DOI: 10.5061/dryad.kd51c5bcz
title: Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region

description: Extreme flooding events are becoming more frequent and costly, and impacts
 have been concentrated in cities where exposure and vulnerability are both
 heightened. To manage risks, governments, the private sector, and
 households now rely on flood hazard data from national-scale models that
 lack  accuracy in urban areas due to unresolved drainage
 processes and infrastructure. The data in this repository supports an
 assessment of the uncertainties of First Street Foundation (FSF) flood
 hazard data, available across the U.S.. For the analysis, FSF data was
 compared to PRIMo-Drain, a flood hazard model that resolves drainage
 infrastructure and fine resolution drainage dynamics. In the linked
 journal manuscript, using the case of Los Angeles, California, we find
 that FSF and PRIMo-Drain estimates of population and property value
 exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of
 governance, for example by 4- to 18-fold at the municipal scale. FSF and
 PRIMo-Drain data often predict opposite patterns of exposure inequality
 across social groups (e.g., Black, White, Disadvantaged). Further, at the
 county scale, we compute a Model Agreement Index of only 24%—a ~1 in 4
 chance of models agreeing upon which properties are at risk. Collectively,
 these differences point to limited capacity of FSF data to confidently
 assess which municipalities, social groups, and individual properties are
 at risk of flooding within urban areas. These results caution that
 national-scale model data at present may misinform urban flood risk
 strategies and lead to maladaptation, underscoring the importance of
 refined and validated urban models.
description: # Comparing First Street Foundation and PRIMo Flood Hazard Data Across the
 Los Angeles Metropolitan Region PI: Brett Sanders, University of
 California Irvine, Department of Civil Engineering
 ([bsanders@uci.edu](mailto:bsanders@uci.edu)) Data Questions Contact:
 Jochen Schubert, University of California Irvine, Department of Civil
 Engineering ([j.schubert@uci.edu](mailto:j.schubert@uci.edu)) Other
 Authors: Mach Katharine, University of Miami Year Published: 2024 Years
 Data Collection: 2020-2024 Geography: Los Angeles, CA, USA ## Description
 of the data file Two data files are provided to calculate parcel level
 flood risk across Los Angeles: parceldatatable.csv contains social data
 (e.g., population estimates, population fractions by race and ethnicity,
 and Neighborhood Disadvantage Index (NDI) values)
 primo_flooddepth_table.csv contains flood hazard data generated by PRIMo
 and PRIMo-Drain parceldatatable.csv contains the following variables: id
 Dataset ID apn Asessors Parcel Number city Parcel City tract Parcel Census
 Tract Number blkgrp Parcel Census Block Group Number blockid Parcel Census
 Block Number popda Parcel Population propvalue Parcel Value (US$) ndi
 Neighborhood Disadvantage Index (UCI) sovi Social Vulnerability Index
 (University of South Carolina) svi Social Vulnerability Index (CDC)
 fracasian Census Fraction non-Hispanic Asian fracblack Census Fraction
 non-Hispanic Black frachispanic Census Fraction Hispanic fracwhite Census
 Fraction non-Hispanic White medinc Block Group Median Income Please note
 that parcels with zero population may exhibit "NaN" values (no
 Data) for population statistics. In some cases, zero population parcels
 may be attributed with population statistics. That is because statistics
 from the US Census and vulnerability indexes are downscaled from Tract or
 Block Group geographies, and zero population parcels may intersect those
 larger geographies. Vice-versa, it can also occur that parcels with a
 non-zero population are attributed with "NaN" population
 statistics due to their intersection with a larger Tract geography for
 which no vulnerability index was calculated. primo_flooddepth_table.csv
 contains the following variables: id Dataset ID apn Asessors Parcel Number
 h_100 PRIMo 1%-annual-chance flood depth h_100_drain PRIMo-Drain
 1%-annual-chance flood depth h_20 PRIMo 5%-annual-chance flood depth
 h_20_drain PRIMo-Drain 5%-annual-chance flood depth For flood risk
 analysis use the included Matlab (.m) codes: inequality_exposure.m
 calculates exposed populations inequality_propvalue.m calculates exposed
 property municipal_exposure_propvalue.m calculates Gini indices which
 quantify social inequalities ## License: For usage and publication with
 the above data please contact: Prof. Brett Sanders -
 [bsanders@uci.edu](mailto:bsanders@uci.edu)