DOI: 10.6078/d15x4n
title: Sea level rise, groundwater rise, and contaminated sites in the San Francisco Bay Area, and Superfund Sites in the contiguous United States

description: Rising sea levels (SLR) will cause coastal groundwater to rise in many
 coastal urban environments. Inundation of contaminated soils by
 groundwater rise (GWR) will alter the physical, biological, and
 geochemical conditions that influence the fate and transport of existing
 contaminants. These transformed products can be more toxic and/or more
 mobile under future conditions driven by SLR and GWR. We reviewed the
 vulnerability of contaminated sites to GWR in a US national database and
 in a case comparison with the San Francisco Bay region to estimate the
 risk of rising groundwater to human and ecosystem health. The results show
 that 326 sites in the US Superfund program may be vulnerable to changes in
 groundwater depth or flow direction as a result of SLR, representing 18.1
 million hectares of contaminated land. In the San Francisco Bay Area, we
 found that GWR is predicted to impact twice as much coastal land area as
 inundation from SLR alone, and 5,297 state-managed sites of contamination
 may be vulnerable to inundation from GWR in a 1-meter SLR scenario.
 Increases of only a few centimeters of elevation can mobilize soil
 contaminants, alter flow directions in a heterogeneous urban environment
 with underground pipes and utility trenches, and result in new exposure
 pathways. Pumping for flood protection will elevate the salt water
 interface, changing groundwater salinity and mobilizing metals in soil.
 Socially vulnerable communities are more exposed to this risk at both the
 national scale and in a regional comparison with the San Francisco Bay
 Area.
description: Data Dryad This data set includes data from
 the California State Water Resources Control Board (WRCB), the California
 Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and
 the US Census.  National Assessment Data
 Processing: For this portion of the project, ArcGIS
 Pro and RStudio software applications were used. Data processing for
 superfund site contaminants in the text and supplementary materials was
 done in RStudio using R programming language. RStudio and R were also used
 to clean population data from the American Community Survey. Packages used
 include: Dplyr, data.table, and tidyverse to clean and organize data from
 the EPA and ACS. ArcGIS Pro was used to compute spatial
 data regarding sites in the risk zone and vulnerable populations. DEM data
 processed for each state removed any elevation data above 10m, keeping
 anything 10m and below. The Intersection tool was used to identify
 superfund sites within the 10m sea level rise risk zone. The Calculate
 Geometry tool was used to calculate the area within each coastal state
 that was occupied by the 10m SLR zone and used again to calculate the area
 of each superfund site. Summary Statistics were used to generate the total
 proportion of superfund site surface area / 10m SLR area for each
 state. To generate population estimates of socially
 vulnerable households in proximity to superfund sites, we followed methods
 similar to that of Carter and Kalman (2020). First, we generated buffers
 at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate
 Intersection, the estimated population of each census block group within
 each buffer zone was calculated. Summary Statistics were used to generate
 total numbers for each state. Bay Area Data
 Processing: In this regional study, we compared the
 groundwater elevation projections by Befus et al (2020) to a combined
 dataset of contaminated sites that we built from two separate databases
 (Envirostor and GeoTracker) that are maintained by two independent
 agencies of the State of California (DTSC and WRCB). We used ArcGIS to
 manage both the groundwater surfaces, as raster files, from Befus et al
 (2020) and the State’s point datasets of street addresses for contaminated
 sites. We used SF BCDC (2020) as the source of social vulnerability
 rankings for census blocks, using block shapefiles from the US Census
 (ACS) dataset. In addition, we generated isolines that
 represent the magnitude of change in groundwater elevation in specific sea
 level rise scenarios. We compared these isolines of change in elevation to
 the USGS geological map of the San Francisco Bay region and noted that
 groundwater is predicted to rise farther inland where Holocene
 paleochannels meet artificial fill near the shoreline. We also used maps
 of historic baylands (altered by dikes and fill) from the San Francisco
 Estuary Institute (SFEI) to identify the number of contaminated sites over
 rising groundwater that are located on former mudflats and tidal
 marshes. The contaminated sites' data from the
 California State Water Resources Control Board (WRCB) and the Department
 of Toxic Substances (DTSC) was clipped to our study area of nine-bay area
 counties. The study area does not include the ocean shorelines or the
 north bay delta area because the water system dynamics differ in deltas.
 The data was cleaned of any duplicates within each dataset using the Find
 Identical and Delete Identical tools. Then duplicates between the two
 datasets were removed by running the intersect tool for the DTSC and WRCB
 point data. We chose this method over searching for duplicates by name
 because some sites change names when management is transferred from DTSC
 to WRCB. Lastly, the datasets were sorted into open and closed sites based
 on the DTSC and WRCB classifications which are shown in a table in the
 paper's supplemental material. To calculate areas
 of rising groundwater, we used data from the USGS paper “Projected
 groundwater head for coastal California using present-day and future
 sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J.,
 & Erikson, L. (2020). We used the hydraulic conductivity of 1
 condition (Kh1) to calculate areas of rising groundwater. We used the
 Raster Calculator to subtract the existing groundwater head from the
 groundwater head under a 1-meter of sea level rise scenario to find the
 areas where groundwater is rising. Using the Reclass Raster tool, we
 reclassified the data to give every cell with a value of 0.1016 meters
 (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of
 that little can leach into pipes and infrastructure. We then used the
 Raster to Poly tool to generate polygons of areas of groundwater
 rise.
description: Proprietary: ArcGIS, R Studio and R, Excel;
 Open Source: QGIS, Google Sheets