In consultation with the Samoa Electric Power Corporation (EPC) and the Samoa Meteorology Division (SMD), the Afulilo Water Storage Outlook Module (AWSOM) that was developed as a manually operated spreadsheet application during COSPPac-1, has now been redeveloped as an automated web application (AWSOM-2). The Afulilo Hydropower Scheme is the largest renewable power scheme in Samoa and is central to Samoa’s goal of becoming 100% renewable in the energy sector by 2025. AWSOM-2 draws on weekly, monthly, and seasonal rainfall forecast products from the ACCESS-S forecasting system, as well as weather and climate forecasts from global models. Additionally, AWSOM-2 draws on rainfall observations from the dam, dam level measurements conducted by EPC and the Samoa Water Resource Division, and power generation rates being operated by EPC. The model incorporates physical relationships derived from studies of how the reservoir responds to rainfall, water runoff from the upper catchment, and losses from evapotranspiration and seepage. Samoan Met staff operationally review model outputs, add interpretive commentary from local knowledge and perspectives, and then forward the reservoir storage outlook report to EPC. This enables EPC to consider options for water use for power generation and optimise water use while maintaining a guaranteed electricity supply. AWSOM-2 has been coded in python by NIWA, through funding from the NZ Ministry of Foreign Affairs and Trade. The application is run on the CliDEsc server at SMD.

The Vaisigano River which flows through the Apia capital of Samoa is in a characteristic short and steep catchment conducive to rapid flash flooding following intense periods of antecedent rainfall. This results in short early warnings and emergency response lead times. Through the Government of Samoa’s Vaisigano Catchment Project (VCP) supported by the Green Climate Fund, technological initiatives to improve the forecasting of imminent flooding in the catchment which enables longer early warnings and response lead times were undertaken within a hazard risk context. In this talk we describe a pilot impacts-based flood monitoring, early warnings, and decision support system developed through the VCP and tailored for the Vaisigano River. The system comprises an integrative real-time automated framework involving the ingestion of numerical weather prediction rain intensity forecasts, real-time rainfall, river level and flow monitoring data, precomputed rainfall-runoff and predictive flood peak and magnitude tools, as well as estimates of flood inundation exposure and threat to safety at buildings and on roads for different return period events. Information is ingested into a centralized, web-based, flood decision support system (FDSS) portal that enables hydrometeorological officers to monitor, forecast and alert relevant emergency or humanitarian responders of imminent flooding with adequate lead time. The FDSS was tested in the lead up to the 18 December 2020 flooding in the Vaisigano and was able to alert duty officers of the estimated timing and magnitude of imminent channel-overtopping with up to 24 hours lead time. We discuss some of the key challenges and gaps to guide system improvements, as well as offer recommendations for future work.

In the last 190 years a total of 39 tsunamis affecting Samoa have been recorded. Many of them caused by earthquakes occurring along the Tonga Trench, which is only 150 km away from the islands. In 1917, an earthquake with a magnitude of 8.3 caused a disastrous tsunami in Samoa. Even though it was a major event, historical records are scarce and little is known about the event. In order to overcome this lack of data, this work has modelled the aspects of the 1917 tsunami event, using available historical records. The tsunami model used an earthquake initiation, propagation along the Tonga Trench and generated inundation footprints for the islands of Samoa. Then using this model output, the impact in present-day Samoa was determined to estimate the likely exposure and damage to buildings within the inundation zone. The study identified a number of inconsistencies between the inundation zone and the anecdotal evidence recorded at the time of the event. In addition, discrepancies were identified between the model and the records from tide gauges in Apia harbour at the time. These recorded a fluctuation of the sea five minutes after the 1917 earthquake.

Juliana Ungaro 1, Herve Damlamian 2, Sachindra Singh 2, Shaun Williams 3, Ryan Paulik 1, Rebecca Welsh 1, Litea Biukoto 2, Doug Ramsay 4 1. NIWA Taihoro Nukurangi, Private Bag 14901, Wellington 6241, Aotearoa New Zealand 2. Geoscience, Energy and Maritime Division, the Pacific Community (SPC), 241 Mead Road, Nabua, Fiji. 3. NIWA Taihoro Nukurangi, PO Box 8602, Christchurch 8440, Aotearoa New Zealand 4. NIWA Taihoro Nukurangi, PO Box 11115, Hillcrest, Hamilton, New Zealand The Pacific region is one of the most vulnerable and disaster-prone areas in the world. This issue is exacerbated by climate change, which is causing the frequency and intensity of climate related hazards to increase. Furthermore, increased urbanisation, population and environmental damage are all contributing to worsening risk levels. Hazard risk modelling tools can enable decision makers to better prepare for and respond to disasters, and to make sound economic and land-use planning decisions. The Pacific Risk Tool for Resilience, Phase 2 (PARTneR-2) is a three-year project that aims to build off the pilot PARTneR project to help Pacific Island Countries (PICs) become more resilient to the impacts of climate change and natural hazards through the effective use of robust information in decision-making. Currently, a critical gap across PICs is the availability and use of low-cost and easily applied tools to assist countries to make their own risk-informed decisions. By developing national risk models and assessment tools, PARTneR-2 will assist six PICs (the Cook Islands, Republic of Marshall Islands, Tuvalu, Tonga, Samoa and Vanuatu) to have the technical and institutional capability to use and apply these to make informed and effective decision-making related to weather, climate, and coastal hazards.