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Session Proposal: Climate-Informed Risk Assessment for Extreme Events (AGU25)

NH008
Climate-Informed Risk Assessment for Extreme Events

Session ID#: 252947

Session Description:
Extreme weather and climate events such as heat waves, floods, tornadoes, hurricanes, droughts, and wildfires can have disastrous impacts on society. Changing climate, population, and development patterns may enhance the risks these extreme events pose to society in many regions globally. A non-analog future may make it increasingly difficult to use historical observations and conventional statistical approaches to assess the future probability of extreme events and their associated risks to society. More complete risk assessment frameworks are required. This session focuses on research that addresses the challenges of climate-informed, time-dependent risk assessment. We invite work that uses a novel assessment framework including, but not limited to, uses of state-of-the-art climate models, machine learning, and new data, for understanding and assessing changes in the extreme event characteristics and their effects on physical risks and impacts across social and economic components of society.
Co-Sponsor(s):
  • A - Atmospheric Sciences
  • GC - Global Environmental Change
  • H - Hydrology
  • IN - Informatics
Index Terms:

1630 Impacts of global change [GLOBAL CHANGE]
4321 Climate impact [NATURAL HAZARDS]
4330 Vulnerability [NATURAL HAZARDS]
4332 Disaster resilience [NATURAL HAZARDS]
Primary Convener:  Joshua Hacker, Organization Not Listed, Washington, DC, United States; Jupiter Intelligence, Boulder, United States
Convener:  Patrick Harr, Jupiter Intelligence, San Mateo, United States
 
The effect of wildfire on drinking water quality (1846307)
Joey Blumberg1, Travis Warziniack2 and Melissa Merritt1, (1)ORISE, USDA Forest Service, Fort Collins, United States, (2)USDA Forest Service, Fort Collins, United States
 
Global supply chains amplify economic costs of future extreme heat risk (1849038)
Zhu Shupeng1, Yida Sun2, Daoping Wang3 and Dabo Guan2, (1)Zhejiang University, Department of Atmospheric Sciences, Hangzhou, China, (2)Tsinghua University, Beijing, China, (3)King's College London, London, United Kingdom
 
A Hierarchical Bayesian Spatial Framework to Assess Nonstationary Rainfall Intensity, Frequency, and Duration in Texas (1903958)
Yuchen Lu1, James Doss-Gollin1, John W Nielsen-Gammon2 and Benjamin Seiyon Lee3, (1)Rice University, Department of Civil and Environmental Engineering, Houston, TX, United States, (2)Texas A&M University, Department of Atmospheric Sciences, College Station, United States, (3)George Mason University, Department of Statistics, Fairfax, VA, United States
 
The Potential of AI Models to Forecast Regional Gray Swan Extreme Events (Invited) (1905836)
Y Qiang Sun, University of Chicago, Department of the Geophysical Sciences, Chicago, United States, Pedram Hassanzadeh, The University of Chicago, Department of the Geophysical Sciences, Chicago, United States, Ashesh Kumar Chattopadhyay, University of California, Santa Cruz, Applied Mathematics, Santa Cruz, United States, Mohsen Zand, University of Chicago, Chicago, United States, Dorian S Abbot, University of Chicago, Geophysical Sciences, Chicago, United States, Jonathan Weare, New York University, Courant Institute of Mathematical Sciences, New York, United States, Hamid Alizadeh Pahlavan, NorthWest Research Associates Boulder, Boulder, United States and Tiffany Shaw, The University of Chicago, Chicago, United States
 
City-Level Heat Stress in Bangladesh: Historical Patterns and Future Climate Projections (1908972)
Fariha Islam Mou and AKM Saiful Islam, Bangladesh University of Engineering and Technology, Institute of Water and Flood Management, Dhaka, Bangladesh
 
Reliably Simulating Extreme Events with Machine Learning Weather Models (Invited) (1923936)
Gregory J Hakim, University of Washington, Department of Atmospheric and Climate Science, Seattle, United States and Zilu Meng, University of Washington, Atmospheric and Climate Science, Seattle, United States
 
Selecting GCMs for fire regime modeling: accounting for spatial and temporal variability in climate (1940031)
Ashley Cale, Benjamin W Sullivan and Erin J Hanan, University of Nevada Reno, Natural Resources and Environmental Science, Reno, United States
 
Louisiana Multi-Hazard Web Map (1955379)
Lakshmi Prasanna Kunku1, Carol J Friedland2, Rubayet Bin Mostafiz2, Meggan Franks3 and Shelly Kleinpeter4, (1)Louisiana State University, Baton Rouge, LA, United States, (2)Louisiana State University Agricultural Center, Biological and Agricultural Engineering, LaHouse Research and Education Center, Baton Rouge, United States, (3)Louisiana State University, Agricultural Center, LaHouse Research and Education Center, Baton Rouge, United States, (4)Louisiana State University, Agricultural Center, LaHouse Research and Education Center, Baton Rouge, United States
 
Next-generation tropical cyclone risk modeling: an integrated, scalable, and climate-responsive approach (1957381)
Justin Rogers1, Hannah Hampson1, Alexis Hoffman2, Hillary Scannell3, Eli Dennis4, Jared Oyler5, Callie McNicholas6, Elizabeth Perry7, Allan Kapoor8, Matthew Woelfle9, Joseph Pucciarelli3, John Exby9 and Stephan R Sain10, (1)Jupiter Intelligence, San Mateo, United States, (2)Pennsylvania State University Main Campus, University Park, PA, United States, (3)Jupiter Intelligence, Boulder, United States, (4)Cooperative Institute for Satellite Earth System Studies, University of Maryland, Department of Atmospheric and Ocean Science, College Park, United States, (5)Jupiter Intelligence, State College, United States, (6)Jupiter Intelligence, Inc., Seattle, United States, (7)Jupiter Intelligence, New York, United States, (8)Jupiter Intelligence, Inc., San Mateo, United States, (9)Jupiter Intelligence, Inc., Boulder, United States, (10)Jupiter, Boulder, United States
 
DeepRisk: An End-to-End AI-based Framework for Real-time Decadal Projections of Extreme Weather and Climate Risk and their Uncertainty Across Assets at Multiple Scales and Fidelity (1968147)
Subhashis Hazarika1, Moein Darman2, Sena Mursel3, Ashesh Kumar Chattopadhyay4, Rohit Vuppala1, Anthony Wong1 and Sushreyo Misra3, (1)Fujitsu Research of America, Santa Clara, United States, (2)University of California Santa Cruz, Santa Cruz, United States, (3)Lehigh University, Bethlehem, United States, (4)University of California Santa Cruz, Santa Cruz, CA, United States
 
Designing for Tropical Cyclone Wind Loads under Non-Stationary Climates (1974624)
Christine Blackshaw, Princeton University, Princeton, NJ, United States and Ning Lin, Princeton University, Department of Civil and Environmental Engineering, Princeton, United States
 
Quantifying Rapidly Growing Chronic Perils’ Physical Risk (1980401)
Mahmoud Khater1, Aaron Bagnell1, Aaron Wang2, Hemanth Vepuri1, Jonathan Sam1, Lawrence Vulis3, Ridwan Siddique2 and Uday Thapa4, (1)Cotality, Oakland, United States, (2)Cotality, Louisville, United States, (3)Cotality, San Diego, United States, (4)Cotality, Delaware, United States
 
Natural Hazard Vulnerability and Wealth Inequality: A Geospatial Analysis of Socioeconomic Disparities and Natural Hazard Outcomes Across CONUS Counties (1992089)
Yilei Yu, Rice University, Center for Coastal Futures & Adaptive Resilience, Houston, United States, Dylan Connor, Stanford University, Center for Advanced Study in the Behavioral Sciences at Stanford University, Stanford, United States, Sara Meerow, Arizona State University, School of Geographical Sciences & Urban Planning (SGSUP), Tempe, United States, Melanie Gall, Arizona State University, Center for Emergency Management and Homeland Security, Phoenix, United States, James Elliott, Rice University, Sociology, Houston, United States and Aaron Flores, Arizona State University, School of Geographical Sciences and Urban Planning, Tempe, United States
See more of: Natural Hazards