H017
Advances in Predicting Hydrologic Extremes: Engineering Tools and Products to Strengthen Infrastructure and System Resilience
Session ID#: 250522
Session Description:
As weather-related extremes intensify, the gap between scientific understanding and real-world adaptation continues to grow. This session will highlight innovative research that bridges this gap with computational hydro-meteorological methods and products translated into actionable strategies for engineering resilience against myriad extreme impacts: from flood and drought, to wildfire. We invite submissions demonstrating how new scientific insights are operationalized into tools that support infrastructure planning, system operations, installation adaptation, and disaster preparedness. Topics of interest include: (1) improved understanding of the physical drivers and interdependence of hydrologic extremes across the hydrologic cycle under anthropogenic change; (2) development of physics-based and data-driven models, observations, and workflows for forecasting and monitoring extreme events; (3) applications of computational hydrology, including AI/ML and probabilistic forecasts with uncertainty estimates, to support engineering and operational planning; and (4) effective strategies for communicating probabilistic forecasts and new tools to stakeholders, with an emphasis on visualization and user engagement.
Co-Sponsor(s):
Index Terms:
1807 Climate impacts [HYDROLOGY]
1817 Extreme events [HYDROLOGY]
1840 Hydrometeorology [HYDROLOGY]
3355 Regional modeling [ATMOSPHERIC PROCESSES]
Primary Convener: Yonas Demissie, Washington State University, Civil and Environmental Engineering, Pullman, WA, United States
Conveners: Hongxiang Yan1, Jeremy Giovando1 and Jeffrey Arnold2, (1)Pacific Northwest National Laboratory, Richland, United States(2)ERT Corp, Greenbelt, United States
Modeling of Unobserved Historical Variability in Atmospheric Rivers in the Pacific Northwest and Implications for Flood Resilience (Invited) (1847659)
Ethan D Gutmann1, Naoki Mizukami2, Jane Harrell3, Michael Warner4, Bert Kruyt5, Andy Wood2 and Bart Nijssen6, (1)NSF National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States, (2)NSF National Center for Atmospheric Research, Boulder, United States, (3)University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle, United States, (4)US Army Corps of Engineers, Seattle District, Seattle, United States, (5)National Center for Atmospheric Research, Research Applications Laboratory, Boulder, CO, United States, (6)University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle, WA, United States
Do We Understand the Resilience of California's Water System to Drought Extremes? (Invited) (1848249)
Patrick M Reed, Cornell University, School of Civil and Environmental Engineering, Ithaca, NY, United States, Rohini S Gupta, Cornell University, Biological and Environmental Engineering, Ithaca, NY, United States and Scott Steinschneider, Cornell University, Ithaca, United States
Developing a two-fold machine learning model to predict catchment-scale 3-day rainfall one week in advance using weekly ensemble forecasts (1877168)
Seiya Aihara, National Agriculture and Food Research Organization (NARO), Institute of Rural Engineering, Tsukuba, Japan, Takeo Yoshida, National Agriculture and Food Research Organization, Institute for Rural Engineering, Tsukuba, Ibaraki, Japan, Hiroki Minakawa, Nat. Inst. Rural Engineering, Tsukuba, Japan and Asari Takada, NARO National Agriculture and Food Research Organization, Institute for Rural Engineering, Ibaraki, Japan
Quantifying Potential Future Shifts in Intensity-Duration-Frequency (IDF) Curves for Precipitation, Wind, and Their Compound Extremes in U.S. Coastal Regions (1887822)
Tangnyu Song, Stony Brook University, School of Marine and Atmospheric Sciences, Stony Brook, United States and Kevin A Reed, Stony Brook University, School of Marine and Atmospheric Sciences, Stony Brook, NY, United States
Deep Learning Models in Gumbel Estimation: Robust Performance under Data Scarcity (1890124)
Jaehwang Lee1, Seo Hyung Choi1 and Young-Oh Kim2, (1)Seoul National University, Seoul, Korea, Republic of (South), (2)Seoul National University, Civil and Environmental Engineering, Seoul, Korea, Republic of (South)
Regional Variability in the Projected Changes in Sub-daily Precipitation IDF Curves across the Contiguous United States (1934397)
Nanditha J S, Princeton University, Princeton, NJ, United States, Gabriele Villarini, Princeton University, Civil and Environmental Engineering & High Meadows Environmental Institute, Princeton, United States, Shubhra Misra, Coastal Engineering and Adaptation Solutions, Houston, United States and Kathleen White, DASD/ER&O, Washington, DC, United States
Improving extreme streamflow estimates across the CONUS Using Physics-Informed ML and Synthetic Rating Curves (1949279)
Soumya Guchhait1, Lalit Pal1, M. Sadegh Riasi2, Sayan Dey3, Lilit Yeghiazarian2 and Siddharth Saksena1, (1)Virginia Polytechnic Institute and State University, Civil and Environmental Engineering, Blacksburg, United States, (2)University of Cincinnati, Chemical and Environmental Engineering, Cincinnati, United States, (3)Saint Louis University Main Campus, Department of Earth, Environmental and Geospatial Science, Saint Louis, United States
Shoreline Change Analysis of Oswego County, NY on Lake Ontario: A Remote Sensing and GIS-Based Study (1960303)
Afeef Ahmad, Clarkson University, Potsdam, NY, United States, Anthony Coronato, Clarkson University, Potsdam, United States, Abul Baki, Clarkson University, Civil and Environmental Engineering, Potsdam, United States and Tyler J Smith, Clarkson University, Department of Civil & Environmental Engineering, Potsdam, NY, United States
Co-Developing CONUS-Wide Projections of Weather and Water Extremes to Support US Agency Management and Planning Initiatives (1968083)
Andrew W Wood1, Mozhgan Askarzadehfarahani2, Naoki Mizukami2, Guoqiang Tang3, Chanel Mueller4, Chris Frans5, Marketa McGuire6 and Brantley Thames4, (1)National Center for Atmospheric Research, Boulder, CO, United States, (2)NSF National Center for Atmospheric Research, Boulder, United States, (3)Wuhan University, School of Water Resources and Hydropower Engineering, Wuhan, China, (4)US Army Corps of Engineers, Headquarters, Washington DC, United States, (5)US Army Corps of Engineers, Seattle, United States, (6)Bureau of Reclamation Denver, Denver, CO, United States
Curbing overconfidence in infrastructure design: Bayesian inference for rainfall return level estimation (1970019)
Omid Emamjomehzadeh, New York University, New York, NY, United States, Dawar Qureshi, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland, Lauren Cook, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland and Omar Wani, New York University, Tandon School of Engineering, New York, NY, United States
Transformer-Based Reinforcement Learning for Flood-Resilient Reservoir Operation (1996432)
Soheyla Tofighi, University of Iowa, Iowa City, IA, United States, Faruk Gurbuz, Turkish Water Institute, Istanbul, Turkey, Ricardo Mantilla, University of Manitoba, Manitoba, Canada and Shaoping Xiao, University of Iowa, Mechanical Engineering, Iowa City, United States
