Activities

PRISM Leading Discussions and Risk Analyses across Scientific Domains at the American Geophysical Union (AGU) Fall Meeting 2020

Convened three trans-disciplinary sessions:

• Thursday, 10 December 2020 13:30 - 14:30 ET

  • IN018 - Data-Driven Exploration of Interconnected Risks in Complex Human–Natural Systems I

• Thursday, 10 December 2020 19:00 - 20:00 ET

  • IN020 - Data-Driven Exploration of Interconnected Risks in Complex Human–Natural Systems II

• Thursday, 10 December 2020 22:00 - 23:00 ET

  • IN022 - Data-Driven Exploration of Interconnected Risks in Complex Human–Natural Systems III eLightning

Our team contributed numerous talks across domains, highlighting the identification of critical risk indicators (CRIs) and how they connect to lead to systemic risk indicators (SRIs), including implications to critical transitions and risk spillover:

• Cross-domain

  • IN018-03 - Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis (Invited)

  • IN022-01Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects

  • IN022-02 - A Data Library to Archive, Analyze, Visualize and Serve Online Datasets from Multiple Domains in an Interoperable Framework

• Space Weather domain

  • IN030-06Open Knowledge Networks for geoscience: A powerful use case through space weather

  • IN018-04Analyzing Interconnectivity and Critical Risk in Space Weather Indexes

• Energy/Power Grid domain

  • IN018-05 - Animal Occurrence Probability and Power Outage Risk in ISO New England

  • IN022-08 - Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

• Ecology

  • IN020-04 - Developing a new biodiversity risk indicator based on finance metrics

  • IN020-07 - Impacts of cascading environmental hazards on Australian plant biodiversity

  • IN022-04 - Clustering future scenarios based on extremal properties of ecological outcomes

  • IN022-09 - Using trait-based biodiversity to identify critical risk indicators in ecology

• Hydrology

  • IN022-03 - Automated deep-learning-based soil moisture planning and forecast system for planning against natural disasters

  • IN022-06 - Improving multi-month streamflow forecast in CONUS using Long-Short Term Memory (LSTM) networks

• Agriculture

  • IN022-07 - Interconnected Risks of Climate, Water and Finance in California’s Agriculture