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Improving access to clean water in Fijian communities

Designing effective tools and models for water treatment authorities

Together with Water Authority of Fiji, UNSW is working to improve water quality in Fiji by increasing the forecasting capabilities of the two water treatment plants.

UNSW design and modelling experts are working to establish a Hydrologic Information Management System (HIS) to enable the storage and maintenance of hydrological data, and a forecasting model to predict turbidity levels within the main tributaries to water treatment processing plants.

The status

The Water Authority of Fiji has two water treatment plants, situated within the Waimanu and Sigatoka catchments, which operate 24/7 at full capacity and at times are unable meet peak demand. Land use in the upper river catchments includes a mixture of forest, agriculture and mining, with changing land use and runoff causing turbidity within the river systems. The raw water is extracted from the Waimanu and Sigatoka rivers via offtake pumps, then entering the water treatment plants.

The challenge

At this stage, plant operators are not forewarned when turbid water is arriving, hence they have insufficient time to prepare. Service interruptions are more pronounced during periods of high rainfall, when the river waters become highly turbid, reducing the output of the plants as the filters require more frequent backwashing. The situation is compounded by lack of data on vast area of ungauged catchments and continuous threat of climate change, with expected changes in rainfall patterns and increased extreme rainfall events forecasting more frequent interruptions to water supply.

The goal

In collaboration with WAF, the UNSW team is developing a remote sensing-land-based hydrological model that uses change of soil use and forecasted rainfall to determine the rate of turbidity in the river. Since there is lack of ground level measurements, the UNSW team is incorporating smart remote sensing techniques in identifying change in river flow rate and in water quality when constructing the model framework. Finally, by using historical data from urban areas especially downstream of the catchments, we will be able to calibrate the models. Once calibrated, the models can act as a forecasting tool to WAF to predict incoming turbidity in the rivers by using forecasted rainfall events. The turbidity forecasting model will enable WAF to better manage chemical consumption, machine downtime and maintenance costs at the water treatment plants.

Project stakeholders

The Water Authority of Fiji (WAF), as both a key stakeholder and client in this project, plays a central role in advancing national water security and resilience objectives. As the statutory body responsible for the provision of clean water and wastewater services across Fiji, WAF is committed to aligning this initiative with the country’s strategic priorities, including the Fijian Government’s 20-Year National Development Plan and Sustainable Development Goal 6. Through its partnership with the University of New South Wales, WAF is seeking to enhance its technical and operational capabilities by establishing a Hydrologic Information System (HIS) and developing hydrological and turbidity forecasting models for the Waimanu and Sigatoka River catchments. These catchments are critical sources for major water treatment plants servicing Suva and surrounding communities. The outcomes of this project will enable WAF to improve data-driven decision-making, reduce treatment costs, strengthen infrastructure planning, and mitigate risks from climate change and land use changes. By integrating these models into its broader strategic and asset management frameworks, WAF aims to build a more resilient, efficient, and future-ready water utility.

MFAT (New Zealand Ministry of Foreign Affairs and Trade) is a funding and strategic stakeholder in the hydrological modelling project for the Water Authority of Fiji (WAF), engaging through its Climate and Disaster Risk Management (DRM) programme. MFAT’s interest is focused on the project's potential to deliver long-term impacts and follow-on activities beyond its current scope. With a strong mandate to support Pacific Island countries in building climate resilience—particularly through infrastructure development, disaster preparedness, and low-carbon solutions—MFAT sees this project as well-aligned with its regional priorities. The involvement of UNSW enhances the project's appeal, as the university offers technical expertise and capacity-building support that contribute to sustainable, value-added outcomes over time.

Suraj Shah is a PhD candidate at the Water Research Centre (WRC) within the School of Civil and Environmental Engineering. His research focuses on developing high-resolution precipitation datasets for the Himalayan region and advancing the understanding of flood characteristics in this complex environment.

He specialises in satellite remote sensing, hydrological modelling, the impacts of climate change on hydrological extremes, and the spatiotemporal analysis of environmental data.

His main responsibilities in the project with Water Authority of Fiji are:

  1. Developing the Hydrological Information System (HIS) platform, which enables data to be accessed, viewed, and visualised via a cloud-based server; and
  2. Conducting SWAT (Soil and Water Assessment Tool) modelling for the catchment area, specifically to assess how land-use changes influence water turbidity.

One challenge faced in this project is the lacked of in-situ data on site. Suraj, together with the team in UNSW, has expertise in interpreting remote sensing data for this exact purpose; developing data where no physical gauge is available.

Project phases

  1. Phase 1: Project initiation – COMPLETE
  2. Phase 2: Establish Hydrologic Information System – COMPLETE
  3. Phase 3: Design conceptual models (including land based hydrological model) - April 2025
  4. Phase 4: Calibration of conceptual models - September 2025
  5. Phase 5: Model finalisation and reporting - December 2025
  6. Phase 6: Presentation and training - February 2026
  7. Phase 7: Incorporation of model results into WAF asset management and water security risk assessment  - April 2026