Preprints
https://doi.org/10.5194/egusphere-2022-52
https://doi.org/10.5194/egusphere-2022-52
 
05 Apr 2022
05 Apr 2022
Status: this preprint is open for discussion.

Intersecting Near-Real Time Fluvial and Pluvial Inundation Estimates with Sociodemographic Vulnerability to Quantify a Household Flood Impact Index

Matthew Preisser1,2, Paola Passalacqua1, R. Patrick Bixler2, and Julian Hofmann3 Matthew Preisser et al.
  • 1Environmental and Water Resources Engineering, University of Texas at Austin, Austin, Texas, USA
  • 2LBJ School of Public Affairs, University of Texas at Austin, Austin, Texas, USA
  • 3Institute of Hydraulic Eng. & Water Resources Management, RWTH Aachen University, Germany

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-government agencies currently lack methods to repeatedly and rapidly create flood impact maps that incorporate local variability of 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.

Matthew Preisser et al.

Status: open (until 31 May 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-52', Anonymous Referee #1, 10 May 2022 reply
  • RC2: 'Comment on egusphere-2022-52', Anonymous Referee #2, 14 May 2022 reply

Matthew Preisser et al.

Matthew Preisser et al.

Viewed

Total article views: 288 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
233 47 8 288 5 4
  • HTML: 233
  • PDF: 47
  • XML: 8
  • Total: 288
  • BibTeX: 5
  • EndNote: 4
Views and downloads (calculated since 05 Apr 2022)
Cumulative views and downloads (calculated since 05 Apr 2022)

Viewed (geographical distribution)

Total article views: 271 (including HTML, PDF, and XML) Thereof 271 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 May 2022
Download
Short summary
There is rising concern in numerous fields regarding the inequitable distribution of human risk to floods. The co-occurrence of river and surface flooding is largely excluded from the leading flood hazard mapping services, therefore underestimating hazards. Using high-resolution elevation data and a region specific social vulnerability index, we developed a method to estimate flood impacts at the household level in near real-time.