Download Modeling Climate Change Impacts on Viti Levu (Fiji) and Aitutaki

Modeling Climate Change Impacts on
Viti Levu (Fiji) and
Aitutaki (Cook Islands)
A Final Report Submitted to Assessments of Impacts and
Adaptations to Climate Change (AIACC), Project No. SIS09
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Modeling Climate Change Impacts on
Viti Levu (Fiji) and
Aitutaki (Cook Islands)
A Final Report Submitted to Assessments of Impacts and
Adaptations to Climate Change (AIACC), Project No. SIS09
Submitted by Kanayathu Koshy
PACE-SD, University of South Pacific, Suva, Fiji
2007
Published by
The International START Secretariat
2000 Florida Avenue, NW
Washington, DC 20009 USA
www.start.org
Contents
About AIACC…………………………………………………………………………...page vii
Summary Project Information………………………………………………………...page viii
Executive Summary………………………………………………………………………page x
1.
Introduction ............................................................................................................................................................... 1
2.
Characterization of Current Climate and Scenarios of Future Climate Change .......................................... 4
2.1
2.1.1
2.1.2
2.1.3
2.2
2.3
2.4
2.4.1
2.4.2
2.4.3
2.5
3.
ACTIVITIES CONDUCTED .................................................................................................................................. 4
Natadola, Viti Levu, Fiji ............................................................................................................................. 4
Navua, Viti Levu, Fiji.................................................................................................................................. 4
Aitutaki, Cook Islands ................................................................................................................................. 4
DESCRIPTION OF SCIENTIFIC METHODS AND DATA ........................................................................................ 4
FIELD SURVEYS ............................................................................................................................................... 17
RESULTS .......................................................................................................................................................... 18
Natadola, Viti Levu, Fiji ........................................................................................................................... 18
Navua, Viti Levu, Fjij................................................................................................................................ 18
Aitutaki, Cook Islands ............................................................................................................................... 18
CONCLUSIONS ................................................................................................................................................. 18
Socio-Economic Futures ........................................................................................................................................ 19
3.2
ACTIVITIES CONDUCTED ................................................................................................................................ 19
3.3.2. Natadola, Viti Levu, Fiji ........................................................................................................................... 19
3.1.2
Navua, Viti Levu, Fjij................................................................................................................................ 19
3.1.3
Aitutaki, Cook Islands ............................................................................................................................... 19
3.2
DESCRIPTION OF SCIENTIFIC METHODS AND DATA ...................................................................................... 19
3.2.1
Navua and Natadola, Viti Levu, Fiji......................................................................................................... 19
3.3
RESULTS .......................................................................................................................................................... 21
3.3.1
Natadola, Viti Levu, Fiji ........................................................................................................................... 21
3.3.3. Navua, Viti Levu, Fiji................................................................................................................................ 22
3.3.3
Aitutaki, Cook Islands ............................................................................................................................... 29
3.4
CONCLUSIONS ................................................................................................................................................. 30
4.
Impacts and Vulnerability..................................................................................................................................... 31
4.1
4.3
4.3
4.4
5.
ACTIVITIES CONDUCTED ................................................................................................................................ 31
DESCRIPTION OF SCIENTIFIC METHODS AND DATA ...................................................................................... 31
RESULTS .......................................................................................................................................................... 32
CONCLUSIONS ................................................................................................................................................. 34
Adaptation ............................................................................................................................................................... 35
5.1
ACTIVITIES CONDUCTED ................................................................................................................................ 35
6.
Capacity Building Outcomes and Remaining Needs ........................................................................................ 36
7.
National Communications, Science-Policy Linkages and Stakeholder Engagement................................... 37
8.
Outputs of the project ............................................................................................................................................ 38
9.
Policy Implications and Future Directions ......................................................................................................... 39
10.
References ................................................................................................................................................................ 43
List of Tables
Table 2.1: Scenario variables........................................................................................................................................... 4
Table 2.2: Projections of global mean sea level rise with different climate and sea level model parameters7
Table 2.3: Results from nine GCM experiments were used to derive regional sea level change patterns. ... 7
Table 2.4: Normalised sea level change for the 21st century at the study sites................................................... 9
Table 2.5: Comparison of PVs of expected damage under climate change........................................................ 16
Table 3.1: Final definition of land use classification scheme ................................................................................. 24
Table 3.2: Transitions between building and non-building land uses ................................................................ 25
Table 3.3: Grid-cell transitions with an end-state of building land use (LUTj=Building) ............................... 25
Table 3.4: Grid-cell transitions with an initial-state of Building land use (LUTi=Building) ........................... 25
Table 3.5: Results of neighbourhood analysis (1994 data) ..................................................................................... 26
Table 3.6: Area-target equations and predictions .................................................................................................... 26
Table 3.7: Masked areas in Navua............................................................................................................................... 27
Table 3.8: Types of housing (Navua - 2002) .............................................................................................................. 27
Table 4.1: Daily consumption versus roof area ........................................................................................................ 34
Table 9.1: Seasonal Mean Climate Change over South Pacific in the 21st Century as simulated* by the
state-of-the-art Global Climate Models (Lal, 2004)......................................................................................... 40
List of Boxes
Box 2.1: Emission scenarios ............................................................................................................................................. 5
Box 2.2: Global Circulation Models ............................................................................................................................... 6
List of Figures
Fig. 1: SimCLIM “Open-Framework” system (adapted from Warrick et al., 2005) ........................................... xi
Fig. 1.1: Natadola, Viti Levu, Fiji.................................................................................................................................... 1
Fig. 1.2: Navua, Viti Levu, Fiji ........................................................................................................................................ 2
Fig. 1.3: Aitutaki, Cook Islands....................................................................................................................................... 2
Fig. 1.4: Inter-related components of the AIACC project for Pacific islands........................................................ 3
Fig. 2.1: Scenarios of CO2 gas emissions and consequential atmospheric concentrations of CO2 (from
IPCC, 2001)................................................................................................................................................................ 5
Fig. 2.2: Sea Level Change over the 21st Century as simulated different GCMs. Each field is the difference
in sea level between the last decade of the experiment and the decade 100 years earlier. This
difference is then normalised by the global sea level change over the same period by the respective
GCM ........................................................................................................................................................................... 8
Fig. 2.3: Sea Level Change (cm) at Suva due to large scale processes with CSIRO GCM-pattern and SRES
A2 scenarios with medium climate & sea level model parameters .............................................................. 9
Fig. 2.4: Observed relative sea level change (with respect to 1990 level) in the recent past at Suva. Discrete
points represent the tide gauge records; the linear equation and the R-square value describe the
linear fitting of the observations......................................................................................................................... 10
Fig. 2.5: The Linkages between climate change, tropical cyclones, coastal impacts, and adaptation
measures. ................................................................................................................................................................. 12
Fig. 2.6: Stage damage curves under different adaptation scenarios ................................................................... 15
Fig. 3.1: Map of Natadola tourism development...................................................................................................... 21
Fig. 3.2: Population growth in Navua and estimate of 2004 population ............................................................. 23
Fig. 3.3: S-curve based on Navua data ........................................................................................................................ 28
Fig. 3.4: Transformed data showing relative damage ............................................................................................. 28
About AIACC
Assessments of Impacts and Adaptations to Climate Change (AIACC) enhances capabilities in the
developing world for responding to climate change by building scientific and technical capacity,
advancing scientific knowledge, and linking scientific and policy communities. These activities are
supporting the work of the United Nations Framework Convention on Climate Change (UNFCCC) by
adding to the knowledge and expertise that are needed for national communications of parties to the
Convention.
Twenty-four regional assessments have been conducted under AIACC in Africa, Asia, Latin America and
small island states of the Caribbean, Indian and Pacific Oceans. The regional assessments include
investigations of climate change risks and adaptation options for agriculture, grazing lands, water
resources, ecological systems, biodiversity, coastal settlements, food security, livelihoods, and human
health.
The regional assessments were executed over the period 2002-2005 by multidisciplinary, multiinstitutional regional teams of investigators. The teams, selected through merit review of submitted
proposals, were supported by the AIACC project with funding, technical assistance, mentoring and
training. The network of AIACC regional teams also assisted each other through collaborations to share
methods, data, climate change scenarios and expertise. More than 340 scientists, experts and students
from 150 institutions in 50 developing and 12 developed countries participated in the project.
The findings, methods and recommendations of the regional assessments are documented in the AIACC
Final Reports series, as well as in numerous peer-reviewed and other publications. This report is one
report in the series.
AIACC, a project of the Global Environment Facility (GEF), is implemented by the United Nations
Environment Programme (UNEP) and managed by the Global Change SysTem for Analysis, Research
and Training (START) and the Third World Academy of Sciences (TWAS). The project concept and
proposal was developed in collaboration with the Intergovernmental Panel on Climate Change (IPCC),
which chairs the project steering committee. The primary funding for the project is provided by a grant
from the GEF. In addition, AIACC receives funding from the Canadian International Development
Agency, the U.S. Agency for International Development, the U.S. Environmental Protection Agency, and
the Rockefeller Foundation. The developing country institutions that executed the regional assessments
provided substantial in-kind support.
For more information about the AIACC project, and to obtain electronic copies of AIACC Final Reports
and other AIACC publications, please visit our website at www.aiaccproject.org.
vii
Summary Project Information
Regional Assessment Project Title and AIACC Project No.
Modeling Climate Change Impacts on Viti Levu (Fiji) and Aitutaki (Cook Islands) (SIS09)
Abstract
The project goal is to enhance the technical and human capacity of Pacific Island Countries (PICs)
to assess vulnerability and adaptation to climate change, including variability. The project has
three main objectives. First, to develop the "next generation" of integrated assessment methods
and models, for application at island and sub-island scales (SimCLIM1). This objective was met
through research and methodological development that focuses on innovative improvements to
the first-order, island-specific models that have been developed over the last several years. The
innovations relate to the development and incorporation of: sea-level scenario generator; human
dimensions components; socio-economic baseline scenario generator; capacity for multi-scale
(island, community-level, site-specific) analyses; improved coastal impact models; explicit
adaptation options; economic tools for evaluation; and "open architecture" features to improve
versatility. Many of these advancements are generic and thus applicable to other small island
developing states. The second objective was to expand the understanding and knowledge
concerning impacts and adaptation to climate change in the Pacific by implementing, testing, and
applying the improved methods in case studies representing low atoll and high volcanic island
situations. The third objective was to build in-country research capacity through training in, and
transfer of, the advanced methods and integrated assessment models.
In principle objectives 1 and 3 have been successfully attained within the duration of the project.
SimCLIM development has been completed and a robust version of the model will be available
shortly, however, this is beyond this project. Objective 2 had been partially accomplished, mainly
because of the paucity of relevant data and information, and the change in data and information
requirements of SimCLIM as it was developed. This was unavoidable since model development
was taking place simultaneously with the field surveys at the three study sites. In addition,
revisions were made to SimCLIM just before the closure of this project. However, vulnerability
and adaptation assessments were carried out in all three study sites and this information had
been relayed to stakeholders in each respective study sites. The third objective was successfully
accomplished with the development of a training version of SimCLIM (TrainClim). About 40
representatives from the Pacific region have been introduced to TrainClim, and arrangements are
already in place to have TrainClim incorporated into the revised USP course on climate change
vulnerability and adaptation assessment and the new short-term training on climate and extreme
events. This Project had also contributed to raising the awareness of various stakeholders on
climate change, climate variability, vulnerability and adaptation assessment through their
interaction with the project team during site visits and workshops organized specifically under
the project.
Administering Institution
PACE-SD, University of South Pacific, Suva, Fiji
Participating Stakeholder Institutions
IGCI, University of Waikato, Hamilton, New Zealand; Fiji Department of Environment, Suva,
Fiji; and Cook Islands Environment Service, Rarotonga, Cook Islands
Countries of Primary Focus
Fiji and Cook Islands
1
SimCLIM is the generic name applied to a software modeling system developed by the International Global
Change Institute (IGCI, University of Waikato, New Zealand) for examining the impacts and adaptations to climate
variability and change.
viii
Case Study Areas
Natadola, Viti Levu, Fiji; Navua, Viti Levu, Fiji; and Aitutaki, Cook Islands
Sectors Studied
Infrastructure (Buildings); Water Resources; Agriculture (Mixed Crops); and Sea Level Rise and
Coasts
Systems Studied
Coastal Settlements; Land Use; Urban, Rural and Peri-Urban Settlements; Flood Plains; Regional
and Small Island Economies; and Food Security
Groups Studied
Farmers; Business People; Urban and Rural Communities; and Government Officials
Sources of Stress and Change
Climate change and variability risks; Climate related extremes such as tropical cyclones and
floods; Population growth; Land use change; Competing government priorities; and Lack of
resources.
Project Funding and In-kind Support
AIACC: US$ 220,000 grant; and University of South Pacific: US$ 24,360 financial contribution for
utilities’ expenses (for a duration of 42 months at $580.00 per month)
Investigators
Principal Investigator: Kanayathu Koshy, Pacific Center for Environment and Sustainable
Development (PACE-SD), University of South Pacific, P.O. Box 1168, Suva, Fiji. Email:
[email protected]
Other Investigators: Patrick Nunn, School of Geography, University of South Pacific, Fiji;
Melchior Mataki, Pacific Center for Environment and Sustainable Development (PACE-SD),
University of South Pacific, Fiji; Richard Warrick, International Global Change Institute (IGCI),
University of Waikato, New Zealand; and Peter Kouwenhoven, International Global Change
Institute (IGCI), University of Waikato, New Zealand.
ix
Executive Summary
Research problem and objectives
An important activity of the Pacific Islands Climate Change Assistance Programme (PICCAP), which was
a Global Environment Fund (GEF) funded enabling activity, was the development of integrated
assessment models to support both Vulnerability and Adaptation (V&A) assessments and capacity
2
building in Pacific island states. The unique aspect of this work was the linking, through
interdisciplinary collaboration, of climate change data, models, and projections with sets of sectoral
impact models at the island scale, for both temporal and spatial analyses. Under PICCAP, there were two
such modeling developments. The first was VANDACLIM–The Islands Version (for a fictitious country),
3
a software tool in support of training in V&A assessment. The generic developments for VANDACLIM
fueled the development of a set of prototype integrated assessment models for real places in the Pacific,
4
like Rarotonga (Cook Islands), Viti Levu (Fiji) and Tawara (Kiribati). These “first-order” models contain
a climate change scenario generator, island-specific climate data, and a mix of agricultural, coastal, water
and health models for impact analyses. The models are designed to be user-friendly, run on a PC and can
be easily updated. In effect, these models serve as evolving platforms for integrating scientific knowledge
and data for purposes of supporting policy and planning in the context of climate change and variability
– a bridge between science and decision-making. Secondly, recent advances in integrated assessment
methods, including those for coastal impacts, adaptation analysis, and economic evaluation were made as
part of a recent World Bank supported V&A study focusing on Fiji and Kiribati, for inclusion in the
5
Bank’s Regional Economic Report (RER) for the region. This study coordinated by the International
Global Change Institute (IGCI), provided support for further development of the existing integrated
models, especially for Fiji.
The major limitation of these “first order” integrated assessment models was the fact that they only
address the bio-physical impacts of climate change with respect to a particular sector. The human
dimension (population, infrastructure, and land use) was not considered although it has implications on
2
These models were built from the software components and tools developed exclusively for CLIMPACTS, an
integrated model system for New Zealand, with funding from the New Zealand Government (Warrick et al., 2001;
Kenny et al., 2000). Various other country applications had been developed from this foundation of work in New
Zealand, including a model for Bangladesh (BDCLIM; Warrick et al., 1996), Australia (OzCLIM), and a training
software package for UNITAR (VANDACLIM; Warrick et al., 1999a, b).
3
Warrick et al (1999). This training model has been also been used for training in small island developing states of
the Caribbean and Indian Oceans, as well as in the Pacific island region where it continues to serve as the basis for a
V&A assessment course offered each year at the University of the South Pacific.
4
Within the framework of the PACific Islands Climate Change Impacts Model system (PACCLIM; IGCI, 1999).
The acronym “PACCLIM” refers both to a sub-regional scenario generator to which each island-specific integrated
model can, optionally, be attached, and, more generally, to the “umbrella” under which the modeling work has been
conducted. As a regional scenario generator, PACCLIM simply offers a capacity for an island country to create and
view climate change scenarios in their regional context; in this regard, it is similar in concept to other regional
scenario generators like SCENGEN (Hulme et al., 2000). However, in its broader “umbrella” function, PACCLIM,
unlike other regional climate models or scenario generators, serves to link explicitly to island-scale models for
purposes of V&A assessment. Thus, the PACCLIM system integrates from global to local.
5
The integrated assessments for the two case studies are: Capacity-Building Activities Related to Climate Change
Vulnerability and Adaptation Assessment and Economic Valuation for FIJI (2000); and Capacity-Building
Activities Related to Climate Change Vulnerability and Adaptation Assessment and Economic Valuation for
Kiribati (2000). These provided the technical basis for the World Bank report (World Bank, 2000).
x
the capacity of sectors to cope with climate risks; and the range of adaptation options that can be
considered. As such, SIS09 was an opportunity to amalgamate bio-physical impacts with the human
dimension (see Figure 1) and capitalize on recent advances pertaining to socio-economic baseline
changes, infrastructure effects, adaptation, and community-level coastal impacts and economic valuation
6
to come up with the “next generation model” for integrated assessment (SimCLIM ).
Fig. 1: SimCLIM “Open-Framework” system (adapted from Warrick et al., 2005)
The summarized research objectives of the project were as follows:
1.
To provide the “next generation” of integrated assessment model.
2.
To implement, test and apply the improved methods for integrated assessment.
3.
To build a sustainable, in-region capacity to conduct research and integrated assessments.
6
SimCLIM is the generic name applied to a software modeling system developed by the International Global
Change Institute (IGCI, University of Waikato, New Zealand) for examining the impacts and adaptations to climate
variability and change. The distinctive advantage of the open system, as opposed to the hard-wired system, is the
flexibility afforded to users for importing their own data and models in order to customise the system for their own
purposes – much like a GIS (Warrick et al., 2005). As indicated in Figure 1, there are tools to allow the user to
import: (1) spatially-interpolated climatologies and other spatial data (e.g. elevation surfaces); (2) site time-series
data; (3) patterns of climate and sea-level changes from General Circulation Models (GCMs); and (4) impact models
that are driven by climate (and other) variables. The geographical size is a matter of user choice (from global to
local), as is the spatial resolution (subject to computational demands and data availability).
xi
Approach
The approach taken in this project was largely determined by the objectives of this project and variations
to the approach were done because of the paucity of data and the revisions to the SimCLIM open
framework. The general approach for each objective is outlined below. The existing three-tier model
structure used in this “first generation” of model serves as the mechanism for integrating methods,
models, data and scenarios (under this second generation model) and allows the user to:
•
Describe and examine baseline climates
•
Create climate change scenarios
•
Evaluate impact models
•
Conduct sensitivity analyses
•
Estimate sectoral impacts of climate and sea level change
•
Examine scientific and modeling uncertainties
For objective 2, a case study approach was used for the purpose of implementing and testing the methods
developed in Objective 1, and for carrying out integrated assessments of impacts and adaptation that
provided new understanding and knowledge regarding the effects of climate and sea level changes in the
Pacific region. The two case study sites were:
•
Navua and Natadola in Viti Levu (Fiji): The project focused on the coastal zone, flood plains, and
the infrastructure effects of climate and sea level changes and variability and the opportunities
for adaptive response. Viti Levu is a high mountainous island with coastal zones highly
populated and subjected to numerous land use changes.
•
Aitutaki in the Cook Islands: Aitutaki is typical of the myriad low-lying atoll islands of the
Pacific. In terms of climate and sea-level change and variability, key issues for integrated
assessment involve pathways for sustainable development in light of resource constraints and
climatic risks. Groundwater and the coastal sector were initially indicated as the sectors to be
studied, however the lack of digital elevation map and the identification of rainwater as key
resource constraint necessitated the change of focus to rainwater harvesting, and the implications
of climate change and variability on its sustainability.
For objective 3, several approaches were taken; firstly the training of a cohort of government and nongovernment officials including USP staff and students, and high school students (in Aitutaki) through
their participation in field surveys, targeted training workshops and sites visits. Secondly, the training
version of SimCLIM (TrainClim) will be used in the USP-based training on climate change vulnerability
and adaptation assessment course. In preparation for this step, a cohort of young professionals from the
region was introduced to TrainClim through a “hand-on” training approach. Awareness-raising about
the project and its outputs, climate change and vulnerability and adaptation assessment were made in
relevant workshops, scientific publications and other public forums. The lessons learnt from this project
will also be given to other climate change projects in the Pacific as a means to share and sustain regional
capacity to conduct integrated assessments on climate change.
Scientific Findings
The finding most pertinent to findings of this project can be summarized as follows:
1.
The “next generation” integrated model suitable for impact and adaptation assessment in small
island states has been developed with following capabilities:
•
Capacity for sub-island (community-scale) assessments within the model system. In order to
provide the capacity for considering different impacts and adaptation at different scales
and to “scale up” from local to national levels, a nested, multi-scale capacity was
developed within the single integrated system. This has increased the potential scope of
xii
case study applications and the flexibility for addressing a range of impact and
adaptation issues.
•
Components for the “human dimensions” (e.g. population, infrastructure, land use) of
vulnerability. The AIACC SIS09 project has developed model components to allow the
incorporation of spatially related demographic, land use and infrastructural data. This
work has included the development of graphical and tabular tools for displaying such
data, including a three-dimensional capacity.
•
A socioeconomic scenario generator to project changes in baseline conditions. In order to fit the
purposes of the case studies related to risks of river and coastal flooding, this activity has
focused specifically on the provision of a land use scenario, particularly with regard to
buildings at risk from such extreme events. It allows the user to evolve patterns of
growth of settlements based on assumptions about trends in population growth, building
types and mix, and land constraints.
•
Develop the capacity for generating island-specific sea-level rise scenarios. The integrated model
system was modified to allow the incorporation local relative sea-level trends (as, for
example, derived from tide gauge data, which include vertical land movements),
regional sea-level change patterns (as derived from coupled A-OGCMs), and globalmean projections. The sea-level scenario generator was linked to an extreme event
analyzer in order to perturb sea-level time-series data and to examine changes in extreme
events (e.g. storm surges) and their return periods. It also is linked to coastal flooding
impact models in order to examine impacts and adaptations under climate change.
•
Developing coastal impact models appropriate for coral and coral-fringed islands. The emphasis
is on impact models relevant to both riverine and coastal flooding, at a scale appropriate
for examining community-level impacts. The basic approach has essentially been
completed, but there remains work to be done in refining the models to make them
“generic” and easily applied in different settings.
•
Develop an explicit adaptation component. Modeling capacity has been developed to
examine explicitly a set of adaptation options related to flood risks, including: raising
minimum floor level requirements for new structures; channelisation; and avoidance of
building in hazardous areas. These actions represent broad categories of adaptation
related to “flood proofing” of structures, engineering works and land use regulation.
•
Modify and incorporate economic tools for both valuation of impacts and evaluation of adaptation
measures. In the first instance, simple and straightforward methods were developed that
could be applied generically across a range of cultural and economic situations found
within the Pacific islands. For assessing economic impacts of flooding, generic flood
height-damage curves for various categories of building types and contents were
developed. These curves can be modified to suit specific situations. As well, basic tools
for benefit-cost analyses were developed in order to evaluate the economic viability of
adaptation measures. The integrated model allows the user to simulate economic impacts
over time for the spectrum of flood events with different return frequencies, which are
then aggregated to give present-value, annualized damages (at a user-selected discount
rate). Multiple runs – with and without climate change, with and without adaptation –
allow the user to separate the benefits and costs of adaptation under climate change from
those occurring under natural climate variability.
•
The capacity to allow models to run in “transient” mode. Typically, the first generation
models (and most impact and adaptation assessments) were run for “time slice”
comparisons (e.g. 1990 versus 2050). Running the models in transient mode provides the
capacity for capturing the effects of climate variability and extremes along with a
changing climate and/or sea level over time. Importantly, this capacity provides the basis
for characterising the costs of impacts (and thus the benefits of adaptation) as “streams”
or “flows” of effects into the future, which are then discounted back to the present for
purposes of evaluation and incremental costing of adaptation options.
xiii
2.
Non-climatic “drivers” such as poor governance and improper land use practices are also
important determinants of the overall vulnerability of people to climate change and its present
variations as well as extreme weather events. A way forward is to implement climate change
adaptation by embracing a connective top-down and bottom-up approach underpinned by
lessons learnt through experiences with addressing climate variability and extreme weather
events guided by climate-proof development plans. Moreover, there should be clear
responsibilities of all stakeholders involved in planning, implementing, and monitoring
adaptation measures.
3.
Local communities in the study sites are “locked” into a vulnerable situation because of their
poor socioeconomic conditions coupled with limited input to government decision-making
processes and access to financial resources, and therefore need assistance to properly adapt to
climate change.
Capacity building outcomes and remaining needs
Project implementation in itself was learning experience because of the paucity of key relevant data and
information, which ultimately dictated variations to be made to the implementation approach and the
change in focus of case study applications of the next generation model in Aitutaki and Natadola.
Secondary and tertiary level students were engaged in the field work, as such, this project had assisted in
raising the capacity of these students to carry out multidisciplinary field assessments where interaction
with stakeholders through interviews and observation were followed by analysis of information and data
gathered. The field experience gained by these students is an important outcome of this project.
About 40 young pacific professionals working with various government and private agencies have been
exposed to the training version (TrainClim) of the SimCLIM model through workshops. The participants
appreciated the capacity of the tool to: (i) give pictorial representations of climate change scenarios, (ii)
allow the user to use climate change information for simple planning problem, (iii) define and evaluate
adaptation options, (iv) perform cost benefit analysis: time-horizon, discount rate for adaptation
measures and (v) the model’s capacity to be used for coastal inundation, freshwater lens, health impact
and shoreline change modeling. All features of TrainClim are also within SimCLIM as such participants
were fully introduced to SimCLIM through its training version.
In terms of long term capacity building, TrainClim will be incorporated into the USP-based climate
change vulnerability and adaptation assessment training. PICs need to be trained in the application of
SimCLIM, but more importantly in the interpretation and analysis of the outputs from the next
generation model. These needs extend beyond the scope of this project, however, the platform to enable
the meeting of this need is the planned incorporation of TrainClim into the USP-based training.
National communications, science-policy linkages and stakeholder
engagement
As far as the National Communications of the two countries (Cook Islands and Fiji) are concerned, when
the project officially started in 2003, Cook Islands had already submitted its National Communication
and Fiji had completed its assessments and was finalizing their report. As such, this project’s direct
contribution to the first national communications was minimal. The project outputs will be useful for
their second national communications. It must however be recognized that researchers were involved
with national communications mainly providing technical expertise within the ambit of their climate
change expertise even before the AIACC project started.
Numerous stakeholders were engaged at various stages of the project, including government officials and
local communities. The project’s official focal points in the Cook Islands and Fiji were the government
departments responsible for environmental services. They were engaged in the project formulation and
implementation stages especially in providing over sight on national priorities with implications on
climate change adaptation in the project sites. Government officials from other PICs were engaged during
the project’s stakeholder workshops and other climate related meetings and training for which the
Principal Investigator and associates organized within Fiji and in the region.
xiv
Policy implications and future directions
In most PICs, policy formulation and implementation are seldom informed by science. Key development
policies pertaining to climate sensitive sectors such as agriculture, water resources, and the coastal zone
are often made without scientific scrutiny. For example, Pacific Islands Countries are known to be among
the most vulnerable to the impacts of climate change (IPCC, 2001), however, climate change does not
feature as one of the key factors in the design and formulation of development plans (Mataki et al, 2006).
Even inter-annual climate variability (El Niño) and extreme weather events (e.g. tropical cyclones, flash
floods) are only responded to as and when such events occur as such, very little effort has been directed
at reducing their impacts with on-going policy variations and implementation of robust adaptive
measures. The capacity building aspects of this project especially in relation to climate change adaptation
planning and the use of scientific data to inform adaptation had contributed to raising the level of
awareness with in government officials, communities, and academics. More importantly, this project had
proven to critics, the significance of modeling in an area such as climate change vulnerability and
adaptation assessment.
Climate change, has risen to the global level backed by a growing pool of evidence for its cause and how
it should be addressed, however, there is scanty scientifically proven data and information to inform
policy formulation and action in most PICs. More over, practitioners and advocates for climate change
adaptation in the Pacific have been challenged internally to ascertain that proposed measures in various
climate change adaptation projects implemented in the Pacific are in fact addressing climate change
impacts at the sectoral level (water, agriculture coastal zone). Concerns about the validity of measures can
be allayed with scientifically proven data, information, and SimCLIM is one such tool that can be used to
generate local level vulnerability and adaptation assessment information and data.
In the past, as popularized by UNEP and US country studies and the IPCC methodology, a top down
scenario driven approach which was based on futuristic climate scenarios generated by computer models,
was used to assess vulnerability of various sectors and determine adaptation options. A more realistic
approach popularized by UNDP through their Adaptation Policy Framework, as a development driven
pathway where the emphasis is to adapt to the present day climate vulnerability based on a bottom up
approach. Scenarios are used only for coming up with win-win adaptations factoring in short term
scenarios. In this approach, an existing climate impact related problem is characterized, assessed, and
managed through incremental adaptations using adaptive management strategies.
In practice, such a risk-based adaptation requires the development of a baseline scenario to which any
development impact, climate change scenario will be added, and to address the resulting vulnerability,
adaptation measures are planned and implemented. This adaptation will be transient and not time sliced
as in the past.
SimCLIM has the capacity to:
•
Describe baseline climates
•
Examine current climate variability and extremes
•
Assess risks – present and future
•
Investigate adaptation – present and future
•
Create climate change scenarios
•
Conduct sensitivity analyses
•
Examine sectoral impacts
•
Examine risks and uncertainties
•
Facilitate integrated impact analyses
xv
1 Introduction
The overall goal of the project is to enhance the knowledge base and the technical and human capacity of
the PICs for assessing impacts and adaptation to climate change, including variability and extremes. As
depicted in Figure 1.4, this integrated approach to integrated assessment sought to link scientific research
to technical and human capacity building. In so doing, it builds systematically upon the approach that
has been developed, and endorsed by countries, over the eight years under UNDP-GEF, APN, and World
Bank funded projects in the Pacific. The detailed research objectives of this project are as follows:
Objective 1: To provide the “next generation” of integrated assessment models applicable to SIDS, in
order to enhance the technical capacity of PICs for assessing impacts, adaptation and vulnerability to
climate change. This objective was met through research and methodological development that focuses
on innovative, generic improvements to the first-order, island-specific models, which have been
developed over the last several years (see below). The innovations relate to the development and
incorporation of: sea-level scenario generator; human dimensions components; socio-economic baseline
scenario generator; capacity for multi-scale (island, community-level, site-specific) analyses; improved
coastal impact models; explicit adaptation options; economic tools for evaluation; and “open
architecture” features to improve versatility.
Objective 2: To expand the understanding and knowledge concerning impacts and adaptation to climate
change in the Pacific, by implementing, testing and applying the improved methods and models for
integrated assessment in three representative case studies, representing low atoll, high volcanic and large
island situations. The two case studies areas are; Aitutaki, Cook Islands (Low Island) and Viti Levu, Fiji
(Volcanic and High Island).
Fig. 1.1: Natadola, Viti Levu, Fiji
1
Fig. 1.2: Navua, Viti Levu, Fiji
Fig. 1.3: Aitutaki, Cook Islands
2
The integrated assessments focused on coasts, infrastructure, water, and agriculture, the mix depending
on the case study circumstances. A major part of achieving this objective is the establishment of
environmental, socio-economic baseline conditions for each case study was established through, field
work, and drawing upon GIS, remote sensing, instrumental and other data resources as required.
Fig. 1.4: Inter-related components of the AIACC project for Pacific islands.
Objective 3: To build a sustainable, in-region capacity to conduct research and integrated assessments.
This objective was achieved by: involving relevant island personnel in the work; transferring the models
to appropriate institutions in the PICs; training key individuals in those countries to train others in model
use and integrated assessment (“training the trainers”); and adapting the integrated assessment models
for use in an existing university-based course on climate change vulnerability and adaptation assessment.
3
2 Characterization of Current Climate and Scenarios of
Future Climate Change
2.1 Activities Conducted
The climate change effects studied in the three pilot areas are different, so different data has been
collected.
Area
Baseline
Climatology
Variables
Phenomena
Time
Horizon
Temporal
Resolution
Spatial
Resolution
2100
Daily
Daily
Daily
Daily
5x5 m
1914 - 1996
Sea level
Rainfall
Sea level
Rainfall
Natadola
Navua
Aitutaki
2100
2100
25x25 m
NA
Table 2.1: Scenario variables
2.1.1 Natadola, Viti Levu, Fiji
Natadola is under flood-risk from storm-surges (aggravated by cyclones). These are influenced by both
sea level and cyclone frequency and intensity. For the sea level changes, tidal data for Lautoka (proxy for
Natadola, only tide gauge close to the study site) was obtained from the National Tidal Facility Australia
as part of the South Pacific Sea Level and Climate Monitoring Project (SPSLCMP) and Cyclone data was
provided by the Fiji Meteorological Service and supplemented by the paper prepared by Terry and
Kostaschuk (2004).
2.1.2 Navua, Viti Levu, Fiji
Navua is under risk from both river flooding and from storm-surges. These are influenced by rainfall, sea
level and cyclone frequency, and intensity. Tidal data for Suva (proxy for Navua, only tide gauge close to
the study site) was obtained from National Tidal Facility Australia as part of the South Pacific Sea Level
and Climate Monitoring Project (SPSLCMP) and Cyclone and rainfall data was provided by the Fiji
Meteorological Service and supplemented by the paper prepared by Terry and Kostaschuk (2004).
2.1.3 Aitutaki, Cook Islands
The Aitutaki case is characterized as a water-supply problem. The SPREP CBDAMBIC project installed
household water-tanks. These tanks will store harvested rainwater. Rainfall data was collected through
the NIWA Climate database.
2.2 Description of Scientific Methods and Data
For generating climate change scenarios, the SimCLIM climate change-modeling tool (Warrick et al, 2005)
was used. The tool supports combinations of GCM’s (global circulation models) and IPCC emissions
scenarios. Depending on the effect studied, different approaches were used.
4
Box 2.1: Emission scenarios
The following IPCC emission scenarios were used to analyze climate change impacts:
A1: Global population peaks around 2050 and declines thereafter. New technologies are rapidly
introduced and economic disparities between regions are substantially reduced. All scenarios in the A1
group use the same basic population, technology, and economic assumptions. They differ in assumptions
about how energy is supplied. A1FI: fossil fuels continue to supply most of the energy. A1T: non-fossil
energy sources dominant. A1B: energy supply is balanced among fossil fuel and non-fossil energy source.
A2: Global population continues to increase throughout the 21st century. Disparities in economic growth
of the regions persist, and technological change occurs more slowly than in any of the other illustrative
scenarios.
B1: Global population peaks around 2050 and declines thereafter. Economies rapidly become service and
information oriented. Income disparities decrease and non-fossil fuel energy technologies are introduced.
B2: Global population continues to increase throughout the 21st century but not as rapidly as in the A2
family. Economic growth is less rapid than in the B1 family and less concentrated in the energy, service,
or information sectors than in either A1 or B1. Decreases in economic disparities occur primarily at local
and regional levels.
Fig. 2.1: Scenarios of CO2 gas emissions and consequential atmospheric concentrations of CO2 (from IPCC,
2001).
5
Box 2.2: Global Circulation Models
Global Circulation Models (GCMs), are physics-based, complex, three-dimensional climate models that
take into account as many factors as possible that could influence climate. They are essentially the only
viable tools for simulating regional patterns of climate change from increasing greenhouse gas
concentrations. GCM outputs are widely used to assess climate change impacts for various geographical
regions of the world. Carter and Hulme (1999) summarized the advantages and disadvantages of using
GCMs for impact assessments. Some disadvantages particularly relevant for the Pacific Islands are:
•
The models normally run on a coarse horizontal resolution (up to several 100-kilometres);
•
Although most GCMs are quite accurate for the global climate, their simulation of regional
climates is often inaccurate;
•
Despite the physical consistency, the internal relationships between the model-variables
produced from GCMs may not always be the same as those found in observed data;
•
Output from GCMs is usually produced at a much coarser temporal and spatial resolution than is
required for regional/local impact studies.
•
To address the first point, those GCM results should be used which have both validated well
globally and in the region of interest. In many cases, an impact model requires a finer spatial
resolution as input. A downscaling technique can be used to satisfy such model requirement.
•
All GCM patterns are standardized per degree of global warming in order to account for the
different climate sensitivities of the various GCMs and make the pattern comparable. GCM
patterns were obtained from the following GCMs:
CSIRO(Mk2): see Gordon and O’Farrell, 1997
HadCM2: see Johns,1996
HadCM3:see Gordon et al., 2000
GFDL_R15_b: Dixon and Lanzante, 1999
GFDL_R30_c: see Knutson et al., 1999
CGM1: see Boer et al., 2000; Flato et al., 2000
CGM2: see Flato and Boer, 2001
NIES/CCSR: see Emori et al., 1999
2.2.1 Sea-level
Year-by-year (1990 - 2100) global sea level rise projections are obtained by running the latest version of
MAGICC, forced by the 6 SRES marker scenarios (i.e., A1B, A1FI, A1T, A2, B2 and B1) with LOW,
MEDIUM, and HIGH climate & sea level model parameters.
These projections are used in the SimCLIM SLR Generator as scaling factors to obtain sea level rise
scenario from GCM-dependent normalized sea level rise patterns for any given year between 1990 - 2100,
under any one of the 6 SRES marker scenarios, and with low, medium or high model parameters.
Global sea level rise projections are stored in an Excel file global_SLR_MAGICCrun.xls, with each SRES
marker scenario (in one sheet) containing the following table of projections:
6
Year
Global Sea Level Rise (cm w.r.t. 1990 level)
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
…
2100
Low
Medium
High
0.00
0.05
0.10
0.15
0.20
0.26
0.31
0.37
0.43
0.49
0.56
0.63
0.70
0.78
0.86
0.95
1.04
…
21.28
0.00
0.16
0.32
0.49
0.66
0.83
1.01
1.20
1.39
1.58
1.78
1.99
2.21
2.43
2.66
2.90
3.15
…
52.70
0.00
0.32
0.65
0.99
1.34
1.70
2.07
2.45
2.84
3.24
3.66
4.08
4.52
4.98
5.45
5.94
6.45
…
91.00
Table 2.2: Projections of global mean sea level rise with different climate and sea level model parameters
GCM experiment
Ocean resolution
(long. × lat. ×
levels)
Data
available
Centre
SLRg(1990~2100)
(cm)*
CGCM1 GS
1.8o×1.8o×29
1900-2100
CCCma
37
CGCM2 GS
1.8o×1.8o×29
1901-2100
CCCma
35
CSIRO Mk2 GS
5.6o×3.2o×29
1881-2100
CSIRO
33
ECHAM4/OPYC3
G
2.8o×2.8o×11
1900-2100
DKRZ
29
GFDL_R15_b GS
3.7o×4.5o×12
1900-2090
GFDL
28
GFDL_R30_c GS
1.875o×2.25o×18
1900-2090
GFDL
28
HadCM2 GS
3.75o×2.5o×20
1900-2090
UKMO
21
HadCM3 GSIO
1.25o×1.25o×20
1900-2090
UKMO
22
MR12 GS
2.0o×2.5o×23
1900-2100
MRI
11
References
Flato et al., 2000;
Boer et al., 2000
Flato and Boer, 2001
Gordon and O’Farrell,
1997; Johns et al., 2001
Roeckner et al. (1996,
1999)
Dixon and Lanzante,
1999
Knutson et al., 1999
Johns et al., 1997;
Mitchell et al., 1995
Gordon et al., 2000;
Johns et al., 2001
Yukimoto et al., 2000
* For experiments ending in 2090, global sea level rise is calculated over the period 1990~2090, instead of 1990~2100.
Table 2.3: Results from nine GCM experiments were used to derive regional sea level change patterns.
7
This range of model simulations demonstrates that:
•
There are significant regional variations in sea level change (Figure 2.2). This warrants the need
for incorporating spatial variations in sea level change into local sea level change scenarios. GCM
experiments have been considered as a primary means to provide information on regional sea
level change variations in a systematic fashion.
Fig. 2.2: Sea Level Change over the 21st Century as simulated different GCMs. Each field is the difference
in sea level between the last decade of the experiment and the decade 100 years earlier. This difference is
then normalised by the global sea level change over the same period by the respective GCM
•
Different model experiment gives different spatial patterns of these variations (Figure 2.2 and
Table 2.3). This requires more vigorous comparison analyses on model configurations and
performances. Meanwhile, techniques such as using the ensemble average of GCM experiments
might yield more robust representation of the regional patterns, compared with taking results
from a single GCM simulation.
Since there are only limited number of GCM experiments available, pattern-scaling technique is required
to generate sea level change scenarios, which represent uncertainties, associated with emissions scenarios
and model parameters. For this purpose, the original GCM patterns of sea level change are normalized by
global mean sea level change as simulated by respective GCM experiments (SLRg (1990 - 2100) in Table
2.2).
8
In order to obtain local relative sea level change, the sea level rise component due to large (global to
regional) scale processes needs to be superimposed onto the component due to local factors (e.g. vertical
land movement, sea level change due to local weather conditions, etc.).
For the former, at a single site, the global sea level rise projection is used to “pattern-scale” the
normalized GCM patterns to obtain a time series of sea level change scenarios. A linear regression can
then be fit into the series and the sea level rise rate (mm/year) (i.e., the slope of the best-fit line) obtained.
For example, for sites Suva and Rarotonga, “normalized” sea level change values are extracted from
Figure 2.2 for each GCM experiment (Table 2.4). Combined with the global sea level change projections, a
linear regression between sea level change (with respect to 1990 level) and time (Figure 2.3) can be
obtained for a given GCM pattern, a SRES marker scenario, and a set of climate & sea level model
parameters.
Model
Site
CGCM1
CGCM2
CSIRO
ECHAM4
GFDL_R15
GFDL_R30
HadCM2
HadCM3
MR
12
Suva
Rarot
onga
0.90
0.97
1.05
1.03
0.85
0.84
1.14
1.00
0.87
0.89
0.74
0.71
1.11
0.94
1.36
0.96
1.17
0.95
Sea Level Change (wrt 1990) (cm)
Table 2.4: Normalised sea level change for the 21st century at the study sites
60
50
40
30
y = 0.353x
R2 = 0.8945
20
10
0
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Fig. 2.3: Sea Level Change (cm) at Suva due to large scale processes with CSIRO GCM-pattern and SRES
A2 scenarios with medium climate & sea level model parameters
The sea level change rate due to contributions from local factors can be derived by differentiating the
large scale and local scale components from observed local relative sea level changes and then apply it
into future for sea level change projections. To “de-couple” the large and local scale components from
observed relative sea level change, one needs to have quality tide gauge records from which to derive the
historical relative sea level change rate. Shown in Figure 2.4 is the derived relative sea level change rate
over the recent past at Suva. Sea level change rate due to local component can be obtained by subtracting
the large-scale component from these relative sea level change rates. Therefore, the key issue here is to
make estimate of historic sea level change rate over the recent past and present time resulting from large
scale processes. This can be done in two different ways:
9
•
Derive sea level change rate associated with large-scale processes directly from GCM simulations.
If year-by-year GCM simulations are available, linear regressions between sea level change and
time (similar to those shown in Figure 2.4) can then be derived to represent large-scale process
contribution to relative sea level change over recent past and present time. Unfortunately, the
only available GCM simulation results for this analysis are decadal.
•
Derive sea level change rate associated with large-scale processes by using the global average
observed sea level change rate over the 20th century (1.5 mm/year) and the normalized sea level
change rate pattern from GCM experiments. For instance, according to CSIRO model, sea level
rise by 0.85 cm at Suva while a sea level rise of 1.00 cm experienced globally (Table 2.3).
Therefore, we could assume that in the past century, large-scale processes contributed 1.275
mm/year (=1.5 x 0.85) to the total relative sea level rise at Suva.
Suva
Sea level change (wrt 1990) (cm)
10
5
0
1973
1977
1986
1989
1991
1993
1995
1998
2000
-5
-10
y = 0.7039x - 8.5294
R2 = 0.4389
-15
Fig. 2.4: Observed relative sea level change (with respect to 1990 level) in the recent past at Suva. Discrete
points represent the tide gauge records; the linear equation and the R-square value describe the linear
fitting of the observations.
Once the estimate of large-scale component is made, the local component can be obtained by subtracting
the large-scale part from the observed sea level change. For instance, at Suva, the tide gauge records show
a relative sea level rise rate of 7mm/year (Figure 2.4). Based on the CSIRO model, large-scale processes
might have contributed 1.275 mm/year over the 20th century. Therefore, sea level rose at a rate of 5.725
mm/year (=7.0-1.275) due to local factors.
With the assumption that local factors-induced sea level rise will not accelerate in the future while large
scale processes (e.g. global warming) would accelerate sea level rise in the 21st century, future scenario of
relative sea level change at a specific site can be developed by superimposing the local factor-induced sea
level change rate onto GCM experiment derived sea level change projections for the future.
10
ΔZi,t-1990 = [ΔZg, t-1990 x ΔZr, t-1990 ] + Znc
ΔZ2x
Where:
ΔZi,t-1990 is the projected sea level change (in cm) at location i, from 1990 to future year t
ΔZg, t-1990 is the change in global-mean sea level (in cm) as projected by simpler climate models for a given
emission scenario and as reported, for example, in IPCC (2001).
ΔZr, t-1990 is the change in regional sea level (in mm) pertaining to location i, as projected by a GCM
ΔZ2x
is the global mean sea-level change (in mm) for an equivalent doubling of atmospheric carbon
dioxide concentration (or, for transient runs of GCMs, the global mean value as averaged over the last
several decades of the GCM simulation).
Znc, t-1990 is the local, non-climate-related change in sea level, usually due to vertical land movements that
affect relative sea level.
2.2.2 Rain-fall
With the extreme-event analyzer of SimCLIM and distribution-curve for extreme-rainfall events is created
from historic rainfall data. This curve is perturbed to create scenarios for future rainfall. Rainfall is used
as input for modeling river-flood-events.
2.2.3 Drought
Drought is a derivative of rainfall: the number of days it has not rained. This derived time-series can also
be analyzed with the extreme-event analyzer of SimCLIM generating a distribution of return-periods for
extreme drought-lengths, which can be perturbed for climate change. As the analysis focuses on the
robustness of the rainwater harvesting system, 2 others methods for creating climate change scenarios
were used:
1.
Perturbing the rainfall-series by decreasing the rainfall with X% (this does not actually change the
length of the drought-periods, but it changes the availability of water for the rainwater tanks)
2.
Perturbing the rainfall-series by decreasing the rainfall by X mm/day (or setting it to 0 when
there is less than X mm/day rainfall originally). This increases the length of some dry periods.
2.2.4 Cyclones/wind
The following figure illustrates the linkages between climate change, tropical cyclones, coastal impacts,
and decision information for risk designs. The white parts (fonts) refer to the cyclone-generated sea
conditions; the blue parts to the components of elevated sea level and run-up that impact coastal areas;
the yellow parts to the key parameters of climate change that affect those relationships; and the red parts
to the major inputs to risk design for adaptation.
11
The Main Components are Cyclone-Generated Sea Conditions (White Lettering), Elevated
Sea Levels and Run-Up that Impact Coastal Areas (Blue Lettering), the Key Parameters of
Climate Change that Affect those Relationships (Yellow Lettering) and the Major Inputs
to Risk Design for Adaptation (Red Lettering)
Climate
Change
MSL change
8 to 88 cm
Mean sea-level
Tropical cyclone
intensity
0 to 20%
+
Atmospheric
Pressure
Barometric set-up
+
Max. Wind
Wind set-up
+
Return Period
50-year
Significant
Wave Height
Wave set-up
+
Wave run-up
10.75m
Run-up elevation
(current choice)
+
Wave energy
Adaptation
measures
Design Risk
Fig. 2.5: The Linkages between climate change, tropical cyclones, coastal impacts, and adaptation measures.
2.2.5 Changes in significant wave height
The relationship between maximum wind speed and significant wave height for a given return period
can be determined using past studies of tropical cyclone risks for the study area.
The relationship between maximum cyclone wind speed (in knots) and return period (Y, in years) is
based on the work of Kirk (1992) that developed the following relationship:
U1.899 = 1,456.265 + 2,046.05 Log Y
This relationship is based on observational records and presumably represents “current” climate.
12
Consideration is given to the impacts of global warming on changes in cyclone intensity and hence
significant wave heights. The literature is equivocal regarding how global warming will affect cyclone
frequency and intensity. Various methods and studies yield different answers, with some indication that
changes in intensity could be region-specific. Nonetheless, a major review undertaken subsequent to the
Intergovernmental Panel on Climate Change (IPCC) Second Assessment Report, conducted by a panel of
the world’s leading experts on the subject, concluded that tropical cyclone intensities (as measured by
maximum cyclone wind speed) are apt to increase as a result of global warming (Henderson-Sellers et al.,
1998). This view was confirmed in the most recent IPCC assessment report (Giorgi and Hewitson, 2001).
Two methods are used to generate time-dependent scenarios of wind speed change. The first relates a
change in wind speed to the corresponding degree of global warming, and scales this ratio by the timedependent projection of global temperature change, viz:
ΔUt-1990 = (ΔU2x / ΔT2x) *ΔTt-1990
Where:
t
future year of the scenario
ΔU wind speed change (ms-1)
ΔT  global mean temperature change
ΔT2x global temperature change under equivalent doubling of atmospheric CO2
ΔU2x wind speed change under equivalent doubling of atmospheric CO2
In order to generate a maximum cyclone wind speed for some future time t, the observed wind speed is
perturbed by this change, viz:
Ut = ΔUt * Uobs
Where:
Uobs is the observed maximum cyclone wind speed
To implement this method a value for ΔU2x / ΔT2x is required. Henderson-Sellers et al., (1998) estimate an
increase of 10-20% in cyclone intensity based on maximum potential intensity models, but unfortunately
do not provide an indication of the corresponding global temperature change. IPCC (Giorgi and
Hewitson, 2001) estimate 5-10%, but again fail to give a corresponding global temperature change. In
reviewing the literature, Lal (2001) concludes that cyclone intensities are projected to increase by 10-20%
for a 2 to 4 degree increase in sea-surface temperature.
In light of these findings, a range of 2.5% to 10% increases in cyclone intensity per degree of warming is
used to implement the first of the two methods. This information is incorporated into SimCLIM as three
options for cyclone intensity change (low, mid and high), as were the relationships between maximum
cyclone wind speed, significant wave height and return periods based on observational data.
The second method is based on daily maximum wind speed for the GCM grid that includes the pilot area,
as estimated by the Canada Climate Modeling Centre GCM2, using the A2 emission scenario and best
judgment of model sensitivity. While these data show changes in the maximum wind speed over time
(from 1961 to 2100), spatial smoothing of the data means that the values underestimate the extreme wind
speed at a specific location. Consequently, the GCM output is scaled such that the maximum speed
13
estimated for the 1972 to 1998 period (16.7 m s-1) coincided with the maximum gust observed over the
same period at the pilot area.
2.2.6 River flooding
Rainfall data is used to estimate the likelihoods of heavy rainfall events associated with flooding in the
study area. These data were adjusted to represent the rain falling over the catchment. For the climate
change projections the CSIRO GCM with SRES A1B emission scenario and best judgment of model
sensitivity is used. Compared to a number of other GCMs, the CSIRO model verifies more favorably in
the South Pacific region.
Global warming can make significant differences to the likelihood, especially for time horizons between 5
and 30 years.
Using a combination of information from topographic maps and a Digital Elevation Model (DEM), a
model is developed for the case study area. This model uses the Rational Method to estimate flows in the
river.
Initial runoff parameters in the model were determined using recorded rainfall and stream flow data.
Validation of the model was undertaken by comparing model outputs with observed flood extent and
depths for the last major rainstorm to affect the area.
The simple flood model used in the case study is based on limited information inputs and has undergone
very limited “ground truthing” of possible hydrodynamics within the flood area. This model has been
developed to assist in understanding how climate change (i.e. changing rainfall patterns) may alter flood
risks, and how adaptation measures may reduce flood risks. The modeling results should not be used in
any way to determine or estimate precise levels or spatial extent of flooding. More detailed modeling
using appropriate hydraulic and hydrodynamic models would need to be used if location specific flood
risk estimates are required.
2.2.7 Cost-benefit analysis
Once the expected risks from climate change at a specific location are understood, decisions need to be
made about how much, if any, of today’s resources should be invested to reduce the risks to an
acceptable level. Adaptation option is the term used to describe a proposed investment of this type.
Different adaptation options may have different degrees of effectiveness at reducing climate-induced
risks, and each will come at a different cost. The purpose of cost-benefit analysis is to compare all feasible
adaptation options at a particular location and select those with the greatest economic value, or ‘bang for
the buck’. The cost of the best adaptation option is the incremental cost of climate change – i.e., the true
cost of the climate-induced changes in risk faced by a community, after all options have been properly
analyzed.
Economic analysis in this context is necessarily a long-run analysis, as it needs to capture the effects of
changes in the physical environment (climate change itself) over decades, and express them in terms of
value at the present. In addition, of course, a long time horizon allows fundamental changes to occur also
in the human environment, i.e., in infrastructure, demographics, and the growth and mix of economic
activity, which need to be taken into account. Changes in vulnerability and risk are the product of the
changing frequency of extreme events and the changing value on the ground of the things that might be
damaged (or the productive activities that might be curtailed).
The first step is to determine the economic value of the expected risks from climate change in the location
being analyzed, which for present purposes is the ‘project site’. The project site might be a coastal
residential community, a port facility, a power plant, a road, or any other definable asset. The discussion
and examples below refer to a coastal residential community, but the concepts apply equally to sites of
different uses.
14
The stock of buildings and important infrastructure are surveyed and valued at replacement cost to
reflect the cost of repairing or, in the extreme, replacing assets damaged by extreme events. In practice, in
most communities, the housing stock can be categorized in three or four different types based on the
main building materials used, e.g., concrete/block, all timber, plywood/corrugated metal, etc. Local
building contractors provide all-inclusive estimates of replacing buildings of each type, per square meter
(m2). The total replacement value of the housing stock can then be calculated from the survey results of
the m2 dimensions of each structure in the project site.
Climate change is a process in which potentially damaging events occur with greater frequency, i.e., with
an increasing probability of occurring in a given year, and with greater severity. As only the frequency
and severity of events over time can be predicted, rather than the occurrence of any discrete event, it is
necessary to analyze average “expected” damage values in each year as a function of climate change.
Weather damage to any particular structure within the project site will depend on the severity of the
event and will run the range from no damage (minor events) to total demolition. As the severity of events
rises, the damage effect takes an “S” shape, i.e., the extent of damage rises slowly with storm severity at
first, and begins to rise steeply once a given ‘threshold’ severity is exceeded. At the high end of the curve,
near-total demolition occurs across a broad range of severe events. This is known as a stage-damage (SD)
curve. In the present example, the damaging event to buildings is floodwater, either in the form of
rainfall runoff (freshwater) or storm surges from the sea (saltwater), with severity measured in meters of
flood height above the ground floor level. A set of typical stage-damage curves is shown in Figure 2.6.
Comparison of SD Curves for Structures under Different Adaptation Scenarios
1.00
0.90
Climate Change
0.70
0.60
0.50
No Adaptation
Adaptation
Damage (% of Replacement Value)
0.80
0.40
Adaptation Option 1
Adaptation Option 2
0.30
0.20
0.10
2.30
2.20
2.10
2.00
1.90
1.80
1.70
1.60
1.50
1.40
1.30
1.20
1.10
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.10
0.20
-
Flood Heights (m above pre-adaptation groundfloor level)
Fig. 2.6: Stage damage curves under different adaptation scenarios
A stage-damage curve is a picture of the vulnerability of a structure to damage from (in this case) floods,
which is rooted in the building’s design characteristics and materials of construction. There will be a
specific stage-damage curve for each building depending on the materials of which it is made. Thus the
blue curve in the picture above shows the initial situation of a particular building (e.g., a residential, 1-
15
storey concrete/block house with a slab ground floor and metal roof, near the shore) as found by
hypothetical survey of the project site. For that building in its present location and configuration, a flood
of about 0.9 meters causes damage to about 10 percent of the replacement value of the building; a flood of
height 1.1 meters causes damage to about 40 percent of replacement value; the building is more than 90
percent destroyed if the flood height reaches 1.7 meters, etc.
Altering a building’s design characteristics will affect its vulnerability to flood damage. For example,
building a floodwater diversion wall around a house, raising the ground floor level of the house, or
moving the house to a new location – all of which are adaptation options – will reduce its exposure to
flood damage and result in a new (shifted) stage-damage curve. This is seen in lower levels of damage
inflicted at every flood height after adaptation is implemented. Thus the benefit of a given adaptation
option can be depicted in a new stage-damage curve drawn next to the initial one. Two stage-damage
curves relevant to hypothetical adaptation options ‘1’ ands ‘2’ have been drawn in the above picture.
The process of climate change results, in the present example, in higher and more damaging flood
heights. Combined with the increasing frequency and severity of events measured and predicted by
models such as SimCLIM, the initial stage-damage curve for each structure can be used to calculate the
damage expected to occur each year as time passes. When these are summed across all structures and the
calculations are repeated for each year in the planning period, a stream of expected annual damage
values results. These are expressed in a present value (PV) representing the total incremental damage
expected in the project site due to climate change.
Adaptation options are analyzed within the same framework. As mentioned, adaptation for a given
structure results in a shifted stage-damage curve showing, for each flood height ‘event’, a lesser degree of
damage than without adaptation. For a particular adaptation option, the above procedures are repeated
to calculate the present value of expected damage after the adaptation option is implemented. The
difference between the PV of expected damage under the no-adaptation case and the PV of the
adaptation option measures the gross benefit of implementing the adaptation option. The procedures are
repeated again for all feasible adaptation options. The table below illustrates the results that might be
obtained from such analysis for a coastal community.
No Adaptation
Adaptation Option 1
Adaptation Option 2
Etc.
Present
Values
of
Expected Damages ($)
6,163,762
Total Savings due to
Adaptation ($)
2,609,961
634,101
3,553,801
5,529,661
-
-
Table 2.5: Comparison of PVs of expected damage under climate change
The PV of savings due to adaptation option 1 in this example – that is, the PV of the damage avoided by
implementing option 1 – is about $3.55 million. If the initial cost of adaptation option 1 is no greater than
$3.55 million, then investment in the option will save the community resources in the long run. Similarly,
if the initial cost of option 2 is not greater than $5.53 million, investment in option 2 will save resources in
the long run. Once the analysis of the benefits of potential adaptation options has been carried through to
this stage, selection between the two options (and others that may also be feasible) will depend upon the
initial cost and hence the economic return available to each.
A discount rate is used to calculate the present value of a stream of future costs, in recognition that a
given cost incurred in the future is worth less than the same cost incurred today. The actual discount rate
chosen reflects a subjective valuation of now versus the future: a lower discount rate reflects a higher
concern about future costs, and vice-versa for a higher discount rate. In particular, use of a lower discount
rate reflects a higher valuation of future costs savings from adaptation relative to the present or initial
costs of adaptation, leading to greater investment in adaptation.
16
The choice of an ‘appropriate’ discount rate to use in cost-benefit analysis depends on the nature of the
economic problem. As climate change is a long-term process, which coincides with major demographic
changes, economic development, and inter-generational transfers of assets, a national and international
concern over climate change itself is indicative of a relatively high concern to avoid future costs that may
affect the well being of the next generation or the country’s long term growth potential. It would
therefore seem anomalous to apply a ‘high’ discount rate to the expected future costs of climate change,
given a high level of confidence in what the climate models predict the future costs to be. A rate as high
as, say, that normally used to evaluate shorter-term financial investments will almost certainly result in a
misallocation of resources away from adaptation. Nevertheless, it is certainly possible that the
‘international’ discount rate for future costs from climate change is consistently lower than the discount
rate implicitly used by many countries and perhaps most individuals. A lower ‘international’ discount
rate may reflect a higher awareness among world bodies of the dynamics of climate change and
consequently higher confidence in the predictions of the rate and the degree by which the climate will
change; or it may reflect a heightened appreciation of the threat that climate changes poses to the world
economy and the pace of development. Whatever the case, international resources are and will be
available to assist small and vulnerable countries to invest in adaptation and to contain risk.
2.3 Field Surveys
Field surveys were carried out in each of the case study sites using pre-set questionnaires designed to
gather physical and socioeconomic data. Each of the sites was visited at least 3 times within the duration
of the project. Information and data gathered include:
•
Socioeconomic data (population, economic activities, and income level);
•
Building types and values;
•
Current land-use and practices;
•
Recollection of extreme weather events (tropical cyclones, flash river floods, coastal flooding as a
result of storm surge);
•
Depth of water in properties recent floods;
•
Spatial extent of storm surges and floods (coastal and river, where applicable);
•
Availability of water (reticulated and rain harvested);
•
Adaptation options presently implemented (depending on type of risk: floods and drought)
•
Barriers experienced in implementing to adaptation measures
•
Perception of about climate change in general.
•
Important ecosystems and their uses (e.g. mangroves and coral reefs)
In addition to the field surveys, a lot of relevant information was gleaned from available literature,
available databases (national-statistics, meteorological services, public works departments, mineral
resources departments and international-NIWA climate database) and focused interviews with key
informants (community leaders, national and local government officials, non-government organizations)
from within and outside the study sites.
However, it is important to note that in general, relevant data and information paucity in addition to
incomplete and disjointed data and information were serious setbacks to the implementation of the
project. In some situations, the researchers used proxy data and information for sea rise level and
meteorological data. On the other hand, genuine attempts were made by the researchers to ensure that
the required information and data were made available where possible.
17
2.4 Results
2.4.1 Natadola, Viti Levu, Fiji
Sea-Level Graphs: Baseline and Scenario
2.4.2 Navua, Viti Levu, Fjij
Sea-Level Graphs: Baseline and Scenario
Rainfall Graphs: Baseline and Scenario
River Flow Graphs
2.4.3 Aitutaki, Cook Islands
Rainfall Graph: Baseline and Scenario
2.5 Conclusions
An important issue is the scale and location of Pacific Islands within the Global Circulation Models: they
are small and “in the middle of nowhere”. This means that they are usually within 1-4 grid-cells of the
GCM, while it is not the highest priority to get the GCM best performance at these locations (as the focus
is on the main lands of the continents). Of course all grid cells in the GCM are connected in will influence
each other, so the final result is still of relevance. In some cases it is possible to deal with climate change
in a different way. For the Aitutaki case study, the performance of the rainwater harvesting system is
depending on the amount and distribution of rainwater over time. Given a historic dataset, the rainfall
events can be changed by either a relative amount (multiplying by a coefficient), or subtracting/adding a
constant. This last method is a good way to increase the length of drought periods, as subtracting certain
values will drop them to zero rainfall. Aitutaki rainfall graph with -10% (report the yearly average) and
with -1 mm (report the yearly average)
18
3 Socio-Economic Futures
3.1 Activities Conducted
3.1.1 Natadola, Viti Levu, Fiji
Field surveys were carried out where important socio-economic (population, income, property values),
ecosystems, land use, and physical characteristics of the site and the adjacent village of Sanasana were
documented. Natadola Beach is of international quality and is currently being developed into a
multimillion tourism establishment.
3.1.2 Navua, Viti Levu, Fjij
Navua town (not yet legally recognized) is located on the southeast of Fiji’s main island, Viti Levu. A
section of Navua River measuring about 163 m wide and 5.81 km in length runs along the town, where in
some homes, including the “central business district” of Navua are only a few meters from the river
banks. The greater Navua area, including the study site, is also crisscrossed by a network of irrigation
channels and flood gates at the coast, previously used to distribute and control water needed for
commercial rice farming.
To study urbanization in coastal zones the land use development in Navua was analyzed. Historic aerial
photographs, field survey, and interview data were used to produce a series of land use maps for Navua,
which were the basis for examining spatial distribution and demand for buildings over time.
•
Land Use Development
•
Property Values
•
Stage Damage Curves
3.1.2.1 Stage Damage Curves
Interviews have been taken to assess the damages from historical flood events. The core information
consists of 3 elements: the property value, the historical flood level, and the associated damage costs
(possibly for more than 1 event).
3.1.3 Aitutaki, Cook Islands
Studies were based on water tanks and water tank distribution. In addition, during the field surveys,
baseline socio-economic conditions island residents, ecosystems, land use, and the physical characteristics
of the island were also established.
3.2 Description of Scientific Methods and Data
3.2.1 Navua and Natadola, Viti Levu, Fiji
For land use change scenarios five steps are taken:
1.
Creating a land use classification scheme encompassing the relevant land use types;
19
2.
Identifying the criteria influencing the spatial distribution of land use types;
3.
Quantifying the change in land use area demanded by each land use type in 30 years time;
4.
Describing the features of infrastructure to ensure that the information outputted by the scenario
generator meets the needs of the flood damage assessment calculation;
5.
Exploring the issues with generic application of the land use change scenario generator in the
Pacific region.
These stages are not strictly sequential, but interlinked and revisited as the research proceeds. In addition,
field survey results were analyzed to assess the ability of communities to cope with present climate
extremes, and its implications for adaptation to climate change.
3.2.1.1 Stage Damage Curves
An S-shaped curve is expected, as there are three stages in the curve:
•
Stage 1, up to a certain flood level there is virtually no damage done.
•
Stage 2, the damage costs will increase rapidly with flood levels.
•
Stage 3, at a certain level, 100% damage is done; further increasing the flood level will not
increase the damage.
An S-curve with maximum value 1 can be described with the following function:
y= 1 / (1 + exp (-a + b.x))
Where:
y = relative damage
a, b = parameters to fit
x = flood level
To fit this curve with a tool like Excel some transformations must be done.
Above equation can be rewritten as:
exp(-a+b.x) = 1/y – 1
Calling the right hand side of this equation z, the fit needed is z = exp (-a+b.x)
Unfortunately, Excel can not fit this equation either (it can only fit z=a.exp(b.x)).
However, by expressing x as a function of z (instead of the other way around), the resulting function can
be fitted by Excel:
z = exp (-a + bx)
ln z = -a + bx
bx = a+ln z
x = a/b + 1/b.ln z
The function in this form can be fitted by Excel: y = c.lnx + b
20
3.3 Results
3.3.1 Natadola, Viti Levu, Fiji
Sanasana villagers are the landowners and rely heavily upon the marine resources for subsistence, cash
crops, and the selling of shells to tourists. Off Sanasana village is Navo Island, a limestone island of
national archaeological and cultural significance. It is proposed for the Natadola Marine Resort to be
located on 300 ha of state and native lease land adjacent to the village and the beach. Current plans
include 4 hotels, condominiums, an international standard golf course, and a marina.
Fig. 3.1: Map of Natadola tourism development
An EIA was commissioned by the Fiji Government to review the competing demands for Natadola, their
environmental impacts and make recommendations that will protect the environment, interests of local
residents, current users of the beach and the economic well being of Fiji. Significant issues identified
included the impact on Sanasana village community, public access, provision of worker accommodation,
and the fate of Natadola Beach Resort. Potential negative effects of the tourism development include:
•
Changes to Sanasana village and villager’s lifestyle such as loss of privacy, loss of access to
7
traditional land for growing crops and reduction of productivity of qoliqoli areas,
7
The Qoliqoli area is any area of seabed or soil under water, sand, reef, mangrove swamp, river, stream, or wetland,
or any other area, recognized and determined within customary fishing grounds under the Fiji Fisheries Act
21
•
Modifications of marine environment and coastal processes such as increase in suspended
sediment from construction works and physical damage to reefs from reef walking and anchors,
•
Adverse effects resulting from the demand for workers housing,
•
Loss of public access,
•
Damage to archaeological sites.
Positive impacts include:
•
A major contribution to the national economy,
•
Significant social and economic opportunities for Sanasana villagers,
•
Protection of beach vegetation and improvement in water quality,
•
Provision of public facilities at beach.
Farming and fishing are the major sources of income for the people of Sanasana. Some villagers are also
involved in tourism i.e. some work in the resort while some sell handicrafts and operate horse rides.
Villagers grow both subsistence and commercial crops in the gardens, subsistence crops being more
popular. Root crop and vegetable farming remains a major source of subsistence for the villagers and
supplemented by goods from shops and fish from the productive coral reefs and mangroves around the
village.
The coral reefs of the Sanasana-Natadola area are all fringing reefs and they retain a high level of
productivity. This is largely because of their great size, particularly their breadth in the Rove area west of
Natadola. The reefs of this area are also comparatively healthy because they are not in the path of any
major rivers, and are regularly washed by waves driven by the tradewinds from southeast to northwest.
Mangrove patches are also located on the study site. In the future it is likely that this situation will change
quite dramatically because of the construction of a series of large-capacity hotels along the back of
Natadola Beach. This is likely to see increased exploitation of both reef and mangrove food resources by
hotel workers and their dependents, and to a lesser extent to provide foods for hotel guests. A lot will
clearly depend on the degree to which hotel managements become aware of and involved in marine
ecosystem conservation in this area.
3.3.2 Navua, Viti Levu, Fiji
Comparing maps showed that in the period 1951-1994 the number of buildings in Navua increased by
251%, a trend primarily driven by population growth, particularly in-migration motivated by economic
and lifestyle factors. Land resources for new buildings were predominantly sourced from land with a
high agricultural capability alongside the river and highway. Fieldwork revealed that flood hazards have
not been a major feature in land use decisions. Incidences of efforts to minimize flood damage to
buildings are elevating and wet-proofing the buildings, rather than constructing in locations safe from
flooding.
Navua had a population of 4183 people, living in 853 households recorded during the 1996 national Fijian
census (Fiji Islands Bureau of Statistics, 2004). Eighty percent of the population identified as Indo-Fijian,
which is a higher proportion than the national average of approximately forty-five percent. Figure 3.2
shows the population growth rate in Navua over the last half century. The uneven rate of population
growth can be partly attributed to periods of labour-intensive developments in the region. These
included commercial-scale sugarcane farming (1890-1920), construction of the Nadi-Suva Highway (19401945), commercial-scale rice farming (1960-2000) and construction of Pacific Harbour (1970-1980). The
drop in the overall growth rate evident in the late 1980s is associated with high emigration rates
following the 1987 coup.
22
5000
4500
Fijian
Population
4000
Indian
3500
Other
3000
Total
2500
2000
1500
1000
500
0
1946
1956
1966
1976
1986
1996
2004
Year
Fig. 3.2: Population growth in Navua and estimate of 2004 population
Since the last census in 1996, the Navua Rural Local Authority has noted a recent surge in the local
population. Evidence of this is the emergence of new, and expansion of existing, informal housing
settlements. Many of these developments are the result of displaced farming families searching for
alternate livelihoods following the expiry of their agricultural land leases (Mataki, et al, 2006 and
Lonergan, 2005). Other population increases are attributed to people moving to Navua from Suva,
attracted by the tranquility of a small town, cheaper land prices, more affordable housing options and the
ability to continue working in Suva (Lonergan, 2005). This population increase has been noted by the Fiji
Sun newspaper and is also apparent in the overstrained power network and overcrowded schools
(Lonergan, 2005).
23
Land Use
Number
1
Land
Use
Type
Building
2
Road
Sub-category
Brief Description
-
This land use covers all buildings (for any use and in any
conditions), including buildings where only foundations
have been laid or those in a derelict state.
This land use designates transport route used to access the
study area (the Nadi-Suva Highway). This road is of higher
standard than other roads and represents a place where
people can sell produce. The highway reserves are also a
common location for informal housing.
This land use designates all other roads, including
unsealed roads and roads in poor condition. If infrequently
used, the road would be overgrown with vegetation and
should not be allocated the road land use type.
This land use is distinguished as tree and shrub canopy
from aerial photographs. A contiguous tract of vegetative
cover, greater than one hectare, can be considered an area
of forest.
Includes all water bodies which are not the ocean. This
represents a different resource from the ocean, because the
water is less salty and the river is used as a transport route.
This type of water is more susceptible to change than the
ocean.
Highway
Arterial road
3
Vegetation
Forest/nonforest
4
Water
River and its
tributaries
5
Other
6
Agriculture
Ocean
Residential
land
Commercial/
Community
land
High-value
agricultural
land
Low-value
agricultural
land
Ocean and the beach.
Includes ornamental gardens, vegetable gardens used for
subsistence farming, driveways and house yards.
Generally this classification is used to describe the land
buffering a building from its surrounding land use, or
between closely located buildings.
Includes sand pits, excavations, bare land, stockpile areas,
buffers around commercial/industrial buildings, school
grounds and community parks.
Designates land used for growing crops or dedicated
grazing pastures. This land is distinguished from lowvalue agriculture by its ordered land cover appearance.
The regular arrangement of crop or vegetable plantations
and orchard trees is generally apparent though land cover.
As opposed to high-value agriculture, vacant, degraded or
weedy land is commonly used for informal grazing and
represents low-value agriculture. Also includes road and
river reserves where land may also be used for informal
agricultural use. These areas are distinguished in land
cover observations as land that has a mottled, uneven
appearance. Some areas may be irregular in shape, with
poorly defined boundaries.
Table 3.1: Final definition of land use classification scheme
24
LUT in 1951
LUT in 1978
LUT in 1994
Building
Building
Building
Number of grid-cells
transition
255
Building
Building
Building
Non-building
Non-building
Building
57
34
Building
Non-building
Non-building
Building
Non-building
Building
172
294
Non-building
Non-building
Building
Non-building
Non-building
Building
60
409
Table 3.2: Transitions between building and non-building land uses
Initial land use (LUTi)
Final land use (LUTj)
(Building land use)
Number of grid-cell
transitions
Percentage of all gridcell transition with
LUTj=Building
Building
Road
Building
Building
861
15
54%
1%
Vegetation
Water
Building
Building
59
0
4%
0%
Other
Agriculture
Building
Building
86
564
5%
36%
1585
100%
Total transitions
Source: Based on comparing the Navua grid-cell maps for 1951, 1978 and 1994
Table 3.3: Grid-cell transitions with an end-state of building land use (LUTj=Building)
Percentage of
all grid-cell
transition with
LUTi = Building
Initial land use (LUTi)
(Building land use)
Final land use (LUTj)
Number of
transitions
Building
Building
Building
Road
861
6
73%
1%
Building
Building
Vegetation
Water
79
3
7%
0%
Building
Building
Other
Agriculture
75
150
6%
13%
1174
100%
Total transitions
grid-cell
Source: Based on comparing the Navua grid-cell maps for 1951, 1978 and 1994
Table 3.4: Grid-cell transitions with an initial-state of Building land use (LUTi=Building)
25
Number of grid-cells
Building
Road
Forest
Water
Other
Agriculture
5 metre (mean number)
5 metre (standard
deviation)
3.64
1.52
0.08
0.43
0.50
1.21
0.00
0.06
3.32
1.86
0.45
1.29
10 metre (mean number)
10 metre (standard
deviation)
15 metre (mean number)
3.08
0.38
1.99
0.02
8.59
1.92
2.42
2.87
1.12
0.92
3.19
3.75
0.27
0.07
3.93
11.89
3.53
4.46
2.90
1.76
4.85
0.67
5.69
5.99
Buffer distances
15 metre (standard
deviation)
Table 3.5: Results of neighbourhood analysis (1994 data)
The equations to generate area-targets for each land use type are listed in Table 3.6. Also shown in this
table are the calculated area-targets for the years 1951, 1978, 1994, 2004 and 2034 with the actual values for
1951, 1978 and 1994 contained in brackets. The predictions for the years 2004 and 2034 were based on
population forecasts for Navua (Lonergan, 2005).
LUT
Equation
Population
Area-targets
1951
1978
1994
2004#
2034*
-
633
2568
4200
4400
8800
Buildingi
0.00009*
(Population_Navua)2 +
0.6803*Population_Navua
+ 1077.3
1544
(1538)
3418
(3383)
5522
(5405)
5961
14034
Roadi
The larger of
4489.9*Ln(Buildingi) –
24845
OR
Roadi-1
8120
(7996)
11688
(11930)
13842
(13566)
14186
18030
Otheri
5.9666* (Buildingi) + 886.81
10099
(10155)
21280
(20897)
33835
(33220)
36455
84619
Wateri
166498 –
7788.8*Ln(Buildingi)
109312
(107239)
103122
(102910)
99386
(95951)
98790
92121
Vegetation
-17124*Ln(Buildingi) +
10536*Ln(Buildingi) +
133338'
84968
(84051)
79733
(82311)
76573
(75130)
76069
70428
Agriculture
665581-(Building + Road +
Other + Water +
Vegetation)i
451538
(453637)
446340
(444029)
436423
(441371)
434120
386349
#
Based on an estimate that the population in the study area was 4400 in 2004.
*
Based on the prediction that the population of urban areas will double in the next 30 years it is estimated that Navua’s population
in the year 2034 will be 8800.
Table 3.6: Area-target equations and predictions
26
Land use type
Roads
Vegetation (steep slopes)
Oceans and rivers
Buildings – community
and civic purpose
Buildings - known
developments
Agriculture
Vegetation - protected
natural areas
Areas masked
All grid-cells attributed Road land use type will not change.
Steep slopes will not transit land use types.
Reclaiming/dredging the Navua River will only occur in some
locations. The alignment of the river will not alter.
Grid-cells attributed to oceans land use type will not change.
Churches, mosques, praying houses, cemeteries, grave sites, schools
and monuments will not change to a different land use type.
However, the features of the buildings may change.
1. Construction of a high school and expansion of the Arya
Pratinidhi Sabha Hindu primary school near the Rewaqa
settlement (eight year development plan).
2. Relocation of the Navua hospital in 2005 – destination is
unknown, but it will be safe from flooding (Naivalu, 2004).
3. Housing Assistance and Relief Trust (HART) village
development involves construction of eight flats in Navua.
4. Squatter and low-income housing initiatives, including a 2005
ADB project which will focus on improving the housing of
squatter and slum settlement in situ (Asian Development
Bank, 2002). The details of these initiatives are not known but
may result in informal settlement areas either becoming more
permanent landscape fixtures or disappearing as residents are
relocated to better quality housing.
1. Agricultural subdivision as part of displaced farmer
relocation scheme means their areas of agriculture land use
will not change.
2. Revitalisation of former Viti Corp agricultural land means
that its use as agriculture land will not change (that is,
retention of use as dairy lands)
There are some mangrove areas outside the study area, which have
been identified as significant (Gray, 2001). This means that the Navua
wetland areas may become more highly valued in the vicinity of this
region.
Table 3.7: Masked areas in Navua
House type
Corrugated iron
Concrete Brick
Wood (good condition)
Wood (poor condition)
Traditional Bure
Makeshift/improvised
Other materials
TOTAL
Number
1005
851
627
361
53
29
18
2944
Table 3.8: Types of housing (Navua - 2002)
27
Percentage %
34.0
29.0
21.0
12.0
1.8
1.0
0.6
100.0
3.3.2.1 Stage Damage Curve
Fitting the field data to the S-curve with the method described yields the following plot.
flood damage curve
1.2
1
relative damage
0.8
interviews
s-curve fit
0.6
0.4
0.2
0
0
0.5
1
1.5
2
2.5
3
3.5
flood level
Fig. 3.3: S-curve based on Navua data
The figure below, displays the transformed data (1/relative damage –1) on the X-axis, and the flood
levels on the Y-axis.
Transformed data
3.5
3
floodlevel
2.5
2
interviews
Log. (interviews)
1.5
1
y = -0.2198Ln(x) + 1.6634
R2 = 0.4472
0.5
0
0
50
100
150
1/relative damage -1
Fig. 3.4: Transformed data showing relative damage
28
200
250
Excel computes 1/b as –0.2198 and a/b as 1.6634 (with a reasonable fit of r²=0.4472, r=-0.67).
From this, a and b can be calculated as a=-7.57, b=-4.55 and this curve can be drawn (see first figure).
3.3.2.2 Socio-economic implications on adaptation
SIS09 surveys showed that on average a Navua resident earns $U.S.35-46 per week, which was
comparable to the average weekly earning recorded by a consulting firm in 2000 (Sinclair Knight Merz,
2000). This indicated that the socioeconomic status of average Navua residents had not improved within
the past five years. From this income levels, it is obvious that ordinary citizens may not be able to
improve their standard of living if they are to sustain damages of the above magnitude on an annual
basis. The net flooding impact experienced by the residents on their homes will depend on adaptive
measures taken, such as building sturdy and raised homes, and shifting to less flood-prone areas within
Navua. However, the full social and economic impacts of previous floods (beyond the scope of this
project) are currently unknown. However, their impacts are deemed substantial, taking into
consideration, destruction of root crops, loss of income and properties, diseases, and in some cases, death.
Apart from the business houses and a few middle-class residents, most of the homes and properties in
Navua area are not insured because of obvious financial constraints and because they cannot meet basic
insurance requirements. It is quite clear that most Navua communities are not coping well with present
climatic risks this trend will continue unless the enabling environment (improvement in socio-economic
conditions, enhanced capacity, and political will) is changed and the communities take upon themselves
the responsibility to adapt.
3.3.3 Aitutaki, Cook Islands
Around 1700 people currently reside on Aitutaki and the trend over recent years has been a “slightly
declining” one. Few younger people elect to make their lives on the island, preferring the attractions and
opportunities on Rarotonga or in New Zealand. Visible testimony to the falling population levels are the
overgrown food gardens throughout the island and a number of abandoned houses. The out-migration of
younger people is offset to some degree by the in-migration of older returnees and a number of retirees
from other countries.
Majority of the houses has concrete foundations with built-in timber ceiling & walls. Most of the houses
were raised off the ground with high cement floors. Heights of cement floors varied between 30cm to
about 80cm, but there were a few (15%) whose floors weren’t raised. There were quite a handful of
abandoned houses on Aitutaki whose owners are overseas. More houses are also currently being built.
About 25% have farms (pigs, banana, chicken, taro, etc) of which 43% was for commercial purposes,
while 57% were solely for subsistence.
Water is a major problem on the island of Aitutaki due to salt intrusion, and water being pumped to
homes are not drinkable but used for other things such as washing, bathing, toilet, but despite this, only
40% of the houses surveyed had water tanks (main source of drinking water), and out of this, only 58%
had suitable roof and good guttering, and well-kept tanks. Some homes are dependent on the village
tanks, which do not have good guttering systems, and gathering roofs, (some were partly rusty), making
it not suitable for drinking. A few were not only collecting rain but basically anything that could land on
it – having no netting/drainage. In addition, the two large resorts are directly linked to the main water
gallery causing water shortages for the people of Aitutaki.
Majority of those interviewed noticed that the vegetation (and forest) seems to be getting less dense as
more development is taking place. From our own observation, more resorts and lodges are being built
and at the same time more houses are being built and expanded. A few of the interviewees said that
vegetation are getting more dense “as before he could see bare ground but now it is all covered in forest
and dense scrubs”.
Many people find the heat extreme now then what it were 10 or 20 years back. People find their crops are
not doing well because of some minor drought conditions that they are encountering nowadays in
Aitutaki. However, some households have mentioned that Aitutaki is experiencing extreme hot and cold
29
weather from time to time. Some nights might be colder than what they have experienced when they
were young and most of the days are very hot. Majority of the people has noticed coastal erosion and that
the high tide mark is more in-land now than it used to be which they feel is due to sea-level rise and other
natural hazards such as the cyclones.
3.4 Conclusions
Conclusions made on development trends informed the programming of a land use change scenario
generator. These scenarios are an essential component of a vulnerability assessment model designed to
calculate the potential cost of flood damage to buildings. These results fulfilled objective 1 of this project,
which sought to develop and incorporate human dimensions components; socio-economic baseline
scenario generator, capacity for multi-scale (island, community-level, site-specific) analyses; improved
coastal impact models; explicit adaptation options; economic tools for evaluation; and “open
architecture” features to improve versatility. In addition, objective 2 was also partly accomplished with
the application of SimCLIM to land use in Navua and the documentation of baseline socio-economic
conditions and human dimensions in Navua, Natadola and Aitutaki.
30
4 Impacts and Vulnerability
4.1 Activities Conducted
•
Natadola: DEM
•
Navua: DEM, socio-economic data (location, elevation and value of properties, stage damage
curves)
•
Aitutaki: water-tank description
4.2 Description of Scientific Methods and Data
For all the three case studies the impact of climate change were studied by applying climate change
scenarios (combinations of a GCM and an IPCC emission scenario) to the climate variable that was
driving a risk factor for the community involved: rain, sea-level or cyclone event. Scenarios were selected
that best reflected the possible extremes.
•
Storm surge model (Natadola, Navua)
In the Natadola and Navua case-studies the risk factors were used to analyze extreme flood events, from
storm-surges and (for Navua) from the river.
•
River flood model (Navua)
•
Land use model (Navua)
•
Economic Cost Benefit Analysis Model (Navua)
In the Navua case, the extreme flood events were used to calculate EAD’s (Expected Annual Damage) to
the town’s infrastructure, and determine the total expected damage costs over a 50 year period, for 4
situations: Without climate change (NC) and without adaptation (NA); with climate change (CC), but
without adaptation; without climate change and with adaptation (AD) and with climate change and with
adaptation. From this the costs from climate change can be calculated, without adaptation (CCNANCNA) and with adaptation (CCAD-NCAD), the difference of these 2 being the incremental benefits of
adaptation. The incremental costs are the difference in costs for adapting with and without climate
change.
•
Water Tank Model
A model is created to analyze the effects of climate change on using a water tank as a buffer against
drought. The model has the following parameters:
Mmax
The size of the tank (in liters): how much water can the tank hold (the tanks installed as part
of the SPREP executed project, "Capacity Building for the Development of Adaptation
Measures in Pacific Island Countries" (CBDAMPIC) are 2000 liters)
D
Daily usage (in l/d): the amount of water subtracted from the tank every day (when
available); as this is per tank, this is also the total amount used per day by the household
that owns the tank; water is used for drinking (3-5 liters per day per person), cooking and
life stock; the average daily usage in the pacific is a little over 100 liters per day per person
(Gleick, 1996 and World Research Institute, 2004)
A
Effective (roof) area (in m2) that is used to catch the rainfall (in many cases only part of the
roof area is used to gather the water); the amount of water gathered per event (in l) is the
31
(mm of rainfall) X (effective area)
M0
The initial amount of water in the tank (at the start of the simulation)
The model calculates the content of the water tank at “the end of the day”, adding any gathered rainfall
and subtracting the daily usage:
Mt= Mt-1 + Rt*A – D
(Rt is the rainfall that day, in mm)
if Mt < 0 then Mt =0
(tank becomes empty)
if Mt > Mmax then Mt = Mmax
(tank becomes full)
(the “if statements” make sure that the tank content does not go below 0, or exceeds the maximum tank
capacity)
4.3 Results
•
Natadola: Flood-Risk Maps
•
Navua: Flood-Maps (both for river-flood and storm-surge),
•
Aitutaki: Performance of different water-tank systems
The analysis focuses on 2 results:
1.
the longest period that insufficient water is available in the 82 year period
2.
the number of periods with an water deficiency of more than 28 days (4 weeks)
In general, the initial amount of water in the tank can have a strong influence on the simulation results as
it determines what is available in the first period. Fortunately, the historical data series shows an extreme
rainfall event of 228.6 mm on the 9th day in the series (which translates into 228.6 liter of water for every
square meter of gathering area). This fills up a regular tank quite easily. Therefore, the analysis skipped
the influence of the initial amount of water in the tank.
With respect to improving the situation (i.e. adapting to climate change), households have the following
options:
1.
increase water storage capacity (by installing additional tanks)
2.
increase effective roof area (by installing gutters and pipes)
3.
decrease the daily usage (by wasting less water)
4.3.1 Outputs
The base situation is defined as follows:
tank size
roof area
daily usage
2000 l
15 m2
80 l/d
32
2000 l tanks are installed as part of the CBDAMPIC project. An effective roof area of 15m2 is a first
“guestimate” (lot of houses only use half the roof; because of spillage, evaporation and pollution, the
actual area needs to be bigger). A daily usage of 80 l/d corresponds to 20 l per person per day for a 4person household.
This corresponds to a longest water shortage period of 133 days (more than 4 months) with 101 drought
periods of more than 4 weeks each in 82 years.
tank size
roof area
daily usage
2000 l
15 m2
50 l/d
Saving water and lowering the daily usage to 50 l/d gives the following improvement: longest water
shortage period becomes 74 days with 30 drought periods of more than 4 weeks in 82 years.
tank size
roof area
daily usage
2000 l
25 m2
80 l/d
Just increasing the effective roof area from 15 to 25 m2 results in 72 days with 35 drought periods.
tank size
roof area
daily usage
3000 l
15 m2
80 l/d
Increasing the storage capacity from 2000 to 3000 liters results in 133 days and 86 drought periods. This
shows that just increasing the tank size does not result in a better performance. The roof area is very
important as well.
tank size
roof area
daily usage
3000 l
25 m2
50 l/d
To get an idea of “best” performance, combining all 3 adaptation measures results in a longest water
shortage period of 48 days, which is one of just 2 shortage periods of over 28 days during the 82 years.
It is also possible to approach the situation as an optimization problem. What is the minimum tank size
that is needed (given the effective roof area and daily consumption) to guarantee that water is always
available.
33
Daily Consumption
50
60
70
80
90
100
Roof Area
15 m2
20 m2
25 m2
22500
np
np
np
np
np
11000
18500
35500
np
np
np
6000
12500
18500
np
np
np
np = not possible
Table 4.1: Daily consumption versus roof area
An interesting result is that no matter how big the tank is, it can not sustain a certain consumption rate if
the roof area is not big enough. So investing in a tank, should coincide with investing in a sufficiently big
catchment area.
4.4 Conclusions
Although, complete vulnerability and adaptation assessments using SimCLIM could not be performed on
each site, the flood risk-maps, flood maps and the evaluation of water tank performance under present
climate and climate change conditions for Natadola, Navua and Aitutaki respectively contributed to
addressing objectives 1 and 2. The water tank evaluation in Aituaki was particularly important in
highlighting the need for proper scientific and economic evaluation of adaptation measures. The
evaluation was done after the installation of water tanks under the CBDAMPIC project on the island and
it clearly showed that investments in large tanks alone will be ineffective without increasing the
catchment area (effective roof area) and saving water.
34
5 Adaptation
This section is not exactly applicable to SIS09, because this project was concerned with developing the
“next generation” of integrated assessment methods and models. The application of these integrated
methods and models was contingent to the successful development of the integrated methods and
models. This has not been quite successful as the SimCLIM, which is the open framework for the next
generation of integrated methods and models under went drastic changes in the course of its
development and therefore render some of the field data collected in Aitutaki, Navua and Natadola
deficient. Furthermore, adaptation implementation was not the focus of this project. However, in the
fieldwork, the research team interacted with a variety of stakeholders such as farmers, business people,
urban and rural communities, and government officials, whose concerns with climate change were
clarified during discussions with them.
5.1 Activities Conducted
Adaptation refers to processes and activities, which individuals, communities, and countries implement
to cope with the consequences of climate change. In the context of this project, adaptation had addressed
indirectly through awareness raising during our interaction with stakeholders (Farmers, Business people,
urban and rural folks, and Government officials). In terms of capacity building, two workshops and three
climate and extreme events training were organized by the Principal Investigator and associates, where
information, lessons learnt and outputs (TrainClim and papers) were disseminated and evaluated by
participants.
On the other hand, the earlier version of SimCLIM had been very successfully used in an Asian
Development Bank (ADB) project in Federated States of Micronesia to examine climate change
vulnerabilities with and without adaptation and vulnerabilities with and without climate change. This
way it was possible to assess vulnerability arising out of climate change and thus could assess impacts
with and without climate change, adaptation with and without climate change, benefit through
prevented damage have been calculated using the above modeling approach and the results have been
used as part of development decision making.
35
6 Capacity Building Outcomes and Remaining Needs
This project has been a “flagship” project for PACE-SD in climate change given that the project started
around the same time the centre was established. As such, it has among other benefits (recognized
partner in a truly international project) contributed to articulating the role for which PACE-SD can play in
climate change and interdisciplinary research within USP. Furthermore, this project had also assisted
PACE-SD gained recognition as a node for climate change research in the Pacific region. This was evident
in the invitation and participation of PACE-SD investigators as advisors to a number of climate change
adaptation implementation projects including a major regional project proposal on adaptation to GEF
involving 11 PICs. In a recent development, the Australian overseas development agency, AusAID had
recently funded for Fiji a community-based climate change adaptation project to be implemented by
PACE-SD and its partners in USP. For IGCI, this project had contributed to enabling them strengthen
their initiatives in integrated assessment for climate change impact assessment. A student also graduated
with a Masters in Philosophy by contributing to modeling work on land use change in Navua. The
foregoing narration clearly demonstrated that this project had contributed to institutional capacity, more
specifically the enabling environment for further climate change research and implementation projects.
Four postgraduate students were engaged by USP as research assistants in this project and three of them
completed some postgraduate units towards their Postgraduate Diplomas and Masters degree and all
these 4 research assistants are currently working with Fiji Government’ Department of Environment and
the World Wild Life Fund for Nature (WWF) Pacific office. Their involvement in this project was certainly
a plus for them given the practical experience gained in carrying out multidisciplinary field surveys,
analysis and the synthesis of field work and relevant climate change vulnerability and adaptation
assessment literature. In fact, two of the former research assistants are senior climate change campaigners
for the WWF Pacific office. The research assistants were also exposed to presenting papers in regional and
international workshop, which is an experience for which many young graduates in other situations may
not have.
About 40 young pacific professionals working with various government and private agencies have been
exposed to the training version (TrainClim) of the SimCLIM model through training and stakeholders
workshops. In addition to these workshops, this project had also contributed to raising public and project
stakeholders’ awareness and understanding of climate change through the project team’s interaction in
various public forums and site visits. An example, which demonstrated this outcome of the project, was
the invitation from the Greenpeace Pacific Office and the Fiji Council of Churches for one of the project
investigators to be a resource person for their joint climate change and development workshop for the
Navua and Serua Provincial Councils. Such invitations were only possible because of the project’s work
in Navua town. In addition, such invitation to these workshops and project had also broaden the project
investigators’ network on climate change research and more importantly contribute scientific climate
change information and data to facilitate adaptation planning and implementation.
Future needs lie mainly in finalizing the arrangements for the incorporation of the TrainClim into the
USP V&A course, and the application of SimCLIM to other islands in the region. Both models (SimCLIM
and TrainClim) have been commercialized, which is a further testimony that SIS09 project’s contribution
to their development had been worthwhile. Nevertheless, more Pacific professionals would need to have
access to these tools and be able to apply and interpret the outputs in order to ensure that climate change
adaptation policy and planning is informed by scientifically sound information and data.
36
7 National Communications, Science-Policy Linkages and
Stakeholder Engagement
As far the National Communications of the two countries (Cook Islands and Fiji) are concerned, when the
project officially started in 2003, Cook Islands had already submitted its National Communication and Fiji
had completed its assessments and was finalizing their report. As such, this project’s direct contribution
to the first national communications was minimal. The project outputs will be useful for their second
national communications. It must however be recognized that researchers were involved with national
communications mainly providing technical expertise within the ambit of their climate change expertise
even before the AIACC project started. For example, the Principal Investigator (PI) was a member of the
Fiji Climate Change Team that was responsible for drafting the Fiji climate change policy. The PI was also
a co-author of a first “Climate Variability and Change and Sea Level Rise in the Pacific Islands Region:
8
Resource Book for Policy and Decision-Makers, Educators and other Stakeholders ”. In addition the
project investigators from USP were also instrumental in the drafting of the Pacific Island Climate
Change Framework, which was endorsed by the Pacific Island Leaders in 2005 and had since been made
part of the grand Pacific Plan. The above examples are cited to illustrate the indirect contribution this
project had made to the national communications and the interaction of the scientific community with
policy-makers and other stakeholders.
Numerous stakeholders were engaged at various stages of the project, government officials especially the
project’s focal points in the Cook Islands and Fiji (in both cases, the government departments responsible
for environmental services). They were engaged in the project formulation and implementation stages
especially in providing over sight on national priorities with implications on climate change adaptation in
the project sites. Government officials from other PICs were engaged during the project’s stakeholder
workshops and other climate related meetings and training for which the PI and associates organized
within Fiji and in the region. Local communities within the study sites were also engaged especially in
the field surveys.
8
This book was published by the Japanese Ministry of Environment and Secretariat of the Pacific Environment
Programme (SPREP) in 2003.
37
8 Outputs of the project
•
Warrick, R., W.Ye, P.Kouwenhoven, J.E.Hay & C.Cheatham, 2005: New Developments of the
SimCLIM Model for Simulating Adaptation to Risks Arising from Climate Variability and
Change in Zerger, A. and Argent, R.M. (eds) MODSIM 2005 International Congress on Modelling
and Simulation. Modelling and Simulation Society of Australia and New Zealand, December
2005, ISBN: 0-9758400-2-9, http://www.mssanz.org.au/modsim05/
•
Mataki, M; Koshy.K; Lal,M (2006): Baseline Climatology of Viti Levu, Fiji and Current Climatic
Trends, Pacific Science, 60 (1)
38
9 Policy Implications and Future Directions
The impact of climatic and natural disasters in terms of human and economic losses has risen in recent
years, and society in general has become more vulnerable to natural disasters. Those usually most
affected by natural, other disasters are the poor and socially disadvantaged groups in the PICs, as they
are least equipped to cope with them. Disaster prevention, mitigation, preparedness, and relief are the
four key elements, which contribute to and should gain from the implementation of sustainable
development policies in PICs. In addition, Environmental protection as a component of sustainable
development consistent with poverty alleviation is imperative in the prevention and mitigation of natural
disasters. Some patterns of consumption, production, and development have the potential for increasing
the vulnerability to natural disasters, particularly of the poor and socially disadvantaged groups.
However, sustainable development can contribute to reduction of this vulnerability, if planned and
managed in a way to ameliorate the social and economic conditions of the affected groups and
communities. Thus, these elements, along with environmental protection and sustainable development,
are closely interrelated. Therefore, Small Island Nations need to incorporate them in their development
plans both at the community and national levels.
Despite significant development in the region over the last decade, natural and environmental disasters
continue to pose enormous threat to sustainable development in the region. Several initiatives have been
undertaken during the past few years to better understand the impacts of environmental disasters in the
region, for example: •
The development of the Environmental Vulnerability Index (EVI) tool for application in the
region to improve understanding and management of vulnerability in SIDS;
•
The development of Comprehensive Hazard and Risk Management (CHARM) as a risk
management decision-making tool in the region; and
•
USP Training Institutes on Climate Variability and Extreme Events.
Among the disasters, climate related catastrophes are the most common in the Pacific. Climate can be
considered as a valuable natural resource, which is of enormous economic and social importance, but
which is usually taken for granted. When we are exposed to climate variability or consider the possibility
of long-term climate change, the practical importance of climate information is recognised. This natural
resource should be protected and adapted to wisely. That’s why in the past three decades there has been
great interest in research dealing with climate variability and climate change. Weather station records and
ship-based observations indicate that most PICs warmed on an average between about 0.3 and 0.8°C
during the 20th century. Although the magnitude of warming varies locally, the warming trend is
spatially widespread and is consistent with an array of other evidences. The heat content of the Pacific
Ocean has also registered a rise since the 1950s. Analyses of surface temperature data over a network of
stations in the PICs suggest that the past four years of the 21st century have continued to record higher
than normal temperatures and that the number of hotter days and nights per year have registered a
significant increasing trends.
Over the PICs, the area-averaged annual mean surface temperature rise by the end of 21st Century is
projected to range between 2.5oC and 3.5oC with no significant seasonal dependency (Table 9.1). In
addition, an average increase of only about 3 to 8% in annual mean precipitation is simulated over the
PICs. Appreciable changes in spatial pattern of mean annual, winter, as well as summer rainfall are,
however, likely. While year to year variability in rainfall during the summer season may not change
significantly, more intense rainfall spells but lesser number of wet days are simulated over the PICs for
the future thus increasing the probability of extreme rainfall events in a warmer atmosphere.
39
Models →
CCCma
CSIRO
Scenarios ↓
CGCM2
mk2
2.67
2.71
1.94
1.89
3.24
5.40
4.27
7.14
Temperature (oC)
DJF – A2
JJA – A2
DJF – B2
JJA – B2
CSM1.3
ECHAM
4
GFDL
R15b
MRI
CCSRNIES
HadCM3
2.34
2.62
2.22
2.03
2.56
2.87
2.20
2.14
2.96
3.41
2.27
2.09
3.05
3.32
2.82
2.79
2.30
2.19
1.96
1.83
3.37
3.98
2.91
2.65
2.87
3.01
2.38
2.16
6.71
2.08
3.12
4.03
5.96
8.73
4.76
5.11
6.54
10.01
2.78
7.64
-1.42
3.68
10.15
-3.71
4.19
-8.91
5.76
4.63
10.17
4.77
9.46
7.63
11.38
Precipitation (%)
DJF – A2
JJA – A2
DJF – B2
JJA – B2
9.43
7.89
5.47
*Data Processing: The original data took the form of a value for each box on a 0.5 degree latitude / longitude grid. The weighted
mean of the values from its constituent grid boxes of aggregated changes for South Pacific Region was calculated for the summer
and winter seasons (DJF and JJA). Each grid box was weighted by surface area, using the cosine of the latitude. The data are from
eight state-of-the-art A-O GCMs and values are for the changes between 1961-90 and 2070-99 (30-year mean). Two SRES Marker
scenarios of anthropogenic emissions of greenhouse gases and sulfate aerosols (A2 and B2) were considered.
Table 9.1: Seasonal Mean Climate Change over South Pacific in the 21st Century as simulated* by the state-of-theart Global Climate Models (Lal, 2004)
Temperature increase in the PICs will be accompanied by rising sea levels, salt water intrusion and large
scale inundation of the coastal areas due to storm surges; more intense precipitation events in some
regions and increased risk of drought in others; and adverse effects on agriculture, human health, fresh
water resources and coastal / marine ecosystems of the PICs. The frequency of extreme temperatures
during summer season is likely to enhance in the PICs thereby increasing the probability of thermal stress
conditions.
The watersheds in the PICs have undergone substantial changes as a result of extensive land use (e.g.,
deforestation, agricultural practices and urbanization) leading to hydrological disasters, enhanced
variability in rainfall and runoff, extensive reservoir sedimentation etc. over the past few decades. One of
the major issues relating to rainfall is the recurrence of extreme events. More frequent heavy rainfall
events are likely in a warmer atmosphere and it would result in serious flash floods. Extreme
precipitation events have geomorphological significance in the mountainous terrain where they may
cause widespread slope failures and land slides. The issue of the response of hydrological systems,
erosion processes and sedimentation could alter significantly in some parts of the Pacific Islands due to
climate change.
Climate variability in the Pacific is a combination of seasonal, multi-annual variability associated with the
El Niño and Southern Oscillation (ENSO) phenomenon and decadal variability, the latter influencing the
ENSO phenomenon itself. The major concern for impacts in the region is not with the mean climate
changes described above, but with the extremes that are super-imposed on those mean changes.
Numerous studies suggest the likely intensification of rainfall when the mean change will be only a
marginal increase. Global climate models currently suggest that the sea surface temperatures in the
region will increase by at least 10C by 2050 and the rainfall intensity in the central equatorial Pacific will
be higher affecting many Small Island States.
Recent changes over the tropical Pacific Ocean and the surrounding land areas are related to the fact that
since the mid-1970s warm episodes (El Niño) have been relatively more frequent or persistent than the
opposite phase (La Niña). The ENSO phenomenon is the primary mode of climate variability on the 2 to 5
year time scales. An increased frequency of ENSO events and a shift in their seasonal cycle in a warmer
atmosphere is likely such that the maximum occurs between August and October rather than around
40
January as currently observed. Thus, the current large inter-annual variability in the rainfall associated
with ENSO is likely to dominate over other impacts attributable to global warming.
Changes in intensity of tropical cyclones could result from changes in sea surface temperature linked to
characteristics of ENSO events. A possible increase of about 10 to 20% in intensity of tropical cyclones has
been suggested under enhanced CO2 conditions. Studies also suggest that, during ENSO events, a
tropical cyclone in the Pacific Ocean has more than a 40% chance of being severe one. Any increase in sea
surface temperature is also likely to cause an increase in wind stress on surface waters. Thus, an increase
in sea surface temperature due to climate change should lead to amplification in storm surge heights and
an enhanced risk of coastal disasters including serious damage to soils and decline in the fresh water
supplies in the fragile and vulnerable coastal regions. Many PICs are, therefore, extremely vulnerable to
climate change and will be among the first to be forced to abandon or relocate from their homes as a
response measure.
It is thus obvious that global warming could well have serious adverse societal and ecological impacts on
the Small Island Nations of the Pacific by the end of this century, especially if surface air temperature
increases approach the upper end of the projections made by the IPCC. Even in the more conservative
scenario, the models project that temperatures and sea levels will continue to increase well beyond the
end of this century, suggesting that the assessments that examine only the next 100 years may well
underestimate the magnitude of the eventual impacts.
Economic development, quality of life, and alleviation of poverty presently constitute the most pressing
concerns of many PICs. With limited resources and low adaptive capacity, these islands are facing the
considerable challenge of charting development paths that are sustainable, without jeopardizing
prospects for economic development and improvements in human welfare. At the same time, given the
inevitability of climate change and sea-level rise, they are forced to find resources to implement strategies
to adapt to increasing threats resulting from enhanced radiative forcing (as a consequence of increases in
greenhouse gases) on the climate system, to which they contribute little.
For almost a decade now, appropriate methods and tools to evaluate impacts of, vulnerability and
adaptation to climate change have been tested and are currently being used for sector specific studies in
many PICs. Following the widespread drought conditions in 1998 and 2003 faced by some Island nations
in this region, there has been an upsurge in interest and concern about adaptation linked to current
climate variability and current vulnerability in addition to the concern with future climate change. The
threat of climate change has added a new dimension to other environmental and social stressors, and
changes in socioeconomic conditions in the context of sustainable development within the risk
management decision-making framework. PICs are committed to address national sustainable
development in the region that takes into account the economic, social, and environmental aspects as also
the issues such as eradicating poverty and improving the livelihoods of their peoples by the
implementation of developmental strategies, which build resilience and capacity to address their
uniquely disproportionate vulnerabilities.
To summarize, the potential impacts of climate change on Small Island Developing States of the Pacific
include, among others, the following:
•
Inundation of deltas, estuaries and coastal wetlands,
•
Destruction of benthic systems, especially sea grass beds,
•
Loss of productivity of coastal ecosystems,
•
Flooding in coastal plains,
•
Increased coastal erosion,
•
Increased saline intrusion leading to aquifer contamination,
•
Displacement of traditional fishing sites,
•
Coral reef deterioration due to thermal stress and sea level rise,
41
•
Damage to coastal infrastructure,
•
Increased vulnerability of human settlements,
•
Loss of agricultural land, and
•
Damage to industrial infrastructure.
PICs view the impacts of climate variability including extreme weather events, climate change, and sea
level rise as an impediment to sustainable development in the region. A Regional Framework for Action
on Climate Change was endorsed by Pacific Island leaders in 2005. It is regarded as the regional blueprint
for collective action by Pacific Island governments, organizations, and individuals and is supported by an
annual multi-stakeholder round table. Regional Meteorological Directors meet annually to examine ways
to plan and prepare for extreme weather events as well as exchange of information on climate variability.
The Pacific Island Global Climate Observing System (PI-GCOS) implementation plans for the region and
the South Pacific Sea Level and Climate Monitoring Project have been functional for some years now. All
PICs are Parties to the UN Framework Convention on Climate Change (UNFCCC) and twelve nations are
signatories to the Kyoto Protocol. A number of intergovernmental regional organizations in the Pacific
provide support and technical assistance to the Parties to meet their treaty obligations.
Recognizing the adverse effects of climate change on the sustainable development pathways, livelihoods
and existence of Small Island Developing Countries of the Pacific, the international community is urged
to take urgent action to:
•
Implement the UNFCCC;
•
Ensure entry into force of the Kyoto Protocol;
•
Reduce domestic greenhouse gas emissions in addition to providing technical assistance to
developing Countries through CDM;
•
Support SIDS in the development and implementation of National Climate Change Actions
Plans; and
•
Remove technology transfer barriers.
A multilateral framework that is more responsive to their financial must facilitate the implementation of
developmental strategies in the PICs and technical needs. SIDS urge the international community to
provide financial and technical support, particularly through the GEF, and call for broadening and
strengthening of regional and national coordination mechanisms using assistance from regional
development banks and other financial institutions.
Realising the magnitude of the problem posed by climate change, the PICs have been taking the issue up
at the highest political level and have been very vocal and persuasive recently in the international fora
such as the World Summit on Sustainable Development and the Mauritius meeting of SIDS. The
commitments PICs have made at these meetings and its regional and national follow-up activities
adequately attest to the resolve of the Pacific nations to address climate change responses as best as they
can. The National Sustainable Development Strategies or the equivalents of PICs highlight climate
mainstreaming in all the national development plans. The National Capacity Self Assessment when
completed will reveal the real capacity of these nations to implement both mitigative and adaptive
measures. Current experience shows that this capacity, especially when it comes to V&A assessment and
climate scenario generation, is rather low.
42
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