High Vulnerability to Water Contamination Exposure (v10.0.1, Adaptive Capacity), Santa Rosa, CA

This dataset identifies areas that are likely to be vulnerable to water contamination exposure following a large scale fire event in Santa Rosa, California. This estimate is based on three primary factors (model branches):

1. Risk of Water Contamination based on the post-fire probability of drinking water contamination exceeding the State of California MCL for Benzene (1 µg/L))

2. Socioeconomic Sensitivity based on socio-economic, landuse, and housing characteristics data extracted from 2010 U.S. Census Blocks and Block Groups. These data were compiled into three main model branches based on the results of a principal components analysis:

i Sensitive Because Of Household Characteristics

ii Sensitive Because Of Poverty/Education/Employment

iii Sensitive Because of Housing Characteristics

3. Adaptive Capacity (which is defined here as a community's ability to adjust to, and recover from, water infrastructure damage.

The final vulnerability scores, calculated from the factors listed above, are stored in the following field:

High_Vulnerability_To_Water_Contamination_Exposure_Fz

This dataset was created using the Environmental Evaluation Modeling System (EEMS) -- a hierarchical, spatially-explicit, fuzzy-logic modeling system developed by the Conservation Biology Institute (CBI). Fuzzy logic is an analytical technique that allows users to evaluate the truthfulness of a proposition along a continuum, rather than being limited to the binary (TRUE/FALSE) determinations of traditional logic.

A value of +1 in the final output field indicates that the proposition “ High Vulnerability to Water Contamination Exposure ” is totally true (i.e., that the polygon does have a High Vulnerability to Water Contamination Exposure). A value of -1 in this field indicates that this proposition is totally false (i.e., that the polygon does not have a High Vulnerability to Water Contamination Exposure). And values between -1 and +1 simply represent degrees of truth along a continuum (the gray areas), and can be interpreted as follows:

* Values greater than 0 indicate that the proposition is more true than false

* Values equal to 0 indicate that the proposition is neither true nor false

* Values less than 0 indicate that the proposition is more false than true

More information on EEMS and fuzzy logic can be found at the following URL:

https://consbio.org/products/tools/environmental-evaluation-modeling-system-eems

The model structure and logical relationships used to create this dataset can be examined in the interactive model diagram in the Wildfire Vulnerability Explorer:

https://wildfirevulnerability.eemsonline.org

The input data used in the water contamination branch of this model was created by Dr. Andres Schmidt with Oregon State University:

https://databasin.org/datasets/24fc4b6fd5cb483aacb6828c06d0233f/

The input data used in the socioeconomic sensitivity and adaptive capacity branches of this model was created by Dr. Jenna Tilt with Oregon State University:

https://databasin.org/datasets/c727fc28aa2a4875acc635c98b60044f/

The original reporting units (polygons) used in this analysis were U.S. Census Blocks. However due to large differences in polygon sizes, the reporting units were subdivided using a 100acre target area (404682.525194m2). This was done in order to help ensure that the water contamination data summarized to each reporting unit (using a zonal max) better captured the spatial distribution of the water contamination risk values in the original 30m input raster. Refer to the dataset lineage in the metadata for additional details.