H023
Advances in Ungauged Flood Prediction: Modeling Approaches, Infrastructure Impacts, and Climate Risks
Session ID#: 252475
Session Description:
Ungauged or poorly gauged watersheds represent one of the greatest uncertainties in global flood risk management. Over the past decade, however, new data streams, process‑based models, and machine‑learning approaches have begun to close this gap. This session invites contributions that advance flood prediction where streamflow records are sparse or absent, by integrating Earth‑observation data, physics‑based hydrologic models, AI/ML frameworks, crowd‑sourced observations, and reservoir‑operation analytics. Our goals are threefold: (1) showcase novel techniques for estimating peak discharge, inundation extent, and flood frequency in both natural and regulated basins; (2) compare the transferability and uncertainty of regionalization, physics-based and data-driven modeling approaches, and remote‑sensing techniques across diverse hydro-climatic settings and non‑stationary conditions; and (3) spark cross‑disciplinary dialogue among hydrologists, data scientists, and practitioners to co‑design open benchmarks and operational toolkits. The broader impacts include enhancing early‑warning systems for vulnerable communities, informing resilient infrastructure design, and guiding climate adaptation in data-poor regions worldwide.
Co-Sponsor(s):
Index Terms:
1807 Climate impacts [HYDROLOGY]
1821 Floods [HYDROLOGY]
1874 Ungaged basins [HYDROLOGY]
4333 Disaster risk analysis and assessment [NATURAL HAZARDS]
Primary Convener: Chandramauli Awasthi, North Carolina State University, Civil, Construction, and Environmental Engineering, Raleigh, United States
Conveners: Mona Hemmati, Gallagher Re, New York, United States, Nancy A. Barth, USGS Wyoming-Montana Water Science Center, Helena, Montana, United States and Nasser Najibi, University of Florida, Department of Agricultural and Biological Engineering, Gainesville, United States
Student/Early Career Convener: Jeongwoo Hwang, NC State University, Department of Civil, Construction, and Environmental Engineering, Raleigh, United States
Flood Hazard and Susceptibility Mapping of the Melamchi River basin in Nepal by Using Analytical Hierarchical Process and GIS Applications (1857956)
Subash Duwadi, Tribhuvan University, Department of Environmental Sciences, Tri-chandra Multiple Campus, Kathmandu, Nepal, Prof. Dr Danda Pani Adhikari, Tribhuvan University, Department of Environmental Science Tri-Chandra Multiple Campus, Kathmandu, Nepal, Prof. Dr. Megh Raj Dhital, Tribhuvan University, Department of Geology , Tri-chandra Multiple Campus, Kathmandu, Nepal and Mr. Ramesh Kathariya, ICIMOD, Nepal, Kathmandu, Nepal
A Comparative Study of Flood Inundation Mapping using Coupled Hydrologic-Hydrodynamic Modelling and Microwave Remote Sensing Approaches in the Kabul River Basin, Pakistan (1934955)
Muhammad Adnan, Asian Institute of Technology Bangkok, Thailand, Water Engineering and Management, Khlong Luang, Thailand and Mohanasundaram Shanmugam, Water Engineering and Management, Asian Institute of Technology, Thailand. Kim Irvine, Faculty of Architecture and Planning, Thammasat University, Thailand
Predicting Annual Maximum Outflow from Reservoirs in Ungauged Basins Using Bayesian Hierarchical Modeling Approach (1937046)
Irshad Ansari, North Carolina State University Raleigh, Raleigh, NC, United States, Chandramauli Awasthi, North Carolina State University, Civil, Construction, and Environmental Engineering, Raleigh, United States and Sankar Arumugam, North Carolina State University Raleigh, Department of Civil, Construction, and Environmental Engineering, Raleigh, United States
Integrated Web-Based Framework for Risk Simulation and Visualization of Potentially Dangerous Glacial Lakes in The Indian Himalayan Region (1939574)
Susmita Saha1, Mr. Rahul Deopa, PhD1, Anil Kumar Gupta2, Simhadri Rao3 and Mohit Prakash Mohanty4, (1)Indian Institute of Technology Roorkee, Roorkee, India, (2)Indian Institute of Technology Roorkee, Water Resources Development & Management, Roorkee, India, (3)Indian Space Research Organization Satellite center, Department of Space and ISRO HQ, Bangalore, India, (4)Indian Institute of Technology Roorkee, Water Resources Development and Management, Roorkee, India
Reproducing the Frequency Distribution of Observed Flood Peaks in Southern Italy Using the QT-DREAM Distributed Hydrological Model (1941929)
Federica Mesto1, Andrea Gioia2, Rocco Bonelli3, Silvano Fortunato Dal Sasso4, Luciana Giuzio3, Margherita Lombardo5, Salvatore Manfreda6, Maria Rosaria Margiotta7, Biagio Sileo4, Pasquale Perrini6, Vincenzo Totaro2, Vito Iacobellis2, Mauro Fiorentino7 and Vera Corbelli3, (1)University of Bari Aldo Moro, Department of Soil, Plant and Food Sciences (DiSSPA), Bari, Italy, (2)Polytechnic University of Bari, Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Bari, Italy, (3)Southern Apennines District Basin Authority, Caserta, Italy, (4)University of Basilicata, Department for Humanistic, Scientific, and Social Innovation, Matera, Italy, (5)University of Calabria, Department of Environmental Engineering (DIAm), Rende, Italy, (6)The University of Naples Federico II, Department of Civil, Building and Environmental Engineering, Naples, Italy, (7)University of Basilicata, Department of Engineering, Potenza, Italy
Using Gauged-Site Flood Records with Routing to Improve Regional Flood Quantile Estimators at “Ungauged” Locations (Invited) (1948766)
Jery R Stedinger, Cornell University, Ithaca, NY, United States, Ken Eng, U.S. Geological Survey, Water Resources Mission Area, Reston, United States and Joseph Moukarzel, Cornell University, CEE-Eng. Management, Ithaca, United States
Bridging Discretization Scale and Routing Parameters for Accurate Peak Flow Prediction in Distributed Model (1952806)
Simon Martinez, University of Iowa, Iowa City, IA, United States, Nicolás Velásquez, Florida Institute of Technology, Mechanical and Civil Engineering Department, Melbourne, FL, United States and Witold F Krajewski, University of Iowa, Iowa Flood Center and IIHR-Hydroscience and Engineering, Iowa City, United States
Advancing Flood Hazard Modeling Across Diverse Storm Types Using 2D Hydrodynamics and Remote Sensing Precipitation (1964422)
Mahjabeen Fatema Mitu, University of Connecticut, Department of Civil and Environmental Engineering, Groton, CT, United States, Rehenuma Lazin, Lawrence Livermore National Laboratory, Atmospheric, Earth, & Energy Division, Livermore, CA, United States and Wen-Ying Wu, Lawrence Livermore National Laboratory, Atmospheric, Earth, & Energy Division, Livermore, United States
SCENARIO-BASED IDF CURVES FOR RESILIENT INFRASTRUCTURE: THE DOD EXTREME PRECIPITATION ASSESSMENT CAPABILITY (1979026)
Ceyda Polatel, US Army Corps of Engineers, Jacksonville, United States, Kathleen White, DASD/ER&O, Washington, DC, United States, Mckenzie Dice, Booz Allen Hamilton DC, Washington, United States and Christopher Cook, U.S. Army Corps of Engineers, Sacramento District, Sacramento, United States
Evaluating the passive microwave remote sensing on daily streamflow forecast in the CONUS (1982993)
Asumi Saito, North Carolina State University Raleigh, Raleigh, NC, United States, Shiqi Fang, North Carolina State University, Civil, Construction, and Environmental Engineering, Raleigh, United States and Sankar Arumugam, NC State University, Department of Civil, Construction, and Environmental Engineering, Raleigh, United States
Impact of Data Uncertainty on Remote Sensing-Based Flood Forecasting Under Data Scarcity: Case Studies of the Naryn River Basin in Kyrgyzstan and the Chikuma River Basin in Japan (1985357)
Zin Thandar Tun1, Mizuki Sakai Prof2, Aye Ei Phyu3, Tsuyoshi Yamajuku3 and Hideki Okumatsu3, (1)Nagano Institute of Technology (Nagano College), Department of Civil Engineering, Nagano, Japan, (2)Nagano Institiute of Technology (Nagano College), Department of Civil Engineering, Nagano, Japan, (3)Katahira & Engineers International, Project Development Department, Tokyo, Japan
Improving Flood Predictions in Ungaged Basins with a Local-Likelihood Spatial Extremes Framework (1985549)
Chandramauli Awasthi, North Carolina State University, Civil, Construction, and Environmental Engineering, Raleigh, United States and Sankar Arumugam, NC State University, Department of Civil, Construction, and Environmental Engineering, Raleigh, United States
Data-Driven Resilience Insights through Network Analysis (Invited) (1986742)
Andrew Jin1, Sukhwan Chung2, Daniel Sardak3, Madison Smith2 and Igor Linkov4, (1)US Army Engineer Research and Development Center, Concord, United States, (2)Credere LLC, Concord, United States, (3)Worcester Polytechnic Institute, Data Science, Worcester, United States, (4)US Army Engineer Research and Development Center, Concord, MA, United States
Improving Extreme Flood Predictions with a Regional Peaks-over-Threshold Approach (1988744)
Kichul Bae, North Carolina State University Raleigh, Raleigh, NC, United States and Sankar Arumugam, North Carolina State University, Civil, Construction, and Environmental Engineering, Raleigh, United States
NADI: Automated Network Analysis and Data Integration Framework for Streamflow Data Quality and Hydrological Modeling (1990606)
Todd Steissberg1, Gaurav Atreya2, Patrick Ray2 and Gaurav Atreya, (1)Organization Not Listed, Washington, DC, United States, (2)University of Cincinnati, Environmental Engineering, Cincinnati, United States
Integrating GeoSWMM and GeoGLOWS for High-Resolution Flood Extent Delineation in Ungauged Watersheds of Gazipur City, Bangladesh (1991029)
Tasfia Mahjarin1, Zeba Tahsin Haque2, Partha Roy3 and Sumaiya Akter2, (1)Brigham Young University, Civil and Construction Engineering, Provo, UT, United States, (2)Bangladesh University of Engineering and Technology, Institute of Water and Flood Management, Dhaka, Bangladesh, (3)Streams Tech Ltd, Environmental Engineering, Dhaka, Bangladesh
Evaluating the Reliability of Flood Inundation Mapping in Ungauged Locations Using Observed, Modeled, and Machine Learning-Based Hydrologic Data (1991194)
Junho Song1, Reza Saleh Alipour2, Savalan Naser Neisary3, Md. Shahabul Alam4, James Halgren4 and Steven J Burian5, (1)University of Alabama, Alabama Water Institute, Tuscaloosa, United States, (2)University of Alabama, Civil, Construction, and Environmental Engineering, Tuscaloosa, United States, (3)The University of Alabama, Department of Civil, Construction, and Environmental Engineering, Tuscaloosa, United States, (4)The University of Alabama, Alabama Water Institute, Tuscaloosa, United States, (5)Cooperative Institute for Research to Operations in Hydrology (CIROH), Alabama Water Institute, The University of Alabama, Tuscaloosa, AL, Alabama Water Institute, Tuscaloosa, United States
Short Term Forecasting of Stage and Discharge in Hysteretic Rivers using Deep Learning (1996078)
Renee Dumars1, Emma House2, Ehab A Meselhe1, Marian Muste3 and Ibrahim Demir1, (1)Tulane University, New Orleans, United States, (2)Tulane University of Louisiana, New Orleans, United States, (3)The University of Iowa, Iowa City, IA, United States
Diffusion-Based Convolutional Learning and Multi-View Data Fusion for Flood Inundation Mapping (2000768)
Ankita Agrawal, Indian Institute of Technology Roorkee, Department of Computer Science and Engineering, Roorkee, India, Balasubramanian Raman, Indian Institute of Technology Roorkee (IITR), Department of Computer Science and Engineering, Roorkee, India and Aparajita Khan, Indian Institute of Technology (BHU) Varanasi, Department of Computer Science & Engineering, Varanasi, India
