Using Flood Inundation Mapping and Social Considerations to Make Decisions

Using Flood Inundation Mapping and Social Considerations to Make Decisions CIROH View Course Problem Statement In the U.S., Floods are the most common weather-related natural disaster and kill more people each year than tornadoes, hurricanes, or lightning (NSSL, 2024). Flood hazards can threaten a local community's infrastructure and, subsequentially, their resilience. Unfortunately, flooding is hard to predict which makes risk reduction tricky. The Federal Emergency Management Agency (FEMA) suggests that "floods are unpredictable. Because flood risk is always changing, communities use tools to understand their risk and take actions to protect their families, homes, and businesses" (FEMA, 2024). The ever-changing risk of flood hazards is what makes flood inundation maps so necessary for scientists, decision-makers, communities, and individuals. Additionally, the hydraulic collapse frequency in the U.S. is estimated at 0.02% per year for a total population of about 504,000 bridges. One such collapse occurred at the intersection of Bear Cabin Branch and Grafton Shop Road (Latitude 39.54491 and Longitude -76.3924) in the City of Bel Air North, MD. As a hydrologic and hydraulic engineer, you are tasked with making a decision on hydraulic design to create a more resilient and sustainable bridge in the future. The goal is to utilize a flood inundation map generated through machine learning to aid in this process. You will need to create a flood inundation map for the collapse site using machine learning techniques, and then compare and evaluate this map against existing physically based models. These flood inundation maps will be utilized in determining specific design elements for the new hydraulic bridge. Module Overview This module will introduce learners to flood inundation mapping tools and the social, ethical considerations involved in flood-based mitigation and protection decision-making. Learners will complete modules and activities that challange them to develop skills in analyzing flood maps, engaging communities, and evaluating vulnerabilities. The goal of this module is to develop skills in social science as they relate to flood-related civil engineering. Topics Covered The following topics will be covered in this module: Natural Hazards and Risks Flood Inundation Mapping Ethical Considerations and Social Vulnerability Flood-Based Mitigation & Protection Decision-Making Prerequisites Learners need to have basic undersanding of: Hydrology Hydraulics Floodplains Climate Change GIS Tools Learning Objectives At the end of this module, learners will be able to: Compare risks and hazards related to flood inundation maps (FIM). Conduct a flood-based social vulnerability analysis. Evaluate resilience-based designs, sustainability-based designs, and nature-based solutions when discussing hydraulic designs. Develop a proposal for flood-based mitigation and protection recommendations. This will be accomplished through Check Your Understanding quizzes and Learning Activities within each section. Course Authors Ann K. Nyambega Ann Nyambega is a graduate student in the Natural Resources and Environmental Management department at the University of Hawaiʻi, specializing in Monitoring and Evaluation of Nature-Based Solutions (NBS). She is currently leading a CIROH-funded project to advance understanding of NBS effectiveness in Hawaiʻi by co-developing an evaluation toolkit to support evidence-based decision-making. Additionally, Ann is part of a NOAA-funded study investigating how ecosystem-based adaptation (EbA) strategies enhance water-related ecosystem services and community resilience in Hawaiʻi. Ann's primary goal is to advance the understanding, implementation and evaluation of NBS strategies to enhance socio-ecological resilience and informed decision-making. annkn@hawaii.edu Lakelyn E. Taylor Lakelyn E. Taylor (she/her/ella) is a postdoctoral associate in the Rubenstein School of Environment and Natural Resources at the University of Vermont. She is working as a communication social scientist on a NOAA grant-funded project that analyzes how local communities receive, share, and perceive flood warning information. Her other research focuses on risk and crisis communication in the contexts of religion, instruction, and ethics. Lakelyn has led research concerning the effectiveness of hurricane warning messages as well as how religion impacts perceptions of natural disasters. Her primary goal is incorporating alternative ways of knowing into the field of communication. ORCID ID: https://orcid.org/0000-0002-7683-180X lakelyn.taylor@uvm.edu Target Audience Juniors/Seniors, Undergraduates/Graduates, enrolled in a Civil Engineering or Hydrology course/program. Tools Needed Learners will need: Computer with access to the Internet Access to Microsoft Office and Excel or Google Docs and Sheets Expected Effort This module will take between approximately 10 to 15 hours to complete. 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 Nyambega, A., & Taylor, L. (2025). Using Flood Inundation Mapping and Social Considerations to Make Decisions. CIROH. https://edx.hydrolearn.org/courses/course-v1:CIROH_HydroLearn+ENSC1990+2025/about. Adapted From This module has been adapted from a previously existing HydroLearn module titled "Flood Inundation Mapping Using Machine Learning for Sustainable vs. Resilient Design." This module focuses more on the social science and community-centered aspects of flood inundation mapping. The module includes distinguishing between risks and hazards, considering ethical components of floods such as social vulnerability, and introducing nature-based solutions as a flood mitigation and protection strategy. 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.