H093
Integrative Approaches to Urban Flooding: Modeling, Monitoring, and Mitigation Strategies
Session ID#: 250712
Session Description:
Urban flooding poses a pressing challenge amidst rapid urbanization and climate change. This session explores integrative approaches to understand, model, monitor, and mitigate urban flooding. We welcome contributions covering:1. Modeling Urban Flood Dynamics: Advances in H&H modeling techniques, integration of multi-dimensional models, and the use of data-driven approaches for flood forecasting and risk assessment.2. Innovative Monitoring Technologies: Development and application of sensors, remote sensing tools, IoT devices, and other technologies to enhance real-time monitoring.3. Urban Flood Risk Assessment and Management: Analysis of vulnerabilities, implementation of NBS solutions, and evaluation of strategies for building resilience.4. Usage of AI in Flood Modeling: Application of artificial intelligence, machine learning, and predictive analytics to enhance the accuracy and efficiency of flood modeling and decision-making processes. 5. Multi-disciplinary Approaches and Case Studies: Collaborative research, cross-sectoral partnerships, and lessons learned from successful urban flood management initiatives.
Co-Sponsor(s):
Index Terms:
1821 Floods [HYDROLOGY]
1847 Modeling [HYDROLOGY]
4321 Climate impact [NATURAL HAZARDS]
6620 Science policy [POLICY SCIENCES & PUBLIC ISSUES]
Primary Convener: Ahmad Haseeb Payab, Drexel University, Civil, Architectural and Environmental Engineering, Philadelphia, PA, United States
Conveners: Franco A Montalto, Drexel University, Civil, Architectural and Environmental Engineering, Philadelphia, PA, United States and William Duncan Solecki, CUNY Hunter College, New York, United States
Physically-Informed Deep Learning Model for Floodwater Depth Estimation in Urban Areas using Remote Sensing (1873484)
Jeffrey Blay, North Carolina A & T State University, Department Of Built Environment, Greensboro, United States, Tewodros Gebre, North Carolina A&T State University, Department Of Built Environment, Greensboro, United States and Leila Hashemi Beni, North Carolina A & T State University, Department of Built Environment, Greensboro, United States
Machine Learning Surrogate Modeling to Improve Urban Flood Predictions (1886211)
Amisha Bhandari1, Yogesh Bhattarai2, Sanjib Sharma2 and Rocky Talchabhadel1, (1)Jackson State University, Department of Civil and Environmental Engineering, Jackson, United States, (2)Howard University, Department of Civil and Environmental Engineering, Washington, DC, United States
Importance of High-Resolution Wind Gust Modeling to Improve Urban Flood Risk Assessment (1887348)
Nehal Ansh Srivastava, Arizona State University, Tempe, AZ, United States, Mikhail Chester, Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, United States and Giuseppe Mascaro, Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, AZ, United States
Transportation and Stormwater Infrastructure (TSI) Planning Integration in an Urbanizing Region near the Dallas-Fort Worth Metroplex (1888545)
Kelli Greenwood1, Sugam Mahat2, Asim Paudel1, Dongfeng Li1, Shannon Abolmaali1, Jerry Cotter3, Matthew Lepinski4, Nick Fang1, Yufan Zhang5, Fouad H. Jaber5, Elizabeth Rophael6, Jeffrey Neal6, Kate Zielke6 and Susan Alvarez6, (1)University of Texas at Arlington, Civil Engineering, Arlington, United States, (2)University of Texas at Arlington, Civil Engineering, Arlington, TX, United States, (3)University of Texas at Arlington, Water Engineering Research Center, Arlington, United States, (4)Independent Consultant, Arlington, United States, (5)Texas A&M, Texas A&M AgriLife, Dallas, United States, (6)North Central Texas Council of Governments, Arlington, United States
Quantifying Sensitivity and Uncertainty in 1D/2D Urban Flood Models: A Case Study from Harrison Avenue, Camden, NJ (1895474)
Ahmad Haseeb Payab1, Timothy Feeney2, Lu Bivona2, Scott Schreiber2 and Franco A Montalto1, (1)Drexel University, Civil, Architectural and Environmental Engineering, Philadelphia, PA, United States, (2)CCMUA, Camden, United States
Scaling Effects of Prairie Restoration as a Flood Mitigation Strategy in Fort Bend County (1899756)
Elena Siemens, Rice University, Houston, TX, United States, True Furrh, Rice University, Houston, United States and Philip B Bedient, Rice University, Civil and Environmental Engineering, Houston, TX, United States
Integrating Hydrodynamic Modeling, Remote Sensing, and Machine Learning for Urban Flood Simulation and Prediction in New York City (1900075)
Shima Kamali, CUNY City College of New York, New York, NY, United States and Reza Khanbilvardi, Department of Civil Engineering, CUNY-CREST Institute, and United Nations University (UNU) Hub on Remote-Sensing and Sustainable Innovations for Resilient Urban Systems (R-SIRUS)-UNU Institute for Water, Environment and Health (UNU-INWEH), The City College of New York, The City University of New York, New York, United States
From Space to Street: Satellite-Driven Urban Flood Intensity Mapping (1900595)
Ali Haider1, Reza Khanbilvardi1, Arpita Mondal2 and Naresh Devineni1, (1)Department of Civil Engineering, CUNY-CREST Institute, and United Nations University (UNU) Hub on Remote-Sensing and Sustainable Innovations for Resilient Urban Systems (R-SIRUS)-UNU Institute for Water, Environment and Health (UNU-INWEH), The City College of New York, The City University of New York, New York, United States, (2)Indian Institute of Technology Bombay, Department of Civil Engineering and Centre for Climate Studies, Mumbai, India
Rapid Evaluation of Nature-Based Solutions for Urban Drainage System Resilience Using Machine Learning Surrogates (1930884)
Shihao Xi, University of Delaware, Geography and Spatial Sciences, Newark, United States and Yao Hu, University of Delaware, Department of Geography & Spatial Sciences; Civil, Construction, and Environmental Engineering, Newark, United States
The Eco-Economic Effects of Perennial Coastal Flooding in Lagos State, Nigeria. (1933226)
Prince Emeka Emeka Ndimele1, Akinloye Emmanuel Ojewole2, Odunayo Temitope Ayodele1, Felix Chinsom Ndimele3, Jamiu Adebayo Shittu1 and Kehinde Moyosola Ositimehin4, (1)Lagos State University, Fisheries, Lagos, Nigeria, (2)Southern Illinois University Edwardsville, Environmental Sciences, Edwardsville, United States, (3)University of Ibadan, Chemistry, Ibadan, Nigeria, (4)Ohio University, Environmental Studies, Athens, United States
AI-Driven Camera Monitoring and PCSWMM Modeling of Current and Future Urban Flood Risk in Marlton, Camden, NJ (1949136)
Mr. Nirajan Adhikari1, Ahmad Haseeb Payab1, Marco Tedesco2 and Franco A Montalto1, (1)Drexel University, Civil, Architectural and Environmental Engineering, Philadelphia, PA, United States, (2)Columbia University, Lamont-Doherty Earth Observatory, Palisades, NY, United States
Investigating near future flood risk in Harris County, Texas (1953464)
Namjeong Choi, US Geological Survey, Fort Worth, United States, Kathryn Powlen, U.S. Geological Survey, Oklahoma-Texas Water Science Center, Denver, CO, United States and Nathan Pasley, USGS Oklahoma-Texas Water Science Center, Austin, United States
Modeling Compound Flood Risks in New York City: Insights for Resilience Planning under Future Storm Scenarios (1955086)
Matteo Benetti1, Gwyneth Nolde2, Marco Pilotti1 and Yuki Miura2, (1)University of Brescia, Brescia, Italy, (2)New York University, Center for Urban Science and Progress, Mechanical and Aerospace Engineering, New York, United States
Spatio-temporal evolution of extreme rainfall and urban flooding in Indian Cities (1959761)
P Akhil Laksman1, Hiren Solanki2, Kumar Gaurav1 and Vimal Mishra3, (1)Indian Institute of Science Education and Research Bhopal, Earth and Environmental Sciences, Bhopal, India, (2)Indian Institute of Technology Gandhinagar, Earth Sciences, Ahmedabad, India, (3)Indian Institute of Technology Gandhinagar, Department of Civil Engineering, Gandhinagar, India
Physics-Informed Deep Learning Urban Flood Forecasting Under Climate Change and Urbanization across the Dallas–Fort Worth Metroplex (1966546)
Amin Tamadoni, University of Texas at Dallas, School of Natural Sciences and Mathematics, Department of Sustainable Earth Systems Sciences, Richardson, United States, Han Qiu, University of Texas at Dallas, Sustainable Earth Systems Sciences, Richardson, United States and David Hyndman, University of Texas at Dallas, School of Natural Sciences and Mathematics, Richardson, United States
Process-Based Hydrologic Modeling of Urban Runoff Dynamics under Climate and Land-Use Change: Application to the Dallas–Fort Worth Metroplex (1977753)
Han Qiu, University of Texas at Dallas, Sustainable Earth Systems Sciences, Richardson, United States, Amin Tamadoni, University of Texas at Dallas, School of Natural Sciences and Mathematics, Department of Sustainable Earth Systems Sciences, Richardson, United States, David Hyndman, University of Texas at Dallas, School of Natural Sciences and Mathematics, Richardson, United States and David Hyndman
Hydrologic Modeling Meets Community Voices: Advancing GSI in Northeast Wilmington (1982304)
Rachel Zobel1, Emily Rodden2, Vaishnavi Tripuraneni2, Victor Perez2 and Carolyn Breden Voter3, (1)University of Delaware, Civil and Environmental Engineering, Newark, DE, United States, (2)University of Delaware, Newark, United States, (3)University of Delaware, Civil and Environmental Engineering; Earth Sciences, Newark, United States
Evaluating a Multivariate Data Assimilation Framework for Urban Flood Forecasting : From Synthetic Tests to Real-World Application (1983705)
Bomi Kim1, Yaewon Lee2, Seungsoo Lee3 and Seong Jin Noh2, (1)Kumoh National Institute of Technology, Civil Engineering, Gumi, Korea, Republic of (South), (2)Kumoh National Institute of Technology, Civil Engineering, Gumi, South Korea, (3)Korea Environment Institute, Sejong-si, South Korea
Informing Compound Flood Mitigation Strategies Across Scales: Blurring the Lines Between Hydrologic and Hydraulic Models. (1990453)
Ethan Coon1, Saubhagya S Rathore2, Mark K Wang3, Sergio Barbosa4, Katherine Lieberknecht5 and Paola Passalacqua3, (1)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, United States, (3)The University of Texas at Austin, Maseeh Department of Civil, Architectural and Environmental Engineering, Austin, United States, (4)Oak Ridge National Laboratory, Oak Ridge, United States, (5)University of Texas at Austin, Austin, United States
