Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index

Abstract

Abstract. Increased interest in combining compound flood hazards and social vulnerability has driven recent advances in flood impact mapping. However, current methods to estimate event-specific compound flooding at the household level require high-performance computing resources frequently not available to local stakeholders. Government and non-governmental agencies currently lack the methods to repeatedly and rapidly create flood impact maps that incorporate the local variability in both hazards and social vulnerability. We address this gap by developing a methodology to estimate a flood impact index at the household level in near-real time, utilizing high-resolution elevation data to approximate event-specific inundation from both pluvial and fluvial sources in conjunction with a social vulnerability index. Our analysis uses the 2015 Memorial Day flood in Austin, Texas, as a case study and proof of concept for our methodology. We show that 37 % of the census block groups in the study area experience flooding from only pluvial sources and are not identified in local or national flood hazard maps as being at risk. Furthermore, averaging hazard estimates to cartographic boundaries masks household variability, with 60 % of the census block groups in the study area having a coefficient of variation around the mean flood depth exceeding 50 %. Comparing our pluvial flooding estimates to a 2D physics-based model, we classify household impact accurately for 92 % of households. Our methodology can be used as a tool to create household compound flood impact maps to provide computationally efficient information to local stakeholders.

A network-based analysis of critical resource accessibility during floods

“…The exclusion of pluvial flooding from most emergency flood mapping sources is another source of inequality/inequity (Grahn and Nyberg, 2017). Pluvial flooding specifically leads to ponded water on impervious surfaces, such as roadways and intersections, that would otherwise not be identified as being inundated using common flood risk maps (Preisser et al, 2022). Our results show that pluvial flooding doubles the number of closed roads at peak flood conditions and predominantly impacts more vulnerable communities.…”

“…We utilized a near-real-time inland compound (fluvial and pluvial) inundation estimate of the Memorial Day flood from previous research and we refer the reader to Preisser et al (2022) for more information on the methods and results. To summarize, we used outputs from NOAA's National Water Model to map fluvial inundation using GeoFlood (Zheng et al, 2018), an implementation of the HAND method (Nobre et al, 2011) utilizing lidar scale topographic data, and pluvial inundation using Fill-Spill-Merge (Barnes et al, 2021), a topographic depression routing algorithm.…”

“…We calculate social vulnerability using a factor analysis approach on 27 variables from the U.S. Census Bureau's American Community Survey 5-Year Estimates from 2011-2015 at the block group boundary (Bixler et al, 2021;Preisser et al, 2022). Our SVI calculation is an adapted methodology first developed by Cutter et al (2003), which utilizes a principal component analysis to similarly reduce the dimensionality of input sociodemographic variables (Supplementary Section 1).…”

“…Lower resolution elevation data ( 30-meters) can also be used, with the understanding that modeling urban flooding with such data comes with its own uncertainties and weaknesses. Our inundation layers are created using the open source tools GeoFlood (Zheng et al, 2018) and Fill-Spill-Merge (Barnes et al, 2021) that estimate fluvial and pluvial flooding respectively (Preisser et al, 2022). This process can be replicated anywhere in the United States using data from NOAA's National Water Model (NWM), which contains the necessary stream flow and ponded water data going back to the 1980s to produce the relevant inundation estimates.…”