- OS21B: Advances in Flood Prediction and Risk Assessment in Coastal, Inland, and Transition Zones II Poster
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NOLA CC
Primary Convener:Generic 'disconnected' Message
Fariborz Daneshvar, Coast Survey Development Laboratory, National Ocean Service, NOAA
Convener:
Albert Cerrone, University of Notre Dame
William Pringle, Environmental Science Division, Argonne National Laboratory
Saeed Moghimi, Coast Survey Development Laboratory, National Ocean Service, NOAA
Early Career Convener:
Atieh Alipour, Coast Survey Development Laboratory, National Ocean Service, NOAA
Chair:
Fariborz Daneshvar, NOAA National Ocean Service
Saeed Moghimi, Coast Survey Development Laboratory, National Ocean Service, NOAA
Bahram Khazaei, Coast Survey Development Laboratory, National Ocean Service, NOAA
Coleman Blakely, Argonne National Laboratory
This session addresses the growing need for accurate short-term predictions and long-term risk assessment of storm-related coastal hazards, including surges, waves, and tides, as well as insights from inland hydrology and baroclinic coupling. It invites studies on four key topics: 1) innovative methods integrating machine learning / artificial intelligence, cloud computing, geospatial tools, and open-source platforms for enhanced storm surge modeling and compound flood prediction; 2) assessing the impact of factors such as urbanization, and land cover scenarios, along with quantifying related uncertainties in compound flooding; 3) applying computational and mathematical techniques to analyze and mitigate uncertainties in flood prediction; and 4) evaluating socio-economic impacts on coastal communities and economic infrastructure. The goal is to improve coastal flood assessment methods through collaboration among researchers and practitioners, aiming to enhance our understanding of coastal flood hazards and their broader impacts.
Index Terms
1821 Floods
1906 Computational models, algorithms
4315 Monitoring, forecasting, prediction
4534 Hydrodynamic modeling
Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Machine Learning and AI
Cross-Listed:
NH - Natural Hazards
H - Hydrology
GC - Global Environmental Change
Neighborhoods:
3. Earth Covering
Scientific DisciplineSuggested ItinerariesNeighborhoodType
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