High Vulnerability to Water Contamination Exposure (v10.0.2, No Adaptive Capacity), Santa Rosa, CA

Jan 24, 2023 (Last modified Mar 8, 2023)

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 two 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

The Adaptive Capacity branch was removed for this version of the model.

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


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:


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


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


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:


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.

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Conservation Biology Institute
Content date:
2010-01-01T00:00:00 - 2016-12-31T00:00:00
Schmidt, Andres, Lisa M. Ellsworth, Jenna H. Tilt, and Mike Gough. 2022. “Predicting Conditional Maximum Contaminant Level Exceedance Probabilities for Drinking Water after Wildfires with Bayesian Regularized Network Ensembles.” Machine Learning with Applications 7 (March): 100227.
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Conservation Biology Institute
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This dataset is licensed under the Creative Commons Attribution-NonCommercial 3.0 License and is available to the public for non-commercial access and use. Attribution is required for redistribution in any medium or format. CBI makes no warranties either expressed or implied with respect to datasets made available for public distribution.
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Conservation Biology Institute

The Conservation Biology Institute (CBI) provides scientific expertise to support the conservation and recovery of biological diversity in its natural state through applied research, education, planning, and community service.