- H13L: Advancing Hydrologic Processes to Improve Flood Prediction II Poster
-
NOLA CC
Primary Convener:Generic 'disconnected' Message
Amar Tiwari, Michigan State University
Convener:
Nanditha J S, Princeton University
Anukesh Krishnankutty Ambika, Oak Ridge National Laboratory
Saran Aadhar, Indian Institute of Technology Jodhpur
Alka Tiwari, University of Texas at Austin
Aparimita Priyadarshini Naik, Indian Institute of Technology Guwahati
Amrutha Suresh, University of Padua
Sreedeep Sekharan, Indian Institute of Technology Guwahati
Sreeja Pekkat, Indian Institute of Technology Guwahati
Chair:
Amar Tiwari, Indian Institute of Technology Gandhinagar
Nanditha J S, Princeton University
Anukesh Krishnankutty Ambika, Oak Ridge National Laboratory
Alka Tiwari, University of Texas at Austin
Floods are among the most devastating natural hazards, driven by complex and interrelated physical processes. These processes vary across catchments and seasons, making it critical to understand the unique drivers of flood events, including extreme rainfall, land use change, river dynamics, atmospheric conditions such as hurricanes, and human interventions. As natural variability reshapes flood behavior, advancing catchment-specific knowledge is essential for improving prediction and preparedness, including in under-resourced regions. This session invites contributions that enhance understanding of the physical processes behind flooding and show how improved process-scale insights can strengthen flood forecasting, impact assessment, and risk management. We welcome contributions in areas including, but not limited to: ● Advancements in understanding flood generation processes ● Incorporating physical knowledge into improving flood forecasting ● Physics-informed statistical or machine learning models. ● Studies on riverine, flash, coastal, urban, and hurricane-induced floods ● Innovative approaches for flood modeling in data-limited environments
Index Terms
1807 Climate impacts
1817 Extreme events
1821 Floods
1847 Modeling
Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Climate Change and Global Policy
Machine Learning and AI
Open Science and Open Data
Global Impacts‚ Solutions‚ & Policies
Co-Organized Sessions:
Global Environmental Change
Atmospheric Sciences
Natural Hazards
Cross-Listed:
NS - Near Surface Geophysics
Neighborhoods:
3. Earth Covering
1. Science Nexus
Scientific DisciplineSuggested ItinerariesNeighborhoodTypeWhere to Watch
Enter Note
Go to previous page in this tab
Session


