Description

1) Enhancing the National Risk Index Inland Flood Hazard with FEMA Risk Rating 2.0 Data
Casey Zuzak, GISP, FEMA, casey.zuzak@fema.dhs.gov
Co-presenters: Matthew Mower, mmowrer@absconsulting.com; Benjamin Roberts, broberts@absconsulting.com; Anne Sheehan, anne.sheehan@fema.dhs.gov

Abstract: The FEMA National Risk Index is an online mapping tool that identifies communities most at risk to 18 different natural hazards and visualizes natural hazard risk metrics. The tool incorporates social vulnerability, community resilience, and natural hazard expected annual loss data, allowing communities to make risk-informed and data-driven decisions. The Risk Index produces a baseline assessment for natural hazard risk in the United States. The baseline riverine flood risk values in the National Risk Index have historically leveraged the FEMA National Flood Hazard Layer and NOAA’s National Centers for Environmental Information Storm Events data to estimate annualized flood frequency. This provides users with a robust understanding of inland and riverine flood risk. In recent years, improvements made to the baseline hazard building exposure database has enabled the National Risk Index team to break down sub-exposure building and occupancy types to incorporate alternate flood hazard datasets. Risk Rating 2.0 average annualized loss values at the Census tract level for single family residential units provide a robust dataset that is easily ingested into the National Risk Index. In replacing previously estimated expected annualized losses based on a variety of sources, the Risk Rating 2.0 data can be aggregated and used for each Census tract and county, improving the quality of the National Risk Index – Inland Flood Hazard Risk Scores. This presentation will cover previous methods and acknowledge limitations, explore how foundational changes in building exposure data enabled the integration of Risk Rating 2.0 data, and will review and discuss results.

2) Leveraging Building-level Data to Improve Flood Risk Modeling in FEMA’s Hazus
Doug Bausch, NiyamIT, dbausch@niyamit.com
Co-presenters: Jesse Rozelle, Jesse.Rozelle@fema.dhs.gov; Jennifer (Ross) Sims, jross@niyamit.com; Ashley Hoke, ahoke@niyamit.com

Abstract: FEMA’s Hazus Program has provided a standard methodology and baseline datasets for modeling risk and estimating natural hazard impacts for over 20 years. This presentation will discuss how newly improved building, infrastructure, and demographic inventories, developed by FEMA and partners, improve flood risk modeling as well as modeling of other Hazus-enabled hazards. The 2022 Hazus 6.0 release incorporated building-level data from the U. S. Army Corps of Engineers National Structure Inventory (NSI), Homeland Infrastructure Foundation-Level Data (HIFLD), Lightbox, FEMA USA Structures, and Open Data DC aggregated into a national dataset and uses this data to improve the methodology for building replacement cost valuations. Methods were updated using data derived from 2022 RSMeans with a detailed assignment of regional modification factors and leveraged the use of building-level data attribution to provide more detailed building areas, basement data, and exterior wall types. Using the enhanced building-level data resulted in over a 44% increase in total national building replacement exposure per capita over the Hazus 5.1 national baseline data. The building level data were aggregated in new dasymetric Census blocks that refined the location of developed areas nationally. This ensures that flood losses only occur in areas that are developed as indicated based on building footprint, parcel, and land use data. We will present several case studies that demonstrate how the new data and methods have greatly improved our capabilities to measure risk. Next steps in leveraging these datasets to improve the vulnerability attribution of the national datasets will be outlined, including the incorporation of pre- and post-FIRM building distributions based on construction year, NFIP entry dates and foundation type data. Past methods have relied upon regional assumptions. However, using the building-level specific data provides the opportunity to more accurately determine losses and measure resilience for flood, as well as other hazards.

3) Methods for Natural Hazard Risk Assessments in Oregon: Flood Hazard Risk Assessments and Beyond
Matt Williams, Oregon Dept. of Geology, matt.williams@dogami.oregon.gov
Co-presenters: None

Abstract:Communities in Oregon need to understand their risk to natural hazards so that they can take steps to lower the risk to people and assets. The Oregon Department of Geology and Mineral Industries (DOGAMI), over the past 6 years, has developed methods for conducting natural hazard risk assessments for communities throughout the state of Oregon. Natural hazard datasets utilized in the risk assessments were produced at DOGAMI using advanced techniques and high resolution lidar data. We developed highly detailed building data from lidar-derived building footprints and county assessor data which were used in the risk assessment analysis. Hazards that were typically examined in DOGAMI’s risk assessments include: earthquake, tsunami, flood (coastal and riverine), landslide, channel migration, volcanic lahar, wildfire, and coastal erosion. Risk analysis was conducted using Hazus-MH for earthquake and flood hazards and exposure analysis for other hazards. From these analyses, we identified high risk areas and determined potential vulnerabilities that were present in the building inventory (including critical facilities). Risk assessments are intended to assist local decision-makers to lower risk from natural hazards within their communities. This presentation will focus on methods used by DOGAMI to conduct natural hazard risk assessments. These methods include identifying the best available hazard data sources, developing a building database, and analyzing the risk to communities from the various natural hazards that are present. Finally and most importantly, the presentation will discuss how we communicate this risk to local stakeholders and decisionmakers.