Limited data from emergency services for the April 2015 East Coast Low (ECL) event initially investigated. SES call-out data provides spatial coverage, but does not capture detail of the damage to buildings. EICU data has detailed information, including indicative damage state, but limited spatial coverage. Neither dataset consistently links the damage to the hazard that caused it. Showing that the impact forecasting process adds value beyond the underlying hazard forecasts in this situation is challenging. EICU data can help to calibrate the vulnerability functions applied to model-based hazard forecast data. The SES callout data can help evaluate whether the indicative damage rates for an area are reasonable, through use of a service demand metric. Service demand is the number of callouts compared to the number of buildings for a statistical area (e.g. mesh block, SA1 or local government area). We use the total building count in each area, as the SES callout data does not differentiate between residential and non-residential buildings. It also includes callouts for downed trees or power lines that may not have directly caused structural damage to buildings. Service demand is compared to mesh block-based impact forecast data for the 2015 ECL, using existing heuristic vulnerability functions for severe wind. We recognise these functions are not calibrated against forecast model data, but provide a starting point from which we can establish the workflow while working towards refined vulnerability functions in parallel. The project has sourced EICU and SES post-event survey data, and high-resolution model (reanalysis) data for two additional severe wind and rain events to improve the calibration of the vulnerability functions. Poster presentation at the 2019 AFAC Conference

Strong surface wind gusts and heavy rain are meteorological hazards that are predominantly produced by storms such as east coast lows, tropical cyclones or thunderstorms. Interest in these hazards from a response agency point of view lies in their impact on the natural and built environment. At present, weather forecast models still predict mostly 'raw' meteorological output such as surface wind speeds at certain times, or rain accumulations over a specified period. This model output needs to be combined with exposure and vulnerability information to translate the forecast hazard into predicted impact. The Bushfire and Natural Hazards CRC project Impact-based forecasting for the coastal zone: East-Coast Lows attempts to demonstrate a pilot capability to deliver impact forecasts for residential housing from an ensemble of weather prediction models runs. The project is a collaborative effort between the Australian Bureau of Meteorology and Geoscience Australia. The project is initially focusing on the wind and rainfall impact from the 20-22 April 2015 east coast low event in NSW. The wind and rainfall hazard data are provided by a 24-member ensemble of the ACCESS model on a 1.3 km grid, with damage data acquired from NSW State Emergency Services (SES) and the Emergency Information Coordination Unit (EICU) for the 2015 event. We will show that the multi-hazard nature of an east coast low event makes attributing the observed building damage to a single hazard difficult. Wind damage to residential housing in this case is largely due to tree fall. This 'damage-by-intermediary' mechanism requires not just the knowledge of building properties in an exposed area, but also additional knowledge of the surrounding vegetation and its response to strong winds. We will discuss enhancements to the SES/EICU damage survey templates that would lead to improvements in the development of the hazard-damage relationships. Abstract presented at the 2018 Bushfire and Natural Hazards CRC (bnhcrc) and Australasian Fire And Emergency Services Authorities Council (AFAC) Conference