Carbon in - carbon out: can carbon inputs keep up with losses in peatland? This project aims to quantify the current and predict the future carbon balance of a high altitude, carbon-dense ecosystem, namely sub-alpine grassy peatland, by measuring how environmental variables including experimental warming control the fluxes of carbon and water into and out of the system. In this way, this project will produce new knowledge on the susceptibility of high-altitude peaty soils to climate change. Expe ....Carbon in - carbon out: can carbon inputs keep up with losses in peatland? This project aims to quantify the current and predict the future carbon balance of a high altitude, carbon-dense ecosystem, namely sub-alpine grassy peatland, by measuring how environmental variables including experimental warming control the fluxes of carbon and water into and out of the system. In this way, this project will produce new knowledge on the susceptibility of high-altitude peaty soils to climate change. Expected outcomes include an enhanced ability to predict future carbon accumulation rates and the resilience of the vital water-storage and filtration services provided by these systems. This project will enhance outputs from new infrastructure and assist planning for future flood and drought management across SE Australia.Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less