliability�of�Vaturu�Reservoir�under�Scenario�11�with�Population�Growth�




Figure 4-11: Reliability of Vaturu reservoir under Scenario 11 with population growth.

Under Scenario 11 where mean annual rainfall was projected to increase by 7.3% in

summer and decrease by 1.1% in winter, reliability of the Vaturu supply was shown to

decrease from 98.9% in 2055 to 96.6% by 2090 with a low population growth rate of

0.4%. At a moderate growth rate of 0.8%, water demand was projected to be met 96% of

the time in 2055, decreasing to 84.7% by 2090. With a high population growth of 1.6%,

Vaturu reservoir was projected to meet water demand 96.8% of the time in 2030,

decreasing to 80.4% in 2055 and eventually just 46.4% by 2090.

72��

100.0%99.6% 98.5% 96.3%100.0%98.9%

95.4%82.3%

100.0%

96.6%

77.8%

43.9%

0

0.2

0.4

0.6

0.8

1

1.2

2000 2020 2040 2060 2080 2100

Reliability

Year







summer and 0.9% in winter, reliability of the Vaturu supply was shown to decrease from

98.5% in 2055 to 96.3% by 2090 with a low population growth rate of 0.4%. At a

moderate growth rate of 0.8%, water demand was projected to be met 95.4% of the time

in 2055, decreasing to 82.3% by 2090. With a high population growth of 1.6%, Vaturu

reservoir was projected to meet water demand 96.6% of the time in 2030, decreasing to

77.8% in 2055 and eventually just 43.9% by 2090.

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100.0% 99.9% 99.2% 97.0%100.0%

99.5%96.4%

86.7%

100.0%

97.2%

83.2%

49.8%

0

0.2

0.4

0.6

0.8

1

1.2

2000 2020 2040 2060 2080 2100

Reliability

Year







summer and decrease by 1.8% in winter, reliability of the Vaturu supply was shown to

decrease from 99.2% in 2055 to 97% by 2090 with a low population growth rate of

0.4%. At a moderate growth rate of 0.8%, water demand was projected to be met 96.4%

of the time in 2055, decreasing to 86.7% by 2090. With a high population growth of

1.6%, Vaturu reservoir was projected to meet water demand 97.6% of the time in 2030,

decreasing to 83.2% in 2055 and eventually 49.8% by 2090.

4.3.5 Estimated�Water�Consumption�

Table 4-20: Estimated Water Consumption in Nadi and Lautoka.�Sector� Consumption�(lpd)�Domestic�(94504�people�x�300�lpd)� 28,351,200�Denarau�Island� 6,000,000�Other�hotels� 5,000,000�Commercial/Industrial� 2,000,000�

�Total�(lpd) 41,351,200�

A breakdown based on modest estimated water consumption in the Nadi and Lautoka

urban areas only accounts for just over 41 megalitres (ML) out of the daily supply of 85

74��

75��

ML from Nagado. This means only 48.6% accounted for and 51.4% unaccounted for.

The Vaturu Water Balance Model assumes that 29% (based on World Bank’s

assessment (2000)) of the treated piped water from Nagado is unaccounted for, and this

is known as non-revenue-water or NRW. Other sources have also proposed estimations

of Fiji’s NRW, including Bridges with 70% (2007) and a WAF personnel with an

estimate of 40 – 50% (Mr Paula Tawakece, 01/11/2011).�

�

4.3.6 Estimated�Domestic�Water�Consumption�

Table 4-21: Daily Water Usage (L and %) for Typical Urban Residents in Fiji. Activity Water Usage (L) % of Daily Total Bathroom 144 41%Toilets 76 22%Tap 30 9%Laundry 42 12%Hand Washing Dishes 45 13%Other Outdoor Uses 11 3%

Total (lpd) 348 100%

The water footprint calculator gave an estimated daily per capita water usage of 348

litres per day (lpd), which is higher than the 300 lpd estimated by World Bank (2000)

and mcuh higher than the 200 lpd provided by GWP Consultants & SOPAC (2007). The

online calculator assumes uninterrupted daily water supply indicating that either both

values cited have been under-estimated or that per capita daily water usage tends to be

actually higher than the national average in areas where there is uninterrupted supply.

The other valuable information obtained from using this calculator is the breakdown of

where water is used. Here, it shows that bathroom and toilet usages take up 63% of the

total daily consumption. This analysis of water daily domestic water usage allows

potential for activities where water conservation can be targeted.

� �

4.4 Vaturu�Reservoir�Historical�Water�Level�Statistical�Analysis�

4.4.1 Annual�Mean�Water�Levels�

514

516

518

520

522

524

526

528

53019

8419

8519

8619

8719

8819

8919

9019

9119

9219

9319

9419

9519

9619

9719

9819

9920

0020

0120

0220

0320

0420

0520

0620

0720

0820

0920

1020

1120

1220

13

Water�Level�(m

�AMSL)

Year

Annual�Vaturu�Reservoir�Water�Levels�(m)�1984�� 2011

Min

Max

Mean

Figure 4-14: Annual Vaturu reservoir water level (m) (1984 – 2011).

The year-to-year minimum water level fluctuated over a larger range compared to the

mean and maximum observed Vaturu reservoir water levels. The lowest ever water

levels recorded were around 520 m in 1992 and 1996. The low values in 1984 and 1985

were likely due the incomplete filling of the reservoir when it first opened. The mean

annual water level was found to be between 524 m and 526 m in most years with notable

high mean values from 1999 to 2001. Maximum levels varied little since overflow via

the spillway prevented water from accumulating higher than 527 m. Levels above 527 m

were due to rapid and momentary accumulation of water from heavy rain events and

when excess water was not discharged through the spillway quickly enough.

� �

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4.4.2 Monthly�Mean�Water�Levels�

522.0

522.5

523.0

523.5

524.0

524.5

525.0

525.5

526.0

526.5

527.0

527.5


Dam�W

ater�Level�(m

)

Month

Monthly�Mean�Water��Level�(m)�(1984�2011)

Figure 4-15: Vaturu reservoir mean monthly water level (m) (1984 – 2011).

The long-term mean monthly trend at the Vaturu reservoir was created from the average

of all the data points belonging to each of the 12 month for all the years. An average

year saw the reservoir close to maximum level around April (526.888 m) and at its

lowest level in November (523.824 m). April is the end of the wet summer season while

November marks its beginning.

� �

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5 CHAPTER�5:� �DISCUSSIONS�

5.1 Vaturu�Rainfall�Statistical�Analysis�

With a mean annual rainfall of 2,972.2 mm, Vaturu receives only slightly less rain than

Suva does. Vaturu’s monthly mean rainfall (provisional normal) showed a rapid

transition from wet to dry season. Rainfall was very unevenly distributed throughout the

year with close to 80% occurring in the wet summer season (November to April). The

three wettest months, from January to March, alone received more than half (51.9%) of

the Vaturu’s mean annual rainfall. Vaturu was amongst the wettest places in Fiji during

the summer and driest during winter. Nadi’s rainfall pattern was comparable to Vaturu

only in seasonal distribution, that is, both areas receiving close to 80% of its respective

mean annual rainfall during summer and the remainder during winter. The quantity of

rainfall received in Vaturu, however, exceeded that of Nadi due to orographic factors

discussed in Chapter 1.4.

Although there were some inter-decadal differences (-3% to 1%) between the mean

annual rainfall for the three 10-year periods from 1982 to 2011, this difference was not

statistically significant and was in agreement with the findings of PCCSP (2011), Mataki

et al. (2006) and Kumar et al. (2012). These inter-decadal differences were most likely a

climate variability caused by the onset of ENSO events within the respective decades.

For example, a mean annual rainfall deficit of 3% for the decade of 1982 – 1991

corresponded to the series of El Niño events which dominated the same period (Figure

1-3).

The range or the difference between extreme high and low rainfall over a season was

much smaller in winter compared to summer. The summer season (November – April)

coincides with the tropical cyclone season in Fiji. The occurrence of a cyclone during

this time usually brought heavy rainfall, which contributed to anomalously high summer

rainfall, for example, in 1988 and 1998. Most El Niño events in Fiji have been found to

cause droughts (McGree et al., 2010) and the summer rainfall deficits experienced in

Vaturu is most likely attributed to the same reason. The findings in this study, and those

of the authors above, indicated an unclear climate change signal for rainfall in Vaturu.

79��

The rainfall distribution in Vaturu does not provide a particularly positive outlook with

regard to water storage since the Vaturu reservoir cannot hold more than its maximum

capacity. All the excess water received during the wettest months will have to be

discharged into the river system. By contrast, there is a risk of insufficient volume of

water stored during the drier months due to rainfall deficit. PCCSP climate projections

for Fiji (2011) predict decreased winter rainfall. By contrast, a slight positive trend

(Figure 4-6) was observed through the historical data examined in this study. PCCSP’s

projections were made for the whole of Fiji and inevitably masked out the effects of

slight differences in the local climate of different parts of the country.

In-depth comparison of rainfall patterns between Vaturu and other stations were not

possible in this study due to the data access limitations. The only comparison of mean

monthly and annual rainfall made with Suva and Nadi was possible only because the

basic statistics of the two latter stations are made freely available on FMS’ website.

5.2 � Rainfall�Projections� �

The Pacific Climate Futures tool is useful and somewhat user-friendly in providing

climate projections for different climate variables. However, some of the projections

were ambiguous or inconclusive because the percentage model agreement for different

outcomes was statistically indistinguishable. Users of this tool were made aware of its

limitations and since this is the first of its kind in the region for projecting climate

futures, the developers will likely make improvements in the future.


The water balance model showed that 8 out of the 19 rainfall projections resulted in a

probable failure of the supply system. These failure rates however were very small and

indicated that changes in rainfall alone at the rates caused by climate change effects in

Fiji are not likely to cause significant water supply shortages. The impact of population

change without the effects of climate change was projected to cause a significant

reduction in the supply’s reliability at the lowest population growth scenario. The

projected reliability was further reduced in the time periods of 2030, 2055 and 2090

80��

under the moderate and high growth scenarios. The combined effect of rainfall change

with water demand under population growth resulted in rates of supply reliability similar

to the effect of population growth alone demonstrating the little effect rainfall changes

had on reliability of the Vaturu reservoir.

5.4 Historical�Vaturu�Reservoir�Water�Levels�

Analyses of historical water level record at the Vaturu reservoir showed a nearly full

water supply level of about 527 m from March to May in an average year. Water level

was found to gradually decline over the duration of the year, reaching consistently low

levels of about 524 m from October to December, when the wet summer season began.

High rainfall during summer most likely recharges the groundwater, maintaining a stable

source of inflow to the reservoir through the catchment’s tributaries. Decrease in rainfall

during the dry season most likely reduced recharge to groundwater; however this

process appeared to occur slowly resulting in the gradual decline in mean water levels at

the reservoir. Historical records indicated that reservoir water level had never fallen

close to the minimum operating level of 506 m, which was stated by Delana (2008),

even during the most severe droughts. Outflow from the reservoir for water supply is

dependent on the capacity of the treatment plant that receives the water. The historical

reliability of the Vaturu reservoir was likely due to the capacity of the treatment plants

preventing drawdown to occur at a rate higher than that at which the supply can be

replenished. It has already been established that population pressure was projected to

result in the Vaturu reservoir’s failure to meet uninterrupted water supply to Nadi and

Lautoka in the future. As highlighted in Chapter 2.4.1, frequent disruption in public

water supply is already affecting the populations of Nadi and Lautoka. With the

assistance of donor aid, the effort to improve water supply security for urban populations

can be accelerated.

5.5 Recent�Improvements�to�Nadi/Lautoka�Regional�Water�Supply�

One external source of funding that contributed to alleviating water disruptions in the

study area was a Japan Government Official Development Assistance (ODA) loan. The

loan of F$25.7 million (1997 value) facilitated major improvement of the Nadi/Lautoka

81��

Regional Water Supply between 1998 and 2004 (Arakawa, 2008). Under this loan, the

supply capacity for the Nadi/Lautoka regional system was increased from 51 to 103

ML/d which included an expansion by 100% of the Nagado Water Treatment Plant

(from 45 to 90 ML/d) and a new plant to supplement Lautoka’s supply. The improved

water treatment plants are currently operating at near maximum capacity and have

helped to reduce the frequency of water cuts in Nadi and Lautoka. When Arakawa

(2008) conducted a post-implementation evaluation of the project, it was found that Nadi

had since installed 3011 new water connections while 1830 were installed in Lautoka. A

survey of a sample population in Lautoka revealed that the water supply situation had

significantly improved since the completion of works under the loan. For example, the

deployment of water trucks had been reduced from 10 trucks per week to a single one

and the areas worst affected by water cuts would have at least 10 – 12 hours of running

water every day.

Even with the completion of major improvement works, the fact that only 30 – 50% of

Nadi and 5 – 25% of Lautoka have uninterrupted water supply (Arakawa, 2008) shows

that the currently water supply system for the area is still inadequate, illustrating the

growing demand of the population for a reliable water supply. Arakawa’s assessment

(2008) demonstrated that the upgrade to Nadi and Lautoka’s water supply made a

positive contribution towards its capacity in meeting the daily water demand of the

population. As a follow-up to these improvements, further assistance was provided by a

subsequent ODA loan to implement a leak detection and control programme for the

network. The widely varying values of NRW ranging from 29% to 70% demonstrates

the amount of uncertainty that is associated with estimating the true volume lost through

the supply network in this way. Non-revenue water is just one of the many issues that

exemplifies the concern raised by scientists at the 2nd World Water Forum in

Netherlands in 2000 that “there was no scarcity of water but only the mismanagement of

resources that has led to inaccessibility of freshwater to the people” (Bouguerra, 2006).

� �

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5.6 Solutions�for�Improving�Water�Security�

For the Nadi/Lautoka supply system to continue serving the population even under

periods of water shortage, several solutions are needed:

1. New water sources – there are many rivers and streams of various sizes and flow

conditions available. Studies are needed to find suitable water sources that could

be provide supplementary supply. This solution is already being investigated by

WAF (Elbourne, 2012, Vuibau and Toroca, 2012).

2. Increase number of dams – there are many retention dams along the main Nadi

River that are used for the primary function of flood mitigation. This same

method could be applied in the Vaturu reservoir’s sub-catchments to retain

excess floodwater during the rainy season and to be used during dry seasons or

periods of high demand.

3. Raise the Vaturu dam wall – this will allow the storage volume to be increased.

Maunsell (2004) investigation of this option showed that despite the capital cost,

this would provide significant extra storage for water supply.

4. Control NRW – estimates of 29% to 70% of treated water lost through illegal

water connections and pipe leakages is a significant volume and there is urgent

need to identify and control the sources.

5. Utilising alternative freshwater sources – groundwater, rainwater and

desalination are viable alternative or supplementary water sources. Groundwater

and rainwater are already being used by many communities in the rural areas

without a reticulated public supply such as Bavu Village near Sigatoka

(Limalevu et al., 2010). Using groundwater resources for large populations pose

a risk of over abstraction and drawdown in the water table. Gale and Booth

(1993) also highlighted this concern when they anticipated the use of Meigunyah

Aquifer as a supplementary source for Nadi’s water supply in the future.

Rainwater is available for free but the high cost of setting up and maintaining

suitable rooftop catchment, guttering and piping connections and storage tanks

may deter households from using this resource. Desalination is being used by

some resorts both on the mainland and in outer islands but this is a cost and

83��

energy intensive option and will increase the operation cost and thus the cost of

water supplied to consumers. The challenges of using alternative water sources

could be managed if they supplement the public supply.

6. Water conservation awareness – consumers in Fiji may have taken the public

water supply system for granted with the perception that abundant rainfall means

a secure water supply. The public needs to be made aware of the importance of

water conservation. In Fiji, several organisations including SPC and Live and

Learn are involved in bringing water conservation awareness especially to young

children in primary schools through their annual World Water Day campaign.

This approach should not be only carried out when external funding is available

but rather be supported by government and incorporated into the teaching

curriculum in schools.

7. Water re-use/recycling – many countries with severe water scarcity problems are

re-using and recycling water for non-potable purposes. This is a relatively new

concept for Fiji and public acceptance may not be very positive but this is

another potential solution where it would be worth trying to change the public’s

perception on.

8. Water pricing structure - water pricing can be used to as a mechanism to manage

demand. Increasing overall water tariff, progressive tariff structures and

increasing tariff for wastewater services are meant to discourage consumers from

using excess water from the reticulated supply and thus controlling wastage

(Asian Development Bank, 1999).

9. Engaging traditional knowledge – Nadi and Lautoka and the whole of Fiji, in

general, has experienced natural water scarcity in its history. Traditional

knowledge and adaptation mechanisms are highly beneficial in modern times in

adapting to similar future situations. Examples include knowledge of the

locations of springs and cultivation of drought resistant crops during dry

conditions. The use of tradition knowledge in adapting to water scarcity is widely

practiced in countries where water shortage is a common problem (Baquhaizel et

al., 1996, Ming-Ko et al., 2007, Lefale, 2010) and is well documented. An

example of a lost traditional drought adaptation strategy in Fiji is the practise of

84��

terraced agriculture, which was discovered by archaeologists to have supported a

substantial population between about AD 750 to AD 1250 during a time

particular aridity (Nunn, 2003, Fiji Guide, 2012). The ancient skill of agriculture

terracing in Fiji has since been lost but other traditional skills from recent history

should be documented for future reference.

5.7 Challenges�

There are many factors that could influence the maintenance of water security in urban

centres. These including the following:

1. Being a small island country in the Pacific, Fiji is vulnerable to natural disasters.

As highlighted in the introductory chapter, tropical cyclones pose the threat of

structural damage to the supply system infrastructure and severe droughts could

reduce freshwater resources.

2. It has already been established that water resource availability is unlikely to be

an issue under normal climatic conditions at current population levels. Effective

management of the water supply system largely determines if the water demand

of the population is reliably met.

3. Delay in legalizing a National Water Policy has made it difficult for necessary

laws to be implemented and enforced for the control and monitoring of water

resources by private parties. An example is the operation of commercial water

bottling companies. Although they need a licence for water abstraction, they are

not required by law to monitor or report the volume of groundwater abstracted.

An estimate can only be made based on how much the companies report their

earnings to be.

4. The existing difficulty in controlling NRW persists due to limited expertise and

human and financial resources to identify and reduce pipe leakages and water

theft. The biggest source of water theft exists in informal “squatter” settlements.

The residents in these areas resort to making illegal connections from main

supply lines to their own houses and are therefore not monitored by or service

fees paid to WAF. Even though this is a known practice, the large number of

such settlements overwhelms the staffing capacity of WAF.

85��

5. Over-reliance on government emergency water supply continues to limit the

population resilience to droughts. Lightfoot (1999), in his impact assessment in

Fiji following the 1997/98 Pacific-wide El Niño event, found that many

communities were unprepared even for short-term water shortages. The

assessment observed that while many communities appeared to have alternative

water sources such rainwater tanks to supplement the public supply, their

reliance on government water deliveries during dry spells have led to the

negligence of alternative sources. Unfortunately, over-reliance on central

supplies will only increase the communities’ vulnerability and reduce their

resilience in coping with water insecurity during droughts. Even with the Fiji

government spending almost $1.7 million at the time for emergency water

delivery in the western Division alone, many communities came close to

completely depleting their stored water while waiting for government to come to

their rescue (Lightfoot, 1999). More diligent maintenance of the domestic and

communal rainwater catchment system would have decreased the impact of

central water supply shortages

For developing small island country in the Pacific, such as Fiji, it would be difficult to

make significant progress in better understanding and managing its water resources

without external financial and technical aid. The issues identified above would also

likely be left unaddressed. Fortunately, donor-funded projects such the examples

showcased in Chapter 2.3 have contributed to the continued improvement of water

resources assessment and management in Fiji.

5.8 Limitations�to�the�Study�

Several limitations that may have affected this research are discussed in this section.

5.8.1 Limitations�to�Data�and�the�Model�

There are some limitations associated with the model itself. While some of these are

related to the design of the model, others may be due to estimations that had to be

86��

applied due to the lack of reservoir and catchment monitoring data. Limitations to the

model include:

- Inaccurate water level records (due to human error)

- Unknown actual scour outlet loss (due to lack of record)

- Rainfall runoff relationship differences

- Tributary flow behavior (due to limited monitoring of stream flow)

Maunsell’s assessment of the Vaturu dam and reservoir in 2004 is perhaps the most

recent and comprehensive one since the commissioning of the dam. The report

concluded that while the dam and spillway were both in good operating condition, the

same could not be said for the monitoring instruments (Maunsell, 2004). Some examples

of this were found during a field visit by the author in November 2011. Several of the

piezometers (for monitoring groundwater levels) located around the perimeter of the

dam have malfunctioned for over 10 years and yet they were still being visited as part of

the monitoring schedule. Hydrology Unit staff confirms that funding has not been

available to carry out routine maintenance on the equipment or for their repair (personal

communication, Mr Paula Tawakece, WAF Hydrology Officer, 01/11/2011). Borehole

monitoring records could have been used to to monitor seepage losses from the dam and

to verify anomalous water level observations that may have been erroneously recorded.

Seismic monitoring equipment has not been in operation since shortly after the

completion of the dam and the only inspection of the equipment has been facilitated by

government seismologist and their consultants (personal communication, Mr Semisi,

Dam Caretaker, 02/11/2011).

Daily inspection around the dam is undertaken by the dam caretakers who keep a daily

log book. Annual inspections are carried out by government engineers and 5-yearly

inspection by an external consultant also forms part of the dam monitoring programme.

The last external inspection was done in 1994 and there is no record of subsequent

inspection since then.

Historical records of dam levels were accessed from daily log books kept at the dam

office in Vaturu during a site visit. Records began from 15th January 1984 – the first

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official day of operation of the Vaturu dam. Between two to four dam caretakers

stationed at the site provide general maintenance and make daily entries of their work in

the log books. The information recorded include reservoir water level (RWL), levels on

the three V-notches and temperature and rainfall from the weather station. Rainfall and

temperature records are also entered in duplicate in a separate FMS-issued climate data

log book. Other comments such caretakers on duty, visitors, maintenance work done

around the dam and trips away from the dam compound by the any of the caretakers for

supplies, medical care, etc are also recorded, at times in great detail. The operation of

scour outlet was not accurately recorded in the log books and could also have

contributed to error in the water balance model.

Deployment of Hydrology staff to the field is heavily dependent on the availability of

vehicles to take them to monitoring sites. During the author’s site visit, a vehicle was

only available on a single day of the week to transport field staff to conduct monitoring

work. Vehicles were often either undergoing maintenance for engaged in transportation

for official businesses. There was no designated vehicle for the Hydrology Unit which

has made it difficult for the Unit to adhere to field monitoring schedules.

5.8.2 Limitations�to�Data�Access�

There was difficulty locating many documents during the visit to WAF’s Lautoka office

indicating that the document management system is in need of improvement. There was

no obvious structure and system in organising documents in a manner that allowed

documents to be retrievable in the absence of the any particular staff. Arakawa also

pointed out this challenge in his report (2008).

There are no written dam operations manual and standard guidelines. The caretakers are

only given verbal instructions on the readings and other dam checks that need to be done

on a daily basis. Log books show frequent visits by water supply engineers,

hydrologists, geologists etc, implying that any monitoring and maintenance work which

is somewhat technical and beyond the knowledge and duty of the caretakers is

undertaken only by specialists.

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The International Commission on Large Dams (ICOLD) is the global organisation that

establishes guidelines for dam construction and operation and it would be highly

beneficial for WAF to produce its own modified written dam operations standard

procedure for Vaturu. It would also benefit future studies if daily operation and

maintenance were to be diligently recorded in a structured manner.

Many of the gaps in the daily observed water level data resulted from the caretakers not

vigilantly taking daily records. Some of the log book entries corresponding to those days

of missing values even explicitly state that caretakers were absent from the dam office

due to reasons such as leave, illness, weekends and public holidays and other business

that required their attention. The Vaturu reservoir, as the primary source for public water

supply in two large urban centres, warrants the need to have staff on duty at all times.

The exchange of climatological data for research purposes is supported by WMO

through the adoption of two policies to facilitate the exchange of data and products.

WMO Resolution 40 (Cg�XII) (1995) for meteorological data and Resolution 25

(Cg�XIII) (1999) for hydrological data “embody the concepts of “essential” and

“additional” data, with a specification of a minimum set of data that should be made

available with free and unrestricted access and that “research and educational

communities should be provided with free and unrestricted access to all data and

products, exchanged under the auspices of WMO, for their non�commercial activities””

(World Meteorological Organization, 2008).

Unfortunately, there is restricted access to climate data in Fiji, and stringent conditions

of data usage must be adhered to for access to be granted.

5.8.3 Limitations�to�Study�Site�Access�

Another source of limitation is the relative difficulty in accessing the dam site. There are

two ways to get to the dam – via the Dam Access Road (enter at Sabeto Junction) and

via the Nausori Highland Road. With fine weather and a good 4WD vehicle, it will take

about 1.5 hours to drive from the main road to the dam. Without a reliable vehicle, it

would be dangerous to travel to Vaturu given the narrow and steep gravel road for most

89��

of the way. This limited accessibility may have been deliberate to prevent general public

access to the site; however this also serves as a challenge for monitoring activities and

for research.

A visit to hydrological monitoring stations within the Vaturu catchment was planned as

part of the site visit; however this was not possible due to insufficient fuel allocation to

the caretakers to operate the boat. The alternative way to reach the monitoring stations

would be on foot, which due to limited field time, was not feasible for the author.

Although the raw water from the Vaturu reservoir undergoes treatment at the Nagado

treatment plant, there should still be measures taken to limit contamination at the source.

Horses were seen grazing on the spillway area during the visit to the site (animals are

prohibited from the dam compound; however, it seems that the caretakers have given

exceptions to some nearby villagers). This practice, while mitigated by the water

treatment process at the Nagado, nevertheless introduces a risk of water contamination.

Another source of contamination is due to sedimentation in the reservoir and increasing

turbidity of the water. The increased sediment load may not be effectively treatment at

Nagado and results in the water delivered to consumers still appearing murky. This has

been widely observed and reported in the daily media. The presence of the scour outlet

is meant, in addition to rapidly discharging excess water and relieving water pressure on

the dam, to also flush out sediment that may collect at the base of the reservoir (British

Dam Society, 2010). There is perhaps improvement that can be made to the operation of

the water treatment plant during times when turbidity is noticeably higher.

� �

90��

6 CHAPTER�6:� CONCLUSION�

The first objective was achieved by incorporating the effect of rainfall changes under

different scenarios and defining rules for the operation of the scour outlet to modify the

original PICPP model. The second and third objectives were achieved by running the

modified water balance model under each of the 19 rainfall scenarios, obtaining the

reservoir failure rates and calculating its reliability under the respective scenarios. The

overall outcome showed that rainfall changes due to climate change did not significantly

reduce the reliability of the Vaturu water supply. The other factor that was investigated

in this study – population growth, was found to be a major driver of water supply

reliability even in the absence of climate change effects. In light of the possibility of

future water shortage, several options were provided to manage its associated risks in

contribution to achieving the final objective. Some of these adaptation and risk

management options include water conservation, increasing storage capacity, using

traditional knowledge and strategies, reducing unaccounted for water and installing

supplementary water supply systems.

The challenges to maintaining urban water security for a supply system, such as the

Vaturu reservoir, are more of a nature related to institutional arrangements and consumer

habits. The results of this study, which further confirms the suggestions by previous

studies, has shown that a scenario of failure of the system to provide a constant supply to

consumers is a result of inefficient resource management rather than water availability.

It is therefore essential that water managers and consumers alike understand the factors

that affect the security of water supply the most and therefore take appropriate

precautionary measures to prevent and mitigate the worst case scenarios.

� �

91��

7 CHAPTER�7:� RECOMMENDATIONS�

Several recommendations emerge from this study to improve future studies and in plans

by WAF to further improve and expand the Nadi/Lautoka Regional Water Supply

network.

1. A partnership between the FMS and the University of the South Pacific (USP) is

needed to enable open access to climate data to serve the dual purpose of

providing an appreciation of the skills required in climate data analyses and the

improved understanding of long-term climate trends in Fiji.

2. An in-depth survey of domestic water consumption habits and activities should

be undertaken to reveal excess water consumers and identify activities where

water conservation efforts can be targeted. Consumers may value their privacy

and be reluctant to disclose this information and this challenge may need to be

overcome before the survey can be conducted. Open access to water

consumption data has provided fruitful water conservation measurements in

other areas of the world such as in Boulder, Colorado in the United States

(Boulder City Council, 2007).

3. A rainwater consumption survey should be carried out to gauge the perceptions,

affordability and future potential for utilising this freely available resource to

supplement domestic supplies.

4. An operations manual would be beneficial to the operation of the Vaturu dam. It

will better guide the personnel directly involved in the operation and

maintenance of the dam and also allow water engineers and researchers to have a

documented record of operations for reference.

5. In order to better understand the relationship between supply and demand for

urban Nadi and Lautoka, it would be beneficial for future studies to take into

account the water that is supplied to Lautoka through the two smaller

supplementary catchments in Saru and Buabua. This study has only considered

the Vaturu supply since it is much larger and provides for bulk of the water

source for both urban centres.

92��

6. A proper survey of the locations and other details of monitoring boreholes, rain

gauge stations and river gauging stations should be undertaken and documented

digitally, for example, in a geographic information system (GIS) database. This

information would be valuable in streamlining and documenting the results of

groundwater, rainfall and stream flow monitoring. A digital database of the

parameters being monitored also allows for efficient data analysis to establish the

impacts of climate and water consumption on the Vaturu reservoir.

7. An integrated water demand and availability analysis, which accounts for both

urban and rural and both human and ecosystem water demand should be

undertaken in the near future for whole of the Nadi Basin. Improving knowledge

in aspects such as occurrence, quality and quantity of the main sources of

freshwater in the whole basin would benefit appropriate utilization of water

resources for both human and agricultural consumption and water demand for

sustaining the natural ecosystems. This would also allow alternative freshwater

sources to be utilized, especially for human consumption, during extended dry

periods.

93��

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104��

APPENDIX�1� �PCCSP�CLIMATE�FUTURES�–�FIJI�

1 �Colour scale for proportion (%) of models in agreement No models < 10% of models 10% - 33% of models 33% - 66% of models 66% - 90% of models > 90% of models

�

1.1 Annual�Rainfall�and�Temperature�Table 1.1.1 Climate futures for 2030 using the B1 - low emissions scenario

Annual Surface Temperature (°C)

Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 1 of 17

models (5%)

Likelihood: 1 of

17 models (5%)

Little Change

-5.00 to 5.00

Likelihood: 4 of 17

models (23%)

Likelihood: 7 of

17 models (41%)

Wetter

5.00 to 15.00

Likelihood: 4 of

17 models (23%)

Much Wetter

> 15.00

Annual Surface Temperature multi-model mean (17 models): 0.6°C, standard deviation 0.2

Annual Rainfall multi-model mean (17 models): 1.7%, standard deviation 5.9

� �

105��

Table 1.1.2 Climate futures for 2030 using the A1B - medium emissions scenario


Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 3 of 18

models (16%)

Little Change

-5.00 to 5.00

Likelihood: 2 of

18 models (11%)

Likelihood: 10 of

18 models (55%)

Wetter

5.00 to 15.00

Likelihood: 3 of 18

models (16%)

Much Wetter

> 15.00


Annual Rainfall multi-model mean (18 models): -0.0%, standard deviation 6.3

Table 1.1.4 Climate futures for 2030 using the A2 - high emissions scenario


Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 1 of

15 models (6%)

Little Change

-5.00 to 5.00

Likelihood: 2 of

15 models (13%)

Likelihood: 9 of

15 models (60%)

Wetter

5.00 to 15.00

Likelihood: 2 of

15 models (13%)

Much Wetter

> 15.00

Likelihood: 1 of

15 models (6%)



106��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 1 of

15 models (6%)

Little Change

-5.00 to 5.00

Likelihood: 7 of

15 models (46%)

Likelihood: 1 of

15 models (6%)

Wetter

5.00 to 15.00

Likelihood: 3 of

15 models (20%)

Likelihood: 2 of

15 models (13%)

Much Wetter

> 15.00

Likelihood: 1 of

15 models (6%)





Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of 18

models (5%)

Drier

-15.00 to -5.00

Likelihood: 2 of

18 models (11%)

Likelihood: 1 of 18

models (5%)

Little Change

-5.00 to 5.00

Likelihood: 8 of

18 models (44%)

Likelihood: 1 of 18

models (5%)

Wetter

5.00 to 15.00

Likelihood: 3 of

18 models (16%)

Likelihood: 1 of 18

models (5%)

Much Wetter

> 15.00

Likelihood: 1 of 18

models (5%)



107��

Table 1.1.6 Climate futures for 2055 using the B1 - low emissions scenario


Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 2 of

17 models (11%)

Likelihood: 1 of

17 models (5%)

Little Change

-5.00 to 5.00

Likelihood: 12 of

17 models (70%)

Wetter

5.00 to 15.00

Likelihood: 1 of

17 models (5%)

Much Wetter

> 15.00

Likelihood: 1 of

17 models (5%)





Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

17 models (5%)

Drier

-15.00 to -5.00

Likelihood: 1 of

17 models (5%)

Little Change

-5.00 to 5.00

Likelihood: 10 of

17 models (58%)

Likelihood: 2 of

17 models (11%)

Wetter

5.00 to 15.00

Likelihood: 2 of

17 models (11%)

Much Wetter

> 15.00

Likelihood: 1 of

17 models (5%)



108��



Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

17 models (5%)

Likelihood: 1 of

17 models (5%)

Drier

-15.00 to -5.00

Likelihood: 2 of

17 models (11%)

Little Change

-5.00 to 5.00

Likelihood: 7 of

17 models (41%)

Wetter

5.00 to 15.00

Likelihood: 1 of

17 models (5%)

Likelihood: 4 of

17 models (23%)

Much Wetter

> 15.00

Likelihood: 1 of

17 models (5%)





Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Annual

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

14 models (7%)

Drier

-15.00 to -5.00

Little Change

-5.00 to 5.00

Likelihood: 5 of

14 models (35%

Likelihood: 1 of

14 models (7%)

Wetter

5.00 to 15.00

Likelihood: 4 of

14 models (28%)

Much Wetter

> 15.00

Likelihood: 2 of

14 models (14%)

Likelihood: 1 of

14 models (7%)



109��

1.2 Summer�Rainfall�and�Temperature�Table 1.2.1 Climate futures for 2030 using the B1 - low emissions scenario

November - April (NDJFMA) Surface Temperature (°C)

Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 2 of

17 models (11%)

Little Change

-5.00 to 5.00

Likelihood: 4 of

17 models (23%)

Likelihood: 3 of

17 models (17%)

Wetter

5.00 to 15.00

Likelihood: 1 of

17 models (5%)

Likelihood: 5 of

17 models (29%)

Much Wetter

> 15.00

Likelihood: 2 of

17 models (11%)

November - April (NDJFMA) Surface Temperature multi-model mean (17 models): 0.7°C,

standard deviation 0.2

November - April (NDJFMA) Rainfall multi-model mean (17 models): 4.0%, standard

deviation 6.4

� �

110��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

Novem

ber -

April

(NDJF

MA)

Rainfal

l (%)

Much Drier

< -15.00

Likelihood: 1 of

18 models (5%)

Drier

-15.00 to -5.00

Likelihood: 2 of

18 models (11%)

Little Change

-5.00 to 5.00

Likelihood: 1 of

18 models (5%)

Likelihood: 9 of

18 models (50%)

Wetter

5.00 to 15.00

Likelihood: 4 of

18 models (22%)

Much Wetter

> 15.00

Likelihood: 1 of

18 models (5%)




deviation 7.6

� �

111��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Little Change

-5.00 to 5.00

Likelihood: 2 of

15 models (13%)

Likelihood: 7

of 15 models

(46%)

Wetter

5.00 to 15.00

Likelihood: 5

of 15 models

(33%)

Much Wetter

> 15.00

Likelihood: 1

of 15 models

(6%)




deviation 6.9

� �

112��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1

of 15 models

(6%)

Drier

-15.00 to -5.00

Little Change

-5.00 to 5.00

Likelihood: 3

of 15 models

(20%)

Likelihood: 2 of

15 models (13%)

Wetter

5.00 to 15.00

Likelihood: 6

of 15 models

(40%)

Likelihood: 2 of

15 models (13%)

Much Wetter

> 15.00

Likelihood: 1 of

15 models (6%)




deviation 8.7

� �

113��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

18 models (5%)

Drier

-15.00 to -5.00

Likelihood: 1

of 18 models

(5%)

Little Change

-5.00 to 5.00

Likelihood: 7

of 18 models

(38%)

Likelihood: 1 of

18 models (5%)

Wetter

5.00 to 15.00

Likelihood: 4

of 18 models

(22%)

Likelihood: 3 of

18 models (16%)

Much Wetter

> 15.00

Likelihood: 1 of

18 models (5%)




deviation 9.5

� �

114��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

17 models (5%)

Drier

-15.00 to -5.00

Likelihood: 1

of 17 models

(5%)

Little Change

-5.00 to 5.00

Likelihood: 8

of 17 models

(47%)

Wetter

5.00 to 15.00

Likelihood: 6

of 17 models

(35%)

Much Wetter

> 15.00

Likelihood: 1

of 17 models

(5%)




deviation 8.5

� �

115��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

17 models (5%)

Drier

-15.00 to -5.00

Likelihood: 1 of

17 models (5%)

Little Change

-5.00 to 5.00

Likelihood: 8

of 17 models

(47%)

Likelihood: 2 of

17 models (11%)

Wetter

5.00 to 15.00

Likelihood: 3

of 17 models

(17%)

Likelihood: 1 of

17 models (5%)

Much Wetter

> 15.00

Likelihood: 1 of

17 models (5%)




deviation 11.3

� �

116��



Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

17 models (5%)

Likelihood: 1 of

17 models (5%)

Drier

-15.00 to -5.00

Little Change

-5.00 to 5.00

Likelihood: 7 of

17 models (41%)

Wetter

5.00 to 15.00

Likelihood: 6 of

17 models (35%)

Much Wetter

> 15.00

Likelihood: 2 of

17 models (11%)




deviation 15.1

� �

117��



Slightly

Warmer

< 0.50

Warmer

0.50 to

1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

November

- April

(NDJFMA)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

14 models (7%)

Drier

-15.00 to -5.00

Little Change

-5.00 to 5.00

Likelihood: 1 of

14 models (7%)

Wetter

5.00 to 15.00

Likelihood: 5 of

14 models (35%)

Likelihood: 1 of

14 models (7%)

Much Wetter

> 15.00

Likelihood: 6 of

14 models (42%)




deviation 14.5

118��

1.3 Winter�Rainfall�and�Temperature�Table 1.3.1 Climate futures for 2030 using the B1 - low emissions scenario

May - October (MJJASO) Surface Temperature (°C)

Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to

3.00

Much Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 2 of

17 models (11%)

Likelihood: 2 of

17 models (11%)

Little Change

-5.00 to 5.00

Likelihood: 3 of

17 models (17%)

Likelihood: 8 of

17 models (47%)

Wetter

5.00 to 15.00

Likelihood: 2 of

17 models (11%)

Much Wetter

> 15.00

May - October (MJJASO) Surface Temperature multi-model mean (17 models): 0.6°C,


May - October (MJJASO) Rainfall multi-model mean (17 models): -1.3%, standard deviation

6.2

� �

119��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to

3.00

Much Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 1 of

18 models (5%)

Likelihood: 6 of

18 models (33%)

Little Change

-5.00 to 5.00

Likelihood: 2 of

18 models (11%)

Likelihood: 6 of

18 models (33%)

Wetter

5.00 to 15.00

Likelihood: 3 of

18 models (16%)

Much Wetter

> 15.00




7.3

� �

120��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 1 of

15 models (6%)

Likelihood: 3 of

15 models (20%)

Little Change

-5.00 to 5.00

Likelihood: 2 of

15 models

(13%)

Likelihood: 7 of

15 models (46%)

Wetter

5.00 to 15.00

Likelihood: 2 of

15 models (13%)

Much Wetter

> 15.00




5.4

� �

121��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

15 models (6%)

Drier

-15.00 to -5.00

Likelihood: 3 of

15 models (20%)

Likelihood: 1 of

15 models (6%)

Little Change

-5.00 to 5.00

Likelihood: 6 of

15 models (40%)

Wetter

5.00 to 15.00

Likelihood: 2 of

15 models (13%)

Likelihood: 2 of

15 models

(13%)

Much Wetter

> 15.00




8.7

� �

122��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 1 of

18 models (5%)

Likelihood: 1 of

18 models (5%)

Drier

-15.00 to -5.00

Likelihood: 4 of

18 models (22%)

Likelihood: 1 of

18 models (5%)

Little Change

-5.00 to 5.00

Likelihood: 9 of

18 models (50%)

Wetter

5.00 to 15.00

Likelihood: 2 of

18 models (11%)

Much Wetter

> 15.00




8.3

� �

123��



Slightly Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much

Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 9 of

17 models (52%)

Likelihood: 1 of

17 models (5%)

Little Change

-5.00 to 5.00

Likelihood: 5 of

17 models (29%)

Wetter

5.00 to 15.00

Likelihood: 2 of

17 models (11%)

Much Wetter

> 15.00




6.7

� �

124��



Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Drier

-15.00 to -5.00

Likelihood: 4 of

17 models (23%)

Likelihood: 4 of

17 models (23%)

Little Change

-5.00 to 5.00

Likelihood: 7 of

17 models (41%)

Wetter

5.00 to 15.00

Likelihood: 1 of

17 models (5%)

Likelihood: 1 of

17 models (5%)

Much Wetter

> 15.00




8.2

� �

125��



Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 3 of

17 models (17%)

Likelihood: 1 of

17 models (5%)

Drier

-15.00 to -5.00

Likelihood: 4 of

17 models (23%)

Little Change

-5.00 to 5.00

Likelihood: 1 of

17 models (5%)

Likelihood: 6 of

17 models (35%)

Wetter

5.00 to 15.00

Likelihood: 2 of

17 models (11%)

Much Wetter

> 15.00




10.0

� �

126��



Slightly

Warmer

< 0.50

Warmer

0.50 to 1.50

Hotter

1.50 to 3.00

Much Hotter

> 3.00

May -

October

(MJJASO)

Rainfall

(%)

Much Drier

< -15.00

Likelihood: 2 of

14 models (14%)

Drier

-15.00 to -5.00

Likelihood: 4 of

14 models (28%

Little Change

-5.00 to 5.00

Likelihood: 4 of

14 models (28%

Wetter

5.00 to 15.00

Likelihood: 2 of

14 models (14%)

Likelihood: 1 of

14 models (7%)

Much Wetter

> 15.00

Likelihood: 1 of

14 models (7%)




11.6

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