a gis approach to flood modelling and mitigation in ...

A GIS APPROACH TO FLOOD VULNERABILITY MODELING IN THE NADI RIVER BASIN, FIJI

JESSY PAQUETTE

A GIS APPROACH TO FLOOD VULNERABILITY MODELING IN THE NADI RIVER BASIN, FIJI

by Jessy Paquette

A thesis submitted in fulfillment of the requirements for the degree of Masters of Science (M.Sc.) in Environmental Sciences

Copyright © 2011 by Jessy Paquette

School of Geography, Earth Science and Environment Faculty of Science, Technology and Environment The University of the South Pacific

July, 2011

Declaration of Authenticity and Originality Statement by the Author I, Jessy Paquette, declare that this thesis is my own work and that, to the best of my knowledge, it contains no material previously published, or substantially overlapping with material submitted for the award of any other degree at any institution, except were due acknowledgement is made in the text.

…………………………. Jessy Paquette S11056209 Date: / /

Statement by Supervisor The research in this thesis was performed under my supervision and to my knowledge is the sole work of Mr. Jessy Paquette.

…………………………. Dr. Eberhard Weber Supervisor Date: / /

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Dedication

In memory of flood victims all around the World, especially those that sadly became “statistics” of the January 2009 Floods in Fiji

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Acknowledgments This project would never have been possible without the financial aid from the University of the South Pacific Research Committee Grant. I am very grateful for the funding I received.

I wish to thank my supervisor, Dr. Eberhard Weber, for his continuous support and logistical aid. I also need to acknowledge the invaluable help of my co-supervisors, Conway Pene, for his advice on GPS and GIS related questions and Dr. Mark Stephens for his contributions on my manuscript.

I would like to extend my sincere thanks to the SOPAC team, Andrick Lal (also for his pictures) and Maleli Turagabeci, for their participation in my field work. I also need to acknowledge my local contacts, Kesho Sharma, Anand Kumar and Mohammed Mukhtar, for their precious help in accomplishing the survey.

I need to commend the excellent work being done by Vinesh Kumar and Maureen Hazelman at the Nadi Basin Catchment Committee (NBCC) and thank them for their support and kind invitations to their meetings.

I want to say merci to my family, Monique, Roxanne and Karyne, for supporting me all along this long process even though they were on the other side of the Earth.

Finally, I am also thankful to all the anonymous Nadi residents that help us along the way and to my friends in Suva for their moral support.

Vinaka Vakalevu!

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Abstract The January 2009 floods in Fiji were reported to be amongst the worst in the history of the country. Nationwide, 11,458 individuals were evacuated, 11 people died, and economic losses exceed F$ 113 million (Holland 2009). The Nadi River Basin, a small and very reactive watershed, was one of the worst hit with flood heights up to 3.5 meters. Little is known about the hydrology of the basin and the inhabitants living in the area are vulnerable seeing as there is no flood model to warn them of approaching floods. This paper presents a simple and affordable approach to flood hazard assessment in a region where primary data are scarce. The objectives of this project were to collect precise topographical and hydrological data, develop an accurate flood model and to create a variety of detailed datasets that could be intergraded in a flood warning system. The resulting multicriteria decision analysis (MCDA) flood vulnerably model incorporates six parameters: elevation, catchments, land-use, slopes, distance (from channel) and soil types. The Analytical Hierarchy Process (AHP) matrices where calculated in MS Excel 2003 and the GIS manipulations were done in ArcGIS 9.3. The final output, a flood vulnerability GIS, was linked to the 2007 census data to evaluate the total risk and exposure of seven focus zones in the greater Nadi area. The GIS model has revealed that 2% of the buildings are in extreme hazard, 10% are in very high hazard, 11% are in high hazard 23% are in moderate hazard, 40% are in low hazard and 14% are in very low hazard. Several interviews were conducted to verify the model’s accuracy and some minor imprecision’s were noted by the participants. However, many of these inaccuracies were caused by the models limitations or by issues that were not identified in the field survey. For that reason, it was determined that the model performed quite well considering monetary and time constraints.

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List of Abbreviations AHP – Analytic Hierarchy Process CAD – Computer Aided Design DEM – Digital Elevation Model DGPS – Differential Geographical Positioning System DISMAC –Fiji Disaster Management Council DTM – Digital Terrain Model DSM – Digital Surface Model ESRI – Environmental Systems Research Institute (company) ETGW – ET GeoWizards (software) FLIS – Fiji Land Information System FMG – Fiji Map Grid FMS – Fiji Meteorological Services GCP – Ground Control Points GIS – Geographic Information System GPS – Geographical Positioning System HEC – Hydrologic Engineering Center (US Army Corps of Engineers) HEC-RAS – Hydrologic Engineering Centers River Analysis System IWRM – Integrated Water Resources Management NBCC – Nadi Basin Catchment Committee NDMO – National Disaster Management Office NTC – Nadi Town Council MCDA – Multicriteria Decision Analysis MSL/ASML – Mean Sea Level / Average Sea Mean Level LiDAR – Light Detection and Ranging PWD – Public Works Department SOPAC – Applied Geoscience and Technology (division of SPC) SPC – Secretariat of the Pacific Community SRTM – Shuttle Radar Topography Mission TC – Tropical Cyclone TIN – Triangulated Irregular Network USP – University of the South Pacific

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Cartographic Information and Metadata Legend:

Author: Jessy Paquette Publication date: July 21st 2011 Projection: Transverse Mercator Coordinate system: WGS 1984 UTM Zone 60S Sources: Worldview 2 and Quickbird imagery: Digitalglobe.inc (via USP and SOPAC) Google Earth imagery: Digitalglobe.inc and others LANDSAT 7 imagery: Courtesy of NASA and USGS CAD data for topographic maps: Fiji Lands and Survey Department Soils map of Fiji: No metadata available 2007 Census map of Fiji: No metadata available Notes: If not specified, maps point to the geographic north and are at a scale of 1:15,000. Unless specified, all maps, graphics and tables were developed by the author. Copyright © 2011 by Jessy Paquette

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Table of Contents DECLARATION OF AUTHENTICITY AND ORIGINALITY ......................................................I DEDICATION..................................................................................................................................... II ACKNOWLEDGMENTS .................................................................................................................III ABSTRACT........................................................................................................................................ IV LIST OF ABBREVIATIONS..............................................................................................................V CARTOGRAPHIC INFORMATION AND METADATA ............................................................ VI TABLE OF CONTENTS................................................................................................................. VII LIST OF FIGURES ........................................................................................................................... IX LIST OF TABLES ...............................................................................................................................X CHAPTER 1: INTRODUCTION ....................................................................................................... 1 STRUCTURE OF THE THESIS ................................................................................................................ 1 PURPOSE OF THIS PROJECT ................................................................................................................. 2 AIMS AND OBJECTIVES ....................................................................................................................... 3 STUDY AREA ...................................................................................................................................... 4 CHAPTER 2: LITERATURE REVIEW ........................................................................................... 5 2.1. DIFFERENT APPROACHES TO FLOOD MODELING ........................................................................... 5 2.1.1. Rainfall-runoff modeling .................................................................................................... 5 2.1.2. River channel modeling...................................................................................................... 7 2.1.3. Multicriteria decision analysis modeling ............................................................................ 8 2.1.4. Variables used in flood models........................................................................................... 9 2.1.5. Limitations and comparison of the cited models .............................................................. 12 2.2. GEOGRAPHIC INFORMATION SYSTEMS (GIS) ............................................................................. 13 2.2.1. Status of digital geographic information in Fiji ................................................................ 13 2.2.2. Fundamental data types: raster information...................................................................... 14 2.2.3. Fundamental data types: vector information..................................................................... 14 2.2.4. Fundamental data types: DEM/DTM/DSM/TIN .............................................................. 15 2.2.5. Fundamental data types: non-spatial information............................................................. 16 2.3. COPING WITH FLOOD HAZARDS ................................................................................................. 17 2.3.1. Structural approaches........................................................................................................ 17 2.3.2. Non-structural approaches ................................................................................................ 18 2.4. PAST STUDIES AND LITERATURE ON FLOODS IN FIJI ................................................................... 18 2.4.1. Geography ........................................................................................................................ 20 2.4.2. Geology and pedology...................................................................................................... 21 2.4.3. Meteorology and hydrology.............................................................................................. 21 2.4.4. Human environment and flood impacts ............................................................................ 23 2.4.5. Lack of proper flood knowledge in Nadi .......................................................................... 24 CHAPTER 3: METHODOLOGY .................................................................................................... 26 3.1. EQUIPMENT AND SOFTWARE SPECIFICATIONS............................................................................ 26 3.2. FIELD SURVEYING OPERATION (PRIMARY DATA COLLECTION) .................................................. 27 3.2.1. Ground control points survey ........................................................................................... 27 3.2.2. Land kinematic / fast static survey.................................................................................... 28 3.2.3. Boat kinematic / fast static survey .................................................................................... 28 3.3. QUALITY CONTROL AND CORRECTION (SECONDARY DATA EDITION)......................................... 29 3.3.1. Topographic data .............................................................................................................. 29 3.3.2. Satellite imagery ............................................................................................................... 30 3.3.3. Other datasets ................................................................................................................... 31 3.4. PREPARING INPUTS FOR THE MODELS (PRIMARY DATA EDITION) ............................................... 32 3.4.1. Topographical input.......................................................................................................... 33 3.4.2. Hydrological inputs .......................................................................................................... 33 3.4.3. Road network input........................................................................................................... 34

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3.4.4. Land-use input .................................................................................................................. 35 3.4.5. Soil type input................................................................................................................... 35 3.4.6. Building input ................................................................................................................... 36 3.4.7. Creating the APH matrices ............................................................................................... 37 3.4.8. Objective A: “elevationnadi”............................................................................................ 38 3.4.9. Objective B: “catchmentnadi” .......................................................................................... 39 3.4.10. Objective C: “landusenadi” ............................................................................................ 39 3.4.11. Objective D: “slopenadi” ................................................................................................ 41 3.4.12. Objective E: “distancenadi”............................................................................................ 41 3.4.13. Objective F: “soilnadi” ................................................................................................... 42 3.5. CREATING A HIGH RESOLUTION DTM MODEL ........................................................................... 43 3.6. CREATING A GIS/MCDA FLOOD VULNERABILITY MODEL ........................................................ 47 3.7. MODEL OUTPUT VALIDATION .................................................................................................... 49 3.7.1. Verification method and corrections................................................................................. 49 3.7.2. Comparing verification method with other flood models ................................................. 52 CHAPTER 4: RESULTS AND ANALYSIS .................................................................................... 54 4.1. RESULTS .................................................................................................................................... 56 4.1.1. Focus on Zone 1................................................................................................................ 57 4.1.2. Focus on Zone 2................................................................................................................ 58 4.1.3. Focus on Zone 3................................................................................................................ 59 4.1.4. Focus on Zone 4................................................................................................................ 60 4.1.5. Focus on Zone 5................................................................................................................ 61 4.1.6. Focus on Zone 6................................................................................................................ 62 4.1.7. Focus on Zone 7................................................................................................................ 63 4.1.8. Focus on Zone 8................................................................................................................ 64 4.2. ANALYSIS .................................................................................................................................. 65 CHAPTER 5: DISCUSSION AND RECOMMENDATIONS ....................................................... 68 5.1. DISCUSSION ............................................................................................................................... 68 5.2. RECOMMENDATIONS ................................................................................................................. 69 5.2.1. Task 1: Acquire and share more and better data............................................................... 69 5.2.2. Task 2: Reducing soil erosion and rapid runoff from the upper catchment ...................... 70 5.2.3. Task 3: Integrate the communities in disaster risk management ...................................... 73 5.2.4. Task 4: Develop flood mitigation strategies in Nadi ........................................................ 74 5.2.5. Task 5: Development of a physical flood model .............................................................. 76 CHAPTER 6: SUMMARY AND CONCLUSION .......................................................................... 78 REFERENCES................................................................................................................................... 81 APPENDICES .................................................................................................................................... 85 Appendix I: Compilation of relevant datasets available at USP and SOPAC ............................ 85 Appendix II: Elevation AHP matrix ........................................................................................... 87 Appendix III: Catchment AHP matrix........................................................................................ 88 Appendix IV: Slope AHP matrix................................................................................................ 88 Appendix V: Land-use AHP matrix ........................................................................................... 89 Appendix VI: Distance from channel AHP matrix..................................................................... 90 Appendix VII: USDA soil types AHP matrix............................................................................. 91 Appendix VIII: Objectives AHP matrix ..................................................................................... 92 Appendix VIV: Flood vulnerability map (without corrections) ................................................. 93 Appendix X: Flood vulnerability map (with corrections)........................................................... 94

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List of Figures Figure 1: Pictures of the 2009 floods taken from the NTC offices (NTC 2009) ......... 2 Figure 2: Aerial view of Nadi Town during the 2009 floods (SOPAC 2009) ............. 3 Figure 3: Nadi River basin and catchments ................................................................. 4 Figure 4: Effect of shape on catchment runoff (Wilson, 1990) ................................... 9 Figure 5: Example of a river cross section (Lawlor 2004)......................................... 11 Figure 6: General topography of Viti Levu................................................................ 20 Figure 7: Yearly climate values at the Nadi Airport (FMS 2010) ............................. 21 Figure 8: GCP Sonaisali jetty and the corresponding imagery (Lal 2011) ................ 27 Figure 9: 4x4 with mounted R8 unit, base station and RF transmitter (Lal 2011) .... 28 Figure 10: Collection of bank heights on the Nadi River .......................................... 29 Figure 11: DEM showing errors in Fiji topographic map data .................................. 30 Figure 12: Control points used to georectify the Google Earth images..................... 31 Figure 13: Development process of the whole flood vulnerability project................ 32 Figure 14: Objective A: elevation input..................................................................... 38 Figure 15: Objective B: catchments input.................................................................. 39 Figure 16: Objective C: land-use input ...................................................................... 40 Figure 17: Objective D: slopes input ......................................................................... 41 Figure 18: Objective E: distance from channel input................................................. 42 Figure 19: Objective F: soils type input..................................................................... 42 Figure 20: Schematic view of the digital terrain model with Etching method .......... 43 Figure 21: Cross-Section view of the Burning-In technique (HEC 2009)................. 45 Figure 22: Densifying the area near the rivers with the Etching method................... 46 Figure 23: Schematic view of the GIS/MCDA flood vulnerability model ................ 47 Figure 24: Discrepancies identified in the interviews (corrections are highlighted) . 49 Figure 25: Test zone (gray grid) and 2009 flood extent provided by SOPAC .......... 54 Figure 26: Focus zones utilized in the flood vulnerability analysis........................... 56 Figure 27: Deforestation from 2001 to 2005 (green forest area / red logged area) ... 72 Figure 28: Cartoon of a defective development cycle (steve-oh.com 2011) ............. 77

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List of Tables Table 1: Scale for pairwise comparisons (Ozcan & Musaoglu 2010) ......................... 8 Table 2: Top 5 floods in the Nadi Basin since 1840 (FMS 2001; Holland 2009) ..... 19 Table 3: Rainfall monitoring performance January floods (Turner 2009)................. 22 Table 4: River level monitoring performance January floods (Turner 2009)............ 23 Table 5: Perceived flood causes / needed responses (Holland 2009) ........................ 25 Table 6: Example of an AHP matrix.......................................................................... 37 Table 7: Land-use classification process.................................................................... 40 Table 8: Inputs, tools and outputs of the digital terrain model .................................. 44 Table 9: Land-use composition within the test zone.................................................. 55 Table 10: Model data for Zone 1 ............................................................................... 57 Table 11: Model data for Zone 2 ............................................................................... 58 Table 12: Model data for Zone 3 ............................................................................... 59 Table 13: Model data for Zone 4 ............................................................................... 60 Table 14: Model data for Zone 5 ............................................................................... 61 Table 15: Model data for Zone 6 ............................................................................... 62 Table 16: Model data for Zone 7 ............................................................................... 63 Table 17: Model data for Zone 8 ............................................................................... 64 Table 18: Social vulnerability factors vs. flood vulnerability.................................... 65 Table 19: Sediment accumulation in the Nadi River ................................................. 71

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Chapter 1: Introduction Flooding represents the most common type of natural hazard and perhaps it affects more individuals and their property than all the other hazards combined (Bell 1999; Ramlal & Baban 2008). Floods can occur almost anywhere and anytime, they affect small and large areas and they can destroy houses, businesses, utilities and livelihoods. Moreover, floods claim around 20,000 lives and adversely affect 20 million individuals worldwide each year (Smith & Petley 2009). Unfortunately, scientists agree that climate change is likely to cause shifts in global weather patterns leading to an increase in the frequency of flood events and in their severity (Few 2003). However, the likelihood of flooding is more predictable than other types of natural hazards. Luckily, it is possible, and advisable, to develop adequate mitigation techniques to diminish economic losses and prevent human fatalities. Studies show that participatory flood hazard mapping is an appropriate methodology in developing countries due to the limited availability of data. This approach also educates the communities to respond and to better understand the information contained in flood hazard maps (Wood 2009). Local vulnerability assessments and GIS studies can be an important tool in achieving sustainability by directing resources and response strategies to the places were they are most needed (Gravelle & Mimura 2008). Therefore, this thesis will focus on developing a geographic information system (GIS) model to assist flood vulnerability mapping in the greater Nadi region.

Structure of the thesis This thesis is divided into six chapters. Chapter 1, the introduction, presents the project and the study area. Chapter 2, the literature review, looks at different flood modeling approaches, gives an overview of geographic information systems, summarizes different mitigation practices and finally offers a rundown of past flood studies in Fiji. Chapter 3, the methodology, focuses on the practical part of this project. A nine day surveying campaign was conducted to acquire the necessary data to create a flood model of the study area. This chapter documents the different techniques used during the survey so the experiment can be reproduced or adapted for other sites. In addition, it clearly explains how to create the topographic terrain model and the flood vulnerability model. Chapter 4, results and analysis, presents the

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outcome of the computations done with the GIS software. Chapter 5, discussion and recommendations, provides an evaluation of the results, a word of warning on geographic data, on model inaccuracies and a few suggestions for the Nadi Basin Catchment Committee (NBCC). Lastly, Chapter 6, the summary and conclusion, sums up the thesis and looks back at this project and offers insight on the difficulties and surprises encountered along the way.

Purpose of this project

Figure 1: Pictures of the 2009 floods taken from the NTC offices (NTC 2009)

The January 2009 floods in Fiji were reported to be the worst in the history of the country since the 1931 floods. Excessive rainfall brought by a large tropical depression cause severe flooding in the North, Central and Western divisions of the country. Low lying areas, like Nadi Town, were submerged for many days before it stopped raining. Some areas experienced flood waters up to three meters and severe landslides

hampered

the

efforts

of

relief

workers.

Water,

electricity,

telecommunications and other utilities were interrupted and were unavailable for several days, in some cases, several weeks. Additionally, critical infrastructure, such as roads, bridges and sewers, were damaged or destroyed by the floods. Nationwide, 11,458 individuals were evacuated, 11 people were killed and economic losses exceed F$ 113 million. On the 11 of January 2009 the Government of Fiji declared a 30 day state of natural disaster (Holland 2009).

Although this natural catastrophe could not have been avoided its impacts could have been alleviated if the proper steps were taken to analyze the dynamics of the Nadi River Basin. Better knowledge of the topography and the hydrology of the catchments will provide some insight on how to mitigate flood risks in this region of Viti Levu. This study will focus on developing a rigorous scientific approach to

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acquire (in the field and remotely), manipulate and interpret floodplain data with the help of ESRI’s ArcGIS 9.3. The results of these analysis will undoubtedly help the National Disaster Management Office (NDMO) and others organizations to better plan for future crisis.

Figure 2: Aerial view of Nadi Town during the 2009 floods (SOPAC 2009)

Aims and objectives The main aim of this experiment is to provide developing countries, like Fiji, the tools and the methodology needed to acquire high quality data, using affordable equipment, and also provide a comprehensive technique to used GIS technology to model and predict the potential impacts of floods. Other more specific objectives include: x

Conduct a field survey to acquire topographic data of the study area;

x

Create detailed maps of the Nadi River Basin;

x

Create a high resolution triangular irregular network (TIN) and a high resolution digital terrain model (DTM) of the Nadi area;

x

Create and edit vector and raster datasets of the study area (drainage area, transport network, land-use, hydrologic network, buildings and slopes) to create a base on which to develop a flood model;

x

Create a flood model of the Nadi area;

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x

Propose different mitigation methods to alleviate future floods in the Nadi River Basin;

x

Provide data for vulnerability mapping to city planers, other academics and to the national authorities.

Study area The Nadi River Basin (Figure 3) is located on the western side of Viti Levu, Fiji’s main island, and is made up of 45 catchments which vary in size: the biggest being 45 km2, the smallest being 0.2 km2 and the median size of the catchments being 11.5 km2. The Nadi is the major river in the area with an estimated river length of 62 km and a drainage area of approximately 520 km2. It flows east to west from the Naloto Range, through the Nausori Highlands, down the Nadi Valley and into the South Pacific Ocean. Its head is located at Vatutu Lake. Its mouth is situated in the intertidal zone and is dominated by mangroves off the coast of Moala village.

Figure 3: Nadi River basin and catchments

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Chapter 2: Literature Review This literature review consists of a thorough search of several online and offline scholarly databases available at the Univeristy of the South Pacific. The main online databases used for the review were Science Direct, Pro-Quest and EBSCO. The main offline database utilized for the review was the Laucala Catalogue. It encompasses the Pacific Collection, the General Collection and the Pacific Islands Marine Resources Information System (PIMRIS). In addition, the SOPAC library, situated at the secretariat in Suva, was also utilized to access documents not available at USP. In all, over fifty publications were selected and analyzed, most of them journal articles, books and technical manuals. However, governmental and non-governmental organizations (NGOs) reports and relevant websites were also consulted to create this literature review.

2.1. Different approaches to flood modeling Although floods can be caused by many different types of events, in the South Pacific, they are usually associated with excessive rainfall that occurs predominately during the cyclone season (Terry & Kostaschuk 2004). Flooding may become more severe in the future since scientists are predicting an increase in tropical cyclone intensity due to climate changes and more sustained El Niño-like conditions (Terry et al. 2004). As pointed out by Few (2003), the magnitude, speed of onset and duration of floods are influenced by many factors such as topography, soils, river alteration, vegetation, land-use and urbanization. Consequently, there are many way to approach flood modeling. In the following sections, three techniques will be presented: the first is to calculate total runoff, the second is to calculate the maximum hydraulic capacity of a river channel and the third is to rank and compare different flood variables. Each of these methods are viable, but time, budget and available data often pushes modelers towards one rather than the other of these approaches. 2.1.1. Rainfall-runoff modeling Total runoff generally consists of four components: direct precipitation, surface runoff, interflow and baseflow. Direct precipitation refers to rain that falls directly onto the streams, lakes and swamps of the basin area. Direct precipitation is usually

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ignored in the calculations since its effects are negligible. However, large lakes and swamps can act like buffers against high peak discharges, provided that they have sufficient storage capacity. Surface runoff consists of all the water that travels over the surface of the ground in sheets or channels. Surface runoff is the most important component to consider in rainfall-runoff models since it greatly affects peak discharges during rainstorms. Interflow is cause by an impermeable layer of soil, such as clay, that forces infiltrated water to flow laterally towards streams below the surface of the ground. The interflow contribution to total runoff depends mainly on the soil characteristics of the catchment and the depth of the water table. In some areas, interflow may account for up to 85% of the total runoff. Baseflow consists of the water that percolates through the soil and replenishes the water table. Eventually, this water reaches the main stream channels and contributes to total runoff. Since water moves very slowly through the ground, the outflow into streams will not only lag behind the occurrence of rainfall by several days but will also be very regular. Baseflow, therefore, normally represents the major long-term component of total runoff and may be ignored in flood modeling since it dose not effect peak discharges (Bell 1999).

Published in 1851, by Thomas James Mulvaney, the first widely used rainfall-runoff model was quite simple: Qp = CAR Were Qp is the peak discharge, C an empirical coefficient or parameter, A the size of the catchment area and R the average rainfall intensity in the catchment. This model estimates how peak discharge is expected to increase according to the total area and rainfall intensity and has been dubbed the rational method. Although the A and R variables are relatively easy to calculate the C variable requires a greater effort. One way to obtain C is to back-calculate the value from past rainfall and peak discharge observations. However, C is dependent on observed R and thus if the value of R exceeds past observations (a bigger storm) the model becomes invalid. Different graphical and mathematical techniques have been developed to avoid this downfall, nevertheless the physical interactions between rainfall and runoff in a catchment seldom follows a linear correlation (Beven 2008).

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Consequently, to calculate accurate peak discharges it is indispensable to identify the physical factors that have an effect on runoff in a catchment. The ideal model would take into consideration all the variables. However, this is impractical since runoff is a very complex process and would require a very large amount of data and calculations. Therefore, only the most important variables are calculated such as rainfall values, runoff coefficients, reservoir storage and catchment size. 2.1.2. River channel modeling Another popular approach for flood modeling is to evaluate the flow of water within a river network. River channel models, also known as hydrodynamic models, focus on the principles of conservation of mass and momentum. The velocity and depth of flow mainly depends on the inflow of water, friction losses, river slope and the width and shape of the river channel. Basic models only take into consideration the previous factors. However, more advanced models have more variables such as backwater influences (tidal and upstream influences.), spillage (overflow), natural floodplains, embanked river channels (water lodging), artificial influences (bridges, dams and levees) and tributary inflows (Sene 2009).

River channel models tend to be very specific for their data needs compared to rainfall-runoff models that can operate with a wide range of inputs. Firstly, a network of river gauges is indispensable to monitor the inflow and outflow of water into the different parts of the basin. Secondly, a set of detailed river cross-sections is needed to calculate different parameters for channel flow. Thirdly, a very accurate digital terrain model (DTM) of the whole vicinity is required to model channel and floodplain geometry. Finally, good knowledge of the area is essential to identify the roughness coefficient (see Manning’s N in section 2.1.4) and other variables that have an effect on flow. On the other hand, these models are far more accurate and are particularly well-suited for the operation critical of flood defense structures and for real-time flood monitoring (Bates & Deroo 2000).

Hydrodynamic models use dimensional approximations of the mass and momentum equations for both flows and river levels. The dimensional approximation refers to the technique used to estimate the flow of water in the river (Sene 2009). Zerodimension models uses a water level versus flow rate rating curve, one-dimensional 7

(1D) models uses the well known HEC-RAS code (storage cell approach developed by the US Army Corps of Engineers), two-dimensional (2D) models use shallowwater equations (the Saint-Venant equations) and three-dimensional (3D) models use the Navier–Stokes equations and hybrids that combine 1D and 2D approaches (Sanders 2007; Wright et al. 2008). The two-dimensional approach is the most widely employed method for flood forecasting applications since flood hydrodynamics can be truly multi-dimensional (zero and one dimension models may be inadequate). Moreover, 2D models have been extensively researched in the past decade therefore there are many robust simulation codes available presently (Sene 2009; Sanders 2007). 2.1.3. Multicriteria decision analysis modeling A final approach to flood modeling is the multicriteria decision analysis. MCDA is a technique utilized to analyze complex decision problems which often involve incommensurable variables. The use of GIS and MCDA has proven successful in multiple natural hazard and suitability analysis. The Analytic Hierarchy Process (AHP) has become one of the most widely used approaches to solve MCDA problems (Fernández & Lutz 2010). AHP pairwise comparison matrices are based on a mathematical model that was developed in the early 1970s by Thomas Saaty (Saaty 1990). For each pairing, the modelers needs to rank (from -9 to +9) criterion following the following table. Table 1: Scale for pairwise comparisons (Ozcan & Musaoglu 2010) *

*

Values

Categories

1

Equally important

2

Equally to moderately more important

3

Moderately more important

4

Moderately to strongly more important

5

Strongly more important

6

Strongly to very strongly important

7

Very strongly important

8

Very strongly important to extremely more important

9

Extremely more important

Negative values will be interpreted as “less important”

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Explanation Two elements contribute equally to the objective Two elements contribute equally to the objective Experience and judgment slightly favor one element over another Experience and judgment slightly favor one element over another Experience and judgment strongly favor one element over another Experience and judgment strongly favor one element over another One element is favored very strongly over another; its dominance is demonstrated in practice One element is favored very strongly over another; its dominance is demonstrated in practice The evidence favoring one element over another is of the highest possible order of affirmation

The mathematical model provides a relative weight for each criterion and the summation is normalized to 100 percent. To counteract uncertainty, AHP provides a mathematical test based on the properties of reciprocal matrices. A consistency ratio of 0.10 or less is a reasonable level while a consistency ratio above 0.10 requires revisions of the ranks used in the matrix (CCI 2005). However, this kind of approach should always have a procedure to analyze the uncertainty produced by geographical interpretation. 2.1.4. Variables used in flood models The first important set of values is linked to the morphology of the catchment. The size, shape, orientation and slope angles of the catchment all have a direct effect on the amount of runoff or on its travel time. Obviously, a bigger catchment area means more water; however it also means lower peak runoff since it takes more time for the water to reach the main stream channels. As seen in figure 4, the shape of the channel system directly effects the discharge time. Thus, a fan shaped catchment (B) will have a faster stream rise and similarly a faster fall than a dendritic shaped catchment (C) because of shorter travel times. It is also important to note that the orientation of the catchment will have an effect on runoff since the prevailing winds will push the storms in a particular direction effecting rain patterns (up stream or down stream) and therefore the travel time of the runoff.

Figure 4: Effect of shape on catchment runoff (Wilson, 1990)

Slopes have a huge influence on runoff speeds, concentration times and on peak discharge. All these factors will be more severe in a steep catchment. Infiltration capacities tend to be lower as slopes get steeper, since vegetation cover is less dense and soil more erodible, thus accentuating runoff (Wilson 1990).

Meteorological information is very important to create runoff models. The amount of precipitation (the total amount of rainfall), its intensity (the amount for a specific

9

time frame), its duration and frequency (the total amount of time and the time between storms), its location (over the entire basin or just part of it) and its direction (if the storm is going up or down the catchment) will all have an effect on the amount of runoff or its travel time.

Geology and pedology can greatly influence the amount of runoff. The geological origin (limestone, sandstone or basalt) and the structure of the soil (grain size, shape, distribution, orientation and arrangement) all affect the permeability of the ground. As a result, clay soils will be less permeable than sandy soils and sandy soils will be less permeable than gravel soils. The infiltration rate can be calculated with the help of infiltration equations such as the Green-Ampt (1911), the Philip (1957) and the Smith-Parlange (1978) equations. Here is the example of Horton’s infiltration equation (1933-1940): ft = ƒc + ( ƒo - ƒc )- kt Were ft is the infiltration rate at time t (mm/h), ƒc is the constant or equilibrium infiltration rate after the soil has been saturated or minimum infiltration rate (mm/h), ƒo is the initial infiltration rate or maximum infiltration rate (mm/h), k is the decay constant for a specific soil and surface (min-1) and t is the time from the beginning of rainfall (min). The infiltration capacity quickly declines during the onset of a rainstorm and then tends towards a rough constant after a few hours of precipitation. Infiltrated water fills the available storage spaces between the soil particles and reduces the capillary forces drawing water into the pores. Clay particles may swell and thereby reduce the size of the pores. In areas were the ground is not protected by vegetation or forest litter, raindrops can detach soil particles from the surface and wash fine particles into surface pores were they can impede the infiltration process. Infiltration is negatively affected if the soil has been compacted by animals or vehicles and is positively affected by the presence of burrowing animals and insects or by a well developed roots system (Wilson 1990; Beven 2008).

Different types of land-use can also effect runoff and overland flow values. For example, the removal of forest from parts of a river basin can lead to higher peak discharges. The most notable increase in runoff and overland flow comes from urbanization because of the creation of large impervious areas such as roads,

10

sidewalks, parking areas and roofs. Not only does this produce higher discharges but lag times are also reduced. The problem is particularly acute where rapid expansion led to urban sprawl without proper planning (lack of a proper drainage systems or the removal of essential vegetation cover in sensitive areas) or is even worse in informal settlements (Bell 1999; USDA 1986).

An important hydrological factor to keep in mind while creating a flood model is the status of the water table. If the ground is already saturated rain will stop percolating into the soil and will travel on the surface thus being more problematic. Stream frequency will have a mark effect on runoff rates because a well-drained catchment will have shorter concentration times. As stated previously in this section, lakes, swamps and other large bodies of water can attenuate the effects of runoff (Bell 1999; Fernández & Lutz 2010). Some factors are distinctive to hydrodynamic models. Channel geometry (such as width, depth and slope) is the first good example of this. These variables effect flow values and are usually acquired via cross-sectional surveys (figure 5) of the river but also can be attained remotely using a GIS program and a detailed digital terrain model (DTM) (Morehouse & Maidment 2002).

Figure 5: Example of a river cross section (Lawlor 2004)

The roughness coefficient, the second distinctive value, is estimated with Manning’s formula which is an empirical formula for open channel flow or free-surface flow driven by gravity.

11

V = (k/n)R2/3S1/2 Where V is the mean velocity of flow (m/s), k is the unit modifier (1.0 for metric units and 1.486 for English units), n is the Manning’s coefficient, R is the hydraulic radius (m) and S is slope of energy grade line (m/m). The most important factors that affect the selection of n values are the type and size of the materials that compose the bed and banks of the channel, water level, channel irregularities, obstructions, vegetation, sinuosity and shape of the channel. The quickest and most commonly utilized method of estimating Manning's roughness coefficient (Manning’s N) is to use tables or photographs of other similar channels (tables: Chow 1959; Van Haveren 1986, Photographs: Chow 1959; Barnes 1967; Arcement & Schneider 1984). However, most hydrologists and river engineers simply estimate n from experience (Lawlor 2004).

Flood defense structures and other obstructing structures, such as raised roads, embankments, dykes and dams, need to be incorporated in the model since they will have a great effect on flood development patterns (Morehouse & Maidment 2002).

Many other physical variables such as evapotranspiration, throughfall and albedo could be added to create more sophisticated models. 2.1.5. Limitations and comparison of the cited models Even the most sophisticated rainfall-runoff model cannot be completely accurate since it cannot take into account the complex processes that are taking place underground. Until techniques are developed to properly track and measure subsurface water these models will always be incomplete. Furthermore, even with today’s best techniques and scientific knowledge, meteorological data is still coarse and sometimes inaccurate. Doppler radar and weather satellites measure rainfall several hundred meters above ground, thus creating a bias since precipitation values can change greatly between the sky and the ground. Alternatively, rain gauges only offer punctual readings thus creating a bias since data needs to be averaged and may not represent reality (Knebl et al. 2005; Beven 2008).

12

Hydrodynamics models are expensive, time-consuming and data dependent. A massive surveying campaign must be conducted to acquire cross-section data from many parts of the catchment to have a representative sample of the river reach. This must be done by qualified technicians with professional equipment to avoid any errors. Hydraulic gauges need to be installed, monitored and maintained to have proper data to feed the model (Sene 2009).

Multicriteria decision analysis models should be employed as a first-stage analysis tool since in-depth modeling requires quantitative information about precipitation and peak flow discharges. There is some uncertainty associated with the spatial outputs of this technique since the whole process relies on the judgment of the modeler and it can be sensitive to changes in the decision of weights associated with the criteria. As for the two previous methods, ground-truthing is an essential part of the process and is recommended to evaluate the accuracy of the model (Fernández & Lutz 2010).

2.2. Geographic information systems (GIS) The term “geographic information system” was first described by Roger Tomlinsion in the early 1960s. He was the leader of the Canadian government’s natural resource mapping project called the Canadian Geographic Information System, or CGIS, which was the earliest governmental GIS program in the world (Carr & Zwick 2007). GIS can be described as follows: “an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes” (Sommer & Wade 2006). In summary, GIS allows the integration of cartography with databases and provides the tools to extract and manipulate valuable spatial information. 2.2.1. Status of digital geographic information in Fiji In Fiji, the implementation of geographic information systems started in the early 1990s; however a lack of management support and awareness of the potential uses of the technology has slowed its development (Pene 2006). Currently, GIS data is available but it is not standardized or centralized by any governmental authority. Metadata is mostly inexistent and the quality of the data varies greatly. Fortunately,

13

the current government has revived the Fiji Land Information System (FLIS) which will act has a central hub for GIS information in the Fiji and will hopefully collect and distribute datasets following ISO 19132:2007 standards (international standards for GIS data). During the writing of this paper, employees at FLIS have achieved a major milestone (setting up a server and making GIS datasets accessible to the different governmental authorities) and will hopefully make this information available to the scientific community. For this project, please refer to appendix I for a compilation of relevant datasets available at USP and SOPAC. 2.2.2. Fundamental data types: raster information Raster data is composed of an array of equally sized cells arranged in rows and columns similar to pixels in a digital image. It can be comprised of a single band or multiple bands, for instance, different wave lengths of the visual spectrum. Unlike a vector structure, which stores coordinates explicitly, raster coordinates are contained in a matrix. Each cell of the matrix contains an attribute value and location coordinates. Groups of cells that share the same value represent the same type of geographic features such as water, forest or land features (Sommer & Wade 2006). Good

examples

of

raster

data

include

scanned/georeferenced

documents

(topographic maps, navigation charts, cadastral plans and aerial photos), satellite imagery and digital aerial photos. The main particularities of raster datasets are: x

They can have multiple bands that can be used in many different scientific applications.

x

They allow easy implementation of overlay operations (E.G. map algebra), which are far more difficult, sometimes impossible, with vector data.

x

Old paper maps, plans and aerial photographs can be easily scanned and georeferenced as raster datasets.

x

They are readily available and come in a variety of formats (TIF, JPGE, IMG, GRID) that can be opened without GIS software.

2.2.3. Fundamental data types: vector information Vector data is a coordinate-based format that represents geographic features as three types of geometry: points, lines and polygons. Points are zero-dimensional features that are represented by a single pair of coordinate. Lines, or polylines, are onedimension features that are represented by vertices joined in a sequence. Polygons,

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also called areas, are two-dimensional features that are represented by vertices joined in a sequence forming a closed shape. Attributes are associated with each vector feature, as opposed to a raster data model, which associates attributes with grid cells (Sommer & Wade 2006). No measurements can be done on points; however polylines can be queried for their length (start, middle and end point) and polygons can be queried for their area, perimeter and centroid. The main particularities of vector datasets are: x

They can be easier to register, scale, and re-project, which avoids blocky appearance for object boundaries.

x

They are more compatible with relational database environments, where they can be part of a relational table as a normal column and processed using a multitude of operators.

x

Vector file sizes are usually smaller than raster data, which can be 10 to 100 times larger than vector data.

x

Vector data is simpler to update and maintain.

x

They allow much more sophisticated spatial and network analysis capabilities.

2.2.4. Fundamental data types: DEM/DTM/DSM/TIN Although definitions may vary from one study to the other, generally speaking, digital elevation model (DEM) is a common term used to identify a raster based dataset (x-y matrix) with continuous elevation values (z-values). The expression digital terrain model (DTM) is used to identify a matrix that corresponds to bare earth elevation values. The term digital surface model (DSM) is used to identify a matrix that corresponds to a mixture of bare earth, tree tops, and buildings elevation values (Sanders 2007). Typically, these datasets are generated with GIS software using spot heights and/or contours acquired from topographic maps or aerial photographs. However, these datasets tend to offer a low vertical accuracy (±1–10m) and have low spatial resolution (90–250m) which is generally unsuitable for flood modeling. To attain higher vertical and horizontal accuracy ground-based or airborne surveys can be conducted. Ground surveys are done with DGPS and can offer decimeter resolution for X-Y-Z values. Airborne surveys are done with LiDAR and offer a vertical resolution of 5–11cm with spatial resolutions of 1–5m. However, DGPS surveys can be difficult to undertake (depending on the topography and the

15

vegetation), they can be time consuming (depending on the size of the area) though LiDAR surveys can be prohibitively expensive (Rayburg et al. 2009). Spaceborne sensors can also provide data to create DEMs. Vertical accuracy of these datasets depends on the sensor type and the altitude of the spacecraft. For example, the shuttle radar topography mission (SRTM) offers a Z accuracy of about 10 m (RMSE). However, vegetation will degrade the vertical precision since radar will penetrate the tree canopy some distance before reflecting to the spacecraft and thus create a bias. Another drawback of radar based DEMs are “speckles”, or random noise, which degrades relative vertical accuracy particularly on floodplains. The global SRTM datasets offer 3 second (90m) horizontal resolution and covers latitudes between 60 N and 56 S (Sanders 2007). Satellites like ASTER and TanDEM-X offers better resolution datasets but suffer the same drawbacks as the SRTM DEMs.

Triangular irregular networks (TINs) are vector data structures that partition geographic space into contiguous, non-overlapping triangles. The vertices of each triangle are sample data points with x-, y-, and z-values. These points are connected by lines to form Delaunay triangles and are used to store and display surface models (Sommer & Wade 2006). TINs can be created with the same data sources as DEMs and each one of them can be converted to one or to the other using GIS software. 2.2.5. Fundamental data types: non-spatial information Non-spatial information is typically stored in tabular format (DBF, XML, XLS) and is linked to vector geometry with a common identifier (Sommer & Wade 2006). For example, in a GIS forestry inventory, a point representing a tree could be linked to a database which will identify its species, its diameter, its height and its health. Although this information is not spatial it could be analyzed spatially to identify patterns. For example, in a plantation all the palms 100 meters from a specific tree suffer from the same parasite. One could deduce that the origin of epidemic must have started inside these boundaries close to that specific palm. Therefore, pest control efforts could concentrate in that specific area instead of the whole plantation. Furthermore, with recent web based applications, this non-spatial information can be queried, modified and even distributed via the Internet using a simple web browser.

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2.3. Coping with flood hazards Most people in developing countries take an ambivalent attitude toward flooding since they have long lived with the duality of flood impacts: on one hand floods bring death and destruction and on the other hand floods bring life and prosperity. The classical example for this is ancient Egypt where agricultural wealth very much depended on the frequent flooding of the Nile River to fertilize the soils along the river. The failure of these floods often caused famine, social and political unrest. Also other cradles of human civilization, like Mesopotamia, India and China are closely connected to rivers (Euphrates, Tigris, Indus, Huang He (Yellow River) and flooding events. In many of these countries even today floods are still an essential part of agriculture because they irrigate and fertilize fields, flush out salts and toxins and recharge water reservoirs (Few 2003). Therefore, it is crucial to recognize that westernized approaches to mitigate flood impacts may not be the best option given that large-scale engineering interventions may have negative effects on the economic and environmental benefits brought by seasonal flooding. Indeed, the canalization of many rivers in so-called developed countries and subsequent settling in the floodplains are major causes for wide-spread flooding disasters 2.3.1. Structural approaches Structural measures generally refer to engineering interventions such as river channel modifications, embankments alterations, river bed dredging, flood diversion schemes, emergency spillways, dike systems, upstream reservoir building, and retention/multipurpose dams designed to control the flow of rivers and abate or control the spread of flooding (Oosterom et al. 2005; Few 2003). Though prominent in the western world, structural approaches have achieved mixed success. Many of these structures have failed, due to design flaws or poor maintenance, and have exacerbated flood hazards or have proven costly in environmental terms (loss of ecological services). The financial cost of these structural solutions, however effective they may be, also makes widespread coverage unfeasible for many floodprone developing countries (Few 2003). Alternatively, proper land-use management is far more effective in alleviating floods in developing countries. Typical strategies include mechanical treatment of slopes, such as terracing (to reduce the runoff) and

17

reforestation (to increase evaporative losses and to reduce soil removal) (Smith & Petley 2009) 2.3.2. Non-structural approaches Non-structural measures refer to mitigation and adaptation techniques designed not to prevent floods but to reduce the short-term and long-term negative impacts caused by flooding. They typically include formal flood warning systems, evacuation programs, flood awareness and education campaigns, building regulations, house construction methods and resettlement (Oosterom et al. 2005). In addition, proper land-use policies that help to prevent wildfires, over-grazing and clear-cutting can reduce flood peaks since they protect vegetation which reduces surface runoff (Smith & Petley 2009). Recently, many NGOs are trying to integrate traditional knowledge into modern mitigation plans. However, changes in population, the environment and flood regimes can make these mitigation techniques obsolete. Therefore, it is important not to over-romanticize these indigenous capacities (Few 2003). Nevertheless, non-structural measures and vulnerability reduction at community level has it merits and is the recommended approach to be taken in developing countries like Fiji.

2.4. Past studies and literature on floods in Fiji Information on floods in Fiji is quite scarce and only a few individuals and organizations have focused their efforts on this topic even though floods have provoked considerable economical and human losses over the past years. This section is a synthesis of relevant sources. The most detailed study on flood control in Fiji was conducted by the Japan International Cooperation Agency (JICA) in 1998. This four volume report, produced by the Yachiyo engineering company, proposes a master plan for watershed management for the four major rivers in Viti Levu (Rewa, Nadi, Ba and Sigatoka River). The report covers a multitude of subjects such as topography, geology, meteorology, hydrology, runoff analysis, flood control plans, water quality, environment, land-use, forest cover, soil erosion, costal erosion and socio-economic indicators. Two structural measures were proposed by the engineering company to control the flooding of the Nadi River. A diversion channel 14 km from the mouth of the river and a cut-off channel for one of the meanders near

18

Nadi Town. All the proper calculations, field studies and environmental impact assessments were made (and are available in the report) but no action was taken by the Fiji government.

Three factors make the Nadi River Basin predisposed to flooding. Firstly, Nadi is on the western side of Viti Levu which makes it more susceptible to be hit by tropical cyclones since TCs have a tendency to come from the north or the west in the South Pacific (Terry 2007). Secondly, the catchment’s orientation (west to east), its size (small), its gradient (steppe > 30°) and its geology (volcanic) makes it very reactive to rainfall events. Thirdly, most of the forest cover in the lower part of the catchment has been removed to make space for sugar cane farms which increases surface runoff in those areas (JICA 1998). A total of 30 floods where observed in the Nadi watershed between 1840 and 2000 (FMS 2001). Some of them were caused by hurricanes/cyclones and others by tropical depressions. Slow moving storms have the greatest impact and the most intense cases of flooding occur during the wet season. Table 2: Top 5 floods in the Nadi Basin since 1840 (FMS 2001; Holland 2009) Date 2009 Jan 9-11 1999 Jan 19

Flood cause Unnamed tropical depression Tropical depression called “The Beast”

1997 Mar 8

Hurricane Gavin

1993 Feb 26-27

Tropical cyclone Polly

1931 Feb-Mar 21-2

Unnamed hurricane

Description Worst floods in Fiji since 1931, F$ 34.8 million in damages in Nadi alone, 11 fatalities. Highest flood levels recorded in Nadi (7.25m a.s.m.l.), F$ 14 million in damages, 7 fatalities. 3rd highest flood levels recorded in Nadi (6.66m a.s.m.l.), no fatalities. 2nd highest flood levels recorded in Nadi (7.06m a.s.m.l.), damage to crops and property, 3 deaths Worst flood in Fiji with major damages all around Viti Levu and more than 206 fatalities.

Future trends in climate and tropical cyclone activity is very hard to determine. One could easily conclude that rising sea-surface temperature, due to continued anthropogenic emissions of green house gases, would increase the total number of cyclones (since high sea-surface temperature is one of the major factors in the creation of TCs), however, this assumption would be false since several other variables, such as vertical lapse rate, wind shear and relative humidity, also play a critical role in TC formation. Then again, when it comes to cyclone intensities an increase in the sea-surface temperatures will cause higher maximum wind speeds, greater precipitations and longer cyclone lifespans. Other possible side-effects of climate change are more persistent El Niño episodes which will change the pattern of cyclone origins (less clustering and more spreading), more storminess east of the

19

180° of longitude, TC track directions tending to be more southerly and extent of TC tracks to be farter poleward before cyclone decay (Terry 2007). 2.4.1. Geography

Figure 6: General topography of Viti Levu

Viti Levu (10,389 km2) is the largest island in the Fiji archipelago. It is divided in two by the Nandrau plateau (figure 6). This plateau contains many nine-hundred meter peaks which are very rugged. It also contains the highest mountain in Fiji: Mount Tomanivi, also know as Mount Victoria, with a summit of 1,323 meters above sea level. The Nandrau plateau is surrounded by the highlands which are comprised of many well rounded hills with a mean elevation from 300 meters to 600 meters. These hills have a strong influence on both climate and vegetation. The east highland is wet and covered with dense tropical rain forest whereas the west highland is dryer and mainly covered with grassland (a result of anthropogenic intervention). In the western half of the island, the upper parts of river channels are steep with many boulders whereas the lower parts of the river channels have hilly terrains with flat alluvial terraces and floodplains at the bottom of the valley. The highlands have natural vegetation while the coastal hinterlands have sugarcane fields (JICA 1998).

20

2.4.2. Geology and pedology Viti Levu is mainly composed of various types of igneous and sedimentary rocks derived from volcanic rocks dating from the Early Cenozoic (40 to 50 millions years ago) to present. Five main series were identified in the existing geological maps and bulletins. The Eocene-Miocene series comprise of basaltic and andesitic flows, rhyolitic and basaltic lava, associated pyroclastic rocks, sandstone, mudstone, massive and minor limestone and various conglomerates. The Miocene-Pliocene series is composed of andesite lava and andesitic pyroclastic rocks, sandstone, mudstone, marl, limestone and various endogenous and sedimentary conglomerates. The Pliocene-Pleistocene series consist of basaltic lava, breccia and conglomerate, andesite lava sandstone, marl, minor limestone and other sedimentary conglomerates. The Holocene series is comprised of alluvium which is recent unconsolidated sand, gravel, silt and clay. Finally, intrusive rocks vary from basic to intermediate plutonic rock ranging from gabbro diorite and tonalite. The southern part of Viti Levu is highly faulted whereas the northern part does not have any major faults. They run in a NE-SW and NW-SE direction prominently. It is thought that these faults were created by the Colo Orogeny which took place in the Middle to Late Miocene (inferred around 12 to 7 millions years ago). The Nandrau plateau was formed in this doming phase and the island was uplifted and tilted (Rodda 1984). 2.4.3. Meteorology and hydrology 1400

28

1200

27

1000

26

800

25

Rainfall 2009 (mm)

600

24

Average temperature (°C)

400

23

200

22

0

Average rainfall (mm)

21 Jan Feb Mar Apr May Jun Jul

Aug Sep Oct Nov Dec

Figure 7: Yearly climate values at the Nadi Airport (FMS 2010)

There is distinct seasonality in precipitation: a hot and wet summer season (from December to May) and a cooler and drier winter season (from June to November) (Terry & Kostaschuk 2004). The Fiji Meteorological Services (FMS) stated in its annual climate summary that 2009 was the fifth wettest year over the last two

21

decades, the mean annual rainfall was 359.7mm above the 1971-2000 long term average (2379.1mm) with significant variation across the country and the wettest January in 52 years (figure 7). From 1970 through 2000, 40 tropical cyclones were tracked through Fijian waters (Terry et al. 2004). Since the upper reaches of the river basins have rugged volcanic topography, there is a high degree of hydrological short-circuiting during the passage of tropical cyclones. Hydrographs show very large concentrated peak discharges influenced by the intensity and duration of the storms and their track direction in relation to river basin orientation. (Terry et al. 2004). Table 3: Rainfall monitoring performance January floods (Turner 2009) * Station Name Tokaravutia Monasavu Nasogo Rewasau Navai/Nasog Navai Nadrau Nadarivatu Ba Wainabua Navala Bukuya Vatutu Dam Moliveitala Naboutini Tubenasolo Magodro

National # 1787014 178708 1786012 1787011 1786010 1776910 177794 177591 1777912 1787016 1776810 1777710 177765 1777512 1777510 1778611 1777613

Location Monasavu Monasavu Monasavu Monasavu Monasavu Monasavu Monasavu Monasavu Koro Monasavu Ba Ba Nadi Nadi

Instrument TBRG - HOBO Pluviograph TBRG - HOBO TBRG - HOBO TBRG - HOBO SIAP TBRG - HOBO TBRG - HOBO TBRG - HOBO SIAP TBRG - HOBO TBRG - HOBO Pluviograph TBRG - HOBO

Nadi

TBRG - HOBO

Performance during floods Data O.K. ? Data O.K. ? Data lost - logger error? Data lost - Chart Error No data on archive Only daily totals for the period No logger installed during event Discontinued - No funding Data O.K. Raingauge blocked in period? Data lost - Chart wasn't changed Data O.K. No data on archive Data O.K. No data on archive

According to the JICA report, there were 7 staffed and 27 automated water level gauging stations (three in the Nadi Catchment) operating in August 1996 on Viti Levu. Of these 27 automated stations, 5 (one in the Nadi Catchment) were located within the tidal influence area. However, gauging stations in western Viti Levu are sparse and are subject to malfunctions, thus hydrological data is incomplete. In addition, there was 112 raingauges stations (77 automatic and 35 manual stations) operating in August 1996 on Viti Levu. Nevertheless, most of these gauges are concentrated around the Monasavu dam (30 gauges within 15 km of the dam) and do not provide sufficient coverage for Viti Levu, as a result, rainfall values need to be extrapolated for many areas on the island (JICA 1998).

A recent Pacific HYCOS / SOPAC survey declared that most raingauges operated by Public Works Department (PWD) recorded high intensity rainfall during the 2009 *

Green : data available / Yellow : data unavailable / Pink : status of data unknown

22

flood, but no extreme values except for the Tikituru gauge which recorded 114.5mm of rainfall in a 10 minute period. Unfortunately this record is too short (less than 12 months long) to conduct a meaningful flow analysis. Moreover, the three PWD raingauges that had long enough records for trend analysis sadly have large data gaps and therefore computations cannot be accomplished (Turner 2009).

Furthermore, a number of the PWD rivergauges were not operational during the flood period due to equipment failure or lack of maintenance. Others did record data but missed the flood peak due to equipment malfunction and flood levels exceeding the range of equipment. Consequently, analysis is impossible and peak levels needed to be estimated from flood marks which the quality is questionable. None of the river flow sites that were rehabilitated for the JICA report recorded the flood peak data. Only one out of the 6 relevant automatic river recorders in the Nadi and Ba catchments managed to capture the peak event. Data archives considered in the report indicate a systematic failure of the hydrological monitoring network within Fiji since the early 1990s (Turner 2009). In summary, there is a lack of good quality, long term hydrological data required for meaningful analysis (Turner 2009; JICA 1998; Yeo et al. 2007). Table 4: River level monitoring performance January floods (Turner 2009)* Station Name Toge Koro Votualevu Yavuna Nadi Bridge Nawaka

National # 127501 129400 425301 425200 424330 425201

Location Ba Ba Nadi Nadi Nadi Nadi

Instrument OTT Chart recorder P/T A-OTT Shaft Encoder/Logger P/T

Performance floods Equipment failure No Data – No logger Equipment failure Data O.K. No Data - Site Inoperable No Data - No logger

The Hydrographic Office Fiji Islands Maritime Safety Administration operates 11 tide gauges in Fiji. The Suva tide gauge has the longest and most reliable record (1975–1995) for tidal data. Using a purely statistical process, Solomon and Kruger (1996) used these records to calculate storm surge return intervals and the following storm surge water level values: 1 year 0.13 m, 2 years 0.28 m, 5 years 0.48 m, 10 years 0.63 m, 25 years 0.83 m and 50 years 0.98 m (Gravelle & Mimura 2008). 2.4.4. Human environment and flood impacts Nadi is Fiji’s third largest town with a population of 42,284. According to the Fiji Islands Bureau of Statistics, the town population was of 11,685 individuals and the peri-urban area population was of 30,599 individuals at the time of the 2007 census 23

(FIBS 2010). Around 9,624 households were identified by SOPAC during their survey for the economic costs of the January 2009 Nadi floods. Nadi is a hub for the tourist industry and harbors Fiji’s international airport and several major tourist hotel chains which are critical assets to Fiji’s economy (Gravelle & Mimura 2008).

The Nadi River is a focal part of life for the local community: 63% of interviewed households stated they relied on the Nadi River for different purposes. The most common answer was for fishing, although people also relied on the river for irrigation and for washing during water cuts. Water quality was reported to have deteriorated significantly during the flood. Of the household sampled, 70% stated the water in their taps and/or in the river was not safe to drink following the floods, or was extremely dirty. Many health impacts arose either directly from the flooding (injuries) or as a result of the subsequent poor environmental conditions (sickness). Of the household interviewed, 31% complained about one or many of the following health problems: diarrhoea/dysentery, scabies, eye infections, cuts, bruises, knocks, dislocations, fever, paralysis, boils/sores/skin infections, headaches/body aches, coughs/asthma, vomiting, colds/flu, typhoid, ringworm and dengue/malaria. On average, only 1% of families and 12% of businesses had any flood insurance, thus flood recovery was impeded or impossible (Holland 2009). 2.4.5. Lack of proper flood knowledge in Nadi Predicting and managing flooding in Nadi is difficult due to the lack of historical records and due to the lack of proper gauging equipment. No proper hydrograph could be found to comment on the average discharge of the river; however, flow capacity of the Nadi River was estimated with non-uniform flow computation based on the river’s profile and cross sections information. According to the JICA engineers the lowest specific discharge of the Nadi River is of 0.59m3/sec/km2 at the confluence of the Nawaka Creek (7.5 km from the mouth, near Nadi Town) and the highest specific discharge (tested) is 20 km from the mouth with a value of 2.07m3/sec/km2. They evaluated the flow capacity at the mouth of the Nadi River to be of 300m3/sec which is relatively small compare to the three other rivers in the report (Rewa: 4,800 m3/sec, Signatoka: 2,600m3/sec and Ba: 2,000 m3/sec) (JICA 1998).

24

Regular dredging of the Nadi River to deepen the channel and reduce flooding can be seen as only a temporary measure (Gravelle & Mimura 2008). Therefore, more sustainable measures are deemed necessary and should be implemented. This includes reforestation in the lower catchment, soil conservation, regulating land development, and protecting natural wetlands. These activities improve the water retention function of drainage basins and maintain river flow capacity by avoiding excessive silting of channels (Terry et al. 2004). Regrettably, soil erosion and rainfall-runoff is exacerbated by increased pre-harvest burning of sugarcane fields, poor agricultural practices, ad hoc housing and other developments in the peri-urban areas around the major towns and cities. All of these recent changes have affected the balance of ecological services and consequently the environment is incapable to cope with large amounts of rainfall (Lal et al. 2009).

Flood perception is also critical since misinformed individuals cannot take proper actions to alleviate risks. The following table, taken from SOPAC’s economic costs report, clearly shows that most of the interviewees’ do not understand the complex factors behind the floods in Nadi. Therefore, most of them cannot provide a proper mitigation response (I.E. reforestation of the upper watershed and proper land management). Table 5: Perceived flood causes / needed responses (Holland 2009) Perceived cause/ needed response Need to address poor drainage system / improve culverts Need to dredge / widen river Need to improve disaster warnings Need to improve infrastructure (build higher bridges, upgrade, build retaining walls etc.) Need to extend the Nadi Bridge Need to improve development processes / planning (it needs to consider flood risk) Location and design of the new Qeleloa Bridge (it impedes the natural flow of water) Need to relocate town (Nadi) Need to improve flood management system

Tot % 56% 18% 6% 5% 4% 4% 4% 2% 1%

Total

100%

Furthermore, awareness of flood risks in Nadi needs to be increased along with proper responses during flood alerts. Even though floods are a natural occurrence in Nadi, only half of the interviewees were aware they were in a flood risk area. Raising community awareness is likely to enhance disaster preparedness for future floods. Community awareness materials on disaster preparedness and dissemination of disaster warnings need to be improved (Holland 2009).

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Chapter 3: Methodology As indicated in section 2.3.4 of the literature review, the rainfall-runoff and the river channel modeling techniques were not feasible for the Nadi River Basin given the lack of accurate data and proper instrumentation to develop such models. Therefore, the multicriteria decision analysis model was selected for this project. Furthermore, the underlying hypothesis of this thesis is that a respectable flood model can be created with limited funds and time. To accomplish this, the logical step forward was to couple the MCDA technique with the ArcGIS program, DGPS technology and available datasets to speed up the process and to avoid additional costs. The first step in developing the flood model was to acquire secondary data to build-up the background information for the model and to prepare an efficient survey of the study zone. Sources of this information included satellite imagery, topographical, hydrological, pedological and statistical datasets. The second step was to conduct the survey to acquire primary data to cover the areas were secondary data was inexistent (mostly the low-lying areas under the 10 meters altitude mark). The third step was to create the new datasets from the collected data and to merge it with existing datasets. The fourth step was to create the AHP tables, the topographic terrain model and the GIS models with Excel and ArcGIS. The fifth step was to run the models and combine the results with the statistical data of the 2007census. The sixth and final step was to verify the accuracy of the model and analyze the results.

3.1. Equipment and software specifications DGPS points were collected with the Trimble GNSS R8 GPS receivers combined with the TSC2 controller. Accuracy levels for static surveying are of 3 mm (+/- 0.1 ppm RSM) for horizontal positions and 3.5 mm (+/- 0.4 ppm RSM) for vertical positions. Accuracy levels for kinematic surveying are 10 mm (+ 1.0 ppm RMS) for horizontal positions and 20 mm (+ 1 ppm RMS) for vertical positions. All computations were done on an Intel base personal laptop running Windows 7 64-bit with a dual-core processor (2.20GHz) and 3.0 GB of RAM. Models and maps were developed with ESRI’s ArcGIS desktop 9.3.1 (with Spatial Analyst and 3D Analyst extensions). Numerous transformations and edits were done using ET GeoWizards (ETGW) that is freely downloadable at http://www.ian-ko.com/. In addition, some

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datasets were created using HEC-GeoRAS and HEC-GeoHMS extension. Both of these ArcGIS plug-ins are freely available at http://www.hec.usace.army.mil/. Finally, the analytic hierarchy process matrices were calculated with Microsoft Excel 2003 based upon Kardi Teknomo’s AHP spreadsheet tutorial available on his website at http://www.people.revoledu.com/kardi/tutoria/AHP/.

3.2. Field surveying operation (primary data collection) The survey was conducted from the 13th to the 21st of January 2011 with the help of Applied Geoscience and Technology Division of SPC (SOPAC) and some local contacts. The team first collected Ground Control Points (GCPs) to verify the accuracy of the satellite imagery (see appendix I for more details on the imagery). Later, a land survey of the study area was carried out on foot and with a 4x4 vehicle. Finally, a boat survey was done 6 km downstream and 4 km upstream from the Nadi Bridge. 3.2.1. Ground control points survey

Figure 8: GCP Sonaisali jetty and the corresponding imagery (Lal 2011)

The 20 ground control points were used to verify and correct the georeferencing of the two satellite scenes that were utilized in this project. Two GCP were taken simultaneously to “trilaterate” their positions with a base station that was installed on a verified geodesic station. Each point was occupied for a period of 20 minutes and GPS readings were collected every 1 second and later corrected with the Trimble software. The locations were selected following specific criteria: 1) they needed to be easily identifiable on the satellite images, 2) they needed to be on the ground to avoid any parallax errors and 3) they needed to be evenly spread out over the two images to maximize the accuracy of the georeferencing (figure 8).

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3.2.2. Land kinematic / fast static survey Over 40,000 points were collected and used in the development of the DTM model. Two R8 units were employed during this task: one was mounted to a 4x4 pickup truck and the other was used for a foot survey. The 4x4 vehicle was driven over the entire study area including streets, back roads, and trails in and around Nadi. The R8 unit was attached to a 2.45 meter pole to get better radio reception and to prevent multipathing of the GPS signal. Points were collected each second, stored in the unit’s memory and downloaded to a laptop at the end of the work day. The foot survey covered the banks and areas around Nadi Town and the adjacent villages. About 200 fast static points were collected (the surveyor needed to stay still for 5 second for the point to be taken) for this part of the task. At all times, positions were corrected by a base station that was installed on a verified geodesic station. The signal was sent via shortwave radio and amplified by a repeater station (figure 9).

Figure 9: 4x4 with mounted R8 unit, base station and RF transmitter (Lal 2011)

3.2.3. Boat kinematic / fast static survey Over 4,000 points were collected onboard a flat bottom boat. The R8 unit was mounted to a 3.45 meter pole that was attached to the center of the craft and coordinates were taken at an interval of 10 seconds. In addition, about 40 fast static 28

points were collected in the center of the channel, on the left bank and the right bank of the river.

Figure 10: Collection of bank heights on the Nadi River

While taking the 5 second fast static positions observations were made on the depth of the river using a weighed line and tape measure. Likewise, the heights of the banks were measured with the tape measure when they were inaccessible or with the R8 unit when they where accessible by foot as seen in figure 10. As with previous methods, positions were corrected via radio link to the base station.

3.3. Quality control and correction (secondary data edition) Secondary data needs to be verified for inconsistencies and errors. This is very important since a single mistake could jeopardize the quality of the GIS model. The first step was to convert all the datasets to the same projection and the same coordinate system (Transverse Mercator / WGS 1984 UTM Zone 60S) to avoid mismatching layers. The second step was to inspect the quality of the data and correct the inaccuracies. The third and final step was to clip the data to fit the study area to avoid extra computations that would slowdown or crash the computer. 3.3.1. Topographic data The Nadi Basin is covered by four topographic map sheets: L27, L28, M27 and M28. These 1:50,000 maps, with 20 m contours line, were created in the 1990s (from aerial

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photography) and cover the Nadi bay area and the Nausori Highlands. The Computer Aided Drawing files (CAD) data for these sheets (13 files in all) were acquired and exported to shapefile format and merged to form a single continuous layer. The map borders and positioning circles were deleted to obtain only contour lines. This was done by isolating the appropriate polylines with the select by attribute SQL interface. A quick look at the attribute table revealed elevation errors for certain polylines. Some were incorrectly coded values (9,485.091 m -8.82 m and 9.97 m) are impossible for these 20 m contour maps. Also, several contours were coded correctly in some sections and, in other sections, had errors. When the contour polylines are converted to TIN/DEM these inconsistencies become obvious as seen in figure 11.

Figure 11: DEM showing errors in Fiji topographic map data

Errors show up as coarse marks in the gradient representing changes in topography. The faulty values needed to be manually corrected in the attribute table before any computations could be done. Furthermore, an assessment of high slope areas (slopes > 39°) is particularly good for finding mislabeled contours. This can be easily achieved using the slope function in 3D Analyst (Yang et al. 2005). 3.3.2. Satellite imagery Georectification of the two satellite scenes was a crucial task since many of the new datasets that needed to be created relied on the proper positioning of these images. This was accomplished using the Georeferencing toolbar and the GCPs collected during the survey. The Georeferencing tool allows the user to position raster datasets (scanned maps, aerial photography and satellite imagery or any other image file) to a defined geographic location using map coordinates, a coordinate system and a map projection. This is an essential step in creating a 1D representation of the earth surface which is in reality a 3D curved surface. This is done by aligning the raster

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dataset with control points. The control points are used to build a polynomial transformation that will shift the raster dataset from its existing location to the spatially correct location (ESRI 2011). Three GCP were employed in each scene (total of six). Another two GCPs, common to the two scenes, were utilized to fit the two scenes together. Finally, two visual points on the images were selected (a part of the Nadi Airport runway and the Narewa Church) to fine-tune the merge. To verify the exactitude of the positioning, the survey points collected with the vehicle were superposed over the rectified images. A visual inspection revealed that the survey points were following close to the centerline of the roads as anticipated by a proper referencing.

Screenshots from Google Earth were also utilized to fill the gaps not covered by the two scenes. ETGW was used to export control points in KML format. This grid of points was imported into Google Earth to identify the specific zones that needed to be collected. To avoid image distortions the simulated relief (used in the 3D perspective) was turned off and a constant eye level/scale of 400 meters was utilized to provide a uniform coverage of the entire area. To maximize the screenshots acquisition all the menus and toolbars were hidden which allowed a bigger viewable area in the display window. The screenshots were exported to TIFF format and then georectified with the help of the control points in ArcGIS (figure 12).

Figure 12: Control points used to georectify the Google Earth images

3.3.3. Other datasets The shapes of the 2007 census Enumeration Areas (EAs) in Fiji were available in a vector file and generously provided by the Fiji Bureau of Statistics. Theses EAs were then linked to an Excel spreadsheet of the 2007 census data of the Ba province (also from the Fiji Bureau of Statistics). The shapes were 165 meters southwest of their

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correct position. This was probably because of a digitizing error or a projection error. Therefore, the whole dataset was shifted to the correct position using the road network to identify the proper location of the enumeration areas.

The flood area and the high water marks, provided by SOPAC, were converted from MapInfo format to ESRI shapefile. The flood area shapefile needed to be shifted 62 meters to the north to be in its true position. This was probably because of a digitizing error or a projection error. However, the high water mark inaccuracies cannot be a projection or digitizing error since these inaccuracies vary from a few meters to fifteen meters and do not follow a clear pattern. Therefore it was assumed that this was the result of poor GPS reception (caused by multipathing) during the data collection phase. Since these marks needed to be taken off structures, the points were shifted to the nearest building.

3.4. Preparing inputs for the models (primary data edition) Many of the existing datasets, such as the roads, the hydrological network and the land-use layer were outdated (based on 1991 topographic maps) and their spatial resolution (1:50,000) was too coarse for this project. Therefore, new datasets needed to be created especially for this project. The new files were created in a Personal Geodatabase which offers many advantages over normal Windows directory format such as data compression, spatial indexing and better performances (Childs 2009). Topology is a key factor to avoid gaps in the final output that could compromise the stability of the model. Consequently, the Topology toolbar, Auto-complete Polygon Task and the Snapping Environment were used extensively. Figure 13 is a flowchart of the development process of the inputs, models and the flood vulnerability map.

Figure 13: Development process of the whole flood vulnerability project

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3.4.1. Topographical input The topographical input called “PointElevations” represents all the DGPS points collected in the survey with a few additions and modifications. Firstly, gaps in the survey data, created by interruptions of the GPS signal under thick vegetation or near high buildings, needed to be filled. The Inverse Distance Weighted (IDW) tool was utilized to predict values of the missing cells. Only the points that were taken on the roads went through this process since, by design, roads follow parameters (low grades and mostly flat) that are well suited to the IDW interpolation others (foot survey) are not so well suited. The Station Point tool in ETGW was used on the road network layer to create points that where spaced 5 meters apart. The Extract Values to point tool was then employed on the IDW raster to give Z values to the points. Subsequently, the Create Point Grid tool in ETGW was utilized to create extra points in the areas that could not be surveyed (mangroves, forests, fields, airport and marshes) and areas that had not been survey because of a lack of time (Denarau Island). Z values were added manually. Finally, the Merge and Spatial Join tool were used to join all the points in a single file. 3.4.2. Hydrological inputs Stream centerlines and bank lines were created with HEC-GeoRAS (ArcGIS plug-in of the HEC-RAS program) and the Editor toolbar. The centers of the streams were visually estimated in order to position the vertex. Subsequently, the banks were digitized in polyline format and ETGW was utilized to create river polygons (using the polyline to polygon feature). The hydrological input called “BankPoints” was created using the station point feature in ETGW. An interval of 3 meters was selected since the grid size of the first DTM created by the Topo to Raster tool is also 3 meters. The second DTM has a grid size of 5 meters. This was the only way to prevent some of the stream lines from becoming “walls” in the final DTM output. This process was to assured that no stream polyline would hit a bank vertex since the error would be easily identified by the program at the 3 meter resolution level but would be ignored or missed at the 5 meter level. (See section 3.5 for more information)

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The catchment input (figure 15) and basin polygon (figure 3) were created with the help of HEC-GeoHMS 4.2.

The ocean polygon was created by a semi-automated process. To begin with, the ISO Cluster tool was use on the Worldview 2 scene to create a signature file. Subsequently, this signature file was utilized with the Maximum Likelihood Classification tool to create 12 classes of land-use. Afterwards, the Reclassification tool was employed to merge all land-use classes to two values: 0 for land and 1 for water. The Raster to Polygon tool was then used to convert the reclass output into polygon format. Following that, all polygons with a 0 value were deleted which left only the water polygons. Finally, a few manual corrections were done with the Topology toolbar to fill some of the gaps and to delete zones that were not in the ocean. On the other hand, lakes and marshes were manually digitized (based on visual interpretation) since this process did not work for those water bodies.

A similar process (to the one utilized for the ocean) was used to identify mangroves. Different bands and classes (near IR and IR bands / 4 classes) were used, but the procedure was the same as the ocean polygon.

The hydrological input called “StationPoints” was created with the ETGW software. A new vector point was automatically created each 500 meters along the Nadi River polyline. Depth values were then added to the attribute table manually. These values were: the thalweg depth from the JICA report, depths and DGPS elevations collected on the boat survey.

The Multiring Buffer tool was used on the stream network polylines to create the distance to channel layer. Values of 100 m, 200 m and 1000 m were used to the first three classes and the Construct Features tool in map topology was utilized to fill the rest of the zone and create the forth class (> 1000 m). The final result can be seen in figure 18. 3.4.3. Road network input The road network was digitized from the two georeferenced scenes using the Editor toolbar in ArcGIS. When possible, the centerlines were use to position the vertex in 34

the middle of the road. However, if there was no line, the modeler visually estimated the center of the road to position the vertex. A “TYPE” datafield was added to the attribute table to identify the category of road (major road = 1, paved road = 2 and dirt road = 3) and was used in the creation of the land-use layer. 3.4.4. Land-use input Many of the previous layers were used to create the land-use polygon. For example, the river, the ocean, lakes, marshes and mangrove polygons were directly added to the file. This was done with the Construct Features tool in map topology to avoid any overlapping edges (this is also true for all the following layers). The Multiring Buffer tool was used to convert the road network to polygon format. An average width was calculated for the three main types of road classes (10 meters for the main roads, and 5 meters for the paved roads and 3 meters for the dirt roads) and these values were then used as the buffers distance. The rest of the classes, except the sugarcane class, were manually digitized by the modeler, using the Auto-Complete Polygon tool, based on visual interpretation of the two satellite scenes of the area. The remaining gaps, representing sugarcane fields, were automatically filled with the Construct Features tool (create new polygons considering existing features in target layer) and one polygon that covered the whole study area. The result can be seen in figure 16. 3.4.5. Soil type input The vector soils map was most likely (no metadata available) manually digitized from the 1:50,000 paper version. The attribute field titled “DRAINAGE” was used to create the 4 USDA soil type groups (which are also partly based on drainage). Group A soils have low runoff potential and high infiltration rates even when thoroughly wetted. They consist chiefly of deep, well to excessively drained sand or gravel and have a high rate of water transmission. Group B soils have moderate infiltration rates when thoroughly wetted and consist chiefly of moderately deep to deep, moderately well to well drained soils with moderately fine to moderately coarse textures. These soils have a moderate rate of water transmission. Group C soils have low infiltration rates when thoroughly wetted and consist chiefly of soils with a layer that impedes downward movement of water and soils with moderately fine to fine texture. These soils have a low rate of water transmission. Group D soils have high runoff potential. They have very low infiltration rates when thoroughly wetted and consist chiefly of 35

clay soils with a high swelling potential, soils with a permanent high water table, soils with a claypan or clay layer at or near the surface, and shallow soils over nearly impervious material. These soils have a very low rate of water transmission (USDA 1986). Since the Fiji soil map uses 8 classes a reclassification was needed to fit the 4 USDA soil groups. Therefore, “excessively drained” and “well drained to excessively drained” where merged to create class A, “well drained” and “moderately well drained” where merged to create class B, “imperfectly drained” and “imperfectly to poorly drained” where merged to create class C, and “poorly drained” and “very poorly drained” where merged to create class D. Missing areas (ocean and rivers) where filled with the Topology toolbar and classified as D. This was necessary step since all areas needed to be classified for the Polygon to Raster transformation to work properly. Theses areas were later on removed (to avoid bias) with the Mask function. The final result can be seen in figure 19. 3.4.6. Building input The ISO Cluster tool was used on the Red/Blue/Green bands of the Quickbird scene to create a signature file. The signature file was then utilized with the Maximum Likelihood Classification tool to create 12 spectral classes. Since many of roofs in this scene were very reflective (tin roofs especially) they were all assigned to the highest classes (dark objects were classified 1 or 2 or 3 and very bright objects 11 or 12). The Reclassification tool was employed to merge all other values (value 1 to 10) so that all the roof signatures would be grouped (value 11 and 12). The resultant output was a bi-color raster file with one color (value 1) representing all the roofs and the other color representing all the other objects (value 0). Subsequently, the Raster to Polygon tool was used to convert the reclass output into polygon format. Following that, all polygons with a value of 0 (objects that were not roofs) were deleted which left only the roof polygons (value of 1) and other very reflective objects like clouds, cars, side walks and patches of bare earth. A field was added to the attribute table and the geometry (area in m2) of each polygon was calculated. All objects smaller than 30 m2 and bigger than 13,795 m2 were deleted being either too small or too big to be buildings. Afterwards, the Spatial Join tool was employed to join land-use values (from the land-use layer) to each one of the polygons. The polygons that were not identified as a residential, commercial, industrial or sugarcane were deleted since buildings are rarely constructed on the other available values 36

(ocean, rivers, mangroves, roads, etc.). In all, 5,406 shapes were automatically identified by the program. However, 412 were not buildings. The polygons were transformed to point format using the Feature to point tool. Theses points can be viewed in the focus zones in section 4.1.

The same process was repeated on 13 Google Earth screenshots (section 3.3.2) to fill the gaps that were not covered by the Quickbird scene. Additionally, some buildings needed to be added manually (since they were not identified by the process) and some errors (other objects that were not buildings) needed to be removed. After theses to additional steps, 2,250 features were added for a grand total of 7,194 structures. Finally, the Extract Values to Point tool and the Spatial Join tool were utilized to join valuable information, such as census information, flood values and land-use codes, to the attribute table. 3.4.7. Creating the APH matrices Once all the datasets where complete, the Analytical Hierarchy Process (AHP) matrices where created for each of the six objectives (see section 3.6). In essence, the matrices are tools to apply Saaty’s mathematical equations. The next table is an example of an APH matrix with the equations. Table 6: Example of an AHP matrix Line Line Line Line Line

1 2 3 4 5

Line 7 Line 8 Line 9 Line 10 Line 11

Row A R.M. Value A Value B Value C Sum N.M. Value A Value B Value C sum

Row B Value A

Row D Value C

1

3

6

=1/C2

1

2

=1/D2

=1/D3

1

=+ SUM(B2:B4)

=+ SUM(C2:C4)

=+ SUM(D2:D4)

Value A

Value B

Value C

Row E

sum

Row F

P. Vector

=B2/B$5

=C2/C$5

=D2/D$5

= SUM(B8:D8)

=E8/E$11

=B3/B$5

=C3/C$5

=D3/D$5

= SUM(B9:D9)

=E9/E$11

=B4/B$5

=C4/C$5

=D4/D$5

= SUM(B10:D10)

=E10/E$11

= SUM(B8:B10)

= SUM(C8:C10)

= SUM(D8:D10) = SUM(E8:E10)

Line 13

N= 3

Line 14

RI= 0.58

Line 15 Line 16

Row C Value B

= SUM(F8:F10)

Lambda Max =+B5*F8+C5*F9+D5*F10 Consistency Index (CI) =(F14-B13)/(B13-1) Consistency Ratio (CR) =F15/0.58

To read it, one needs to look at the row letters and line numbers like in a spreadsheet. For example, “= SUM(B8:D8)” is the summation of the values in row B to D on line 8. The R.M. box is the reciprocal matrix and the N.M. box is the normalized matrix.

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The P. Vector values are the priority vectors, the N value is the number of values/classes and the RI value is the Random consistency Index (determined statistical values developed by Saaty). The dollar character ($) is an Excel function that locks the row or the line during calculations and should be ignored. For more information on the analytical hierarchy process please refer to section 2.1.3. Additionally, all six of the matrices used in the flood model can be found in the appendices. Lastly, theses matrices were also used in the development of the following inputs that were the core of the GIS/MCDA flood vulnerability model. 3.4.8.Objective A: “elevationnadi”

Figure 14: Objective A: elevation input

Historically, low-lying areas were known to be the worst hit in Nadi and many residents mentioned that these areas were also the first to be inundated. Furthermore, floods are also governed by gravity which pulls the water towards low-lying areas and eventually the ocean. Therefore, elevation was selected as the most important objective. The original input for this process was the GRID (figure 14) which is the output of the digital terrain model (see section 3.5). Seven classes were created from the original input: < 1 m, 1 to 2 m, 2 to 4 m, 4 to 6 m, 6 to 8 m, 8 to 10 m and > 10m. These classes were selected because flooding in Nadi occurs in the areas that are below 10 meters. Objective A has a weight of 48.10% on the total model and has an inconsistent ratio of 0.0155 (theses values refer to the elevation AHP matrix).

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3.4.9. Objective B: “catchmentnadi”

Figure 15: Objective B: catchments input

Logically, to be flooded by the Nadi River the areas need to be part of the Nadi Catchment. Consequently, the catchment extent was selected as the second most important objective for the flood vulnerability model. This objective was added in later iterations since areas near the shoreline (low elevations) always showed as highly vulnerable. However, this was a bias since some of these areas could not be flooded by the Nadi River since they were not part of the catchment. Storm surge could flood these areas, but a lack of proper data prevented integration of this factor in the model. The original input was a GRID file (figure 15) which was created by the HEC-GeoHMS extension. Two classes were selected: Nadi Catchment and other catchments. Objective B has a weight of 20.80% on the total model and has an inconsistent ratio of 0.0000 since only 2 classes were used. 3.4.10. Objective C: “landusenadi” Development in the lower part of the Nadi Catchment has undoubtedly influenced the severity of flood impacts over the past 50 years. As stated in the literature review, urbanization has a great affect on flooding. Thus, the area was classified following the premise that developed areas would be more vulnerable than natural areas. In all, 16 classes were created: commercial, dense commercial, residential, dense residential, industrial, main roads, paved roads, dirt roads, sand, cane, mixed crops,

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forest, mangrove, marsh, open spaces and water. Afterwards, the USDA’s Urban Hydrology for Small Watersheds Report (1986) was used to group and rank these different land-use classes. In this report, different curve values were proposed to calculate surface runoff. The higher the curve values; the less infiltration there will be. Based on this information a flood resilience rank was attributed to each classes and then the AHP matrix was used to evaluate the priority vector. The final result can be observed in the following table. Table 7: Land-use classification process * Land-use Forest Sand Open areas Mix crops Residential Cane field Dirt Road Dense Residential Commercial Industrial Dense commercial Main roads Mangroves Marshes Water

USDA curve values 30 30 39 55 61 67 72 77 80 81 89 98 n/a n/a n/a

Resilience rank 1 1 2 2 3 4 5 5 6 6 7 7 8 8 8

Priority vector 0.3426 0.3426 0.2263 0.2263 0.1532 0.1049 0.0700 0.0700 0.0476 0.0476 0.0326 0.0326 0.0228 0.0228 0.0228

The original vector file (figure 16) was converted to GRID format (5x5 meters as the previous files) with the new resilience ranks. Objective C represents 13.65% of the total model and has an inconsistent ratio of 0.0377.

Figure 16: Objective C: land-use input *

Curve numbers are for type A soils, if not listed in the source, the values were estimated by the modeler.

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3.4.11. Objective D: “slopenadi”

Figure 17: Objective D: slopes input

The slopes objective was the most difficult factor to create since no documentation was found that proposed a standardized way to categorize slope angles in relation to flood vulnerability. Many articles agreed that a higher value would be beneficial since it would prevent the accumulation of runoff that would be temporarily stored in depressions creating ponding (USDA 1986; Fernández & Lutz 2010; Ramlal & Baban 2008). Many attempts were made with different values and different number of classes. The best results were achieved with two classes: areas that were less than one degree and more than one degree. The original input (figure 17) was reclassified to fit these new parameters. Objective D has a weight of 8.39% on the total model and has an inconsistent ratio of 0.0000 since only 2 classes were used. 3.4.12.Objective E: “distancenadi” As for the slopes, the channel objective was hard to classify since only the Fernández and Lutz (2010) proposed a clear way to categorize this factor. Therefore, similar values were used for the Nadi model. However, visual interpretation of the flood depths values (provided by SOPAC) was used to justify the distances: < 100 m, between 100 and 200 m (new value), between 200 and 1000 m (new value), and > 1000 m. The original vector file (figure 18) was converted to GRID format (5x5

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meters as the previous files) before being added to the model. Objective E has a weight of 5.43% on the total model and has an inconsistent ratio of 0.0346.

Figure 18: Objective E: distance from channel input

3.4.13. Objective F: “soilnadi” As mentioned in the literature review, pedology has a noticeable influence on drainage capacity. Thus, the final objective, USDA soil types, was incorporated in the flood vulnerability model. The input, a reclassified vector file (figure 19), was converted to GRID format (5x5 meters) before processing.

Figure 19: Objective F: soils type input

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Since there was some uncertainty about this reclassification process and on the accuracy of the soil polygons (no metadata was provided in the original file) this objective has the lowest weight. Its weight is 3.64% and it has an inconsistent ratio of 0.0124.

3.5. Creating a high resolution DTM model

Figure 20: Schematic view of the digital terrain model with Etching method

The creation of a high resolution DTM was a core element in the development of the flood vulnerability model since it relied on accurate topographic values. Seeing as many operations needed to be executed to combine all the collected data into one DTM the ModelBuilder in ArcGIS was utilized to create the final output. Figure 20 is a schematic view of the operations needed to build the DTM. The inputs (in blue) are processed by the tools (in yellow) and subsequently the outputs (in green) are created. On the following page, there is a table that explains each item found in figure 20.

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Table 8: Inputs, tools and outputs of the digital terrain model Name StationPoints Streams BankPoints PointElevations Lakes Contours Ocean Area Topo to Raster Extract Values to DEM SinksDEM/SkinsHYDRODEM ParameterDEM/ParameterHYDRODEM DiagnosticDEM/DiagnosticHYDRODEM StreamDEM/StreamHYDRODEM Ext_BankPoints HydroDEM

Description Points in the Nadi River channel with bathymetric values Polylines that represent the rivers and streams in the area Points (at 3m intervals) along the banks of the Nadi River Points with the elevation values (DGPS/Spot heights) Polygons that represent the lakes and marshes in the area Polylines that represent the 20m contours of the topomap Polygon that represent the ocean Polygon that represent the test area (used as a clip) ANUDEM algorithm used to create the DEMs Tool that allows to extract the raster values to points First output that dose not incorporate the hydrology Point output that represent the modeled skins Txt output with the parameters used in Topo to Raster Txt output with a set of diagnostic information Polyline output that represent the modeled streams Output of the BankPoints with the DEM values (elevation) Final output that incorporates the hydrological network

The Topo to Raster tool is a topographic interpolation method specifically designed to create hydrologicaly correct DEMs. It is based on the ANUDEM (version 4.6.3) algorithm developed by Michael Hutchinson in the early 1990’s at the Australian National University (ESRI 2011). This tool was designed to take advantage of various elevation inputs (contours, spot heights, GPS points, LiDAR) and it calculates abrupt changes in terrain such as streams and ridges. It also includes a drainage enforcement algorithm that attempts to clear spurious sinks by modifying the DEM inferring drainage lines via the lowest saddle points. Nevertheless, it does not attempt to clear real sinks as supplied by the sink function, thus, producing higher accuracy surfaces with less input data. Topo to Raster uses a multiresolution interpolation method, starting with a coarse raster and working toward the finer, user-specified resolution. At each resolution, drainage conditions are enforced, interpolation is performed, and the number of remaining sinks is recorded in the output diagnostic file. The drainage enforcement can be turned on or off and stream polylines can be used when more accurate placement of streams is required. The stream data should be composed of single arcs pointing downstream (first vertex at the source and last vertex at the ocean) given that this orientation affects the drainage enforcement algorithm. Complex hydrogeomorphic forms such as dendritic, braided and parallel streams need to be cleaned up by interactive editing (must be converted to single arcs pointing downstream). This is also true for lake polygons and rivers represented by polygons. When editing lake polygons out of the network, a single arc is placed from the beginning to end of the impounded area. The arc must follow the path of a historic streambed if one is known or exists. If the elevation of the lake is known, the lake polygon and its elevation can be used as a lake input (ESRI 2011). 44

About fifty version of the digital terrain model (the final version being figure 20) were developed and tested to find the best combination of parameters to create a DTM that would be true to the topography and hydrography of the lower catchment. The reason why this is so complicated is that the whole area is very flat and contains almost no topographical features. Therefore, the hydrological network needs to be forced upon the model. This is know as “burning” or “fencing” and was developed by Ferdi Hellweger at University of Texas in 1997 (HEC 2009).

Figure 21: Cross-Section view of the Burning-In technique (HEC 2009)

As seen in figure 21, this technique permits to drop or raise the cell value along the path of a vector line (predominantly a stream polyline). The user needs to decide on a single input value (in meters) for the whole stream polyline. A buffer value can also be included since technically vector lines have no width. However, there are two major flaws to this technique: in reality, (1) stream depths and (2) stream width vary in different sections depending on topographical and morphological constraints. Thus, the channel depth and width stays the same over the whole area, which, in the case of the high resolution DTM, is not realistic and may cause a bias in the flood model. The Etching method, developed in this thesis, is a novel approach that eliminates both of these problems. The Etching method requires several steps: firstly, the Topo to Raster tool is used without the drainage enforcement algorithm to create a basic landscape (the first Topo to Taster in figure 20). Secondly, the Extract Values to Points tool is employed to extract elevations values from the previous operation. These points are then used to prepare the terrain before the Etching process

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commences by densifying the geometry (figure 22) in and around the banks and by marking the true width of the desired stream. This step ensures that the banks are well defined to avoid a process failure.

Figure 22: Densifying the area near the rivers with the Etching method

Finally, the second Topo to Raster is run with the stream polylines, the extrapolated BankPoints and the StationPoints. StationPoints (bathymetric values) were forced upon the model, but only in the stream bed, creating well defined stream channels. The resulting DTM is closer to the true topography and hydrology of the area.

A 1979 orthophoto map of Nadi area (sheet 10) was utilized to verify the accuracy of the topography. The orthophoto map was georectify to the model and then 417 spot heights (with 0.5 meters accuracy) were manually digitized. The Extract Values to Point tool was used to extract elevation values from the DTM to the 417 spot heights points. The attribute table of this point layer was exported to Excel to undergo a Pearson's chi-square test. All the DTM values needed to be rounded to the closes multiple of 0.5 since the benchmark was set on 0.5 meters (the 1979 spot heights). The correlation between the aerial photo and the “DEM” output (DTM without the forced hydrological network) is 95.1% and for the “HydroDEM” output is 94.4% (DTM with the forced hydrological network). However, when values in the channel are tested (same procedure but with the 52 stations points) the results for the “DEM” output is 71.2% and for the “HydroDEM” output is 93.1%. Moreover, the ANUDEM diagnostics logs (DiagnosticDEM.txt/DiagnosticHYDRODEM.txt) revealed that the “DEM” output leaves behind 2,921 skins and the “HydroDEM” output left 1,037 skins (a lower value is better since this means more streams were correctly connected).

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3.6. Creating a GIS/MCDA flood vulnerability model

Figure 23: Schematic view of the GIS/MCDA flood vulnerability model

The model for this project is based on the urban flood hazard model developed by Fernández and Lutz (2010) for Tucumán Province, Argentina. However, the objectives and the priority vectors where developed especially for the Nadi Basin. Six objectives were selected: elevation (“elevationnadi”), catchment area (“catchmentnadi”), land-use (“landusenadi”), slopes (“slopenadi”), distance from channel (“distancenadi”), and soil types (“soilnadi”). The AHP matrices were calculated in Excel and the GIS manipulations done in ArcGIS. The 2009 flood 47

extent and flood height, provided by SOPAC, were used to calibrate the priority vectors. A heuristic method was used to find the proper values. The final result, the GIS/MCDA flood vulnerability model, can be observed in figure 23 which is a schematic view in ModelBuilder. As for the last model, the inputs are in blue, the tools in yellow and the outputs in green. Before starting the process, all the inputs need to be converted in raster format since the Weighted Sum tool cannot operate with shapefiles. The Polygon to Raster tool was employed for this but several software “crashes” were observed. This was probably due to the high resolution demanded (5 meters pixels). Therefore, the Clip tool was used to cut the shapefiles in to four sections. Each one of these sections went through the Polygon to Raster tool and then was reassembled with the Mosaic to New Raster tool.

The first line of the flood vulnerability model (figure 23) consists of all six inputs from section 3.4. The second line reclassifies the inputs with the calculated AHP weights values. Since the Reclassify tool cannot compute decimal values the AHP weights needed to be multiplied by 10,000 to allow the reclassification process. The third line represents all the outputs of this first operation. The forth line adds a field to the outputs to allow decimal calculations. Theses fields were set on float to allow decimal values to be added to the raster files. The fifth line consists of the outputs of last operation. The sixth line uses the Calculate field tool to divide all values by 10,000, thus reconverting the AHP values to their decimal forms. The seventh line represents the outputs of the last operation. The eighth line weighs each one of the outputs with the Weighted Sum tool. Theses are the values calculated in the Objectives AHP matrix (appendix VIII). This process can be seen in. The tenth line consists of the output of this operation (“SUM”) and the “Mask” input which is used on the following line. On the eleventh line the Mask tool is used to eliminate the zones which are already permanently flooded such as the ocean, rivers, lakes, marshes and mangroves. Finally, on line twelve, the final output, the flood vulnerability index (FVI), represent resilience of each areas. Higher values are good and lower values are bad. These values can be left as it is or classified using natural breaks, quantiles or standard deviations. However, these values do not represent any physical phenomena, thus selection of the intervals was done carefully to avoid bias. This output is used to create the final flood vulnerability map.

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3.7. Model output validation By no means should the GIS/MCDA flood vulnerability model be considered a “physical” flood model since it cannot compute the complex interactions of runoff and river flow. This model was designed to identify high risk areas in the lower part of the Nadi Catchment and not to evaluate flood heights. Its main use is to focus present flood prevention efforts in areas that need it the most. 3.7.1. Verification method and corrections

Figure 24: Discrepancies identified in the interviews (corrections are highlighted)

A second visit to Nadi was conducted from the 14th to the 21st of April 2011 to ground proof the results of the model. During this visit several key individuals, from the Nadi Town Council, the NBCC and the local DIMAC office, were ask to evaluated and comment the model. Local merchants and village chiefs were also consulted to have the complete perspective of the model’s worthiness. In all, ten interviews were conducted to verify the accuracy of the model. A blank map was presented to the participants (all individuals that witness the 2009 floods) and they were asked to identify vulnerable areas based on their knowledge. Afterwards, the flood model was presented and they were asked to comment on the results. All these observations were collated to create figure 24 which outlines the model’s minor inaccuracies. For the most part, the model performed very well and identified correctly the most vulnerable areas. However, some discrepancies were noted by the

49

interviewees. Below, a description of these errors (marked from 1 to 9 in figure 24) and explanations on why the model failed to identify the proper values.

1) This area was originally classified has a high hazard zone but was downgraded to a moderate hazard zone since flood waters do not pose a true threat in that particular region. The error was caused by channel distance objective. The vulnerability values given to the different distances (< 100 m, 100-200 m, 200-1000 m and > 1000 m) were fixed and did not compensate for the channel’s risk level. For example, the Nadi River causes a higher risk than a small creek. Several irrigation ditches traverse the area and since they had the same threat value as the major rivers the model overestimated the potential risk. The removal of the ditches from the stream layer should take care of this problem. However, storm surge is a definite risk since some wrecked buildings were observed along the sea side during the field survey and a respondent confirmed that they were destroyed by the waves in 2009. Therefore, a storm surge objective is needed to estimate the exact threat value to the area.

2) This zone was initially classified as being of moderate hazard but was upgraded to very high hazard. The inaccuracy was caused by an inability of the model to compute flow values. The drain running along this zone seems to be a high threat to the surrounding buildings. With the current model it was impossible to predict this. Fortunately, the interviews help to identify this weakness. A river channel model could have identified this if cross-sections of the drain were made. However, this is very unlikely since these models mostly concentrate on rivers and not on drains. Further analysis of this drain will be needed before an exact vulnerability value could be calculated.

3) As for the previous point, a storm drain is a great menace for neighboring buildings. The danger level was increased from moderate to extreme since this area experienced floods up to 2.4 meters in 2009. This ditch is one of the only drainage points for the entire area. Most of the runoff from the runway is channeled through it. A study needs to be made to calculate its flow capacity and to determine the area that it drains. Unfortunately, the current model lacks the data to make such calculations thus it cannot predict an exact vulnerability value.

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4) This area was firstly classified as being of a very high hazard zone but was downgraded to a moderate hazard zone since flood waters do not pose a great threat to the vicinity. The source of the error is unclear, but it is probably due to the fact that this zone is far enough from the Nadi/Malakua confluence and that there is sufficient drainage capacity. The current model lacks these two variables that are difficult to model with the AHP method. Therefore, one of the two other proposed methods should be developed to overcome these limitations and give accurate values.

5) The gold color zones next to the arrow and under the number five were reclassified from low hazard to high hazard since they represent a greater threat than anticipated. These inaccuracies were not caused by the model but rather by data generalization. It was impossible to cover 100% of the area with the DGPS technique. Some compromises needed to be made to save time and money. Thus, these areas were not surveyed because they were not easily accessible with the survey vehicle. Therefore, it was impossible to identify these depressions that are prone to ponding. Furthermore, storm drains in the area get clogged by rubbish and cannot properly evacuate runoff. Even the best physical model could not anticipate this factor. Consequently, local knowledge, gained in interviews, is vital to the creation of a conclusive flood model.

6) Part of this zone was initially classified has extreme hazard and very high hazard but was downgraded to moderate hazard. As for the previous point, the inaccuracies were caused by some micro topography that was not picked up in the survey. Luckily, an interview with the headman of the village revealed these inaccuracies. LiDAR data would have not missed these features, but the data was not available during the development of the model. At the time of writing, a combined World Bank/SOPAC project is underway to acquire LiDAR information of the whole catchment. Therefore, when available, this data should be integrated into the model to improve it.

7) Some parts of this area were classified as being subject to moderate hazard but were upgraded to high hazard. The inaccuracies were caused by a failure of the model to compute flow values. As for point four, the current model lacks the ability to calculate the complex interactions between the topography and channel flow. This 51

area is just before the Nadi/Malakua confluence and a set of very tight meander. For that reason, the water slows down and floods the banks of the river. Fortunately, most of this area is uninhabited and the main land-use is mixed corps that can actually benefit from the nurturance left behind by the floods. However, on the south side of the Nadi River, the Back Road gets flooded frequently. Therefore, people should avoid this road in times of flood to prevent accidents.

8) This area was originally classified as being expose to extreme hazard and very high hazard but was downgraded to a high hazard zone because flood waters did not pose a true threat to the population since the entire area is covered in sugarcane fields. The error was caused by the inability of the model to verify human presence in the area. Since there was no variable included to verify population concentration, the model cannot discriminate between populated and unpopulated areas. Therefore, in a future version, this variable should also be included. As for point seven, floods are actually beneficial in this area. However, people should stay clear of this zone during heavy rains.

9) This zone was initially classified as being subject to very high hazard but was downgraded to moderate hazard. The inaccuracy was caused by an inability of the model to compute flow values and some micro topography around the temple area. According to the Nadi Town Council, the Sri Siva Subramaniya temple was never flooded since it was built in 1972. According to the same source, the design of the new Qeleloa Bridge, just south of the temple, is the cause of flow problems further down stream which results in flooding of the adjacent areas. This may or may not have protected the temple during the 2009 floods. However, future analysis would be needed to verify this hypothesis and to compute exact vulnerability values for this whole area. 3.7.2. Comparing verification method with other flood models Fernández and Lutz (2010) (hereafter referred to as the authors) used statistical methods (Taylor's series error propagation and Fourier Amplitude Sensitivity Test) to assess the accuracy of their model instead of interviews. According to the authors, an important source of model uncertainty was caused by the variation of the two first variables. This is quite logical since the combined weight of these values represent 52

61.9% of the final output of their model and 68.9% for the Nadi model. Consequently, any inconsistencies in the initial inputs, in the case of the Nadi model, the elevation objective and catchment objective, or an incorrect criterion weight attribution of these two objectives would jeopardize validity of the model. Fortunately, for the Nadi model, both of these were thoroughly examined before the model was run. The result of their statistical tests implies that by reducing errors of the first three inputs the final map accuracy would be improved. This is also reasonable since these values represent 82.5% of the final output of their model and 82.55% for the Nadi model. Furthermore, the authors investigated the sensitivity of criterion weights by forcing a variation of 25, 50 and 75% on the original values. According to them, the final results were very similar for the three analyses showing a robust behavior of the model. In all, the authors concluded that the MCDA technique within a GIS environment have proved to be a powerful method to generate hazard maps with a good degree of accuracy (Fernández & Lutz 2010).

In many other papers (Bates & Deroo 2000; Horritt & Bates 2002; Knebl et al. 2005; Oosterom et al. 2005; Wright et al. 2008) remote sensing is used to verify the accuracy of the models. Radar based sensors can penetrate cloud cover and measure flood extent and sometimes flood depths. These sensors can be mounted on airplanes, for example airborne synthetic aperture radar (ASAR), or on satellites, for example RADARSAT, ENVISAT or ERS-2. Unfortunately, for the Nadi model, this method cannot be implemented since the only available image was taken after the flood peak and would give an incorrect estimation of the full extent of the 2009 flood. Therefore, in a future event, the Fijian authorities need to act quickly to purchase this type of data which is critical in the development of a flood model.

Finally, Duan et al. (2009) combined the previous method (using a LANDSAT ETM+ image) with a ground proofing campaign in Northern Thailand. Thirty sites (in three separate provinces) were visited and several interviews were conducted to validate the model. The results of the interviews in two provinces matched closely the results of the LANDSAT ETM+ image while the results in another province were slightly less conclusive. However, it was judged that the overall project was a success (Duan et al. 2009).

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Chapter 4: Results and Analysis The focus of the project was on the lower part of the Nadi Basin. An area of 84 square kilometers was selected as the test zone. The area spans 12 km inland from the coast and covers 90% of the area that was flooded in 2009 (figure 25).

Figure 25: Test zone (gray grid) and 2009 flood extent provided by SOPAC

Each square kilometer of this zone was methodically analyzed except for Denarau Island (2.62 km2) which was not part of the flood area nor the Nadi Basin. The lower part of the catchment is delineated to the north by the main road axes: Votualevu Road, Queen’s Road and Denarau Road and to the south by Moala Road. Two other catchments can be found in this area: the one belonging to Malakua Creek and one which is not identified but lays southwest of the airport. The low gradient of the Nadi plain has forced the river to carve its bed, in some places several meters deep in soft deltaic deposits. Three major tributaries flow into the Nadi River just to the southwest of town, the Malakua, Nawaka and the Masi Creeks, which undoubtedly contribute to the severity of the flooding in that particular area. Altogether, 148.84 km of rivers, creeks and irrigation ditches were digitized in the test zone. According to the soils layer, 0.8% (0.69 km2) of the zone is covered with type A soils, 25% (21.2 km2) of type B soils, 32.3% (27.2 km2) of type C soils and 41.6% (34.9 km2) of type D soils. Low hills, with a maximum altitude of 100 meters, can be

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found along the east perimeter and to the south near Rasusuva. Three other noticeable topographic features are located in the center of town, at the Kerebula settlement and along the Queen’s Road near the runway. All these areas seem to be safe from flooding. Slope values vary from 44.35° to 0°. Most of the test zone is very flat with a mean elevation of 8.5 meters. Beside Nadi town, the major villages and settlements in the area are: Dratabu, Kerebula, Moala, Nakavu, Namotomoto, Narewa, Nawaka, Rasusuva, Saunaka, Sikituru, Vatutu, Votualevu, Vunayasi and Yavusania. According to the 2007 census file, 86 enumeration areas are in the zone, two Tikinas (Nadi and Nawaka) and about 47,351 individuals. As seen in the following table, sugarcane fields is the dominant land-use type and the Nadi central business district represents less than one percent of the total area. Table 9: Land-use composition within the test zone Land-use Sugarcane fields Mangroves Water (ocean and rivers) Open spaces Residential Dense Residential Mixed crops Forest Industrial Marshes Main Road Commercial Dense Commercial Paved Road Dirt Road Sand / Beach

Area (in km2) 45.10 10.90 9.45 3.48 2.65 2.41 1.78 1.16 1.03 0.79 0.63 0.59 0.46 0.42 0.34 0.19

Area (in %) 55.41 13.39 11.62 4.28 3.26 2.96 2.19 1.42 1.26 0.97 0.78 0.72 0.57 0.52 0.41 0.24

In all, 132 km of roads was digitized: 32 km of main roads, 40 km of paved roads and 59 km of dirt roads. Several dirt roads were not digitized since this is a very time consuming job and not essential to the model. Automated and visual inspection of the satellite images revealed the presence of 7,628 buildings in the test zone. Of this total, 526 (7%) were commercial, 165 (2%) were industrial and 6,939 (91%) were residential. According to the census file, 6,404 building (83%) were in urban areas and 1,226 (17%) were in rural areas. According to the flood outline provided by SOPAC 3,603 (47%) were in the 2009 flood zone and 4,027 (53%) were not.

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4.1. Results

Figure 26: Focus zones utilized in the flood vulnerability analysis

Six classes were created to categorize flood vulnerability in the test zone. The high water marks were used to verify the validity of these classes. After several attempts, natural breaks (Jenks) were selected since they proved to be the most suitable way to group the values. The final raster output was converted to polygon format and can be seen in appendices VIV and X. The geometry of each zone was calculated to estimate the general flood threat. As shown in the following table, less than a third of the area is under severe risk and more than a third is apparently safe from floods. Table 8: Land-use composition of the test site Classification Extreme hazard Very high hazard High hazard Moderate hazard Low hazard Very low hazard

Area (in km2) 1.84 9.10 6.58 18.56 22.43 4.14

Area (in %) 2.94 14.53 10.50 29.63 35.80 6.60

The most vulnerable areas are located in Nadi town, to the south of the CBD, along the north bank of the Nadi River after town and close to the coast southwest of the runway. The less vulnerable areas are located along the Queen’s Highway near the airport, near Vatutu village, Dratabu village and near Vunayasi village and Rasusuva. A more detailed overview will be given in the following subsection. Figure 26 locates the general areas were the “focus zones” are. The map inserts in the following subsection locates the EAs which are identified by their 8 digits code numbers.

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4.1.1. Focus on Zone 1 Table 10: Model data for Zone 1 Census data Satellite data Enumeration areas in the zone 15 Buildings in the zone 1,307 Total population (2007) 6,441 Difference census / satellite 87 Total number of house holds 1,394 Building types Education level Dense commercial (dark blue) 247 No formal education / primary diploma 1,343 Commercial (light blue) 93 Secondary / specialist diploma 4,353 Dense residential (dark orange) 247 Tertiary diploma 745 Residential (light orange) 680 House condition Industrial (yellow). 94 Good condition 812 # of buildings in each hazard zones Average condition 503 Extreme vulnerability 92 Poor condition 79 Very high vulnerability 226 House wall building materials High vulnerability 472 Concrete 816 Moderate vulnerability 298 Tin / iron 418 Low vulnerability 161 Very low vulnerability 58 Wood 153 Bure materials 3 Makeshift materials 3 Comments Zone 1 is centered on Nadi Town. It also covers the Namotomoto village and the Nakavu village (to the north of the CBD) and a part of the Nawaka village (to the south of the CBD). Vulnerability map of Zone 1

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4.1.2. Focus on Zone 2 Table 11: Model data for Zone 2 Census data Satellite data Enumeration areas in the zone 3 Buildings in the zone 518 Total population (2007) 2,575 Difference census / satellite 67 Total number of house holds 451 Building types Education level Dense commercial (dark blue) 0 No formal education / primary diploma 589 Commercial (light blue) 0 Secondary / specialist diploma 1,835 Dense residential (dark orange) 0 Tertiary diploma 151 Residential (light orange) 514 House condition Industrial (yellow). 4 Good condition 268 # of buildings in each hazard zones Average condition 150 Extreme vulnerability 40 Poor condition 33 Very high vulnerability 330 House wall building materials High vulnerability 48 Concrete 287 Moderate vulnerability 86 Tin / iron 90 Low vulnerability 14 Very low vulnerability 0 Wood 66 Bure materials 6 Makeshift materials 1 Comments Zone 2 is centered on three villages: Narewa, Sikituru and Yavusania. Vulnerability map of Zone 2

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4.1.3. Focus on Zone 3 Table 12: Model data for Zone 3 Census data Satellite data Enumeration areas in the zone 9 Buildings in the zone 661 Total population (2007) 4,909 Difference census / satellite 507 Total number of house holds 1,168 Building types Education level Dense commercial (dark blue) 0 No formal education / primary diploma 1,081 Commercial (light blue) 8 Secondary / specialist diploma 3,376 Dense residential (dark orange) 17 Tertiary diploma 452 Residential (light orange) 621 House condition Industrial (yellow). 15 Good condition 614 # of buildings in each hazard zones Average condition 481 Extreme vulnerability 12 Poor condition 73 Very high vulnerability 32 House wall building materials High vulnerability 35 Concrete 635 Moderate vulnerability 30 Tin / iron 410 Low vulnerability 356 Very low vulnerability 196 Wood 120 Bure materials 0 Makeshift materials 3 Comments Zone 3 is centered on two settlements: Rasasuva and Vunayasi. Data from Yavusania was excluded from the analysis since it was covered in Zone 2 Vulnerability map of Zone 3

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4.1.4. Focus on Zone 4 Table 13: Model data for Zone 4 Census data Satellite data Enumeration areas in the zone 6 Buildings in the zone 864 Total population (2007) 3,941 Difference census / satellite 9 Total number of house holds 855 Building types Education level Dense commercial (dark blue) 0 No formal education / primary diploma 992 Commercial (light blue) 15 Secondary / specialist diploma 2,751 Dense residential (dark orange) 49 Tertiary diploma 198 Residential (light orange) 770 House condition Industrial (yellow). 30 Good condition 465 # of buildings in each hazard zones Average condition 305 Extreme vulnerability 18 Poor condition 85 Very high vulnerability 8 House wall building materials High vulnerability 105 Concrete 360 Moderate vulnerability 539 Tin / iron 397 Low vulnerability 152 Very low vulnerability 42 Wood 80 Bure materials 10 Makeshift materials 8 Comments Zone 4 is centered on the area southwest of the international airport runway. Vulnerability map of Zone 4

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4.1.5. Focus on Zone 5 Table 14: Model data for Zone 5 Census data Satellite data Enumeration areas in the zone 10 Buildings in the zone 1,039 Total population (2007) 4,612 Difference census / satellite 58 Total number of house holds 1,097 Building types Education level Dense commercial (dark blue) 37 No formal education / primary diploma 859 Commercial (light blue) 115 Secondary / specialist diploma 2,675 Dense residential (dark orange) 809 Tertiary diploma 1,078 Residential (light orange) 74 House condition Industrial (yellow). 4 Good condition 784 # of buildings in each hazard zones Average condition 287 Extreme vulnerability 0 Poor condition 26 Very high vulnerability 21 House wall building materials High vulnerability 9 Concrete 989 Moderate vulnerability 103 Tin / iron 39 Low vulnerability 364 Very low vulnerability 542 Wood 66 Bure materials 0 Makeshift materials 2 Comments Zone 5 is centered on the area east of the international airport runway, near Namaka. Vulnerability map of Zone 5

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4.1.6. Focus on Zone 6 Table 15: Model data for Zone 6 Census data Enumeration areas in the zone Total population (2007) Total number of house holds Education level No formal education / primary diploma Secondary / specialist diploma Tertiary diploma House condition Good condition Average condition Poor condition House wall building materials Concrete Tin / iron Wood Bure materials Makeshift materials

1 867 165 214 565 88 93 64 8

Satellite data Buildings in the zone 129 Difference census / satellite 36 Building types Dense commercial (dark blue) 0 Commercial (light blue) 2 Dense residential (dark orange) 0 Residential (light orange) 127 Industrial (yellow). 0 # of buildings in each hazard zones Extreme vulnerability 4 Very high vulnerability 14 High vulnerability 17 Moderate vulnerability 94 Low vulnerability 0 Very low vulnerability 0

98 43 19 4 1 Comments Zone 6 is centered on the lands owned by the people of Moala village. Vulnerability map of Zone 6

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4.1.7. Focus on Zone 7 Table 16: Model data for Zone 7 Census data Satellite data Enumeration areas in the zone 5 Buildings in the zone 737 Total population (2007) 4,259 Difference census / satellite 155 Total number of house holds 892 Building types Education level Dense commercial (dark blue) 0 No formal education / primary diploma 1,037 Commercial (light blue) 0 Secondary / specialist diploma 3,030 Dense residential (dark orange) 0 Tertiary diploma 192 Residential (light orange) 737 House condition Industrial (yellow). 0 Good condition 483 # of buildings in each hazard zones Average condition 360 Extreme vulnerability 6 Poor condition 49 Very high vulnerability 40 House wall building materials High vulnerability 7 Concrete 263 Moderate vulnerability 266 Tin / iron 495 Low vulnerability 418 Very low vulnerability 0 Wood 108 Bure materials 18 Makeshift materials 8 Comments Zone 7 is centered on the Kerebula settlement and Nawaka village. Vulnerability map of Zone 7

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4.1.8. Focus on Zone 8 Table 17: Model data for Zone 8 Census data Satellite data Enumeration areas in the zone 9 Buildings in the zone 593 Total population (2007) 5,511 Difference census / satellite 553 Total number of house holds 1,146 Building types Education level Dense commercial (dark blue) 0 No formal education / primary diploma 1,215 Commercial (light blue) 26 Secondary / specialist diploma 3,485 Dense residential (dark orange) 234 Tertiary diploma 811 Residential (light orange) 333 House condition Industrial (yellow). 0 Good condition 769 # of buildings in each hazard zones Average condition 319 Extreme vulnerability 1 Poor condition 58 Very high vulnerability 2 House wall building materials High vulnerability 0 Concrete 878 Moderate vulnerability 4 Tin / iron 207 Low vulnerability 491 Very low vulnerability 95 Wood 57 Bure materials 1 Makeshift materials 3 Comments Zone 8 is centered on the Votualevu village. Vulnerability map of Zone 8

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4.2. Analysis Even though flood vulnerability values can be high in an area; the individuals living there can be more resilient if they have enough income and reserves to recover after the floods. For example, someone with a low income, no bank savings and a poorly constructed house would definitely be more vulnerable than someone that did not have these constrains. The level of education was selected to estimate wealth since economic data is not available for the public 2007 census files. Also, building quality can be an indicator of prosperity but also an indication of the resilience against flood damage. Finally, the model’s flood vulnerability values were tabulated to identify the zones which are under the greatest flood risk. Table 18: Social vulnerability factors vs. flood vulnerability * Focus zones

Tertiary

Good

Average

Poor

Extreme

Very high

High

Moderate

Low

Very low

Flood vulnerability

Secondary

Building quality

Primary

Education

Zone 1

20.9

67.6

11.6

58.2

36.1

5.7

7.0

17.3

36.1

22.8

12.3

4.4

Zone 2

22.9

71.3

5.9

59.4

33.3

7.3

7.7

63.7

9.3

16.6

2.7

0.0

Zone 3

22.0

68.8

9.2

52.6

41.2

6.3

1.8

4.8

5.3

4.5

53.9

29.7

Zone 4

25.2

69.8

5.0

54.4

35.7

9.9

2.1

0.9

12.2

62.4

17.6

4.9

Zone 5

18.6

58.0

23.4

71.5

26.2

2.4

0.0

2.0

0.9

9.9

35.0

52.2

Zone 6

24.7

65.2

10.1

56.4

38.8

4.8

3.1

10.9

13.2

72.9

0.0

0.0

Zone 7

24.3

71.1

4.5

54.1

40.4

5.5

0.8

5.4

0.9

36.1

56.7

0.0

Zone 8

22.0

63.2

14.7

67.1

27.8

5.1

0.2

0.3

0.0

0.7

82.8

16.0

The highest floods marks may be in the Nadi CBD, nonetheless, about 72% of the building in Narewa, Sikituru and Yavusania are in greater danger (Zone 2). Fortunately, most of the houses in the villages are well built (59.4%). On the other hand, most of the villagers (71.3%) only have a secondary school level of education (people in Nadi town have more tertiary diplomas). Consequently, Narewa, Sikituru and Yavusania villages were selected as the most vulnerable areas in Nadi.

The town zone (Zone 1) is the second most vulnerable area; especially in the squatter settlements to the south and in the Namotomoto and Nakavu villages. In these settlements, poor drainage, clogged-up by rubbish, impedes the proper evacuation of runoff thus creating ponding and water accumulation (Sovau 2011). This is also true for the center of town (bus station) but even worse since this area acts like a pool

*

Values are in percent derived from the data from the previous subsection

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(bowl shaped topography combined with asphalted areas) that is filled up by rainfall and surging water from the Nadi/Malakua confluence. The main drain, which is poorly designed and positioned, actually worsens the floods by providing a way for the rivers to fill-up this “pool” (La Cuira & Narayan 2011). Once filled, the water cannot percolate or be evacuated, as a result creating the worst floods in Nadi.

Moala Village (Zone 6) is the third most vulnerable zone since all the buildings are in danger (moderate to extreme). Building quality and education indicated that the residents probably have more resilience, but not much. Moreover, the village is just next to the ocean and storm surge may also be a threat.

The zone southwest of the runway (Zone 4) may be less vulnerable to floods than other areas, but its inhabitants are socially more vulnerable. Far less individuals have tertiary level education and almost 10% of the buildings in the zone are in poor condition. The model indicates that much of the runoff coming from the Nadi airport runway is evacuated by a drain that runs from the southeast to the northwest in this zone. It appears that this drain cannot accommodate the runoff in severe downpours thus it overflows and floods the surrounding buildings. Therefore, this area was selected as the fourth most vulnerable area.

The fifth area, Zone 7, is a relatively safe area. Most of the buildings are built on higher grounds which reduce the flood risk. However, the houses closer to the Nawaka creek and in low lying areas are more at risk. Social vulnerability is moderate, most people (71.1%) have a secondary school level of education and most of the houses (54.1%) are well built.

As for the previous zone, Rasusuva (Zone 3) is quite safe with 83.6% of the buildings in low or very low risk areas. Low-lying residences near creeks are more at risk. Social vulnerability is also lower with almost twice the tertiary school level of education (9.2% Rasusuva vs. 4.5% Kerebula), but a slightly more vulnerable buildings 6.3% versus 5.5% (poor building construction).

The seventh’s most vulnerable zone, Namaka (Zone 5), is the wealthiest area with the most tertiary diplomas (23.4%) and the highest number of well constructed house 66

(71.5%). The higher grounds, on which most of the buildings are constructed, provide some safety against flood events. Furthermore, the Nadi Catchment follows the Queen’s Roads; consequently buildings north of the street are far less vulnerable since they do not belong to the catchment. On the other hand, buildings near storm drains and in lowing areas are more susceptible to flooding. For example, the Colonial Plaza: its proximity to a major drain, its asphalt surface and its bowl shape topography makes it vulnerable like the bus station in Nadi town.

Votualevu (Zone 8), the highest elevated and least vulnerable zone, presents the lowest flood vulnerability (98.8% of the buildings are in low or very low risk areas) and some of the best social conditions: 14.7% of the individuals in the enumeration areas have a tertiary school level of education and 67.1% of the houses are in good condition. This was predictable since most of the enumeration areas in this zone are not part of the Nadi Catchment.

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Chapter 5: Discussion and Recommendations The results presented in the previous chapter should be interpreted with care. Even though all inputs are based on the best available physical data the outputs are purely a result of mathematical equations based on Saaty’s Analytic Hierarchy Process and on the modeler’s judgment. Therefore, interactions between variables may not concur with physical or natural laws. However, the model does present a valuable first-stage analysis of flood vulnerability in the lower part of the Nadi Basin. The data provided by the model can help decision makers to focus their flood risk awareness efforts and gives an important tool for disaster management authorities. With more time and funds, physical based model should be developed to counteract some of the uncertainties produces by the GIS/MCDA method.

5.1. Discussion Even though this GIS/MCDA model could be regarded as a simplistic attempted to measure flood vulnerability in the lower Nadi Basin it was still a monumental task to accomplish. Firstly, there is very little information about past flooding in the Nadi region (the Ba and Rewa rivers are better documented). Secondly, many of the datasets required to build any type of flood model are inexistent or lack the proper accuracy. Thirdly, there is no benchmark information to test or verify the models. Finally, no one really knows the true extent of the 2009 floods since Fijian authorities and international NGOs were to busy trying no manage the crisis instead of measuring it. Shockingly, during the research phase of this project, it was discovered that the 2009 flood extent (provided by SOPAC but originally created by DISMAC) was exactly the same as the 1993 flood extent that was documented in the 1998 JICA report. Furthermore, during the interviews in Nadi, no one at the DIMAC office could answer questions about the 2009 floods or provide any maps/data on the extent of the disaster. Finally, when ask about future floods, employees seemed to be clueless and could not provide any clear action plan if a similar flood would occur. Consequently, it is impossible to systematically compare the predictions of the GIS/MCDA flood model with the actual flood potential of the Nadi River.

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5.2. Recommendations The following recommendations are based on the knowledge, experience and the opinion of the author of this thesis. These were written for an academic purpose and should be interpreted with care. Flood modeling is a complex process that requires the input of a pluridisciplinary team of experts. Consequently, if any of these suggestions should be adopted, they should be revised by professionals. 5.2.1. Task 1: Acquire and share more and better data From the onset of this project it was clear that there was a huge void of scientific knowledge on the Nadi Basin. Most of the available data was outdated, erroneous or simply unsuitable for the task. Furthermore, to acquire this data one must go through a long and tedious process which is a major drawback to any modeler/analyst. Some suitable data was available, but the owners refused to share it which was a huge disappointment. Therefore, most of the data layers needed to be created from scratch which required an enormous amount of effort and time; effort and time that could have been invested in creating a better model instead of creating data which was already available or in development.

Consequently, the first step would be to setup a database of all available datasets in Fiji: a centralized archive, freely accessible through the Internet, with a variety of datasets (physical and social) from governmental, NGO and private sources. All these datasets should be properly verified and have metadata that complies with international standards. Stakeholders need to understand that a synergy is created when data is shared (errors are identified, models are developed and data merging can develop even more information) and that they have more to lose if they decide to withhold this information.

The second step would be to acquire multispectral aerial photography or satellite imagery to conduct a detail assessment of the upper basin (section 5.2.2 is an example of this). This information would greatly help any attempts to monitor/reduce high runoff rates (water and soil) from the highlands which are one of the root problems in the Nadi Basin. Theses datasets could also be used in a physical based flood model.

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The third step would be to secure financial support to assure the operating cost and maintenance cost of the newly installed rainfall gauges and river gauges in the Nadi Basin. The National Hydrological Service needs to be funded appropriately. Money needs to be made available for vehicles, fuel, boats, instrumentation, standby allowances and overtime to ensure a sustainable hydrological monitoring network is maintained” (Turner 2009). Moreover, the collected data needs to be verified, shared and secured to uphold international standards and provide a viable long-term record.

The fourth step would be to conduct more studies to understand the impact of climate change, storm surge and tides on the flood zones in Nadi. According to Yeo (2000), the most rapid rise of several floods has coincided with rising tides and wind (storm surge) tends to back up floodwater in the gently sloping lower reach of the (Ba) river. Thus, tertiary institutions, NGOs and the Fijian government need to promote and fund research in the Nadi Catchment to evaluate these variables.

The fourth step would be to convince the Fiji Bureau of Statistics to release more specific socio-economic values to draw a better portrait of the economic vulnerabilities of the individuals living in flood zones. 5.2.2. Task 2: Reducing soil erosion and rapid runoff from the upper catchment One of the major problems of the Nadi Basin is soil erosion and runoff. Deforestation and poor agricultural practices have increased flood risk since they increase flood runoff and sediment deposition and decrease channel capacity (Yeo 2000; Smith & Petley 2009). During the 2011 river survey, several measurements were made to determine the bathymetry of the Nadi River. These depths were compared to the data in the 1998 JICA report. Since the values in the JICA report were not depths but rather elevations over the Mean Sea Level (MSL) it was necessary to adjust the values. This was done by subtracting the surveyed depths to the DGPS elevations values (elevation of the boat on the water) that were also set on the MSL. Afterward, the 1998 values (JICA) were subtracted to the 2011 values (this survey) to give a rough estimation of the sediment accumulation in the past thirteen years.

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Table 19: Sediment accumulation in the Nadi River * Site C1 C79 C165 C264 C731 C892 C1287 C1655 C1861 C1935 C2005 C2219 C2287 C2567 C2789 C3145

Latitude -17.80873 -17.80609 -17.80659 -17.80655 -17.80691 -17.80772 -17.80354 -17.80496 -17.80123 -17.79899 -17.79751 -17.80102 -17.80220 -17.79868 -17.79520 -17.79288

Longitude 177.39648 177.39712 177.39926 177.39925 177.40571 177.40831 177.41080 177.40653 177.41206 177.41665 177.42113 177.42458 177.42849 177.43068 177.43598 177.43367

Elevation 1.08 1.00 0.72 0.87 0.74 0.70 0.68 1.00 1.34 1.36 1.36 1.43 1.42 1.47 1.62 1.63

Depth 3.27 2.84 2.68 2.68 1.34 0.60 0.70 2.65 0.83 4.76 1.90 1.69 2.26 0.95 2.00 1.00

Survey -2.19 -1.84 -1.96 -1.81 -0.61 0.10 -0.02 -1.65 0.51 -3.40 -0.54 -0.26 -0.84 0.52 -0.38 0.63

JICA -1.82 -0.37 -1.52 -1.84 -1.06 -1.60 -0.87 -2.07 0.09 -3.35 -1.09 -1.53 -1.58 -0.90 -2.67 -2.20

Difference -0.37 -1.47 -0.44 0.03 0.46 1.70 0.85 0.42 0.42 -0.05 0.55 1.27 0.74 1.42 2.29 2.83

The term “rough estimation” was used because three factors may contribute to errors in the table above. Firstly, two different sampling techniques were used (DGPS survey/Total station survey). Secondly, it was impossible to sample the exact same point because the data in the JICA report had no geographical coordinates (a georeferenced image of a cross-section map taken from the report was the only way to extrapolate the coordinates). Thirdly, during the 2011 survey, it was determined that the thalweg was in the middle of the river (lack of time to sample the whole cross-section) which may not be the case in reality. However, considering the circumstances and the void of data on the subject, these values should be considered until a proper study is done. It is important to note that all sample sites before C1935 were dredged just before the survey. This explains the three negative values at the start of the table. Nevertheless, even with this operation almost all the other sites, except C1935 (just passed the bridge), still have more sediments than 1998. The sedimentation budget is clearly positive and shows that some areas are more affected than others. The mean accumulation with all the values is 67 centimeters and 1.29 meters for the values that were not dredged. Thus, one could estimate that, in the past ten years, about a meter of new sediments were deposited in the Nadi River.

Moreover, an analysis of two LANDSAT 7 images, one from 2001 and one from 2005, has exposed some signs of deforestation in the catchment. As for the model, the Maximum Likelihood Classification tool was utilized to classify both images. However, only the infrared bands were used (B40 near infrared, B50 middle infrared and B70 middle infrared 2). The hypothesis was that concentrating only on the *

The C1935 site is upstream a few meters after the Nadi bridge

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infrared bandwidths this would amplify the spectral signature of chlorophyll and assist in plant classification. As predicted, this process helped identify different landuses but also helped to classify vegetated areas such as grass, shrubs, pine plantation, tropical forest and mangroves. From the 12 original classes two classes were created: “others” and “forest”. The result was clipped with the outline of the basin and then by a topographic mask. The mask was used to delete areas below 100 meters of elevation, thus eliminating the bias made by sugarcane fields (fallow fields were classified as “others” and cultivated ones as “forest”). The arbitrary value of 100 meters was selected after visual inspection of the images revealed that sugarcane cultivation halted around 100 m. The two outputs were converted to polygons and their area calculated to estimate the deforestation in the Nadi Catchment. Figure 27 is the result of this operation. The rectangular grid is the study area, the black outlines the sub-catchments, the green areas the forest cover in 2005 and the red areas the forest that was removed between 2001 and 2005.

Figure 27: Deforestation from 2001 to 2005 (green forest area / red logged area)

The forest cover was 243.17 km2 in 2001 and was reduced to 231.52 km2 in 2005 for a difference of 11.66 km2. In other terms, 3.4% of the total vegetation cover was removed in only four years. This figure is probably much more significant in 2011.

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Thus, it is clear that one of the problems in the Nadi Basin is runoff due to a lack of forest cover in the upper reach. Dredging is only a temporary solution since it does not address the root of the problem (Gravelle & Mimura 2008). To be successful three important steps need to be accomplished.

A soil erosion model, based on the universal soil loss equation (USLE), developed by Wischmeier and Smith in 1978, should be developed to identify the most vulnerable areas. The USLE equation, which lends itself well to GIS analysis, was used by Ramlal & Baban (2008) to develop a successful GIS soil erosion model in the West Indies.

The government will need to enact better legislation to penalize poor forestry and agricultural practices, such as clear cutting and slash/burn techniques, and reward good practices, such as agroforestry and contouring/terracing measures.

A reforestation campaign should be conducted and, in the very high risk areas, the land should be declared a natural reserve to prevent future development and deterioration of the forest cover. During the whole process, the communities must be involved and consulted. Given that the whole catchment will benefit from these interventions, everyone, from the hotels in Denarau to the goat herder in the highlands, should monetarily contribute to this project. 5.2.3. Task 3: Integrate the communities in disaster risk management In developing countries, poverty and vulnerability go hand in hand. Frequently, poor migrants, moving from rural areas to urban areas, end up in squatter settlements in flood-prone zones around cities. These sites are often seen unfit for legitimate private of public developments because of this risk, but not much concern is given to squatters. At the household level, these individuals have fewer resources to draw upon to counteract the negative impacts of flooding (Few 2003). This is the case of many citizens living in the lower part of the Nadi Catchment. These squatters need to be reminded of the risk and be properly warned in the event of a dangerous flood. Dependency on flood relief efforts need to be reduced as they are only a temporary solution. Fostering community-based hazard reduction and interventions can strengthen social capacity (Few 2003). Some of these people understand their 73

surrounding, vulnerabilities and risks and have developed disaster coping mechanisms. However, this local knowledge is rarely recorded and some of these mechanisms have become obsolete because they could not be adapted to the present environmental conditions. Therefore, traditional knowledge needs to be combined with modern disaster management plans. This process needs to respect a grass root approach (each member of the community needs to participate actively in the decision-making process) since involving the citizens promotes trust, respect and an exchange of information among local communities and local the authorities. This also helps to educate the masses and to ensure long-term commitment of the communities. Finally, this approach will increase the plan’s acceptance among the villagers (Tran et al. 2009). 5.2.4. Task 4: Develop flood mitigation strategies in Nadi As stated in the literature, non-structural approaches would be preferable over engineered approaches since they cannot suffer catastrophic failures and they are generally more cost-effective. It is necessary to have an Integrated Water Resources Management (IWRM) plan to promote the co-coordinated development and management of water, land, and related resources in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems. This was achieved with funding of the Global Environment Facility (GEF) and the creation of the Nadi Basin Catchment Committee (NBCC).

According to Yeo 2000, it was estimated that up to 65% of damage from the 1999 flood at Ba (almost $10 million) might have been avoided with an improved flood warning system. He later adds “an effective and sustainable flood forecasting system depends not only on technology for the communication of observational data, but also on the availability of trained workers to obtain and process the data”.

Therefore, a comprehensive flood management plan which takes into consideration the local communities needs to be developed. This includes input from the citizens, local businesses, NGOs (like the Red Cross), the police and fire services and local authorities (like the Nadi Town Council and NDMO). Evacuation routes and areas need to be designated, a clear information dissemination process needs to be defined, 74

specific roles need to be given to the different authorities, an alert message needs to be composed, warning methods (television, radio, cell phone, loudspeakers and siren) need to be devised and tested (Yeo 2000).

The Fijian government needs to review regulations and laws in regards to flood mitigation. This consists of better land-use (zoning restrictions in flood-prone areas, creation of protected forest areas, regulate new developments, promote commercial and residential development in areas above the reach of floods), building codes (floor-level regulations, flood proofing techniques and building materials in flood zones), agricultural and forestry regulations that will decrease runoff and soil erosion (stiffer penalties and rewards for good practices), accountability of government institutions and public servants towards the security of the communities they are suppose to serve and protect (JICA 1998).

Soil erosion control must be improved in the Nadi Basin. This includes reforestation of critical areas, prevention of wildfires, conservation of forest belts along river banks and in the upper catchment, the promotion of agroforestry and the planting of hedgerows in-between fields to slow runoff (Smith & Petley 2009).

Flood education needs to be conducted in schools and in communities. People from all steps of life need to be informed of their community’s flood vulnerability, on the actions they need to take to prepare themselves and on what to do if there is a flood. Simple information on why floods happen and on how to prevent them should also be transmitted.

Each village and settlement should have “clean-up days” to keep storm drains clear of rubbish to prevent their clogging during the wet season.

At last, relocation to safer grounds should be considered in the most vulnerable areas. However, the concerned individuals and businesses need to be active participants in the relocation process. Everyone has different perceptions of risk and this need to be taken into account. Moreover, low-income populations often ignore the risks in order to satisfy immediate living and work objectives. Priority should be given to maintaining support networks (familial, social, and economic) of the people that need 75

to be relocated. A 2006 World Bank report has revealed that if these networks are disrupted individuals will move back into risky areas to reconnect with these networks. (Arnold 2006)

Some suitable structural approaches have been devised for the Nadi Basin. The Fijian government is currently installing small retention dams to slow down the flow of water during the flood season and to raise the river level during the dry season. Currently, there are two dams (Vatutu and Namulomulo) and two more are to be built in the upcoming year (Kumar 2011).

The Nadi Town Council is looking into creating water retention areas (small lakes and ponds) upstream from Nadi town to decrease flood peaks. Water would be pumped into these areas by windmill. Restoration of sloughs, swamps and other wetland environments would also produce a similar response (La Cuira & Narayan 2011).

Mechanical land treatment of slopes, such as contour ploughing or terracing could be done in the upper catchment to reduce the runoff coefficient (Smith & Petley 2009).

Finally, in the 1998 JICA report, a sort cut channel was proposed for one of the meanders just west of Nadi town. In this area, just before the Nawaka creek, the river goes through a set of tight meander that greatly slows its flow. This 250 meters sort cut channel would link the up the 9 km point to the 7.5 km point (distance from the mouth). According to the JICA engineers, this structural approach would increase flow capacity by 0.3 m in terms of stage and 50 m3/sec in terms if discharge. 5.2.5. Task 5: Development of a physical flood model It is the opinion of this author that the NBCC should not try to develop a river channel model. First off, river channel models demand bigger monetary investments for equipment, surveys, maintenance and trained personnel. Second off, the banks of the river are not easily accessible (topography and vegetation) and would hamper the cross-section data collection. Third off, this type of model demands a long-term commitment (maintenance, data collection and calibration) which may not be possible if the NBCC budget runs out or if the government does not support the 76

project. Finally, a good flood warning system in Nadi will not require the level of precision that is offered by a hydrological model.

Consequently, a rain-runoff model would be better suited for the needs of an early warning system in Nadi. The equipment (hydrographs and rain gauges) required to build such a model have already been installed in the catchment. These new tools are already linked, by a cellular network, to a computer server that is collecting real-time data (Kumar 2011). Now enough data needs to be collected to calibrate the equipment and built the model. This process could be abbreviated if past hydrological and meteorological data could be reconstructed or extrapolated from archived records. Lastly, the use of GIS could greatly help the development of the model by incorporating datasets developed for this thesis and also new the new ones proposed in section 5.2.1. However, a comprehensive study of the different plausible models needs to be made, specific needs need to be identified, a clear plan needs to be develop and good communications between the NBCC and the model developer need to kept to avoid the following mishaps:

Figure 28: Cartoon of a defective development cycle (steve-oh.com 2011)

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Chapter 6: Summary and conclusion This project is the first of its kind in Fiji; never before have GIS and DGPS been combined to provide highly accurate flood model data for the Nadi Basin. The methodology developed for this research takes in to consideration the economic and technical limitations of developing countries. Nevertheless, the datasets developed for this model, such as the road network, the hydrological network, the land-use and the DTM are the most up-to-date and the most accurate datasets available in Fiji. The nine days survey has also provided some much needed insight on the topography and the hydrology of the catchment. Lastly, the results in this section will undoubtedly help to draw better plans for disaster preparedness which helps to increase community resilience against natural hazards. This work is crucial since climate change will most likely increase the risk of floods in the Nadi Basin.

The main goal of this thesis, to create an affordable and accurate flood model of the Nadi area, was accomplished. However, this technique should be used only as a firststage analysis and a physical based model, that requires more precise meteorological and hydrological data, should be developed for the flood warning system. The secondary goals, to acquire field data from the study zone, create a detailed elevation model, update and create vector and raster datasets of the area, were also achieved. Nevertheless, data users should be aware that inaccuracies can be caused by uncontrollable factors such as equipment inaccuracy, scale effect, projection distortion and cartographic generalization (Carr & Zwick 2007).

As revealed in the literature review, it was impossible to develop a rainfall-runoff or a hydrological model because of the lack of accurate data and the lack of proper instrumentation in the catchment. Consequently, the MCDA technique was selected since it was the best alternative. However, accurate positional, topographical and hydrological data was needed to develop such a model, thus a field survey was undertaken with the help of SOPAC. Very precise ground control points were taken to georectify satellite imagery and over 40,000 DGPS points were taken all over the 84 square kilometers study zone to create a detailed DEM. Afterwards, a digital topographic model was developed with the help of ANUDEM program to create a

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detailed DTM. The Etching method, an innovative channel definition technique, was devised to improve the overall accuracy of the DTM and to make it hydraulically correct. Subsequently, a GIS/MCDA flood vulnerability model with the aid of ArcGIS ModelBuilder and several datasets were produced to feed this model. Based on the work of Fernández and Lutz in (2010), six parameters were incorporated in the model: elevation, catchments, land-use, slopes, distance from channel and soil types. The AHP matrices were calculated in Excel and the GIS manipulations done in ArcGIS. The final output, a flood vulnerability index, was linked to the 2007 census data to evaluate the total risk exposure of seven focus areas.

The model has revealed many interesting facts. There are 86 enumeration areas and 47,351 individuals living in the study area. Most of the test zone is very flat with a mean elevation of 8.5 meters. Sugarcane fields are the dominant land-use type (almost 56% of the zone) and the Nadi central business district represents less than one percent of the total area. A semi-automated classification process of recent satellite imagery has identified 7,628 buildings of which 84% are in urban areas and 16% are in rural areas. According to the 2007 census file, there was 6,404 building (an increase of 1,224) in the same area of 83% were in urban areas and 17% were in rural areas. According to the flood model index 2% of the buildings are in extreme hazard, 10% are in very high hazard, 11% are in high hazard 23% are in moderate hazard, 40% are in low hazard and 14% are in very low hazard. An analysis of the flood model and of socio-economic values has allowed the identification of seven areas in regards to their total risk exposure. The most vulnerable area is the Narewa, Sikituru and Yavusania village group, followed by the Nadi central business district, then the Moala Village, the zone southwest of the runway, the Kerebula Settlement, the Rasusuva settlement, the Namaka area and finally the Votualevu area. A closer look at the data has revealed that the Nadi River is not the only flood vector in the study zone. The runway sub-catchment poses a great threat to the zone to its southwest and several poorly designed storm drains also pose a risk to the surrounding buildings.

Several interviews were conducted to verify the model’s accuracy and some errors in the model results were noted by the participants. Many of these inaccuracies were caused by the models limitations or by issues that were not identified in the field 79

survey. Overall, it was determined that the model performed quite well considering the monetary and time constraints.

Numerous recommendations were arranged for the relevant authorities such as suggestions regarding data sharing and data acquisition, analyses on soil erosion and runoff, ideas on how to integrate the communities in disaster risk management, hints on how to develop flood mitigation strategies in Nadi and some insight on which type of physical flood model should be developed by the NBCC.

As a final point, this project was a very interesting and gratifying journey. It allowed the author to acquire new skills, attain new knowledge, made him understand how to work in a developing country, gave him lots of headache (working with Fiji time!), provided him with many pleasant memories and prepared him for a variety of future challenges.

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Appendices

Coordinate system

Projection

Data type

Appendix I: Compilation of relevant datasets available at USP and SOPAC Orthophotomap of Nadi area (hard & soft copies)

Topographic map: Fiji map series 31 (hard & soft copies)

Soil map of Fiji (NorthWestern Viti Levu, Malolo, Mamanuca…)

Geology of Viti Levu map

Fiji Islands DEM & Viti Levu DEM

LANDSAT 7 satellite imagery

N/A

Transverse Mercator

Cassini

Not indicated

Fiji map grid

WGS_1984_ UTM_ Zone_60S

UTM 60S WGS 84 Clarke 1880

WGS 72

Not indicated

Not indicated

Original Scale

1:50 000

1:126720 (two miles for one inch)

1:250 000

Pixel size 100mX100m

Pixel size 30mX30m

Pixel size 90mX90m 1979

1991

1961

1966

Most likely late 1980 early 1990

06/08/2001

Australian Survey Office

Fiji Lands and Survey Department

Geological Survey of Fiji

NASA/SOPAC

NASA

B&W images taken 16/7/1977 @ a original scale of 1:1600

Based on aerial photography 1986 / ground checked 1990

Soils Bureau of NewZealand & Fiji Department of agriculture Combination of many sources and scales. Data from images, surveys and admiralty charts

First edition

1st done by Shuttle Radar Topography Mission & 2nd by SOPAC unknown method

6 bands

Publication year Produced by

GCS_WGS 1984

GCS WGS_1984

1:5 000

Extra information

GCS WGS_1972

85

smaller scale produced but not available @ USP

Data type Projection Coordinate system Original Scale Publication year Produced by Extra information

World view 2 satellite imagery of costal Nadi

Quickbird satellite imagery of the Nadi-Ba corridor

Vector layers for the Fiji topo-maps (vegetation, roads, rivers, topography, places, etc.) Fiji map grid (FMG)

Vector soils (Digital)

Vector geology (Digital)

Vector cadastral (Digital)

WGS_1984_ UTM_ Zone_60S

WGS_1984_ UTM_ Zone_60S

Fiji map grid (FMG)

Fiji map grid (FMG)

Fiji map grid (FMG)

GCS_WGS 1984

GCS_WGS 1984

GCS_WGS 1972

GCS_WGS 1972

GCS_WGS 1972

GCS_WGS 1972

Pixel size 50cmX50cm

Pixel size 60cmX60cm

1:50 000

Most likely 1:50 000

Most likely 1:50 000

No metadata

18/12/2009

Most likely late 1980 early 1990

Most likely late 1980 early 1990

No metadata

No metadata

Probably post 1991 since many lots on Denarau Is.

Geoeye.inc

Geoeye.inc

Fiji Lands and Survey Department

No metadata

Most likely Geological Survey of Fiji

FLIS?

Available in multi spectral pan and pan sharpen formats

Provided by SOPAC

In CAD data probably from the topo-map since all layers concur with the 31 series topomaps

Was in shapefile format on the USPGIS server: No metadata available

Was in shapefile format on the USP GIS server: No metadata available

Was in format shapefile on the USPGIS server: No metadata available

8 bands

pan sharpen 3 bands

86

87 7 7.1227 0.0204 0.0155

Lambda Max Consistency Index (CI) Consistency Ratio (CR)

10 and + 0.290 0.290 0.145 0.097 0.072 0.058 0.048 1.000

Normalized matrix 10 and + 8 to 10 6 to 8 4 to 6 2 to 4 1 to 2 1 and sum N=

10 and + 1.00 1.00 0.50 0.33 0.25 0.20 0.17 3.45

Reciprocal matrix 10 and + 8 to 10 6 to 8 4 to 6 2 to 4 1 to 2 1 and Sum

Elevation (in meters)

8 to 10 0.264 0.264 0.132 0.132 0.088 0.066 0.053 1.000

8 to 10 1.00 1.00 0.50 0.50 0.33 0.25 0.20 3.78 6 to 8 0.304 0.304 0.152 0.076 0.076 0.051 0.038 1.000

6 to 8 2.00 2.00 1.00 0.50 0.50 0.33 0.25 6.58 4 to 6 0.321 0.214 0.214 0.107 0.054 0.054 0.036 1.000

4 to 6 3.00 2.00 2.00 1.00 0.50 0.50 0.33 9.33 2 to 4 0.308 0.231 0.154 0.154 0.077 0.038 0.038 1.000

2 to 4 4.00 3.00 2.00 2.00 1.00 0.50 0.50 13.00 1 to 2 0.286 0.229 0.171 0.114 0.114 0.057 0.029 1.000

1 to 2 5.00 4.00 3.00 2.00 2.00 1.00 0.50 17.50 1 and 0.261 0.217 0.174 0.130 0.087 0.087 0.043 1.000

1 and 6.00 5.00 4.00 3.00 2.00 2.00 1.00 23.00 sum Priority Vector 2.034 0.2905 1.749 0.2499 1.142 0.1632 0.810 0.1158 0.568 0.0812 0.411 0.0587 0.285 0.0408 7.000 1.0000

Appendix II: Elevation AHP matrix

Appendix III: Catchment AHP matrix Catchment Reciprocal matrix Other Nadi Sum

Other 1.00 0.50 1.50

Nadi 2.00 1.00 3.00

Normalized matrix Other Nadi sum

Other 0.667 0.333 1.000

Nadi sum Priority Vector 0.667 1.333 0.6667 0.333 0.667 0.3333 1.000 2.000 1.0000

N= Lambda Max Consistency Index (CI) Consistency Ratio (CR)

2 2.0000 0.0000 0.0000

Appendix IV: Slope AHP matrix Slope (in degrees) Reciprocal matrix 1 and + 0 to 1 Sum

1 and + 1.00 0.17 1.17

0 to 1 6.00 1.00 7.00

Normalized matrix 1 and + 0 to 1 sum

1 and + 0.857 0.143 1.000

0 to 1 sum Priority Vector 0.857 1.714 0.8571 0.143 0.286 0.1429 1.000 2.000 1.0000

N= Lambda Max Consistency Index (CI) Consistency Ratio (CR)

2 2.0000 0.0000 0.0000

88

89 8 8.3721 0.0532 0.0377

Lambda Max Consistency Index (CI) Consistency Ratio (CR)

Forest 0.380 0.190 0.127 0.095 0.063 0.054 0.048 0.042 1.000

Normalized matrix Forest Mix crops/Open areas R Cane Dense R/Dirt Road C/I Roads/Dense C Wet areas sum

N=

Forest 1.00 0.50 0.33 0.25 0.17 0.14 0.13 0.11 2.63

Reciprocal matrix Forest Mix crops/Open areas R Cane Dense R/Dirt Road C/I Roads/Dense C Wet areas Sum

LandUse

Mix crops/Open areas 0.437 0.219 0.109 0.073 0.055 0.044 0.036 0.027 1.000

Mix crops/Open areas 2.00 1.00 0.50 0.33 0.25 0.20 0.17 0.13 4.58 R 0.403 0.268 0.134 0.067 0.045 0.034 0.027 0.022 1.000

Cane 0.355 0.266 0.177 0.089 0.044 0.030 0.022 0.018 1.000

R Cane 3.00 4.00 2.00 3.00 1.00 2.00 0.50 1.00 0.33 0.50 0.25 0.33 0.20 0.25 0.17 0.20 7.45 11.28 Dense R/Dirt Road 0.351 0.234 0.176 0.117 0.059 0.029 0.020 0.015 1.000

C/I 0.307 0.219 0.175 0.131 0.088 0.044 0.022 0.015 1.000

Dense R/Dirt Road C/I 6.00 7.00 4.00 5.00 3.00 4.00 2.00 3.00 1.00 2.00 0.50 1.00 0.33 0.50 0.25 0.33 17.08 22.83

Roads/Dense C Wet areas sum Priority Vector 0.271 0.237 2.741 0.3426 0.203 0.211 1.810 0.2263 0.169 0.158 1.226 0.1532 0.136 0.132 0.839 0.1049 0.102 0.105 0.560 0.0700 0.068 0.079 0.381 0.0476 0.034 0.053 0.261 0.0326 0.017 0.026 0.182 0.0228 1.000 1.000 8.000 1.0000

Roads/Dense C Wet areas 8.00 9.00 6.00 8.00 5.00 6.00 4.00 5.00 3.00 4.00 2.00 3.00 1.00 2.00 0.50 1.00 29.50 38.00

Appendix V: Land-use AHP matrix

Lambda Max Consistency Index (CI) Consistency Ratio (CR)

4.0934 0.0311 0.0346

4

> 1000 200 to 1000 0.424 0.400 0.424 0.400 0.106 0.133 0.047 0.067 1.000 1.000

NORMALIZED MATRIX > 1000 200 to 1000 100 to 200 < 100 sum N=

> 1000 200 to 1000 1.00 1.00 1.00 1.00 0.25 0.33 0.11 0.17 2.36 2.50

Reciprocal matrix > 1000 200 to 1000 100 to 200 < 100 Sum

Channel distance (in meters)

100 to 200 0.485 0.364 0.121 0.030 1.000

100 to 200 4.00 3.00 1.00 0.25 8.25 < 100 0.450 0.300 0.200 0.050 1.000

< 100 9.00 6.00 4.00 1.00 20.00 sum Priority Vector 1.758 0.4396 1.487 0.3718 0.560 0.1401 0.194 0.0485 4.000 1.0000

Appendix VI: Distance from channel AHP matrix

90

Appendix VII: USDA soil types AHP matrix USDA Soil Types Reciprocal matrix Class A Class B Class C Class D Sum

Class A 1.00 0.33 0.17 0.13 1.63

Class B 3.00 1.00 0.50 0.25 4.75

Class C 6.00 2.00 1.00 0.50 9.50

Class D 8.00 4.00 2.00 1.00 15.00

Normalized matrix Class A Class B Class C Class D sum

Class A 0.615 0.205 0.103 0.077 1.000

Class B 0.632 0.211 0.105 0.053 1.000

Class C 0.632 0.211 0.105 0.053 1.000

Class D 0.533 0.267 0.133 0.067 1.000

N= Lambda Max Consistency Index (CI) Consistency Ratio (CR)

4 4.0335 0.0112 0.0124

91

sum Priority Vector 2.412 0.6030 0.893 0.2232 0.446 0.1116 0.249 0.0622 4.000 1.0000

92 6 6.1836 0.0367 0.0296

Lambda Max Consistency Index (CI) Consistency Ratio (CR)

Elevation 0.510 0.170 0.127 0.073 0.064 0.057 1.000

Normalized matrix Elevation Catchment Land-use Slopes Distance Soil sum N=

Elevation 1.00 0.33 0.25 0.14 0.13 0.11 1.96

Reciprocal matrix Elevation Catchment Land-use Slopes Distance Soil Sum

Objectives

Soil 0.375 0.208 0.167 0.125 0.083 0.042 1.000

Slopes Distance Soil 7.00 8.00 9.00 3.00 4.00 5.00 2.00 3.00 4.00 1.00 2.00 3.00 0.50 1.00 2.00 0.33 0.50 1.00 13.83 18.50 24.00

Catchment Land-use LandUse Distance 0.568 0.495 0.506 0.432 0.189 0.247 0.217 0.216 0.095 0.124 0.145 0.162 0.063 0.062 0.072 0.108 0.047 0.041 0.036 0.054 0.038 0.031 0.024 0.027 1.000 1.000 1.000 1.000

Catchment Land-use 3.00 4.00 1.00 2.00 0.50 1.00 0.33 0.50 0.25 0.33 0.20 0.25 5.28 8.08

sum Priority Vector 2.886 0.4810 1.248 0.2080 0.819 0.1365 0.503 0.0839 0.326 0.0543 0.218 0.0364 6.000 1.0000

Appendix VIII: Objectives AHP matrix

Appendix VIV: Flood vulnerability map (without corrections)

93

Appendix X: Flood vulnerability map (with corrections)

94