U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the California State Water Resources Control Board

A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program

Status and Understanding of Groundwater Quality in the South Coast 

Interior Groundwater Basins, 2008: California GAMA Priority Basin Project

Photo placement

Scientific Investigations Report 2014–5023
Front Cover Map: Groundwater basins categorized by sampling priority. Location of groundwater basin 

boundaries from California Department of Water Resources (CDWR, 2003).

Cover photographs:

Front cover: Farm in Gilroy, California. (Photograph taken by Michael Land, U.S. Geological Survey.)

Back cover: Golf course irrigation well, Pleasanton, California. (Photograph taken by Tracy Davis, 

U.S. Geological Survey.) 

Sampling priority

Study area

Low-use basins

Priority basins

Areas that are outside CDWR-defined

 groundwater basins
Status and Understanding of Groundwater 

Quality in the South Coast Interior 

Groundwater Basins, 2008: California 

GAMA Priority Basin Project

By Mary C. Parsons, Justin T. Kulongoski, and Kenneth Belitz

A product of the California Groundwater Ambient Monitoring and Assessment 

(GAMA) Program

Prepared in cooperation with the California State Water Resources Control Board

Scientific Investigations Report 2014–5023

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Parsons, M.C., Kulongoski, J.T., and Belitz, Kenneth 2014, Status and understanding of groundwater quality in the 

South Coast Interior groundwater basins, 2008—California GAMA Priority Basin Project: U.S. Geological Survey 

Scientific Investigations Report 2014–5023, 68 p., http://dx.doi.org/10.3133/sir20145023.

ISBN 978-1-4113-3781-7

ISSN 2328-031X (print)

ISSN 2328-0328 (online) 
iii

Contents

Abstract...........................................................................................................................................................1

Introduction ....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................4

Hydrogeologic Setting of the South Coast Interior Basins Study Unit .................................................4

Livermore Study Area...........................................................................................................................6

Gilroy Study Area..................................................................................................................................6

Cuyama Study Area..............................................................................................................................6

Methods ..........................................................................................................................................................8

Relative-Concentrations and Water-Quality Benchmarks ............................................................8

Datasets for Status Assessment........................................................................................................9

U.S. Geological Survey Grid Data .............................................................................................9

California Department of Public Health Grid Data ...............................................................11

Data Used for Spatially Weighted Calculations....................................................................13

Selection of Constituents for Additional Evaluation ............................................................13

Calculation of Aquifer-Scale Proportions..............................................................................14

Understanding-Assessment Methods ............................................................................................15

Statistical Analysis ....................................................................................................................16

Potential Explanatory Factors...........................................................................................................16

Land Use......................................................................................................................................16

Well Depth and Depth to Top-of-Perforations ......................................................................16

Groundwater Age ......................................................................................................................20

Geochemical Conditions...........................................................................................................20

Status and Understanding of Water Quality ...........................................................................................22

Inorganic Constituents.......................................................................................................................22

Trace Elements...........................................................................................................................28

Understanding Assessment for Arsenic.......................................................................28

Understanding Assessment for Boron..........................................................................32

Understanding Assessment for Molybdenum .............................................................32

Uranium and Radioactive Constituents..................................................................................32

Nutrients......................................................................................................................................32

Understanding Assessment for Nitrate ........................................................................32

Inorganic Constituents with Aesthetic Benchmarks...........................................................36

Understanding Assessment for Total Dissolved Solids (TDS) ..................................36

Understanding Assessment for Sulfate .......................................................................36

Understanding Assessment for Manganese ...............................................................36

Organic Constituents..........................................................................................................................40

Understanding Assessment for Trihalomethanes ................................................................40

Understanding Assessment for Herbicides ..........................................................................40

Special-Interest Constituents...........................................................................................................45

Understanding Assessment for Perchlorate.........................................................................45

Summary .......................................................................................................................................................47

Acknowledgments.......................................................................................................................................47
iv

References....................................................................................................................................................48

Appendix A. Use of Data from the California Department of Public Health (CDPH) Database ......53

Appendix B. Ancillary Datasets.................................................................................................................59

Appendix C. Comparison of California Department of Public Health and U.S. Geological 

Survey-GAMA Data........................................................................................................................67

Contents—Continued

Figures

1. Map showing location of the South Coast Interior Basins study unit, California 

GAMA Priority Basin Project, and the California hydrogeologic provinces.......................3

2. Map showing the geographic features and study areas of the South Coast Interior 

Basins study unit, California GAMA Priority Basin Project...................................................5

3. Map showing the geologic formations and areal distribution of USGS-grid and 

understanding wells sampled in the South Coast Interior Basins study unit, 

California GAMA Priority Basin Project....................................................................................7

4. Map showing the locations of study area grid cells, USGS grid and understanding 

wells, and CDPH wells, South Coast Interior Basins study unit, California GAMA 

Priority Basin Project, August–December 2008 ....................................................................10

5. Ternary diagrams showing percentage of urban, agricultural, and natural land 

use in the study unit and study areas, and the area surrounding each grid and 

understanding well in the South Coast Interior Basins study unit, California 

GAMA Priority Basin Project....................................................................................................17

6. Map of land use in the South Coast Interior Basins study unit, California GAMA 

Priority Basin Project.................................................................................................................18

7. Boxplots showing well depths, depths to top-of-perforations, and perforation 

lengths for grid and understanding wells, South Coast Interior Basins study unit, 

California GAMA Priority Basin Project..................................................................................19

8. Bar chart showing age classification in relation to the depth of well perforations, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............20

9. Graph showing maximum relative-concentration of constituents detected in grid 

wells, by constituent class, South Coast Interior Basins study unit, California 

GAMA Priority Basin Project....................................................................................................26

10. Graph showing relative-concentrations of arsenic, boron, chromium, fluoride, 

molybdenum, selenium, strontium, gross alpha radioactivity, and nitrate with 

health-based benchmarks, and total dissolved solids, chloride, iron, manganese, 

and sulfate with aesthetic benchmarks in USGS- and CDPH-grid wells, South 

Coast Interior Basins study unit, California GAMA Priority Basin Project........................27

11. Maps showing relative-concentrations of arsenic for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time 

period used for the status assessment for the Livermore, Gilroy, and 

Cuyama study areas, South Coast Interior Basins study unit, 

California GAMA Priority Basin Project..................................................................................31
v

12. Maps showing relative-concentrations of boron for USGS-grid and USGSunderstanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............33

13. Maps showing relative-concentrations of molybdenum for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............34

14. Maps showing relative-concentrations of nitrate for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............35

15. Maps showing relative-concentrations of total dissolved solids for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............37

16. Maps showing relative-concentrations of sulfate for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............38

17. Maps showing relative-concentrations of manganese for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............39

18. Graph of detection frequency and maximum relative-concentrations of organic 

and special-interest constituents detected in USGS-grid wells in the South Coast 

Interior Basins study unit, California GAMA Priority Basin Project...................................41

19. Graph of detection frequency and relative-concentrations of selected organic

and special-interest constituents in USGS-grid wells in the South Coast 

Interior Basins study unit, California GAMA Priority Basin Project, 

August–December 2008.............................................................................................................42

20. Maps showing relative-concentrations of chloroform for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............43

21. Maps showing relative-concentrations of simazine for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............44

22. Maps showing relative-concentrations of perchlorate for USGS-grid and 

USGS-understanding wells and for CDPH wells with data during the time period 

used for the status assessment for the Livermore, Gilroy, and Cuyama study areas, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............46

Figures—Continued
vi

Tables

1. Number of wells sampled by the U.S. Geological Survey for the fast and slow 

sampling schedules and number of constituents sampled in each constituent class 

for the South Coast Interior Basins study unit, California GAMA Priority Basin 

Project, August–December 2008..............................................................................................11

2A. Benchmark type and value for constituents, and number of grid cells with 

U.S. Geological Survey-GAMA data and California Department of Public Health 

data, for each constituent, South Coast Interior Basins study unit, California 

Priority Basin Project.................................................................................................................12

2B. Benchmark type and value for organic and special-interest constituents selected 

for additional evaluation in the status assessment of groundwater quality in the 

South Coast Interior Basins study unit, California Priority Basin Project.........................13

3. Constituents reported at concentrations greater than benchmark values in the 

CDPH database between January 8, 1980, and August 10, 2005, South Coast 

Interior Basins study unit, California GAMA Priority Basin Project...................................14

4A. Results of non-parametric (Spearman’s rho method) analysis of correlations in grid 

and understanding wells between selected potential explanatory factors, South 

Coast Interior Basins study unit, California GAMA Priority Basin Project........................20

4B. Results of Wilcoxon rank-sum tests for differences in values of selected potential 

explanatory factors between samples classified by groundwater age, geochemical 

condition, study area, and well depth, South Coast Interior Basins study unit, 

California GAMA Priority Basin Project..................................................................................21

5. Number of constituents analyzed and detected by the U.S. Geological Survey by 

constituent class and benchmark type, South Coast Interior Basins study unit, 

California GAMA Priority Basin Project, August–December 2008 .....................................22

6. Aquifer-scale proportions from grid-based and spatially weighted approaches for 

constituents with (1) high relative-concentrations during August 11, 2005, to August 10, 

2008, from the CDPH database, (2) moderate or high relative-concentrations in 

samples collected from USGS-grid wells (August–December 2008), or (3) detection 

frequencies of organic constituents greater than 10 percent in USGS-grid wells, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............23

7A. Aquifer-scale proportions for inorganic constituent classes, South Coast Interior 

Basins study unit, California GAMA Priority Basin Project.................................................25

7B. Aquifer-scale proportions for organic constituent classes, South Coast Interior 

Basins study unit, California GAMA Priority Basin Project.................................................25

8A. Results of non-parametric (Spearman’s rho method) analysis for correlations 

between selected water-quality constituents and potential explanatory factors, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............29

8B. Results of Wilcoxon rank-sum tests to determine significant differences between 

selected water-quality constituents grouped by explanatory factor classifications, 

South Coast Interior Basins study unit, California GAMA Priority Basin Project............30
vii

Conversion Factors, Datums, and Abbreviations and 

Acronyms 

Inch/foot/mile to International System of Units (SI)

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square foot (ft2

) 0.09290 square meter (m2)

square mile (mi2

) 2.590 square kilometer (km2) 

Radioactivity

picocurie per liter (pCi/L) 0.037 becquerel per liter (Bq/L)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32.

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F–32)/1.8.

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 

25 °C).

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) 

or micrograms per liter (μg/L). One milligram per liter is equivalent to 1 part per million (ppm); 

1 microgram per liter is equivalent to 1 part per billion (ppb).

Datums

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).
viii

Abbreviations and Acronyms

AL-US U.S. Environmental Protection Agency action level

CUY Cuyama study area

GAMA Groundwater Ambient Monitoring and Assessment Program

GIL Gilroy study area

HAL-US U.S. Environmental Protection Agency lifetime health advisory level 

HBSL health-based screening level

LIV Livermore study area

LSD land-surface datum

LUFT leaking underground fuel tank

MCL-CA California Department of Public Health maximum contaminant level 

MCL-US U.S. Environmental Protection Agency maximum contaminant level 

NL-CA California Department of Public Health notification level 

pmc percent modern carbon

RSD5-US U.S. Environmental Protection Agency risk-specific dose at a risk factor of 10–5

SCI South Coast Interior Basins study unit

SMCL-CA California Department of Public Health secondary maximum contaminant level 

SMCL-US U.S. Environmental Protection Agency secondary maximum contaminant level 

TT-US U.S. Environmental Protection Agency treatment technique levels

TU tritium unit

Organizations

CDPH California Department of Public Health (Department of Health Services prior to 

July 1, 2007)

CDPR California Department of Pesticide Regulation

CDWR California Department of Water Resources

LLNL Lawrence Livermore National Laboratory

NAWQA National Water-Quality Assessment Program (USGS)

SWRCB State Water Resources Control Board (California)

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey
ix

Selected chemical names

DO dissolved oxygen

MTBE methyl tert-butyl ether

NDMA N-nitrosodimethylamine

PCE tetrachloroethene

TDS total dissolved solids

THM trihalomethane

VOC volatile organic compound

Units of Measure

δ delta notation; the ratio of a heavier isotope to the more common lighter isotope of an 

element, relative to a standard reference material, expressed as per mil

> greater than

< less than

% percent
x

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Abstract

Groundwater quality in the approximately 653-squaremile (1,691-square-kilometer) South Coast Interior Basins 

(SCI) study unit was investigated as part of the Priority 

Basin Project of the Groundwater Ambient Monitoring and 

Assessment (GAMA) Program. The South Coast Interior 

Basins study unit contains eight priority groundwater 

basins grouped into three study areas, Livermore, Gilroy, 

and Cuyama, in the Southern Coast Ranges hydrogeologic 

province. The GAMA Priority Basin Project is being 

conducted by the California State Water Resources Control 

Board in collaboration with the U.S. Geological Survey 

(USGS) and the Lawrence Livermore National Laboratory. 

The GAMA South Coast Interior Basins study was 

designed to provide a spatially unbiased assessment of 

untreated (raw) groundwater quality within the primary 

aquifer system, as well as a statistically consistent basis for 

comparing water quality between basins. The assessment was 

based on water-quality and ancillary data collected by the 

USGS from 50 wells in 2008 and on water-quality data from 

the California Department of Public Health (CDPH) database. 

The primary aquifer system was defined by the depth intervals 

of the wells listed in the CDPH database for the SCI study 

unit. The quality of groundwater in the primary aquifer system 

may be different from that in the shallower or deeper waterbearing zones; shallow groundwater may be more vulnerable 

to surficial contamination.

The first component of this study, the status of the current 

quality of the groundwater resource, was assessed by using 

data from samples analyzed for volatile organic compounds 

(VOCs), pesticides, and naturally occurring inorganic 

constituents, such as trace elements and minor ions. This 

status assessment is intended to characterize the quality of 

groundwater resources within the primary aquifer system of 

the SCI study unit, not the treated drinking water delivered to 

consumers by water purveyors.

Relative-concentrations (sample concentration divided 

by the health- or aesthetic-based benchmark concentration) 

were used for evaluating groundwater quality for those 

constituents that have Federal or California regulatory or nonregulatory benchmarks for drinking-water quality. A relativeconcentration greater than 1.0 indicates a concentration 

greater than a benchmark, and a relative-concentration less 

than or equal to 1.0 indicates a concentration equal to or 

less than a benchmark. Relative-concentrations of organic 

constituents and special-interest constituents were classified 

as “high” (relative-concentration greater than 1.0), “moderate” 

(relative-concentration greater than 0.1 and less than or equal 

to 1.0), or “low” (relative-concentration less than or equal to 

0.1). Relative-concentrations of inorganic constituents were 

classified as “high” (relative-concentration greater than 1.0), 

“moderate” (relative-concentration greater than 0.5 and less 

than or equal to 1.0), or “low” (relative-concentration less than 

or equal to 0.5). 

Aquifer-scale proportion was used as the primary 

metric in the status assessment for evaluating regional-scale 

groundwater quality. High aquifer-scale proportion is defined 

as the percentage of the area of the primary aquifer system 

with a relative-concentration greater than 1.0 for a particular 

constituent or class of constituents; percentage is based on 

an areal rather than a volumetric basis. Moderate and low 

aquifer-scale proportions were defined as the areal percentage 

of the primary aquifer system with moderate and low relativeconcentrations, respectively. Two statistical approaches—gridbased and spatially weighted—were used to evaluate aquiferscale proportions for individual constituents and classes of 

constituents. Grid-based and spatially weighted estimates 

were comparable in the SCI study unit (within 90-percent 

confidence intervals).

Inorganic constituents (one or more) with health-based 

benchmarks were detected at high relative-concentrations 

in 29 percent of the primary aquifer system, at moderate 

relative-concentrations in 37 percent, and at low relativeconcentrations in 34 percent. High aquifer-scale proportions 

of inorganic constituents primarily reflected high aquiferscale proportions of nitrate (14 percent), boron (8.6 percent), 

molybdenum (8.6 percent), and arsenic (5.7 percent). In 

contrast, the relative-concentrations of organic constituents 

(one or more) were high in 1.6 percent, moderate in 

Status and Understanding of Groundwater Quality in the 

South Coast Interior Groundwater Basins, 2008: California 

GAMA Priority Basin Project

By Mary C. Parsons, Justin T. Kulongoski, and Kenneth Belitz
2 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

2.0 percent, and low or not detected in 96 percent of the 

primary aquifer system. Of the 207 organic and specialinterest constituents analyzed for, 15 constituents were 

detected. Perchlorate was found at moderate relativeconcentrations in 34 percent of the aquifer. Two organic 

constituents were frequently detected (in greater than 

10 percent of samples): the trihalomethane chloroform and the 

herbicide simazine. 

The second component of this study, the understanding 

assessment, identified natural and human factors that may 

have affected groundwater quality by evaluating land use, 

physical characteristics of the wells, and geochemical 

conditions of the aquifer. This evaluation was done by 

using statistical tests of correlations between these potential 

explanatory factors and water-quality data. Concentrations 

of arsenic, molybdenum, and manganese were generally 

greater in anoxic and pre-modern groundwater than other 

groundwater. In contrast, concentrations of nitrate and 

perchlorate were significantly higher in oxic and modern 

groundwater. Concentrations of simazine were greater in 

modern than pre-modern groundwater. Chloroform detections 

were positively correlated with greater urban land use. Boron 

concentrations and chloroform detections were higher in the 

Livermore study area than in the other study areas of the SCI; 

total dissolved solids and sulfate concentrations were greater 

in the Cuyama study area.

Introduction 

To assess the quality of ambient groundwater in aquifers 

used for drinking-water supply and to establish a baseline 

groundwater-quality monitoring program, the California State 

Water Resources Control Board (SWRCB), in collaboration 

with the U.S. Geological Survey (USGS) and Lawrence 

Livermore National Laboratory (LLNL), implemented the 

Groundwater Ambient Monitoring and Assessment (GAMA) 

Program (California State Water Resources Control Board, 

2013, website at http://www.waterboards.ca.gov/gama). The 

statewide GAMA Program currently consists of four projects: 

(1) the GAMA Priority Basin Project, conducted by the USGS 

(U.S. Geological Survey, 2013, website at http://ca.water.

usgs.gov/gama/); (2) the GAMA Domestic Well Project, 

conducted by the SWRCB; (3) the GAMA Special Studies, 

conducted by LLNL; and (4) the online database GeoTracker 

GAMA, conducted by the SWRCB. On a statewide basis, 

the GAMA Priority Basin Project focused on the primary 

aquifer systems, typically the deep portion of the groundwater 

resource, and the SWRCB Domestic Well Project generally 

focused on the shallow aquifer systems. The deeper aquifers 

may be at less risk of contamination than the shallow wells, 

such as private domestic or environmental monitoring wells, 

which are closer to surficial sources of contamination. As a 

result, concentrations of contaminants, such as volatile organic 

compounds (VOCs) and nitrate, in wells screened in the deep 

aquifers may be lower than concentrations of constituents 

in shallow wells (Kulongoski and others, 2010; Landon and 

others, 2010).

The SWRCB initiated the GAMA Program in 2000 in 

response to Legislative mandates (California State Water 

Resources Control Board, 1999; State of California, 2001a). 

The GAMA Priority Basin Project was initiated in response 

to the Groundwater Quality Monitoring Act of 2001 to assess 

and monitor the quality of groundwater in California (State 

of California, 2001b). The GAMA Priority Basin Project 

is a comprehensive assessment of statewide groundwater 

quality designed to help better understand and identify risks 

to groundwater resources and to increase the availability of 

information about groundwater quality to the public. For the 

GAMA Priority Basin Project, the USGS, in collaboration 

with the SWRCB, developed a monitoring plan to assess 

groundwater basins through direct sampling of groundwater 

and other statistically reliable sampling approaches (Belitz and 

others, 2003; California State Water Resources Control Board, 

2003). Additional partners in the GAMA Priority Basin Project 

include the California Department of Public Health (CDPH), 

the California Department of Pesticide Regulation (CDPR), the 

California Department of Water Resources (CDWR), and local 

water agencies and well owners (Kulongoski and Belitz, 2004). 

The range of hydrologic, geologic, and climatic 

conditions in California were considered in this statewide 

assessment of groundwater quality. Belitz and others (2003) 

partitioned the State into 10 hydrogeologic provinces, 

each with distinctive hydrologic, geologic, and climatic 

characteristics (fig. 1). These hydrogeologic provinces 

include groundwater basins and subbasins designated by the 

CDWR (California Department of Water Resources, 2003). 

Groundwater basins generally consist of relatively permeable, 

unconsolidated deposits of alluvial origin. Eighty percent 

of California’s approximately 16,000 public-supply wells 

are in designated groundwater basins. Groundwater basins 

and subbasins were prioritized for sampling on the basis 

of the number of public-supply wells, with secondary 

consideration given to municipal groundwater use, agricultural 

pumping, the number of historically leaking underground 

fuel tanks, and the number of square-mile sections having 

registered pesticide applications (Belitz, and others, 2003). 

The 116 priority basins, a subset of the approximately 350 

lower-priority basins, and additional areas outside defined 

groundwater basins, were grouped into 35 study units, which 

include approximately 95 percent of public-supply wells in 

California’s groundwater basins. The South Coast Interior 

Basins study unit (hereinafter referred to as the SCI study unit) 

contains eight priority groundwater basins grouped into three 

study areas, Livermore, Gilroy, and Cuyama, in the Southern 

Coast Ranges hydrogeologic province. 
Introduction 3

Basin and Range

Central

Valley

Klamath

Mountains

Desert

Cascades and

Modoc Plateau

Southern Coast

Ranges

Sierra

Nevada

Northern

Coast

Ranges

SOUTH COAST

INTERIOR BASINS

STUDY UNIT

(figure 2)

San Diego

Drainages

Transverse Ranges and

selected Peninsular Ranges

Livermore study area

Gilroy study area

Cuyama study area

Bakersfield

San

Francisco

Sacramento

OREGON

NEVADA

MEXICO

ARIZONA

Redding

Los Angeles

San Diego

P a c i f i c O c e a n

0 200 MILES

0 200 KILOMETERS

100

100

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

Provinces from Belitz and others, 2003.

IP026177_Figure 01

Figure 1. Location of the South Coast Interior Basins study unit, California GAMA Priority Basin Project, and the 

California hydrogeologic provinces.
4 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Purpose and Scope

The purposes of this report are to provide (1) a study unit 

description: description of the hydrogeologic setting of the 

SCI study unit (fig. 1); (2) a status assessment: assessment of 

the status of the current quality of groundwater in the primary 

aquifer system in the SCI study unit; and (3) an understanding 

assessment: identification of the natural and human factors 

affecting groundwater quality. 

Water-quality data for samples collected by the USGSGAMA Program in the SCI study unit and details of sample 

collection, analysis, and quality-assurance procedures for the 

SCI study unit are reported by Mathany and others (2009). 

Using those same data, this report describes methods used 

in designing the sampling network, identifying CDPH data 

for use in the status assessment, estimating aquifer-scale 

proportions of relative-concentrations, analyzing ancillary 

datasets, classifying groundwater age, and assessing the status 

of groundwater quality by statistical and graphical approaches. 

The status assessment includes analyses of water-quality 

data for 35 wells selected by the USGS for spatial coverage 

of one well per grid cell (hereinafter referred to as USGSgrid wells) across the SCI study unit. Most of the USGS-grid 

wells were public-supply wells, but a few other types of wells 

with perforation depth intervals similar to the public-supply 

wells also were sampled. Samples were collected for analysis 

of anthropogenic constituents, including volatile organic 

compounds (VOCs) and pesticides, and naturally occurring 

inorganic constituents, such as trace and minor elements. 

Water-quality data from the California Department of Public 

Health (CDPH) database also were used to supplement data 

collected by the USGS for the GAMA Program. The resulting 

set of water-quality data from USGS-grid wells and selected 

CDPH wells was considered to be representative of the 

primary aquifer system in the SCI study unit; the primary 

aquifer system is defined by the depth of the screened/

perforated intervals of the wells listed in the CDPH database 

for the SCI study unit. GAMA status assessments were 

designed to provide a statistically robust characterization 

of groundwater quality in the primary aquifer system at the 

basin-scale (Belitz and others, 2003). The statistically robust 

design also allows basins to be compared and results to be 

synthesized regionally and statewide. 

To provide context, the water-quality data discussed in 

this report are compared to California and Federal regulatory 

and non-regulatory benchmarks for drinking water. The 

assessments in this report are intended to characterize the 

quality of untreated groundwater resources in the primary 

aquifer system within the study unit, not the drinking water 

delivered to consumers by water purveyors. This study does 

not attempt to evaluate the quality of water delivered to 

consumers; after withdrawal from the ground, water typically 

is treated, disinfected, and (or) blended with other waters to 

maintain acceptable water quality. Regulatory benchmarks 

apply to drinking water that is delivered to the consumer, not 

to untreated groundwater.

The understanding assessment included data from 

15 wells sampled by the USGS (hereinafter referred to as 

USGS-understanding wells) in addition to the 35 USGSgrid wells sampled for the status assessment to identify the 

natural and human factors affecting groundwater quality, 

and to explain the relations between water quality and 

selected potential explanatory factors. Potential explanatory 

factors examined included land use, well depth, indicators of 

groundwater age, and geochemical conditions of the waters. 

Hydrogeologic Setting of the South 

Coast Interior Basins Study Unit

The SCI GAMA study unit covers approximately 

653 square miles (mi2) in Alameda, Contra Costa, Santa 

Clara, San Benito, San Luis Obispo, Santa Barbara, Ventura, 

Kern, and Los Angeles Counties. The SCI study unit lies in 

the Southern Coast Ranges hydrogeologic province (fig. 1) 

and contains eight groundwater basins: Livermore, Gilroy–

Hollister (subbasins: Llagas, Bolsa Area, Hollister Area, and 

San Juan Bautista Area), Cuyama Valley, Castac Lake Valley, 

Cuddy Canyon Valley, Cuddy Ranch Area, Cuddy Valley, 

and Mil Potrero. For the purpose of this study, these eight 

groundwater basins were grouped into three study areas: 

Livermore, Gilroy, and Cuyama (figs. 2A–C). As part of the 

Priority Basin Project, untreated groundwater samples were 

collected from 50 wells that were considered representative 

of the primary aquifer systems in the SCI study unit from 

August 11 to November 17, 2008 (Mathany and others, 2009). 

Samples collected from four additional monitoring wells in the 

Livermore basin were not included in this report because the 

monitoring wells were screened at much shallower depths than 

the primary aquifer system and were therefore not considered 

representative of the primary aquifer system (table B2).

The climate of the SCI study unit is characterized by hot, 

dry summers and cold winters. Average annual precipitation 

in the SCI study unit varies slightly among the three study 

areas. In the Livermore study area, values range from more 

than 20 inches along the southeast and northwest study area 

margins to 16 inches on the valley floor, occurring as rain 

during the winter (California Department of Water Resources, 

2006). In the Gilroy study area, average annual precipitation 

ranges from 17 inches in the northern reaches of the study 

area to 13 inches in the south, occurring as rain in the winter 

and early spring (California Department of Water Resources, 

2004a, b, c, d). In the Cuyama study area, precipitation ranges 

from less than 8 inches in the Cuyama Valley up to 17 inches 

in the surrounding mountains, occurring as rainfall in the 

winter and early spring (PRISM Climate Group, Oregon State 

University, 2010).
Hydrogeologic Setting of the South Coast Interior Basins Study Unit 5

IP026177_fig 02abc

5

5

California

Aqueduct

Castac

Lake

Cuyama River

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de la Laguna

Altamont Hills

Engineered

recharge ponds

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

EXPLANATION

Stream

Public-supply well—

 With a record in the

 California Department

 of Public Health

 (CDPH) database

Livermore

STUDY AREA

Gilroy

Cuyama

0 5 10

0 105 Kilometers

Miles

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

A B

C

Figure 2A,B,C. Geographic features and study areas of the South Coast Interior Basins study unit, California GAMA Priority Basin 

Project.
6 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Livermore Study Area

The Livermore (LIV) study area is approximately 109 mi2

in area and lies about 40 miles (mi) east of San Francisco 

within a structural trough of the Diablo Range. The LIV study 

area is located within the Livermore Valley Groundwater 

Basin (California Department of Water Resources, 2006) 

and was defined by the extent of alluvium of Quaternary 

age within the groundwater basin (fig. 3A). The parts of the 

groundwater basin having surficial geology consisting of 

sediments of Plio-Pleistocene age were not included in the 

study area. Elevations within LIV range from about 280 feet 

(ft) above land-surface datum (LSD) in the southwest, where 

Arroyo de la Laguna exits the study area, to about 600 ft 

above LSD in the east, near the Altamont Hills (fig. 2A). 

The main surface drainage features of the LIV study 

area are creeks and human-made canals that drain into the 

Arroyo de la Laguna on west side of the basin (fig. 2A). 

Groundwater recharge in the LIV study area occurs from 

infiltration of precipitation and irrigation waters, engineered 

recharge (percolation ponds and lakes), and interbasin lateral 

flow (California Department of Water Resources, 2006). 

Groundwater discharge occurs as pumping for municipal and 

irrigation supply and as evaporation (California Department of 

Water Resources, 2006).

Primary water-bearing formations in the LIV study 

area include valley-fill materials of Quaternary age, the 

Livermore Formation of Plio-Pleistocene age in the south and 

southwest, and the Tassajara Formation of Pliocene age in 

the northern region of LIV (California Department of Water 

Resources, 2006). The LIV study area has three major fault 

zones (the Pleasanton and Parks faults and the Livermore fault 

zone), each inhibiting the lateral movement of groundwater 

(California Department of Water Resources, 2006) (fig. 3A). 

Gilroy Study Area

The Gilroy (GIL) study area is approximately 287 mi2 in 

area and lies within the Gilroy–Hollister groundwater basin, 

which contains four CDWR-defined subbasins: Llagas, Bolsa 

Area, Hollister Area, and San Juan Bautista Area (California 

Department of Water Resources, 2004a, b, c, d). The study 

area was defined by the extent of alluvium of Quaternary age 

within these defined groundwater basins (fig. 3B). The parts 

of the groundwater basin having surficial geology consisting 

of sediments of Plio-Pleistocene age were not included in the 

study area. Elevations within the study area range from about 

140 ft above LSD in the center of the basin, along the Santa 

Clara County and San Benito County border, to approximately 

800 ft above LSD in the south tip of the study area. 

The Pájaro River and its major tributaries drain the 

northern portion of the GIL study area to the Pacific Ocean 

through Monterey Bay. The southern portion of the GIL study 

area is drained by the San Benito River and its tributaries 

(fig. 2B). Groundwater recharge in the GIL study area occurs 

from a variety of sources: stream losses along the Pájaro 

and San Benito Rivers and their tributaries; areal recharge 

of precipitation and irrigation waters; seepage along canals; 

and interbasin lateral flow. A number of engineered-recharge 

facilities (percolation ponds and channels) enhance ambient 

recharge in the northern portion of the GIL study area. In the 

southern region of the GIL study area, engineered recharge 

occurs from the San Benito River as a result of river regulation 

at the Hernandez Reservoir, which is located approximately 

20 mi southeast of the study area boundary (California 

Department of Water Resources, 2004a, b, c, d). Groundwater 

discharge occurs primarily as pumping for municipal and 

irrigation supply (Kilburn, 1973; California Department of 

Water Resources, 2004a, b, c, d).

The primary water-bearing formation in GIL is a 

sedimentary sequence consisting mainly of clays, silts, 

sands, and gravels ranging in age from Tertiary to Holocene 

(California Department of Water Resources, 2004a, b, c, d). 

The major faults that bound the GIL study area (Calaveras, 

San Andreas, and Sargent) are relatively impermeable barriers 

to groundwater flow (fig. 3B). An unnamed clay confining 

layer in the Llagas Subbasin restricts vertical groundwater 

flow in the northern region of GIL (California Department of 

Water Resources, 2004a). 

Cuyama Study Area

The Cuyama (CUY) study area is approximately 

257 mi2 in area, is composed largely of the Cuyama Valley 

groundwater basin (230 mi2), and also includes five small 

CDWR-defined groundwater basins [Castac Lake Valley 

(6 mi2), Cuddy Canyon Valley (5 mi2), Cuddy Ranch Area 

(7 mi2), Cuddy Valley (5 mi2), and Mil Potrero (4 mi2)] 

(California Department of Water Resources, 2004e, f, g, h, 

i, j). The study area was defined by the extent of alluvium 

of Quaternary age within the groundwater basin (fig. 3C). 

The parts of the groundwater basin having surficial geology 

consisting of sediments of Plio-Pleistocene age were not 

included in the study area. Elevations within CUY range from 

about 1,400 ft above LSD in the west, where the Cuyama 

River exits the CUY study area, to about 5,000 ft above LSD 

in the east, in the small alluvial valleys in the San Emigdio 

Mountains (fig. 2C).

The CUY study area is drained by the Cuyama River 

and its major tributaries) (fig. 2C). Groundwater recharge in 

the CUY study area occurs almost exclusively from seepage 

from the Cuyama River, which is fed by runoff from the Sierra 

Madre Mountains (Singer and Swarzenski, 1970; Everett and 

others, 2013). Groundwater recharge in the five small CDWRdefined groundwater basins in the CUY study area occurs from 

the percolation of precipitation, spring flows, and septic tank 

effluent (California Department of Water Resources, 2004f, g, 

h, i, j). Groundwater discharge in the CUY study area occurs 

as pumping for irrigation and municipal supply, discharge 

to rivers and streams, and evapotranspiration (Upson and 

Worts, 1951). 
Hydrogeologic Setting of the South Coast Interior Basins Study Unit 7

IP026177_fig 03abc

5

5

California

Aqueduct

Castac

Lake

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

Cuyama River

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

Sargent Fault

Calaveras Fault

San Andreas fault

San Andreas fault

South Cuyama fault

Ozena fault

Morales fault

680

580

Arroyo de

la Laguna

Altamont Hills

Pleasanton fault

Livermore Fault Zone

Parks fault

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Stream

Livermore

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Fault

USGS-grid

 well

USGS-

 understanding

 well

Franciscan complex

Tertiary marine sediment

Metamorphic rocks

Metasediment

Ultramafic/mafic

Plio-Pleistocene sediment

Quaternary alluvium

SEDIMENTARY ROCKS

OTHER

Quaternary other sediment

Tertiary nonmarine sediment

Tertiary, mafic volcanics

Granitic rocks

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

A B

C

Figure 3A,B,C. Geologic formations and areal distribution of USGS-grid and understanding wells sampled in the South Coast 

Interior Basins study unit, California GAMA Priority Basin Project.
8 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

The Cuyama Valley consists of a downfaulted block or 

graben between faults (Singer and Swarzenski, 1970). The 

Cuyama Valley is bordered on the north by the Morales Fault, 

and on the south by the Ozena and South Cuyama faults 

(fig. 3C). The eastern part of the Cuyama Valley is underlain 

by a large syncline, the axis of which strikes roughly parallel 

to the elongation of the valley and dips towards the northwest 

(Singer and Swarzenski, 1970; Sweetkind and others, 2013). 

The Cuyama Valley has sedimentary deposits 3,000–4,000 ft 

in thickness, which range in age from Cretaceous to Holocene 

(Singer and Swarzenski, 1970). The basement complex and all 

sedimentary deposits older than the Morales Formation (clay, 

silt, sand, and gravel deposits of Pliocene age) are considered 

non-water-bearing. The Morales Formation, up to 10,000 ft 

thick along the northern margin, is overlain by alluvium of 

Pleistocene and Holocene age (Singer and Swarzenski, 1970).

The primary water-bearing formations in the CUY 

study area are mainly unconfined and are composed of 

younger (Holocene-age) and older (Pleistocene-age) 

alluvium (consisting of alternating layers of sands, gravels, 

boulders, silts, and clays) (Singer and Swarzenski, 1970). 

Under natural conditions, groundwater flow in the Cuyama 

Valley groundwater basin is northwestward, down the valley 

(Singer and Swarzenski, 1970; Everett and others, 2013). 

It is postulated that there are also additional faults that do 

not intersect land surface, yet compartmentalize the basin 

and create water-level offsets as much as 110 ft (Singer and 

Swarzenski, 1970).

Five small CDWR-defined groundwater basins (Castac 

Lake Valley, Cuddy Canyon Valley, Cuddy Ranch Area, 

Cuddy Valley, and Mil Potrero) located east of the Cuyama 

Valley groundwater basin were included in the CUY study 

area to gain information on the quality of groundwater in the 

San Andreas fault zone (fig. 3C). These groundwater basins lie 

adjacent to the San Emigdio Mountains, contain alluvial fill 

material of Tertiary, Pleistocene, or Quaternary age, and are 

drained by a series of streams and creeks. Only the parts of the 

basins containing alluvium of Quaternary age were included in 

the CUY study area.

Methods 

The status assessment provides a spatially unbiased 

assessment of groundwater quality in the primary aquifer 

system, and the understanding assessment was designed 

to evaluate the natural and human factors that may affect 

groundwater quality of the SCI study unit. This section 

describes the methods used for (1) defining groundwater 

quality, (2) assembling the datasets used for the status 

assessment, (3) determining which constituents warranted 

additional evaluation, (4) calculating aquifer-scale proportions, 

and (5) analyzing explanatory factors and water-quality 

datasets as part of the understanding assessment. Methods 

used for compilation of data on potential explanatory factors 

are described in appendix B.

The primary metric for defining groundwater quality 

is relative-concentration, which references concentrations 

of constituents measured in groundwater to regulatory and 

non-regulatory benchmarks used to evaluate drinking-water 

quality. Some benchmarks are established for protection 

of human health, and others are established for aesthetic 

properties, such as taste and odor. Constituents were selected 

for additional evaluation in the assessment on the basis of 

objective criteria by using these relative-concentrations. 

Groundwater-quality data collected by the U.S. Geological 

Survey for the GAMA Priority Basin Project (USGS-GAMA) 

and data compiled in the CDPH database were used in the 

status assessment. Two statistical approaches based on 

spatially unbiased equal-area grids were used to calculate 

aquifer-scale proportions of low, moderate, or high relativeconcentrations: (1) the “grid-based” approach uses one value 

per grid cell to represent groundwater quality, and (2) the 

“spatially weighted” approach uses many values per grid cell 

(Belitz and others, 2010).

The CDPH database contains historical records from 

more than 25,000 wells, necessitating targeted retrievals to 

effectively access relevant water-quality data. For example, 

for the area representing the SCI study unit, the historical 

CDPH database contains more than 240,000 records from 

330 wells. The CDPH data were used in three ways in the 

status assessment: (1) to supplement the USGS data for the 

grid-based calculations of aquifer-scale proportions, (2) to 

select constituents for additional evaluation in the assessment, 

and (3) to provide the majority of the data used in the spatially 

weighted calculations of aquifer-scale proportions.

Relative-Concentrations and Water-Quality 

Benchmarks

Concentrations of constituents are presented as relativeconcentrations in the status assessment:

Relative concentraton Sample concentration

Benchmark concen - = tration

Regulatory and non-regulatory benchmarks apply to treated 

water that is served to the consumer, not to untreated 

groundwater. However, to provide some context for the 

water-quality data collected for this study, concentrations 

of constituents measured in the untreated groundwater 

were compared with benchmarks established by the 

U.S. Environmental Protection Agency (USEPA) and 

CDPH (U.S. Environmental Protection Agency, 1999, 

2009, 2012; California Department of Public Health, 

2010, 2012). Relative-concentrations less than (<) 1.0 

indicate sample concentrations less than the benchmark, 

and relative-concentrations greater than (>) 1.0 indicate 

sample concentrations greater than the benchmark. The 

use of relative-concentrations also permits comparison of 
Methods 9

constituents present at a wide range of concentrations on a 

single scale. Relative-concentrations can only be computed 

for constituents with water-quality benchmarks; therefore, 

constituents without water-quality benchmarks were not 

included in the status assessment. 

The benchmarks used for each constituent were selected 

in the following order of priority:

1. Regulatory, health-based CDPH and USEPA maximum 

contaminant levels (MCL-CA and MCL-US), action 

levels (AL-US), and treatment technique levels (TT-US). 

2. Non-regulatory, aesthetic-based CDPH and USEPA 

secondary maximum contaminant levels (SMCL-CA and 

SMCL-US). For constituents with recommended and 

upper SMCL-CA levels, the values for the upper levels 

were used. 

3. Non-regulatory, health-based CDPH notification levels 

(NL-CA), USEPA lifetime health advisory levels 

(HAL-US), and USEPA risk-specific dose at a risk factor 

of 10–5 (1 in 100,000 lifetime risk of cancer, RSD5-US).

Note that for constituents with multiple types of benchmarks, 

this hierarchy may not result in selection of the benchmark 

with the lowest concentration. Additional information on the 

types of benchmarks and listings of the benchmarks for all 

constituents analyzed are provided by Mathany and others 

(2009).

Toccalino and others (2004), Toccalino and Norman 

(2006), and Rowe and others (2007) previously used the 

ratio of the measured sample concentration to the benchmark 

concentration [either USEPA MCLs or health-based screening 

levels (HBSLs)] and defined this ratio as the benchmark 

quotient. HBSLs were not used in this report because they are 

not currently used as benchmarks by California drinking-water 

regulatory agencies. Because different water-quality benchmarks 

may be used to calculate relative-concentrations and benchmark 

quotients, the values of these ratios may not be the same for all 

constituents (for example, Fram and Belitz, 2012). 

For ease of discussion, relative-concentrations of 

constituents were classified into low, moderate, and high

categories:

Category

Relative-concentrations 

for organic and specialinterest constituents

Relativeconcentrations for 

inorganic constituents

High > 1 > 1

Moderate > 0.1 and ≤ 1 > 0.5 and ≤ 1

Low ≤ 0.1 ≤ 0.5

The boundary between “moderate” and “low” relativeconcentrations was set at 0.1 for organic and special-interest 

constituents for consistency with other studies and reporting 

requirements (U.S. Environmental Protection Agency, 1998; 

Toccalino and others, 2004). For inorganic constituents, 

the boundary between “moderate” and “low” relativeconcentrations was set at 0.5. The primary reason for using 

a higher threshold was to focus attention on the inorganic 

constituents of most immediate concern (Fram and Belitz, 

2012). Most inorganic constituents are naturally occurring 

and tend to be more prevalent than organic constituents in 

groundwater. Although more complex classifications could be 

devised based upon the properties and sources of individual 

constituents, use of a single moderate/low boundary value 

for each of the two major groups of constituents provided a 

consistent objective criterion for distinguishing constituents 

occurring at moderate rather than low concentrations.

Datasets for Status Assessment

The wells selected for sampling by the USGS in the SCI 

study provide a statistically unbiased, spatially distributed set 

of wells for the assessment of the quality of groundwater in 

the primary aquifer system (USGS-grid wells). Water-quality 

data from the USGS-grid wells were supplemented with 

data from selected wells in the CDPH database (CDPH-grid 

wells; see the “California Department of Public Health Grid 

Data” section) to obtain more complete spatial coverage and 

to include constituents that were not analyzed for in every 

USGS-grid well. These data were used to assess proportions 

of the primary aquifer system with high, moderate, and low 

relative-concentrations.

U.S. Geological Survey Grid Data

The primary data used for the grid-based calculations of 

aquifer-scale proportions of relative-concentrations were data 

from wells sampled by USGS for the GAMA Priority Basin 

Project (USGS-GAMA). Detailed descriptions of the methods 

used to identify wells for sampling are given in Mathany and 

others (2009). Briefly, each study area was divided into 10-mi2

(~26-square-kilometer) equal-area grid cells (figs. 4A–C), and 

in each cell, one well was randomly selected to represent the 

cell (Scott, 1990). Wells were selected from the population of 

wells in a Statewide database maintained by the CDPH. If a 

cell had no accessible wells listed in the CDPH database, then 

appropriate wells were selected from the statewide National 

Water Information System (NWIS) database maintained by 

the USGS or information from other sources. The SCI study 

unit contained 44 grid cells, and the USGS sampled wells in 

35 of those cells (USGS grid wells); the USGS did not sample 

a well in the remaining 9 cells (Mathany and others, 2009). 

The range of groundwater types sampled by the USGS-grid 

wells was comparable to the range of groundwater types 

represented in the larger number of wells with data in the 

CDPH database (appendix C). USGS-grid wells were named 

with an alphanumeric GAMA ID consisting of a prefix 

identifying the study area and a number indicating the order 

of sample collection within each study area (figs. A1A–C;

table A1). The following prefixes were used to identify the 

study area: CUY, Cuyama study area; GIL, Gilroy study area; 

and LIV, Livermore study area. 
10 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

IP026177_fig 04abc

5

5

California

Aqueduct

Castac

Lake

Cuyama River

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Stream

Livermore

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Equal-area

 randomized-

 sampling

 grid cell

USGS-grid well

USGS- and CDPH-grid well

CDPH-grid well

USGS-understanding well

Other CDPH well

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

A B

C

Figure 4A,B,C. Locations of study area grid cells, U.S. Geological Survey (USGS) grid and understanding wells, and California 

Department of Public Health (CDPH) wells, South Coast Interior Basins study unit, California GAMA Priority Basin Project, 

August–December 2008.
Methods 11

Samples collected from USGS-grid wells were 

analyzed for 205 to 269 constituents, depending on the 

sampling schedule (table 1). Water-quality indicators 

(field parameters), volatile organic compounds, pesticides, 

perchlorate, nutrients, major and minor ions, trace elements, 

noble gases, and selected isotopes used as hydrologic tracers 

were analyzed in samples from all wells. Polar pesticides and 

metabolites, redox species, radiochemical constituents, and 

N-nitrosodimethylamine (NDMA) were analyzed in samples 

from 11 wells. The collection, analysis, and quality-control 

data for the analytes listed in table 1 are described by Mathany 

and others (2009). Water-quality data collected by USGSGAMA are tabulated in Mathany and others (2009) and also 

are available from the SWRCB’s database GeoTracker GAMA 

(website at http://www.waterboards.ca.gov/gama/geotracker_

gama.shtml).

California Department of Public Health Grid Data

The 3 study areas were divided into 44 grid cells; of 

these, 9 cells did not have a USGS-grid well, 28 cells had 

a USGS-grid well but no USGS data for radiochemical 

constituents, and 7 cells had a USGS-grid well with data 

for all of the inorganic constituents evaluated. The CDPH 

database was queried to provide the missing inorganic data for 

these cells. CDPH wells with data for the most recent 3 years 

available at the time of sampling (August 11, 2005–August 10, 

2008) were considered. If a well had more than one analysis 

for a constituent in the 3-year interval, the most recent data 

were selected. 

For 10 of the 28 cells with a USGS-grid well but no 

USGS data for radiochemical constituents, the USGS-grid well 

was a well in the CDPH database with data for radiochemical 

constituents, and these data were selected to represent the 

cell (figs. 4A,B; USGS- and CDPH-grid wells). For another 

6 of the 28 cells, the USGS-grid well was not a well in the 

CDPH database with data for radiochemical constituents, but 

at least one other well in the cell had data for radiochemical 

constituents (figs. 4B,C; CDPH-grid wells). The procedures 

used to select one of those wells and all of the radiochemical 

data selected from the CDPH database to complete the grid 

dataset are described in appendix A. The CDPH database 

generally did not contain data for all missing radiochemical 

constituents at every CDPH grid well; therefore, the number of 

wells used for the grid-based assessment differed for various 

radiochemical constituents (table 2A). The 9 cells without 

USGS-grid wells also did not contain CDPH wells; thus, no 

data for inorganic constituents were selected to represent those 

cells. Although other organizations also collect water-quality 

data, the CDPH database is the only statewide database of 

groundwater-chemistry data available for comprehensive 

analysis. 

Table 1. Number of wells sampled by the U.S. Geological 

Survey for the fast and slow sampling schedules and number 

of constituents sampled in each constituent class for the South 

Coast Interior Basins study unit, California GAMA Priority Basin 

Project, August–December 2008.

[NDMA, N-nitrosodimethylamine; TDS, total dissolved solids; ns, not 

sampled]

Schedule

Fast Slow

Total number of wells 43 11

Number of grid wells sampled 28 7

Number of understanding wells sampled 15 4

Constituent Class Number of 

constituents

Inorganic constituents

Specific conductance 1 1

Nutrients 5 5

Major ions, alkalinity, and TDS 11 11

Trace elements 23 23

Uranium and radioactive constituents

Uranium 1 1

Radon-2221 ns 1

Gross alpha/beta2 ns 2

Organic constituents3

Volatile organic compounds4 85 85

Pesticides and degradates 63 63

Polar pesticides and metabolites5 ns 57

Special-interest constituents

Perchlorate 1 1

NDMA ns 1

Geochemical and age-dating tracers

Dissolved oxygen, pH, and temperature 3 3

Ar, Fe, Cr redox species ratios ns 3

Carbon-14 and δ13C of dissolved carbonates 2 2

Tritium6 1 1

Noble gases (He, Ne, Ar, Kr, Xe), 3

He/4He, and tritium7 7 7

δ2

H and δ18O of water 2 2

Total 205 269

1

 Radon-222 collected at five fast wells in the Gilroy study area as part of a 

special study. 2

 Both gross alpha and gross beta activities were measured after 72-hour 

and 30-day holding times; the 72-hour results are used in this report. 3

 Fourteen pharmaceutical compounds were analyzed at slow wells and 

results are discussed in Fram and Belitz (2011). 4

 Includes nine constituents classified as fumigants or fumigant synthesis 

byproducts. 5

 Does not include three constituents in common with pesticides and 

degradates. 6

 Analyzed at USGS Stable Isotope and Tritium Laboratory, Menlo Park, 

California. 7

 Analyzed at Lawrence Livermore National Laboratory, Livermore, 

California.
12 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Table 2A. Benchmark type and value for constituents, and number of grid cells with U.S. Geological Survey-GAMA data and California 

Department of Public Health data, for each constituent, South Coast Interior Basins study unit, California Priority Basin Project.

[Benchmark type: Regulatory, health-based benchmarks: MCL-US, USEPA maximum contaminant level; Prop MCL-US, proposed USEPA maximum 

contaminant level; AL-US, USEPA action level; MCL-CA, CDPH maximum contaminant level. Non-regulatory, health-based benchmarks: HAL-US, USEPA 

lifetime health advisory level; NL-CA, CDPH notification level. Non-regulatory, aesthetic/technical-based benchmarks: SMCL-CA, CDPH secondary maximum 

contaminant level. Benchmark types and values as of October 2013. Benchmark units: μg/L, micrograms per liter; mg/L, milligrams per liter; 

μS/cm, microsiemens per centimeter; pCi/L, picocuries per liter. Other Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient 

Monitoring and Assessment Program; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health]

Constituent Benchmark 

type1

Benchmark 

value

Benchmark 

units

Number of grid cells 

with USGS-GAMA data

Number of grid cells 

with CDPH data

Trace and minor elements with health-based benchmarks

Aluminum MCL-CA 1,000 μg/L 35 0

Antimony MCL-US 6 μg/L 35 0

Arsenic MCL-US 10 μg/L 35 0

Barium MCL-CA 1,000 μg/L 35 0

Beryllium MCL-US 4 μg/L 35 0

Boron NL-CA 1,000 μg/L 35 0

Cadmium MCL-US 5 μg/L 35 0

Chromium MCL-CA 50 μg/L 35 0

Copper AL-US 1,300 μg/L 35 0

Fluoride MCL-CA 2 mg/L 35 0

Lead AL-US 15 μg/L 35 0

Molybdenum HAL-US 40 μg/L 35 0

Nickel MCL-CA 100 μg/L 35 0

Selenium MCL-US 50 μg/L 35 0

Strontium HAL-US 4,000 μg/L 35 0

Thallium MCL-US 2 μg/L 35 0

Vanadium NL-CA 50 μg/L 35 0

Uranium and radioactive constituents

Uranium MCL-US 30 μg/L 35 0

Radon-222 Prop. MCL-US 4,000 pCi/L 1 2

Gross alpha particle activity MCL-US 15 pCi/L 7 15

Gross beta particle activity MCL-CA 50 pCi/L 7 2

Nutrients

Ammonia, as nitrogen HAL-US 2

 24.7 mg/L 35 0

Nitrite, as nitrogen MCL-US 1 mg/L 35 0

Nitrate, as nitrogen3 MCL-US 10 mg/L 35 0

Trace and minor elements with aesthetic benchmarks

Chloride SMCL-CA 500 mg/L 35 0

Iron SMCL-CA 300 μg/L 35 0

Manganese SMCL-CA 50 μg/L 35 0

Silver SMCL-CA 100 μg/L 35 0

Specific Conductance SMCL-CA 1,600 μS/cm 35 0

Sulfate SMCL-CA 500 mg/L 35 0

Total dissolved solids (TDS) SMCL-CA 1,000 mg/L 35 0

Zinc SMCL-US 5,000 μg/L 35 0

1

 Maximum contaminant level benchmarks are listed as MCL-US when the MCL-US and MCL-CA are identical, and as MCL-CA when the MCL-CA is 

lower than the MCL-US or no MCL-US exists. 2

 HAL-US benchmark is 30 mg/L for ammonia, as ammonia. 

3

 Concentrations of nitrate, as nitrate reported in the CDPH database are converted to concentrations of nitrate, as nitrogen, for comparison with USGSGAMA data. 
Methods 13

CDPH data were not used to supplement USGS-grid 

well data for VOCs, pesticides, or perchlorate because a larger 

number of VOCs and pesticide compounds are analyzed 

for the USGS-GAMA Program than are available from the 

CDPH database. USGS-GAMA collected data for 85 VOCs 

plus 63 pesticides and pesticide degradates at every well in 

the SCI study unit, plus an additional 57 polar pesticides and 

degradates analyzed at 7 grid wells (table 1). In addition, 

method detection limits for USGS-GAMA analyses of organic 

constituents were typically one to two orders of magnitude 

lower than the reporting limits for analyses compiled by the 

CDPH (Fram and Belitz, 2012). 

Data Used for Spatially Weighted Calculations

The spatially weighted calculations of aquifer-scale 

proportions of relative-concentrations were made using data 

from the USGS-grid wells, from selected additional wells 

sampled by USGS-GAMA, and from 171 other wells in the 

CDPH database with water-quality data during the 3-year 

interval August 11, 2005, to August 10, 2008. For wells with 

both USGS and CDPH data for the same constituents, only 

the USGS data were used. Water-quality data collected by 

the CDPH are available from the SWRCB’s internet database 

GeoTracker GAMA (website at http://www.waterboards.

ca.gov/gama/geotracker_gama.shtml).

Nineteen additional wells (12 production wells and 

7 monitor wells) were sampled by the USGS to increase 

the sampling density in the SCI study unit to better 

understand specific groundwater-quality issues. These 

“USGS-understanding” wells were numbered with prefixes 

modified from those used for the USGS grid wells (GILU, 

LIVU, or CUYU) (fig. A1; table A1). With the exception of 

four monitoring wells in the LIV study area, these USGSunderstanding wells were screened or open at similar depths 

to the grid wells (tables B1 and B2) and were included in 

the dataset for the spatially weighted calculations. The four 

LIV monitoring wells were not included in the calculations 

of aquifer-scale proportions for the status assessment or 

in statistical tests of correlations for the understanding 

assessment.

Selection of Constituents for Additional 

Evaluation

As many as 133 constituents with benchmarks were 

analyzed in samples from SCI study unit wells; however, 

only a subset of these constituents is identified for additional 

evaluation in this report based on the following criteria:

1. Constituents present at high relative-concentrations in the 

CDPH database within the 3-year interval (August 11, 

2005–August 10, 2008);

2. Constituents present at high or moderate relativeconcentrations in the USGS-grid wells; or

3. Organic constituents with detection frequencies of greater 

than 10 percent in the USGS-grid well dataset for the 

study unit.

Constituents discussed in the understanding assessment 

had high relative-concentrations in greater than 2 percent of the 

primary aquifer system, or were organic constituents detected 

in greater than 10 percent of the USGS-grid well dataset. A 

complete list of the constituents investigated by USGS-GAMA 

in the SCI study unit may be found in the related SCI USGS 

Data Series Report (Mathany and others, 2009).

The CDPH database also was used to identify 

constituents with historically high (but not currently high) 

relative-concentrations. The historical period was defined 

as the earliest record maintained in the CDPH database to 

August 10, 2005 (January 8, 1980–August 10, 2005). 

Constituent concentrations may be historically high, but 

not currently high, because of changes in groundwater quality 

with time or abandonment of wells with high concentrations. 

Historically high concentrations of constituents that do not 

otherwise meet the criteria for additional evaluation are not 

considered representative of potential groundwater-quality 

concerns in the study unit from 2005 to 2008. For the SCI 

study unit, 15 constituents were measured at high relativeconcentrations prior to August 11, 2005 (table 3). Two-thirds 

of the constituents reported at high concentrations during the 

historical period were reported at high concentrations in only 

one well.

Table 2B. Benchmark type and value for organic and specialinterest constituents selected for additional evaluation in the 

status assessment of groundwater quality in the South Coast 

Interior Basins study unit, California Priority Basin Project.

[Benchmark type: MCL-US, USEPA maximum contaminant level; 

MCL-CA, CDPH maximum contaminant level. Benchmark types and values 

as of October 2013. Benchmark units: μg/L, micrograms per liter. Other 

abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater 

Ambient Monitoring and Assessment Program; USEPA, U.S. Environmental 

Protection Agency; CDPH, California Department of Public Health]

Constituent Benchmark 

type

Benchmark 

value

Benchmark 

units

Number 

of grid 

cells with 

USGSGAMA 

data

Bromoform MCL-US 1 80 μg/L 35

Chloroform MCL-US 1 80 μg/L 35

Tetrachloroethene 

(PCE)

MCL-US 5 μg/L 35

Simazine MCL-US 4 μg/L 35

Perchlorate MCL-CA 6 μg/L 35

1

 The MCL-US threshold for trihalomethanes is for the sum of chloroform, 

bromoform, bromodichloromethane, and dibromochloromethane.
14 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Calculation of Aquifer-Scale Proportions

Aquifer-scale proportions are defined as the percentage 

of the area (rather than the volume) of the primary aquifer 

system with concentrations greater than or less than specified 

thresholds relative to regulatory or aesthetic water-quality 

benchmarks. Two statistical approaches were used to evaluate 

the proportions of the primary aquifer system (Belitz and 

others, 2010) in the SCI study unit with high, moderate, or low 

relative-concentrations of constituents relative to benchmarks. 

• Grid-based: One value per grid cell from either USGSgrid or CDPH-grid wells was used to represent the 

primary aquifer system. The proportion of the primary 

aquifer system with high relative-concentrations 

was calculated by dividing the number of grid cells 

represented by a high relative-concentration for a 

particular constituent by the total number of grid 

cells with data for that constituent. Proportions of 

moderate and low relative-concentrations were 

calculated similarly. The grid-based estimate is 

spatially unbiased. However, the grid-based estimate 

may not identify constituents that are present at high 

relative-concentrations in small proportions of the 

primary aquifer system. Confidence intervals for gridbased detection frequencies of high concentrations 

were computed by using the Jeffreys interval for the 

binomial distribution (Brown and others, 2001; Belitz 

and others, 2010). The confidence intervals provide 

a range that is likely (with 90-percent confidence) to 

bracket the true high aquifer-scale proportion. 

• Spatially weighted: All available data from the 

following sources were used to calculate the aquiferscale proportions—all CDPH wells in the study unit 

(most recent analysis from each well with data for 

that constituent during the current period August 11, 

2005–August 10, 2008); USGS-grid wells; and 

Table 3. Constituents reported at concentrations greater than benchmark values in the California Department of Public Health (CDPH)

database between January 8, 1980, and August 10, 2005, South Coast Interior Basins study unit, California GAMA Priority Basin Project.

[High concentrations are concentrations greater than the benchmark value. Benchmark type: MCL-US, USEPA maximum contaminant level; Prop MCL-US, 

proposed USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; SMCL-CA, CDPH secondary maximum contaminant level. 

Benchmark types and values as of October 2013. Benchmark units: μg/L, micrograms per liter; pCi/L, picocuries per liter. Other abbreviations: USEPA, U.S. 

Environmental Protection Agency; CDPH, California Department of Public Health]

Constituent Benchmark 

type

Benchmark 

value

Benchmark 

units

Number of 

wells 

with analysis

Number of wells 

with a high 

concentration

Date of most 

recent high value 

(mm/dd/yyyy)

Trace elements

Aluminum MCL-CA 1,000 μg/L 254 6 7/6/2005

Barium MCL-CA 1,000 μg/L 259 1 7/20/2000

Cadmium MCL-US 5 μg/L 258 4 1/17/2002

Mercury MCL-US 2 μg/L 256 1 4/22/1988

Nickel MCL-CA 50 μg/L 230 2 10/18/2001

Thallium MCL-US 2 μg/L 330 1 3/29/1994

Zinc SMCL-CA 5,000 μg/L 262 2 5/4/1998

Solvents

Carbon tetrachloride MCL-CA 0.5 μg/L 267 1 9/24/1992

1,1,2-Trichloroethane MCL-US 5 μg/L 267 1 9/24/1992

Fumigants

1,2-Dibromoethane (EDB) MCL-US 0.05 μg/L 28 1 8/18/1992

Other VOCs

Benzene MCL-CA 1 μg/L 267 1 12/26/1989

Methyl tert-butyl ether (MTBE) MCL-CA 13 μg/L 226 1 12/13/2004

Uranium and radioactive constituents

Uranium MCL-CA 20 pCi/L 54 2 4/2/1991

Radon-222 Prop. MCL-US 4,000 pCi/L 28 1 8/18/1992

Radium-228 MCL-US 1

 5 pCi/L 50 1 7/7/2004

1

The MCL-US benchmark for radium is for the sum of radium-226 and radium-228.
Methods 15

USGS-understanding wells with perforation depth 

intervals representative of the primary aquifer system. 

The proportion of high relative-concentrations for 

each constituent for the primary aquifer system was 

computed by (1) calculating the proportion of wells 

with high relative-concentrations in each grid cell 

and (2) averaging together the grid-cell proportions 

computed in step 1 (Isaaks and Srivastava, 1989; 

Belitz and others, 2010). Similar procedures were 

used to calculate the proportions of moderate and 

low relative-concentrations of constituents. The 

resulting proportions are spatially unbiased (Isaaks and 

Srivastava, 1989). Confidence intervals for spatially 

weighted estimates of aquifer-scale proportion are not 

described in this report.

Grid-based and spatially weighted aquifer-scale 

proportions for constituent classes were calculated by using 

the maximum relative-concentration for any constituent in the 

class to represent the relative-concentration for the cell (gridbased) or for each well (spatially weighted).

In addition, for each constituent, the raw detection 

frequencies of high and moderate relative-concentrations 

for individual constituents were calculated by using the 

same dataset as used for the spatially weighted calculations. 

However, these raw detection frequencies are not spatially 

unbiased because the wells in the CDPH database are 

not uniformly distributed throughout the SCI study unit 

(figs. 2A–C). For example, if a constituent was present at high 

relative-concentrations in a small region of the aquifer that 

had a high density of wells, the detection frequency of high 

relative-concentrations would be greater than the high aquiferscale proportion. Raw detection frequencies are provided for 

reference but were not used to characterize the groundwater 

resource. 

Aquifer-scale proportions discussed in this report 

primarily were estimated by using the grid-based approach, 

and secondarily by using the spatially weighted approach. The 

grid-based high aquifer-scale proportions were used unless 

the spatially weighted proportions were significantly different 

from the grid-based values. Significantly different results were 

defined as follows:

1. If the grid-based high aquifer-scale proportion was zero 

and the spatially weighted proportion was non-zero, then 

the spatially weighted result was used. This situation can 

happen when the concentration of a constituent is high in 

a small fraction of the primary aquifer system.

2. If the grid-based high aquifer-scale proportion was nonzero and the spatially weighted proportion was outside 

the 90-percent confidence interval (based on the Jeffreys 

interval for the binomial distribution, Brown and others, 

2001), then the spatially weighted proportion was used.

The grid-based moderate and low proportions were used 

in most cases because the reporting levels for many organic 

constituents and some inorganic constituents in the CDPH 

database were higher than the threshold between moderate 

and low categories. However, if the grid-based moderate 

proportion was zero and the spatially weighted proportion 

non-zero, then the spatially weighted value was used as a 

minimum estimate for the moderate proportion. 

Aquifer-scale proportions of high relative-concentrations 

also were determined for classes of constituents. The 

classes of organic constituents for which aquifer-scale 

proportions were calculated were trihalomethanes, solvents, 

and herbicides. The classes of inorganic constituents with 

health-based benchmarks for which aquifer-scale proportions 

were calculated were trace and minor elements, radioactive 

constituents, and nutrients. 

Understanding-Assessment Methods

The potential explanatory factors—land use, well depth, 

depth to the top-of-perforations of the well, groundwaterage class, and geochemical conditions (appendix B)—were 

analyzed in relation to constituents of interest for the 

understanding assessment to establish context for physical 

and chemical processes within the groundwater system. In 

addition, comparisons of groundwater quality between study 

areas are discussed in this report. Statistical tests were used 

to identify significant correlations between the constituents of 

interest and potential explanatory factors. 

The data selected for the understanding assessment varied 

by whether the variables tested were expected to be primarily 

areally distributed (land use and study area) or vertically 

distributed (depth, groundwater age, geochemical conditions). 

Correlations between explanatory factors and groundwater 

constituents were tested using data from the USGS- and 

CDPH-grid wells only, or the USGS- and CDPH-grid wells 

plus USGS-understanding wells. Data from CDPH-other wells 

were not used in the understanding assessment because some 

geochemical and age-dating tracers and well-construction data 

were not available. Because the USGS-understanding wells 

were not randomly selected on a spatially distributed grid, 

these wells were excluded from analysis of relations between 

water-quality constituents and areally distributed explanatory 

variables to avoid areal-clustering bias. For variables 

expected to be primarily vertically distributed, data from 15 

understanding wells that were representative of the primary 

aquifer system (production wells or monitor wells that had 

similar well construction characteristics as the grid wells) were 

included to provide more detailed vertical information on water 

quality across a wider range of depth, age, and geochemical 

conditions in the portions of the aquifer used for public supply 

than were present using the grid data alone.
16 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Statistical Analysis

Nonparametric statistical methods were used to test the 

significance of correlations between water-quality variables 

and potential explanatory factors. Nonparametric statistics 

are robust techniques that are generally not affected by 

outliers and do not require that the data follow any particular 

distribution (Helsel and Hirsch, 2002). The significance level 

(p) used to test hypotheses for this study was compared to a 

threshold value (α) of 5 percent (α = 0.05) to evaluate whether 

the relation was statistically significant (p < α). Throughout 

this report, the term “correlated” is used to indicate that the 

relation was statistically significant.

Two different statistical tests were used because the set 

of potential explanatory factors included both categorical and 

continuous variables. Groundwater age class and oxidationreduction class were treated as categorical variables because 

there were a finite number of values a well could be assigned: 

for example, groundwater ages were classified as modern, 

pre-modern, or mixed. Land use, well depth, depth to topof-perforations, dissolved oxygen, and pH were treated as 

continuous variables because there were an infinite number 

of values a well could be assigned: for example, land use was 

represented by percentages of land-use types. Concentrations 

of water-quality constituents were treated as continuous 

variables.

Relations between potential explanatory factors, and 

between potential explanatory factors and water-quality 

constituents, were tested for significance. Correlations 

between continuous variables were evaluated using the 

Spearman’s rho test to calculate the rank-order coefficient (ρ, 

rho) and the significance level of the correlation (p). Relations 

between categorical variables and continuous variables were 

evaluated using the Wilcoxon rank-sum test. The test was 

applied pair-wise to the groups to determine which pairs 

had significantly different median values of the continuous 

variable. For example, the relation between well depth and 

groundwater age was evaluated by testing for differences in 

median value of well depth between each of the three pairs of 

groundwater age classes: modern and mixed, modern and premodern, and mixed and pre-modern.

Potential Explanatory Factors

Brief descriptions of potential explanatory factors, 

including land use, physical characteristics of the wells, 

indicators of groundwater age, and geochemical conditions 

of the aquifer, are given in this section. Data sources 

and methodology used for assigning values for potential 

explanatory factors are described in appendix B. Apparent 

correlations between an explanatory factor and a water-quality 

constituent could actually reflect correlations between two or 

more explanatory factors. Therefore, it is important to identify 

statistically significant correlations between explanatory 

factors (tables 4A,B). Implications of correlations between 

explanatory variables are discussed later in the report as part 

of the analysis of factors affecting individual constituents. 

Values of potential explanatory factors for the four monitoring 

wells not used in the assessments are listed in the tables 

in appendix B, but these wells are not included in the 

descriptions in this section.

Land Use

Land use was described by three land-use types: urban, 

agricultural, and natural (appendix B). Land classified as 

agricultural made up the greatest percentage (45 percent) of 

the total land area in the study unit, while natural land use was 

42 percent of the area, and urban land use was 13 percent of 

the area (fig. 5A).

Land use around grid wells (radius of 1,640 ft 

[500 meters]) in the SCI study unit was different in the 

three study areas (fig. 5A). A 500-meter (m) buffer centered 

on the well has been shown to be effective at correlating 

urban land use with VOC occurrence for the purposes of 

statistical characterization (Johnson and Belitz, 2009). In the 

Livermore study area, average land use around grid wells 

was 63 percent urban, 24 percent agricultural, and 13 percent 

natural (figs. 5A,B, 6A). In the Gilroy study area, average 

land use around grid wells was 64 percent agricultural, 

23 percent urban, and 13 percent natural (figs. 5A,B, 6B). 

In the Cuyama study area, average land use around grid 

wells was 54 percent natural, 35 percent agricultural, and 11 

percent urban (figs. 5A,B, 6C). In the SCI study unit, average 

land use around grid wells was 44 percent agricultural, 29 

percent urban, and 27 percent natural land use (figs. 5A,B). 

The higher percentage of urbanized land surrounding the grid 

wells (29 percent)—compared to the land in the entire study 

unit (13 percent)—reflects the association of public-supply 

wells with population density. Agricultural lands are mostly 

orchards, vineyards, or pastures. The largest urban areas are 

the cities of Livermore and Gilroy.

Well Depth and Depth to Top-of-Perforations

Well construction information was available for 24 of the 

35 grid wells sampled in the SCI study unit. Grid wells had 

depths ranging from 93 to 800 ft below LSD; the median was 

455 ft below LSD (fig. 7; table B2). Depths to the tops of the 

perforated interval ranged from 37 to 640 ft below LSD, with 

a median of 200 ft below LSD. The perforation length was 

up to 501 ft with a median of 220 ft. The median depth to the 

tops of the perforated intervals and median perforation lengths 

of the USGS-understanding wells used in the understanding 

assessment are similar to those of the USGS-grid wells (fig. 7).
Methods 17

B

20

40

60

80

100

0

PERCENT URBAN

100 80 60 40 20 0

PERCENT AGRICULTURE

100

80

60

40

20

0

PERCENT NATURAL

SOUTH COAST INTERIOR BASINS

STUDY UNIT

Livermore

Gilroy

Cuyama

STUDY AREA

UNDERSTANDING WELLS

A

20

40

60

80

100

0

PERCENT URBAN

100 80 60 40 20 0

PERCENT AGRICULTURE

100

80

60

40

20

0

PERCENT NATURAL

Livermore

Gilroy

Cuyama

SOUTH COAST INTERIOR BASINS

STUDY UNIT

Data point:

Land use

based on

satellite

imagery

Data point:

Land use

based on a

500-meter-radius

area around

grid wells

STUDY AREA

STUDY UNIT

IP026177_Figure 05ab

Figure 5. Percentage of urban, agricultural, and natural land use in (A) the study unit and study areas, and (B) the area 

surrounding each grid and understanding well in the South Coast Interior Basins study unit, California GAMA Priority 

Basin Project.
18 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Figure 6A,B,C. Land use in the South Coast Interior Basins study unit, California GAMA Priority Basin Project.

IP026177_fig 06abc

5

5

California

Aqueduct

Castac

Lake

Cuyama River

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Stream

Livermore

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Urban

Natural

Agricultural

LAND-USE CLASSIFICATION

USGS-grid

 well

USGS-

 understanding

 well

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

A B

C
Methods 19

Well

depth

Top-ofperforationsPerforation

length

Well

depth

Top-ofperforationsPerforation

length

Grid wells

24

Understanding wells

19 19 14 12 9

1,000

800

600

400

200

0

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

19 Number of wells with data

75th percentile

Median

25th percentile

Data point that is outside of

1.5 × IQR of the 25th percentile

Nearest data point that is within

1.5 × IQR of the 75th percentile

Nearest data point that is within

1.5 × IQR of the 25th percentile

Interquartile

range (IQR)

EXPLANATION

IP026177_Figure 07_box plot

Figure 7. Well depths, depths to top-of-perforations, and perforation lengths for grid and understanding wells, South Coast Interior 

Basins study unit, California GAMA Priority Basin Project.
20 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Groundwater Age

Groundwater samples were assigned age classifications 

based on the tritium and carbon-14 content of the samples 

(appendix B). Age classifications were assigned to the 

50 USGS-grid and understanding well groundwater samples; 

26 were classified as modern (recharged after 1952), 12 were 

mixed (having a component of both modern and pre-modern 

waters), 1 was modern or mixed, and 11 were pre-modern 

(recharged before 1952) (table B3).

Classified groundwater ages were used to create a 

3-factor classification system for well depth. Wells of depth 

less than a critical depth were defined as shallow; wells with 

depth to top-of-perforations beginning above the critical depth 

and ending below the critical depth were defined as mixed; 

and wells with depth of perforations entirely below the critical 

depth were defined as deep. The critical depth of 250 ft below 

LSD was selected by optimizing the segregation of modernage groundwater into shallow wells, mixed-age groundwater 

into mixed-depth wells, and pre-modern-age groundwater into 

deep wells (fig. 8).

Geochemical Conditions

An abridged classification of oxidation-reduction 

(redox) conditions adapted from the framework presented by 

McMahon and Chapelle (2008) was used to classify redox 

conditions for the 50 USGS-grid and understanding well 

samples (table B4). Groundwater samples were classified 

as oxic, anoxic, or mixed (anoxic/oxic) (Jurgens and others, 

2009). Groundwater was oxic in 80 percent of the wells, 

anoxic in 16 percent of the wells, and mixed in 4 percent 

of the wells. Dissolved oxygen (DO) concentrations were 

significantly greater in mixed-age or modern-age groundwater 

compared with premodern-age groundwater (table 4B). 

DO concentrations were also significantly greater in the 

CUY study area than in the GIL study area. 

The pH values ranged from 6.7 to 7.9 in the USGS-grid 

wells and USGS-understanding wells (table B4) and were 

significantly greater in pre-modern-age groundwater compared 

with mixed-age or modern-age groundwater (table 4B). Values 

of pH were not significantly different between wells with 

modern-age groundwater and mixed-age groundwater. pH 

showed a negative correlation with DO (table 4A).

NUMBER OF WELLS

0 2 4 6 8 10 12

DEPTH OF PERFORATIONS BELOW LAND SURFACE

Entire perforation interval

is within 250 feet of land

surface

Entire perforation interval

is equal to or greater than

250 feet of land surface

Perforation interval

is unknown

Top of perforation interval

less than 250 feet;

Bottom of perforation

interval equal to or greater

than 250 feet

Modern

Mixed

Pre-modern

GROUNDWATER AGE

 CLASSIFICATION

EXPLANATION

Figure 8. Age classification in relation to the depth of well perforations, South Coast Interior Basins study unit, California GAMA 

Priority Basin Project.

Table 4A. Results of non-parametric (Spearman’s rho method) 

analysis of correlations in grid and understanding wells between 

selected potential explanatory factors, South Coast Interior Basins 

study unit, California GAMA Priority Basin Project.

[Spearman’s correlation statistic (ρ) shown for correlations determined to be 

significant on the basis of p-values (not shown) less than threshold value (α) 

of 0.05. nc, no significant correlation]

Explanatory factor

Depth 

to top of 

perforations

Depth 

of 

well

Dissolved 

oxygen 

concentration

pH

Grid wells

Percent urban land 

use

nc nc nc nc

Percent agricultural 

land use

nc nc nc nc

Percent natural land 

use

nc nc nc 0.43

Grid and understanding wells

Depth to top of 

perforations

0.51 nc nc

Depth of well nc nc

Dissolved oxygen 

concentration

–0.31

pH
Table 4B. Results of Wilcoxon rank-sum tests for differences in values of selected potential explanatory factors between samples classified by groucondition, study area, and well depth, South Coast Interior Basins study unit, California GAMA Priority Basin Project.[Wilcoxon rank-sum test statistic (Z) values shown for differences determined to be significant on the basis of p-values (not shown) less than threshold value (α) of 0.05. Positivthe median value for the factor in samples classified in the first group is significantly greater than the median concentration of the constituent in samples classified in the second of Z indicate that the median concentration of the constituent in samples classified in the first group is significantly less than the median concentration of the constituent in sampgroup. Well depth classification: shallow, well depth < 250 ft; overlapping, top-of-perforations < 250 ft and well depth > 250 ft; deep, well depth > 250 ft and top-of-perforationclassification: modern, sample with water recharged after 1952; pre-modern, sample with water recharged before 1952; mixed age, sample with modern and pre-modern compono significant difference (p-value > 0.05); ≤, less than or equal to; >, greater than; <, less than]

Selected 

water-quality 

constituents

Potential explanatory factors

Groundwater age 

classification

Geochemical conditions 

classification

Study 

area

Modern age 

compared 

with 

mixed age

Mixed age 

compared 

with premodern age

Modern age 

compared 

with premodern age

Oxic 

compared 

with 

mixed 

Mixed 

compared 

with 

anoxic 

Oxic 

compared 

with 

anoxic 

Cuyama 

compared 

with 

Gilroy

Cuyama 

compared 

with 

Livermore

Gilroy 

compared 

with 

Livermore

Shallow 

compared 

with 

overlappin

Z: Test statistic for Wilcoxon test

Percent urban land 

use

nd nd nd nd nd nd –2.71 –2.96 –2.28 nd 

Percent agricultural 

land use

nd nd nd nd nd nd nd nd 2.70 nd 

Percent natural 

land use

nd nd nd nd nd nd 2.46 nd nd nd 

Depth to top-ofperforations nd nd nd nd nd nd nd nd nd –2.65 

Depth of well nd nd –2.29 nd nd nd nd nd nd –3.23 

Dissolved oxygen nd 2.61 3.24 1.98 2.10 3.14 2.04 nd nd nd 

pH nd –2.64 –3.24 nd nd nd nd nd nd nd 
22 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Status and Understanding of 

Water Quality 

The status assessment was designed to identify the 

constituents or classes of constituents most likely to be 

of water-quality concern because of their high relativeconcentrations or their prevalence. USGS sample analyses, 

plus additional data from the CDPH database, were included 

in the assessment of groundwater quality for the SCI study 

unit. The spatially distributed, randomized approach to gridwell selection and data analysis yields a view of groundwater 

quality in which all areas of the primary aquifer system are 

weighted equally; regions with a high density of groundwater 

use or with high density of potential contaminants were not 

preferentially sampled or represented (Belitz and others, 

2010). The understanding assessment identifies the natural and 

human factors that may affect water quality in the SCI study 

unit and focuses on the constituents selected for additional 

evaluation in the status assessment.

The following discussion of the status and understanding 

assessment results is divided into inorganic, organic, and 

special-interest constituents. The status assessment begins 

with a survey of how many constituents were detected at 

any concentration compared to the number analyzed, and 

with a graphical summary of the relative-concentrations of 

constituents detected in the grid wells. Results are presented 

for the subset of constituents that met criteria for selection for 

additional evaluation based on concentration, or for organic 

constituents, prevalence (see Selection of Constituents for 

Additional Evaluation). 

The aquifer-scale proportions calculated by using the 

spatially weighted approach were within the 90-percent 

confidence intervals for their respective grid-based aquifer 

high proportions for all of the constituents selected for 

additional evaluation in the status assessment, providing 

evidence that the grid-based and spatially weighted approaches 

yield statistically equivalent results.

Inorganic Constituents

Inorganic constituents generally occur naturally in 

groundwater, although their concentrations may be affected 

by human factors. All but 1 of the 44 inorganic constituents 

analyzed by the USGS-GAMA Priority Basin Project were 

detected in the SCI study unit; 32 of the 44 had health-based 

or aesthetic-based benchmarks (table 5). Nine inorganic 

constituents with health-based benchmarks (the trace and minor 

elements arsenic, boron, chromium, fluoride, molybdenum, 

strontium, and selenium, the radioactive constituent gross alpha 

particle activity, and the nutrient nitrate) and five inorganic 

constituents with aesthetic-based benchmarks (the trace metals 

iron and manganese and the salinity indicators chloride, 

sulfate, and TDS) met the selection criterion of having 

maximum relative-concentrations greater than 0.5 (moderate 

or high) in the grid wells (figs. 9, 10; table 6). Inorganic 

constituents with health-based benchmarks, as a group, had 

high relative-concentrations in 29 percent of the primary aquifer 

system, moderate relative-concentrations in 37 percent, and 

low relative-concentrations in 34 percent (table 7A). Inorganic 

constituents with aesthetic-based benchmarks, as a group, had 

high relative-concentrations in 49 percent of the primary aquifer 

system, moderate relative-concentrations in 31 percent, and low 

relative-concentrations in 20 percent.

Table 5. Number of constituents analyzed and detected by the 

U.S. Geological Survey by constituent class and benchmark type, 

South Coast Interior Basins study unit, California GAMA Priority 

Basin Project, August–December 2008.

Benchmark type

Number of Constituents

Analyzed Detected 

Volatile organic compounds

Regulatory health-based 33 7

Non-regulatory health-based 25 1

No benchmark 27 0

Total: 85 8

Pesticides

Regulatory health-based 11 3

Non-regulatory health-based 28 2

No benchmark 81 1

Total: 120 6

Special interest

Regulatory health-based 1 1

Non-regulatory health-based 1 0

No benchmark 0 0

Total: 2 1

Inorganic

Regulatory health-based 19 18

Non-regulatory health-based 5 5

Non-regulatory aesthetic-based 8 8

No benchmark 12 12

Total: 44 43

Geochemical and age-dating tracers

Regulatory 2 2

Non-regulatory 0 0

No benchmark 16 16

Total: 18 18

All constituents

Regulatory health-based 66 31

Non-regulatory health-based 59 8

Non-regulatory aesthetic-based 8 8

No benchmark 136 29

Total: 269 76
Table 6. Aquifer-scale proportions from grid-based and spatially weighted approaches for constituents with (1) high relative-concentrations during August 10, 2008, from the California Department of Public Health (CDPH) database, (2) moderate or high relative-concentrations in samples collected f

(August–December 2008), or (3) detection frequencies of organic constituents greater than 10 percent in USGS-grid wells, South Coast Interior Basins GAMA Priority Basin Project.

[Relative-concentration categories: high; concentrations of inorganic or organic or special-interest constituents greater than water-quality benchmark; moderate, concentrationthan or equal to 0.5 of benchmark but less than benchmark or concentrations of organic or special-interest constituents greater than or equal to 0.1 of benchmark but less than beinorganic constituents less than 0.5 of benchmark or concentrations of organic or special-interest constituents less than 0.1 of benchmark or not detected. Benchmark types and vtables 2A,B]

Constituent

Raw detection frequency1 Spatially weighted aquifer- scale proportion1 Grid-based aquifer-scale proportion 

Number 

of 

wells

Moderate 

values 

(percent)

High 

values 

(percent)

Number 

of cells

Moderate 

values 

(percent)

High 

values 

(percent)

Number 

of 

wells

Moderate 

values 

(percent)

High 

values 

(percent)

Inorganic constituents with health-based benchmarks

Trace and minor elementsArsenic 137 4.4 3.6 35 3.3 5.3 35 8.6 5.7 

Boron 63 16 11 35 14 9.2 35 11 8.6 

Chromium 135 1.5 0.7 35 0.8 1.0 35 2.9 0 

Fluoride 144 7.6 1.4 35 3.2 0.4 35 5.7 0 

Molybdenum 55 5.5 5.5 35 1.7 8.6 35 0 8.6 

Selenium 136 0.7 0 35 0.6 0 35 2.9 0 

Strontium 50 6.0 0 35 8.6 0 35 8.6 0 

Uranium and radioactive onstituentsGross alpha3 107 10 4.7 24 12 1.2 22 18 0 

NutrientsNitrate 191 21 7.9 35 14 16 35 14 14 

Inorganic constituents with aesthetic-based benchmarks

Chloride 130 2.3 0 35 2.6 0 35 2.9 0 

Sulfate 128 9.4 7.8 35 14 23 35 8.6 26 

TDS 131 33 12 35 42 27 35 43 29 

Iron 132 3.8 6.1 35 6.5 4.8 35 5.7 0 

Manganese 132 3.8 16 35 3.9 18 35 5.7 23 
Constituent Raw detection frequency1 Spatially weighted aquifer- scale proportion1 Grid-based aquifer-scale proportion 

Number of wells Moderate values (percent) High values (percent) Number of cells Moderate values (percent) High values (percent) Number of wells Moderate values (percent) High values (percent) 

Organic constituents Trihalomethanes Bromoform 126 0.8 0.8 35 0.3 0.7 35 0 0 

Chloroform 123 0 0 35 0.0 0.0 34 0 0 

Solvents Tetrachloroethene (PCE) 126 4.0 1.6 35 2.0 0.9 35 0 0 

Herbicides Simazine 112 0 0 35 0 0 35 0 0 

Special-interest constituents Perchlorate 84 19 1.2 35 24 0.3 35 34 0 

1 Based on most recent analysis for each CDPH well between August 11, 2005, and August 10, 2008, combined with USGS-GAMA grid-based data. 2 Based on the Jeffreys interval for the binomial distribution (Brown and others, 2001). 3 Gross alpha activities were not adjusted for uranium activity. The MCL-US for gross alpha activity applies to adjusted gross alpha activity. Table 6. Aquifer-scale proportions from grid-based and spatially weighted approaches for constituents with (1) high relative-concentrations during 

August 10, 2008, from the California Department of Public Health (CDPH) database, (2) moderate or high relative-concentrations in samples collected f(August–December 2008), or (3) detection frequencies of organic constituents greater than 10 percent in USGS-grid wells, South Coast Interior Basins GAMA Priority Basin Project.—Continued [Relative-concentration categories: high; concentrations of inorganic or organic or special-interest constituents greater than water-quality benchmark; moderate, concentrationthan or equal to 0.5 of benchmark but less than benchmark or concentrations of organic or special-interest constituents greater than or equal to 0.1 of benchmark but less than beinorganic constituents less than 0.5 of benchmark or concentrations of organic or special-interest constituents less than 0.1 of benchmark or not detected. Benchmark types and vtables 2A,B]
Status and Understanding of Water Quality 25

Table 7A. Aquifer-scale proportions for inorganic constituent 

classes, South Coast Interior Basins study unit, California GAMA 

Priority Basin Project.

[Relative-concentration categories: high, concentrations of constituents 

greater than water-quality benchmark; moderate, concentrations of 

constituents greater than or equal to 0.5 of benchmark but less than 

benchmark; low, concentrations of constituents less than 0.5 of benchmark. 

Values are grid-based except where footnoted]

Constituent class

Aquifer-scale proportion

Low relativeconcentration 

(percent)

Moderate 

relativeconcentration 

(percent)

High relativeconcentration 

(percent)

Inorganics with health-based benchmarks

Trace and minor 

elements

57 23 20

Uranium and 

radioactive 

constituents1

82 18 0

Nutrients 71 14 14

Any inorganic 

with healthbased 

benchmarks1

34 37 29

Inorganics with aesthetic-based benchmarks

Salinity 

indicators2

29 40 31

Manganese and 

(or) iron

71 5.7 23

Any inorganic 

with aestheticbased 

benchmarks

20 31 49

1

 Aquifer-scale proportions for the classes uranium and radioactive 

constituents and all inorganic constituents with health-based benchmarks were 

calculated using unadjusted gross alpha activity.

2

 Salinity indicators are chloride, sulfate, and total dissolved solids.

Table 7B. Aquifer-scale proportions for organic constituent 

classes, South Coast Interior Basins study unit, California GAMA 

Priority Basin Project.

[Relative-concentration categories: high, concentrations of constituents 

greater than water-quality benchmark; moderate, concentrations of 

constituents greater than or equal to 0.1 of benchmark but less than 

benchmark; low, concentrations of constituents less than 0.1 of benchmark. 

Values are grid-based except where footnoted]

Constituent class

Low relativeconcentration 

(percent)

Moderate 

relativeconcentration

(percent)

High 

relativeconcentration

(percent) Not 

detected

Detected 

low

Trihalomethanes 88 11 0 1

 0.7

Solvents 89 8.3 1

 2.0 1 0.9

Herbicides 80 20 0 0

Any organic with 

health-based 

benchmarks

65 31 1

 2.0 1 1.6

1

 Spatially weighted value. All other values are grid-based.
26 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Moderate

Low

Moderate

Low

High High

Organic and specialinterest constituentsInorganic

constituents

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Volatile

organic

compounds

Pesticides Special

interest

Nutrients Trace

elements

Major and

minor ions

and trace

elementsSMCL

Uranium and

radioactive

constituents

0.001

0.01

0.1

1

10

Simazine

Atrazine

Perchlorate

Tetrachloroethene (PCE)

Carbon tetrachloride

Chloroform (THM)

Bentazon

Nitrite-N

Ammonia-N

Nitrate-N

TDS

Sulfate

Iron

Manganese

Chloride Fluoride

Molybdenum

Boron

Lead

Zinc

Copper

Gross alpha

Gross beta

Radon-222

Uranium

Vanadium

Chromium

Antimony

Selenium

Beryllium

Arsenic

Aluminum

Barium

Nickel

Strontium

1,1-Dichloroethene

Cadmium

Copper Name and center of symbol is the maximum relative-concentration for that constituent-

 Unless indicated by following location line: (35 grid wells sampled) 

Name and center of symbol is the maximum relative-concentration for that constituent-

 Unless indicated by following location line: (3 to 22 grid wells sampled) 

EXPLANATION

IP026177_Figure 09

Radon-222

Number of 

compounds

detected

< 0.001

2 2 0 0 0 0 0

Figure 9. Maximum relative-concentration of constituents detected in grid wells, by constituent class, South Coast Interior Basins 

study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 27

0.001

0.01

0.1

1

10

RELATIVE-CONCENTRATION, DIMENSIONLESS

Arsenic Boron Chromium Fluoride Molybdenum Selenium Strontium

A

IP026177_Figure 10a

Gross alpha Nitrate

Cuyama

Gilroy

Livermore

CONSTITUENTS THAT HAVE HEALTH-BASED THRESHOLDS

(MAXIMUM CONTAMINANT LEVEL, HEALTH

ADVISORY LEVEL, NOTIFICATION LEVEL) 

STUDY AREA

SOUTH COAST INTERIOR BASINS

RELATIVE-CONCENTRATION

High

Moderate

Low

Figure 10. Relative-concentrations of (A) arsenic, boron, chromium, fluoride, molybdenum, selenium, strontium, gross alpha 

radioactivity, and nitrate with health-based benchmarks, and (B) total dissolved solids, chloride, iron, manganese, and sulfate with 

aesthetic benchmarks in USGS- and CDPH-grid wells, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
28 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Figure 10. —Continued

IP026177_Figure 10b

0.001

0.01

0.1

1

10

RELATIVE-CONCENTRATION, DIMENSIONLESS

B

Chloride Iron Manganese Sulfate Total

dissolved

solids

CONSTITUENTS THAT HAVE SECONDARY

MAXIMUM CONTAMINANT LEVELS

Cuyama

Gilroy

Livermore

STUDY AREA

SOUTH COAST INTERIOR BASINS

RELATIVE-CONCENTRATION

High

Moderate

Low

Trace Elements

Trace elements, as a class, were detected at high relativeconcentrations (for one or more constituents) in 20 percent 

of the primary aquifer system, moderate values in 23 percent, 

and low values in 57 percent (table 7A). Arsenic, boron, and 

molybdenum were detected at high relative-concentrations 

in more than 2 percent of the primary aquifer system, 

and chromium and fluoride were detected at high relative 

concentrations in less than or equal to 1 percent (spatially 

weighted) of the primary aquifer system (table 6). 

Relative-concentrations for some trace elements—

aluminum, barium, cadmium, mercury, nickel, thallium, and 

zinc—were high in at least one well as reported in the CDPH 

database before August 11, 2005, but not during the current 

period of study (August 11, 2005–August 10, 2008) (table 3); 

these high values represent historical values rather than current 

values.

Understanding Assessment for Arsenic

Arsenic is a naturally occurring semi-metallic trace 

element. Potential sources of arsenic to groundwater are 

both natural and anthropogenic. Natural sources include the 

dissolution of arsenic-rich minerals such as arsenopyrite 

(FeAsS), a common constituent of shales, and apatite, a 

common constituent of phosphorites. Arsenic also can be 

used as a wood preservative, in glass production, in paints, 

dyes, metals, drugs, soaps, and semi-conductors, and in the 

mining of copper and gold (Welch and others, 2000). Arsenic 

solubility increases with increasing water temperature, 

such that hydrothermal fluids often exhibit higher arsenic 

concentrations (Ballantyne and Moore, 1988; Webster and 

Nordstrom, 2003), as do older groundwaters that have had 

extended exposure to arsenic-bearing minerals. Previous 

investigations and literature reviews of arsenic have indicated 

two mechanisms for elevated arsenic concentrations in 
Status and Understanding of Water Quality 29

groundwater: the release of arsenic resulting from reductive 

dissolution of iron or manganese oxyhydroxides under 

anoxic conditions and the desorption of arsenic from these 

oxyhydroxides under high-pH, oxic conditions (for example, 

Welch and others, 2000; Smedley and Kinniburgh, 2002; 

Belitz and others, 2003; Stollenwerk, 2003).

Arsenic has an MCL-US of 10 micrograms per liter 

(μg/L) and was detected at high relative-concentrations in 

5.7 percent of the primary aquifer system and at moderate 

relative-concentrations in 8.6 percent (table 6). High and 

moderate relative-concentrations occurred in the CUY and 

GIL study areas (fig. 11).

Arsenic concentrations were negatively correlated to 

DO (table 8A), and concentrations were significantly lower 

in samples having a redox classification of oxic than in those 

samples classified as mixed (table 8B). Arsenic concentrations 

Table 8A. Results of non-parametric (Spearman’s rho method) analysis for correlations between selected water-quality constituents 

and potential explanatory factors, South Coast Interior Basins study unit, California GAMA Priority Basin Project.

[Spearman’s correlation statistic (ρ) shown for correlations determined to be significant on the basis of p-values (not shown) less than threshold value (α) of 

0.05. nc, no significant correlation]

Selected 

water-quality 

constituent

Potential explanatory factors

Grid and understanding wells combined Grid wells

Well 

depth

Depth to top-ofperforations pH Dissolved 

oxygen 

Percent urban 

land use1

Percent agricultural 

land use1

Percent natural 

land use1

ρ: Spearman’s correlation statistic

Inorganic constituents

Arsenic nc nc 0.32 –0.40 nc –0.34 nc

Boron nc nc nc nc nc nc nc

Molybdenum nc nc 0.43 –0.28 nc nc 0.46

Nitrate nc nc –0.32 0.52 nc nc –0.53

Total dissolved solids (TDS) nc nc nc nc –0.39 nc nc

Sulfate nc nc nc nc –0.46 nc 0.41

Manganese nc nc nc –0.52 nc nc nc

Organic constituents

Chloroform nc –0.28 nc nc 0.52 –0.36 nc

Simazine nc nc –0.35 nc nc nc nc

Special-interest constituent

Perchlorate nc nc –0.29 0.46 nc nc –0.53

1

 Land-use percentages are within a radius of 500 meters centered around each well included in analysis. 

were positively correlated with manganese concentrations 

(Spearman’s test, p=0.003, ρ=0.40), but were not correlated 

with iron concentrations (Spearman’s test, p=0.129). 

These relations are consistent with release of arsenic by 

reductive dissolution of manganese oxyhydroxides. Arsenic 

concentrations in samples having a groundwater age classified 

as pre-modern were significantly higher than in samples 

classified as having modern or mixed ages (table 8B). Arsenic 

concentrations were not correlated to well depth, despite the 

significantly greater well depths and lower DO concentrations 

in wells with pre-modern groundwater compared to wells with 

modern groundwater (table 4B).
Table 8B. Results of Wilcoxon rank-sum tests to determine significant differences between selected water-quality constituents grouped by explanaSouth Coast Interior Basins study unit, California GAMA Priority Basin Project. [Wilcoxon rank-sum test statistic (Z) values shown for differences determined to be significant on the basis of p-values (not shown) less than threshold value (α) of 0.05. Positivmedian concentration of the constituent in samples classified in the first group is significantly greater than the median concentration of the constituent in samples classified in the values of Z indicate that the median concentration of the constituent in samples classified in the first group is significantly less than the median concentration of the constituent igroup. Well depth classification: shallow, well depth < 250 ft; overlapping, top-of-perforations < 250 ft and well depth > 250 ft; deep, well depth > 250 ft and top-of-perforationclassification: modern, sample with water recharged after 1952; pre-modern, sample with water recharged before 1952; mixed age, sample with modern and pre-modern compono significant difference (p-value > 0.05); ≤, less than or equal to; >, greater than; <, less than] Selected water-quality constituent Potential explanatory factors Groundwater age classification Geochemical conditions classification Study area 

Modern compared with mixed Mixed compared with pre- modern Modern compared with pre- modern Oxic compared with mixed Mixed compared with anoxic Oxic compared with anoxic Cuyama compared with Gilroy Cuyama compared with Livermore Gilroy compared with Livermore Shallow compared with overlappin

Z: Test statistic for Wilcoxon test Arsenic nd –2.17 –2.78 –2.22 nd –2.26 nd nd nd nd 

Boron nd nd nd 1.98 nd nd nd –3.32 nd nd 

Molybdenum nd –2.18 –3.16 nd nd –2.59 nd 2.36 nd nd 

Nitrate nd nd 2.66 nd nd 2.82 nd nd nd nd 

Total dissolved solids nd nd nd nd nd nd 2.75 2.29 nd 2.36 

Sulfate nd nd nd nd nd nd 3.87 3.23 nd nd 

Manganese nd nd –2.49 –2.36 nd –3.18 nd nd nd nd 

Chloroform nd nd nd nd nd nd nd –2.06 –2.99 nd 

Simazine nd nd 2.01 nd nd nd nd –2.52 nd nd 

Perchlorate nd nd 2.87 nd nd 3.18 nd nd nd nd 
Status and Understanding of Water Quality 31

IP026177_fig 11abc

5

5

California

Aqueduct

Castac

Lake

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

Cuyama River

99

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO 101

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Livermore Stream

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Fault

Franciscan complex

Tertiary marine sediment

Metamorphic rocks

Metasediment

Ultramafic/mafic

Plio-Pleistocene sediment

Quaternary alluvium

SEDIMENTARY ROCKS

OTHER

Quaternary other sediment

Tertiary nonmarine sediment

Tertiary, mafic volcanics

Granitic rocks

USGS-grid well 

USGS-understanding well 

CDPH-other well 

ARSENIC

Measured concentration,

in micrograms per liter

Relative-concentration

Low

Moderate

High

5.1 to 10

< 5.0

> 10

< 5.0

< 5.0

5.1 to 10

> 10

A B

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

C

Figure 11. Relative-concentrations of arsenic for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and for California 

Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the (A) Livermore, 

(B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
32 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Understanding Assessment for Boron

Boron is a naturally occurring semi-metallic element that 

occurs in many minerals. Natural sources of boron include 

igneous rocks such as granite and pegmatite (as the mineral 

tourmaline), and evaporite minerals such as borax, kernite 

and colemanite (Hem, 1985; Reimann and de Caritat, 1998). 

Borax, a boron-containing evaporate mineral that is mined in 

California, is used as a cleaning agent and therefore may be 

present in sewage and industrial wastes. Seawater contains 

4.5 milligrams per liter (mg/L) of boron (Summerhayes and 

Thorpe, 1996), and boron also is associated with thermal 

springs (Hem, 1985; Kulongoski and others, 2010). Boron also 

is used to produce semiconductors, insecticides, preservatives, 

and chemical reagents. Boron is highly mobile because no 

mineral has a low enough solubility to provide an upper limit 

to its concentration range.

Boron has an NL-CA of 1,000 μg/L and was detected 

at high relative-concentrations in 8.6 percent of the primary 

aquifer system and at moderate relative-concentrations 

in 11 percent (table 6). High and moderate relativeconcentrations occurred in all three study areas (fig. 12). 

Boron concentrations were significantly greater in the 

LIV study area than in the CUY study area (table 8B). Boron 

concentrations are known to be elevated in the LIV study area 

due to marine sediments neighboring the groundwater basin 

(California Department of Water Resources, 2006). Boron 

concentrations were greater in oxic groundwater compared 

with mixed groundwater (table 8B).

Understanding Assessment for Molybdenum

Molybdenum is a trace element that is found in the major 

ore mineral molybdenite, and to a lesser extent, in the mineral 

wulfenite (Evans and Barabash, 2010). High concentrations 

of molybdenum also are found in shales and phosphate 

deposits formed in strongly reducing marine environments (for 

example, Krauskopf, 1955). Industrial uses of molybdenum 

include steel and iron alloys, electrodes, and catalysts. 

Molybdenum commonly is present in biosolids, such as 

sewage sludges, and can accumulate in vegetation and forage 

crops (particularly legumes) grown on lands fertilized with 

these materials (Evans and Barabash, 2010). 

Molybdenum has a HAL-US of 40 μg/L and was detected 

at high relative-concentrations in 8.6 percent of the primary 

aquifer system and at moderate relative-concentrations in 

1.7 percent (spatially weighted) (table 6). High and moderate 

relative-concentrations occurred only in the western and 

eastern parts of the CUY study area, respectively (fig. 13). 

Molybdenum concentrations were significantly greater in 

pre-modern-age groundwater compared to modern or mixedage groundwater, and greater in anoxic groundwater compared 

to oxic groundwater (table 8B). Similarly, molybdenum 

was negatively correlated with dissolved oxygen (table 8A). 

Molybdenum was positively correlated with pH and 

percentage of natural land use; the CUY study area has the 

greatest percentage of natural land.

Uranium and Radioactive Constituents

All concentrations of radioactive constituents 

in samples from grid wells were below health-based 

benchmarks (table 7A). Moderate relative-concentrations 

of gross alpha were detected in 18 percent of the primary 

aquifer system (table 6). None of the wells with moderate 

relative-concentrations of gross alpha had moderate relativeconcentrations of uranium or radon-222.

The radioactive constituents radon-222, radium-228, and 

uranium had high relative-concentrations in at least one well 

reported in the CDPH database before August 11, 2005, but 

not during the current period of study (table 3).

Nutrients

Nutrients, as a class, were detected at high relativeconcentrations in 14 percent of the primary aquifer system and 

at moderate relative-concentrations in 14 percent (table 6). 

High and moderate relative-concentrations of nitrate occurred 

in all three study areas (figs. 10A, 14).

Understanding Assessment for Nitrate

Nitrate has both natural and anthropogenic sources to 

groundwater; however, concentrations greater than 2 mg/L 

(as nitrogen) (relative-concentration of 0.2) generally are 

considered to indicate presence of nitrate from anthropogenic 

sources (Mueller and Helsel, 1996). Potential anthropogenic 

sources of nitrate include use of fertilizers in agricultural and 

urban areas, nitrate in water used for engineered recharge, 

seepage from septic and sewage systems, and animal and 

human wastes.

Nitrate concentrations were significantly higher in 

modern groundwater than in pre-modern groundwater and 

in oxic groundwater than in anoxic groundwater (table 8B). 

Nitrate was also positively correlated with dissolved oxygen 

and negatively correlated with pH and percentage of natural 

land use (table 8A). Some of the explanatory variables related 

to nitrate, such as dissolved oxygen and groundwater age, are 

generally related to each other (table 4A).
Status and Understanding of Water Quality 33

IP026177_fig 12abc

5

5

California

Aqueduct

Castac

Lake

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

Cuyama River

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Livermore Stream

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Fault

Franciscan complex

Tertiary marine sediment

Metamorphic rocks

Metasediment

Ultramafic/mafic

Plio-Pleistocene sediment

Quaternary alluvium

SEDIMENTARY ROCKS

OTHER

Quaternary other sediment

Tertiary nonmarine sediment

Tertiary, mafic volcanics

Granitic rocks

USGS-grid well 

USGS-understanding well 

CDPH-other well 

BORON

Measured concentration,

in micrograms per liter

Relative-concentration

Low

Moderate

High

501 to 1,000

< 500

> 1,000

501 to 1,000

< 500

> 1,000

< 500

501 to 1,000

> 1,000

A B

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

C

Figure 12. Relative-concentrations of boron for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and for California 

Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the (A) Livermore, 

(B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
34 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

IP026177_fig 13abc

5

5

California

Aqueduct

Castac

Lake

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

Cuyama River

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Livermore Stream

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Fault

Franciscan complex

Tertiary marine sediment

Metamorphic rocks

Metasediment

Ultramafic/mafic

Plio-Pleistocene sediment

Quaternary alluvium

SEDIMENTARY ROCKS

OTHER

Quaternary other sediment

Tertiary nonmarine sediment

Tertiary, mafic volcanics

Granitic rocks

USGS-grid well 

USGS-understanding well 

MOLYBDENUM

Measured concentration,

in micrograms per liter

Relative-concentration

Low

Moderate

High

< 20.0

> 40

21 to 40

< 20.0

A B

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

C

Figure 13. Relative-concentrations of molybdenum for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and 

for California Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the 

(A) Livermore, (B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 35

IP026177_fig 14abc

5

5

California

Aqueduct

Castac

Lake

Cuyama River

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Stream

Livermore

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Urban

Natural

Agricultural

LAND-USE CLASSIFICATION

USGS-grid well 

USGS-understanding well 

CDPH-other well 

NITRATE AS NITROGEN

Measured concentration,

in milligrams per liter

Relative-concentration

Low

Moderate

High

5.1 to 10

< 5.0

> 10

5.1 to 10

< 5.0

< 5.0

5.1 to 10

> 10

A B

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

C

Figure 14. Relative-concentrations of nitrate for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and for California 

Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the (A) Livermore, 

(B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
36 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Inorganic Constituents with Aesthetic 

Benchmarks

One or more inorganic constituents with aesthetic-based 

benchmarks were detected at high relative-concentrations 

in 49 percent of the primary aquifer system and at moderate 

relative-concentrations in 31 percent (table 7A). The 

constituents most commonly detected at high relativeconcentrations were TDS, sulfate, and manganese (table 6).

Understanding Assessment for Total Dissolved Solids 

(TDS) 

Natural sources of TDS include mixing of groundwater 

with deep saline groundwater (connate water) that is 

influenced by interactions with deep marine or lacustrine 

sediments, concentration of salts by evaporation in discharge 

areas, and (or) water-rock interactions. Potential anthropogenic 

sources of TDS to groundwater in the SCI study unit include 

agricultural and urban irrigation, increased evaporation from 

irrigation water, disposal of wastewater and industrial effluent, 

and leaking water and sewer pipes. 

TDS has an SMCL-CA of 1,000 mg/L and was detected 

at high relative-concentrations in 29 percent of the primary 

aquifer system and at moderate relative-concentrations in 

43 percent (table 6). TDS concentrations were negatively 

correlated with percentage of urban land use (table 8A). 

High relative-concentrations of TDS were measured in wells 

representative of the primary aquifer system in the GIL and 

CUY study areas, and moderate relative-concentrations were 

detected in all three study areas (fig. 15). TDS concentrations 

were significantly greater in the CUY study area than in 

the GIL or LIV study areas, and were significantly greater 

in shallow and deep wells than in wells perforated in both 

the shallow and deep depth intervals (“overlapping” wells) 

(table 8B). These relations in part reflect the distribution of 

well types among the study areas: the percentage of wells 

classified as overlapping was lowest in CUY.

Understanding Assessment for Sulfate 

Natural sources of sulfate include the dissolution of 

natural sulfur and its oxidation to the anion sulfate, or the 

biochemical oxidation of sulfide minerals or species. Sulfate 

occurs in evaporite sediments as gypsum or anhydrite and 

in soils in arid and semi-arid environments as gypsum, 

and commonly exceeds 1 mg/L in rainfall (Hem, 1985). 

Anthropogenic sources include sulfuric acid used in some 

agriculture to lower the pH of irrigation water and assist with 

nutrient uptake.

Sulfate has an SMCL-CA of 500 mg/L and was detected 

at high relative-concentrations in 26 percent of the primary 

aquifer system and at moderate relative-concentrations 

in 8.6 percent (table 6). Similar to TDS concentrations, 

sulfate concentrations were significantly greater in the 

CUY study area than in the GIL or LIV study areas (fig. 16; 

table 8B). Sulfate concentrations were correlated with TDS 

concentrations, and all samples with high or moderate relativeconcentrations of sulfate also had high or moderate relativeconcentrations of TDS. Sulfate was negatively correlated with 

percentage of urban land use and positively correlated with 

percentage of natural land use (table 8A).

Understanding Assessment for Manganese

Potential natural sources of manganese to groundwater 

include the dissolution of manganese oxyhydroxide minerals 

present in many sediments and rocks and of primary 

manganese-bearing silicate minerals found in igneous and 

metamorphic rocks (Hem, 1985). Potential anthropogenic 

sources of manganese to groundwater include effluents 

associated with the steel and mining industries (Reimann 

and de Caritat, 1998) and soil amendments, in the form of 

manganese sulfates, that are added to deficient soils in order 

to stimulate crop growth. Concentrations of manganese are 

strongly influenced by redox conditions in the aquifer. In 

sediments, the manganese oxyhydroxide minerals are common 

as suspended particles and as coatings on mineral surfaces 

(Sparks, 1995). These minerals are stable in oxygenated 

systems at neutral pH. However, under anoxic conditions, the 

process of reductive dissolution can mobilize these minerals 

from oxyhydroxides, increasing their concentrations in 

groundwater (Sparks, 1995). 

Manganese has an SMCL-CA of 50 μg/L and was 

detected at high relative-concentrations in 23 percent 

of the primary aquifer system and at moderate relativeconcentrations in 5.7 percent (table 6). High relativeconcentrations of manganese were observed in all three 

study areas (figs. 10B, 17). Manganese concentrations were 

significantly greater in pre-modern-age groundwater compared 

with modern-age groundwater (table 8B). Manganese 

concentrations were significantly less in oxic groundwater 

compared with mixed or anoxic groundwater. Manganese was 

also negatively correlated to dissolved oxygen (table 8A).
Status and Understanding of Water Quality 37

IP026177_fig 15abc

5

5

California

Aqueduct

Castac

Lake

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

Cuyama River

99

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO 101

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Livermore Stream

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Fault

Franciscan complex

Tertiary marine sediment

Metamorphic rocks

Metasediment

Ultramafic/mafic

Plio-Pleistocene sediment

Quaternary alluvium

SEDIMENTARY ROCKS

OTHER

Quaternary other sediment

Tertiary nonmarine sediment

Tertiary, mafic volcanics

Granitic rocks

USGS-grid well 

USGS-understanding well 

CDPH-other well 

TOTAL DISSOLVED SOLIDS

Measured concentration,

in milligrams per liter

Relative-concentration

Low

Moderate

High

501 to 1,000

< 500

> 1,000

501 to 1,000

< 500

< 500

501 to 1,000

> 1,000

A B

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

C

Figure 15. Relative-concentrations of total dissolved solids for U.S. Geological Survey (USGS)-grid and USGS-understanding wells 

and for California Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the 

(A) Livermore, (B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
38 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

IP026177_fig 16abc

5

5

California

Aqueduct

Castac

Lake

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emidgio Mts

Cuyama River

99

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO 101

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Livermore Stream

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Fault

Franciscan complex

Tertiary marine sediment

Metamorphic rocks

Metasediment

Ultramafic/mafic

Plio-Pleistocene sediment

Quaternary alluvium

SEDIMENTARY ROCKS

OTHER

Quaternary other sediment

Tertiary nonmarine sediment

Tertiary, mafic volcanics

Granitic rocks

USGS-grid well 

USGS-understanding well 

CDPH-other well 

SULFATE

Measured concentration,

in milligrams per liter

Relative-concentration

Low

Moderate

High

251 to 500

< 250

> 500

251 to 500

< 250

< 250

251 to 500

> 500

A B

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

C

Figure 16. Relative-concentrations of sulfate for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and for California 

Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the (A) Livermore, 

(B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 39

IP026177_fig 17abc

5

5

California

Aqueduct

Castac

Lake

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

Cuyama River

99

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO 101

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Livermore Stream

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Fault

Franciscan complex

Tertiary marine sediment

Metamorphic rocks

Metasediment

Ultramafic/mafic

Plio-Pleistocene sediment

Quaternary alluvium

SEDIMENTARY ROCKS

OTHER

Quaternary other sediment

Tertiary nonmarine sediment

Tertiary, mafic volcanics

Granitic rocks

USGS-grid well 

USGS-understanding well 

CDPH-other well 

MANGANESE

Measured concentration,

in micrograms per liter

Relative-concentration

Low

Moderate

High

25.1 to 50

> 50

< 25

< 25

> 50

< 25

25.1 to 50

> 50

A B

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

C

Figure 17. Relative-concentrations of manganese for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and 

for California Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the 

(A) Livermore, (B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
40 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Organic Constituents

The organic compounds are organized by constituent 

class, including volatile organic compounds (VOCs) and 

pesticides. VOCs are characterized by their tendency to 

evaporate and are present in paints, solvents, fuels, fuel 

additives, refrigerants, fumigants, and disinfected water. 

VOCs typically persist longer in groundwater than in surface 

water because groundwater is isolated from the atmosphere. 

Pesticides are used to control weeds, fungi, or insects in 

agricultural and urban settings.

Organic constituents, as a class, were detected at 

high relative-concentrations in 1.6 percent of the primary 

aquifer system and at moderate relative-concentrations in 

2.0 percent (both values based on the spatially weighted 

approach) (table 7B). The constituents detected at high 

relative-concentrations were the solvent tetrachloroethene 

(PCE) and the trihalomethane bromoform. The trihalomethane 

chloroform and the herbicide simazine were detected (at low 

relative-concentrations only) in more than 10 percent of the 

primary aquifer system (figs. 18, 19). Of the 205 organic 

constituents analyzed for in the SCI study unit, 14 were 

detected, of which 13 had health-based benchmarks (table 5). 

Ten organic constituents with health-based benchmarks were 

detected in grid wells (fig. 18). 

Solvents are used for various industrial, commercial, 

and domestic purposes. Solvents had a high spatially 

weighted proportion of 0.9 percent, reflecting a high relativeconcentration of PCE in two wells in the LIV study area 

reported in the CDPH database during the current period of 

study (August 11, 2005–August 10, 2008). The proportions of 

the aquifer having moderate or low (or not detected) relativeconcentrations of solvents were 2 percent and 97 percent, 

respectively (table 7B). PCE was detected in one USGS-grid 

well at low relative-concentration (fig. 19). Historically high 

relative-concentrations for the solvent carbon tetrachloride and 

1,1,2-trichloroethane were recorded in the CDPH database for 

the period before August 11, 2005, but not during the current 

period of study (table 3).

Understanding Assessment for Trihalomethanes

Trihalomethanes (THMs) may form during disinfection 

of water supplies and may enter groundwater by infiltration 

of landscape irrigation water. THMs, as a class, were detected 

at high relative-concentrations in 0.7 percent (spatially 

weighted approach) of the primary aquifer system and at low 

relative-concentrations (or not detected) in 99 percent of the 

primary aquifer system (table 7B). The THM chloroform 

was detected in 11 percent of the 35 grid wells in the study 

unit (figs. 18, 19). Chloroform was only detected at low 

relative-concentrations in the primary aquifer system (table 6). 

Chloroform also was the most frequently detected VOC in 

groundwater according to the USGS National Water-Quality 

Assessment (NAWQA) Program (Zogorski and others, 2006). 

Most detections of chloroform were in the LIV study area 

(fig. 20).

The THM bromoform was detected at high relativeconcentrations in one well reported in the CDPH database 

during the current period of study (August 11, 2005–

August 10, 2008), resulting in a spatially weighted high 

relative-concentration of 0.7 percent in the primary aquifer 

system (table 6). Bromoform was not detected in the 

35 USGS-grid wells. The spatially weighted approach includes 

data from a larger number of wells than the grid-based 

approach and therefore is more likely to include constituents 

that are detected at high relative-concentrations in small 

proportions of the primary aquifer system. 

Chloroform concentrations were positively correlated 

with percentage of urban land use and negatively correlated 

with percentage of agricultural land use (table 8B). Nationally, 

THM concentrations have been positively correlated with 

percentage of urban land use (Zogorski and others, 2006). 

Potential urban sources of THMs include recharge from 

landscape irrigation that uses disinfected water and leakage 

from distribution lines or sewer systems, as well as industrial 

and commercial sources (Ivahnenko and Barbash, 2004). 

The LIV study area has the highest percentage of urban 

land (fig. 5A), consistent with the higher concentrations 

of chloroform than the CUY or GIL study areas (table 8B; 

fig. 20). Chloroform concentrations were negatively 

correlated with depth to top-of-perforations, indicating that 

concentrations decrease with depth in the primary aquifer 

system.

Understanding Assessment for Herbicides

Herbicides are applied to crops, gardens, lawns, around 

buildings, and along roads to control weeds. As a class, 

herbicides were detected at low relative-concentrations in 

20 percent of the primary aquifer system (table 7B), reflecting 

low relative-concentrations of simazine (figs. 9, 18, 21). 

Simazine also was among the most commonly detected 

herbicides in groundwater in major aquifers across the United 

States (Gilliom and others, 2006) and was the most frequently 

detected herbicide in groundwater in California (Troiano and 

others, 2001). No herbicides were detected in grid wells from 

the CUY study area (fig. 21).

Simazine was detected in seven grid wells in the GIL 

and LIV study areas (fig. 21), three of which had construction 

information available and had depth to top-of-perforations of 

less than 250 ft. Simazine was negatively correlated with pH 

(table 8A) as a result of other explanatory factors correlating 

to each other. Because there were no detections of simazine 
Status and Understanding of Water Quality 41

Moderate

Low

High

RELATIVE-CONCENTRATION, DIMENSIONLESS 0.001

0.0001

0.00001

0.01

0.1

1

10

EXPLANATION

Simazine Name and center of symbol is the maximum relative-concentration for that constituent-

 Unless indicated by following location line: (35 grid wells sampled) 

1 10 100

DETECTION FREQUENCY AT ANY CONCENTRATION, IN PERCENT

IP026177_Figure 18. 

Simazine

Atrazine

Chloroform (THM)

Perchlorate

Prometon

CFC-12

1,1-Dichloroethene

Tetrachloroethene (PCE)

Carbon tetrachloride

CFC-11

Tebuthiuron

Figure 18. Detection frequency and maximum relative-concentrations of organic and special-interest constituents detected in USGSgrid wells in the South Coast Interior Basins study unit, California GAMA Priority Basin Project.

in USGS-grid wells in the CUY study area, pH and simazine 

concentrations present a negative correlation. Simazine 

concentrations were significantly greater in modern-age 

groundwater compared with pre-modern-age groundwater 

(table 8B).

Simazine concentrations were not significantly correlated 

with percent agricultural or percent urban land use in the SCI 

study unit. Nationally, simazine has been used in agricultural 

applications on citrus and vineyards and in urban settings for 

weed control (Gilliom and others, 2006).
42 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

RELATIVE-CONCENTRATION

High

Moderate

Low

0 20 40 60 80 100

Chloroform

(THM)

Simazine

Perchlorate

Tetrachloroethene

(PCE)

0.0001 0.001 0.01 0.1

RELATIVE-CONCENTRATION, DIMENSIONLESS

1 10

STUDY UNIT

DETECTION FREQUENCY,

IN PERCENT STUDY AREA

Cuyama

Gilroy

Livermore

A B

IP026177_Fig19ab

10% detection line

Figure 19. (A) Detection frequency and (B) relative-concentrations of selected organic and special-interest constituents in USGS-grid 

wells in the South Coast Interior Basins study unit, California GAMA Priority Basin Project, August–December 2008.
Status and Understanding of Water Quality 43

IP026177_fig 20abc

5

5

California

Aqueduct

Castac

Lake

Cuyama River

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

680

580

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Stream

Livermore

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Urban

Natural

Agricultural

LAND-USE CLASSIFICATION

USGS-grid well 

USGS-understanding well 

CDPH-other well 

CHLOROFORM

Measured concentration,

in micrograms per liter

Relative-concentration

Not detected

Low

0.04 to 8

< 0.04

0.04 to 8

< 0.04

< 0.05

0.5 to 0.61

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

A B

C

Figure 20. Relative-concentrations of chloroform for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and 

for California Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the 

(A) Livermore, (B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
44 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

IP026177_fig 21abc

5

5

California

Aqueduct

Castac

Lake

Cuyama River

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Stream

Livermore

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Urban

Natural

Agricultural

LAND-USE CLASSIFICATION

USGS-grid well 

SIMAZINE

Measured concentration,

in micrograms per liter

Relative-concentration

Not detected

Low

 0.005 to 0.009

< 0.005

USGS-understanding well 

 0.005 to 0.014

< 0.005

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

A B

C

Figure 21. Relative-concentrations of simazine for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and 

for California Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the 

(A) Livermore, (B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 45

Special-Interest Constituents

Constituents of special interest analyzed for in the SCI 

study unit were perchlorate and N-nitrosodimethylamine 

(NDMA). These constituents were of special interest at the 

inception of the GAMA Priority Basin Project in 2001 because 

they recently had been observed in, or were considered to 

have the potential to reach, drinking-water supplies (California 

Department of Public Health, 2008a, b). An MCL-CA for 

perchlorate was established in 2007. Perchlorate was analyzed 

for in samples from all grid wells (table 1). Perchlorate had 

a high spatially weighted proportion of 0.3 percent and was 

detected at moderate relative-concentrations in 34 percent 

of the primary aquifer system (table 6). Perchlorate was not 

detected at high relative-concentrations in the USGS-grid 

wells (table 6). Perchlorate was detected, at any concentration, 

in 75 percent of grid wells. NDMA, a semi-volatile organic 

chemical, was not detected in the seven grid wells sampled in 

the SCI study unit (Mathany and others, 2009). 

Understanding Assessment for Perchlorate

Perchlorate has natural and anthropogenic sources to 

groundwater. Perchlorate forms naturally in the atmosphere 

and is present dissolved in precipitation and deposited 

in unsaturated zones (Rajagopalan and others, 2006, 

2009). Perchlorate can be present at low concentrations in 

groundwater under natural conditions (Fram and Belitz, 

2011). Potential anthropogenic sources of perchlorate include 

industrial, manufacturing, or commercial uses such as rocket 

fuel, explosives, road flares, automobile air-bag systems, and 

other products, and nitrate fertilizers mined from the Atacama 

Desert of Chile that have been used historically on some 

orchard crops (U.S. Environmental Protection Agency, 2005; 

Dasgupta and others, 2006; Böhlke and others, 2009). 

Perchlorate concentrations were negatively correlated 

with percentage of natural land use (table 8A). Concentrations 

of perchlorate were significantly higher in modern waters 

than in pre-modern-age waters (table 8B). Perchlorate 

concentrations were negatively correlated with pH and 

positively correlated with DO concentrations (table 8A). 

Concentrations of perchlorate were significantly higher 

in oxic waters compared with anoxic waters (table 8B). 

Although perchlorate biodegrades under anoxic conditions in 

some aquifers (Sturchio and others, 2007), the occurrence of 

perchlorate in oxic groundwater in the study unit may result 

from relations of both DO and perchlorate with modern-age 

groundwater.
46 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

IP026177_fig 22abc

5

5

California

Aqueduct

Castac

Lake

Cuyama River

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

KERN CO

LOS

ANGELES

CO

Sierra Madre Mountains

Caliente Range

San Emigdio Mts

99

101

101

Hollister Hills

Mts

Diablo

Range

Pajaro

San Benito

River

Uvas

Creek

Creek

Llagas

River

Santa Cruz

152

Coyote Creek

San Luis

Reservoir

Diablo

Range

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS

CO

680

580

Arroyo de

la Laguna

Altamont Hills

121°40’ 121°45’

120° 119°30’ 119°

121°50’ 121°30’ 121°15’

37°

45’

37°

36°

45’

34°

45’

35°

37°

35’

37°

40’

Dublin

Livermore

Gilroy

Hollister

Pleasanton

ALAMEDA CO

CONTRA COSTA CO

EXPLANATION

Stream

Livermore

STUDY AREA

Gilroy

Cuyama

0 10 20 Miles

0 10 20 Kilometers

Cuyama

Lebec

New Cuyama

Urban

Natural

Agricultural

LAND-USE CLASSIFICATION

USGS-grid well 

PERCHLORATE

Measured concentration,

in milligrams per liter

Relative-concentration

Not detected

Low

Moderate

High

0.1 to < 0.6

< 0.1

0.6 to 6.0

USGS-understanding well 

0.1 to < 0.6

< 0.1

> 6

0.6 to 6.0

Shaded relief derived from

U.S. Geological Survey

National Elevation Dataset, 2006,

Albers Equal Area Conic Projection

0 5 10

0 105 Kilometers

Miles

A B

C

Figure 22. Relative-concentrations of perchlorate for U.S. Geological Survey (USGS)-grid and USGS-understanding wells and 

for California Department of Public Health (CDPH) wells with data during the time period used for the status assessment for the 

(A) Livermore, (B) Gilroy, and (C) Cuyama study areas, South Coast Interior Basins study unit, California GAMA Priority Basin Project.
Acknowledgments 47

Summary 

Groundwater quality in the approximately 653-squaremile South Coast Interior Basins (SCI) study unit was 

investigated as part of the California State Water Resources 

Control Board’s Groundwater Ambient Monitoring and 

Assessment (GAMA) Program Priority Basin Project. 

The GAMA SCI study was designed to provide a spatially 

unbiased characterization of untreated groundwater quality in 

the primary aquifer system. The assessment is based on waterquality and ancillary data collected by the U.S. Geological 

Survey (USGS) from 50 wells in 2008 and water-quality data 

from the California Department of Public Health (CDPH) 

database.

The first component of this study, the status of the 

current quality of the groundwater resource, was assessed on 

the basis of data from samples analyzed for volatile organic 

compounds (VOCs), pesticides, and naturally occurring 

inorganic constituents such as trace and minor elements. The 

status assessment characterizes the quality of groundwater 

resources in the primary aquifer system of the SCI study unit, 

not the treated drinking water delivered to consumers by water 

purveyors.

Relative-concentrations (sample concentration divided 

by the benchmark concentration) were used to evaluate 

groundwater quality for those constituents that have Federal 

and (or) California regulatory or non-regulatory benchmarks 

for drinking-water quality. Aquifer-scale proportion was 

used as the primary metric for evaluating regional-scale 

groundwater quality. High aquifer-scale proportion is 

defined as the percentage of the primary aquifer system 

with relative-concentration greater than 1.0 for a particular 

constituent or class of constituents; proportion is based on 

an areal rather than a volumetric basis. Moderate and low 

aquifer-scale proportions were defined as the percentage of 

the primary aquifer system with moderate and low relativeconcentrations, respectively. Two statistical approaches, 

grid-based and spatially weighted, were used to evaluate 

aquifer-scale proportion for individual constituents and classes 

of constituents. Grid-based and spatially weighted estimates 

were comparable in the SCI study unit (within 90-percent 

confidence intervals). However, the grid-based estimate may 

not identify constituents that are present at high relativeconcentrations in small proportions of the primary aquifer 

system.

Inorganic constituents with health-based benchmarks 

were detected at high relative-concentrations in 29 percent of 

the primary aquifer system, moderate in 37 percent, and low 

in 34 percent. The high aquifer-scale proportion of inorganic 

constituents primarily reflected the high aquifer-scale 

proportions of nitrate (14 percent), molybdenum (8.6 percent), 

boron (8.6 percent), and arsenic (5.7 percent). The inorganic 

constituents with secondary maximum contaminant levels 

were detected at high relative-concentrations in 49 percent of 

the primary aquifer system, moderate in 31 percent, and low 

in 20 percent. The high aquifer-scale proportion primarily 

reflected the high aquifer-scale proportions of total dissolved 

solids (29 percent), sulfate (26 percent), and manganese 

(23 percent). 

Relative-concentrations of organic constituents (one or 

more) were high in 1.6 percent, moderate in 2.0 percent, and 

low or not detected in the remaining 96 percent of the primary 

aquifer system. The high aquifer-scale proportion of organic 

constituents reflected the high spatially weighted aquifer-scale 

proportions of tetrachloroethene (PCE) (0.9 percent) and 

bromoform (0.7 percent). Of the 207 organic and specialinterest constituents analyzed, 15 constituents were detected. 

The trihalomethane chloroform and the herbicide simazine 

were detected in greater than 10 percent of the primary aquifer 

system, but only at low relative-concentrations. 

In the second component of this study, the understanding 

assessment, some of the primary natural and human factors 

that affect groundwater quality were identified through 

evaluation of correlations between land use, physical 

characteristics of the wells, geochemical conditions of the 

aquifer, water temperature, and relative-concentrations of 

constituents. Results from these analyses may be used to 

explain the occurrence and distribution of constituents in 

the SCI study unit. Concentrations of arsenic, molybdenum, 

and manganese were generally greater in anoxic and premodern groundwater than other groundwater. In contrast, 

concentrations of nitrate and perchlorate were significantly 

higher in oxic and modern groundwater. Concentrations 

of simazine were significantly greater in modern than premodern groundwater. Chloroform detections were positively 

correlated with greater urban land use. Boron concentrations 

and chloroform detections were significantly higher in 

the LIV study area; TDS and sulfate concentrations were 

significantly greater in the CUY study area.

Acknowledgments

The authors thank the following cooperators for their 

support: the California State Water Resources Control 

Board, Lawrence Livermore National Laboratory, California 

Department of Public Health, and California Department of 

Water Resources. We especially thank the cooperating well 

owners and water purveyors for their generosity in allowing 

the USGS to collect samples from their wells. Funding for this 

work was provided by State of California bonds authorized by 

Proposition 50 and administered by the State Water Board.
48 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

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U.S. Geological Survey Fact Sheet 2004–3088, 2 p. 

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Kulongoski, J.T., Belitz, Kenneth, Landon, M.K., and 

Farrar, Christopher, 2010, Status and understanding 

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Landon, M.K., Belitz, Kenneth, Jurgens, B.C., 

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Appendix A. Use of Data from the California Department of Public Health (CDPH) Database 53

The grid-based calculation of aquifer-scale proportion 

uses one value per grid cell. Where USGS data for inorganic 

constituents were not available, additional data to represent 

a cell were selected from the CDPH database. Of the 44 grid 

cells in the SCI study unit, 7 cells had USGS-grid wells with 

the full complement of inorganic constituent data collected 

by USGS-GAMA, 28 cells had USGS-grid wells with the full 

complement of inorganic constituent data with the exception 

of gross alpha particle, gross beta particle, and radon-222 

activities, and 9 cells did not have USGS-grid wells. The 

CDPH database was queried to provide these missing data for 

inorganic constituents. CDPH wells with data for the most 

recent 3 years available at the time of sampling (August 11, 

2005–August 10, 2008) were considered. If a well had more 

than one analysis for a constituent in the 3-year interval, then 

the most recent data were selected.

The first choice was to select CDPH data for the grid well 

sampled by the USGS for other constituents. This approach 

resulted in the selection of radiochemical data from the CDPH 

database for 10 USGS-grid wells. To identify the USGS-grid 

wells that incorporated CDPH radiochemical data, a well ID 

was created that added “DG” to the GAMA ID for these wells 

(for example, LIV-01 with CDPH data was assigned the well 

ID: LIV-DG-01; table A1; figs. A1A–C).

If the first step did not yield CDPH radiochemical data 

for the USGS-grid well, the second step was to assign random 

rankings to the CDPH wells and find the highest ranked 

well in each grid cell that had radiochemical data. This step 

resulted in selecting CDPH radiochemical data from additional 

CDPH wells that were not USGS-grid wells for six grid cells. 

To identify these new CDPH grid wells, a well ID was created 

that added “DPH” after the study unit prefix (for example, 

CDPH-grid well GIL-DPH-03). 

The result of these steps was that for each constituent, 

data were obtained from 1 grid well per cell from the USGS 

database or the CDPH database for 35 of the 44 grid cells. 

Radiochemical data from the CDPH database were used for 

15 grid wells (table A2). Data were available from the CDPH 

database for 2 grid wells for radon-222 and gross beta and 

for 15 grid wells for gross alpha (table A2). Estimates of 

aquifer-scale proportion for constituents based on a smaller 

number of wells are subject to a larger error associated with 

the 90-percent confidence intervals (on the basis of Jeffreys 

interval for the binomial distribution) (Brown and others, 

2001).

Differences in constituent laboratory reporting levels 

associated with USGS and CDPH data did not affect 

analysis of high or moderate relative-concentrations because 

concentrations greater than one-half of water-quality 

benchmarks were substantially higher than the reporting levels. 

Appendix A. Use of Data from the California Department of Public Health 

(CDPH) Database
54 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

sac11-0410_fig A01a

Lake del Valle

San Antonio Reservoir

Frick Lake

Tule Pond

Patterson Reservoir Laurel Creek

ALAMEDA CO

CONTRA COSTA CO

Dublin

Livermore

Pleasanton

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

EXPLANATION

Stream USGS-grid well USGS- and CDPH-grid well

USGS-understanding well

 Note: LIVU-05, -06, -07, -08 were not used for the status

 and understanding assessments presented in this report

 and are therefore not shown on any of the other figures

Livermore

STUDY AREA

A

5

4

3

1

2

6

7

LIVU-02

LIVU-03

LIVU-01 LIV-06

LIV-05

LIV-01, -DG-01

LIV-03, -DG-03

LIV-02

LIVU-04

LIV-04

Arroyo de la Laguna

LIVU-08

LIVU-07

LIVU-06

LIVU-05

Figure A1. Identifiers and locations of USGS-grid, USGS-understanding, and CDPH-grid wells in the (A) Livermore study area, 

(B) Gilroy study area, and (C) Cuyama study area of the South Coast Interior Basins study unit, California GAMA Priority Basin Project.
Appendix A. Use of Data from the California Department of Public Health (CDPH) Database 55

IP010121_figA01b

San Luis

Reservoir

SAN BENITO CO

SANTA CLARA CO

MONTEREY CO

MERCED CO

SANTA CRUZ CO

STANISLAUS CO

USGS-grid well CDPH-grid well

USGS-understanding well

USGS- and CDPH-grid well

B

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

EXPLANATION

Stream

Gilroy

STUDY AREA

Gilroy

Hollister

Morgan Hill

4

2

3

9

7

8

1

11

13

18

5

12

15

6

14

16

10

17

19

GILU-05

GILU-04

GILU-03

GILU-02

GILU-01

GIL-DPH-04

GIL-DPH-03

GIL-DPH-08

GIL-DPH-05

GIL-DPH-06

GIL-DPH-12

GIL-13

GIL-12

GIL-11

GIL-09

GIL-08

GIL-06

GIL-05

GIL-04 GIL-03

GIL-01, -DG-01

GIL-10, -DG-10

GIL-02, -DG-02

GIL-07, -DG-07

GIL-15, -DG-15

GIL-16, -DG-16

GIL-17,

-DG-17 GIL-14,

-DG-14

Figure A1. —Continued
56 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Figure A1. —Continued IP026177_fig A01c

Ventura River

Outlet Canal

California

Aqueduct

Santa Ynez River

Kern River

P a c i f i c O c e a n

KERN CO

VENTURA CO

SANTA BARBARA CO

SAN LUIS OBISPO CO

LOS

ANGELES

CO

Lebec

New Cuyama Cuyama

0 10 20 MILES

0 10 20 KILOMETERS

Aqueduct

Canal Dam

Stream

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

EXPLANATION

Cuyama

STUDY AREA

C

USGS-grid well

CDPH-grid well

USGS-understanding well

CDPH-grid well

CUYU-06

CUYU-05

CUYU-04 CUYU-03

CUYU-02

CUYU-01

CUY-12

CUY-11

CUY-10

CUY-09

CUY-08

CUY-07

CUY-05 CUY-06

CUY-04

CUY-03

CUY-02

CUY-01

CUY-DPH-06

3 4 5

7

11

9

8

2 6

12

10

1

13

18

15

14

16

17
Appendix A. Use of Data from the California Department of Public Health (CDPH) Database 57

USGSGAMA well 

identification 

number

Grid cell 

number

Grid 

supplemented by 

CDPH data from 

same well

Grid 

supplemented by 

CDPH data from 

different well

Cuyama study area grid wells

CUY-01 5 — —

CUY-02 11 — —

CUY-03 14 — —

CUY-04 9 — —

CUY-05 10 — —

CUY-06 13 — —

CUY-07 7 — —

CUY-08 12 — —

CUY-09 18 — —

CUY-10 17 — —

CUY-11 16 — —

CUY-12 15 — —

Gilroy study area grid wells

GIL-01 2 GIL-DG-01 —

GIL-02 5 GIL-DG-02 —

GIL-03 17 — GIL-DPH-03

GIL-04 15 — GIL-DPH-04

GIL-05 19 — GIL-DPH-05

GIL-06 12 — GIL-DPH-06

GIL-07 7 GIL-DG-07 —

GIL-08 3 — GIL-DPH-08

GIL-09 4 — —

GIL-10 1 GIL-DG-10 —

GIL-11 8 — —

GIL-12 6 — GIL-DPH-12

GIL-13 11 — —

GIL-14 18 GIL-DG-14 —

GIL-15 13 GIL-DG-15 —

GIL-16 14 GIL-DG-16 —

GIL-17 16 GIL-DG-17 —

USGSGAMA well 

identification 

number

Grid cell 

number

Grid 

supplemented by 

CDPH data from 

same well

Grid 

supplemented by 

CDPH data from 

different well

Livermore study area grid wells

LIV-01 3 LIV-DG-01 —

LIV-02 1 — —

LIV-03 2 LIV-DG-03 —

LIV-04 1 2

 5 — —

LIV-05 4 — —

LIV-06 7 — —

USGS-understanding wells

CUYU-01 18 — —

CUYU-02 17 — —

CUYU-03 18 — —

CUYU-04 17 — —

CUYU-05 18 — —

CUYU-06 18 — —

GILU-01 4 — —

GILU-02 5 — —

GILU-03 4 — —

GILU-04 4 — —

GILU-05 4 — —

LIVU-01 2 — —

LIVU-02 3 — —

LIVU-03 3 — —

LIVU-04 3 — —

LIVU-05 2 2 — —

LIVU-06 2 2 — —

LIVU-07 2 2 — —

LIVU-08 2 2 — —

1

 LIV-04 is located in cell 4 near to the boundary with cell 5. It was used to 

represent cell 5 because cell 5 contained no accessible wells.

2

 Well not used in calculation of spatially weighted aquifer-scale proportions 

or in testing of statistical relations among potential explanatory factors or 

between potential explanatory factors and water-quality constituents.

Table A1. Cell number and USGS-GAMA well identification numbers for well data used in the South Coast Interior Basins study unit, 

California GAMA Priority Basin Project.

[A USGS-GAMA well identification number indicates the use of USGS data from the grid well; a CDPH-grid well identification number with “DG” indicates 

the use of CDPH data from the USGS-grid well; a CDPH-grid well identification number with “DPH” indicates the use of CDPH data from a different well. 

—, no wells sampled or selected; CUY, Cuyama study area well; GIL, Gilroy study area well; LIV, Livermore study area well; CUYU, GILU, or LIVU, 

understanding well]
58 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Table A2. CDPH well identification number and radiochemical 

data from the CDPH database used in the South Coast Interior 

Basins study unit, California GAMA Priority Basin Project.

[A CDPH-grid well identification number with “DG” indicates the use of 

CDPH data from the USGS-grid well; a CDPH-grid well identification 

number with “DPH” indicates the use of CDPH data from a different 

well. GIL, Gilroy study area well; LIV, Livermore study area well. pCi/L, 

picocuries per liter; na, no data available; <, less than]

CDPH well 

identification 

number

Gross alpha 

particle activity 

(pCi/L)

Gross beta 

particle activity 

(pCi/L)

Radon-222 

(pCi/L)

GIL-DG-01 0.29 na na

GIL-DG-02 0.21 na na

GIL-DPH-03 1.2 na na

GIL-DPH-04 0.09 na na

GIL-DPH-05 9.4 na na

GIL-DPH-06 1.1 na na

GIL-DG-07 0.30 na na

GIL-DPH-08 0.20 na na

GIL-DG-10 0.74 na na

GIL-DPH-12 9.4 na na

GIL-DG-14 0.16 na na

GIL-DG-15 1.8 na na

GIL-DG-16 5.2 na na

GIL-DG-17 8.4 na na

LIV-DG-01 <3.0 <3.0 330

LIV-DG-03 <3.0 <3.0 286
Appendix B. Ancillary Datasets 59

Land-Use Classification

Land use was classified by using an enhanced version 

of the satellite-derived [98-ft (30-m) pixel resolution] USGS 

Enhanced National Land Cover Dataset (Nakagaki and others, 

2007). This dataset has been used in previous national and 

regional studies relating land use to water quality (Gilliom 

and others, 2006; Zogorski and others, 2006). The dataset 

characterizes land cover during the early 1990s. The imagery 

was classified into 25 land-cover categories (Nakagaki 

and Wolock, 2005). These 25 land-cover categories were 

aggregated into 3 principal land-use classes for the purpose of 

general characterization of land use—urban, agricultural, and 

natural. Average land use (proportions of urban, agricultural, 

and natural) for the study unit, for each study area, and for 

areas within a radius of 1,640 ft (500 m) surrounding each 

well (table B1) were calculated using ArcGIS (version 9.2) 

(Johnson and Belitz, 2009). 

Septic Systems and LUFTs

Septic tank density was determined from housing 

characteristics data from the 1990 U.S. Census (U.S. Census 

Bureau, 1990). The density of septic tanks in each housing 

census block was calculated from the number of tanks and 

block area. The density of septic tanks around each well was 

then calculated from the area-weighted mean of the block 

densities for blocks within a 500-m buffer around the well 

location (Tyler Johnson, U.S. Geological Survey, written 

commun., 2009) (table B1).

The density of leaking or formerly-leaking underground 

fuel tanks (LUFTs) was determined from the locations of 

tanks in the SWRCB’s GeoTracker database of environmental 

cleanup sites (California Environmental Protection Agency, 

2001). The density of LUFTs was calculated by using Theissen 

polygons (Tyler Johnson, U.S. Geological Survey, written 

commun., 2007). The boundaries of the Theissen polygon 

around a particular LUFT are created by bisecting the linear 

distances between the LUFT and all the surrounding LUFTs. 

The density of LUFTs in the polygon is the number of tanks in 

the polygon (generally one) divided by the area of the polygon 

in square kilometers. A well is assigned the LUFT density of 

the Theissen polygon in which it is located. 

Well Construction Information and 

Hydrologic Conditions

Well construction data primarily were obtained from 

drillers’ logs filed with CDWR. Other sources were ancillary 

records of well owners and the USGS National Water 

Information System database. Well construction data are 

not available in the CDPH database. Well identification 

verification procedures are described by Mathany and others 

(2009). Well depths, depths to the tops and bottoms of the 

perforated intervals, and lengths of the perforated intervals for 

wells are listed in table B2.

The climate at each well site was represented by 

an aridity index (table B2) (United Nations Educational, 

Scientific, and Cultural Organization, 1979; United Nations 

Environment Programme, 1997):

aridity index average annual precipitation

average annual e

 

 = vapotranspiration

Higher values of the index correspond to wetter 

conditions. Average annual precipitation for each well 

site was extracted from the PRISM average annual 

precipitation for 1971–2000 GIS coverage (PRISM 

Climate Group, Oregon State University, 2010). Average 

annual potential evapotranspiration for each well site was 

extracted from a GIS coverage modified from Flint and 

Flint (2007). The modification consisted of calibrating 

the potential evapotranspiration values to the California 

Irrigation Management Information System actual reference 

evapotranspiration values for well-watered grass (California 

Irrigation Management Information System, 2005; Alan Flint, 

U.S. Geological Survey California Water Science Center, oral 

commun., 2009).

Groundwater Age Classification

Groundwater dating techniques indicate the time since 

the groundwater was last in contact with the atmosphere. 

The techniques used in this report to estimate groundwater 

residence times or ‘age’ were those based on tritium (for 

example, Tolstikhin and Kamenskiy, 1969; Torgersen and 

others, 1979) and carbon-14 activities (for example, Vogel and 

Ehhalt, 1963; Plummer and others, 1993).

Tritium (3

H) is a short-lived radioactive isotope 

of hydrogen with a half-life of 12.32 years (Lucas and 

Unterweger, 2000). Tritium is produced naturally in the 

atmosphere from the interaction of cosmogenic radiation 

with nitrogen (Craig and Lal, 1961), by above-ground 

nuclear explosions, and by the operation of nuclear reactors. 

Tritium enters the hydrological cycle following oxidation to 

tritiated water (3HOH). Natural background levels of tritium 

in precipitation are approximately 3 to 15 tritium units (TU) 

(Craig and Lal, 1961; Clark and Fritz, 1997). Above-ground 

nuclear explosions resulted in a large increase in tritium values 

in precipitation, beginning in about 1952 and peaking in 1963 

at values over 1,000 TU in the northern hemisphere (Michel, 

1989). Radioactive decay over a period of 60 years would 

decrease tritium values of 10 TU to 0.6 TU. 

Appendix B. Ancillary Datasets
60 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Previous investigations have used a range of tritium 

values from 0.3 to 1.0 TU as thresholds for indicating presence 

of water that has exchanged with the atmosphere since 

1952 (Michel, 1989; Plummer and others, 1993; Michel and 

Schroeder, 1994; Clark and Fritz, 1997; Manning and others, 

2005; Kulongoski and others, 2010; Landon and others, 2010). 

For samples collected for the SCI study unit in 2008, tritium 

values greater than or equal to a threshold of 0.3 TU were 

defined as indicating the presence of groundwater recharged 

since 1952 (Kulongoski and others, 2010; Landon and others, 

2010). Water recharged since 1952 is defined as “modern” 

groundwater.

Carbon-14 (14C) is a widely used chronometer based 

on the radiocarbon content of organic and inorganic 

carbon. Dissolved inorganic carbon species, carbonic acid, 

bicarbonate, and carbonate typically are used for 14C dating 

of groundwater. 14C is formed in the atmosphere by the 

interaction of cosmic-ray neutrons with nitrogen and, to a 

lesser degree, with oxygen and carbon, and by above-ground 

nuclear explosions. 14C is incorporated into carbon dioxide and 

mixed throughout the atmosphere. The carbon dioxide enters 

the hydrologic cycle because it dissolves in precipitation and 

surface water in contact with the atmosphere. 14C activity in 

groundwater, expressed as percent modern carbon (pmc), 

reflects the time since groundwater was last exposed to the 

atmospheric 14C source. 14C has a half-life of 5,730 years and 

can be used to estimate groundwater ages ranging from 1,000 

to approximately 30,000 years before present.

The 14C age (residence time, presented in years) is 

calculated on the basis of the decrease in 14C activity as 

a result of radioactive decay since groundwater recharge, 

relative to an assumed initial 14C concentration (Clark and 

Fritz, 1997). An average initial 14C activity of 99 pmc is 

assumed for this study, with estimated errors on calculated 

groundwater ages up to ± 20 percent. Calculated 14C ages in 

this study are referred to as “uncorrected” because they have 

not been adjusted to consider exchanges with sedimentary 

sources of carbon (Fontes and Garnier, 1979). Groundwater 

with a 14C activity of >88 pmc is reported as having an 

age of <1,000 years; no attempt is made to refine 14C ages 

<1,000 years. Measured values of pmc can be >100 in 

groundwater samples containing a significant component of 

water recharged after 1952 because the definition of pmc is 

based on 14C activity in the absence of 14C contributed by 

above-ground nuclear explosions (Clark and Fritz, 1997). 

For the SCI study unit, 14C activity <88 pmc was defined 

as indicating the presence of groundwater recharged before 

the modern era (Kulongoski and others, 2010). Water 

recharged before the modern era is defined as “pre-modern” 

groundwater.

Tritium and 14C data and age classifications are reported 

in table B3. Because of uncertainties in age distributions, 

particularly the uncertainties caused by mixing of waters of 

different ages in wells with long screened or open intervals 

and high withdrawal rates, the uncorrected 14C ages were not 

specifically used for quantifying the relation between age 

and water quality in this report. While more sophisticated 

lumped parameter models for analyzing age distributions that 

incorporate mixing are available (Cook and Böhlke, 2000), 

use of these alternative models to understand age mixtures was 

not needed for the assessments in this report. Classification 

into modern, mixed, and pre-modern categories was sufficient 

to provide an appropriate and useful characterization for the 

purposes of examining groundwater quality.

For the SCI study unit, groundwater samples were 

assigned the following age classifications:

Classification Tritium (TU) 14C (pmc)

Modern ≥0.3 ≥88

Modern or Mixed ≥0.3 No data

Mixed ≥0.3 <88

Pre-modern <0.3 <88

Classification of Geochemical Condition

Geochemical conditions investigated as potential 

explanatory variables in this report include oxidationreduction characteristics and pH (table B4). Oxidationreduction (redox) conditions influence the mobility of many 

organic and inorganic constituents (McMahon and Chapelle, 

2008). Along groundwater flow paths, redox conditions 

commonly proceed along a well-documented sequence 

of terminal electron acceptor processes (TEAPs); one 

TEAP typically dominates at a particular time and aquifer 

location (Chapelle and others, 1995; Chapelle, 2001). The 

predominant TEAPs are oxygen-reduction, nitrate-reduction, 

manganese-reduction, iron-reduction, sulfate-reduction, and 

methanogenesis. The presence of redox-sensitive chemical 

species suggesting more than one TEAP may indicate mixed 

waters from different redox zones upgradient of the well, a 

well screened across more than one redox zone, or spatial 

heterogeneity in microbial activity in the aquifer. 

Redox conditions were assigned to each sample by 

using a modified version of the classification scheme of 

McMahon and Chapelle (2008) and Jurgens and others (2009) 

(tables B4). Samples with DO > 1.0 mg/L were classified 

as oxic, and samples with DO ≤ 1.0 mg/L were classified as 

anoxic. The anoxic samples were further classified according 

to the TEAPs inferred from data for nitrate, manganese, and 

iron. Data for these constituents were obtained from USGSGAMA for all wells.
Appendix B. Ancillary Datasets 61

Table B1. Well type, percent land use by category, land-use classification, septic density, leaking underground fuel tank density, and 

USGS-GAMA well identification number for GAMA well data and CDPH-grid well data used in the South Coast Interior Basins study unit, 

California GAMA Priority Basin Project.

[USGS-GAMA well identification number: CUY, Cuyama study area well; GIL, Gilroy study area well; LIV, Livermore study area well; CUYU, GILU, or 

LIVU, understanding well; LUFT, Leaking underground fuel tank. Well types: PROD, production well; MON, monitor well. Other abbreviations: USGS, U.S. 

Geological Survey; CDPH, California Department of Public Health; LUFT, leaking (or formerly leaking) underground fuel tank; na, not available; tanks/km2, 

number of tanks per square kilometer; <, less than]

USGS-GAMA well 

identification number

Well 

type

Land use (in percent) 1 Land-use 

classification

Septic density 

(tanks/km2

) 2

LUFT density 

(tanks/km2

) 3 Agricultural Natural Urban 

Cuyama study area grid wells

CUY-01 PROD 1 95 4 Natural 0.1 <0.01

CUY-02 PROD 1 99 0 Natural 0.1 <0.01

CUY-03 PROD 78 18 4 Agricultural 0.1 <0.01

CUY-04 PROD 36 57 7 Natural 0.1 <0.01

CUY-05 PROD 42 49 9 Mixed 0.1 <0.01

CUY-06 PROD 95 5 0 Agricultural 0.1 <0.01

CUY-07 PROD 99 1 0 Agricultural 0.1 <0.01

CUY-08 PROD 53 46 1 Agricultural 0.1 <0.01

CUY-09 PROD 42 55 2 Natural 9.6 0.02

CUY-10 PROD 2 38 60 Urban 4.8 <0.01

CUY-11 PROD 33 65 2 Natural 0.1 <0.01

CUY-12 PROD 91 5 4 Agricultural 0.1 <0.01

Gilroy study area grid wells

GIL-01 PROD 61 3 37 Agricultural 17.2 0.40

GIL-02 PROD 71 3 26 Agricultural 6.8 0.53

GIL-03 PROD 40 43 17 Mixed 6.5 0.03

GIL-04 PROD 71 4 25 Agricultural 13.4 1.19

GIL-05 PROD 62 13 24 Agricultural 7.0 0.02

GIL-06 PROD 100 0 0 Agricultural 2.1 <0.01

GIL-07 PROD 80 9 11 Agricultural 3.7 0.02

GIL-08 PROD 80 10 10 Agricultural 35.3 0.44

GIL-09 PROD 87 1 11 Agricultural 18.5 0.47

GIL-10 PROD 92 0 8 Agricultural 15.2 0.06

GIL-11 PROD 67 3 30 Agricultural 7.9 0.12

GIL-12 PROD 50 35 15 Agricultural 7.5 0.03

GIL-13 PROD 24 68 8 Natural 1.0 <0.01

GIL-14 PROD 49 48 4 Mixed 10.6 0.01

GIL-15 PROD 95 4 1 Agricultural 2.0 <0.01

GIL-16 PROD 37 12 51 Urban 13.1 0.31

GIL-17 PROD 40 14 46 Mixed 31.2 7.10

Livermore study area grid wells

LIV-01 PROD 41 11 48 Mixed 2.9 0.57

LIV-02 PROD 34 44 22 Mixed 0.3 0.31

LIV-03 PROD 0 2 98 Urban 1.3 0.81

LIV-04 PROD 53 14 32 Agricultural 13.3 0.28

LIV-05 PROD 20 57 24 Natural 4.0 0.28

LIV-06 PROD 33 1 66 Urban 8.1 0.28
62 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

USGS-GAMA well 

identification number

Well 

type

Land use (in percent) 1 Land-use 

classification

Septic density 

(tanks/km2

) 2

LUFT density 

(tanks/km2

) 3 Agricultural Natural Urban 

CDPH-grid wells

CUY-DPH-06 PROD 88 4 8 Agricultural na na

GIL-DPH-03 PROD 13 11 76 Urban na na

GIL-DPH-04 PROD 41 59 0 Natural na na

GIL-DPH-05 PROD 68 8 24 Agricultural na na

GIL-DPH-06 PROD 88 6 6 Agricultural na na

GIL-DPH-08 PROD 29 7 64 Urban na na

GIL-DPH-12 PROD 66 31 2 Agricultural na na

USGS-understanding wells

CUYU-01 PROD 1 35 64 Urban 123.7 0.01

CUYU-02 PROD 0 88 11 Natural 13.0 <0.01

CUYU-03 PROD 1 76 23 Natural 13.7 0.02

CUYU-04 PROD 7 93 1 Natural 12.4 0.02

CUYU-05 PROD 0 100 0 Natural 4.5 0.01

CUYU-06 PROD 41 51 7 Natural 0.3 0.12

GILU-01 PROD 29 2 69 Urban 18.9 1.88

GILU-02 PROD 72 3 25 Agricultural 6.8 0.53

GILU-03 MON 67 3 30 Agricultural 6.7 0.77

GILU-04 MON 67 3 30 Agricultural 6.7 0.77

GILU-05 MON 67 3 30 Agricultural 6.7 0.77

LIVU-01 PROD 1 2 96 Urban 0.0 0.37

LIVU-02 PROD 18 1 81 Urban 1.3 0.93

LIVU-03 PROD 14 1 85 Urban 0.7 0.93

LIVU-04 PROD 11 40 48 Mixed 1.3 0.35

LIVU-05 4 MON 38 2 60 Urban 0 2.10

LIVU-06 4 MON 36 2 62 Urban 0 2.10

LIVU-07 4 MON 14 3 84 Urban 0 0.91

LIVU-08 4 MON 22 4 74 Urban 0 0.91

1 Land-use percentages within 500-meter radius of well site (Johnson and Belitz, 2009).

2

 Septic tank density within 500-meter radius of well site, based on 1990 U.S. Census data.

3

 LUFT density within 500-meter radius of well site, based on GEIMS LUFT database (California Environmental Protection Agency, 2001).

4

 Well not used in calculation of spatially weighted aquifer-scale proportions or in testing of statistical relations among potential explanatory factors or 

between potential explanatory factors and water-quality constituents.

Table B1. Well type, percent land use by category, land-use classification, septic density, leaking underground fuel tank density, and 

USGS-GAMA well identification number for GAMA well data and CDPH-grid well data used in the South Coast Interior Basins study unit, 

California GAMA Priority Basin Project.—Continued

[USGS-GAMA well identification number: CUY, Cuyama study area well; GIL, Gilroy study area well; LIV, Livermore study area well; CUYU, GILU, or 

LIVU, understanding well; LUFT, Leaking underground fuel tank. Well types: PROD, production well; MON, monitor well. Other abbreviations: USGS, U.S. 

Geological Survey; CDPH, California Department of Public Health; LUFT, leaking (or formerly leaking) underground fuel tank; na, not available; tanks/km2, 

number of tanks per square kilometer; <, less than]
Appendix B. Ancillary Datasets 63

Table B2. Well construction information, well depth class, and aridity index for wells used in the South Coast Interior Basins study unit, 

California GAMA Priority Basin Project.

[USGS-GAMA well identification number: CUY, Cuyama study area well; GIL, Gilroy study area well; LIV, Livermore study area well; CUYU, GILU, or 

LIVU, understanding well. Well depth class: Shallow, well depth ≤ 250 ft; overlapping, well depth > 250 ft and depth to top of perforations ≤250 ft; deep, well 

depth > 250 ft and depth to top of perforations > 250 ft. Other abbreviations: USGS, U.S. Geological Survey; CDPH, California Department of Public Health; 

na, not available; ft, feet; LSD, land-surface datum; <, less than; >, greater than; ≤, less than or equal to]

USGS-GAMA 

well 

identification 

number

Construction information

Well 

depth 

class

Aridity 

index 

(dimensionless)1

Well 

depth 

(ft below LSD)

Top of 

perforations 

(ft below LSD)

Bottom of 

perforations 

(ft below LSD)

Length from top of 

perforations to 

bottom of well 

(ft)

Cuyama study area grid wells

CUY-01 188 45 125 80 Shallow 0.17

CUY-02 790 340 790 450 Deep 0.16

CUY-03 na na na na na 0.17

CUY-04 720 420 720 300 Deep 0.14

CUY-05 na na na na na 0.15

CUY-06 800 640 800 160 Deep 0.16

CUY-07 750 250 750 500 Deep 0.15

CUY-08 550 na na na Overlapping or Deep 0.16

CUY-09 361 80 361 281 Overlapping 0.41

CUY-10 298 146 288 142 Overlapping 0.32

CUY-11 357 na na na Overlapping or Deep 0.23

CUY-12 93 na na na Shallow 0.16

Gilroy study area grid wells

GIL-01 484 157 480 323 Overlapping 0.41

GIL-02 470 240 460 220 Overlapping 0.41

GIL-03 na na na na na 0.29

GIL-04 500 147 500 353 Overlapping 0.31

GIL-05 na na na na na 0.37

GIL-06 na na na na na 0.40

GIL-07 na na na na na 0.41

GIL-08 na na na na na 0.41

GIL-09 na na na na na 0.41

GIL-10 378 na na na Overlapping or Deep 0.42

GIL-11 na na na na na 0.41

GIL-12 700 na na na Overlapping or Deep 0.43

GIL-13 460 100 460 360 Overlapping 0.31

GIL-14 na na na na na 0.29

GIL-15 382 360 382 22 Deep 0.39

GIL-16 339 282 337 55 Deep 0.33

GIL-17 450 314 325 11 Deep 0.29

Livermore study area grid wells

LIV-01 500 200 490 290 Overlapping 0.34

LIV-02 113 37 107 70 Shallow 0.41

LIV-03 650 149 650 501 Overlapping 0.36

LIV-04 147 101 146 45 Shallow 0.30

LIV-05 300 270 290 20 Deep 0.31

LIV-06 na na na na na 0.32
64 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

USGS-GAMA 

well 

identification 

number

Construction information

Well 

depth 

class

Aridity 

index 

(dimensionless)1

Well 

depth 

(ft below LSD)

Top of 

perforations 

(ft below LSD)

Bottom of 

perforations 

(ft below LSD)

Length from top of 

perforations to 

bottom of well 

(ft)

CDPH-grid wells

CUY-DPH-06 na na na na na na

GIL-DPH-03 590 142 590 448 Overlapping na

GIL-DPH-04 na na na na na na

GIL-DPH-05 na na na na na na

GIL-DPH-06 na na na na na na

GIL-DPH-08 na na na na na na

GIL-DPH-12 na na na na na na

USGS-understanding wells

CUYU-01 193 39 193 154 Shallow 0.37

CUYU-02 270 51 256 205 Overlapping 0.33

CUYU-03 212 na na na Shallow 0.41

CUYU-04 na na na na na 0.34

CUYU-05 351 191 351 160 Overlapping 0.28

CUYU-06 295 na na na Overlapping or Deep 0.25

GILU-01 420 190 420 230 Overlapping 0.41

GILU-02 800 530 790 260 Deep 0.41

GILU-03 285 225 280 55 Overlapping 0.41

GILU-04 395 350 390 40 Deep 0.41

GILU-05 615 560 610 50 Deep 0.41

LIVU-01 315 235 310 75 Overlapping 0.39

LIVU-02 530 150 510 360 Overlapping 0.37

LIVU-03 745 515 730 215 Deep 0.37

LIVU-04 420 200 410 210 Overlapping 0.30

LIVU-05 2 35 20 35 15 Shallow 0.42

LIVU-06 2 70 45 65 20 Shallow 0.42

LIVU-07 2 52 42 52 10 Shallow 0.40

LIVU-08 2 50 30 50 20 Shallow 0.40

1

Aridity index is average annual precipitation divided by average annual evapotranspiration.

2

 Well not used in calculation of spatially weighted aquifer-scale proportions or in testing of statistical relations among potential explanatory factors or 

between potential explanatory factors and water-quality constituents.

Table B2. Well construction information, well depth class, and aridity index for wells used in the South Coast Interior Basins study unit, 

California GAMA Priority Basin Project.—Continued

[USGS-GAMA well identification number: CUY, Cuyama study area well; GIL, Gilroy study area well; LIV, Livermore study area well; CUYU, GILU, or 

LIVU, understanding well. Well depth class: Shallow, well depth ≤ 250 ft; overlapping, well depth > 250 ft and depth to top of perforations ≤250 ft; deep, well 

depth > 250 ft and depth to top of perforations > 250 ft. Other abbreviations: USGS, U.S. Geological Survey; CDPH, California Department of Public Health; 

na, not available; ft, feet; LSD, land-surface datum; <, less than; >, greater than; ≤, less than or equal to]
Appendix B. Ancillary Datasets 65

USGS 

GAMA well 

identification 

number

Tritium, 

tritium 

units

Percent 

modern 

carbon 

(percent)

Uncorrected 

carbon-14 

age 

(years)

Age 

classification

Cuyama study area grid wells

CUY-01 0.9 74 2,350 Mixed

CUY-02 <0.3 2 33,020 Pre-modern

CUY-03 2.3 95 <1,000 Modern

CUY-04 <0.3 66 3,300 Pre-modern

CUY-05 <0.3 4 25,430 Pre-modern

CUY-06 2.1 89 <1,000 Modern

CUY-07 1.3 72 2,530 Mixed

CUY-08 2.8 76 2,160 Mixed

CUY-09 <0.3 50 5,490 Pre-modern

CUY-10 0.9 na na Modern or 

Mixed

CUY-11 0.8 66 3,260 Mixed

CUY-12 2.0 89 <1,000 Modern

Gilroy study area grid wells

GIL-01 1.2 75 2,230 Mixed

GIL-02 1.8 88 1,000 Modern

GIL-03 1.3 97 <1,000 Modern

GIL-04 <0.3 39 7,470 Pre-modern

GIL-05 2.3 100 <1,000 Modern

GIL-06 2.8 96 <1,000 Modern

GIL-07 2.7 105 <1,000 Modern

GIL-08 2.5 112 <1,000 Modern

GIL-09 2.9 112 <1,000 Modern

GIL-10 1.3 90 <1,000 Modern

GIL-11 3.0 104 <1,000 Modern

GIL-12 <0.3 60 4,040 Pre-modern

GIL-13 1.8 100 <1,000 Modern

GIL-14 <0.3 21 12,590 Pre-modern

GIL-15 <0.3 88 1,000 Pre-modern

GIL-16 <0.3 67 3,120 Pre-modern

GIL-17 <0.3 79 1,870 Pre-modern

USGS 

GAMA well 

identification 

number

Tritium, 

tritium 

units

Percent 

modern 

carbon 

(percent)

Uncorrected 

carbon-14 

age 

(years)

Age 

classification

Livermore study area grid wells

LIV-01 3.5 100 <1,000 Modern

LIV-02 2.7 83 1,450 Mixed

LIV-03 4.9 68 3,050 Mixed

LIV-04 6.5 97 <1,000 Modern

LIV-05 0.7 88 <1,000 Modern

LIV-06 6.6 109 <1,000 Modern

USGS-understanding wells

CUYU-01 2.3 96 <1,000 Modern

CUYU-02 2.6 79 1,820 Mixed

CUYU-03 2.1 100 <1,000 Modern

CUYU-04 1.4 83 1,430 Mixed

CUYU-05 2.3 92 <1,000 Modern

CUYU-06 2.3 98 <1,000 Modern

GILU-01 2.0 88 <1,000 Modern

GILU-02 1.2 49 5,710 Mixed

GILU-03 1.5 101 <1,000 Modern

GILU-04 1.8 89 <1,000 Modern

GILU-05 <0.3 40 7,380 Pre-modern

LIVU-01 0.3 95 <1,000 Modern

LIVU-02 10.3 39 7,550 Mixed

LIVU-03 2.7 2 33,130 Mixed

LIVU-04 3.2 94 <1,000 Modern

LIVU-05 1 3.7 78 1,910 Mixed

LIVU-06 1 <0.3 45 6,400 Pre-modern

LIVU-07 1 2.9 94 <1,000 Modern

LIVU-08 1 3.0 99 <1,000 Modern

1

 Well not used in calculation of spatially weighted aquifer-scale proportions 

or in testing of statistical relations among potential explanatory factors or 

between potential explanatory factors and water-quality constituents.

Table B3. Tritium, percent modern carbon, and age classification of samples, South Coast Interior Basins study unit, California Priority 

Basin Project.

[na, not available; <, less than; modern, groundwater recharged after 1952; pre-modern, groundwater recharged before 1952; mixed, groundwater with modern 

and pre-modern components]
66 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

Dissolved 

oxygen 

(mg/L)

Redox 

classification

Redox 

process

pH 

(standard 

units)

Cuyama study area grid wells

CUY-01 3.8 Oxic O2 7.3

CUY-02 1.3 Oxic O2 7.5

CUY-03 8.8 Oxic O2 7.2

CUY-04 6.2 Oxic O2 7.2

CUY-05 1.8 Oxic O2 7.3

CUY-06 5.6 Oxic O2 7.2

CUY-07 9.4 Oxic O2 7.2

CUY-08 10 Oxic O2 7.2

CUY-09 1.4 Mixed O2

-Mn 7.6

CUY-10 1.9 Mixed O2

-Mn 7.2

CUY-11 3.4 Oxic O2 7.3

CUY-12 6.7 Oxic O2 7.2

Gilroy study area grid wells

GIL-01 3.7 Oxic O2 7.3

GIL-02 4.6 Oxic O2 7.3

GIL-03 5.9 Oxic O2 7.2

GIL-04 0.7 Anoxic suboxic 7.8

GIL-05 2.4 Oxic O2 7.0

GIL-06 <0.2 Anoxic NO3

-Mn 7.2

GIL-07 5.6 Oxic O2 6.7

GIL-08 2.3 Oxic O2 7.1

GIL-09 2.0 Oxic O2 7.1

GIL-10 5.8 Oxic O2 7.1

GIL-11 5.4 Oxic O2 6.9

GIL-12 <0.2 Anoxic Mn 7.8

GIL-13 0.2 Anoxic Mn 7.2

GIL-14 <0.2 Anoxic Fe-Mn 7.8

GIL-15 <0.2 Anoxic NO3

-Mn 7.0

GIL-16 1.1 Oxic O2 7.5

GIL-17 4.7 Oxic O2 7.5

Dissolved 

oxygen 

(mg/L)

Redox 

classification

Redox 

process

pH 

(standard 

units)

Livermore study area grid wells

LIV-01 3.5 Oxic O2 7.2

LIV-02 0.2 Anoxic Fe-Mn 6.9

LIV-03 4.0 Oxic O2 7.0

LIV-04 4.5 Oxic O2 7.4

LIV-05 5.1 Oxic O2 7.7

LIV-06 5.3 Oxic O2 7.1

USGS-understanding wells

CUYU-01 4.5 Oxic O2 7.3

CUYU-02 6.4 Oxic O2 7.1

CUYU-03 5.2 Oxic O2 7.2

CUYU-04 2.5 Oxic O2 7.2

CUYU-05 6.3 Oxic O2 7.4

CUYU-06 6.3 Oxic O2 7.4

GILU-01 5.3 Oxic O2 7.0

GILU-02 1.5 Oxic O2 7.6

GILU-03 3.4 Oxic O2 7.3

GILU-04 4.1 Oxic O2 7.4

GILU-05 <0.2 Anoxic NO3

-Mn 7.9

LIVU-01 5.0 Oxic O2 7.2

LIVU-02 3.8 Oxic O2 7.2

LIVU-03 3.2 Oxic O2 7.4

LIVU-04 5.1 Oxic O2 7.2

LIVU-05 1 4.7 Mixed O2

-Mn 6.8

LIVU-06 1 0.6 Anoxic Fe-Mn 6.9

LIVU-07 1 0.3 Anoxic suboxic 6.8

LIVU-08 1 0.3 Anoxic Mn 7.1

1

 Well not used in calculation of spatially weighted aquifer-scale proportions 

or in testing of statistical relations among potential explanatory factors or 

between potential explanatory factors and water-quality constituents.

Table B4. Dissolved oxygen concentrations, pH values, and oxidation-reduction classifications for samples from the South Coast 

Interior Basins study unit, California GAMA Priority Basin Project.

[Redox class: oxic, DO > 1.0 mg/L and not Mn- or Fe-reducing; anoxic, DO < 1.0 mg/L; mixed, DO > 1.0 mg/L and Mn- or Fe-reducing. Redox process: O2, 

oxygen reducing (DO > 1.0 mg/L); suboxic, anoxic and not NO3-, Mn-, or Fe-reducing; NO3, anoxic and nitrate reducing (NO3 < 0.5 mg/L); Mn, manganese 

reducing (Mn > 50 μg/L); Fe, iron reducing (Fe > 100 μg/L). Nitrate, manganese, and iron concentration data from Mathany and others (2009). Other 

abbreviations: DO, dissolved oxygen; mg/L, milligrams per liter; μg/L; micrograms per liter; <, less than]
Appendix C. Comparison of California Department of Public Health and U.S. Geological Survey-GAMA Data 67

Major ion data for grid wells with sufficient data (USGS 

and CDPH data) were plotted on a trilinear diagram (Piper, 

1944) along with all CDPH major ion data to determine 

whether the groundwater types in grid wells were similar 

to groundwater types observed historically in the study 

unit. Trilinear diagrams show the relative abundance of 

major cations and anions (on a charge equivalent basis) as 

a percentage of the total ion content of the water (fig. C1). 

Trilinear diagrams are often used to define groundwater type 

(Hem, 1985). All samples with major ion data in the CDPH 

database with a cation/anion imbalance of less than 10 percent 

were retrieved and plotted on the trilinear diagram for 

comparison with USGS- and CDPH-grid well data.

Appendix C. Comparison of California Department of Public Health and

U.S. Geological Survey-GAMA Data

The ranges of water types for USGS-grid wells and 

other wells from the historical CDPH database were similar 

(fig. C1). In most water samples from wells, no single cation 

accounted for more than 60 percent of the total cations, and 

bicarbonate accounted for more than 60 percent of the total 

anions. Waters in these wells are described as mixed cation–

bicarbonate type waters. There were also sodium/potassiumbicarbonate waters, for which sodium plus potassium and 

bicarbonate account for more than 60 percent of the cations 

and anions, respectively. 

The determination that the range of relative abundance of 

major cations and anions in grid wells is similar to the range of 

those in all CDPH wells indicates that the grid wells represent 

the types of water present within the primary aquifer system in 

the SCI study unit.
68 Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008

PERCENT

Calcium

 100 

 80 

 60 

 40 

 20  0 

 0 

 20 

 40 

 60 

 80 

 100 

Magnesium

 0 

 20 

 40 

 60 

 80 

 100 

Sodium plus Potassium

Chloride, Fluoride, Nitrite plus Nitrate

 0 

 20 

 40 

 60 

 80 

 100 

 100 

 80 

 60 

 40 

 20 

 0 

Bicarbonate

 100 

 80 

 60 

 40 

 20 

 0 

Sulfate

 0 

 20 

 40 

 60 

 80 

 100 

Sulfate plus Chloride

 0 

 20 

 40 

 60 

 80 

 100 

Calcium plus Magnesium

 100 

 80 

 60 

 40 

 20 

 0 

 100 

 80 

 60 

 40 

 20 

 0 

PERCENT

PERCENT

EXPLANATION

USGS-grid well

CDPH well

IP026177_Figure D01 Grid wells_SCR-I

Figure C1. Trilinear diagram of selected inorganic data from the U.S. Geological Survey (USGS)-grid wells and from all wells in the 

California Department of Public Health (CDPH) database that have a charge imbalance of less than 10 percent, South Coast Interior 

Basins study unit, California GAMA Priority Basin Project.
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For more information concerning the research in this report, contact the

 Director, California Water Science Center

U.S. Geological Survey

6000 J Street, Placer Hall

Sacramento, California 95819

http://ca.water.usgs.gov
Printed on recycled paper

Parsons and others—Status and Understanding of Groundwater Quality in the South Coast Interior Groundwater Basins, 2008—

California GAMA Priority Basin Project—SIR 2014–5023

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ISSN 2328-031X (print)

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20145023