Fiji Disaster Risk Reduction and Resilience Activity Scope and Design Flood Warning System Recommendation Report

Report

Prepared for Ministry of Foreign Affairs and Trade Government of New Zealand

Prepared by Tonkin & Taylor International Ltd

Date June 2020

MFAT Activity Reference Number WPG0101781 ACT-0100374

Document Control

Title: Fiji Disaster Risk Reduction and Resilience Activity Scope and Design Date Version Description Prepared by: Reviewed Authorised by: by: 24/04/2020 1 First draft for client comment Laura Tilly Richard Richard Bapon Reinen- Reinen-Hamill Fakhruddin Hamill 18/06/2020 2 DRAFT Final Report Laura Tilly Richard Richard Bapon Reinen- Reinen-Hamill Fakhruddin Hamill

Distribution: Ministry of Foreign Affairs and Trade Government of New Zealand 1 PDF copy GHD 1 PDF copy Tonkin & Taylor International Ltd (FILE) 1 copy

Table of contents

1 Introduction 1 1.1 Scope of work 1 1.2 Methodology 1 2 Background 2 2.1 River catchments in Fiji 2 2.2 Institutional arrangement for flood management in Fiji 3 3 Flood risk assessment 5 3.1 Gap analysis of the data for flood risk assessment 5 3.1.1 Hydrological observation database 5 3.1.2 Flood modelling 7 3.1.3 Historical record of estimated flood-related damages and losses 9 3.1.4 Strengthening data collection and management 10 3.2 Recommendations for data and risk assessment 10 4 Flood prediction, warning and monitoring system 12 4.1 Flood forecasting and monitoring system 13 4.2 Flood warning system 15 4.3 Current operation and maintenance arrangements for flood warning systems 17 4.4 Recommendations on flood prediction, warning, and monitoring 17 5 Assessment of options and overall recommendations 21 6 Applicability 27

Appendix A : List of people met Appendix B : Summary of previous projects

Tonkin & Taylor International Ltd June 2020 Fiji Disaster Risk Reduction and Resilience Activity Scope and Design - Flood Warning System Job No: MFAT Activity Reference- WPG0101781 Recommendation Report ACT-0100374 Ministry of Foreign Affairs and Trade Government of New Zealand

Executive summary

Fiji is highly vulnerable to natural hazards, especially to tropical cyclones, heavy rains and floods, making disaster risk reduction a key priority. The New Zealand Ministry of Foreign Affairs and Trade has partnered with the Government of the Republic of Fiji to discharge core disaster risk reduction and disaster risk management functions, to better coordinate and manage their partners, and thereby reduce the country’s vulnerability to natural disasters. This report outlines the outcomes of a gap analysis of the flood risk and flood data and a gap analysis of the flood prediction, warning and monitoring systems operated by the FMS. The basis for this assessment included a review of available literature and datasets, in-country stakeholder consultations and field observations. The datasets assessed included real-time weather data (precipitation, temperature, soil moisture and humidity), river flow data, and historical flood damage estimates, which are required for assessing flood risk. It makes recommendations on strengthening data collection and management, the operation and maintenance arrangements for flood warning systems and options for enhancing flood warning systems. Recommendations to improve data and data management include: strengthening institutional arrangements and coordination, improving and expanding rainfall and flow data collection from key river basins, improving data on land use and vegetation cover in flood watersheds, increasing the use of remote sensing data collection for non-gauged basins, and development of a centralised database. Recommendations for improving the flood prediction, warning and monitoring system include: providing the needed equipment, setting up the catchment-based flood forecasting system for each major catchment, developing standard operation procedures for flood warning system, developing a communication and warning dissemination strategy, and strengthen capacity of the relevant agencies. This report also provides the overall recommendations for enhancing flood warning in Fiji, outlining medium to low and least-cost options for strengthening flood prediction, warning and monitoring systems under three scenarios: business as usual, defensive scenario, and resilience scenario.

Glossary of terms

CAP Common Alerting Protocol

DEM Digital Elevation Model DRM Disaster Risk Management DRR Disaster Risk Reduction EWS Early Warning System FMS Fiji Meteorological Service GIS Geographical Information Systems ICAO International Civil Aviation Organisation IMO International Maritime Organisation MFAT Ministry of Foreign Affairs and Trade MIT Ministry of Transport MLMR Ministry of Lands and Mineral Resources MOWE Ministry of Water and Environment NDMO National Disaster Management Agency NEOC National Emergency Operations Centre O&M Operations and Management PDaLO Pacific Damage and Loss Database TCAC Tropical Cyclone Advisory Centre WAF Water Authority Fiji WMO World Meteorological Organization SOP Standard Operating Procedure

1 Introduction

The New Zealand Ministry of Foreign Affairs and Trade (MFAT) has partnered with the Government of the Republic of Fiji, supporting Fiji’s Ministry of Infrastructure and Transport (MIT) and the National Disaster Management Office (NDMO) to discharge core disaster risk reduction (DRR) and disaster risk management (DRM) functions, to better coordinate and manage their partners, and thereby reduce the country’s vulnerability to natural disasters.

1.1 Scope of work Tonkin + Taylor International (T+TI) has been engaged by GHD Ltd. to lead the system strengthening for flood warning systems and tsunami sirens. This report is outlining recommended options for delivering a flood end-to-end early warning system (EWS). This is to include: • Gap analysis of the flood risk and flood data • Gap analysis of the flood prediction, warning and monitoring systems operated by the FMS • Strengthening data collection and management • Operation and maintenance arrangements for flood warning systems • Assessment of the options for flood warning systems. The report provides sufficient detail on the options for flood warning systems, including technical specifications, implementation requirements, operations and maintenance agreements, which will enable tendering for required services. Feedback provided from relevant stakeholders and MFAT will be integrated into the final version of the Flood Warning System Recommendation Report.

1.2 Methodology A desktop review of available literature was conducted to identify gaps in flood related data, EWS for flood, and how the system is operated and maintained. Assessment of the gaps identified from the literature review informed the recommendations for delivering an end-to-end flood EWS. A two-member team visited Fiji during 17– 19 February 2020. The purpose of the field mission was to discuss with key stakeholders the ways for improving the current flood and tsunami hazard warning and monitoring systems. Key stakeholders included NDMO, Fiji Meteorological Service (FMS), the Pacific Community (SPC), GeoSciences, Ministry of Lands and Mineral Resources (MOLMR), Ministry of Waterways and Environment (MOWE), and Fijian Broadcasting Corporation (FBC). During the mission, T+TI provided an overview of the approach and methodology to enhance tsunami siren and flood warning systems in Fiji. Information provided by key stakeholders was essential to get perspectives of residents and responsible agencies. The people consulted during the in-country visit are listed in Appendix A.

2 Background

Fiji is an archipelago of approximately 330 islands with an estimated 884,887 population based on the 2017 Fiji Bureau of Statistics’ census. The two largest islands Viti Levu and Vanua Levu (87% of the total land area) have mountainous landscapes with peaks rising to 1,323m ad 1,032m respectively (Ministry of Lands and Mineral Resources, 2020). Urban centres including Nadi, Lautoka, Ba and Labasa are situated near the mouths of significant rivers and are some of the most vulnerable areas to flood hazard. Fiji is highly vulnerable to natural hazards, including tropical cyclones, floods, storm surges, earthquakes and tsunamis (UNDRR, 2019). The most common natural hazards in Fiji are tropical cyclones, heavy rains and floods, occurring frequently during the monsoon season (Government of the Republic of Fiji, 2019). Recent flood events including riverine and flash floods have caused significant damage to vulnerable urban communities, resulting in substantial financial and economic losses, and causing negative impacts to infrastructure, environment, and health and education sectors (UFCOP, 2017). Losses from natural hazards are expected to increase in the future, driven in part by socioeconomic trends such as increasing urbanisation and by the impacts of climate change (UFCOP, 2017).The major concern for the Fijian Government is the increasing exposure of populations residing or working in flood prone areas. Fiji’s goal to substantially reduce disaster risk and related losses is a key priority. A risk reduction and preparedness initiative identified in the Republic of Fiji National Disaster Risk ReductionPolicy 2018- 2030 is to develop multi-hazard early warning systems (EWSs) to cover information on flood, cyclones, landslides, tsunamis, storm surges and coastal inundation. Notable ongoing DRR initiatives include the Flash Flood Guidance System (Fiji FFGS1, a name chosen to ensure ownership of the project and show that its applications are specifically targeting the islands of Fiji) and operational flood forecasting in the Nadi basin, both supported by the World Meteorological Organization (WMO). Other risk reduction measures include construction of seawalls, establishment of mangroves on coastal areas, and footbridge developments.

2.1 River catchments in Fiji Fiji’s river systems range from small mountain streams and steep torrents to large mature rivers that meander between flood plains and out to lagoons and ocean deltas. Over 70% of the island is drained by three large river systems: the Rewa, Navua and Sigatoka (Figure 2-1). The Navua River is particularly vulnerable to flooding; it is believed that about 12,000 people would be concerned about flood risk (WFP, 2012). The other significant river catchments on Viti Levu are the Ba and Nadi catchments. The Ba catchment promotes particularly rapid-rising floods during heavy rain due to its topography of steep valleys in the upper catchment. Major floods occur in the area on average once in four years, with average flood depths of around 6m. The Nadi River is the largest in western Fiji with an estimated length of 62km and is made up of 45 sub-catchments (Paquette & Lowry, 2012).

1 Fiji FFGS is funded by the Climate Risk Early Warning System Initiative and Environment and Climate Change Canada and implemented by the World Meteorological Organization and the Hydrological Research Center. It uses the best science of meteorology and hydrology to further improve the quality and lead-time for early warning of flash flooding.

Figure 2-1: Major flood catchments in Viti Levu, Fiji. Source: T+T, 2020

Over 170 ﬂoods from 1840 to 2020 have been recorded by the FMS, as illustrated in Figure 2-2. This equates to an average of 10 ﬂoods per decade. Thus, it is essential to develop flood management plans and further enhance flood EWS for these major river catchments due to the frequent nature of flood events affecting vulnerable areas.

Figure 2-2 Decadal floods history in Fiji since 1840 (source: updated Yeo, 2015 based on FMS, 2020)

2.2 Institutional arrangement for flood management in Fiji FMS is responsible for severe weather and flood warning and monitoring in Fiji. FMS collects hydrological data by monitoring major river catchments, analyses and interprets data, and issues

4 timely flood alerts, warnings and advisories. FMS also serves as the Regional Specialized Meteorological Centre (RSMC) under the WMO’s Tropical Cyclone Programme, responsible for providing information on cyclones in the South Pacific. Moreover, it functions as a Tropical Cyclone Advisory Centre for International Aviation as designated by the International Civil Aviation Organisation, responsible for maritime warnings for international shipping within the framework of the International Maritime Organization. The MOWE‘s mandate covers some elements of water management such as flood control2 and river basin management programmes, but it has no formal flood management role. The Water Authority of Fiji (WAF) is responsible for flow monitoring and records the inflow or estimated inflow from the catchments where it has operations (ADB, 2019). The NDMO is responsible for flood warning. During emergencies and disasters, the National Emergency Operations Centre (NEOC) assumes emergency coordination role. Local government plays a crucial role in flood risk management and is responsible for planning (including through building guidelines), drainage works, development of infrastructure to minimise flood risk, and communicating flood warnings at the local level. Other agencies involved in reducing flood risk are the Ministry of Forests and the Ministry of Agriculture for catchment management, and emergency authorities for post-disaster response. The arrangement distributed across government agencies and sectors is currently believed to be effective towards managing flood risk and flood early warning in Fiji (ADB, 2019).

2 An example of flood mitigation is construction of footbridges for hazard-prone communities.

3 Flood risk assessment

3.1 Gap analysis of the data for flood risk assessment This section presents results of the gap analysis of the real-time weather data (precipitation, temperature, soil moisture and humidity), river flow data, and historical flood damage estimates, which are required for assessing flood risk. Based on the assessment of the gaps, a set of recommendations for the system strengthening is presented.

3.1.1 Hydrological observation database Observational data is collected directly from hydrometeorological stations network in Fiji through phone and mobile networks, email, fax and wireless communication networks. The FMS hydro-meteorological observation network consists of 42 telemetered stations (Figure 3-1), including: • 10 rain gauge NIWA TelemtricTB3, which report rainfall data in millimetres (mm) • 11 water level NIWA PumpPro, which report water level data in mm • 21 water level and rainfall stations, which report water level and rainfall data in mm. The telemetric stations communicate through the GPRS Vodafone network, the Broadband Global Area Network (BGAN) and Iridium satellite. Every station transmits information in real time to the server located at the FMS. This information is then linked to the servers at the WAF Lautoka and the WAF Wailoku. The Hydrology Division of WAF Lautoka utilizes the data for flood forecasting and monitoring, and this data is then stored in the database Tideda and Xconnect. FMS also has 26 manual rainfall stations (Figure 3-2). The observations are recorded manually in the F20 forms and sent to FMS at the end of each month. The FMS Climate Division staff then uploads this data into the CLiDE (Climate Data for the Environment)3 database. Data management and control is conducted manually. This manual system can lead to frequent errors and observations cannot be used with confidence to support forecasting. The quality of hydrological data is generally poor, largely due to limited resources to maintain and provide quality assurance of the datasets. Issues include limited ratings to convert river stages into flows, vandalism of infrastructure, and access to infrastructure for maintenance(UFCOP, 2017). Due to this, there is limited observation data on surface water flows from FMS (which has a database on river flows) and the WAF. The sparse network of hydro-meteorological stations distributed among the major river catchments on Viti Levu and Vanua Levu, and limited staff to monitor and maintain the hydrological observation data, means that building capability in these areas is critical for improving the operation of flood monitoring, modelling and forecasting required for an effective EWS.

3 CliDE is a Climate Data Management System developed as part of the Pacific Climate Change Science Program, providing a central database for climate records, with key entry forms, quality assurance tools, reports and data dumps. It is free and open-source software, using a web-based user interface and high reliability relational database system.

Figure 3-1: FMS hydrometric stations (green = rain gauges, yellow = water level stations, blue = water level and rainfall stations)

Figure 3-2: FMS rainfall stations location map (Source: Government of the Republic of Fiji, 2019)

Yeo (2015) extended, reviewed and explained the historical flood database for the Ba River, which had previously been described by Yeo et al. (2007) in order to quantify flood levels for the Ba River largely based on the correspondence from Rawawai Sugar Mill. Combining the work of Yeo 2015 and Yeo et al. 2007, the longest historical flood database of the Ba River (from 1892 to 2014) was developed. The results of this work revealed that at an average, once every 3.8 years the Rarawai Sugar Mill has been inundated due to flooding. There remains a gap on land data including land use and vegetation cover in watershed that is essential for understanding risk for flood and tsunami. There are reasonably good exposure datasets for some of the most vulnerable urban areas, however only a few studies have assessed the risk to property and life that may be experienced in future flood events. It is essential to record past flood loss and impact data in a systematic way to estimate and predict hazard loss for future events.

3.1.2 Flood modelling All five major catchments in Viti Levu have evidence of historic flooding and are thus flood prone. Over the last few years there have been significant advancements in the quality of flood mapping for some urban areas. Modelling has been done for Nadi, Navua and Ba river basins (Table 3-1). During the in-country visit, the FMS representatives expressed the need for flood hazard mapping for all river catchments, but they expressed a major concern for flood modelling for the Rewa and Sigatoka River, largely as these areas are of significant importance for the tourism industry. The FMS staff mentioned that hazard mapping is of great value and that it should take priority over enhancing the flood forecasting system. This will be useful for resource mobilisation in future disasters, to understand impacts for all major river basins. A summary of the five major catchments in Viti Levu is shown in Table 3-1. Information for Vanua Levu flooding and catchment information proved to be limited.

Table 3-1: Flood modelling completed in five catchments in Fiji (Source: T+T, 2020 )

Flood Catchment Flood frequency Models Population catchment area (km2) Nadi 517 1 every 2 years NIWA 2D flood model 42,000 Ba 937 1 every 4 years DEM (3D) /historical 247,708 flood series Rewa 3,000 Unknown HEC-RAS 1D hydraulic 108,016 model4 Sigatoka 1,530 Unknown None available 9,622 Navua 1,070 1 every 10 years Flood warning system 5,421 (unknown type)

Paquette and Lowry (2012) modelled spatial extent of flood inundation of the Nadi basin using a multicriteria decision analysis coupled with GIS layers. NIWA (the National Institute of Water and Atmospheric Research)(2014) used topographic data derived from high-resolution LiDAR, hydraulic inundation and risk models to produce 100-year return flood inundation maps to calculate estimated economic damages and human losses for the Nadi flood catchment area (Figure 3-3). Based on the results of the 1 in 100-year flood inundation scenario, more than 525 buildings in Nadi are exposed and may be damaged by flood inundation. Total direct and indirect financial loss related to these

4 HEC-RAS is a one-dimensional steady flow hydraulic model designed to aid hydraulic engineers in channel flow analysis and floodplain determination. The results of the model can be applied in floodplain management and flood insurance studies.

8 buildings is similar to the present day 1 in 100-year scenario (estimated at FJ$794 million) and to the scenario with the airport-Denarau link road in place (estimated at FJD$796 million). The flood model incorporates the proposed flood diversion, where loss estimates would be reduced by 31% to the total financial loss estimated at FJD$551 million.

Figure 3-3: Flood hazard rating for Nadi flood catchment (Source: NIWA, 2014)

More recently in 2019, a Dutch Risk Reduction Team conducted flood assessment of the Ba and Rakiraki catchments to inform flood mitigation options (Kingdom of the Netherlands, 2019). Key findings from this study include:

• Ba has been developed on an active floodplain and is expected to experience flood events in the future, therefore flood EWS for Ba is important. • Flood waters (depths and velocity) have a major impact on Ba Town, but also erosion and sedimentation are serious issues, aggravating the impact of floods directly and indirectly. • The need for increasing community preparedness is strong and urgent, and EWS may play a crucial role in this. • Proposed interventions include short-term engineering interventions (e.g., dredging, opening elevated ramps and lowering of the floodplain on west and east riverbank next to Ba Town) and medium- to long-term interventions (e.g., construction of dikes, flow-through dams). • DEM) rainfall/flood scenarios are recommended to model the flow of flood water and the water depths during floods for improving preparedness. FMS is currently undertaking major improvements on EWS in Ba (among others). From the literature review, it is concluded that there remains a gap in modelling flood hazard for three river catchments of Viti Levu (Sigatoka, Navua and Rewa rivers). No flood modelling has been done for Vanua Levu and the surrounding outer islands. This should be considered as a top priority for enhancing flood forecasting capabilities and impact assessments as well as for providing scientific evidence for decision makers to implement food risk management measures. Further research on Fiji flood risk is summarised in Appendix B.

3.1.3 Historical record of estimated flood-related damages and losses There have been efforts to capture loss and impact data from past flood events. In 2013, SPC developed the Pacific Damage and Loss database (PDaLo, 2020) which holds records on events from 1567 to 2014. The system was established to record damage and losses from significant hazard events for the Pacific. Results shows that the most frequent hazards recorded were tropical cyclones, floods, earthquakes and tsunamis. The database estimated the loss from floods equated to US$310 million, with an average cost of US$2.8 million per event (Cook, 2013). The majority of flood events (79%) occurred in Fiji and Papua New Guinea (Cook, 2013). The loss of lives and economic damages of floods in Fiji are shown in Figure 3-4. The Government of Fiji, through the NDMO, is making in greater effort to capture loss and impact data from past events. Figure 3-4 shows impact and loss information from flood events from the year 1964-2014. Historical record of the flood-related damage and loss remains sparse in certain areas, therefore there is a need to capture this information in a systematic way or into a standardised central repository to easily access this data if needed for future loss estimations.

Figure 3-4: Loss of life and economic damages (per annum) due to floods in Fiji (Source: SPC PDaLo, 2020)

Based on the literature review and open-source data, the available GIS risk and flood hydrological data for Fiji are listed below:

• Satellite imagery: FMS uses geostationary Himawari-8 satellite to provide precise information on the location of weather fronts, cyclones and clouds • 90m Fiji DEM: Shuttle Radar Topography Mission (SRTM,2000) to derive slope and elevation information • Fiji Waterways – extracted from Open StreetMap (OSM)5 • Nadi = 100-year return period flood depths developed by NIWA (2014) • Nadi = 100-year flood risk to life developed by NIWA (2014) • Nadi basin/catchment = LiDAR (2014) • Ba = 1m Digital Terrain Model (2012)

5 OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world.

• Ba = Aerial LiDAR survey (accuracy of 500mm horizontally and 150mm vertically) • Exposure data including census data (2017), building outlines and infrastructure (transportation networks) • Specific hydrological monitoring equipment (identified in section 4).

3.1.4 Strengthening data collection and management There have been significant technological advancements in the collection of data for monitoring and forecasting flood early warnings. There is increased availability of ground and remote sensing data (satellite imagery and radar-based data) accessed in real time or near real time, and spatial and temporal resolution data. These data, together with weather prediction models and seasonal forecasts, improve the accuracy of flood predictions and enhance flood EWS. It is recommended that FMS shifts from manual data collection to automatic systems as a necessary procedure for maintaining and recording continuous records. Since forecasting and monitoring equipment is implemented by multiple agencies (e.g. NIWA), the Government of Fiji should look at ways to streamline this process. The current data management system does not allow the use of much more than real-time observations and handling of large amounts of data coming from Numerical Weather Prediction System, radar stations, etc. It is standard practice to store the data received from automatic observations stations in climatological or hydrological databases for quality control. Digital climate databases are essential for the production of climate information, different climatological and applied studies and services for research on climate change and vulnerability and also the production of hazard maps for disaster risk awareness and preparedness. An example of a common sharing data platform could be The Fiji DRM GeoPortal which is a data sharing platform for DRR and sustainable development. This would enable presenting information to assist with planning and coordination for disaster preparedness and response. The data available on this portal is currently being provided by Fiji’s data repository.

3.2 Recommendations for data and risk assessment Recommendations to improve data and data management systems for flood warning systems are:

• Strengthen institutional arrangements and coordination: Strengthen institutional coordination such as producing concept of operations which will provide consensus among stakeholders so that there is a common understanding and support of the current flood prediction and alert system. FMS currently has stronger capacity and a hydrological database, however MOWE holds the water management mandate in Government of Fiji and it does not have sufficient capacity on flood gauges monitoring. NDMO needs to work closely with FMS to understand the flood predictions and their uncertainty to ensure community response. Since forecasting and monitoring equipment is implemented by multiple agencies (e.g., NIWA), the Government of Fiji should look at ways to streamline this process. • Observation stations: Improve and expand rainfall and flow data collection from key river basins. The data collected should be capable of interfacing with flood monitoring and warning systems. A number of automatic weather and hydrological stations need to expand the rainfall and flow data collection from key river catchments. There needs to be appropriate resource for provision of national hydrological services, by expanding and properly maintaining hydrological networks and data storage repositories as well as increasing the number of hydrometeorological services and products. • Baseline flood hazard information: Improve data on land use and vegetation cover in flood watersheds, including a GIS mapping of activity. GIS data needs on high-resolution DEM,

vegetation and land use database, accurate exposure data including mapping or rural populations and spatio-temporal models of urban populations, accurate infrastructure data including buildings, critical infrastructure, lifelines etc. • With the assistance of accurate baseline data mentioned above, detailed flood risk assessments can be accurately modelled for areas that have currently not been modelled or has had limited modelling competed including Sigatoka, Rewa, Navua and major river catchments on Vanua Levu. Flood modelling for these areas will also inform exposure and vulnerability assessments and assist the Government of Fiji with implementing disaster risk management measures for flood hazard in vulnerable areas. Fiji Meteorological Service expressed the need for flood modelling to assist with flood forecasting for future events in addition to inform resource mobilization. Flood modelling should be completed for all major catchments and careful consideration should be given to the inclusion of future climate change scenarios. • Remote sensing and satellite estimation: Increase the use of remote sensing data collection for non-gauged basins. • Development of a centralised database: Flood data archives are fundamental to comprehensive assessment of socially, temporal and spatially disaggregated impact data. Risk interpretation, with standardised loss data, can be used in loss forecasting and historical flood loss modelling. These would provide valuable opportunities to acquire better information about the economic, ecological and social costs of flood hazard, and provide risk-based information for policy, practice, and investment of flood risk. A systematic flood data collection and archiving system need to be established at FMS and NDMO (Fakhruddin et al., 2019).

4 Flood prediction, warning and monitoring system

Early warning systems are a major component in DRR, through the emphasis of disaster preparedness. Flood EWS can save many lives and reduce flood loss and impact, which allows people time to act. In recent years, science and technology advancement has rapidly improved data observation, modelling and the flood forecasts for DRR. Fiji has a well-established meteorological observation and predictions capability under the FMS. As a Regional Meteorological Service (RSMS), FMS has developed good capacity to provide rainfall and extreme weather forecasts in the region. However, hydrological observation and prediction system is in the development stage. It currently lacks the subsequent analysis and synthesis of data for water resources policy and decision makers. The existence of flood prediction, warning and monitoring systems differ across different areas in Fiji. A gap analysis involved assessing Fiji rivers to determine whether they have previously flooded and whether there is a flood warning, monitoring and/or prediction model in place. Information for all rivers in Fiji is summarised in Table 4-1. Existing flood prediction, monitoring and warning systems are summarised in Table 4-2.

Table 4-1: Existing flood prediction, warning, and monitoring systems

River Has this river flooded? Flood warning /monitoring/ prediction systems in place? Sigatoka River Yes No Navua River Yes Yes Rewa River Yes Partially Ba River Yes Partially Nadi River Yes Yes Dreketi River Yes No Qawa River Yes No Wailevu River Yes No Labasa River Yes No Samambula River Yes No

Table 4-2: Existing flood prediction, monitoring and warning systems in Fiji

Project Year River basin Implemented Model type System type by Flash flood 2015- Whole country WMO Rainfall runoff Warning, guidance system current HRC model/ hydrologic prediction, (FFGS) year model monitoring 2D/1D NIWA 2D flood 2013- Nadi River NIWA 1D/2D Hazard model Nadi 2014 assessment Ba flood series 2007 Ba River Yeo et al.2007 DEM, historical flood Hazard series assessment Wadina 2015 Rewa River Sri Lanka HEC-RAS 1D hydraulic Hazard tributary Institute of model assessment Information Technology

Project Year River basin Implemented Model type System type by CIFDP-F sub- 2019 Nadi River WMO Logical tree model Warning, project (CIA-SS NIWA monitoring Nadi) SWFDP Mid Whole country WMO Cascading weather Warning, 2000s forecasting Process prediction CIFDP-F 2019- Whole country T+T, JMA, Hydro-dynamic Prediction, onward WMO models warning Navua flood 2007 Navua River NIWA Analysing data to Hazard warning system predict flood arrival assessment times and river levels

4.1 Flood forecasting and monitoring system FMS’s provides weather forecast products and weather warnings is currently operating on a 24/7 basis. The FMS is responsible for producing: • Daily weather forecasts • 7-day outlook • Heavy rain alert • Heavy rain warning • Tropical cyclone alerts and warnings − Three-day tropical cyclone outlook − Tropical disturbance summary − Tropical disturbance advisory − Tropical cyclone warnings − Special weather bulletins − Tropical cyclone track and uncertainty maps • Flood forecasting • Marine forecasting. FMS have a Forecast Workstation Upgrade Plan in place, whereby FMS have a backup office in Laucala Bay in Suva for redundancy purposes. This will host the disaster recovery system on a hyper- converged infrastructure that will mirror the forecasting system in Nadi HQ. This will enable FMS to immediately shift operations to Laucala Bay online and continue with business and response. This office does not yet have forecast capabilities however there is a plan to develop this resource in the future. The Government of Fiji recognises the importance of continuing to improve flood EWS for all major river catchments, as there is a concern of flooding events increasing due to the effects of climate change. Hydrological services embedded into the FMS (several years ago) include: • Flood alerts and warnings • Hydrological data collection (river and rainfall) • Intensity-duration-frequency curves.

FMS has limited capacity to conduct hydrological modelling; however, they are looking at expanding this role in the future. With support from WMO, a Fiji FFGS commenced in late 2018 at FMS with support from Hydrological Research Centre (HRC). Two staffs were fully trained on FFGS. FFGS is a tool designed for hydro meteorologists to be able to forecast flash flood events. Implementation of the Fiji FFGS involves modelling two types of information: rainfall information and information on the land surface. Fiji FFGS defines the amount of precipitation over a given duration and catchment area to cause flooding at the outlet of a river or stream. Each catchment within Fiji was delineated using a watershed analysis, and soil information was used to create a model of the land surface. Then rainfall data such as gauge data, satellite precipitation and radar precipitation were used to determine the rainfall events which trigger flash floods. This model was integrated with weather forecasting to predict flash floods (Figure 4-1).

Figure 4-1 Fiji-FFGS system at FMS (WMO, 2018)

A simplified coastal inundation alert support system for the Nadi floodplain was developed by FMS and NIWA in 2019. It uses telemetered rainfall, river stage and sea level data to predict future inundation hazard up to 12 hours ahead. The present version shows the status of 10 measurement stations and gives alerts of inundation hazard for an area including Denarau and Nadi town. The cyclone inundation model (CIF) recently implemented by Tonkin +Taylor International and the Japan Meteorological Agency (JMA) supported by WMO has flood inundation integrated with storm surge, wave and tides.

Figure 4-2 Coastal inundation at Ba area due to Cyclone Harold (CIFDP-F, 2020)

There are currently limited operational flood forecasting capabilities for the Rewa, Navua and Nadi River basins based on rainfall forecasting. The lack of trained and dedicated hydrologists at FMS is not allowing the forecasting models to run operationally. The IT infrastructure is also inadequate to support overall functions. FMS currently does not implement impact-based forecasting, which uses hazard vulnerability, impact and risk assessment to communicate likely impacts, but rather produces generic hazard information such as wind speeds. FMS acknowledged during the in-country visit that impact-based forecasting will be needed in the future.

4.2 Flood warning system Warnings and other infrastructure products which include how the hazard information is presented in the form of different observations such as watches, advisories or statements is of critical importance to the effectiveness of an end-to-end flood EWS. These are required to be updated at a frequency which is appropriate to the warning lead time relevant for flooding. The warning needs to ensure accurate community interpretation of the key message; it fundamentally ensures that the appropriate response plans and safe evacuation procedures are adequately resourced. The FMS has added the duty to issue flood warnings including for the Navua flood warning system. FMS weather warning information is currently produced manually and is disseminated only in text format. This type of format is somewhat informative and would not raise enough attention from the public. FMS has an open-source tool, SmartMet Alert, which produces warnings in a map format, but this is not utilized due to inadequate training during installation phase and is not integrated in any flood modelling. The system supports WMO endorsed Common Alerting Protocol (CAP) format messages for warning dissemination to the public and stakeholders.

The EWS is currently in place in the Navua flood plain. The Navua catchment is short and steep, therefore warning time may likely be short thus development planning and preparedness initiatives are key to reducing impact from future flood events. Below are the specifications and process for flood early warning for the wider Navau region: • Gauge network: three river level stations (one existing at Nakavu, two new at Sabata and Nabukalevu) • Data sent to hydrology and meteorology service: six rainfall stations giving catchment-wide coverage • Data is sent by VHF radio to hydrology in Wailoku/Suva and then by broadband internet connection to FMS in Nadi • A flood forecasting model is in place, but data is needed to calibrate the new stations • Available warning time: time between flood peaks in Sabata and Nakavu = about one hour. Time between flood peaks in Nabukelevu and Nakavu = about three hours or more. Time between rainfall in Sabata and flood in Nakavu = about five hours • System operation: The water level or rainfall dataloggers sent alarm messages to the base stations, once critical levels are reached to operators at Suva (8/5) and FMS (24/7) monitor river level rise. The forecasting model provides predictions of downstream levels and FMS warns to NDMO, media and general public. The flood codes are defined below for the Navua flood EWS. Following this the national warning dissemination process (Table 4-3).

Table 4-3: Navua Flood Early Warning System

Flood Alert Flooding is possible. Be aware of this. Issues at least 24 hrs in advance by (Yellow) Be Prepared! Watch out! FMS to media and key agencies. NDMO Divisional Commissioner Provincial Administrator (Serua) Navua Police Station Navua Fire Station SDMO/ Navua Health Centre LAWRM FRCS Flood Flooding of homes, businesses, and main Issued by FMS 2-3 hrs prior to Warning roads is expected. flooding event to media, all (Red) Act now! Please Evacuate! relevant agencies and txt alerts Severe Flooding expected. There is Issued by FMS (as soon as practical) immediate danger to life and property. prior to event to all agencies and Evacuate now! media

All Clear There are no flood alerts or warnings Issues by FMS after consultation (Green) currently in force. with Hydrology and sent to all agencies and media

Currently, NDMO is expanding the multi-hazards warning communications using VHF radio communications. The VHF system is connected to the sirens and can control them remotely. The console system integrates all systems and enables them to connect with each other interactively. It can communicate with landlines or mobile phones through the console.

Figure 4-3 NDMO concept note on multi-hazards warning dissemination (NDMO, 2020)

4.3 Current operation and maintenance arrangements for flood warning systems It is important that all hydrological equipment is appropriately calibrated, maintained and replaced if necessary. Equipment requiring ongoing operation and maintenance arrangements includes meteorological and hydrological equipment and sensors, transmission systems, computing and information, and warning sirens. FMS have a calibration laboratory that is equipped with instruments that calibrates barometers, thermometers and relative humidity sensors. The laboratory is also used for assisting the other Pacific Island meteorological organisations in calibrating their sensors to improve data quality not only for individual countries but for the whole South Pacific region. Instruments in FMS networks have annual inspections for calibration. Calibration instruments are sent to the Bureau of Meteorology’s Regional Instruments Centre in Melbourne for calibration to retain the laboratory’s calibration certificate. Recommendations from Government of Fiji (2019) mentioned that the calibration room is not meeting the current standards for light and temperature conditions. There are also equipment pieces lacking that should meet the minimum required meteorological calibration laboratory, including rain gauge calibration rig, solar radiation verification/calibration, and wind direction/speed verification kit.

4.4 Recommendations on flood prediction, warning, and monitoring Recommendations include providing the needed equipment stated above to meet requirements.

• Numerical weather forecasts: Implement localised numerical weather forecasting (4 m to 1 km resolution) models and integrate the NWP models results to generate flood forecasting, including modelling of catchments and river flows, flash flood, coastal flood and fluvial flood. • National flood forecasting system: Fiji recently operationalised the FFGS which is designed for hydro meteorologists to be able to forecast flash flood events. The FFGS has option to increase lead time up to 36 hours and is able to quantitate risk. But the system has limitation on magnitude and requires dense observation stations for localised forecasts. This system

could be used to understand hotspot of the area under threat and a localised catchment model could be used to provide accurate flood level forecast and warning.

Figure 4-4 FFGS system for structure (WMO, 2020)

• Catchment based flood forecasting system: The catchment-based flood forecasting system would be set up for each catchment/river such as Nadi, Ba, Sigatoka and other major flood prone rivers. A rainfall-runoff model using NWP models and hydrological parameters ensembles flood prediction and warning system could be established. The CIF model of Fiji uses LisFlood-FP model. The LisFlood-FP is 2D flood inundation model. It reduces the computational burden of dry cells and data movements between the central processing unit (CPU) and the random access memory (RAM), and vectorise the core numerical solver used for operational flood forecasting. This model could be set up for catchment level or other suitable models that available within FMS.

Figure 4-5 Catchment wise flood prediction and warning using NWP and other products (T+T, 2020)

• Standard operation procedures (SOPs): There are currently no SOPs for flood EWS in Fiji. This is strongly encouraged to be developed in the immediate future. There is an operational logistics contingency plan for floods. This document is supported by the Fijian Government and funded by the European Commission. The purpose of this emergency logistics response plan for floods is to provide a toll for logistics preparedness and response arrangements for floods in Fiji. However, this plan remains sparse and the government structure has changed since the development of this plan. • Flood response and recovery plan: The existing Greater Nauva Flood Response Plan outlines roles, capacities and responsibilities of agencies. It is recommended to develop a communication and warning dissemination strategy (including maps, zones, emergency contacts, location and capacity of community outreach). • FMS strategic plan for 2018 – 2023: This plan provides guidance taking into account the risks and opportunities from both external and internal factors affecting the organisation and scientific and technological advancements over the next five years. • Enhance climate services: The Fiji Implementation Plan for Enhanced Climate Services provides guidance to the Fijian Government on how to enhance the development and delivery of the climate services of the FMS. It addresses priorities to inform decision making and planning. • Training and capacity building: The FMS have well documented staff training records and remain actively training staff on a regular basis for weather and climate whereas there is a lack of hydrological forecasting training. FMS provides training for participants and experts from neighbouring countries, by overseas experts from BoM and Meteorological Service of New Zealand on topics such as tropical cyclone analysis, storm surges, numerical weather predictions, radar, satellites and aviation weather forecasting. Some of this training is financially supported by FMS, but majority relies on external funding from agencies such as

the WMO, JICA or the United Nations Development Programme (UNDP). Most staff members are technicians or professionals with lower-level academic degrees, whilst the number of staff with higher academic education such as a master’s degree remains relatively low. It is suggested that the educational levels of staff and the number of specialised technical people do need meet the current demands. This should be considered when thinking about wanting to improve forecasting systems and flood modelling.

5 Assessment of options and overall recommendations

Provision of a well-developed impact-based flood prediction and warning service is expected to significantly enhance the risk reduction capacity within Fiji. These services should be developed in consistency with WMO No. 1150 Guidelines (impact-based EWS) and should be appropriate for the Fiji context. It is also needed to ensure that 10 EWS elements are well covered towards the successful implementation of flood EWS. The enhanced and full fledge impact-based flood warning service development is an iterative and evolving process that needs to be undertaken in close communication with all relevant stakeholders. Table 5-1 discusses medium to low and least cost options for strengthening flood prediction, warning, and monitoring systems operated by the Government of Fiji. These options are proposed under three scenarios: business as usual, defensive scenario, and resilience scenario. The business as usual scenario refers to the current system with limited future enhancement taking place. The defensive scenario ensures all catchments has flood prediction and warning system. The resilience scenario ensures a robust system is in place with advance lead time and state of the art technologies. Table 5-2 provides in depth information on these recommendations including identifying overall objectives with specific tasks, suggested sites for prioritisation, and indicative costs to implement these measures. Recommendations in both Table 5-1 and Table 5-2 will help assist the Government of Fiji to prioritise measures to enhance overall flood prediction, warning and monitoring systems in Fiji.

Figure 5-1: End-to-end impact-based EWS (Fakhruddin & Schick, 2019)

Table 5-1 Recommendations to enhance Fiji's flood prediction, warning and risk assessment under three scenarios

Elements Future status under business as usual Future status under defensive scenario Future status under resilience scenario scenario Institutional assessment Strengthen institutional collaboration Strengthen institutional collaboration Continuous feedback from stakeholders in place with NDMO and MOWE with all stakeholders Flood observation system Maintain existing telemetric and manual Ensure all weather and hydrological Baseline flood hazard information is high- and data management gauge regularly observation stations are telemetric and resolution and accurate Install at least one EWS in each high-risk feeding directly in the models A centralised database is in place catchment Baseline flood hazard information is in place

Flood prediction National FFGS is operational National FFGS is tested for several Probabilistic ensemble forecasts are in place for catchments and able to produce all catchments Simple rainfall-runoff model is hotspot 3-5 days in advance based on operational at Nadi and other basins rainfall forecasts NWP products are integrated in the catchment models

Flood warning and impact- Flood magnitude and qualitative flood Flood inundation and depth and impact- Tailored made impact and response option are based products warning in place with colour code based sectoral information are in place provided to sectors Flood warning Flood information disseminates in Rapid alert notification system in place CAP system integrates rapid alert and dissemination email, TV, radio, internet and SMS using CAP system community feedback mechanism Flood risk assessment Flood risk assessment for major flood Flood risk assessment completed for all FMS strategic plan fully implemented affected rivers (Nadi, Ba, Rewa, Navua) catchment using future climate change scenarios SOPs are in place Flood response and recovery plan in place

Tonkin & Taylor International Ltd June 2020 Fiji Disaster Risk Reduction and Resilience Activity Scope and Design - Flood Warning System Recommendation Report Job No: MFAT Activity Reference- WPG0101781 ACT-0100374 Ministry of Foreign Affairs and Trade Government of New Zealand

Elements Future status under business as usual Future status under defensive scenario Future status under resilience scenario scenario Training and capacity Three full-time hydrologists in place at Five full-time hydrologists in place at Impact-based forecasts well understood by the building FMS FMS community and they developed their own Community-level flood awareness and FMS and NDMO ensure flood response response plan response plan at each community

Table 5-2: Overall recommendations to enhance end-to-end flood early warning system for Fiji

ID Objective Initiative Specific Task Description Suggested Site Potential Indicative implemen cost ting NZD partners

FEW01 Flood risk Base-line data • High resolution Improve data on land-use and vegetation cover in Ba, Rewa, FMS, $2.0 assessment digital flood watershed. GIS data needs include high Sigatoka, NDMO, Million elevation data resolution DEM, vegetation and land-use database. Navua MLMR (per square

Baseline data is essential for accurate flood basin km $300 • Land-cover modelling, forecasting and risk assessments. @7000 sq data km)

Risk • Hazard With the assistance of accurate base-line data, Nadi, Ba, NDMO, $400K assessment modelling flood exposure and vulnerability assessments can Rewa, FMS ($100K per modelling be accurately modelled such as determining Sigatoka, watershed) • Exposure population, buildings, critical infrastructure at risk Navua modelling to flood hazard. Flood risk assessments should be • Vulnerability completed for all catchments with the inclusion of modelling future climate change scenarios.

ID Objective Initiative Specific Task Description Suggested Site Potential Indicative implemen cost ting NZD partners

Flood • National flood A national flood response plan should be developed National/Catch FMS, $200K response response plan to help outline procedures and institutional ment NDMO, plans response arrangements. This should be improved Divisional • Catchment on the existing operational logistics contingency Offices wise flood plan for floods due to it being sparse and the response plan change of government structure since the • SOPs development of this plan. There is currently no SOPs for flood EWS and is strongly encouraged to be developed in the immediate future. Both a national response plan and SOP should be informative of one another.

FEW02 Rainfall and Enhance and • Additional There is a need to improve and expand on existing Nadi, Ba, FMS $150K flow data expand on weather and rainfall and flow data collection system for key river Rewa, ($30K per equipment current hydrological basins. The data should be capable of interfacing Sigatoka, catchment) data/observat stations with flood monitoring and warning systems. There Navua ion system needs to be appropriate resource for provision of national hydrological services, by expanding and properly maintain hydrological networks and data storage repositories as well as increasing the number of hydrometeorological services and products.

FEW03 Systematic Flood • Flood data Flood data archives are fundamental for National FMS, $100K centralised database collection comprehensive assessment and understanding of NDMO flood impacts and loss from past flooding events. This database • Flood data database would be used for loss forecasting and archiving historical flood loss modelling. It is recommended system that FMS shifts from manual data collection to an

ID Objective Initiative Specific Task Description Suggested Site Potential Indicative implemen cost ting NZD partners automatic system for maintaining and recording continuous records. The Government of Fiji should look at ways to streamline this process. A centralised database would provide valuable information to better inform policy decision-making and investment of flood risk.

FEW04 Strengthen Institutional • Develop a There is a need to strengthen institutional National FMS, $50K institutional assessment concept of coordination across all stakeholders including FMS, MOWE, arrangeme operations for MOWE, NDMO, so there is a common NDMO nts and flood understanding and support of the current flood coordinatio forecasting prediction and alert system. NDMO should work n and impact- more closely with FMS to understand the flood based warning predictions and their uncertainty to ensure community response. The development of COP will identify and clearly outline roles and responsibilities within each organisation.

Training and • Employ Five full-time hydrologists would be needed at FMS National/ FMS, $100K capacity additional staff to build capacity and expertise and have enough Nadi, Ba, NDMO, building staff to monitor flood early warnings. FMS have Rewa, Communit technology in place to enhance the overall early Sigatoka, ies • Development warning system however, is inadequately utilized Navua of community- due to limited staff and training. level flood awareness and response plan To enhance community resilience to flood early and training warnings and for the communities to comprehensively understand impact-based forecasts it would beneficial with the help of NDMO

ID Objective Initiative Specific Task Description Suggested Site Potential Indicative implemen cost ting NZD partners to develop their own community response plans so it is site specific and detailed for their community.

FEW05 Flood Enhance • Rapid alert Currently, FMS weather warning information is National FMS,NDMO $200K warning warning notification produced manually. A rapid alert notification disseminati dissemination system design system should be developed using a Common on products Alerting Protocol (CAP) system, which should • Rapid alert integrate community feedback mechanism. notification system implementatio n

6 Applicability

This report has been prepared for the exclusive use of our client Ministry of Foreign Affairs and Trade Government of New Zealand, with respect to the particular brief given to us and it may not be relied upon in other contexts or for any other purpose, or by any person other than our client, without our prior written agreement.

Tonkin & Taylor International Ltd

Report prepared by: Authorised for Tonkin & Taylor International Ltd by:

...... …...... …...... Laura Tilley Bapon Fakhruddin Richard Reinen-Hamill Senior Natural Hazards Specialist Project Director

SHAF p:\1012221\issueddocuments\18062020_flood warning system report_ final draft_clientfeedback(v1).docx

References ADB, 2019. Urban water supply and wastewater management investment programme, Fiji CIFDP, 2020. Model outcomes from TC Harold. http://stormsurge.met.gov.fj/ Cook, S. (2013). Pacific Damage and Loss (PDaLo) Regional Disaster Impact Report. SPC SOPAC published report (PR180). Fakhruddin, S. & Schick, L. (2019). Benefits of economic assessment of cyclone early warning systems – A case study on Cyclone Evan in Samoa. Progress in Disaster Science (2). Retrieved from: https://www.sciencedirect.com/science/article/pii/S2590061719300341 FMS, 2020. Fiji Meteorological Service. Consultation with staffs. Japan International Cooperation Agency (JICA) (2016). The Project for the Planning of The Nadi River Flood Control Structures. Ministry of Agriculture, Rural and Maritime Development and National Disaster Management Global Logistics Cluster -WFP (2012). Fiji: Operational Logistics Contingency Plan. Government of the Republic of Fiji. Department of Meteorology. (2019). Fiji’s Hydrometeorological Observation Equipment Maintenance and Service Production: A roadmap of actions. Ministry of Disaster Management & Meteorological Services, Ministry of Economy, NAP Global Network and Finnish Meteorological Institute. Kingdom of the Netherlands (2019). Dutch Risk Reduction Team: Reducing the risk of water relation disasters. DRR Mission Report Fiji: Scoping Mission for Flood Alleviation Measures for Ba & Rakiraki Towns (and associated water catchments). Ba & Rakiraki Flood Alleviation Project. Kuleshov, Y., et al. (2014) Extreme Weather and Climate Events and Their Impacts on Island Coun- tries in the Western Pacific: Cyclones, Floods and Droughts. Atmospheric and Climate Sciences, 4, 803-818. National Institute for Water and Atmospheric Research Ltd. (NIWA) (2014): Nadi River Flood Risk Assessment. Ministry of Lands and Mineral Resources, 2020, last access February 2020. http://www.lands.gov.fj/ Paquette, J., & Lowry, J. (2012). Flood hazard modelling and risk assessment in the Nadi River Basin, Fiji, using GIS and MCDA. The South Pacific Journal of Natural and Applied Sciences, 30, pg.33-43.Pacific Damage and Loss (PDaLo) Information System (2020). DesInventar Sendai: Country Profile - Pacific Islands. Retrieved from: https://www.desinventar.net/DesInventar/profiletab.jsp SOPAC, (n.d). Navua Flood Early Warning System & Response Plan. National Disaster Management Office, Government of the Republic of Fiji. SOPAC. Navua Flood Warning System. Presentation. T+T, 2020. Developed by T+T expert analysis UNDRR (2019). Disaster Risk Reduction in the Republic of Fiji: Status Report 2019. United Nations Office for Disaster Risk Reduction (UNDRR), Regional Office for Asia and the Pacific. Urban Floods Community of Practice (UFCOP) (2017). Urban Flood Risk Management In the Pacific: Tracking Progress and Setting Priorities. Yeo, S. (2015). Refining the historical flood series for Ba, Fiji. Technical Report. Yeo, S.W. & Blong, R.J. (2010). ‘Fiji’s worst natural disaster: the 1931 hurricane and flood’, Disasters, Vol. 34, N o. 3, 657‐683, doi: 10.1111/j.1467‐7717.2010.01163.x. Yeo, S.W., Blong, R.J. & McAneney, K.J. (2007). ‘Flooding in Fiji: findings from a 100‐ year historical series’, Hydrological Sciences Journal, Vol. 52, No. 5, 1004‐1015.

Yila, O., Weber, E. & Neef, A. (2013). ‘The role of social capital in post‐ flood response and recovery among downstream communities of the Ba River, western Viti Levu, Fiji Island s. WFP, 2012. Operational logistic contingency plan, https://reliefweb.int/sites/reliefweb.int/files/resources/Fiji%20- %20Operational%20Logistics%20Contingency%20Plan%20for%20Floods.pdf World Meteorological Organization (WMO) (2020). Flash Flood Guidance System: Response to one of the deadliest hazards. Retrieved from: https://public.wmo.int/en/resources/bulletin/flash-flood-guidance- system-response-one-of-deadliest-hazards World Meteorological Organization (WMO) (2004). The Associated Programme on Flood Management. Integrated Flood Management Case Study Fiji Islands: Flood Management Rewa River Basin.

Appendix A: List of people met

Stakeholder People met and position (if Date and time of visit known)

GHD Fiji Timothy Stats 17/02/2020 – 10:00am

National Disaster Management Mitieli 17/02/2020 – 11:00am Office Mr Ben – Communications 18/02/2020 – 8:00am Tavita Sonko – GIS - Policy

Fijian Broadcasting Nitendra Prasad 18/02/2020 – 9:00am Corporation

Ministry of Lands and Mineral Irena D. Nayacalevu 18/02/2020 – 10:00am Resources Emilini K N Tagicakibau Meizyanne Hicks Rigieta Ravuiwasa

Ministry of Waterways and Joshua Wycliffe (Permanent 18/20/2020 – 2:00pm Environment Secretary)

SPC Geosciences Herve Damlamian 18/02/2020 – 3:00pm Litea Biukoto

Mineral Resources Staff working at time in 18/02/2020 – 4:30pm Department National Tsunami Warning Centre

Fiji Meteorological Service Misa Funaki 19/02/2020 – 10:00am

Appendix B: Summary of previous projects

Flood hazard and modelling Agency/author Project literature

Technical report: An economic SOPAC, 2008 An economic assessment of the Navua flood analysis of flood warning in warning system was conducted to determine Navua, Fiji the investment potential or ‘economic return’ of investing in the Navua flood warning system and the issues that affect the likelihood of economic returns eventuating. Which concluded that every dollar spent on the warning system would be most likely to save FJD $3.7 – 7.3 in return. Flood hazard modelling and Paquette and Lowry, Using a multicriteria decision analysis (MCDA) risk assessment in the Nadi 2012 approach coupled with GIS layers for River Basin, Fiji, using GIS and elevation, catchment, land use, slope, MCDA distance from channel and sail types, we model the spatial extent of flood hazard in the Nadi River Basin in western Fiji. Over 40,000 elevation points were taken using Differential GPS. Extreme weather and climate Kuleshov et al. 2014 The research aim was to improve the events and their Impacts on understanding of climate change impacts on island countries in the the natural environment for Pacific Island Western Pacific: cyclones, Countries. Impacts from past extreme events floods and droughts including the 2012 Floods in Fiji to analyse past and future climate to inform decision- making for adaptation to climate variability and change.

Nadi River flood risk NIWA, 2013 – 2014 Provided hydrological analysis, LiDAR based assessment topographic mapping, roughness mapping, historic flood modelling, hydrodynamic modelling, option assessment and risk analysis using RiskScape.

Fiji’s Hydrometeorological The Government of the This document provides FMS with a roadmap Observation Equipment Republic of Fiji (2019) of actions for sustainable development of the Maintenance and Service organisation and sustainable utilization of the Production: A roadmap of overall infrastructure. actions

Urban flood risk management UFCOP, 2017 This document describes a benchmarking in the Pacific method developed to track the progress Pacific Island Countries towards best-practice for flood risk management. It identifies eight priorities or advancing current practice in Pacific Island Countries.

Flood hazard and modelling Agency/author Project literature

The project for the planning of JICA, 2016 Flooding list near Nadi basin between 1870 the Nadi River flood control and 2014. Table list of the flood description structures and areas affected with associated loss and impact data. Deconstructing vulnerability and adaptation in a coastal river basin ecosystem: a participatory analysis of flood risk in Nadi, Fiji Islands

PTC: rising waters in Fiji – flood The University of the Students assessed flood risk for the Village of risk modelling South Pacific, Suva, Fiji KoroToga, which is bounded by the Rewa River. The students developed a simulation model based on digital terrain models derived from drone digital surface models tied to the Fiji Map Grid. DRR mission report Fiji: Dutch DRR Group Conducted an assessment of the floods in Ba Scoping mission for flood and the Penang catchments to identify alleviation measures for Ba mechanisms that’s causing floods and and Rakiraki towns suggest possible flood mitigation measures.