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 ACTIVITIES CONDUCTED ...... 4 2.1.1 Natadola, Viti Levu, Fiji ...... 4 2.1.2 Navua, Viti Levu, Fiji...... 4 2.1.3 Aitutaki, Cook Islands...... 4 2.2 DESCRIPTION OF SCIENTIFIC METHODS AND DATA ...... 4 2.3 FIELD SURVEYS ...... 17 2.4 RESULTS ...... 18 2.4.1 Natadola, Viti Levu, Fiji ...... 18 2.4.2 Navua, Viti Levu, Fjij...... 18 2.4.3 Aitutaki, Cook Islands...... 18 2.5 CONCLUSIONS ...... 18 3. 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 ACTIVITIES CONDUCTED ...... 31 4.3 DESCRIPTION OF SCIENTIFIC METHODS AND DATA ...... 31 4.3 RESULTS ...... 32 4.4 CONCLUSIONS ...... 34 5. 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, multi- institutional 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 building in Pacific island states.2 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), a software tool in support of training in V&A assessment.3 The generic developments for VANDACLIM fueled the development of a set of prototype integrated assessment models for real places in the Pacific, like Rarotonga (Cook Islands), Viti Levu (Fiji) and Tawara (Kiribati).4 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 Bank’s Regional Economic Report (RER) for the region.5 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 to come up with the “next generation model” for integrated assessment (SimCLIM6).
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.
6SimCLIM 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 non- government 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 global- mean 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 Variables Time Temporal Spatial Climatology Phenomena Horizon Resolution Resolution Natadola Sea level 2100 Daily 5x5 m Rainfall Daily Navua Sea level 2100 Daily 25x25 m Aitutaki 1914 - 1996 Rainfall 2100 Daily 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 Global Sea Level Rise (cm w.r.t. 1990 level) Year Low Medium High 1990 0.00 0.00 0.00 1991 0.05 0.16 0.32 1992 0.10 0.32 0.65 1993 0.15 0.49 0.99 1994 0.20 0.66 1.34 1995 0.26 0.83 1.70 1996 0.31 1.01 2.07 1997 0.37 1.20 2.45 1998 0.43 1.39 2.84 1999 0.49 1.58 3.24 2000 0.56 1.78 3.66 2001 0.63 1.99 4.08 2002 0.70 2.21 4.52 2003 0.78 2.43 4.98 2004 0.86 2.66 5.45 2005 0.95 2.90 5.94 2006 1.04 3.15 6.45 … … … … 2100 21.28 52.70 91.00
Table 2.2: Projections of global mean sea level rise with different climate and sea level model parameters
Ocean resolution Data SLRg(1990~2100) GCM experiment (long. × lat. × Centre References available (cm)* levels) Flato et al., 2000; CGCM1 GS 1.8o×1.8o×29 1900-2100 CCCma 37 Boer et al., 2000 CGCM2 GS 1.8o×1.8o×29 1901-2100 CCCma 35 Flato and Boer, 2001 Gordon and O’Farrell, CSIRO Mk2 GS 5.6o×3.2o×29 1881-2100 CSIRO 33 1997; Johns et al., 2001 ECHAM4/OPYC3 Roeckner et al. (1996, 2.8o×2.8o×11 1900-2100 DKRZ 29 G 1999) Dixon and Lanzante, GFDL_R15_b GS 3.7o×4.5o×12 1900-2090 GFDL 28 1999 GFDL_R30_c GS 1.875o×2.25o×18 1900-2090 GFDL 28 Knutson et al., 1999 Johns et al., 1997; HadCM2 GS 3.75o×2.5o×20 1900-2090 UKMO 21 Mitchell et al., 1995 Gordon et al., 2000; HadCM3 GSIO 1.25o×1.25o×20 1900-2090 UKMO 22 Johns et al., 2001 MR12 GS 2.0o×2.5o×23 1900-2100 MRI 11 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 0.90 1.05 0.85 1.14 0.87 0.74 1.11 1.36 1.17 Rarot 0.97 1.03 0.84 1.00 0.89 0.71 0.94 0.96 0.95 onga
Table 2.4: Normalised sea level change for the 21st century at the study sites
) 60 m c (
)
0 50 9 9 1
t
r 40 y = 0.353x w
( 2
R = 0.8945 e 30 g n a h 20 C
l e v
e 10 L
a e 0 S 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 10 ) m c (
) 5 0 9 9 1
t r 0 w (
e 1973 1977 1986 1989 1991 1993 1995 1998 2000 g n
a -5 h c
l e v e l
-10 a
e y = 0.7039x - 8.5294 S 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 MSL change 8 to 88 cm Change
Mean sea-level + Tropical cyclone Atmospheric Barometric set-up intensity Pressure + 0 to 20% Max. Wind Wind set-up + Significant Wave set-up Return Period Wave Height + Wave run-up 50-year 10.75m Run-up elevation (current choice) + Wave energy
Adaptation Design Risk measures
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:
1.899 U = 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 time- dependent 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) T global mean temperature change Δ 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
0.80 ) e e g u n l a a 0.70 h V
t C
n e e 0.60 t a m A e m
d No Adaptation i c l a a l C p Adaptation Option 1 p
0.50 t a e t
i Adaptation Option 2 R o
f n o
0.40 % (
e
g 0.30 a m a
D 0.20
0.10
- 2 0 0 1 1 1 2 2 0 0 0 1 1 0 0 0 1 1 1 1 1 2 - 0 ...... 8 0 5 7 9 1 4 2 3 6 2 3 1 0 4 9 2 3 6 5 7 8 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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.
Present Values of Total Savings due to Expected Damages ($) Adaptation ($) No Adaptation 6,163,762 Adaptation Option 1 2,609,961 3,553,801 Adaptation Option 2 634,101 5,529,661 Etc. - -
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 traditional land for growing crops and reduction of productivity of qoliqoli7 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 (1940- 1945), 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 4000 Indian 3500 Other
n Total o 3000 i t a l 2500 u p
o 2000 P 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 Land Use Sub-category Brief Description Number Type 1 Building - 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. 2 Road Highway 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. Arterial road 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. 3 Vegetation Forest/non- This land use is distinguished as tree and shrub canopy forest from aerial photographs. A contiguous tract of vegetative cover, greater than one hectare, can be considered an area of forest. 4 Water River and its Includes all water bodies which are not the ocean. This tributaries 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. Ocean Ocean and the beach. 5 Other Residential Includes ornamental gardens, vegetable gardens used for land 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. Commercial/ Includes sand pits, excavations, bare land, stockpile areas, Community buffers around commercial/industrial buildings, school land grounds and community parks. 6 Agriculture High-value Designates land used for growing crops or dedicated agricultural grazing pastures. This land is distinguished from low- land value agriculture by its ordered land cover appearance. The regular arrangement of crop or vegetable plantations and orchard trees is generally apparent though land cover. Low-value As opposed to high-value agriculture, vacant, degraded or agricultural weedy land is commonly used for informal grazing and land 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 Number of grid-cells LUT in 1951 LUT in 1978 LUT in 1994 transition Building Building Building 255 Building Building Non-building 57 Building Non-building Building 34 Building Non-building Non-building 172 Non-building Building Building 294 Non-building Building Non-building 60 Non-building Non-building Building 409
Table 3.2: Transitions between building and non-building land uses
Percentage of all grid- Final land use (LUT ) Number of grid-cell Initial land use (LUT ) j cell transition with i (Building land use) transitions LUTj=Building Building Building 861 54% Road Building 15 1% Vegetation Building 59 4% Water Building 0 0% Other Building 86 5% Agriculture Building 564 36% Total transitions 1585 100%
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
Initial land use (LUTi) Number of grid-cell all grid-cell Final land use (LUTj) (Building land use) transitions transition with
LUTi = Building Building Building 861 73% Building Road 6 1% Building Vegetation 79 7% Building Water 3 0% Building Other 75 6% Building Agriculture 150 13% Total transitions 1174 100%
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 Buffer distances Building Road Forest Water Other Agriculture 5 metre (mean number) 3.64 0.08 0.50 0.00 3.32 0.45 5 metre (standard 1.52 0.43 1.21 0.06 1.86 1.29 deviation) 10 metre (mean number) 3.08 0.38 1.99 0.02 8.59 1.92 10 metre (standard deviation) 2.42 1.12 3.19 0.27 3.93 3.53 15 metre (mean number) 2.87 0.92 3.75 0.07 11.89 4.46 15 metre (standard deviation) 2.90 1.76 4.85 0.67 5.69 5.99
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).
Area-targets LUT Equation 1951 1978 1994 2004# 2034* Population - 633 2568 4200 4400 8800
0.00009* (Population_Navua)2 + 1544 3418 5522 Buildingi 5961 14034 0.6803*Population_Navua (1538) (3383) (5405) + 1077.3
The larger of 4489.9 Ln(Building ) – * i 8120 11688 13842 Road 24845 14186 18030 i (7996) (11930) (13566) OR
Roadi-1 10099 21280 33835 Other 5.9666 (Building ) + 886.81 36455 84619 i * i (10155) (20897) (33220) 166498 – 109312 103122 99386 Wateri 98790 92121 7788.8*Ln(Buildingi) (107239) (102910) (95951)
-17124*Ln(Buildingi) + 84968 79733 76573 Vegetation 10536*Ln(Buildingi) + 76069 70428 133338' (84051) (82311) (75130)
665581-(Building + Road + 451538 446340 436423 Agriculture Other + Water + 434120 386349 (453637) (444029) (441371) Vegetation)i
#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 Areas masked Roads All grid-cells attributed Road land use type will not change. Vegetation (steep slopes) Steep slopes will not transit land use types. Oceans and rivers 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. Buildings – community Churches, mosques, praying houses, cemeteries, grave sites, schools and civic purpose and monuments will not change to a different land use type. However, the features of the buildings may change. Buildings - known 1. Construction of a high school and expansion of the Arya developments 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. Agriculture 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) Vegetation - protected There are some mangrove areas outside the study area, which have natural areas 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 Number Percentage % Corrugated iron 1005 34.0 Concrete Brick 851 29.0 Wood (good condition) 627 21.0 Wood (poor condition) 361 12.0 Traditional Bure 53 1.8 Makeshift/improvised 29 1.0 Other materials 18 0.6 TOTAL 2944 100.0
Table 3.8: Types of housing (Navua - 2002)
27 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
0.8 e g a m
a interviews
d 0.6
e s-curve fit v i t a l e r
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
2.5
2 l e v
e interviews l d
o Log. (interviews) o l f 1.5
1 y = -0.2198Ln(x) + 1.6634 R2 = 0.4472
0.5
0 0 50 100 150 200 250 1/relative damage -1
Fig. 3.4: Transformed data showing relative damage
28 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 (CCNA- NCNA) 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 2000 l roof area 15 m2 daily usage 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 4- person 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 2000 l roof area 15 m2 daily usage 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 2000 l roof area 25 m2 daily usage 80 l/d
Just increasing the effective roof area from 15 to 25 m2 results in 72 days with 35 drought periods.
tank size 3000 l roof area 15 m2 daily usage 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 3000 l roof area 25 m2 daily usage 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 Roof Area 15 m2 20 m2 25 m2 50 22500 11000 6000 60 np 18500 12500 70 np 35500 18500 80 np np np 90 np np np 100 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: Resource Book for Policy and Decision-Makers, Educators and other Stakeholders8”. 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 CSM1.3 ECHAM GFDL MRI CCSR- HadCM3 4 R15b NIES Scenarios ↓ CGCM2 mk2 Temperature (oC) DJF – A2 2.67 2.34 2.56 2.96 3.05 2.82 3.37 2.87 JJA – A2 2.71 2.62 2.87 3.41 3.32 2.79 3.98 3.01 DJF – B2 1.94 2.22 2.20 2.27 2.30 1.96 2.91 2.38 JJA – B2 1.89 2.03 2.14 2.09 2.19 1.83 2.65 2.16
Precipitation (%) DJF – A2 3.24 6.71 2.08 8.73 10.01 2.78 -8.91 4.77 JJA – A2 5.40 9.43 3.12 4.76 7.64 10.15 5.76 9.46 DJF – B2 4.27 7.89 4.03 5.11 -1.42 -3.71 4.63 7.63 JJA – B2 7.14 5.47 5.96 6.54 3.68 4.19 10.17 11.38
*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-the- art 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.
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