Flood Inundation Mapping And High Risk Facility Identification - UNDER REVIEW

Flood Inundation Mapping And High Risk Facility Identification - UNDER REVIEW CIROH View Course Problem Statement In the U.S., extreme flooding events are becoming increasingly frequent and severe, posing significant risks to communities and infrastructure. For instance, Hurricane Helen, an event that exceeded all expectations, caused extensive flooding in Asheville and its surrounding areas. The unprecedented nature of this event highlighted the critical need for effective tools to assess flood risks and identify high-risk areas. Flood inundation maps, such as those produced by FEMA and the National Water Model (NWM), are essential for visualizing the extent and impacts of flooding. These maps provide valuable insights into flood-prone areas, enabling a better understanding of vulnerabilities within communities, critical facilities, and infrastructure. The insights derived from these maps play a vital role in supporting flood risk management strategies and mitigating the impacts of future extreme weather events. Module Overview This module focuses on flood inundation mapping and its application in vulnerability assessment. Learners will explore different flood mapping approaches using FEMA flood products and the National Water Model(NWM) to assess flood exposure and high risk facilities. It consists of the following sections: Section 1: Introduction Section 2: Flood Inundation Mapping Using FEMA and NWM products Section 3: Evaluation of Flood Inundation Mapping Section 4: Flood Risk Assessment and Critical Facility Analysis. Topics Covered - Fundamentals of flood inundation mapping - Introduction to FEMA flood maps and how to access them - National Water Model (NWM) flood mapping products - Comparison of FEMA and NWM flood maps - Visualization techniques using Google Earth Engine (GEE) and GIS Prerequisites To successfully complete this course, learners should have a basic understanding of: Hydrology , Hydraulics , Floodplains , and Geospatial Analysis Learning Objectives By the end of this module, learners will be able to: - Understand the principles of flood inundation mapping - Access and utilize FEMA flood maps for their case studies - Explore the National Water Model and its flood prediction capabilities - Compare FEMA and NWM flood maps to assess flood risk - Visualize and analyze flood inundation data using Google Earth Engine and GIS tools - Identify high-risk facilities based on flood exposure "This will be accomplished through activities within each section. Results from each activity will be recorded in specified results templates. The results templates for each activity can be found at the beginning of each activity. The results templates are organized such that results from one activity can easily be used in successive activities." Suggested Implementation If applicable, take this opportunity to address your suggested implementation mode to an instructor that is interested in adopting your module. Have you any adivce to offer? Particular methods that have worked for you? Or perhaps you designed your module to be used in a certain way? Course Authors Saide Zand Saide Zand is a second-year PhD student at the University of Alabama, specializing in coastal hydrology and flood risk assessment. As a member of the Coastal Hydrology Lab, Saide's research focuses on leveraging machine learning for flood inundation mapping, enhancing the accuracy and efficiency of hindcast flood mapping.Saide is honored to be part of the Cooperative Institute for Research to Operations in Hydrology (CIROH) and contributes to the "Physics-Informed Machine Learning for Compound Flood Mapping" project. This research integrates hydrodynamic modeling and physical principles with machine learning to improve flood mapping capabilities, particularly in complex coastal environments. szand@crimson.ua.edu Sushree Swagatika Swain Dr. Sushree is a postdoctoral employee at the Scripps Institute of Technology, University of California San Diego. Her research focuses on the analysis of compound extreme events and risk assessment, with an emphasis on understanding the interactions between multiple extreme events and their cascading impacts on infrastructure, ecosystems, and communities. She employs statistical modeling, machine learning, climate data analysis, and risk assessment frameworks to enhance predictive capabilities for extreme weather events. Her work aims to improve disaster preparedness and inform policy decisions related to climate resilience. As a part of the Cooperative Institute for Research to Operations in Hydrology (CIROH) initiated HydroLearn Hackathon Program, Dr. Sushree contributes to the Compound Flood Mapping and Vulnerability Analysis project, advancing research in hydrological hazards and risk mitigation. ssswain@ucsd.edu Target Audience This course is designed for: - Researchers working on flood risk analysis and hydrology - Operational hydrologists involved in flood forecasting and emergency management Tools Needed - Google Earth Engine (GEE) - Geographic Information System (GIS) software (such as QGIS or ArcGIS) - FEMA flood map resources - National Water Model datasets Expected Effort Learners should expect to dedicate approximately 10 to 15 hours to complete this module, including lectures, hands-on exercises, and case study analysis. Course Sharing and Adaptation This course is available for export by clicking the "Export Link" at the top right of this page. You will need a HydroLearn instructor studio account to do this. You will first need to sign up for a hydrolearn.org account, then you should register as an instructor by clicking 'studio.hydrolearn' and requesting course creation permissions. Recommended Citation Last name, First Initial., (Year) Module Title. CIROH. URL to about page. Example: Zand, S., Swain, S.S. (2025). Flood Inundation Mapping And High Risk Facility Identification. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:CIROH_HydroLearn+OP_080+2025/about Adapted From If your module has been adapted from a previously existing module, please mention that here. Go into detail about how this module differs from the original. If your module is an original creation, you can delete this section. Acknowledgement This project received funding under award NA22NWS4320003 by National Oceanic and Atmospheric Administration (NOAA) Cooperative Institute Program to the Cooperative Institute for Research on Hydrology (CIROH) through the University of Alabama. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the opinions of NOAA.