H065
Extreme Flood Dynamics in a Changing World: Catchment Responses, Modeling, Mapping, and Management
Session ID#: 250067
Session Description:
Climate variability and rapid environmental change are altering catchment responses, making floods more extreme and unpredictable. These changes, characterized by evolving flood dynamics and connectivity patterns, challenge existing prediction tools and require a deeper understanding of ongoing shifts in flood behavior. We invite contributions addressing these challenges by advancing the understanding of flood dynamics in changing environments to inform the management of extreme fluvial and pluvial floods. We seek submissions on (but not limited to): (i) flood generation mechanisms and catchment responses through experimental, physically-based, or data-driven approaches, (ii) advancements in hydrological modeling and theory, (iii) scalable approaches for flood mapping and modeling, and (iv) case studies of extreme floods with lessons on resilience. Additionally, we welcome contributions exploring how these (v) findings can be effectively communicated to decision-makers, bridging the science-policy gap. This session aims to integrate process understanding, innovative methods, and actionable solutions to address real-world flood challenges.
Index Terms:
1804 Catchment [HYDROLOGY]
1821 Floods [HYDROLOGY]
Primary Convener: Arianna Borriero, Munich Re, Munich, Germany
Conveners: Stefano Basso, Norwegian University of Science and Technology - NTNU, Trondheim, Norway, Pallav Kumar Shrestha, Helmholtz Centre for Environmental Research - UFZ, Computational Hydrosystems, Leipzig, Germany and Vinh Tran, University of Michigan, Ann Arbor, United States
NeuralSWWM Python Package: A Hybrid Hydrologic-Machine Learning Algorithm for Stormwater Management (1849954)
Nima Zafarmomen1, Vidya Samadi1 and Edoardo Borgomeo2, (1)Clemson University, Department of Agricultural Sciences, Clemson, SC, United States, (2)University of Cambridge, Department of Engineering, Cambridge, United Kingdom
Probabilistic Flood Mapping via a Simulation–Learning Framework: Evidence from the Lower Mekong Floodplain (1877182)
Giang V Nguyen, Kyungbook National University, School of Advanced Science and Technology Convergence, Sangju-si, South Korea, Minho Yeon, Kyungpook National University, Climate Crisis Institute, Sangju, South Korea and Giha Lee, Kyungpook National University, School of Advanced Science and Technology Convergence, Sangju, South Korea
Flood Inundation Mapping within Operational Hydrological Forecasting: the July 4 Texas Flooding Case Study (Invited) (1880038)
Sagy Cohen1, Anupal Baruah1, Dinuke Munasinghe2 and Dipsikha Devi1, (1)The University of Alabama, Department of Geography and the Environment, Tuscaloosa, AL, United States, (2)Department of Geography and Environment, University of Alabama, Tuscaloosa, United States
Flood mitigation effects of utilizing storage facilities within river basins, including agricultural facilities, and probabilistic evaluation of those effects under various rainfall patterns (1908675)
Hiroki Minakawa1, Seiya Aihara2, Takeo Yoshida2 and Asari Takada3, (1)Nat. Inst. Rural Engineering, Tsukuba, Japan, (2)National Agriculture and Food Research Organization, Institute for Rural Engineering, Tsukuba, Ibaraki, Japan, (3)NARO National Agriculture and Food Research Organization, Institute for Rural Engineering, Ibaraki, Japan
Floods at the Crossroads of Climate and Human Impact: A Process-Based, Explainable AI Framework for Indian Catchments (1920715)
Vaibhav Tripathi1, Subhankar Karmakar2, Amir AghaKouchak3, Mohit Prakash Mohanty1 and Lina Stein4, (1)Indian Institute of Technology Roorkee, Water Resources Development and Management, Roorkee, India, (2)Indian Institute of Technology Bombay, Environmental Science and Engineering Department, Centre for Climate Studies, Mumbai, India, (3)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States, (4)University of Bristol, Civil Engineering, Bristol, United Kingdom
Advancing Operational Flash Flood Forecasting Through Efficient Deep Neural Networks (1949496)
Amirmoez Jamaat, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States, Alfonso Mejia, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, State College, United States and Cibin Raj, Pennsylvania State University Main Campus, Agricultural and Biological Engineering, University Park, United States
The Anatomy of a Hydrologic Extreme (1965049)
Mark S Bartlett Jr, The Water Institute, Baton Rouge, United States, Elizabeth Cultra, Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States and Amilcare M Porporato, Princeton University, Civil and Environmental Engineering, Princeton, United States
Sensitivity Analysis of an AI Hydrologic Emulator under Ensemble Climate Projections (1998339)
Seong Jin Noh1, Yaewon Lee2, Hyuna Woo3, Bomi Kim4, Hyeongjin Choi4 and Yusuke Hiraga5, (1)Kumoh National Institute of Technology, Civil Engineering, Gumi, South Korea, (2)Kumoh National Institute of Technology, Department of Civil Engineering, Gumi, South Korea, (3)Kumoh National Institute of Technology, Gumi-si, South Korea, (4)Kumoh National Institute of Technology, Gumi, South Korea, (5)Tohoku University, Department of Civil and Environmental Engineering, Sendai, Japan
Pathways to Resilience: FORTIFIED® Elevated Manufactured Homes for Affordable Housing in Hazard-Prone Regions (2002071)
Rubayet Bin Mostafiz1, Ayat Salah Al Assi2, Md Adilur Rahim3, Nehal Mahmud Khan4, Isabelina Nahmens5, Robert V Rohli6, Laura Ikuma5 and Muzna Hamza5, (1)Louisiana State University Agricultural Center, Biological and Agricultural Engineering, LaHouse Research and Education Center, Baton Rouge, United States, (2)Louisiana State University Agricultural Center, Biological and Agricultural Engineering-LaHouse Research & Education Center, Baton Rouge, United States, (3)Louisiana State University AgCenter, Biological and Agricultural Engineering, Baton Rouge, United States, (4)Louisiana State University, Baton Rouge, Louisiana, UNITED STATES, (5)Louisiana State University, Mechanical & Industrial Engineering, Baton Rouge, United States, (6)Louisiana State University, Oceanography and Coastal Sciences, Baton Rouge, LA, United States
