Risk Frontiers Seminar Series 2023
Tuesday 24th October 2023, Museum of Sydney
Guest access from 2pm, Seminar 2:30-4:30, Drinks and canapes afterwards
Risk Frontiers is excited about the return of its Seminar for 2023 as an in person event.
Meet Our Speakers
All about El-Niño– what it is and what to expect.
I am an Associate Professor at the Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney. My research focus is on climate variability, atmospheric teleconnection patterns, interaction across tropical ocean basins, and future projections. I investigate the mechanisms by which the oceans affect regional climate, in particular rainfall and extreme events such as droughts. I am particularly interested in understanding how different types of El Niño Southern Oscillation influence atmospheric circulation, regional climate, and interact with other oceanic basins.
In this presentation, I talk about the El Niño-Southern Oscillation, a phenomenon that has received a lot of media attention lately. After experiencing three years of wet weather due to La Nina, Australia may now face hot and dry seasons due to the development of El Niño. I will explain what El Nino is, how it starts and ends, its different types, and the implications for Australia’s weather patterns. Additionally, I will discuss the current outlook for El Niño, why it has been declared by international weather agencies, but not by the Australian Bureau of Meteorology. I will also discuss how it affects global mean temperatures, and its relation to the current record-breaking winter temperatures and upcoming seasons.
Advancing Drought Prediction using Machine Learning and Drought Impact Reports.
Sanaa Hobeichi is a post-doctoral researcher at the University of New South Wales (UNSW) and the ARC Centre of Excellence for Climate Extremes (CLEX). Her research spans Climate Science and Machine Learning, focusing on developing Machine Learning methods for downscaling climate data and improving drought predictions. She is also interested in explainable and physics-informed machine learning.
Sanaa obtained her PhD in Climate Science from UNSW. She also holds a Bachelor’s degree in Computer Science and Applied Mathematics, and a Master’s degree in Environmental Remote Sensing.
Advancing Drought Prediction using Machine Learning and Drought Impact Reports.
Leveraging a comprehensive archive of drought impact reports, we used machine learning to associate these impacts with concurrent local climate conditions and large-scale modes of variability. This led to the development of a novel drought indicator that outperforms existing drought indices in predicting impactful drought events. Rigorous testing in Texas and southeast Australia demonstrated the robustness of this approach. In Texas, this drought indicator surpasses the current state-of-the-art US Drought Monitor, particularly in automation and forecasting capabilities. In southeast Australia, the drought indicator offered insights into the temporal and spatial evolution of the Tinderbox Drought. This new drought indicator improves our ability to predict and manage impactful drought events effectively.
Implementation of a machine learning based storm surge model within Risk Frontiers’ tropical cyclone CAT-loss model.
Maxime is a risk scientist at Risk Frontiers and helps develop and manage Risk Frontiers’ suite of models. He holds a PhD in quantitative Marine Science from the University of Tasmania. His research investigated the drivers and impacts of marine heatwaves.
Maxime’s role within Risk Frontiers focuses on Risk Frontiers’ Cyclone model CyclAUS and implementing a new machine learning based storm surge hazard component. He also uses his strong background in extreme and climate science to improve the development of other models including Flood, Hail and assist the ClimateGLOBE project.
In the context of global sea level rise, coastal flooding losses due to storm surge events are a growing threat for Australians. Such events are commonly observed during tropical cyclones (TCs). Although Australia is baring down a hot and dry El Nino summer, Australian coastlines are still at risk. The loss associated with storm surge during a TC is often ignored in traditional TC Catastrophe-Loss models due to the complexity of modelling extreme sea level events in Monte-Carlo simulation infrastructures. Here, we present a machine learning modelling approach to include storm surge risk within Risk Frontiers’ TC loss model – CyclAUS. Storm surge water levels are modelled using Convolutional Neural Networks trained on a coastal sea level reanalysis dataset, spanning the Australian coastline at a 25 km resolution. Outputs from CyclAUS, simulating 50000 years of current climatology TCs around Australia, are then fed to point-based individual storm surge models to extract a maximum sea level height. Storm surge model validation shows that our machine learning approach has comparable skills to hydrodynamic solutions, while demanding far fewer computational resources, thus enabling its implementation into stochastic CAT-loss models.
Dr. Behnam Beheshtian, Risk Engineer at Risk Frontiers
Vulnerability Assessment of Highly Populated Buildings in the Face of Earthquake and Tsunami Hazards
Behnam is a Risk Engineer at Risk Frontiers, responsible for developing and maintaining the QuakeAUS and QuakeNZ models, in addition to contributing to the development of other models within the group. He is passionate about researching the risk and resilience of the built environment in the face of natural hazards, including earthquakes, tsunamis, and bushfires. Holding a bachelor’s in civil engineering, along with his industry experience, means he is well-versed in the structural design of a wide variety of commercial, industrial, and infrastructure projects. Behnam’s PhD is in Earthquake Engineering from Swinburne University of Technology, where he researched the risk and resilience of the built environment to tsunami and seismic hazards.
In this presentation, I will talk about my recent study that focused on the vulnerability of highly
populated buildings, using the AsiaWorld-Expo mega-structure in Hong Kong as a case study. This research used OpenSees software to develop a 3D numerical model of the building. Incremental dynamic analysis was conducted to analyse the seismic vulnerability of the structure, and a physics-based methodology was applied to determine the structure’s tsunami response under various scenarios. The primary outcomes of the research included structural fragility curves, reflecting the building’s damage potential in the face of seismic and tsunami hazards. I found that the structure is more vulnerable along its shorter dimension (parallel to the roof trusses, the Y direction) due to fewer shear walls. Moreover, for assessing tsunami-induced damage, instead of flow depth, the use of the kinematic moment of momentum flux was the preferred intensity measure. The findings provide crucial insights for risk-informed design strategies in earthquake- and tsunami-prone areas.
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