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 Santa Clara River 

Valley, 2007: California GAMA Priority Basin Project

Photo placement

Scientific Investigations Report 2011–5052
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: Looking west over Santa Clara River, Ventura County, California. (Photograph taken by 

Jill Densmore, U.S. Geological Survey.)

Back cover: Pumping well in Ventura River Valley, Ventura County, California. (Photograph taken by 

Cathy Munday, 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 Santa Clara River Valley, 2007: 

California GAMA Priority Basin Project

By Carmen A. Burton, Joseph Montrella, Matthew K. Landon, 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 2011–5052

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2011

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

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U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Burton, C.A., Montrella, Joseph, Landon, M.K., and Belitz, Kenneth, 2011, Status and understanding of groundwater 

quality in the Santa Clara River Valley, 2007—California GAMA Priority Basin Project: U.S. Geological Survey 

Scientific Investigations Report 2011–5052, 86 p.
iii

Contents

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

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

Purpose and Scope ..............................................................................................................................3

Description of the Santa Clara River Valley Study Unit...........................................................................6

Ventura River Valley, Ojai Valley, and Upper Ojai Valley Basins.................................................12

Simi Valley and Arroyo Santa Rosa Valley Basins ........................................................................12

Santa Clara River Valley Basin .........................................................................................................12

Las Posas Valley and Pleasant Valley Basins................................................................................13

Methods ........................................................................................................................................................13

Status Assessment Methods............................................................................................................13

Relative-Concentrations and Water-Quality Benchmarks .................................................13

Design of Sampling Network for Status Assessment..........................................................14

California Department of Public Health (CDPH)-Grid Well Selection ...............................17

Identification of Constituents of Interest ...............................................................................17

Estimation of Aquifer-Scale Proportions ...............................................................................18

Understanding Assessment Methods.............................................................................................20

USGS-Understanding Wells.....................................................................................................20

Statistical Analysis ....................................................................................................................21

Evaluation of Potential Explanatory Factors ...........................................................................................21

Land Use...............................................................................................................................................21

Septic Systems....................................................................................................................................21

Depth ..................................................................................................................................................22

Groundwater Age................................................................................................................................22

Geochemical Conditions....................................................................................................................25

Correlations Between Explanatory Variables ................................................................................25

Status and Understanding of Water Quality............................................................................................28

Inorganic Constituents ......................................................................................................................29

Trace Elements and Minor Ions...............................................................................................29

Factors Affecting Arsenic ...............................................................................................44

Factors Affecting Vanadium............................................................................................44

Factors Affecting Boron ..................................................................................................44

Gross Alpha Radioactivity and other Radioactive Constituents ........................................44

Nutrients......................................................................................................................................44

Factors Affecting Nitrate ................................................................................................45

Inorganics with Aesthetic Benchmarks (SMCL) ..................................................................47

Factors Affecting Total Dissolved Solids (TDS) ...........................................................47

Factors Affecting Sulfate.................................................................................................49

Factors Affecting Manganese .......................................................................................49

Factors Affecting Iron ......................................................................................................50
iv

Contents—Continued

Status and Understanding of Water Quality—Continued

Organic and Special-Interest Constituents....................................................................................51

Pesticides....................................................................................................................................54

Factors Affecting Pesticides...........................................................................................54

Trihalomethanes ........................................................................................................................54

Factors Affecting Trihalomethanes................................................................................54

Solvents.......................................................................................................................................54

Factors Affecting Solvents..............................................................................................54

Other Volatile Organic Compounds (VOCs) ...........................................................................54

Special-Interest Constituent....................................................................................................54

Factors Affecting Perchlorate .......................................................................................59

Summary........................................................................................................................................................60

Acknowledgments.......................................................................................................................................61

References Cited..........................................................................................................................................61

Appendix A. Selection of California Department of Public Health (CDPH)-Grid Wells...................69

Appendix B. Comparison of CDPH and USGS-GAMA Data.................................................................74

Appendix C. Calculation of Aquifer-Scale Proportions ........................................................................77

Appendix D. Attribution of Potential Explanatory Factors ..................................................................78

Figures

Figure 1. Map showing location of the Santa Clara River Valley study unit and California 

hydrogeologic provinces, California GAMA Priority Basin Project .................. 4

Figure 2. Map showing study area and geographic features of the Santa Clara River Valley 

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

Figure 3. Ternary diagram showing proportions of urban, agricultural, and natural land 

use for USGS- and CDPH-grid wells, USGS-understanding wells, and other 

CDPH wells, Santa Clara River Valley study unit, California GAMA Priority 

Basin Project................................................................................. 7

Figure 4. Map showing land use in the Santa Clara River Valley study unit, California 

GAMA Priority Basin Project ............................................................ 8

Figure 5. Map showing geology of the Santa Clara River Valley study unit, California 

GAMA Priority Basin Project............................................................... 11

Figure 6. Map showing location of study-unit grid cells, USGS-grid and USGSunderstanding wells, California Department of Public Health (CDPH) wells used 

to supplement inorganic constituents (CDPH-grid wells), and other CDPH wells 

located in the Santa Clara River Valley study unit, California GAMA Priority 

Basin Project................................................................................. 16

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

lengths, for USGS- and CDPH-grid and USGS-understanding wells, Santa Clara 

River Valley study unit, California GAMA Priority Basin Project ..................... 22
v

Figures

Figure 8. Boxplots and bar chart showing relation of groundwater age classification to 

well depth and depth to top-of-perforations, and numbers of wells with each 

groundwater age class in each of the three depth categories, Santa Clara River 

Valley study unit, California GAMA Priority Basin Project ........................... 23

Figure 9. Graphs showing relation of dissolved-oxygen concentration (an oxidationreduction condition indicator), depth to top-of-perforations, and groundwater 

age class, and relation of pH, depth to top-of-perforations, and groundwater 

age class, Santa Clara River Valley study unit, California GAMA Priority Basin 

Project ....................................................................................... 26

Figure 10. Graph showing maximum relative-concentration in USGS-grid and CDPH-grid 

wells for constituents detected, grouped by type of constituent, Santa Clara 

River Valley study unit, California GAMA Priority Basin Project ..................... 30

Figure 11. Graphs showing relative-concentrations of inorganic constituents with humanhealth-based or aesthetic benchmarks categorized as high, medium, and low 

in USGS- and CDPH-grid wells, Santa Clara River Valley study unit, California 

GAMA Priority Basin Project............................................................... 34

Figure 12. Maps showing relative-concentrations of selected inorganic constituents 

with human-health-based and aesthetic benchmarks in USGS-grid and CDPHgrid wells and CDPH-other wells in the Santa Clara River Valley study unit, 

California GAMA Priority Basin Project................................................... 35

Figure 13. Graphs showing relation of nitrate concentrations to depth to top-ofperforations and dissolved-oxygen concentrations classified by groundwater 

age for grid and understanding wells, Santa Clara River Valley study unit, 

California GAMA Priority Basin Project................................................... 46

Figure 14. Graphs showing relations of total dissolved solids and sulfate concentrations 

to pH, classified by groundwater age, for grid and understanding wells, Santa 

Clara River Valley study unit, California GAMA Priority Basin Project ............... 48

Figure 15. Graphs showing relation of manganese concentrations to depth to the top-ofperforations and to dissolved-oxygen concentrations, classified by groundwater 

age, in grid and understanding wells in the Santa Clara River Valley study unit, 

California GAMA Priority Basin Project................................................... 50

Figure 16. Graphs showing relation of iron concentrations to depth to top-of-perforations 

and dissolved oxygen concentrations, classified by groundwater age, in grid 

and understanding wells in the Santa Clara River Valley study unit, California 

GAMA Priority Basin Project............................................................... 51

Figure 17. Graphs showing detection frequency and maximum relative-concentration for 

organic and special-interest constituents detected in USGS-grid wells, Santa 

Clara River Valley study unit, California GAMA Priority Basin Project ............... 52

Figure 18. Graphs showing detection frequency and relative-concentration of selected 

organic constituents and perchlorate in USGS-grid wells in the Santa Clara 

River Valley study unit, California GAMA Priority Basin Project ..................... 53

Figure 19. Maps showing number of pesticide detections and relative-concentrations

of selected organic constituents for USGS-grid wells and CDPH wells, Santa 

Clara River Valley study unit, California GAMA Priority Basin Project ............... 55

Figure 20. Graph showing relation of pesticide concentrations to depth to top-ofperforations, classified by groundwater age, in USGS-grid wells, Santa Clara 

River Valley study unit, California GAMA Priority Basin Project ..................... 59
vi

Tables

Table 1. Stratigraphic column and related designations of geologic units by source and 

aquifer system model layers in the groundwater and surface-water flow model 

of the Santa Clara–Calleguas Basin, Ventura County, California ..................... 9

Table 2. Analytes and numbers of wells sampled for each analytical schedule, Santa 

Clara River Valley study unit, GAMA Priority Basin Project, April–June 2007 ...... 15

Table 3. Inorganic constituents and number of grid wells per constituent, Santa Clara 

River Valley study unit, GAMA Priority Basin Project ................................. 17

Table 4. Comparison of number of compounds and median method-detection levels 

or laboratory reporting levels by type of constituent for data stored in the 

California Department of Public Health (CDPH) database and data collected 

by the Santa Clara River Valley study unit, GAMA Priority Basin Project, 

April through June 2007..................................................................... 19

Table 5. Constituents in wells in the California Department of Health (CDPH) database 

at historically high concentrations from March 5, 1984, to October 30, 2003, 

Santa Clara River Valley study unit, California, GAMA Priority Basin Project ...... 19

Table 6. Results of non-parametric (Wilcoxon) statistical tests for differences in values 

of potential explanatory factors and selected water-quality constituents between 

modern, mixed, and pre-modern groundwater age classes, Santa Clara River 

Valley study unit, California, GAMA Priority Basin Project ........................... 24

Table 7. Results of non-parametric (Spearman’s rho method) analysis of correlations 

between selected potential explanatory factors, Santa Clara River Valley study 

unit, California, GAMA Priority Basin Project.` .......................................... 27

Table 8. Number of constituents analyzed for, and number of constituents detected in, 

U.S. Geological Survey (USGS) grid wells listed by human-health-based or 

aesthetic benchmark type and constituent type, Santa Clara River Valley 

study unit, California, GAMA Priority Basin Project .................................... 28

Table 9. Raw-detection frequency and aquifer-scale proportions calculated by using 

spatially weighted and grid-based approaches for constituents never detected 

at concentrations greater than water-quality benchmarks during the most recent 

3 years of available data (November 1, 2003–October 31, 2006) from the California 

Department of Public Health (CDPH) database, detected at high or moderate 

relative-concentrations in USGS-grid wells sampled April–June 2007, or organic 

constitutents detected in greater than 10 percent of USGS-grid wells sampled, 

Santa Clara River Valley study unit, California, GAMA Priority Basin Project ...... 31

Table 10. Aquifer-scale proportions calculated for constituent classes, Santa Clara 

River Valley study unit, California, GAMA Priority Basin Project ..................... 33

Table 11. Summary of Spearman's rho correlation analysis between selected 

water-quality constituents and potential explanatory factors, Santa Clara 

River Valley study unit, California, GAMA Priority Basin Project ..................... 45
vii

Conversion Factors, Datums, and Abbreviations and Acronyms

Conversion Factors

Inch/Pound to 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 mile (mi2

) 2.590 square kilometer (km2)

Flow rate

gallon per minute (gal/min) 0.06309 liter per second (L/s)

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

kilometer (km) 0.6214 mile (mi)

meter (m) 3.281 foot (ft) 

Volume

liter (L) 0.2642 gallon (gal)

Mass

kilogram (kg) 2.205 pound avoirdupois (lb)

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

°F=(1.8×°C)+32.

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). Milligrams per liter is equivalent to parts per million (ppm) and 

micrograms per liter is equivalent to parts per billion (ppb).

cm3

 STP g–1 cubic centimeters at standard temperature and pressure per gram 

δE delta notation; the ratio of the heavier isotope (i) to the more common lighter isotope

of an element (E), relative to a standard reference material, expressed as per mil

mL milliliter

pCi/L picocuries per liter

per mil parts per thousand

pmc percent modern carbon

TU tritium unit

% percent
viii

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).

Abbreviations and Acronyms

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

BLS below land surface

E estimated or having a higher degree of uncertainty

GAMA Groundwater Ambient Monitoring and Assessment Program

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

HBSL health-based screening level

LRL laboratory reporting level

LSD land-surface datum

LT-MDL long-term method detection level

MCL-CA California Department of Public Health maximum contaminant level 

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

MDL method detection limit

MRL minimum reporting level

NL-CA California Department of Public Health notification level

NWIS National Water Information System (USGS)

PSW public-supply wells

RPD relative percentage difference

RSD relative standard deviation

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

SCRV Santa Clara River Valley study unit

SCRVU Santa Clara River Valley study unit understanding well

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

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

TEAP terminal electron acceptor process

US United States

VSMOW Vienna Standard Mean Ocean Water

> greater than

≥ greater than or equal to

< less than

≤ less than or equal to

 

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
ix

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued

Organizations

CDPH California Department of Public Health (was California Department of Health

Services prior to July 1, 2007)

CDWR California Department of Water Resources

LLNL Lawrence Livermore National Laboratory

NAWQA National Water-Quality Assessment Program (USGS)

NWQL National Water Quality Laboratory (USGS)

SWRCB State Water Resources Control Board (California)

UWCD United Water Conservation District

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

Selected chemical names

Ammonia-N ammonia as nitrogen

CaCO3 calcium carbonate

DBCP 1,2-dibromo-3-chloropropane

DO dissolved oxygen

DOC dissolved organic carbon

EDB 1,2-dibromoethane (ethylene dibromide)

Fe iron

Nitrate-N nitrate as nitrogen

Nitrite-N nitrite as nitrogen

Mn manganese

PCE perchloroethene (tetrachloroethene)

S sulfur

TCE trichloroethene

TDS total dissolved solids

THM trihalomethane

VOC volatile organic compound
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Status and Understanding of Groundwater Quality in the 

Santa Clara River Valley, 2007: California GAMA Priority 

Basin Project 

By Carmen A. Burton, Joseph Montrella, Matthew K. Landon, and Kenneth Belitz

Abstract

Groundwater quality in the approximately 460-squaremile Santa Clara River Valley study unit was investigated 

from April through June 2007 as part of the Priority Basin 

Project of the Groundwater Ambient Monitoring and 

Assessment (GAMA) Program. The GAMA Priority Basin 

Project is conducted by the U.S. Geological Survey (USGS) 

in collaboration with the California State Water Resources 

Control Board and the Lawrence Livermore National 

Laboratory. The Santa Clara River Valley study unit contains 

eight groundwater basins located in Ventura and Los Angeles 

Counties and is within the Transverse and selected Peninsular 

Ranges hydrogeologic province.

The Santa Clara River Valley study unit was designed 

to provide a spatially unbiased assessment of the quality of 

untreated (raw) groundwater in the primary aquifer system. 

The assessment is based on water-quality and ancillary data 

collected in 2007 by the USGS from 42 wells on a spatially 

distributed grid, and on water-quality data from the California 

Department of Public Health (CDPH) database. The primary 

aquifer system was defined as that part of the aquifer system 

corresponding to the perforation intervals of wells listed in 

the CDPH database for the Santa Clara River Valley study 

unit. The quality of groundwater in the primary aquifer system 

may differ from that in shallow or deep water-bearing zones; 

for example, shallow groundwater may be more vulnerable to 

surficial contamination. Eleven additional wells were sampled 

by the USGS to improve understanding of factors affecting 

water quality.

The status assessment of the quality of the groundwater 

used data from samples analyzed for anthropogenic 

constituents, such as volatile organic compounds (VOCs) 

and pesticides, as well as naturally occurring inorganic 

constituents, such as major ions and trace elements. The 

status assessment is intended to characterize the quality of 

untreated groundwater resources in the primary aquifers of the 

Santa Clara River Valley study unit, not the quality of treated 

drinking water delivered to consumers.

Relative-concentrations (sample concentration divided 

by health- or aesthetic-based benchmark concentration) were 

used for evaluating groundwater quality for those constituents 

that have Federal and (or) California benchmarks. A relativeconcentration greater than 1.0 indicates a concentration 

greater than a benchmark. For organic and special interest 

constituents, relative-concentrations were classified as high 

(greater than 1.0); moderate (greater than 0.1 and less than or 

equal to 1.0); and low (less than or equal to 0.1). For inorganic 

constituents, relative-concentrations were classified as high 

(greater than 1.0); moderate (greater than 0.5 and less than or 

equal to 1.0); and low (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 areal percentage of the primary aquifer system with 

relative-concentrations greater than 1.0. Moderate and low 

aquifer-scale proportions are 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 Santa Clara River Valley study unit (within 

90 percent confidence intervals).

The status assessment showed that inorganic constituents 

were more prevalent and relative-concentrations were higher 

than for organic constituents. For inorganic constituents 

with human-health benchmarks, relative-concentrations (of 

one or more constituents) were high in 21 percent of the 

primary aquifer system areally, moderate in 30 percent, and 

low or not detected in 49 percent. Inorganic constituents 

with human-health benchmarks with high aquifer-scale 

proportions were nitrate (15 percent of the primary aquifer 

system), gross alpha radioactivity (14 percent), vanadium 

(3.4 percent), boron (3.2 percent), and arsenic (2.3 percent). 

For inorganic constituents with aesthetic benchmarks, 

relative-concentrations (of one or more constituents) were 

high in 54 percent of the primary aquifer system, moderate in 

41 percent, and low or not detected in 4 percent. The inorganic 
2 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

constituents with aesthetic benchmarks with high aquifer-scale 

proportions were total dissolved solids (35 percent), sulfate 

(22 percent), manganese (38 percent), and iron (22 percent).

In contrast, the results of the status assessment for 

organic constituents with human-health benchmarks 

showed that relative-concentrations were high in 0 percent 

(not detected above benchmarks) of the primary aquifer 

system, moderate in 8.4 percent, and low or not detected in 

95 percent. Relative-concentrations of the special interest 

constituent, perchlorate, were moderate in 12 percent of the 

primary aquifer system and low or not detected in 88 percent. 

Relative-concentrations of two VOCs—carbon tetrachloride 

and trichloroethene (TCE)—were moderate in 2.4 percent 

of the primary aquifer system. One VOC—chloroform 

(water disinfection byproduct)—was detected in more than 

10 percent of the primary aquifer system but at low relativeconcentrations. Of the 88 VOCs and gasoline oxygenates 

analyzed, 71 were not detected. Pesticides were low or not 

detected in 100 percent of the primary aquifer system. Of the 

118 pesticides and pesticide degradates analyzed, 13 were 

detected and 5 of those had human-health benchmarks. Two of 

these five pesticides—simazine and atrazine—were detected in 

more than 10 percent of the primary aquifer system.

The second component of this study, the understanding 

assessment, was to identify the natural and human factors 

that affect groundwater quality on the basis of the evaluation 

of land use, physical characteristics of the wells, and 

geochemical conditions of the aquifer. Results from these 

analyses are used to explain the occurrence and distribution 

of selected constituents in the primary aquifer system of the 

Santa Clara River Valley study unit.

The understanding assessment indicated that water 

quality varied spatially primarily in relation to depth, 

groundwater age, reduction-oxidation conditions, pH, and 

location in the regional groundwater flow system. High 

and moderate relative-concentrations of nitrate and low 

relative-concentrations of pesticides were correlated with 

shallow depths to top-of-perforation, and with high dissolved 

oxygen. Groundwater of modern and mixed ages had higher 

nitrate than pre-modern-age groundwater. Decreases in 

concentrations of total dissolved solids (TDS) and sulfate were 

correlated with increases in pH. This relationship probably 

indicates relations of these constituents with increasing 

depth across most of the Santa Clara River Valley study unit. 

Previous studies have indicated multiple sources of high 

concentrations of TDS and sulfate and multiple geochemical 

processes affecting these constituents in the Santa Clara River 

Valley study unit. Manganese and iron concentrations were 

highest in pre-modern-age groundwater at depth and in the 

downgradient area of the Santa Clara River Valley study unit 

(closest to the coastline), indicating the prevalence of reducing 

groundwater conditions in these aquifer zones.

Introduction 

Groundwater composes almost one-half of the water used 

for public supply in California (Hutson and others, 2004). 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 Environmental Protection Agency, 2010, website at 

http://www.waterboards.ca.gov/water_issues/programs/gama). 

The statewide GAMA program currently consists of three 

projects: (1) the GAMA Priority Basin Project, conducted 

by the USGS (U.S. Geological Survey, 2010, website at 

http://ca.water.usgs.gov/gama); (2) the GAMA Domestic 

Well Project, conducted by the SWRCB; and (3) the GAMA 

Special Studies, conducted by LLNL. On a statewide basis, the 

GAMA Priority Basin Project primarily focused on the deep 

portion of the groundwater resource (primary aquifer system) 

and the SWRCB Domestic Well Project generally focused 

on the shallow aquifer systems. The primary aquifer system 

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

as private domestic or monitoring wells, that are closer to 

surficial sources of contaminants. As a result, concentrations 

of contaminants such as VOCs and nitrate can be higher in 

shallower wells than deeper wells (Nolan and Hitt, 2006; 

Zogorski and others, 2006; Landon and others, 2010).

The SWRCB initiated the GAMA Program in 2000 in 

response to a legislative mandate (State of California, 1999, 

2001a, Supplemental Report of the 1999 Budget Act 1999–00 

Fiscal Year). The GAMA Priority Basins Project was initiated 

in response to the Groundwater Quality Monitoring Act of 

2001 (State of California, 2001b, Sections 10780–10782.3 

of the California Water Code, Assembly Bill 599) to assess 

and monitor the quality of groundwater in California. 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 the 

monitoring plan to assess groundwater basins through direct 

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.
Introduction 3

The range of hydrologic, geologic, and climatic 

conditions that exist in California must be considered 

in an 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). All these hydrogeologic 

provinces include groundwater basins designated by the 

CDWR (California Department of Water Resources, 2003). 

Groundwater basins generally consist of relatively permeable, 

unconsolidated deposits of alluvial or volcanic origin. Eighty 

percent of California’s approximately 16,000 active and 

standby drinking-water wells listed in the statewide database 

maintained by the CDPH (hereinafter referred to as CDPH 

wells) are located in designated groundwater basins within 

these hydrogeologic provinces. Some groundwater basins, 

such as the Santa Clara River Valley basin, cover large 

areas and are further divided into groundwater subbasins by 

CDWR. Groundwater basins and subbasins were prioritized 

for sampling on the basis of the number of CDPH wells, with 

secondary consideration given to municipal groundwater 

use, agricultural pumping, the number of historical leaking 

underground fuel tanks, and registered pesticide applications 

(Belitz and others, 2003). Of the 472 basins and subbasins 

designated by the CDWR, 116 priority basins, containing 

approximately 95 percent of the CDPH wells located in 

basins, were selected for the project. These priority basins 

and additional areas outside defined groundwater basins were 

grouped into 35 study units. 

The Santa Clara River Valley GAMA study unit 

(hereinafter referred to as SCRV) is located in the Transverse 

and selected Peninsular Ranges hydrogeologic province 

(fig. 1) (Belitz and others, 2003). SCRV consists of eight 

groundwater basins (Ventura River Valley, Ojai Valley, Upper 

Ojai Valley, Santa Clara River Valley, Pleasant Valley, Las 

Posas Valley, Arroyo Santa Rosa Valley, and Simi Valley) 

(fig. 2).

Purpose and Scope

This report is one in a series of published and planned 

Scientific Investigation Reports presenting the status and 

understanding of current water-quality conditions in GAMA 

Priority Basin Project study units. Tabulated USGS data are 

available from several study units and are available as USGS 

Data Series reports (for example, Montrella and Belitz, 2009), 

and planned subsequent reports will address changes or trends

in water-quality across time.

The status and understanding assessments for the SCRV 

study unit are presented in this report. The purposes of this 

report are to provide a (1) study unit description: briefly 

describe the hydrogeologic setting of the SCRV study unit, 

(2) status assessment: assessment of the current status of 

untreated-groundwater quality in the primary aquifer system 

in the SCRV study unit, and (3) understanding assessment: 

identification of the natural and human factors affecting 

groundwater quality, and explanation of the relations between 

water quality and selected potential explanatory factors. An 

explanation of the causative factors of any relations between 

water quality and explanatory factors is beyond the scope of 

this report.

The status assessment in this report includes analysis of 

water-quality data from 42 wells selected for sampling by the 

USGS within spatially distributed grid cells across the SCRV 

(hereinafter referred to as USGS-grid wells). The USGS-grid 

wells mostly were public-supply wells (PSW) but included 

other wells with perforation intervals similar to wells listed 

in the CDPH database. Samples were collected from USGSgrid wells for analysis of anthropogenic constituents, such 

as volatile organic compounds (VOCs) and pesticides, as 

well as naturally occurring constituents, such as major ions, 

nutrients, and trace elements. Water-quality data from the 

CDPH database also were used to supplement data collected 

by USGS for the GAMA Program. The resulting set of waterquality data from USGS-grid and selected CDPH-grid wells 

were considered to be representative of the primary aquifer 

system in the SCRV study unit.

To provide context, the water-quality data discussed in 

this report were compared to California and Federal regulatory 

and non-regulatory benchmarks for treated drinking water. 

The assessments in this report are intended to characterize 

the quality of untreated groundwater resources in the primary 

aquifers in the study unit, not the treated drinking-water 

delivered to consumers by water purveyors. Benchmarks 

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

groundwater.

The understanding assessment for SCRV includes 

data from 11 wells sampled by USGS for the purpose of 

understanding (hereinafter referred to as USGS-understanding 

wells). Some of the USGS-understanding wells selected for 

this purpose had perforations in shallow or deep zones, above 

or below the primary aquifer system. Potential explanatory 

factors examined included land use, well depth and depth 

to top-of-perforations, groundwater age, and geochemicalcondition indicators (redox status). A comprehensive analysis 

of all possible explanatory factors is beyond the scope of this 

report.

Water-quality data for samples collected by the USGS 

for the GAMA Program in the SCRV study unit and details of 

sample collection, analysis, and quality-assurance procedures 

were reported by Montrella and Belitz (2009). Using the 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, analyzing 

ancillary datasets, classifying groundwater age, and assessing 

the status and understanding of groundwater quality and its 

relation to selected explanatory factors.
4 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Basin and Range

Klamath

Mountains

Desert

Cascades and

Modoc Plateau

Southern 

Coast

 Ranges

Sierra

Nevada

Central

Valley

Northern

Coast

Ranges

San Diego

Drainages

Transverse Ranges and Selected Peninsular Ranges

Bakersfield

San Francisco

OREGON

NEVADA

MEXICO

ARIZONA

Redding

Los Angeles

San Diego

Pacific Ocean

0 200 MILES

0 200 KILOMETERS

40

42 124 122 120 118 116 114

38

36

34

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection,

North American Datum of 1983 (NAD 83)

Provinces from Belitz and others, 2003

Sacramento

Santa Clara River Valley 

GAMA Study Unit

9677-0301 Montrella._Figure 1. Location in California

Figure 1. Location of the Santa Clara River Valley study unit and California hydrogeologic provinces (modified from 

Belitz and others, 2003), California GAMA Priority Basin Project.
Introduction 5

IP010121_fig 02

OXNARD

OXNARD

FOREBAY

SANTA CLARA

RIVER VALLEY

EAST

PIRU

1

33

126

27

118

14

101

5

5

10

210

405

34

00’

34

30’

119 00’ 118 30’

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,

North American Datum of 1983 (NAD 83)

LOS ANGELES CO

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

South Mtn

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Ventura

Clara

Calleguas

Creek

Santa Susana Mountains

VENTURA CO

Santa Ynez Mts

UPPER

VENTURA

RIVER

LOWER

VENTURA

RIVER

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

PIRU

OXNARD

FOREBAY

FILLMORE

MOUND

SANTA

PAULA

Study unit boundary

Ojai Valley

Groundwater basins within the study unit

 (from California Department of Water Resources, 2003) Subbasins within the 

 Santa Clara River and

 Ventura River

 groundwater basins

Subarea within the 

 Oxnard groundwater

 subbasin

EXPLANATION

Water bodies

Stream or river

PACIFIC OCEAN

Upper Ojai Valley

Ventura River Valley

Santa Clara River

 Valley

Pleasant Valley

Arroyo Santa Rosa Valley

Las Posas Valley

Simi Valley

Figure 2. Study area and geographic features of the Santa Clara River Valley study unit, California GAMA Priority Basin Project.
6 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Description of the Santa Clara River 

Valley Study Unit

The SCRV study unit lies in the northwestern part 

of the Transverse Ranges and selected Peninsular Ranges 

hydrogeologic province described by Belitz and others 

(2003) and covers about 460 mi2 (1,192 km2) in parts of Los 

Angeles and Ventura Counties, California. The SCRV study 

unit includes eight groundwater basins as defined by the 

California Department of Water Resources (2004a–2004n): 

Ojai Valley, Upper Ojai Valley, Ventura River Valley, Santa 

Clara River Valley, Pleasant Valley, Arroyo Santa Rosa Valley, 

Las Posas Valley, and Simi Valley. The study unit is bounded 

to the south by the Santa Monica Mountains and to the north 

by the Topatopa and Santa Ynez Mountains. The San Gabriel 

Mountains form the eastern boundary, and the Pacific Ocean 

lies to the west of the study area (fig. 2).

The land-surface altitude of the SCRV rises from sea 

level in the western part of the SCRV to more than 1,440 ft 

(440 m) in the eastern part of the Santa Clara River Valley 

basin. However, the land-surface altitudes of the surrounding 

mountains are as high as 5,800 ft (1,768 m). The climate in the 

SCRV is characterized as a Mediterranean climate with cool, 

moist winters, and dry, warm summers. The average annual 

precipitation ranges from 12 to 28 in/yr (30 to 71 cm/yr), 

with most rainfall occurring in the winter months and at high 

altitudes (California Department of Water Resources 2004c, 

2004e, 2004i, 2004m).

Land use in the study unit is 40 percent natural, 

37 percent agricultural, and 23 percent urban (fig. 3), based 

on the classification of USGS National Land Cover Data 

(Nakagaki and others, 2007). The natural land use consists 

of shrubs and grassland, with wetlands along the rivers and 

forested areas at higher altitudes. Natural land use is primarily 

in the Santa Clara River Valley East subbasin. Agricultural 

and urban land uses predominate throughout the remainder 

of the study unit (fig. 4). However, there is natural land use 

in the areas of wetlands and lagoons near the mouths of 

the Santa Clara River and Calleguas Creek in the western 

part of the study unit. The primary crops in the SCRV study 

unit are citrus, avocados, alfalfa, pasture, strawberries, dry 

beans, along with additional vegetables and subtropical fruits 

(California Department of Water Resources, 2001; Hanson and 

others, 2003). The largest urban areas in the western part of 

the study unit are the cities of Ventura, Oxnard, Camarillo, and 

Simi Valley (fig. 4). Large urban areas in the eastern part of 

the study unit include the cities of Newhall and Santa Clarita.

Regionally, groundwater primarily flows from the 

upland or elevated areas of the study unit toward the Pacific 

Ocean. Hanson and others (2003) developed a numerical 

groundwater flow model of the aquifer systems in the Santa 

Clara–Calleguas groundwater system, which includes the 

Santa Clara River Valley, Pleasant Valley, Las Posas Valley, 

and Arroyo Santa Rosa Valley groundwater basins, as 

defined by the CDWR. Hanson and others (2003) included 

a stratigraphic column and a related aquifer designation 

for geologic units used in the flow model (table 1). Publicsupply and irrigation wells that withdraw water from the 

Santa Clara–Calleguas groundwater system are screened in 

the Upper- and Lower-aquifer systems. The Upper-aquifer 

system as defined by Turner (1975) and Hanson and others 

(2003) includes the Shallow, Oxnard, and Mugu aquifers. The 

Lower-aquifer system includes the Hueneme, Fox, and Grimes 

Canyon aquifers. Prior to urban and agricultural development, 

groundwater discharge was through evapotranspiration, 

discharge to rivers, and discharge to the sea through seeps 

in submarine canyons and cliffs (Hanson and others, 2003). 

After development, groundwater pumping for agricultural use 

accounts for the greatest amount of discharge from the aquifer 

system (Hanson and others, 2003), followed by municipal use. 

Recharge to the groundwater system is through percolation 

from the three main river systems—Ventura River, Santa 

Clara River, and Calleguas Creek—along with infiltration 

of direct precipitation on the landscape (Hanson and others, 

2003; California Department of Water Resources, 2004a, 

2004b, 2004c, 2004d, 2004e, 2004f, 2004g, 2004h, 2004i, 

2004j, 2004k, 2004l, 2004m, 2004n). The source of water for 

streams is from runoff of precipitation, reservoir discharge, 

dewatering wells, and treated wastewater effluent. Percolation 

of irrigation-return water also contributes to groundwater 

recharge, but the amount of recharge from this source varies 

across the study unit.

The groundwater basin and subbasin boundaries in the 

SCRV study unit are largely controlled by faulting, folding, 

and deformation associated with the regional tectonic regime 

of the Transverse Ranges (Emery, 1960; Vedder and others, 

1969; Dahlen and others, 1990; Izbicki, 1996b; Izbicki and 

Martin, 1997) (fig. 5). The present-day geomorphic features 

of the SCRV study unit are mostly a result of Middle through 

Late Pleistocene north-south compressional tectonics (Greene 

and others, 1978; Yeats and others, 1988). Uplift on the eastern 

part of the Oak Ridge fault and other faults have created 

east-west trending mountain chains (South, Oak Ridge, and 

Santa Susana Mountains) that separate groundwater basins 

to the north (Santa Clara River Valley) and south (Las Posas, 

Pleasant, Arroyo Santa Rosa, and Simi Valleys, fig. 5). The 

bedrock underlying the SCRV study unit consists of lowpermeability upper Cretaceous and Tertiary sedimentary, 

volcanic, igneous, and metamorphic rocks. Many of the 

geologic formations outcropping in and around the study unit 

consist of marine sedimentary deposits of Tertiary age (fig. 5). 

The Tertiary marine rocks include the Miocene Monterey 

Formation, which consists of thick units of sulfur-rich organic 

siliceous and carbonaceous shale containing oil, tar, and metal 

sulfide minerals [for example, pyrite (FeS2)] (Davis, 1961; 

Stankiewicz and others, 1996; Gutierrez-Alonso and Gross, 

1997). The marine sedimentary formations were uplifted and 

became some of the source material for younger formations 

that make up the alluvium and aquifer material in the SCRV 

study unit (Hsü and others, 1980; Mangelodorf and Rullkötter, 

2003).
Description of the Santa Clara River Valley Study Unit 7

IP010121_fig 03

20

40

60

80

100

0

100 80 60 40 20 0

100

80

60

40

20

0

PERCENT URBAN

PERCENT AGRICULTURE

PERCENT NATURAL

Grid well (USGS or CDPH)

Understanding well (USGS)

Average for entire study unit

Average for grid wells

Average for CDPH wells

EXPLANATION

Land use based on

500-meter

radius around wells

Figure 3. Proportions of urban, agricultural, and natural land use for USGS- and 

CDPH-grid wells, USGS-understanding wells, and other CDPH wells, Santa Clara 

River Valley study unit, California GAMA Priority Basin Project. (Land use was 

determined from USGS National Land Cover Data from Nakagaki and others, 2007.)
8 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Figure 4. Land use in the Santa Clara River Valley study unit, California GAMA Priority Basin Project.

1

33

126

27

118

14

101

5

5

10

210

405

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

LOS ANGELES CO

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

VENTURA CO

Santa Ynez Mts

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

Las

Posas

Valley

Santa Clara

River Valley

Santa Clara

River Valley

Pleasant

Valley

Urban

LAND USE CLASSIFICATION

Agricultural

Natural

Land use data from Nagasaki and others (2007).

Study unit boundary

Groundwater basins within the 

 study unit (from California Department

 of Water Resources, 2003)

EXPLANATION

Arroyo Santa

Rosa Valley

Simi Valley

Arroyo Santa

Rosa Valley

Water bodies

Stream or river

PACIFIC OCEAN
Description of the Santa Clara River Valley Study Unit 9

Geologic 

era

Geologic 

system

Geologic 

series 

(epoch)

Weber and others (1976) Dibblee 1

Turner (1975)

Green and 

others (1978) 2

RASA 3 Aquifer system 

model layers

Lithologic units and formations Aquifers

Cenozoic

Quaternary

Holocene Recent Alluvium 

(Lagoonal, beach, river and flood plain deposits, 

artificial fill, and alluvial fan deposits)

Recent 

alluvial and 

semiperched

Shallow Upper-aquifer 

system 4, layer 1 

Recent Alluvium

(Lagoonal, beach, river and flood plain 

deposits, and alluvial fan deposits)

Oxnard 5

Late (Upper) 

Pleistocene 6

Older Alluvium 

(Lagoonal, beach, river and flood plain, alluvial 

fan, terrace, and marine terrace deposits)

Mugu 2

Saugus Formation 7 

(Terrestrial fluvial 

sediments)

Saugus Formation Hueneme Upper 

Hueneme

Lower-aquifer 

system, layer 2

San Pedro Formation 8 

(Marine clays and sands 

and terrestrial fluvial 

sediments)

Lower 

Hueneme

Las Posas Sand 

(Marine shallow 

regressive sands)

Fox Canyon Fox 

Canyon

Early (Lower) 

Pleistocene 6

Santa Barbara Formation 8 

(Marine shallow regressive 

sands)

Grimes 

Canyon 9,10

Grimes 

Canyon

Pico Formation 11 

(Marine siltstones, sandstones, and 

conglomerates)

Formation not included in 

regional flow model

Formation not 

included in 

regional flow 

model

Tertiary

Pliocene 6 Repetto Formation

(Terrestrial fluvial sandstones, sandstones, and 

shales)

Miocene Santa Margarita Formation, Monterey Shale, 

Rincon Mudstone, Towsley Formation 12

(Terrestrial fluvial sandstones and fine-grained 

lake deposits)

Not included Santa 

Margarita 

sandstones 

included 

in northeastern 

Santa Rosa 

Valley

Lower-aquifer 

system, layer 2

Conejo Volcanics

(Terrestrial and marine extrusive and intrusive, 

felsic-andesites to basalts)

Formation not included in 

regional flow model

Formation not 

included in 

regional flow 

Lower Topanga Formation, Topanga-Vaqueros model

Sandstones, Modelo Formation, Sisquoc 

Formation

(Marine transgressive sands and siltstones)

Table 1. Stratigraphic column and related designations of geologic units by source and aquifer system model layers in the 

groundwater and surface-water flow model of the Santa Clara–Calleguas Basin, Ventura County, California. 

[Modified from Hanson and others, 2003]
10 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Table 1. Stratigraphic column and related designations of geologic units by source and aquifer system model layers in the 

groundwater and surface-water flow model of the Santa Clara–Calleguas Basin, Ventura County, California.— Continued

[Modified from Hanson and others, 2003]

Geologic 

era

Geologic 

system

Geologic 

series 

(epoch)

Weber and others (1976) Dibblee 1

Turner (1975)

Green and 

others (1978) 2

RASA 3 Aquifer system 

model layers

Lithologic units and formations Aquifers

Cenozoic

Tertiary

Oligocene Sespe Formation 

(Terrestrial fluvial 

claystones and sandstones)

Formation not included in regional flow model Formation not 

included in 

regional flow 

Eocene Llajas Formation, model

Coldwater Sandstone, 

Cozy Dell Shale, Matilija 

Sandstone, Juncal 

Formation, Santa 

Susanna Formation 

(Marine sandstones, 

mudstones, and 

claystones)

Paleocene Martinez Formation 

(Terrestrial conglomerate, 

sandstones, and marine 

shales)

Mesozoic

Upper 

Cretaceous

Chico Formation

(Sandstones with shales)

1

 Formations from Dibblee (1988, 1990a, 1990b, 1991, 1992a, 1992b, 1992c, 1992d).

2

 Perched aquifer designated in parts of the Oxnard Plain only.

3

 From Hanson and others (2003) as part of the Southern California Regional Aquifer-System Analysis Program of the U.S. Geological Survey.

4

 Shallow aquifer included in the Oxnard Plain Forebay and inland subbasins. Semiperched part of Shallow aquifer not included in remainder of Oxnard Plain.

5

 Restricted to the Oxnard Plain and Forebay by Turner (1975).

6

 Modified on the basis of ash-deposit age dates (Yerkes and others, 1987, fig. 11.2).

7

 Mapped in eastern Ventura County subbasins of Santa Paula, Fillmore, Piru, and Las Posas Valley and may be time equivalent to parts of the San Pedro and 

Santa Barbara Formations (Weber and others, 1976, fig. 3).

8

 Mapped in western Ventura County subbasins.

9

 San Pedro Formation everywhere in Pleasant Valley where the Santa Barbara Formation was assigned to the Grimes Aquifer.

10 Las Posas and Pleasant Valley Basins only.

11 Includes mud pit and claystone members.

12

 Also known as Santa Margarita Sandstone.
Description of the Santa Clara River Valley Study Unit 11

IP010121_fig 05

Co

Los Angeles

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

South Mtn

Oak Ridge Mts

Sulphur Mtn

Santa Susana Mountains

Ventura

Co

Santa Ynez Mts

Ojai

Ventura

Simi Valley

Port Hueneme

Newhall

Camarillo

Oxnard

Santa Paula

Santa

Clarita

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Santa Clara

River Valley

Santa

Clara

River

Valley

Las Posas Valley Simi Valley

Pleasant Valley Arroyo Santa

Rosa Valley

Pacific

Ocean

EXPLANATION

Water bodies

Study unit boundary

Stream or river

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Geology from Jennings (1977),

Saucedo and others (2000)

Groundwater basins within the 

 study unit (from California

 Department of Water Resources, 2003)

Fault-Dashed where approximate,

 dotted where concealed

Other

Volcanic Rocks

Sedimentary Rocks

Quaternary alluvium

Plio-Pleistocene non-marine,

 Pliocene non-marine

Pliocene marine

Miocene marine

Tertiary volcanic rocks

Tertiary pyroclastic rocks

Tertiary intrusive rocks

Schist, various ages & type

Mesozoic granitic rocks

Mafic granitic rocks

Precambrian rocks

Oligocene nonmarine

Eocene marine

Paleocene marine

Upper Cretaceous marine

Jurassic marine

Fillmore

Narrows

Piru

Narrows

Country Club

Fault 

Oak Ridge Fault River

River

Santa

Clara

Ventura

Calleguas

Creek

Q

Q

Q

Q

E

M

P

M

Q

P

Tv

P

M

pC

M

QPc

Tv

E E

J

grMz

Q

sch

Ku

Q

Ep

Q

P

Oc

Q

M

P

Oc

pC

M

pC

E

M

E

Ku

M

Oc

Oc

Ep

QPc

Oc

QPc

Oc

grMz

M

Ep

Oc

grMz

QPc

QPc

Oc

Q

E

Oc

QPc

M

QPc

QPc

QPc

Ku

P

grMz

pC

QPc

Oc

Ep

QPc

Ti

pC

Oc

M

Tv

Oc

gr-m

M

QPc

E

P

Q

Q

Oc

Ep

Oc Oc

Tv

P

P

QPc

Tvp

pC

K

Ep Ku

Ti

Ti

Tv

P

Oc

Tvp

Q

QPc

Ku

Tv

Q

QPc

P

M

Oc

E

Ep

Ku

J

Tv

Tvp

sch

grMz

gr-m

pC

Figure 5. Geology of the Santa Clara River Valley study unit, California GAMA Priority Basin Project.
12 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Ventura River Valley, Ojai Valley, and Upper Ojai 

Valley Basins

The primary freshwater-bearing deposits vary across the 

SCRV study unit. The water-bearing formations in the Upper 

and Lower Ventura River Valley subbasins and Ojai Valley 

and Upper Ojai Valley basins are alluvium of Pleistocene and 

Holocene age and largely are unconfined. The thickness of 

the alluvium typically ranges from 50 to 100 ft, although the 

thickness can be as much as 300 ft in parts of the Upper Ojai 

Valley basin. In the Lower Ventura River Valley subbasin, 

groundwater also is pumped from the middle Pleistocene age 

San Pedro Formation, which is partially confined near the 

mouth of the Ventura River (California Department of Water 

Resources, 2004d, 2004f, 2004m, 2004n).

Simi Valley and Arroyo Santa Rosa 

Valley Basins

The water-bearing formations in the Simi Valley and 

Arroyo Santa Rosa Valley groundwater basins are alluvium of 

Pleistocene and Holocene age and generally are unconfined 

(California Department of Water Resources, 2004a, 2004l). 

The alluvium reaches a maximum thickness of 730 ft in the 

Simi Valley basin but becomes shallow in the western part of 

the valley. The alluvium reaches a thickness of 200 ft in the 

Arroyo Santa Rosa Valley basin. The San Pedro Formation 

also is a major water-bearing unit in the Arroyo Santa Rosa 

Valley. The San Pedro Formation reaches a thickness of 700 ft, 

and is a confined aquifer in the western part of the basin.

Santa Clara River Valley Basin

The Santa Clara River Valley groundwater basin consists 

of six subbasins (fig. 2): Santa Clara River Valley East, 

Piru, Fillmore, Santa Paula, Mound, and Oxnard. The Piru, 

Fillmore, and Santa Paula subbasins, located along the Santa 

Clara River, are separated by a series of narrows created by 

reverse faults on the northern and southern sides of the valley. 

The Piru and Fillmore narrows (fig. 5) are caused by the more 

competent Tertiary rock reducing the width of the subbasins, 

causing groundwater to discharge back to the surface 

(Reichard and others, 1999). The Mound subbasin, also 

located along the Santa Clara River, is mostly separated from 

Santa Paula subbasin by the Country Club fault (figs. 2 and 5), 

which impedes groundwater flow (Reichard and others, 1999).

In the Santa Clara River Valley East subbasin, the 

primary water-bearing deposits are Holocene-age alluvium, 

Pleistocene-age terrace deposits, and the Pleistocene-age 

Saugus Formation (California Department of Water Resources, 

2004k). The alluvial deposits are thickest (as much as 240 ft) 

beneath the Santa Clara River channel. Terrace deposits are 

located along the low-lying flanks of the foothills and the 

upper reaches of tributaries to the Santa Clara River but yield 

little groundwater. The Saugus Formation consists of two 

parts in the Santa Clara River Valley East subbasin. Water in 

the lower part is brackish and well yield is low. The upper 

part of the Saugus Formation contains poorly consolidated 

lenses of conglomerate and sandstone interbedded with sandy 

mudstone. The maximum depth of the upper part reaches 

5,500 ft below land surface. The upper part provides much 

higher well yields and water that is of better quality than the 

lower part.

 Upper- and Lower-aquifer systems (Hanson and others, 

2003) can be recognized in the Piru, Fillmore, Santa Paula, 

and Mound subbasins. The Lower-aquifer system generally is 

unconfined in the Piru and Fillmore subbasins, but becomes 

confined in the western parts of the Santa Paula and Mound 

subbasins. Hanson and others (2003) identify the Hueneme 

aquifer of the Saugus Formation as the uppermost aquifer 

within the Lower-aquifer system in these four groundwater 

subbasins (table 1). The California Department of Water 

Resources (2004b, 2004e, 2004h, 2004j) identifies the primary 

water-bearing formation as the San Pedro Formation in the 

Lower-aquifer system. Yeats and others (1988) point out that 

the geologic formation name, “Saugus,” has been used by 

previous authors in reference to shallow marine and nonmarine deposits of Pleistocene age, typically referred to as the 

San Pedro Formation (Weber and others, 1973). The depths 

of production wells sampled as part of this study in the Piru, 

Fillmore, Santa Paula, and Mound subbasins range from 275 

to 1,190 ft below land surface. The shallow wells are screened 

in the Upper-aquifer system, the deeper wells usually are 

screened in the Lower-aquifer system, and a few wells are 

screened in both aquifers. 

The Upper- and Lower-aquifer systems are better defined 

in the Oxnard subbasin than in the Piru, Fillmore, and Santa 

Paula subbasins. The Upper-aquifer system primarily consists 

of the Pleistocene and Holocene age sands and gravels. The 

Lower-aquifer system consists of Pleistocene marine and 

non-marine deposits. The Lower-aquifer system is unconfined 

in the Oxnard forebay (fig. 2) located adjacent to the Santa 

Clara River in the northeastern part of the Oxnard subbasin 

and is hydraulically connected to the Upper-aquifer system. 

The United Water Conservation District (UWCD) recharge 

facilities, which are located in the Oxnard forebay, play an 

important role in recharging both aquifer systems of the 

Oxnard subbasin (United Water Conservation District, 2003). 

Wells sampled as part of this study are screened in the Upperaquifer, the Lower-Aquifer or both aquifer systems.

Because of pumping along coastal areas of the Oxnard 

subbasin, seawater intrusion is a concern for local water 

management authorities (California Department Water 

Resources, 1965; Greene and others, 1978; Izbicki and others, 

1995; Izbicki, 1996a, 1996c; United Water Conservation 

District, 2003; Fox Canyon Groundwater Management 

Agency, 2007).
Methods 13

Las Posas Valley and Pleasant Valley Basins

The Las Posas Valley and Pleasant Valley groundwater 

basins also have defined Upper- and Lower-aquifer systems. 

The Upper-aquifer system in these basins consists of 

unconfined alluvial deposits of Pleistocene and Holocene age. 

The thickness of these alluvial deposits reaches as much as 

300 ft in the Las Posas Valley basin. A regional unconformity, 

which is caused by a deformation during deposition of older 

alluvium, exists at the base of the Upper-aquifer system 

separating the overlying alluvial deposits from the underlying 

San Pedro Formation and Santa Barbara Formation that 

make-up the Lower-aquifer system (Turner, 1975; Greene 

and others, 1978). This unconformity is more distinct in the 

eastern part of the Las Posas Valley basin. Alluvial deposits 

in Pleasant Valley are not a major source of groundwater 

(California Department of Water Resources, 2004i). The 

Lower-aquifer system is composed of the Hueneme, Fox 

Canyon, and Grimes Canyon aquifers (table 1). The Hueneme 

Aquifer consists of the upper part of the San Pedro and Saugus 

Formations; the Fox Canyon aquifer consists of the lower part 

of the San Pedro Formation, which is composed of coarse 

gravel 100–200 ft thick in Las Posas Valley basin and as much 

as 300 ft thick in Pleasant Valley basin (Hanson and others, 

2003; California Department of Water Resources, 2004c, 

2004i). The Santa Barbara Formation, deposited during the 

early Pleistocene, underlies the San Pedro Formation; and 

is composed of shallow marine sand. The Grimes Canyon 

aquifer is composed of the upper part of the Santa Barbara 

Formation, which is as much as 1,000 ft thick in the Las Posas 

Valley basin (Hanson and others, 2003; California Department 

of Water Resources, 2004c). Most of the wells sampled in this 

part of the study unit are screened from about 500 to 1,000 ft 

deep.

Methods 

Methods used for the GAMA Priority Basin Project 

were selected to achieve the following objectives: (1) design 

a sampling plan suitable for statistical analysis, (2) combine 

CDPH data with data collected in 2007 by the USGS 

for assessing water quality, (3) determine proportions of 

the primary aquifers that have high, moderate, and low 

concentrations for constituent classes and individual 

constituents of interest, (4) identify constituents of interest 

to be discussed further, (5) compile and classify relevant 

ancillary data to identify relations of potential explanatory 

factors to water quality, and, (6) investigate statistical relations 

between potential explanatory factors and water quality. 

The status assessment was designed to provide a spatially 

unbiased assessment of untreated groundwater quality in the 

primary aquifer systems. The primary metric for defining 

groundwater quality in this report is relative-concentration, 

which compares groundwater chemical concentrations to regulatory 

and non-regulatory benchmarks used to evaluate drinking-water 

quality. Constituents are included or not included in the assessment 

on the basis of relative-concentration criteria. Groundwater-quality 

data collected by the USGS for the GAMA Program and data 

compiled in the CDPH database are used in the status assessment. 

Two statistical approaches based on spatially unbiased grids with 

equal-area cells in each study area are used to calculate aquiferscale proportions.

The understanding assessment was designed to evaluate 

the natural and human factors that affect groundwater quality at 

the study-unit level. A finite set of potential explanatory factors 

were analyzed in relation to constituents of interest to place the 

observed water quality within the context of physical and chemical 

processes. Statistical tests were used to identify significant 

correlations between the constituents of interest and potential 

explanatory factors. 

Status Assessment Methods

The status assessment included two primary steps. First, 

water-quality data were normalized to their respective waterquality benchmarks by calculating their relative-concentrations 

(Toccalino and others, 2004; Toccalino and Norman, 2006). 

Second, aquifer-scale proportions were determined for categories 

of “high,” “moderate,” and “low” relative-concentrations by using 

two approaches: (1) grid-based and (2) spatially weighted (Belitz 

and others, 2010). The “grid-based” approach uses one well per 

cell to represent groundwater quality—water-quality data are from 

wells sampled by the USGS, supplemented with data from selected 

wells in the CDPH database. The spatially weighted approach 

uses data for wells sampled by the USGS and all wells in the 

CDPH database, and weights each well such that each grid cell 

contributes equally to represent groundwater quality. Results for 

the two approaches were compared, and results from the preferred 

approach were used to identify constituents of interest for further 

discussion.

Relative-Concentrations and Water-Quality 

Benchmarks

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

Sample concentration Relative-concentration

Water-quality benchmark concentration = .

Relative-concentrations provide a means to relate concentrations 

of constituents in groundwater samples to water-quality 

benchmarks. Relative-concentrations less than 1.0 indicate sample 

concentrations less than the benchmark; relative-concentrations 

greater than 1.0 indicate sample concentrations greater than the 

benchmark. The use of relative-concentrations also normalizes 

a wide range of concentrations for different constituents to a 
14 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Category Relative-concentrations for 

organic constituents

Relativeconcentrations 

for inorganic 

constituents

High > 1 > 1

Moderate > 0.1 and < 1 > 0.5 and < 1

Low < 0.1 < 0.5

A relative-concentration of 0.1 was used as a threshold 

between low and moderate values of organic and specialinterest constituents compared with a relative-concentration 

of 0.5 for inorganic constituents. Organic and specialinterest constituents, which generally are anthropogenic and 

uncommon in groundwater, are usually less prevalent and have 

smaller relative-concentrations than inorganic constituents. 

The USEPA also established a relative-concentration of 0.1 

of the regulatory benchmark as a threshold concentration 

so that the agency would be notified if the presence of a 

pesticide in surface water or groundwater equals or exceeds 

that threshold (U.S. Environmental Protection Agency, 1998). 

In contrast, inorganic constituents typically occur naturally 

at concentrations that could be greater than 0.1 of regulatory 

benchmarks; consequently, it would be difficult to identify 

the highest-priority inorganic constituents that may have 

elevated concentrations greater than background levels if 

a relative-concentration of 0.1 were used as the threshold 

between moderate and low relative-concentrations. Therefore, 

a relative-concentration of 0.5 was used as a threshold 

between low and moderate values of inorganic constituents, 

rather than 0.1 as was used for the organic and special-interest 

constituents.

Design of Sampling Network for Status 

Assessment

The wells selected for sampling by the USGS in this 

study were selected to provide a statistically unbiased, 

spatially distributed set of wells for the assessment of the 

quality of groundwater in the primary aquifers. Water-quality 

data from the USGS-grid wells were supplemented with 

data from selected wells from the CDPH database [CDPHgrid wells—discussed in more detail in the California 

Department of Public Health (CDPH)-Grid Well Selection 

section] to obtain more complete grid coverage and to include 

constituents that were not analyzed for in every USGS-grid 

well. These data were used to assess the proportions of the 

primary aquifer system that have high, moderate, and low 

relative-concentrations. 

common scale relative to benchmarks. Toccalino and others 

(2004), Toccalino and Norman (2006), and Rowe and 

others (2007) used the ratio of measured concentration to a 

benchmark [either MCLs or Health-Based Screening Levels 

(HBSLs)] and defined this ratio as the Benchmark Quotient. 

Relative-concentrations used in this report are equivalent to 

the Benchmark Quotient reported by Toccalino and others 

(2004) for constituents with water-quality benchmarks. 

HBSLs were not used in this report because HBSLs are not 

currently used as benchmarks by California drinking-water 

regulatory agencies. Relative-concentrations were computed 

only for compounds with water-quality benchmarks; therefore, 

constituents lacking water-quality benchmarks were not 

included in the status assessment.

Regulatory and non-regulatory water-quality benchmarks 

apply to water that is served to the consumer, not to untreated 

groundwater. However, to provide context for the waterquality results, concentrations of constituents measured in the 

untreated groundwater were compared with regulatory and 

non-regulatory human-health-based water-quality benchmarks 

established by the U.S. Environmental Protection Agency 

(USEPA) and CDPH (U.S. Environmental Protection Agency, 

2006; California Department of Health Services, 2007). The 

human-health benchmarks used include MCLs, notification 

levels (NLs), health advisory levels (HALs), action levels 

(ALs), and risk-specific dose (1 in 100,000 lifetime risk 

of cancer, RSD5-US). Non-regulatory benchmarks set for 

aesthetic concerns, secondary maximum contaminant levels 

defined by CDPH and USEPA (SMCL-CA and SMCL-US), 

also were used. For a constituent with multiple types of 

benchmarks, the benchmark used for calculation of relativeconcentration was selected according to the following order 

of priority: regulatory human-health (MCL and AL), nonregulatory aesthetic (SMCL), and non-regulatory humanhealth (in the order NL-CA, HAL-US, and RSD5-US). For 

the regulatory human-health benchmarks, Federal benchmark 

levels were used unless the California levels were lower. 

California public health goals were not used in this report. 

Additional information on the types of benchmarks and the 

benchmarks for all constituents analyzed is provided by 

Montrella and Belitz (2009).

Relative-concentrations were classified into high, 

moderate, and low categories. 
Methods 15

The primary data used for the 

grid-based calculations of aquifer-scale 

proportions were data from wells sampled 

by the GAMA Priority Basin Project. 

Detailed descriptions of the methods used 

to identify wells for sampling are given in 

Montrella and Belitz (2009). Briefly, the 

study unit was divided into 48 equal-area 

grid cells of about 10 mi2 (25 km2) each 

(fig. 6); one well was randomly selected 

to represent each cell (Scott, 1990). Wells 

were selected from the population of 

wells in statewide databases maintained 

by CDPH and USGS. If a grid cell did 

not contain accessible CDPH wells, then 

commercial, irrigation, or domestic wells 

with perforation intervals at similar depths 

as the CDPH wells were considered for 

sampling. One USGS-grid well was 

sampled in 42 of the 48 grid cells (fig. 6). 

The six grid cells where samples were 

not collected had few, if any, wells, 

and (or) permission to sample was not 

granted for wells in those cells. The 42 

USGS-grid wells were sampled during 

the period April through June 2007 and 

included 27 CDPH wells, 12 irrigation 

wells, 2 dewatering wells, and 1 domestic 

well. USGS-grid wells in the study unit 

were numbered in the order of sample 

collection and with the prefix SCRV 

(fig. A1A). The USGS-grid wells were 

sampled by the USGS for the GAMA 

Priority Basin Project but are owned by 

other organizations or individuals.

Samples collected from USGSgrid wells were analyzed for 232 to 

374 constituents (table 2). VOCs, 

pesticides, potential wastewater indicator 

compounds, perchlorate, noble gases, 

tritium, and stable isotopes of hydrogen 

and oxygen were analyzed in water 

samples from all wells. Nutrients, 

dissolved organic carbon, isotopes of 

nitrogen and oxygen in nitrate, major 

and minor ions, trace elements, and 

redox species were analyzed in samples 

from many wells. Gasoline oxygenates, 

pharmaceuticals, additional pesticides, 

carbon isotopes, isotopes of chlorine and 

bromine, and radiochemical constituents 

were analyzed in samples from some 

wells. The collection, analysis, and 

quality-control data for the analytes listed 

in table 2 are described by Montrella and 

Belitz (2009).

Table 2. Analytes and numbers of wells sampled for each analytical schedule, Santa 

Clara River Valley study unit, GAMA Priority Basin Project, April–June 2007.

Schedule

 Fast Intermediate Slow 

Total number of wells 27 17 9

Number of grid wells sampled 26 11 5

Number of understanding wells sampled 1 6 4

 Analyte groups Number of constituents

Dissolved oxygen, pH, specific 

conductance, temperature

4 4 4

Volatile organic compounds (VOCs) 1 85 85 85

Pesticides and degradates 63 63 63

Potential wastewater-indicator compounds 69 69 69

Perchlorate 1 1 1

Noble gases and tritium 2 7 7 7

Tritium 3 1 1 1

Hydrogen and oxygen isotopes of water 4 2 2 2

Nutrients 5 5

Dissolved organic carbon 1 1

Nitrogen and oxygen isotopes of nitrate 2 2

Major and minor ions, and trace elements 35 35

Arsenic, chromium, and iron species 6 6

Alkalinity, turbidity 2

Gasoline oxygenates 5 3

Pharmaceuticals 4 14

Polar pesticides and degradates 6 59

Carbon isotopes 2

Chlorine and bromine isotopes 4 2

Radon-222 1

Radium isotopes 2

Gross alpha and beta radioactivity 4

Microbial constituents 4 4

 Sum: 232 281 374

1

 Includes 10 constituents classified as fumigants or fumigant synthesis byproducts.

2

 Analyzed at Lawrence Livermore National Laboratory, Livermore, California.

3

 Analyzed at U.S. Geological Survey Stable Isotope and Tritium Laboratory, Menlo Park, California.

4

 Not discussed in this report.

5

 Does not include five constituents in common with VOCs.

6

 Does not include four constituents in common with pesticides and degradates.
16 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig 06

1

33

126

27

118

14

101

5

5

10

210

405

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

LOS ANGELES CO

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

South Mtn

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

VENTURA CO

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

EXPLANATION

Water bodies

Stream or river

PACIFIC OCEAN

Santa Clara study

 unit boundary

Equal-area randomized-sampling

 grid cell California Department of

 Public Health (CDPH) -grid well

 (CDPH data only)

All CDPH-other wells

USGS-understanding well

USGS-grid well (GAMA and

 supplemental CDPH data)

USGS-grid well (GAMA data only)

Figure 6. Location of study-unit grid cells, USGS-grid and USGS-understanding wells, California Department of Public Health (CDPH) 

wells used to supplement inorganic constituents (CDPH-grid wells), and other CDPH wells located in the Santa Clara River Valley 

study unit, California GAMA Priority Basin Project.
Methods 17

California Department of Public Health (CDPH)-

Grid Well Selection

Data for VOCs, pesticides, and perchlorate were collected 

at all 42 USGS-grid wells. The USGS-grid-well data included 

more VOC and pesticide constituents, and reporting levels 

were lower, than reporting levels from the CDPH database. 

Therefore, CDPH data for these constituents were not used to 

supplement USGS-grid well data for the status assessment.

Samples to be analyzed for inorganic constituents were 

collected from 16 of 42 USGS-grid wells. Because the GAMA 

Priority Basin Project did not collect a complete suite of 

inorganic constituents for all grid cells, the CDPH database 

was used to provide data for inorganic constituents for the 

cells that lacked this data (table 3). In addition, the GAMA 

Priority Basin Project was not able to sample wells in six of 

the grid cells. CDPH wells were selected to represent as many 

of these grid cells as possible. CDPH wells that were selected 

to supplement USGS-grid wells are referred to as “CDPHgrid” wells. The approach used to identify suitable CDPH 

wells is described in appendix A. Briefly, the first choice was 

to use CDPH data from the same well as the USGS-grid well 

(“DG” CDPH-grid wells; fig. A1B; table A1). If the DG well 

did not have all needed data, then a second well in the same 

grid cell was randomly selected from the subset of CDPH 

wells with data (“DPH” CDPH-grid wells; fig. A1B; table A1). 

No more than one “DPH” CDPH-grid well was selected for 

each cell. Combining data from CDPH-grid wells with data 

from USGS-grid wells produced inorganic data for 47 of the 

48 cells. All other CDPH wells with data from the current 

period (November 1, 2003, through October 31, 2006) not 

selected to be CDPH-grid wells are referred to as “CDPHother” wells. Comparisons of data from USGS and CDPH 

wells to assess the validity of using these different sources in 

combination are presented in appendix B.

Identification of Constituents of Interest

The GAMA Priority Basin Project used monitoring data 

in the CDPH database along with newly collected data for 

characterization of the groundwater resource. The statewide 

CDPH database contains data for regulated constituents that 

have water-quality benchmarks. Although other organizations 

also collect water-quality data, the CDPH database is the only 

database containing statewide public-supply well waterquality data. Data for some constituents, including VOCs, 

pesticides, inorganic constituents, and radioactive constituents, 

are available from both the USGS-GAMA and CDPH 

databases. However, more VOCs and pesticides were analyzed 

for by the USGS Priority Basin Project than were available 

from the CDPH database (table 4). In addition, laboratory 

reporting levels (LRLs) for USGS-GAMA analyses typically 

were one to two orders of magnitude lower than the method 

Table 3. Inorganic constituents and number of grid wells per 

constituent, Santa Clara River Valley study unit, GAMA Priority 

Basin Project. 

[GAMA, Groundwater Ambient Monitoring and Assessment Program; CDPH, 

California Department of Public Health; MCL-US, U.S. Environmental 

Protection Agency (USEPA) maximum contaminant level; MCL-CA, CDPH 

maximum contaminant level; SMCL-CA, CDPH secondary maximum 

contaminant level; SMCL-US, USEPA secondary maximum contaminant 

level; NL-CA, CDPH notification level; AL-US, USEPA action level; 

HAL-US, USEPA lifetime health advisory; USGS, U.S. Geological Survey]

Constituent Benchmark 

type

Number of 

grid wells 

sampled 

by GAMA

Number of 

grid wells 

selected 

from CDPH

Nutrients with health-based benchmarks

Ammonia as nitrogen HAL-US 16 4

Nitrite as nitrogen MCL-US 16 26

Nitrate as nitrogen MCL-US 16 30

Trace elements and minor ions with health-based benchmarks

Aluminum MCL-CA 16 29

Antimony MCL-US 16 28

Arsenic MCL-US 16 28

Barium MCL-CA 16 27

Beryllium MCL-US 16 28

Boron NL-CA 16 17

Cadmium MCL-US 16 28

Chromium MCL-CA 16 29

Copper AL-US 16 29

Lead AL-US 16 20

Molybdenum HAL-US 16 0

Nickel MCL-CA 16 28

Selenium MCL-US 16 28

Strontium HAL-US 16 0

Thallium MCL-US 16 28

Vanadium MCL-US 16 13

Fluoride MCL-CA 16 29

Trace elements and major ions with 

secondary maximum contaminant levels

Iron SMCL-CA 16 29

Manganese SMCL-CA 16 29

Silver SMCL-CA 16 28

Zinc SMCL-US 16 29

Chloride SMCL-CA 16 29

Sulfate SMCL-CA 16 29

Total dissolved solids SMCL-US 16 30

Radioactive constituents with health-based benchmarks

Gross alpha radioactivity MCL-US 5 24

Gross beta radioactivity MCL-US 5 9

Radon-222 MCL-US 5 1

Radium-226 plus -228 MCL-US 5 21

Uranium MCL-US 16 19
18 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

detection levels (MDLs) used for analyses compiled by CDPH 

(table 4). Thus, the newly acquired GAMA data were selected 

to enhance the CDPH data by providing a larger number of 

analytes and lower laboratory reporting levels than are found 

in the CDPH database. Both datasets are used in the status 

assessment and understanding assessment.

The CDPH database contains more than 235,000 

historical records from about 340 wells in the SCRV, 

necessitating targeted retrievals to effectively access the waterquality data. CDPH data were used with USGS-grid data to 

identify constituents in the SCRV study unit that were detected 

at concentrations greater than water-quality benchmarks in 

any sample during the period of record. Data were retrieved 

from the CDPH database for samples from all wells located 

in the SCRV study unit for the full period of record (March 5, 

1984, to October 31, 2006). Constituent concentrations were 

identified as historically high (table 5) if (1) concentrations 

were high (greater than benchmarks) at any time during the 

full period of record (March 5, 1984–October 31, 2006) and 

(2) concentrations were not high in the most recent 3-year 

period of CDPH data (November 1, 2003, through October 31, 

2006, hereinafter referred to as current period) or in the 

USGS-grid data. These constituents do not reflect current 

conditions on which the status assessment is based. 

Of the more than 300 constituents analyzed in the SCRV 

study unit, only those of greatest importance to water quality 

in the primary aquifer systems are discussed in this report. 

Constituents examined in the status assessment include:

1. Constituents that had high or moderate relativeconcentrations in the CDPH database for samples 

collected during the 3-year period (November 1, 2003–

October 31, 2006) prior to USGS-GAMA well sampling,

2. Constituents that had high or moderate relativeconcentrations in the USGS-grid wells or USGSunderstanding wells sampled April-June 2007 (discussed 

later in this report) or,

3. Organic constituents that were detected in greater than 

10 percent of the USGS-grid wells, even if all relativeconcentrations were low, because of their prevalence in 

the aquifer.

The status assessment particularly focused on any 

constituent with aquifer-scale proportions that had high values 

in greater than 2 percent of the primary aquifer system. 

Estimation 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 benchmarks. Two 

statistical approaches were selected to evaluate the proportions 

of the primary aquifer system (Belitz and others, 2010) in 

the SCRV study unit with high, moderate, or low relativeconcentrations 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 wells (cells) 

represented by a high relative-concentration for a 

particular constituent by the total number of wells (grid 

cells) with data for that constituent (see appendix C

for details of methods). Proportions of moderate 

and low relative-concentrations were calculated 

similarly. Confidence intervals for grid-based detection 

frequencies of high concentrations were computed 

using the Jeffreys interval for the binomial distribution 

(Brown and others, 2001). Although the grid-based 

estimate is spatially unbiased, the grid-based approach 

may not identify constituents that are present at high 

relative-concentrations in small proportions of the 

primary aquifers.

• 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, November 1, 

2003, to October 31, 2006), USGS-grid wells, and 

USGS-understanding wells with perforation depth 

intervals representative of the primary aquifer system. 

USGS-understanding wells that were monitoring 

wells were excluded because these wells were 

perforated at shallower or deeper depths than wells 

typically used for public supply in the areas in which 

the monitoring wells were located. For the spatially 

weighted approach, proportions were computed on a 

cell-by-cell basis (Isaaks and Srivastava, 1989) rather 

than as an average of all wells. The proportion of high 

relative-concentrations for each constituent for the 

primary aquifers was computed by (1) computing the 

proportion of wells with high relative-concentrations 

in each grid-cell; and (2) averaging together the gridcell proportions computed in step (1) (see appendix C

for details of methods). 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).

The raw detection frequencies of wells with high relativeconcentrations for constituents calculated by using the same 

data that were used for the spatially weighted approach 

are provided for reference in this report, but were not used 

to assess aquifer-scale proportions. These raw detection 

frequencies are not spatially unbiased because the wells in the 

CDPH database are not uniformly distributed. Consequently, 

high relative-concentrations in spatially clustered wells in 

a particular area representing a small part of the primary 

aquifers could be given a disproportionately high weight 

compared to spatially unbiased methods. 
Methods 19

Table 4. Comparison of number of compounds and median method-detection levels or laboratory reporting levels by type of 

constituent for data stored in the California Department of Public Health (CDPH) database and data collected by the Santa Clara River 

Valley study unit, GAMA Priority Basin Project, April through June 2007.

[GAMA, Groundwater Ambient Monitoring and Assessment Program; MDL, method-detection level; LRL, laboratory reporting level; mg/L, milligrams per 

liter; μg/L, micrograms per liter; pCi/L, picocuries per liter; ssLc, sample-specific critical level; nc, not collected]

Constituent type

CDPH GAMA

Concentration 

or activity units Number of 

compounds

Median 

MDL

Number of 

compounds

Median 

LRL

Volatile organic compounds (including 

fumigants) plus gasoline oxygenates

83 0.5 88 0.08 μg/L

Pesticides plus degradates 74 1.0 122 0.009 μg/L

Pharmaceuticals nc nc 14 2

0.027 μg/L

Perchlorate 1 4.0 1 0.5 μg/L

Trace elements 20 8.0 24 0.16 μg/L

Radioactive constituents (ssLc) 5 1.0 8 3

1.0 pCi/L

Nutrients, major and minor ions 14 Unknown1 17 0.06 mg/L

1

 Median MDL for nutrients is 0.4 mg/L; MDLs for major and minor ions were not available.

2

 Value reported is a median MDL.

3

 Value reported is a median ssLc for eight radioactive constituents collected and analyzed by GAMA.

Table 5. Constituents in wells in the California Department of Health (CDPH) database at historically high concentrations from 

March 5, 1984, to October 30, 2003, Santa Clara River Valley study unit, California, GAMA Priority Basin Project.

[A high analysis is defined as a concentration that is greater than human-heath benchmark for that constituent. GAMA, Groundwater Ambient Monitoring and 

Assessment Program; MCL-US; U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; mg/L, 

milligrams per liter; μg/L, micrograms per liter]

Constituent

Number of wells 

with historical 

data

Benchmark

type

Benchmark 

value Unit

Number of wells 

with at least one 

historically high 

value

Date of most 

recent high value

Trace elements

Antimony 237 MCL-US 6 μg/L 1 10/01/1997

Cadmium 245 MCL-US 5 μg/L 2 07/12/1989

Chromium 245 MCL-CA 50 μg/L 2 12/12/1989

Mercury 244 MCL-US 2 μg/L 1 06/07/1999

Major and minor ions

Fluoride 248 MCL-CA 2 mg/L 8 02/05/2001

Fumigants

Dibromochloropropane (DBCP) 227 MCL-US 0.2 μg/L 2 01/14/1997

Dibromoethane (EDB) 226 MCL-US 0.05 μg/L 1 09/04/1992

Other organics

Carbon tetrachloride 247 MCL-CA 0.5 μg/L 1 10/04/1991

Bis(2-ethylhexyl)phthalate 111 MCL-US 5 μg/L 4 10/01/1996
20 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Aquifer-scale proportions discussed in this report 

primarily were estimated using the grid-based approach, and 

secondarily using the spatially weighted approach. The gridbased aquifer-scale proportions were used unless the spatially 

weighted proportions were significantly different. Significantly 

different results were defined as follows:

1. If the aquifer proportion for the high category was zero 

by using the grid-based approach and non-zero by using 

the spatially weighted approach, then the result from the 

spatially weighted approach was used. This situation can 

arise when the concentration of a constituent is high in a 

small fraction of the aquifer.

2. If the grid-based aquifer proportion for the high 

category was non-zero, then the 90 percent confidence 

interval (based on the Jeffreys interval for the binomial 

distribution, Brown and others, 2001) was used to 

evaluate the difference. If the spatially weighted 

proportion was within the 90 percent confidence 

interval, then the grid-based proportion was used. If the 

spatially weighted proportion was outside the 90 percent 

confidence interval, then the spatially weighted proportion 

was used.

Aquifer-scale proportions for the moderate and low 

categories primarily were determined from the grid-based 

estimates because for some constituents the reporting levels 

for analyses in CDPH were too high to distinguish between 

moderate and low relative-concentrations.

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 include trihalomethanes, solvents, and 

pesticides. The classes of inorganic constituents with humanhealth benchmarks for which aquifer-scale proportions were 

calculated include trace elements, radioactive constituents, 

and nutrients. Classes of inorganic constituents with aesthetic 

benchmarks, for which aquifer-scale proportions were 

calculated, include major and minor ions (which include 

sulfate and chloride), total dissolved solids (TDS), and trace 

elements (manganese and iron). 

Understanding Assessment Methods

The purpose of the understanding assessment is to place 

the observed groundwater-quality data into a physical and 

chemical context. A finite set of potential explanatory factors, 

including land use, well depth, depth to top-of-perforations, 

groundwater age classes, and geochemical-condition 

indicators, were analyzed in relation to constituents of interest. 

Statistical tests were used to identify significant correlations 

between the constituents of interest and potential explanatory 

factors. Selected correlated data that were most valuable for 

improving understanding of factors affecting water quality are 

shown graphically. 

The USGS-GAMA data include hydrologic tracers 

and geochemical indicators that are not regulated waterquality constituents with human-health benchmarks. These 

constituents are important for understanding groundwater 

quality and are discussed for that purpose in this report. 

USGS-Understanding Wells

For the understanding assessment, the grid-based data 

from USGS- and CDPH-grid wells were further supplemented 

with information from USGS-understanding wells for the 

purpose of assessing relations of selected water-quality 

constituents to explanatory factors (fig. A1A). Eleven wells 

(USGS-understanding wells) were selected for sampling by 

the USGS to increase the data density in several areas to assess 

spatial changes in water quality. The USGS-understanding 

wells sampled in the SCRV study unit were numbered in the 

order of collection, with a prefix modified from those used for 

the USGS-grid wells (SCRVU). These wells were selected on 

the basis of two design objectives. 

First, USGS-understanding wells were sampled to 

assess changes in water quality with depth in the primary 

aquifer system. Four depth-dependent groundwater samples 

were collected from one long-screened understanding well, 

(SCRVU-04) located in the Pleasant Valley basin, by using 

sampling methods described by Izbicki (2004). A sample 

of the surface discharge of well SCRVU-04 was collected 

approximately 2 months before the depth-dependent sampling. 

A velocity log was collected prior to sample collection to 

determine which parts of the perforated interval in SCRVU-04 

were contributing most of the flow into the well. Discussion 

of the results of the depth-dependent sampling is beyond the 

scope of this report.

Second, USGS-understanding wells were selected to 

identify changes in water quality in near-coastal aquifers 

in the Oxnard subbasin and Pleasant Valley basin. Previous 

investigations have identified elevated concentrations of 

total dissolved solids in groundwater in parts of these basins, 

partially caused by both seawater intrusion and the underlying 

aquifer material (California Department of Water Resources, 

1965; Izbicki, 1996b). In an attempt to differentiate among 

the potential sources of salinity, selected USGS-grid and 

USGS-understanding wells were sampled for chlorine and 

bromine isotopes, which are constituents that may aid in 

identifying sources of salinity (table 2). USGS-understanding 

wells were selected to characterize source waters that may 

mix in the aquifers of the Oxnard subbasin. These source 

waters are referred to as “end members” because water 

samples representing mixtures tend to plot along mixing lines 

bounded by the “end member” compositions. Targeted “end 

members” include (1) an understanding well located adjacent 

to the UWCD Saticoy recharge facility, representing modern 

recharge (SCRVU-03), (2) deep wells representing the Loweraquifer system (SCRVU-01, -02, -04), and (3) a shallow 
Evaluation of Potential Explanatory Factors 21

monitoring well adjacent to the coast, representing seawater 

composition (SCRVU-10). Discussions of the salinity source 

characterization using isotopic and other geochemical tracers 

are beyond the scope of this report. 

Statistical Analysis

Non-parametric statistical methods were used to identify 

significant correlations between water-quality variables and 

potential explanatory variables. Non-parametric statistics 

are robust techniques that generally are not affected by 

outliers and do not require that the data follow any particular 

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

(p) generated from hypothesis testing for this report was 

compared to a threshold value (α) of 5 percent (α = 0.05) 

to evaluate whether the relation was statistically significant 

(p < α). Two different statistical tests were used because 

the set of potential explanatory factors included continuous 

and categorical variables. Relations between categorical 

variables (for example, groundwater age or land-use classes) 

and continuous variables were evaluated using the Wilcoxon 

rank-sum test. Correlations between continuous variables 

were evaluated using Spearman’s method. Correlations 

between potential explanatory factors, between water-quality 

parameters, and between potential explanatory factors and 

water-quality constituents were tested for significance. 

Correlations on pesticides, THMs, and solvents were 

performed on the sum of their concentrations. For example, 

the sum of solvent concentrations for SCRV-13 is 1.27 μg/L 

[0.09 (perchloroethene) + 0.07 (carbon tetrachloride) + 0.12 

(trans-1,2-dichloroethene) + 0.99 (trichloroethene)].

The wells selected for the statistical tests were USGSand CDPH-grid wells and USGS-understanding wells, with 

the exception of well SCRVU-10. Well SCRVU-10 is a 

shallow monitoring well located on the coast that has been 

significantly affected by seawater and was excluded because 

samples collected from the well do not represent the primary 

aquifer system. Data from CDPH-other wells were not used 

in the understanding assessment because carbon isotope, 

tritium, dissolved oxygen, and some well construction data 

were not available. Correlations between explanatory factors 

and groundwater constituents were tested using data from the 

USGS- and CDPH-grid wells only, or the USGS- and CDPHgrid wells plus USGS-understanding wells. Because the 

USGS-understanding wells primarily represented groundwater 

in agricultural areas that were not randomly selected on a 

spatially distributed grid, the USGS-understanding wells were 

excluded from analysis of relations between water quality and 

areally distributed explanatory variables (land use and lateral 

position), to avoid areal-clustering bias. However, USGSunderstanding wells were included in the analysis of relations 

between water quality and vertically distributed explanatory 

variables (depth, groundwater age, and oxidation-reduction 

characteristics). 

Evaluation of Potential Explanatory 

Factors

Brief descriptions of potential explanatory factors, 

including land use, septic systems density, well depth, 

depth to the top-of-well perforations, groundwater age, and 

geochemical conditions, are included in this section. Data 

sources and methodology used for assigning values for 

potential explanatory factors are described in appendix D.

Land Use

In the SCRV study unit, land use is a combination of 

agricultural, urban, and natural; however, land use in the areas 

surrounding the study unit primarily is natural (fig. 4). Land 

use in the study unit was 37 percent agricultural, 23 percent 

urban, and 40 percent natural on the basis of Nagasaki and 

others (2007). Land use within a 500-m (1,640-ft) radius 

around each of the grid wells, on average, had a higher 

proportion of agricultural (42 percent) and urban (30 percent) 

land uses, and a lower proportion of natural land use 

(28 percent) (fig. 3), probably because wells tend to be located 

in areas of human settlement. In contrast, the land use within 

a 500-m radius around each of the CDPH wells, on average, 

had lower agricultural land use (33 percent), and even higher 

urban land use (37 percent) than the study unit as a whole. In 

general, the average agricultural and urban land use within 

the 500-m (1,640-ft) radius of grid wells is within 10 percent 

of the agricultural and urban land use of the study unit as a 

whole.

The predominant land use varies spatially across the 

study unit. For example in the Santa Clara River Valley 

East subbasin (easternmost part of the Santa Clara River 

Valley basin), the land use is predominantly natural, with 

the exception of the urban areas in and around the cities of 

Newhall and Santa Clarita (fig. 4). The remainder of the 

Santa Clara River Valley basin (excluding Santa Clara River 

Valley East subbasin) is predominantly agricultural, with 

distinct urban land use in and around the cities of Santa Paula, 

Ventura, and Oxnard, and natural land use around the Santa 

Clara River floodplain. The Simi Valley groundwater basin is 

predominantly urban. 

Septic Systems

Septic system density within the 500-m (1,640-ft) radius 

around the USGS-grid and USGS-understanding wells ranged 

from 0 to 137 septic tanks per km2, with a median of 5 septic 

tanks per km2. Many of the wells in areas with septic system 

densities greater than median were located in the Oxnard 

subbasin or the Ventura River Valley, Ojai Valley, or upper 

Ojai Valley basins (table D1).
22 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Depth

Depths of USGS- and CDPH-grid wells varied across 

the study unit. Well depths in grid wells ranged from 60 to 

1,440 ft below land surface, with a median of 541 ft (fig. 7). 

Depth to top-of-perforations ranged from 15 to 800 ft, with a 

median of 403 ft. The maximum perforation length was 690 ft, 

with a median of 220 ft. These values represent a subset of 

the USGS- and CDPH-grid wells because the well depths 

and depth to top-of-perforations were not known for every 

well. Only the wells with construction information available 

were included in the analyses of the explanatory variables 

(table D2).

The median depth of USGS-understanding wells was 

deeper than the median depth for USGS- and CDPH-grid 

wells (fig. 7). The median depth for USGS-understanding 

wells was 750 ft. However, the median of the depth to top-ofperforations and perforation lengths were similar for grid and 

understanding wells.

Groundwater Age

Groundwater samples were assigned age classifications 

based on the tritium, helium-3, carbon-14, and helium-4 

content of the samples (appendix D). Of the 53 grid and 

understanding wells sampled by USGS-Priority Basin Project, 

groundwater samples were classified as modern age in 17 

wells, mixed age in 18 wells (evidence of modern and premodern groundwater in the same sample), and pre-modern age 

in 13 wells (table D3). Samples from five wells could not be 

classified because the age-tracer data were incomplete or did 

not meet all quality-assurance checks. 

Wells with groundwater classified as pre-modern age 

generally were deeper than the wells with groundwater 

classified as modern or mixed age (fig.8A, table 6). The depths 

to top-of-perforations of wells with groundwater classified as 

pre-modern age generally were deeper than the depths to topof-perforations of wells with groundwater classified as modern 

or mixed age (fig. 8B, table 6). 

Figure 7. Well depths, depths to top-of-perforations, and perforation lengths, for 

USGS- and CDPH-grid and USGS-understanding wells, Santa Clara River Valley study 

unit, California GAMA Priority Basin Project.

Well

depth 

Depth to

top-ofperforations

Depth to

top-ofperforations

Length of

perforations

Well

depth 

Length of

perforations

GRID WELLS

39

UNDERSTANDING WELLS

33 31 10 9 9

39 Number of values

75th percentile

Median

25th percentile

10th percentile

90th percentile

Value greater than

90th percentile

Value less than

10th percentile

1,600

1,200

800

400

0

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

EXPLANATION
Evaluation of Potential Explanatory Factors 23

Figure 8. Relation of groundwater age classification to (A) well depth and (B) depth to top-of-perforations, and (C) numbers 

of wells with each groundwater age class in each of the three depth categories, Santa Clara River Valley study unit, California 

GAMA Priority Basin Project.

Modern

Mixed

Pre-modern

NUMBER OF WELLS

DEPTH OF PERFORATED INTERVAL

BELOW LAND SURFACE

0 2 4 6 8 10 12 14

15 17 13 0

400

DEPTH TO TOP-OF-PERFORATIONS, IN FEET

800

1,200

GROUNDWATER AGE CLASSIFICATION

12 0

200

400

WELL DEPTH, IN FEET BELOW LAND SURFACE

600

800

1,000

1,600

15 13

GROUNDWATER AGE CLASSIFICATION

15 Number of values

EXPLANATION

EXPLANATION

75th percentile

Median

25th percentile

10th percentile

90th percentile

Value greater than

90th percentile

Value less than

10th percentile

A

B

C

Modern Mixed Pre-modern

Modern Mixed Pre-modern

Entire perforated

interval less than

250 feet 

Entire perforated

interval equal to or

greater than 250 feet 

Top-of-perforated

interval less than

250 feet;

Bottom of perforated

interval equal to or

greater than 250 feet

IP010121_Figure 08abc
24 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Nearly all wells with perforation intervals entirely 

between land surface and 250 ft below land surface (9 of 

10 wells) had groundwater ages that were modern or mixed 

(fig. 8C). Likewise, many wells with perforation intervals 

entirely at depths equal to or greater than 250 ft (12 of 25 

wells) had groundwater ages that were pre-modern. Wells 

with perforation intervals that crossed the 250 ft depth 

had groundwater ages that were modern or mixed. The 

250 ft depth criterion was arbitrarily selected; groundwater 

age generally was younger, dissolved oxygen and nitrate 

concentrations generally were higher, and more pesticide and 

volatile organic compounds were detected at depths less than 

250 ft than at depths greater than 250 ft.

The presence of one pre-modern age sample from a well 

less than 250 ft deep and four modern age samples from wells 

greater than or equal to 250 ft deep (fig. 8C) indicate that 

there are local variations in the general groundwater age-depth 

relations. These variations may indicate the position of the 

well relative to regional recharge and discharge areas. For 

example, three wells with the top-of-perforation greater than 

250 ft deep and with modern ages are located adjacent to the 

Santa Clara River (wells SCRV-15, SCRV-16, and SCRV27, fig. A1A) (Montrella and Belitz, 2009); and another well 

(SCRV-24) is located in close proximity to a recharge facility 

and a golf course; the groundwater system may have relatively 

higher recharge rates in the vicinity of these wells than in 

other parts of the system, allowing modern age groundwater to 

infiltrate to greater depths than generally is the case elsewhere 

in the study unit. 

Table 6. Results of non-parametric (Wilcoxon) statistical tests for differences in values of potential explanatory factors and selected 

water-quality constituents between modern, mixed, and pre-modern groundwater age classes, Santa Clara River Valley study unit, 

California, GAMA Priority Basin Project.

[Wilcoxon rank sum tests; Z, test statistic for Wilcoxon test; Z statistic is shown when the difference between age classes is significant (p < 0.05); positive Z 

statistic (values of the selected explanatory factor or constituent of the first age class generally are greater than second age class); negative Z statistic (values 

of selected explanatory factor or constituent of first age class generally are smaller than the second age class); GAMA, Groundwater Ambient Monitoring and 

Assessment Program; ns, no significant difference]

Groundwater age class comparisons

Modern compared 

with mixed

Mixed compared 

with pre-modern

Modern compared 

with pre-modern

Z Z Z

Potential explanatory factors

 Depth to top-of-perforations ns −2.051 −3.265

 Well depth ns −2.470 −3.250

 Dissolved oxygen ns ns 2.835

 pH ns −2.042 −2.440

Selected water-quality constituents

 Total dissolved solids (TDS) ns ns ns

 Sulfate ns ns ns

 Manganese ns ns ns

 Iron ns ns −2.244

 Nitrate as nitrogen ns 2.806 3.993

 Vanadium ns ns 2.298

 Arsenic ns ns ns

 Boron ns ns ns

 Gross alpha radioactivity ns ns ns

 Perchlorate ns ns ns

 Sum of pesticide concentrations ns ns ns

 Sum of solvent concentrations ns ns ns

 Total trihalomethane concentrations ns ns ns
Evaluation of Potential Explanatory Factors 25

Geochemical Conditions

An abridged classification of oxidation-reduction 

(redox) conditions adapted from the framework presented by 

McMahon and Chapelle (2008) is given in appendix D for 

the USGS-grid and USGS-understanding wells in the SCRV 

(table D4). In general, groundwater in the Santa Clara River 

Valley study unit primarily is oxic in shallow wells (fig. 9A) 

and in wells located in the upgradient basins (that is, Ojai 

Valley and Ventura River Valley basins, and Santa Clara River 

Valley East subbasin) but becomes more reducing or anoxic 

with depth and near the western (distal or downgradient) 

end of the study unit. Redox conditions for about one-half 

of groundwater samples (25 of 52 wells with complete data 

for redox characterization) are anoxic, ranging from nitratereducing to sulfate-reducing conditions (table D4). Redox 

conditions in the Santa Clara River Valley East, Piru, and 

Fillmore subbasins, and the Ventura River Valley, Ojai Valley, 

Upper Ojai Valley, and Arroyo Santa Rosa Valley basins 

primarily were oxic. Redox conditions in the Santa Paula, 

Mound, and Oxnard subbasins, and the Las Posas Valley basin 

primarily were anoxic. Groundwater samples from the Simi 

Valley and Pleasant Valley basins indicated oxic or anoxic 

conditions in different parts of these basins.

Dissolved-oxygen (DO) concentrations were used as the 

primary indicator variable for redox conditions for comparison 

with concentrations of water-quality constituents in this report. 

DO was used because DO data were available for 52 of the 

53 USGS-GAMA sampled wells; more wells than other redox 

indicators. DO concentrations were not available for CDPHgrid wells that were used to supplement the USGS-grid well 

data. 

DO concentrations were significantly greater for samples 

from wells with modern or mixed groundwater ages than for 

wells with pre-modern groundwater ages (table 6, fig. 9). 

DO concentrations were not significantly different between 

samples from wells with modern groundwater age and wells 

with mixed groundwater age. DO concentrations ranged from 

less than 0.2 to 8 mg/L across the study unit (table D4).

Wilcoxon tests indicate pH values were significantly 

lower for wells with mixed and modern groundwater ages 

than for wells with pre-modern groundwater ages (table 6). 

Values of pH were not significantly different between the 

wells with modern groundwater ages and the wells with mixed 

groundwater ages. Values of pH ranged from 6.8 to 7.8 in the 

study unit (table D4).

Correlations Between Explanatory Variables

Significant correlations between explanatory variables are 

important to identify because apparent correlations between 

an explanatory variable and a water-quality constituent could 

indicate relations between two explanatory variables and 

not between an explanatory variable and a water-quality 

constituent. Significant correlations between explanatory 

variables are shown in table 7 and are discussed along with the 

relations between groundwater age and depth. Implications of 

cross-correlations between explanatory variables are discussed 

later in the report as part of analysis of factors affecting 

individual water-quality constituents.

DO concentrations were negatively correlated with 

depth to top-of-perforations and well depth (table 7). These 

correlations indicate that DO concentrations decrease with 

increasing depth to top-of-perforations. These correlations 

were expected based on previous studies in other aquifers 

(McMahon and Chapelle, 2008). The wells in figure 9A

represent all the wells with construction information (depth 

to top-of-perforations), DO, and groundwater age data in the 

study unit. The highest DO concentrations are from wells 

with shallow perforations, which typically have modern or 

mixed ages. DO concentrations were less than 1.0 mg/L for 

groundwater classified as pre-modern, with the exception of 

one well (SCRV-14, 1.8 mg/L, table D4). DO concentrations 

were more variable for groundwater ages classified as modern 

or mixed than for groundwater ages classified as pre-modern 

(fig. 9A). 

The pH was positively correlated with well depth and 

depth to top-of-perforations (table 7), indicating increasing 

pH with increasing depth (fig. 9B). These correlations were 

expected based on previous studies in other aquifers in 

California (Jurgens and others, 2010) and reflect dissolution of 

primary minerals, causing the pH of groundwater to increase 

with depth and continued contact of groundwater with aquifer 

materials. Izbicki and Martin (1997) used geochemical 

modeling to conclude that weathering of silicate minerals, 

which causes increased pH, was an important process along 

groundwater flow paths in the Las Posas basin. The pH was 

negatively correlated with urban land use (table 7), indicating 

increasing pH with decreasing percentage of urban land use. 

This correlation was to be expected because most wells in 

the SCRV with urban land use less than 20 percent were in 

predominantly agricultural areas (fig. 3); and wells in the 

SCRV with predominantly agricultural land use tend to be 

relatively deep (on the basis of the positive correlation of 

agricultural land use and well depth); therefore, wells with 

lower percentage of urban land use are likely to have a higher 

pH (on the basis of the positive correlation of pH and well 

depth; for example, well SCRV-20). 
26 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Figure 9. (A) Relation of dissolved-oxygen concentration (an oxidation-reduction 

condition indicator), depth to top-of-perforations, and groundwater age class, and 

(B) relation of pH, depth to top-of-perforations, and groundwater age class, Santa Clara 

River Valley study unit, California GAMA Priority Basin Project.

100

0

200

300

400

500

600

700

800

900

0 1 2 3 4 5 6 7 8

DEPTH TO TOP-OF-PERFORATION, IN FEET 

DISSOLVED OXYGEN, IN MILLIGRAMS PER LITER

100

0

200

300

400

500

600

700

800

900

6.5 7.0 7.5 8.0

DEPTH TO TOP-OF-PERFORATION, IN FEET 

pH, IN STANDARD UNITS

Modern

Mixed

Pre-modern

B

IP010121_Figure 09ab

Modern

Mixed

Pre-modern

A
Evaluation of Potential Explanatory Factors 27

The number of septic tanks or cesspools was negatively 

correlated with percentage of natural land use (table 7). 

This correlation was expected because fewer septic tanks or 

cesspools would be expected because fewer people live in 

natural land-use areas than in areas of agricultural or urban 

land use.

Well depth (and depth to top-of-perforations) was 

positively correlated with percentage of agricultural land use 

(table 7). These correlations indicate that wells are deeper 

in predominantly agricultural areas in this study unit than 

in areas that are predominantly urban or natural land use. In 

particular, seven wells with more than 80 percent agricultural 

land use were greater than 800 ft deep, putting these wells 

in the deepest 25 percent of wells included in the study. 

Intensive use of groundwater for agriculture has lowered the 

water table; thus, wells need to be deeper. Correspondingly, 

well depth (and depth to top-of-perforations) was negatively 

correlated with percentage of natural land use (table 7). These 

correlations indicate that wells are shallower in predominantly 

natural areas. 

Table 7. Results of non-parametric (Spearman’s rho method) analysis of correlations between selected 

potential explanatory factors, Santa Clara River Valley study unit, California, GAMA Priority Basin Project.

[GAMA, Groundwater Ambient Monitoring and Assessment Program; Spearman’s rho test used and rho values shown when 

correlations between selected explanatory factors are significant (p<0.05). Land-use percentages and number of septic tanks or 

cesspools within a circle with a radius of 500 meters centered around each well. <, less than; ns, not significant]

Wells 

included in 

analysis

Explanatory 

factor

Dissolvedoxygen 

concentration

pH

Number of 

septic tanks 

or cesspools

Depth to topof-perforationWell 

depth

Grid wells

Percentage of 

urban 

land use

ns -0.336 ns ns ns

Percentage of 

agricultural 

land use

ns ns ns 0.518 0.516

Percentage of 

natural 

land use

ns ns −0.360 −0.501 −0.467

Number of 

septic tanks or 

cesspools

ns ns ns ns

Grid and 

understanding 

wells

Depth to top-ofperforation -0.614 0.329

Well depth -0.585 0.345
28 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

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. The assessment applies 

only to constituents with regulatory (MCL and AL) or 

non-regulatory (HAL, RSD5-US, or NL) human-health 

benchmarks or aesthetic benchmarks (SMCL) established by 

the USEPA or the CDPH (California Department of Public 

Health, 2008a; U.S. Environmental Protection Agency, 2008a, 

2008b). The spatially distributed, randomized approach to 

well selection and data analysis yields a view of groundwater 

Benchmark type

Number of 

constituents 

analyzed

Number of 

constituents 

detected

VOCs plus gasoline oxygenates

Regulatory – HHB 33 13

Nonregulatory – HHB 26 2

No benchmark 29 2

Total: 88 17

Pesticides and degradates (NWQL Schedule 2003)

Regulatory – HHB 3 2

Nonregulatory – HHB 17 3

No benchmark 43 8

Total: 63 13

Polar pesticides and degradates (NWQL Schedule 2060)

Regulatory – HHB 10 0

Nonregulatory – HHB 9 0

No benchmark 36 0

Total: 55 0

Potential wastewater indicator compounds

Regulatory – HHB 2 0

Nonregulatory– HHB 2 0

No benchmark 50 8

Total: 54 8

quality in which all areas of the primary aquifer system are 

weighted equally.

The understanding assessment was designed to help 

answer the question of why specific constituents are, or are 

not, observed in groundwater. The understanding assessment 

addresses a subset of the constituents discussed in the status 

assessment and is based on statistical correlations between 

water quality and a finite set of potential explanatory factors. 

This assessment may improve our understanding of how 

human and natural sources of contaminants affect groundwater 

quality in the SCRV; however, it was not designed to identify 

specific sources of constituents to specific wells. 

Human-health benchmarks are established for about 

one-half of the organic and special interest constituents 

(21 of 39) that were detected in USGS-grid wells (table 8). 

Table 8. Number of constituents analyzed for, and number of constituents detected in, U.S. Geological Survey (USGS) grid wells listed 

by human-health-based or aesthetic benchmark type and constituent type, Santa Clara River Valley study unit, California, GAMA Priority 

Basin Project.

[Regulatory health-based benchmarks include U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH) maximum 

contaminant levels. Nonregulatory health-based benchmarks include USEPA lifetime health advisory levels, risk specific dose level at 10−5 lifetime cancer risk, 

and CDPH notification level. GAMA, Groundwater Ambient Monitoring and Assessment Program; HHB, human-health benchmark; NWQL, U.S. Geological 

Survey (USGS) National Water Quality Laboratory; SMCL, secondary maximum contaminant level (aesthetic benchmark); VOC, volatile organic compound]

Benchmark type

Number of 

constituents 

analyzed

Number of 

constituents 

detected

Special interest (perchlorate)

Regulatory – HHB 1 1

Total: 1 1

Sum organic and special interest constituents

Regulatory – HHB 48 16

Nonregulatory – HHB 53 5

No benchmark 159 18

Total: 260 39

Sum inorganic and radioactive constituents

Regulatory – HHB 22 21

Nonregulatory – HHB 8 8

Nonregulatory – SMCL 6 6

No benchmark 17 17

Total: 53 52
Status and Understanding of Water Quality 29

Seventeen VOCs, including gasoline oxygenates, were 

detected; human-health benchmarks are established for all 

but two VOCs. Thirteen pesticides were detected; humanhealth benchmarks are established for 5 of the 13 pesticides. 

Five of the pesticides with no benchmarks (de-ethylatrazine, 

desulfinylfipronil, 3,4-dichloroanaline, fipronil sulfide, and 

fipronil sulfone) are degradates; human-health benchmarks are 

established for two of the three parent compounds (atrazine 

and diuron) of these degradates. Human-health benchmarks 

have not been established for the parent compound (fipronil) 

of the remaining degradates that were detected. Thus, the 

organic constituents that are regulated include most of these 

constituents that were detected in groundwater in the SCRV. 

Human-health benchmarks have not been established for 

the eight potential wastewater indicator compounds that 

were detected (table 8). Four of the wastewater compounds 

were detected at concentrations greater than the compounds 

respective reporting levels (Montrella and Belitz, 2009).

In contrast to organic constituents, inorganic constituents 

were nearly always detected (52 of 53, table 8) in USGS- and 

CDPH-grid wells. Human-health or aesthetic benchmarks 

have been established for 36 of the 53 inorganic constituents. 

Most of the constituents without benchmarks are major or 

minor ions that are naturally present in nearly all groundwater.

The maximum relative-concentration for each constituent 

with a water-quality benchmark in grid wells is shown in 

figure 10. Eight inorganic constituents (including radioactive 

constituents) were detected at high relative-concentrations 

in one or more grid wells; boron was also detected at high 

relative-concentrations, but only in a few CDPH-other 

wells. In contrast, none of the organic and special-interest 

constituents were detected at high relative-concentrations 

in grid wells, and only four organic and special-interest 

constituents were detected at moderate relative-concentrations. 

Aquifer-scale proportions were calculated for each constituent 

detected at high or moderate relative-concentrations and 

for each organic and special-interest constituent detected in 

more than 10 percent of the grid wells. Spatially weighted, 

high aquifer-scale proportions were within the 90 percent 

confidence intervals for their respective grid-based aquifer 

high proportions (16 of 16 constituents; see table 9), providing 

evidence that the grid-based approach yields statistically 

equivalent results to the spatially weighted approach. 

Inorganic Constituents 

Inorganic constituents generally occur naturally 

in groundwater, although their concentrations may be 

influenced by humans as well as natural factors. Inorganic 

constituents with human-health benchmarks, as a group (trace 

elements, fluoride, radioactive constituents, and nutrients), 

had high aquifer-scale proportions in 21 percent, moderate 

in 30 percent, and low or not detected in 49 percent of the 

primary aquifer system (table 10). Inorganic constituents 

with aesthetic benchmarks, as a group, had high aquifer-scale 

proportions in 54 percent, moderate in 41 percent, and low in 

4.3 percent of the primary aquifer system.

Trace Elements and Minor Ions

Trace elements and minor ions with human-health 

benchmarks, as a class, had high relative-concentrations 

for one or more constituents in 4.4 percent, moderate in 

38 percent, and low or not detected in 58 percent of the 

primary aquifer system (table 10). Percentages may not add 

to 100 as a result of rounding. Relative-concentrations of 

arsenic, vanadium, and boron were high in at least one grid 

well (table 9).

Arsenic was detected at high relative-concentrations in 

2.3 percent of the primary aquifer system and was low or not 

detected in 98 percent (table 9, fig. 11). Arsenic was detected 

at a high relative-concentration of in one well in the foothills 

of the San Gabriel Mountains in the Santa Clara River Valley 

East subbasin (fig. 12A). 

Vanadium was detected at high relative-concentrations 

in 3.4 percent of the primary aquifer system and, like 

arsenic, was not detected at moderate relative-concentrations 

(table 9, fig. 11). Vanadium was detected at a high relativeconcentration in one well in the Arroyo Santa Rosa Valley 

groundwater basin (fig. 12B). 

Boron was detected at high relative-concentrations in 

3.2 percent of the primary aquifer system, as determined by 

using the spatially weighted approach. Boron was detected 

at high relative-concentrations in the Fillmore and Oxnard 

subbasins and in the Simi Valley and Pleasant Valley 

basins (fig. 12C). Boron was detected at moderate relativeconcentrations mostly in the Santa Clara River Valley, Pleasant 

Valley, and Simi Valley basins (fig. 12C).
30 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

0.001

0.01

0.1

1

10

100

IP010121_Figure 10

Silver

EXPLANATION

Strontium

Abbreviations: N, Nitrogen; TDS, Total Dissolved Solids; HHB, Human-Health Benchmark

Moderate

Low

Moderate

Low

Moderate

High High

Organic and specialinterest constituentsInorganic

constituents

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Volatile

Organic

Compounds

Pesticides Special

Interest

Nutrients Trace

Elements

and Minor

Ions with

HHB

Major and

Minor Ions

and Trace

ElementsSMCL

Radioactive

Constituents

2 0 1 0 0 0 0

Number of 

compounds

detected

< 0.001

Constituents with analyses in ≥24 grid wells and wells are spatially representative

Name and center of symbol is location of data unless indicated by following location line:

Constituents with analyses in <24 grid wells and wells are spatially representative

Name and center of symbol is location of data unless indicated by following location line:

Simazine

Carbon tetrachloride

Trichloroethene

Total trihalomethanes

Bromodichloromethane

Dibromochloromethane

Bromochloromethane

Bromoform

Benzene

Toluene

Carbon disulfide

Chloroform

Tetrachloroethene (PCE)

CFC-11

CFC-113

trans-1,2-Dichloroethene

Perchlorate

Atrazine

Nitrite-N

Ammonia-N

Nitrate-N

Iron

TDS

Sulfate

Chloride

Manganese

Gross alpha

Gross beta

Uranium

Radium

Radon

Antimony

Zinc

Cadmium

Barium

Strontium

Molybdenum

Lead

Chromium

Fluoride

Arsenic

Boron

Selenium

Vanadium

Aluminum

Nickel

Copper

Figure 10. Maximum relative-concentration in USGS-grid and CDPH-grid wells for constituents 

detected, grouped by type of constituent, Santa Clara River Valley study unit, California GAMA 

Priority Basin Project.
Status and Understanding of Water Quality 31

Table 9. Raw-detection frequency and aquifer-scale proportions calculated by using spatially weighted and grid-based approaches for constituents detected at concentrationsgreater than water-quality benchmarks during the most recent 3 years of available data (November 1, 2003–October 31, 2006) from the California Department of Public Health (CDPH) database, detected at high or moderate relative-concentrations in USGS-grid wells sampled April–June 2007, or organic constitutents detected in greater than 10 percent of USGS-grid wells sampled, Santa Clara River Valley study unit, California, GAMA Priority Basin Project. [High, concentrations greater than water-quality benchmark; moderate, concentrations less than or equal to the water-quality benchmark, but greater than 0.1 of the benchmark (for organic and special interest constituents) or greater than 0.5 of the benchmark (for inorganic constituents); low, concentrations less than or equal to 0.1 of benchmark (for organic and special interest constituents) or less than or equal to 0.5 of the benchmark (inorganic constituents); GAMA, Groundwater Ambient Monitoring and Assessment Program]

Constituent

Raw detection frequency 1

Spatially weighted aquifer-scale 

proportion 1 

(Cell declustered)

Grid-based aquifer-scale proportion

90-percent confidence 

interval for grid-based 

high aquifer-scale 

proportion 2

Number of 

wells

Number of 

high wells

High 

aquiferscale 

proportion

(percent)

Number of 

cells

Moderate 

values 

(percent)

High 

values 

(percent)

Number of 

cells

Moderate 

values 

(percent)

High 

values 

(percent)

Lower 

limit

(percent)

Upper

limit

(percent)

Major and minor elements Total dissolved solids (TDS) 166 47 28 45 54 33 46 56 35 24 47Sulfate 163 25 15 44 32 20 45 27 22 13 34Manganese 168 48 29 44 3.3 32 45 2.2 38 27 50Iron 165 30 18 44 6.0 24 45 4.4 22 13 34Chloride 163 0 0.0 44 2.3 0.0 45 2.2 0.0 0.0 2.9Fluoride 163 0 0.0 44 0.3 0.0 45 0.0 0.0 0.0 2.9NutrientsNitrate plus nitrite, as nitrogen 193 14 7.3 45 8.9 11 46 8.7 15 8.2 25

Trace elementsVanadium 96 3 3.1 33 0.0 3.0 29 0.0 3.4 0.6 13

Arsenic 159 1 0.6 43 0.0 1.2 44 0.0 2.3 0.4 8.5

Boron 117 5 4.3 34 50 3.2 32 45 0.0 0.0 4.0

Selenium 159 0 0.0 43 5.8 0.0 44 6.8 0.0 0.0 3.0

Aluminum 159 0 0.0 44 0.4 0.0 45 0.0 0.0 0.0 2.9

Lead 123 0 0.0 38 1.3 0.0 36 0.0 0.0 0.0 3.7

Radioactive constituentsGross-alpha radioactivity, 

72-hr count

148 6 4.1 39 11 7.3 29 6.9 14 5.9 27

Uranium 91 0 0.0 39 13 0.0 35 20 0.0 0.0 3.8

Trihalomethanes (disinfection by products) 3

Total trihalomethanes 128 0 0.0 45 0.7 0.0 42 2.4 0.0 0.0 4.4Chloroform 128 0 0.0 45 0.0 0.0 42 0.0 0.0 0.0 4.4
32 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project Table 9. Raw-detection frequency and aquifer-scale proportions calculated by using spatially weighted and grid-based approaches for constituents detected at concentrations greater than water-quality benchmarks during the most recent 3 years of available data (November 1, 2003–October 31, 2006) from the California Department of Public Health (CDPH) database, detected at high or moderate relative-concentrations in USGS-grid wells sampled April–June 2007, or organic constitutents detected in greater than 10 percent of USGS-grid wells sampled, Santa Clara River Valley study unit, California, GAMA Priority Basin Project. —Continued. [High, concentrations greater than water-quality benchmark; moderate, concentrations less than or equal to the water-quality benchmark, but greater than 0.1 of the benchmark (for organic and special interest constituents) or greater than 0.5 of the benchmark (for inorganic constituents); low, concentrations less than or equal to 0.1 of benchmark (for organic and special interest constituents) or less than or equal to 0.5 of the benchmark (inorganic constituents); GAMA, Groundwater Ambient Monitoring and Assessment Program] Constituent Raw detection frequency 1 Spatially weighted aquifer-scale proportion 1 (Cell declustered) Grid-based aquifer-scale proportion 90-percent confidence interval for grid-based high aquifer-scale proportion 2 Number of wells Number of high wells High aquifer- scale proportion (percent) Number of cells Moderate values (percent) High values (percent) Number of cells Moderate values (percent) High values (percent) Lower limit (percent) Upper limit (percent) Solvents Carbon tetrachloride (tetrachloromethane) 132 0 0.0 45 0.7 0.0 42 2.4 0.0 0.0 4.4 Trichloroethene (TCE) 132 0 0.0 45 0.7 0.0 42 2.4 0.0 0.0 4.4 Pesticides Simazine 121 0 0.0 45 0.0 0.0 42 0.0 0.0 0.0 4.4 Atrazine 121 0 0.0 45 0.0 0.0 42 0.0 0.0 0.0 4.4 Special interest Perchlorate 70 0 0.0 45 10.7 0.0 43 12 0.0 0.0 4.4

1 Based on most recent CDPH analysis during November 1, 2003–October 31, 2006 (most recent data available at time of analysis) combined with grid-based data.2 Based on the Jeffreys interval for the binomial distribution (Brown and others, 2001).3 The MCL-US (U.S. Environmental Protection Agency maximum contaminant level) threshold for trihalomethanes is the sum of chloroform, bromoform, bromodichloromethane, and dibromochloromethane.
Status and Understanding of Water Quality 33

Table 10. Aquifer-scale proportions calculated for constituent classes, Santa 

Clara River Valley study unit, California, GAMA Priority Basin Project.

[Aquifer-scale proportions are given in percentage of area of the primary aquifer system. GAMA, 

Groundwater Ambient Monitoring and Assessment Program; THM, trihalomethane; TDS, total 

dissolved solids; SO4, sulfate; Cl, chloride; VOC, volatile organic compound; SMCL, secondary 

maximum contaminant level. All values greater than 10 percent are rounded to the nearest 

1 percent; values less than 10 percent are rounded to the nearest 0.1 percent; values may not add 

up to 100 percent because of rounding]

Constituent

Aquifer-scale proportion (percent)

High 

values

Moderate 

values

Low values or 

not detected

Inorganics with human-health benchmarks

Trace elements and minor ions 4.4 38 58

Radioactive constituents 14 11 75

Nutrients 15 8.7 76

Any inorganic with human-health benchmarks 21 30 49

Inorganics with aesthetic benchmarks (SMCL)

Major and minor ions (TDS, SO4, Cl) 35 56 8.7

Manganese and (or) iron 44 0.0 56

Any inorganic constituent with an SMCL 54 41 4.3

Organics with human-health benchmarks

Pesticides 0.0 0.0 100

Total THMs 0.0 2.4 98

Solvents 0.0 2.4 98

Other VOCs 0.0 0.0 100

Any organic constituent 0.0 4.8 95

Constituents of special interest

Perchlorate 0.0 12 88
34 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Moderate

Low

High

Moderate

Low

High

RELATIVE-CONCENTRATION (MCL),

DIMENSIONLESS

RELATIVE-CONCENTRATION (SMCL),

DIMENSIONLESS

0.001

0.01

0.1

1

10

100

0.001

0.01

0.1

1

10

100

Arsenic Boron Gross alpha Nitrate

Iron Manganese Sulfate TDS

Vanadium

Figure 11. Relative-concentrations of inorganic constituents with 

human-health-based or aesthetic benchmarks categorized as high, 

medium, and low in USGS- and CDPH-grid wells, Santa Clara River 

Valley study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 35

Figure 12. Relative-concentrations of selected inorganic constituents with human-health-based and aesthetic benchmarks in 

USGS-grid and CDPH-grid wells and CDPH-other wells in the Santa Clara River Valley study unit, California GAMA Priority Basin 

Project: (A) arsenic, (B) vanadium, (C) boron, (D) gross alpha radioactivity, (E) nitrate, (F) total dissolved solids, (G) sulfate, 

(H) manganese, and (I) iron.

1

33

126

27

118

14

101

5

5

10

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405 OXNARD

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PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

LOS ANGELES CO

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

VENTURA CO

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

Land-use data from Nagasaki and others, 2007.

EXPLANATION

PACIFIC OCEAN

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

A. Arsenic
36 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig 12B

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

LOS ANGELES CO

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

VENTURA CO

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

Land-use data from Nakagaki and others, 2007.

PACIFIC OCEAN

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

B. Vanadium

Figure 12.—Continued
Status and Understanding of Water Quality 37

Figure 12.—Continued

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

C. Boron

Land-use data from Nakagaki and others, 2007.

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

LOS ANGELES CO

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

VENTURA CO

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

PACIFIC OCEAN
38 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Figure 12.—Continued

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

PACIFIC OCEAN

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

D. Gross alpha radioactivity
Status and Understanding of Water Quality 39

Figure 12.—Continued

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

PACIFIC OCEAN

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

E. Nitrate
40 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

PACIFIC OCEAN

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

F. Total dissolved solids

Figure 12.—Continued
Status and Understanding of Water Quality 41

IP010121_fig 12G

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

PACIFIC OCEAN

G. Sulfate

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

Figure 12.—Continued
42 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig 12H

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

PACIFIC OCEAN

H. Manganese

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

Figure 12.—Continued
Status and Understanding of Water Quality 43

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

PACIFIC OCEAN

I. Iron

EXPLANATION

USGS- or

CDPH-grid

wells

USGSunderstanding

wells

California

Department of

Public Health

(CDPH-other) wells

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Low or not

detected

Moderate

High

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

Figure 12.—Continued
44 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Factors Affecting Arsenic

Arsenic concentrations in the SCRV were not 

significantly different between groundwater-age classes 

(table 6) and were not significantly correlated to any other 

explanatory factors (table 11). The low frequency of high 

and moderate relative-concentrations of arsenic in the SCRV 

may make identification of explanatory factors for arsenic 

concentrations difficult. An arsenic concentration greater than 

the MCL-US was detected in one CDPH-grid well (SCRVDPH-48) in the Santa Clara River Valley East subbasin 

(fig. 12A). DO data were not available for this CDPH-grid 

well; the sample pH was 7.8.

Factors Affecting Vanadium

Vanadium concentrations in the SCRV were significantly 

different between modern and pre-modern groundwater-age 

classes (table 6) but were not significantly correlated to any 

explanatory factor (table 11). The low frequency of high and 

moderate relative-concentrations of vanadium in the Santa 

Clara River Valley study unit may make identification of 

explanatory factors for vanadium concentrations difficult. 

High relative-concentrations of vanadium were detected in 

one CDPH-grid well (SCRV-DG-13) and two CDPH-other 

wells located in close proximity to the CDPH-grid well and to 

each other in the Arroyo Santa Rosa Valley basin (fig. 12B). 

Desorption of vanadium from aquifer material can occur under 

oxic conditions (DO >0.5 mg/L) and relatively high pH (>8.0) 

(Reimann and Caritat, 1998; Wright and Belitz, 2010). The 

DO and pH of CDPH-grid well SCRV-DG-13 were 2.7 and 

7.4 mg/L, respectively, so other processes may account for the 

high concentration of vanadium in this well.

Factors Affecting Boron

Boron concentrations in the SCRV were negatively 

correlated with agricultural land use and positively correlated 

with urban land use (table 11). These correlations indicate 

that wells surrounded predominantly by urban land use 

tend to have higher concentrations of boron than wells 

surrounded by agricultural land use. Boron concentrations 

were not significantly different between groundwater-age 

classes (table 6) and were not significantly correlated to any 

explanatory factor other than land use (table 11).

High boron concentrations in groundwater have been 

attributed to several sources. Boron can be concentrated 

in municipal sewage because boron is found in household 

detergents (washing powders). Because standard treatment 

processes do not remove boron, it can become concentrated 

in the treated wastewater effluent (Dotsika and others, 2005). 

Several wastewater plants are located in the study unit that 

discharge treated effluent to surface water and (or) allow 

treated effluent to percolate to groundwater (United Water 

Conservation District, 2008). In addition to wastewater 

sources, boron may be found at high concentrations (greater 

than 1,000 μg/L) in groundwater where the aquifer material is 

composed of organic-rich sediments (Goodarzi and Swaine, 

1994; Williams and Hervig, 2004). High boron concentrations 

often occur near the coast. It is likely the high concentrations 

are caused by seawater intrusion because boron concentrations 

in seawater typically are greater than 4,000 μg/L. High boron 

concentrations of greater than 2,000 μg/L in understanding 

wells SRVU-09 and SRVU-10, which are located near the 

coast, possibly indicate the effects of the mixing of fresh 

groundwater with seawater in these areas. Additional work is 

required to more fully explore sources and controls on boron 

concentrations in the Santa Clara River Valley study unit.

Boron concentrations are greater than the NL-CA in 

CDPH-other wells in the Fillmore subbasin and Simi Valley 

basin (fig. 12C). Boron also has been reported as a constituent 

of concern in the Upper Ojai Valley basin, and in the Piru and 

Fillmore subbasins (California Department of Public Health, 

2004b, 2004h, 2004m). In addition, previous groundwater 

and surface-water studies have reported boron concentrations 

greater than the MCL-CA of 1,000 μg/L in the Santa Clara 

River Valley basin (Izbicki and others, 1995).

Gross Alpha Radioactivity and other Radioactive 

Constituents

Relative-concentrations of radioactive constituents, 

as a class, were high in 14 percent, moderate in 11 percent, 

and low or not detected in 75 percent of the primary aquifer 

system (table 10). Gross alpha radioactivity was detected at 

high relative-concentrations in 14 percent and at moderate 

concentrations in 6.9 percent of the primary aquifer system 

(table 9, fig. 11). Relative-concentrations of another 

radioactive constituent, uranium, were moderate in 20 percent 

of the primary aquifer system (table 9).

Gross alpha radioactivity was detected at high relativeconcentrations in the Pleasant Valley and Simi Valley basins 

and the Oxnard subbasin (fig.12D). Moderate concentrations 

of gross alpha radioactivity were detected in the same areas as 

high concentrations, and in the Las Posas Valley basin, and the 

Fillmore and Santa Paula subbasins. Gross alpha radioactivity 

was not significantly different between groundwater-age 

classes (table 6) and was not significantly correlated to any 

explanatory factors (table 11).

Nutrients

The relative-concentrations of nutrients, as a class, were 

high in 15 percent, moderate in 8.7 percent, and low or not 

detected in 76 percent of the primary aquifer system (table 10). 

The only nutrient detected at high or moderate relativeconcentrations was nitrate plus nitrite (hereinafter referred to 

as nitrate) (table 9, fig. 10). High or moderate concentrations 

of nitrate were detected throughout the study unit (fig. 12E).
Status and Understanding of Water Quality 45

Table 11. Summary of Spearman's rho correlation analysis between selected water-quality constituents and potential explanatory 

factors, Santa Clara River Valley study unit, California, GAMA Priority Basin Project.

[Aquifer-scale proportions are from the grid-based method unless otherwise stated; rho value is shown when the correlations between selected explanatory factors 

and water-quality constituents are significant (p < 0.05); GAMA, Groundwater Ambient Monitoring and Assessment Program; ns, no significant correlation; 

THM, trihalomethane; TDS, total dissolved solids; MCL-US, U.S. Environmental Protection Agency maximum contaminant level; NL-CA, CDPH notification 

level; SMCL-CA, CDPH secondary maximum contaminant level; CDPH, California Department of Public Health; <, less than; >, greater than]

Constituent Benchmark 

type

Aquifer-scale 

proportion 

with high 

values 

(percent) 

Grid and understanding wells combined Grid wells

Depth to 

top-ofperforations 

Well 

depth pH Dissolved oxygen

Percentage of land use1 Number 

of septic 

tanks or 

cesspools1 Agricultural Urban Natural

rho rho rho rho rho rho rho rho

Inorganic constituents

Vanadium2 NL-CA 3.4 ns ns ns ns ns ns ns ns

Arsenic2 MCL-US 2.3 ns ns ns ns ns ns ns ns

Boron2 NL-CA 33.2 ns ns ns ns −0.404 2.104 ns ns

Nitrate2 MCL-US 15 −0.618 −0.474 ns 0.575 ns ns ns ns

TDS2 SMCL-CA 35 ns ns –0.430 ns ns ns ns ns

Sulfate2 SMCL-CA 22 ns ns –0.473 ns ns ns −0.341 ns

Manganese SMCL-CA 38 0.549 0.465 ns −0.824 ns ns ns −0.376

Iron2 SMCL-CA 22 0.436 0.325 ns −0.391 ns ns ns ns

Radioactive constituents

Gross alpha 

radioactivity2

MCL-US 14 ns ns ns ns ns ns ns ns

Organic constituents and constituent classes

Total THMs4 MCL-US 0.0 ns ns ns ns ns ns ns ns

Solvents, sum of 

concentrations4

variable 0.0 ns ns ns ns ns ns ns ns

Pesticides, sum of 

concentrations4

MCL-US 0.0 −0.551 −0.481 ns 0.385 ns ns ns ns

Constituents of special interest

Perchlorate4 MCL-US 0.0 ns ns −0.292 ns ns ns ns ns

1

Land-use percentages and number of septic tanks are within a circle with a radius of 500 meters centered around each well included in analysis. 

2

Constituents with greater than 2 percent high aquifer proportion. 

3

Based on the spatially weighted approach.

4

Classes of compounds that include constituents with high values less than 2 percent, moderate values, or detection frequencies at any concentration greater 

than 10 percent.

Factors Affecting Nitrate 

Nitrate was negatively correlated with depth to the topof-perforations and well depth, and positively correlated with 

DO (table 11, fig. 13). As depth to top-of-perforations, or well 

depth, increases, the nitrate concentrations decrease (fig. 13A). 

Nitrate concentrations also were significantly greater in 

wells with modern or mixed groundwater ages than in wells 

with pre-modern groundwater ages (table 6, fig. 13A). The 

occurrence of high nitrate and DO concentrations in shallow 

and younger groundwater indicates surficial or near-surface 

sources of nitrate. The distribution of nitrate concentrations in 

SCRV was expected and is consistent with the distribution of 

nitrate concentrations observed in other aquifers (Hallberg and 

Keeney, 1993; Burow and others, 2007; Landon and others, 

2010). Some of the explanatory variables related to nitrate 

concentrations are themselves related; for example, DO and 

groundwater age are both correlated to depth to the top-ofperforations (tables 6 and 7, fig. 9A).
46 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Figure 13. Relation of nitrate concentrations to depth to top-of-perforations 

and dissolved-oxygen concentrations classified by groundwater age for 

grid and understanding wells, Santa Clara River Valley study unit, California 

GAMA Priority Basin Project. IP010121_fig 13ab

0 

100 

200 

300 

400 

500 

600 

700 

800 

900 

0 10 20 30 

0 10 20 30 

DEPTH TO TOP-OF-PERFORATIONS, IN FEET 

A

B

0 

1 

2 

3 

4 

5 

6 

7 

8 

DISSOLVED OXYGEN, IN MILLIGRAMS PER LITER 

NITRATE PLUS NITRITE AS NITROGEN, IN MILLIGRAMS PER LITER 

Modern

Mixed

Pre-modern

CDPH-grid wells

 (no age data) 

Modern

Mixed

Pre-modern
Status and Understanding of Water Quality 47

Available information indicates that denitrification 

may contribute to decreasing nitrate concentrations at 

increasing depths, where anoxic conditions occur. As DO 

concentrations in groundwater decrease with depth (fig. 9A), 

nitrate becomes the next most favorable electron acceptor; 

therefore, microbially mediated denitrification of nitrate to 

dissolved nitrogen gas (N2) and to intermediate products 

can occur (Kendall, 1998). Previous groundwater studies 

in the Santa Clara River Valley study unit have confirmed 

that denitrification occurs in parts of the aquifer (Izbicki 

and others, 2005). Under the generally widespread anoxic 

conditions in the deep aquifers in the Santa Clara River Valley 

study unit, the distribution of nitrate in the aquifer generally is 

expected to be influenced by denitrification.

Relations between nitrate concentrations and 

agricultural land use or the density of septic tanks or 

cesspools surrounding grid wells did not meet the criteria 

for being significant (table 11). The land use within a 500-m 

radius surrounding the seven grid wells that had a nitrate 

concentration greater than the MCL-US of 10 mg/L varied 

from predominantly agricultural or urban land use to mixed 

land use (fig. 12E). Relations between agricultural land use 

and nitrate concentrations (for example, Kendall, 1998; Burow 

and others, 2007) and between septic systems and nitrate 

concentrations (for example, Moore and others, 2006) have 

been noted in other aquifers. The absence of relations between 

nitrate concentration and land use in this study unit may reflect 

multiple factors, including many sources of nitrate, generally 

mixed land uses on the landscape, or mixing of water and 

solutes from multiple land uses, especially in relatively deep 

wells with long perforation intervals.

Inorganics with Aesthetic Benchmarks (SMCL)

As a class, inorganics with aesthetic benchmarks 

(SMCLs) had high relative-concentrations in 54 percent, 

moderate in 41 percent, and low in 4.3 percent of the primary 

aquifer system (table 10). The constituents detected at high 

relative-concentrations were TDS, sulfate, manganese, and 

iron (table 9, figs. 10 and 11). Chloride was detected at 

moderate relative-concentrations in 2.2 percent of the primary 

aquifer system (table 9).

TDS was detected at high relative-concentrations in 

35 percent and moderate in 56 percent of the primary aquifer 

system (table 9). TDS relative-concentrations were high 

throughout the Santa Clara River Valley basin but were more 

prevalent in the downgradient areas of the Santa Clara River 

Valley (closest to the coastline), as well as in the Pleasant 

Valley and Simi Valley basins (fig. 12F).

Sulfate was detected at high relative-concentrations in 

22 percent and moderate in 27 percent of the primary aquifer 

system (table 9). Sulfate relative-concentrations were high 

and moderate in the downgradient areas of the Santa Clara 

River Valley basin (closest to the coastline), as well as in the 

Pleasant Valley and Simi Valley basins (fig.12G).

Manganese was detected at high relative-concentrations 

in 38 percent and moderate in 2.2 percent of the primary 

aquifer system (table 9). Manganese relative-concentrations 

generally were high in the downgradient areas of the Santa 

Clara River Valley, and in the Las Posas Valley, Pleasant 

Valley, Ojai Valley, and Ventura River Valley basins (fig. 12H).

Iron was detected at high relative-concentrations in 

22 percent and moderate in 4.4 percent of the primary 

aquifer system (table 9). The distribution of high relativeconcentrations of iron was similar to manganese, with high 

concentrations in the downgradient areas of the Santa Clara 

River Valley and in the Las Posas Valley and Pleasant Valley 

basins (fig. 12I). However, at least one relative-concentration 

also was high in the Piru and Santa Clara River Valley East 

subbasins and in the Ventura River Valley basin (fig. 12I).

Factors Affecting Total Dissolved Solids (TDS)

TDS concentrations were negatively correlated with 

pH (table 11), indicating that TDS concentrations generally 

decrease as pH increases (fig. 14A). In general, groundwater 

in wells with high TDS (>800 mg/L) and low pH (<7.5) was 

modern or mixed ages, whereas groundwater in wells with 

low TDS (≤800 mg/L) and high pH (≥7.5) more commonly 

was pre-modern age (fig. 14A). There are exceptions to these 

general relations of TDS, pH, and age. TDS concentrations 

for four understanding wells (SCRVU-07, -08, -09, -10) in 

the southwestern corner of the study unit (fig. A1A) near the 

coast were 8,740 to 30,000 mg/L. The chemistry of these wells 

indicates the effects of mixing with seawater or the upwelling 

of brines from deep formations (Izbicki and others, 2005).

In general, TDS concentrations are higher in wells in the 

downgradient parts of the regional groundwater flow system 

of the study unit (fig. 12F), for example, in the downgradient 

areas of the Santa Clara River Valley and Pleasant Valley 

basins. TDS concentrations greater than the SMCL-CA of 

1,000 mg/L were detected in only two grid wells in Simi 

Valley basin, which could be considered in the upgradient 

area of the study unit. Marine sediments surround and 

underlie Simi Valley (fig. 5) and could potentially affect the 

TDS concentration in the groundwater where these wells are 

located. The proportions of the primary aquifer system with 

high and moderate relative-concentrations are larger in SCRV 

than in many of the study units evaluated by the Priority Basin 

Project (Bennett and others, 2010; Kulongoski and others, 

2010; Landon and others, 2010; Bennett and others, 2011).
48 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Figure 14. Relations of total dissolved solids and sulfate concentrations to pH, classified by groundwater 

age, for grid and understanding wells, Santa Clara River Valley study unit, California GAMA Priority Basin 

Project.

IP010121_fig 14ab

Modern Mixed Pre-modern CDPH grid wells (no age data) 

EXPLANATION

100

1

10

100

1,000

10,000

1,000

10,000

100,000

6.5 7 7.5 8 

TOTAL DISSOLVED SOLIDS, MILLIGRAMS PER LITER SULFATE, MILLIGRAMS PER LITER 

6.5 

A

B

7 7.5 8 

pH, STANDARD UNITS 
Status and Understanding of Water Quality 49

Concentrations of TDS and sulfate were strongly 

positively correlated (ρ = 0.775, not shown in table 10), and 

high relative-concentrations of these two constituents often 

co-occurred (figs. 12F and 12G). The correlation of TDS and 

sulfate was expected because sulfate is one of the two most 

abundant anions in groundwater in the study unit (appendix B, 

fig. B2). Moreover, sulfate typically was the most abundant 

anion in groundwater with high TDS concentrations because 

the concentration of the other major anion, bicarbonate, is 

limited by precipitation of calcite at high concentrations 

(Izbicki and others, 2005).

The high and moderate TDS concentrations in the SCRV 

study unit could indicate a variety of factors, both natural and 

anthropogenic, including historical groundwater pumping and 

evapotranspiration patterns, irrigation return and irrigation 

recycling, mixing of shallow, fresh groundwater with deep, 

more saline groundwater influenced by interactions with 

deep marine sediments, intrusion of seawater or upwelling of 

deep saline waters, and rock/water interaction along regional 

groundwater flow paths. High salinity water is present in 

the perched and semi-perched aquifers and also in the deep 

aquifer zones in parts of the study unit (Izbicki, 1996a; Izbicki 

and others, 2005). Further discussion of sources of salinity in 

the SCRV study unit is beyond the scope of this report.

Factors Affecting Sulfate

Similar to TDS, sulfate concentrations were negatively 

correlated with pH (table 11), indicating that sulfate 

concentrations decrease as pH increases (fig. 14B). Sulfate 

concentrations were not significantly different between 

groundwater age classes (table 6). 

Similar to TDS, the source of sulfate can vary across 

the study unit. For example, a core sample from relatively 

shallow clay layers (about 130 ft below land surface) 

underlying the Santa Paula subbasin contained pore water 

with sulfate concentrations of 3,000 mg/L (Reichard and 

others, 1999). High concentrations of sulfur may be attributed 

to the presence of Miocene and Pliocene marine sediments 

from the surrounding mountains (fig. 5). Izbicki and others 

(2005) analyzed the sulfuric isotopic composition (δ34S) 

of sulfate (SO4) from different depths of the aquifer in the 

Pleasant Valley basin and in the southwestern part of the Santa 

Clara River Valley basin to evaluate the source of sulfate in 

groundwater. Izbicki and others (2005) concluded that the 

sources of elevated sulfate include (1) evaporative irrigation 

return water, (2) oxidation of reduced sulfur in sulfide minerals 

(for example, pyrite) and (or) organic matter in sediments, 

and (3) dissolution of marine evaporite minerals (for example, 

gypsum). The concentrations of sulfate in different parts of 

the study unit vary depending on the depth at which samples 

were collected and the redox condition of the groundwater. 

Sulfate-reducing conditions, which result in consumption of 

sulfate and generation of hydrogen sulfide gas, have been 

documented to occur widely, particularly in deep parts of the 

aquifer (Izbicki and Martin, 1997; Izbicki and others, 2005). 

Precipitation of metallic sulfide minerals, such as pyrite also 

may occur in parts of the aquifer (Izbicki and others, 2005). 

The sources and sinks for sulfate determined in these previous 

investigations are consistent with the decrease in sulfate 

concentrations with increasing pH.

The negative correlation of sulfate with percentage of 

natural land use (table 11) may not indicate a direct relation to 

land use but rather that geochemical processes are producing 

high sulfate concentrations in the downgradient end of the 

study unit, where land use is dominantly agricultural or urban. 

The moderate and low relative-concentrations of sulfate along 

the coastal areas (near Port Hueneme and the southern tip of 

the Oxnard subbasin) of the study unit (fig. 12G) may indicate 

sulfate-reduction processes occurring in the aquifer, with 

the exception of well SCRVU-10. SCRVU-10 is a shallow 

monitoring well on the coast near the southern tip of the 

Oxnard subbasin that is affected by seawater intrusion (Izbicki 

and others, 2005). Public-supply wells in this area have been 

inactive or on standby because of high TDS or major-ion 

concentrations as a result of seawater intrusion or upwelling of 

brine from underlying sediments (Izbicki and others, 2005).

Factors Affecting Manganese 

Manganese concentrations primarily vary in response 

to redox conditions and depth in the SCRV study unit. 

Manganese concentrations were negatively correlated with 

DO (table 11, fig. 15B). DO concentrations less than 0.5 mg/L 

and manganese concentrations greater than 50 μg/L generally 

are consistent with reducing aquifer conditions (predominant 

redox process is manganese-reduction, table D4). Manganese 

concentrations were positively correlated with well depth 

and the depth to top-of-perforations (table 11), indicating 

increasing concentrations with increasing depth (fig. 15A, 

y-axis scale is reversed so depths increase from top to bottom 

of plot). These correlations are consistent with increasingly 

reduced conditions (decreasing DO) in the groundwater 

system with increasing depth. Manganese concentrations were 

not significantly different between groundwater age classes 

(table 6). Most of the wells with manganese concentrations 

greater than the SMCL-CA of 50 μg/L (or high relativeconcentrations) are in the downgradient area of the Santa 

Clara River Valley basin, and in the Pleasant Valley, Las 

Posas Valley, Ventura River Valley, or Ojai basins (fig. 12H). 

The negative correlation of manganese concentrations with 

number of septic systems or cesspools (table 11) likely is not 

a direct relation but may be an artifact of relations between 

explanatory factors.
50 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Factors Affecting Iron

Similar to manganese, iron concentrations were 

negatively correlated with DO (table 11, fig.16D), and 

positively correlated well depth (table 11) and the depth to 

top-of-perforations (table 11, fig.16C), indicating increasing 

concentrations with increasing depth. Concentrations were 

higher in groundwater samples with pre-modern than in 

groundwater samples with modern ages (table 6, figs.16C and 

16D). Most of the wells with iron concentrations greater than 

the SMCL-CA of 300 μg/L (or high relative-concentrations) 

are in the Oxnard, Mound, Fillmore, and Santa Paula 

subbasins of the Santa Clara River Valley basin, or in the 

Pleasant Valley and Simi Valley basins (fig. 12I). For most 

samples with iron concentrations greater than the SMCL-CA 

of 300 μg/L (high relative-concentration), manganese 

concentrations also were greater than the SMCL-CA of 

50 μg/L 

Iron concentrations may decrease in deep parts of the 

primary aquifers under anoxic conditions. Sulfate reducing 

and methanogenesis conditions are present in the deep 

parts (typically greater than 800 ft below land surface) of 

the Lower-aquifer system of the Pleasant Valley basin and 

Oxnard subbasin (Izbicki and others, 1995, 2005). These 

anoxic conditions persist in these aquifers because of the 

abundance of electron donors, usually in the form of dissolved 

organic carbon (DOC), present in marine sediments that 

makeup the aquifer material. If redox conditions for sulfate 

reduction exist, then sulfide is produced, which reacts with 

the dissolved Mn2+ and Fe2+ ions. This reaction can lead 

to the precipitation of Mn- and Fe-sulfide minerals (Izbicki 

and others, 2005), thus contributing to decreasing iron 

concentrations. 

IP010121_fig 15AB

0

100

200

300

400

500

600

700

800

900

1,000

ND 0.2 1 10 100 1,000 10,000

ND 0.2 1 10 100 1,000 10,000

DEPTH TO TOP-OF-PERFORATIONS,

IN FEET 

0

1

2

3

4

5

6

7

8

DISSOLVED OXYGEN, 

IN MILLIGRAMS PER LITER 

MANGANESE, IN MICROGRAMS PER LITER 

A

B

Modern

Mixed

Pre-modern

Age unknown 

No detection 

EXPLANATION

ND

Figure 15. Relation of manganese concentrations to depth to the topof-perforations and to dissolved-oxygen concentrations, classified by 

groundwater age, in grid and understanding wells in the Santa Clara River 

Valley study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 51

IP010121_fig 16AB

0

100

200

300

400

500

600

700

800

900

1,000

ND 2 10 100 1,000 10,000 100,000

ND 2 10 100 1,000 10,000 100,000

DEPTH TO TOP-OF-PERFORATIONS,

IN FEET 

0

1

2

3

4

5

6

7

8

DISSOLVED OXYGEN, 

IN MILLIGRAMS PER LITER 

IRON, IN MICROGRAMS PER LITER 

A

B

Modern

Mixed

Pre-modern

Age unknown 

No detection 

EXPLANATION

ND

Figure 16. Relation of iron concentrations to depth to top-of-perforations and 

dissolved-oxygen concentrations, classified by groundwater age, in grid and 

understanding wells in the Santa Clara River Valley study unit, California GAMA 

Priority Basin Project.

Organic and Special-Interest Constituents

Organic and special-interest constituents, unlike 

inorganic constituents, usually are of anthropogenic origin. 

The organic and special interest constituents discussed in 

this report include VOCs, pesticides, and perchlorate. VOCs 

may be present in paints, solvents, fuels, refrigerants, can 

be byproducts of water disinfection, and are characterized 

by their tendency to evaporate. In this report, VOCs are 

classified as trihalomethanes (THMs), solvents, and other 

VOCs. Pesticides are used to control weeds, insects, or 

fungi in agricultural, urban, and suburban settings. The only 

special-interest constituent of concern is perchlorate because 

perchlorate has been detected recently in, or is considered to 

have the potential to reach, water resources used for drinkingwater supply (California Department of Public Health, 2008b).

Organic constituents with human-health benchmarks, 

as a whole, were not detected at high relative-concentrations 

in the primary aquifer system and were detected at moderate 

relative-concentrations (>0.1 but ≤1) in 4.8 percent of 

the primary aquifers (table 10). The maximum relativeconcentrations for most of the detected organic constituents 

with human-health benchmarks were less than 0.1 (fig. 17). 

Organic and special-interest constituents with one or more 

moderate relative-concentrations were the solvents carbon 

tetrachloride and trichloroethene (TCE), total THMs, and the 

special-interest constituent perchlorate (fig. 17). A few organic 

constituents—THM chloroform, pesticides atrazine and 

simazine, and the special-interest constituent perchlorate—

were prevalent (detection frequency greater than 10 percent 

in USGS-grid wells) in the primary aquifers (figs. 17 and 

18), but at concentrations that were less than their respective 

benchmarks.
52 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig 17

1 10 100

DETECTION FREQUENCY, IN PERCENT

0.00001

0.0001

0.001

0.01

0.1

1

10

RELATIVE-CONCENTRATION, DIMENSIONLESS

Benzene

Bromochloromethane

Bromodichloromethane Bromoform

Carbon disulfide

Carbon tetrachloride

CFC-11

CFC-113

Chloroform

Total trihalomethanes

Dibromochloromethane

m- and p-Xylene

Tetrachloroethene

(PCE)

Toluene

trans-1,2-Dichloroethene

Trichloroethene (TCE)

Atrazine

Hexazinone

Metolachlor

Simazine

Tebuthiuron

Perchlorate

1,1,1-Trichloroethane

(TCA)

Moderate

Low

High

Toluene

EXPLANATION

Constituents with analyses in ≥24 grid wells and wells are spatially representative

Name and center of symbol is location of data unless indicated by following location line:

Figure 17. Detection frequency and maximum relative-concentration for organic and special-interest 

constituents detected in USGS-grid wells, Santa Clara River Valley study unit, California GAMA Priority 

Basin Project.
Status and Understanding of Water Quality 53

IP010121_fig 18AB

0.0001 0.001 0.01 0.1 1 10

STUDY UNIT RELATIVE-CONCENTRATION, DIMENSIONLESS

DETECTION FREQUENCY,

IN PERCENT

0 10 20 30 40 50 10% detection

Low Moderate High

Perchlorate

Simazine

Trichlorethene

Carbon

tetrachloride

Chloroform

Total

trihalomethanes

Atrazine

A B

NUMBER OF

WELLS CONTAINING

THE CONSTITUENT

7

7

8

1

1

5

4

Figure 18. (A) Detection frequency and (B) relative-concentration of selected organic constituents and perchlorate in USGS-grid 

wells in the Santa Clara River Valley study unit, California GAMA Priority Basin Project.
54 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Pesticides

Pesticides, as a class, were detected at low relativeconcentrations or were not detected in 100 percent of the 

primary aquifer system (table 10). Most grid wells with 

detections had one to four pesticides or degradates detected; 

however, there were two wells that had five or more pesticides 

detected (fig. 19A). Several of the pesticide detections were 

located in the Santa Clara River Valley East subbasin. The 

pesticides with detection frequencies greater than 10 percent 

were the herbicides simazine and atrazine. Simazine was the 

most frequently detected pesticide in SCRV, with a detection 

frequency of 26 percent. The detection frequency for atrazine 

was 17 percent (fig. 18) (Montrella and Beltiz, 2009). The 

relative-concentration for all concentrations of simazine and 

atrazine was less than 0.01 (fig. 18). 

Factors Affecting Pesticides

As a class, pesticide concentrations were higher in 

shallower groundwater than in deeper groundwater. Pesticides 

were negatively correlated with well depth and depth to topof-perforations and positively correlated with DO (table 11). 

These relations indicate that pesticide concentrations decrease 

with increasing depth of well perforations (fig. 20). The 

correlation of DO and pesticide concentrations probably 

indicates a parallel decrease in DO with increasing depth 

(fig. 9A). The sum of pesticide concentrations was not 

significantly different between groundwater age classes 

(table 6). However, pesticides were detected more frequently 

in groundwater with modern (45 percent) and mixed 

(46 percent) ages than in groundwater of pre-modern age 

(10 percent, fig. 20).

Pesticide concentrations were not correlated with land 

use in the Santa Clara River Valley study unit (table 11), as 

has been observed in some studies (Gilliom and others, 2006). 

Pesticide concentrations were not correlated with land use 

even in wells with depths to top-of-perforations less than 

250 ft. This result was not unexpected because pesticides, in 

general, are used in both agricultural and urban areas (Gilliom 

and others, 2006).

Trihalomethanes 

THMs, as a class, were detected at moderate relativeconcentrations in 2.4 percent and low or not detected 

in 98 percent of the primary aquifer system (table 10). 

Chloroform was the most prevalent THM with a detection 

frequency of 17 percent (fig. 18) and was the most frequently 

detected VOC in the SCRV (Montrella and Belitz, 2009). 

Some THMs were detected in most basins and subbasins in the 

SCRV (fig. 19B).

Factors Affecting Trihalomethanes

Total THM concentrations were not correlated with any 

explanatory variables (table 11). Total THM concentrations 

were not significantly different between groundwater age 

classes (table 6). The absence of correlations of total THM 

with explanatory variables may reflect the low detection 

frequency for total THM, making it difficult to identify 

relations.

Solvents

Solvents, as a class, were detected at moderate relativeconcentrations in 2.4 percent, and low or not detected in 

98 percent of the primary aquifer system (table 10). The two 

solvents detected at moderate relative-concentrations were 

carbon tetrachloride and TCE (fig. 17). None of the solvents 

were detected in more than 10 percent of the grid wells.

Carbon tetrachloride and TCE were detected at moderate 

relative-concentrations (fig. 18) in one USGS-grid well in the 

Arroyo Santa Rosa Valley basin (fig. 19C). Other solvents 

were detected at low relative-concentrations in a few USGSgrid wells across the SCRV (fig. 19C) (Montrella and Belitz, 

2009).

Factors Affecting Solvents

Detections of solvents, as a class, were not significantly 

different between groundwater age classes (table 6) and also 

were not correlated with any explanatory variables (table 11). 

The absence of correlations of solvents with explanatory 

variables may reflect the low detection frequency for solvents, 

making it difficult to identify relations.

Other Volatile Organic Compounds (VOCs)

For the other VOCs, relative-concentrations were neither 

high nor moderate in any proportion of the primary aquifer 

system based on the grid wells (table 10). The relativeconcentrations also were neither high nor moderate in CDPHother wells during the current period. None of the other VOCs 

were detected in more than 10 percent of the grid wells.

Special-Interest Constituent

Perchlorate was the only constituent of special interest 

sampled for in the SCRV. Perchlorate was detected at 

moderate relative-concentrations in 12 percent of the primary 

aquifer system (table 9, fig. 17). All perchlorate detections 

were at moderate relative-concentrations (fig. 17). Perchlorate 

was detected in the Ojai Valley, Simi Valley, and Arroyo 

Santa Rosa Valley basins as well as in the Fillmore subbasin 

(fig. 19D).
Status and Understanding of Water Quality 55

Figure 19. Number of pesticide detections and relative-concentrations of selected organic constituents for USGS-grid wells and 

CDPH wells, Santa Clara River Valley study unit, California GAMA Priority Basin Project.

IP010121_fig 19A

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

EXPLANATION

PACIFIC OCEAN

Water bodies

Subbasin boundary

Number of

pesticide

detections

Stream or river

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003) Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

A. Pesticides

Not detected

1 to 4 pesticides

5 to 10 pesticides

USGS-grid

wells

California Department

of Public Health

(CDPH-other) wells

Study unit boundary
56 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig 19B

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

EXPLANATION

PACIFIC OCEAN

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003) Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

B. Trihalomethanes

USGS-grid

wells

California Department

of Public Health

(CDPH-other) wells

Not detected

Low

Moderate

Study unit boundary

Figure 19.—Continued
Status and Understanding of Water Quality 57

IP010121_fig 19C

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

EXPLANATION

PACIFIC OCEAN

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003) Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

C. Solvents

Not detected

Low

Moderate

Study unit boundary

USGS-grid

wells

California Department

of Public Health

(CDPH-other) wells

Figure 19.—Continued
58 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig 19D

1

33

126

27

118

14

101

5

5

10

210

405 OXNARD

FILLMORE

MOUND

PIRU

SANTA

PAULA

Santa Clara River Valley

Santa

Clara

River

Valley

Las Posas Valley

Simi Valley

Pleasant Valley

Ojai

Valley Upper

Ojai

Valley

Ventura

River

Valley

Arroyo Santa

Rosa Valley

SANTA CLARA

RIVER VALLEY

EAST

South Mtn

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

Land-use data from Nakagaki and others, 2007.

EXPLANATION

PACIFIC OCEAN

D. Perchlorate

Water bodies

Subbasin boundary

Relativeconcentration

Stream or river

Groundwater basins within the 

study unit (from California

Department of Water Resources, 2003)

Study unit boundary

Urban MOUND

LAND-USE

CLASSIFICATION

Agricultural

Natural

Not detected

Low

Moderate

USGS-grid

wells

California Department

of Public Health

(CDPH-other) wells

Figure 19.—Continued
Status and Understanding of Water Quality 59

IP010121_fig 20

0

100

200

300

400

500

600

700

800

900

0.02 0.04 0.06 0.08 

DEPTH TO TOP-OF-PERFORATIONS, IN FEET 

SUM OF PESTICIDE CONCENTRATIONS, IN MICROGRAMS PER LITER 

ND

Modern

Mixed

Pre-modern

ND No detection 

Figure 20. Relation of pesticide concentrations to depth to top-of-perforations, classified by groundwater age, in 

USGS-grid wells, Santa Clara River Valley study unit, California GAMA Priority Basin Project.

Factors Affecting Perchlorate 

Perchlorate was negatively correlated with pH (table 11) 

but was not correlated with other explanatory factors or 

groundwater age class (tables 6 and 11). The absence of 

correlation may be due to the relatively low detection 

frequency. The negative correlation of perchlorate to pH 

(table 11) may indicate that perchlorate concentrations 

decrease with increasing depth, with increasing pH serving 

as a proxy indicator for increasing depth (table 7, fig. 9B). A 

direct correlation of perchlorate to depth may not have been 

discernible because well construction data were available for 

only four of the five wells with detections of perchlorate.

In addition, perchlorate concentrations were positively 

correlated with nitrate concentrations (ρ = 0.419, ρ = 0.033; 

not included in table 11). Nitrate (as nitrogen) concentrations 

were greater than the MCL-US of 10 mg/L for four of the 

five wells (SCRV-04, -13, -28, -35, table A1, fig. 19D) with 

perchlorate concentrations greater than the reporting level. The 

fifth well (SCRV-38) with reported perchlorate greater than 

the reporting level was not sampled by USGS Priority Basin 

Project for nutrients, and nitrate values were not reported in 

the CDPH database. Agricultural land use was the dominant 

land use around four of the five wells with perchlorate 

detections, and urban land use was the dominant land use 

around the other well (SCRV-35). High nitrate concentrations 

can be associated with several sources, including fertilizers. 

Possible anthropogenic sources of perchlorate could include 

industrial, manufacturing, or commercial uses, such as 

explosives, road flares, automobile air-bag systems, or other 

products (Parker and others, 2008), or nitrate fertilizers, 

which were mined from the Atacama Desert of Chile and 

used historically on some orchard crops (Dasgupta and others, 

2006). Elevated concentrations of perchlorate can also result 

from mobilization of naturally-occurring perchlorate by 

downward moving irrigation return flows (Fram and Belitz, 

2011). The association of perchlorate detections and elevated 

nitrate in four wells, along with surrounding agricultural land 

use, is consistent with the mobilization of naturally-occuring 

perchlorate by irrigation or from application of perchloratebearing fertilizer; additional investigation is needed to clarify 

sources of perchlorate in the study unit.
60 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Summary

Groundwater quality in the approximately 460 mi2 Santa 

Clara River Valley study unit (SCRV) was investigated as 

part of the Priority Basin Project of the Groundwater Ambient 

Monitoring and Assessment (GAMA) program. Samples were 

collected by the U.S. Geological Survey (USGS) from April 

through June 2007.

The GAMA Priority Basin Project is designed to provide 

a statistically robust characterization of untreated groundwater 

quality in the primary aquifers at the basin-scale. Forty-two 

grid wells were randomly selected within spatially distributed 

grid cells across the SCRV to assess the quality of the 

groundwater. An additional 11 wells were sampled to improve 

understanding of the relation of water quality to explanatory 

factors. Samples from USGS-grid wells were analyzed for 

as many as 374 constituents. CDPH inorganic data from the 

prior 3-year period (November 1, 2003, to October 31, 2006) 

were used to complement USGS-grid well data and to provide 

additional information about groundwater quality. 

Relative-concentrations (sample concentration divided 

by water-quality benchmark concentration) were used for 

evaluating groundwater quality. Selected constituents with 

high (relative-concentration greater than 1) or moderate 

relative-concentrations or detection frequencies greater than or 

equal to 10 percent were selected to focus the understanding 

assessment on those constituents with the greatest effect 

on water quality. The relative-concentration threshold for 

classifying inorganic constituents as moderate was 0.5 and 

for classifying organic constituents 0.1; the lower threshold 

value for organic constituents was selected because these 

constituents, which are typically from anthropogenic sources, 

have smaller relative-concentrations and generally are less 

prevalent than naturally occurring inorganic constituents.

Aquifer-scale proportion was used as a metric for 

assessing the quality of untreated groundwater for the SCRV. 

Aquifer-scale proportions are defined as the percentage of 

the area of the primary aquifer system with concentrations 

greater than or less than specified thresholds relative to 

regulatory or aesthetic benchmarks. Grid-based and spatially 

weighted statistical approaches were used to assess aquiferscale proportions of constituents at high, moderate, and low 

relative-concentrations in the primary aquifers. The gridbased approach was used for most constituents unless the 

spatially weighted proportions were significantly different. 

Raw detection frequencies also were calculated by using 

all available data and are provided for comparison but 

are not used in the analysis because detection frequencies 

are potentially biased because the wells are not uniformly 

distributed.

For inorganic constituents with human-health 

benchmarks, the relative-concentrations of at least one 

constituent were high in 21 percent of the primary aquifer 

system, and were moderate in 30 percent of the primary 

aquifer system. The inorganic constituents with humanhealth benchmarks and the largest aquifer-scale high 

proportions were nitrate (15 percent), gross alpha radioactivity 

(14 percent), vanadium (3.4 percent), boron (3.2 percent), and 

arsenic (2.3 percent). 

For inorganic constituents with aesthetic benchmarks 

(SMCL), the relative-concentrations of at least one 

constituent were high in 54 percent of the primary aquifer 

system, and were moderate in 41 percent of the primary 

aquifer system. Inorganic constituents with SMCLs and high 

relative-concentrations included TDS (35 percent), sulfate 

(22 percent), manganese (38 percent), and iron (22 percent).

Of the 88 VOCs and gasoline oxygenates analyzed, 17 

were detected. Of the 17 VOCs detected, 15 had human-health 

benchmarks. None of the VOCs were detected at high relativeconcentrations. The relative-concentrations of two solvents, 

carbon tetrachloride and TCE, were moderate in 2.4 percent 

of the primary aquifer system. The relative-concentrations of 

total THMs also were moderate in 2.4 percent of the primary 

aquifer system. The remaining VOCs that were detected were 

detected at low relative-concentrations. Of the 118 pesticides 

analyzed, 13 were detected. Five of these 13 had human-health 

benchmarks. Relative-concentrations for pesticides were 

neither high nor moderate in any part of the primary aquifer 

system. Two pesticides, simazine and atrazine, were detected 

at low relative-concentrations in 26 percent and 17 percent, 

respectively, of the primary aquifer system. Perchlorate was 

detected at moderate relative-concentrations in 12 percent of 

the primary aquifer system.

Water quality primarily varied in relation to depth, 

groundwater age, reduction-oxidation conditions, and pH. 

Concentration of nitrate and detections of pesticides were 

high in wells with shallow perforation depths and were 

correlated with high dissolved-oxygen concentrations. Nitrate 

concentrations were higher in groundwater with modern 

and mixed ages than in groundwater with pre-modern age. 

Decreases in concentrations of total dissolved solids and 

sulfate with increasing pH probably indicates correlations 

between these constituents and increasing depth across 

most of the SCRV. Increases in pH with depth likely are 

the result of dissolution of primary silicate minerals in the 

aquifer. Manganese and iron concentrations were largest in 

deeper wells, in pre-modern age groundwater, and in the 

downgradient part of the SCRV (closest to the coastline), 

indicating the prevalence of reducing oxidation-reduction 

conditions in these aquifer zones.
References Cited 61

Acknowledgments

The authors thank the following cooperators for 

their support: the California State Water Board, Lawrence 

Livermore National Laboratory, California Department of 

Public Health, and California Department of Water Resources. 

We especially thank the well owners and water purveyors 

for their cooperation in allowing the U.S. Geological Survey 

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.

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Appendix A 69

Appendix A. Selection of California Department of Public 

Health (CDPH)-Grid Wells

California requires samples to be collected regularly from 

public-supply wells under Title 22 (California Department 

of Health Services, 2007). Historical data derived from these 

samples are available from the California Department of 

Public Health (CDPH) database. Assembly Bill (AB) 599 

directs the Groundwater Ambient Monitoring and Assessment 

(GAMA) Program to use available data and to collect new 

data as needed. The GAMA Priority Basin Project uses 

this monitoring data along with newly collected data to 

characterize the water quality of the primary aquifers. The 

CDPH database provided additional water-quality data for the 

grid-based and spatially weighted approaches to estimating 

aquifer-scale proportions for a wide range of constituents. 

CDPH data were not used to provide data for grid-wells for 

VOCs, pesticides, or perchlorate because these constituents 

were sampled for by the USGS at all grid wells, and because 

reporting levels for these constituents in the CDPH database 

generally were not sufficiently low enough to differentiate 

between “low” and “moderate” relative-concentrations.

Of the 48 grid cells in the Santa Clara River Valley 

study unit (SCRV), 42 cells had USGS-grid data for organic 

and special interest constituents; 16 of these 42 cells also 

had USGS-grid data for inorganic constituents; and 6 of the 

48 grid cells did not have USGS-grid data because no well was 

sampled (figs. A1A and A1B). Three approaches were used 

to select CDPH inorganic constituent data for each grid cell 

where the USGS did not sample for inorganic constituents. 

The first approach was to select CDPH data for the grid 

well sampled by the USGS for other constituents, provided the 

CDPH data met quality-control criteria. Cation-anion balance 

was used as the quality-control assessment metric. Because 

water is electrically neutral and must have a balance between 

positive (cations) and negative (anions) electrically charged 

dissolved species, the cation/anion imbalance commonly is 

used as a quality-assurance check for water sample analysis 

(Hem, 1985). Cation-anion balance was calculated as the 

difference between the total cations and total anions divided 

by the average, expressed as a percentage:

*100

where

cations is the sum of calcium, magnesium, 

sodium, and potassium in

milliequivalents per liter (meq/L)

and,

anions is the sum of c

cations anions

percent difference cations anions

æ ö ç - ÷ ç ÷ =ç ÷

÷ ç + ÷ ç ÷ è ø

å å

å å

å

å hloride, sulfate, 

fluoride, nitrate, and bicarbonate in

milliequivalents per liter (meq/L).

An imbalance, or percentage difference, greater than 

or equal to 10 percent can indicate uncertainty in the quality 

of the data. The most recent CDPH data for the USGS-grid 

wells with missing data were evaluated to determine whether 

cation-anion imbalances for the CDPH data were less than 

10 percent. If so, the CDPH data for those wells were selected 

for use as the grid-well data for inorganic constituents. It 

was assumed that if analyses met acceptable-quality-control 

criteria for major ion data, then the data quality for the 

analyses at these wells also would be acceptable for trace 

elements, nutrients, and radiochemical constituents. This 

approach resulted in the selection of inorganic data from 

CDPH at 14 USGS-grid wells. For identification purposes, 

data from the CDPH for these grid wells were assigned 

GAMA identifications numbers equivalent to the GAMA 

USGS-grid well numbers, but with “DG” inserted between the 

study area prefix and sequence number (for example, CDPHgrid well SCRV-DG-05 is the same well as USGS-grid well 

SCRV-05, table A1).

If the first approach did not yield CDPH inorganic data 

for a grid cell, then the second approach was to search the 

CDPH database to identify the well with the highest rank, 

from the random ranking of CDPH wells during the original 

selection of USGS-grid wells for each cell, with a cationanion imbalance less than 10 percent. This approach resulted 

in selecting CDPH inorganic data for wells not sampled by 

USGS for 15 grid cells. Each of these 15 CDPH-grid wells 

was located in the same cell as the corresponding USGS-grid 

well but not necessarily right next to that USGS-grid well. To 

identify these new CDPH-grid wells, well IDs were created 

that added “DPH” after the study unit prefix and then added 

the grid-cell number for the study unit (for example, CDPHgrid well SCRV-DPH-26).
70 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig A01A

1

33

126

27

118

14

101

5

5

10

210

405

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

South Mtn

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

LOS ANGELES CO

VENTURA CO

EXPLANATION

Water bodies

Stream or river

PACIFIC OCEAN

Santa Clara study

 unit boundary

Equal-area randomized-

 sampling grid cell

USGS-grid well

USGS-understanding well 

SCRV-42

SCRV-41

SCRV-40

SCRV-39

SCRV-38

SCRV-37

SCRV-36

SCRV-35

SCRV-34

SCRV-33

SCRV-32

SCRV-31

SCRV-30

SCRV-29

SCRV-28

SCRV-27

SCRV-26

SCRV-25

SCRV-24 SCRV-22

SCRV-21

SCRV-20

SCRV-19

SCRV-18

SCRV-17

SCRV-16

SCRV-15

SCRV-14

SCRV-13

SCRV-12

SCRV-11

SCRV-10

SCRV-09

SCRV-08

SCRV-07

SCRV-06

SCRV-05

SCRV-04

SCRV-03

SCRV-02

SCRV-02

SCRV-01

SCRV-23

SCRVU-11

SCRVU-10

SCRVU-09

SCRVU-09

SCRVU-05

SCRVU-02

SCRVU-01

SCRVU-07 SCRVU-08

SCRVU-06

SCRVU-04

SCRVU-03

A

Figure A1. Identifiers and locations of (A) USGS-grid and USGS-understanding wells sampled during April through June 2007, and 

(B) CDPH-grid wells at which data for inorganic constituents from the California Department of Public Health were used, Santa Clara 

River Valley study unit, California GAMA Priority Basin Project.
Appendix A 71

IP010121_fig A01B

1

33

126

27

118

14

101

5

5

10

210

405

34

00’

34

30’

119 00’ 118 30’

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

North American Datum of 1983 (NAD 83)

Santa Monica Mountains

Topatopa Mountains

San Gabriel Mountains

South Mtn

Oak Ridge Mts

Sulphur Mtn

River

River

Santa

Clara

Ventura

Calleguas

Creek

Santa Susana Mountains

Santa Ynez Mts

Ojai

Ventura

Newhall

Camarillo

Simi Valley

Port Hueneme

Oxnard

Santa Paula

Santa

Clarita

EXPLANATION

Water bodies

Stream or river

PACIFIC OCEAN

Santa Clara study

 unit boundary

Equal-area randomized-

 sampling grid cell

USGS-grid well (GAMA and

 supplemental CDPH data)

SCRV-DG-13

SCRV-DPH-26

LOS ANGELES CO

VENTURA CO

California Department of

 Public Health (CDPH) -grid well

 (CDPH data only)

SCRV-DG-31

SCRV-DG-30

SCRV-DG-26

SCRV-DG-25

SCRV-DG-05

SCRV-DG-21

SCRV-DG-22

SCRV-DG-11

SCRV-DG-13

SCRV-DG-20

SCRV-DG-15

SCRV-DG-24

SCRV-DG-08 SCRV-DG-29

SCRV-DG-18

SCRV-DPH-7

SCRV-DPH-5

SCRV-DG-41

SCRV-DPH-48

SCRV-DPH-45

SCRV-DPH-44 SCRV-DPH-43

SCRV-DPH-42

SCRV-DPH-40

SCRV-DPH-38

SCRV-DPH-26

SCRV-DPH-25

SCRV-DPH-21

SCRV-DPH-17

SCRV-DPH-16

SCRV-DPH-35

B

Figure A1—Continued
72 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

USGS GAMA well 

identification No. 

(USGS data only)

CDPH GAMA well 

identification No. 

(USGS and CDPH 

data)

CDPH 

GAMA well 

identification 

No. (CDPH data 

only)

Grid 

cell 

No.

USGS- and CDPH-grid wells

SCRV-01 – – 16

SCRV-02 – – 17

SCRV-03 – – 15

SCRV-04 – – 8

SCRV-05 SCRV-DG-05 – 34

SCRV-06 – – 12

SCRV-07 – – 6

SCRV-08 SCRV-DG-08 – 18

SCRV-09 – – 9

SCRV-10 – – 1

SCRV-11 SCRV-DG-11 – 29

SCRV-12 – – 13

SCRV-13 SCRV-DG-13 – 28

SCRV-14 – – 26

SCRV-15 SCRV-DG-15 – 20

SCRV-16 – – 23

SCRV-17 – – 5

SCRV-18 SCRV-DG-18 – 10

SCRV-19 – – 33

SCRV-20 SCRV-DG-20 – 24

SCRV-21 SCRV-DG-21 – 31

SCRV-22 SCRV-DG-22 – 30

SCRV-23 – – 35

SCRV-24 SCRV-DG-24 – 19

SCRV-25 SCRV-DG-25 – 36

SCRV-26 SCRV-DG-26 – 37

SCRV-27 – – 3

SCRV-28 – – 22

SCRV-29 SCRV-DG-29 – 11

SCRV-30 SCRV-DG-30 – 46

SCRV-31 SCRV-DG-31 – 47

SCRV-32 – – 40

SCRV-33 – – 27

SCRV-34 – – 21

SCRV-35 – – 41

Table A1. Nomenclature for wells sampled by USGS or selected from the CDPH database in the Santa Clara River Valley study unit, 

California, GAMA Priority Basin Project. 

[CDPH, California Department of Public Health; GAMA, Groundwater Ambient and Monitoring Assessment Program; USGS, U.S. Geological Survey; 

SCRV, Santa Clara River Valley USGS-grid well; SCRVU, USGS-understanding well; SCRV-DG, CDPH-grid well with USGS and CDPH data; 

SCRV-DPH, CDPH-grid well with CDPH data only; –, no data]

USGS GAMA well 

identification No. 

(USGS data only)

CDPH GAMA well 

identification No. 

(USGS and CDPH 

data)

CDPH 

GAMA well 

identification 

No. (CDPH data 

only)

Grid 

cell 

No.

USGS- and CDPH-grid wells—Continued

SCRV-36 – – 38

SCRV-37 – – 14

SCRV-38 – – 7

SCRV-39 – – 4

SCRV-40 – – 48

SCRV-41 SCRV-DG-41 – 2

SCRV-42 – – 39

– – SCRV-DPH-5 5

– – SCRV-DPH-7 7

– – SCRV-DPH-16 16

– – SCRV-DPH-17 17

– – SCRV-DPH-21 21

– – SCRV-DPH-25 25

– – SCRV-DPH-26 26

– – SCRV-DPH-35 35

– – SCRV-DPH-38 38

– – SCRV-DPH-40 40

– – SCRV-DPH-42 42

– – SCRV-DPH-43 43

– – SCRV-DPH-44 44

– – SCRV-DPH-45 45

– – SCRV-DPH-48 48

USGS-understanding wells

SCRVU-01 – – 11

SCRVU-02 – – 26

SCRVU-03 – – 10

SCRVU-04 – – 17

SCRVU-05 – – 48

SCRVU-06 – – 14

SCRVU-07 – – 14

SCRVU-08 – – 15

SCRVU-09 – – 15

SCRVU-10 – – 15

SCRVU-11 – – 15
Appendix A 73

If the cation-anion balance for data from the well in the 

CDPH database in a grid cell was not less than 10 percent, 

then the third approach was to select the highest randomly 

ranked well in the CDPH database with any of the needed 

inorganic data. These wells may not have met the chargebalance criteria because a complete set of major-ion data 

was not available to calculate a cation-anion balance. This 

approach resulted in selection of two USGS-grid wells 

(SCRV-11 and SCRV-41) from which CDPH inorganic data 

were used, but for which those CDPH data had not passed the 

cation-anion balance check. Because these wells were USGSgrid wells, a well ID was created that added “DG” to the 

GAMA ID (for example, well SCRV-DG-41).

The result of these approaches was one well per grid cell 

with data from the USGS database, the CDPH database, or 

both databases. Inorganic data for 31 CDPH-grid wells in the 

CDPH database were used (fig. A1B). Data were not available 

for all inorganic constituents from all 31 CDPH-grid wells. 

However, data for most of the inorganic constituents were 

available for 47 of the 48 grid cells. Table 3 shows the number 

of USGS- and CDPH-grid wells with data for each inorganic 

constituent. 

Estimates of aquifer-scale proportions for constituents 

made on the basis of a small number of wells have a larger 

error associated with the 90 percent confidence intervals 

(based on the Jeffreys interval for the binomial distribution, 

Brown and others, 2001). Analysis of the combined data 

sets to evaluate the occurrence of high or moderate relativeconcentrations for inorganic constituents was not affected by 

differences in reporting levels between USGS- and CDPH-grid 

data because concentrations greater than one-half of waterquality benchmarks (relative-concentration greater than 0.5) 

generally were substantially greater than the highest reporting 

levels. Comparisons between USGS- and CDPH-grid data are 

described in appendix B.
74 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Appendix B. Comparison of CDPH and USGS-GAMA Data

CDPH and USGS-GAMA data were compared to 

assess the validity of combining data from these different 

sources. Because laboratory reporting levels for most organic 

constituents and trace elements were substantially lower for 

USGS-GAMA data than for CDPH data (table 4), it was 

not possible to directly compare concentrations of many 

constituents in individual wells in any meaningful way. 

However, concentrations of major ions and nitrate, which 

generally are prevalent and have concentrations substantially 

above reporting levels, could be compared for each well using 

data from both sources. 

Comparisons were made for wells that were analyzed by 

GAMA Priority Basin Project for inorganic and radiochemical 

constituents that also had CDPH data within the most recent 

3-year interval. Major ion and nitrate data were available for 

thirteen wells in the USGS and CDPH databases. The small 

number of wells prevented a statistically robust analysis of 

the paired results for each individual constituent. However, 

the paired analyses for nine different constituents (calcium, 

magnesium, potassium, sodium, alkalinity, chloride, sulfate, 

TDS, nitrate-N) with values above the reporting levels in both 

databases was a large enough dataset (72 pairs) for meaningful 

statistical comparison.

A non-parametric signed rank test indicated no significant 

differences between the paired USGS-GAMA and CDPH data 

(z = 0.694, p = 0.4873). Although differences between the 

paired datasets occurred for a few wells, most sample pairs 

plotted close to a 1-to-1 line (fig. B1). The relative percent 

difference (absolute difference of the two values divided 

by the average of the two values, RPD) was calculated for 

each data pair. The median RPD was 6.7 percent; 91 percent 

of the RPD values were less than 20 percent. These direct 

comparisons indicated that these GAMA and CDPH inorganic 

data were not significantly different, which gave support to the 

use of the CDPH data for inorganic constituents to supplement 

the USGS-grid well data for the cells with an incomplete suite 

of inorganic analyses.

Piper 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. B2). Piper 

diagrams often are used to define groundwater type (Hem, 

1985). Combined USGS-GAMA and CDPH major-ion data 

for grid wells were plotted on piper diagrams (Piper, 1944) 

along with all CDPH major-ion data from November 1, 2003, 

to October 31, 2006, to determine whether the groundwater 

types in grid wells were similar to groundwater types 

historically observed in the study unit. All cation/anion data 

in the CDPH database with a cation/anion balance less than 

10 percent were retrieved and plotted on the piper diagrams 

for comparison with USGS- and CDPH-grid well data.

The ranges of water types for grid wells and other wells 

from the CDPH database for the current period were similar 

(fig. B2). Most water samples from wells were classified 

as mixed cation-mixed anion type waters; no single cation 

accounted for more than 60 percent of the total cations, and no 

single anion accounted for more than 60 percent of the total 

anions. The most common cation was calcium, and the major 

anions were sulfate and bicarbonate. Waters in a minority of 

wells were classified as mixed cation-bicarbonate, calciummixed anion, or sodium-mixed anion type waters, indicating 

that bicarbonate, calcium, or sodium accounted for more than 

60 percent of the total anions and cations, respectively.

The determination that the range of relative abundances 

of major cations and anions in grid wells (47 wells) was 

similar to the range of those in the selected CDPH-other wells 

(151 wells) indicates that the grid wells represent most of the 

diversity of water types present in the SCRV. However, three 

minor differences between USGS- and CDPH-grid data and 

CDPH-other data were evident.

First, chloride is the predominant anion in one USGSgrid well (lower right of the lower right triangle of the piper 

diagram, fig. B2). There are no CDPH wells that plot in this 

area of the piper diagram. This USGS-grid well is an irrigation 

well near the southern tip of the SCRV where there are no 

active CDPH wells.

Second, sodium is the predominant cation for three 

CDPH-other wells (lower right of the lower left triangle of the 

piper diagram, fig. B2). Two of these CDPH-other wells are 

in the Oxnard subbasin; there were no USGS- or CDPH-grid 

wells that had sodium as the predominant cation. One CDPHother well is in the Santa Clara River Valley East subbasin, 

where USGS-grid wells were not sampled for major ions.

Third, calcium in one CDPH-other well was greater than 

75 percent (lower left of the lower left triangle of the piper 

diagram, fig. B2), which is much higher than in any grid well. 

This well is in the Santa Clara River Valley East subbasin 

between wells SCRV-30 and SCRV-31, both with much lower 

concentrations of calcium.
Appendix B 75

IP010121_fig B01

0.01 0.1 1 10 100 1,000 10,000

0.01

0.1

1

10

100

1,000

10,000

Magnesium

Potassium

Sodium

Alkalinity

Chloride

Sulfate

Total dissolved solids

Nitrate

CONCENTRATION, USGS-GAMA DATA, IN MILLIGRAMS PER LITER

CONCENTRATION, CDPH DATA, IN MILLIGRAMS PER LITER

Calcium

1 to 1 correlation line

Figure B1. Paired inorganic concentrations from wells sampled by the USGS, April–June 2007, and the most recent available 

analysis in the California Department of Public Health for the same wells, November 1, 2003, to October 31, 2006, Santa Clara River 

Valley study unit, California GAMA Priority Basin Project.
76 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

IP010121_fig B02

20

40

0

60

80

100

100

80

60

40

20

0

0

20

40

60

80

100

80

60

100

40

20

0

60

80

100

20

40

0

100

80

60

40

20

0

CALCIUM

PERCENT, IN MILLIEQUIVALENTS PER LITER

PERCENT

PERCENT

SODIUM PLUS POTASSIUM

MAGNESIUM

CHLORIDE, FLUORIDE, NITRITE PLUS NITRATE

SULFATE

CARBONATE PLUS BICARBONATE

20

0

40

60

80

100

20

0

40

60

80

100

80

100

40

20

60

0

80

100

60

40

20

0

CALCIUM PLUS MAGNESIUM

SULFATE PLUS CHLORIDE

Grid well (USGS GAMA)

CDPH wells (most recent)

EXPLANATION

Well type—

Figure B2. Comparing water types in for USGS- and CDPH-grid wells with water types in all wells in the 

California Department of Public Health (CDPH) database that have a charge imbalance of less than 10 percent, 

Santa Clara River Valley study unit, California GAMA Priority Basin Project.
Appendix C 77

Appendix C. Calculation of Aquifer-Scale Proportions

Two statistical approaches, grid-based and spatially 

weighted, were selected to evaluate the aquifer-scale 

proportions of the primary aquifers in the SCRV study unit 

that had high, moderate, or low relative-concentrations 

(concentration relative to its water-quality benchmark) of 

constituents. Raw detection frequencies also were calculated 

for individual constituents, but were not used for estimating 

aquifer-scale proportions because this method creates spatial 

bias towards regions with large numbers of wells.

Grid-Based Calculation

One well in each grid cell, a “grid well,” is used to 

represent the primary aquifer system (Belitz and others, 2010). 

The relative-concentration for each constituent (concentration 

relative to its water-quality benchmark) was then evaluated 

for each grid well. The proportion of the primary aquifers 

with high relative-concentrations was calculated by dividing 

the number of cells with concentrations greater than the 

benchmark (relative-concentration greater than 1) by the total 

number of grid wells in the SCRV. Proportions containing 

moderate and low relative-concentrations were calculated 

similarly. Confidence intervals for grid-based aquifer-scale 

proportions were computed using the Jeffreys interval for the 

binomial distribution (Brown and others, 2001). The gridbased estimate is spatially unbiased. However, the grid-based 

approach may not identify constituents that are present at high 

relative-concentrations in small proportions of the primary 

aquifer system.

The grid-based aquifer-scale proportions for constituent 

classes also were calculated on a one-value–per-grid-cell 

basis. A cell with a high relative-concentration for any 

constituent in the class was defined as a high cell, and the high 

proportion was calculated as the number of high cells divided 

by the number of cells with data for any of the constituents 

in that class. The moderate proportion for the constituent 

class was calculated similarly, except that a cell already 

defined as high could not also be defined as moderate. A cell 

with a moderate relative-concentration for any constituent 

in the class without a high value for any constituent in the 

class was defined as moderate. The grid-based aquifer-scale 

proportion for the low category was calculated similarly, such 

that a cell could only be low if the relative-concentration was 

neither moderate nor high for any constituent in the class. The 

proportions for the high, moderate, and low categories were 

expected to total 100 percent, except for small differences as a 

result of rounding.

Spatially Weighted Calculation

The spatially weighted calculations of aquifer-scale 

proportions uses the most recent value for a constituent 

from all wells in the CDPH database with data in the 3-year 

interval prior to USGS-GAMA sampling (November 1, 2003, 

to October 31, 2006) in the SCRV, from all USGS-grid well 

data, and from selected USGS-understanding well data. 

The spatially weighted approach computes the aquifer-scale 

proportion using the percentage of wells with high relativeconcentrations from all wells in each cell, instead of using data 

from only one well. For each constituent, the high aquiferscale proportion was computed by calculating the proportion 

of wells with high relative-concentrations in each grid cell and 

dividing by the number of cells (Belitz and others, 2010): 

1

th

th

,

where

is the proportion of wells in the i cell with 

high relative-concentrations,

is the number of wells in the i cell with data 

for the constituent,

is the nu

high

i

total

n

i

i

SU

i

total

high

W

P

W

P

P n

P

W

W

=

=

=

å

th mber of wells in the i cell with high

relative-concentrations,

is the aquifer-scale proportion for the study

unit, and

is the number of cells with data for the

constituent.

PSU

n

Similar procedures were used to calculate the proportions 

of moderate and low relative-concentrations. The resulting 

proportions are expected to be spatially unbiased (Isaaks and 

Srivastava, 1989).

Raw Detection Frequencies

The raw detection frequencies of wells with high 

relative-concentrations for constituents were calculated using 

the same data used for the spatially weighted approach. 

This approach is the percentage (frequency) of wells in the 

study unit with high relative-concentrations. However, raw 

detection frequencies are not spatially unbiased because the 

wells in the CDPH database and USGS-understanding wells 

are not uniformly distributed. Consequently, high relativeconcentrations for wells clustered in a particular area represent 

a small part of the primary aquifer system and could be given 

a disproportionately high weight compared to that given by 

spatially unbiased approaches. Raw detection frequencies of 

high relative-concentrations are provided for reference in this 

report but were not used to assess aquifer-scale proportions.
78 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Appendix D. Attribution of Potential Explanatory Factors

Land-Use Classification

Land use was classified using an enhanced version of the 

satellite-derived (30-m pixel resolution) nationwide USGS 

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 is classified 

into 25 land-cover classifications (Nakagaki and Wolock, 

2005). These 25 land-cover classifications were assigned 

to 3 general land-use classifications: urban, agricultural, 

and natural. Land-use statistics were assigned using USGS 

National Land Cover Dataset for the study unit, and for areas 

within a radius of 500 m around each grid, understanding, 

and all CDPH wells (table D1) (Johnson and Belitz, 2009). 

Any overlapping of the areas within a radius of 500 m around 

adjacent wells was not accounted for in calculating land-use 

statistics.

Septic Systems

Septic tank density was determined from housing 

characteristics data from the 1990 U.S. Census (U.S. Census 

Bureau, 1990, accessed September 28, 2010, at ftp://ftp2.

census.gov/census_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 500 m around each well location. 

(Tyler Johnson, U.S. Geological Survey, written commun., 

2009) (table D1).

Well-Construction Information

Well-construction data were from driller’s logs. Other 

sources of well-construction data were ancillary records 

of well owners and the USGS National Water Information 

System database. Well identification verification procedures 

are described by Montrella and Belitz (2009). Well depths 

and depths to the top- and bottom-of-perforations for USGSgrid, USGS-understanding, and CDPH-grid wells are listed 

in table D2. Wells were classified as production, monitoring, 

or domestic wells (table D2). Production wells pump the 

groundwater from the aquifer to a distribution system. 

Monitoring wells tend to be short-screened wells installed 

exclusively for monitoring purposes. Domestic wells pump 

groundwater from the aquifer for home use.

Groundwater Age Classification

Groundwater dating techniques indicate the time that 

has elapsed after the groundwater was last in contact with 

the atmosphere. Techniques used to estimate groundwater 

residence times or ‘age’ include those based on tritium (3H; 

Tolstikhin and Kamenskiy, 1969; Torgersen and others, 1979) 

and 3H in combination with its decay product helium-3 (3He) 

(Schlosser and others, 1988), carbon-14 (14C) activities (Vogel 

and Ehhalt, 1963; Plummer and others, 1993: Kalin, 2000), 

and dissolved noble gases, particularly helium-4 accumulation 

(Davis and DeWiest, 1966; Andrews and Lee, 1979; Cey and 

others, 2008; Kulongoski and others, 2008).

Tritium (3H) 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. Aboveground nuclear-bomb testing between 1951 and 1980 (peak 

production in 1963) introduced much larger quantities of 3H 

than natural production into the atmosphere (Michel, 1989; 

Solomon and Cook, 2000). Tritium enters the hydrological 

cycle as precipitation following oxidation to tritiated water. 

Consequently, the presence of 3H in groundwater may be used 

to identify water that has exchanged with the atmosphere 

in the past 50 years. By determining the ratio of 3H to its 

decay product 3He, the time that the water has resided in the 

aquifer can be calculated more precisely than by using 3H 

alone (Takaoka and Mizutani, 1987; Poreda and others, 1988). 

Tritium activities and tritium-helium ages of the water samples 

are given in table D3. 14C is a radioactive isotope of carbon with a half-life 

of 5,730 years that is formed naturally in the atmosphere 

by the interaction of cosmic-ray neutrons with nitrogen, 

and to a lesser degree, interaction with oxygen and carbon. 

14C is incorporated into carbon dioxide, which is mixed 

throughout the atmosphere; the carbon dioxide is dissolved in 

precipitation and incorporated into the hydrologic cycle. 14C 

activity in groundwater, expressed as percent modern carbon 

(pmc), reflects exposure to the atmospheric 14C source. 14C can 

be used to estimate groundwater ages ranging from 1,000 to 

less than 30,000 years before present (Clark and Fritz, 1997). 

Calculated 14C ages (table D3) 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; Kalin, 2000). The 14C age (residence time) is 

calculated based on the decrease in 14C activity as a result of 

radioactive decay after groundwater recharge, relative to an 

assumed initial 14C concentration (Clark and Fritz, 1997). A 

mean initial 14C activity of 99 pmc was assumed for this study, 

with estimated errors on calculated groundwater ages as much 

as ±20 percent.
Appendix D 79

USGS 

GAMA well 

identification 

No.

CDPH 

GAMA well 

identification 

No.

Land use (percent)

Land-use 

classification

Septic 

density 

(tanks/

km2

)

Agricultural Natural Urban 

USGS- and CDPH-grid wells

SCRV-01 – 84.8 7.3 7.9 Agricultural 3.8

SCRV-02 – 88.2 6.2 5.6 Agricultural 1.6

SCRV-03 – 88.4 9.6 1.9 Agricultural 1.5

SCRV-04 – 68.0 2.4 29.6 Agricultural 52

SCRV-05 SCRV-DG-05 19.7 48.8 31.5 Mixed 0.3

SCRV-06 – 0.0 6.3 93.7 Urban 16

SCRV-07 – 31.0 26.9 42.0 Mixed 22

SCRV-08 SCRV-DG-08 67.4 17.0 15.7 Agricultural 27SCRV-09 – 81.4 14.7 3.9 Agricultural 2.5

SCRV-10 – 0.0 20.8 79.2 Urban 0.2

SCRV-11 SCRV-DG-11 59.0 41.0 0.0 Agricultural 15SCRV-12 – 0.2 2.1 97.7 Urban 0.0

SCRV-13 SCRV-DG-13 75.4 21.9 2.7 Agricultural 12

SCRV-14 – 37.8 10.7 51.5 Urban 4.3

SCRV-15 SCRV-DG-15 0.1 5.5 94.4 Urban 6.7

SCRV-16 – 8.2 42.0 49.7 Mixed 4.8

SCRV-17 – 0.2 81.6 18.2 Natural 13

SCRV-18 SCRV-DG-18 53.6 8.1 38.3 Agricultural 33

SCRV-19 – 49.0 50.5 0.5 Mixed 1.4

SCRV-20 SCRV-DG-20 87.1 12.5 0.5 Agricultural 8.2

SCRV-21 SCRV-DG-21 84.5 15.5 0.0 Agricultural 8.2

SCRV-22 SCRV-DG-22 86.3 13.4 0.3 Agricultural 8.2

SCRV-23 – 17.2 55.2 27.6 Natural 0.3

SCRV-24 SCRV-DG-24 75.3 16.5 8.2 Agricultural 3.6

SCRV-25 SCRV-DG-25 26.3 55.7 18.0 Natural 0.6

SCRV-26 SCRV-DG-26 26.2 41.8 32.0 Mixed 8.0

SCRV-27 – 57.0 33.4 9.5 Agricultural 0.0

SCRV-28 – 71.6 22.0 6.4 Agricultural 6.8

SCRV-29 SCRV-DG-29 2.7 12.0 85.2 Urban 137

SCRV-30 SCRV-DG-30 14.4 79.2 6.4 Natural 0.6

SCRV-31 SCRV-DG-31 0.7 35.6 63.7 Urban 6.7

SCRV-32 – 0.0 50.5 49.5 Mixed 0.1

SCRV-33 – 70.7 5.6 23.7 Agricultural 1.5

SCRV-34 – 44.8 54.2 1.0 Natural 6.9

SCRV-35 – 0.0 8.7 91.3 Urban 23

Table D1. Land-use classification and septic tank density for USGS-grid and -understanding wells sampled by USGS for April–June 2007, and for CDPH-grid wells for inorganic constituents, Santa Clara River Valley study unit, California, GAMA Priority Basin Project.[Land-use classification is based on a 500-meter radius around each well (Johnson and Belitz, 2009). Density of septic tanks in a circle with a 500-meter radius around each well (U.S. Census Bureau, 1990). CDPH, California Department of Public Health; USGS, U.S. Geological Survey; SCRV, Santa Clara River Valley USGS-grid well; SCRVU, USGS-understanding well; SCRV-DG, CDPH-grid well with USGS and CDPH data; SCRV-DPH, CDPH-grid well with CDPH data only; km, kilometer, –, no data]

USGS 

GAMA well 

identification 

No.

CDPH 

GAMA well 

identification 

No.

Land use (percent)

Land-use 

classification

Septic 

density 

(tanks/

km2

)

Agricultural Natural Urban 

USGS- and CDPH-grid wells—Continued

SCRV-36 – 36.0 14.1 49.9 Mixed 5.7

SCRV-37 – 57.2 33.0 9.9 Agricultural 2.4

SCRV-38 – 55.2 11.6 33.2 Agricultural 18

SCRV-39 – 15.9 11.5 72.6 Urban 11

SCRV-40 – 1.5 98.5 0.0 Natural 0.4

SCRV-41 SCRV-DG-41 72.4 24.2 3.4 Agricultural 1.3SCRV-42 – 65.2 34.6 0.2 Agricultural 4.3

– SCRV-DPH-5 0.0 52.6 47.4 Natural 65– SCRV-DPH-7 55.2 11.6 33.2 Agricultural 18– SCRV-DPH-16 87.6 12.4 0.0 Agricultural 4.0– SCRV-DPH-17 33.0 25.0 42.0 Mixed 0.7– SCRV-DPH-21 0.0 100.0 0.0 Natural 6.3– SCRV-DPH-25 70.9 28.9 0.2 Agricultural 73– SCRV-DPH-26 12.0 7.6 80.4 Urban 59– SCRV-DPH-35 21.1 47.0 32.0 Mixed 0.4– SCRV-DPH-38 40.4 40.2 19.4 Mixed 5.0

– SCRV-DPH-40 0.0 2.3 97.7 Urban 33– SCRV-DPH-42 0.3 4.1 95.5 Urban 52– SCRV-DPH-43 7.0 53.4 39.6 Natural 45– SCRV-DPH-44 0.1 37.2 62.7 Urban 16– SCRV-DPH-45 0.8 80.3 18.9 Natural 11– SCRV-DPH-48 15.1 46.8 38.0 Mixed 5.3

USGS-Understanding WellsSCRVU-01 – 45.4 5.8 48.8 Mixed 17

SCRVU-02 – 43.3 9.6 47.1 Mixed 65SCRVU-03 – 80.9 11.0 8.1 Agricultural 5.0

SCRVU-04 – 49.9 29.2 20.8 Mixed 1.2

SCRVU-05 – 33.4 40.9 25.7 Mixed 3.8SCRVU-06 – 78.4 19.2 2.4 Agricultural 2.3SCRVU-07 – 54.2 37.1 8.7 Agricultural 2.3SCRVU-08 – 0.1 57.3 42.6 Natural 5.1SCRVU-09 – 0.1 57.3 42.6 Natural 5.1

SCRVU-10

– 0.0 86.6 13.4 Natural 3.2

SCRVU-11

– 93.9 5.6 0.5 Agricultural 1.4
80 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Table D2. Well type and construction information for USGS-grid and -understanding wells sampled April–June 2007, and CDPH-grid 

wells for inorganic constituents, Santa Clara River Valley study unit, California, GAMA Priority Basin Project.

[Well depth determined from completed well depth reported on driller’s log. Top of perforations determined from depth to bottom of solid (unperforated) casing 

reported on driller’s log. CDPH, California Department of Public Health; USGS, U.S. Geological Survey; SCRV, Santa Clara River Valley USGS-grid well; 

SCRVU, USGS-understanding well; SCRV-DG, CDPH-grid well with USGS and supplemental CDPH data; SCRV-DPH, CDPH-grid well with CDPH data 

only; ft, foot; LSD, land-surface datum; na, not available]

USGS GAMA 

well identification 

No.

CDPH GAMA 

well identification No. Well type

Construction information

Well depth 

(ft below LSD)

Top-of-perforations 

(ft below LSD)

Bottom-of-perforations 

(ft below LSD)

USGS- and CDPH-grid wells

SCRV-01 – Production 1,300 590 1,280

SCRV-02 – Production 1,310 750 1,290

SCRV-03 – Production 1,023 443 1,003

SCRV-04 – Production na na na

SCRV-05 SCRV-DG-05 Production 300 50 280

SCRV-06 – Production 220 120 220

SCRV-07 – Production 242 92 232

SCRV-08 SCRV-DG-08 Production 910 700 890

SCRV-09 – Production 863 703 863

SCRV-10 – Production 766 610 738

SCRV-11 SCRV-DG-11 Production 636 316 636

SCRV-12 – Production 1,200 720 1,180

SCRV-13 SCRV-DG-13 Production 399 204 375

SCRV-14 – Production 830 512 740

SCRV-15 SCRV-DG-15 Production 670 452 653

SCRV-16 – Production 700 260 700

SCRV-17 – Production 60 15 60

SCRV-18 SCRV-DG-18 Production 420 300 400

SCRV-19 – Production 100 na na

SCRV-20 SCRV-DG-20 Production 1,440 800 1,440

SCRV-21 SCRV-DG-21 Production 980 650 na

SCRV-22 SCRV-DG-22 Production 980 670 980

SCRV-23 – Production na na na

SCRV-24 SCRV-DG-24 Production 1,042 642 1,042

SCRV-25 SCRV-DG-25 Production 203 60 165

SCRV-26 SCRV-DG-26 Production 142 na na

SCRV-27 – Production 873 403 853

SCRV-28 – Production 334 105 240

SCRV-29 SCRV-DG-29 Production 252 107 na

SCRV-30 SCRV-DG-30 Production 208 na na

SCRV-31 SCRV-DG-31 Production 150 56 150

SCRV-32 – Production 300 50 230

SCRV-33 – Production 504 224 504

SCRV-34 – Production 920 500 920

SCRV-35 – Production 300 70 290

SCRV-36 – Production 300 na na

SCRV-37 – Production 541 421 521
Appendix D 81

Table D2. Well type and construction information for wells sampled by USGS for April–June 2007, and CDPH-grid wells for inorganic 

constituents, Santa Clara River Valley Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California.—Continued

[Well depth determined from completed well depth reported on driller’s log. Top of perforations determined from depth to bottom of solid (unperforated) casing 

reported on driller’s log. CDPH, California Department of Public Health; USGS, U.S. Geological Survey; SCRV, Santa Clara River Valley USGS-grid well; 

SCRVU, USGS-understanding well; SCRV-DG, CDPH-grid well with USGS and supplemental CDPH data; SCRV-DPH, CDPH-grid well with CDPH data 

only; ft, foot; m, meter; LSD, land-surface datum; na, not available]

USGS GAMA 

well identification 

No.

CDPH GAMA 

well identification No. Well type

Construction information

Well depth 

(ft below LSD)

Top-of-perforations 

(ft below LSD)

Bottom-of-perforations 

(ft below LSD)

USGS- and CDPH-grid wells—Continued

SCRV-38 – Production 644 281 644

SCRV-39 – Production 1,190 580 1,080

SCRV-40 – Domestic na na na

SCRV-41 SCRV-DG-41 Production 300 na na

SCRV-42 – Production 275 110 275

– SCRV-DPH-5 Production 29 20 29

– SCRV-DPH-7 Production 644 281 644

– SCRV-DPH-16 Production 200 150 190

– SCRV-DPH-17 Production 1,071 801 1,051

– SCRV-DPH-21 Production na na na

– SCRV-DPH-25 Production 830 467 830

– SCRV-DPH-26 Production 884 564 864

– SCRV-DPH-35 Production na na na

– SCRV-DPH-38 Production 300 na na

– SCRV-DPH-40 Production 600 350 600

– SCRV-DPH-43 Production 1,290 485 1,280

– SCRV-DPH-42 Production na na na

– SCRV-DPH-44 Production na na na

– SCRV-DPH-45 Production na na na

– SCRV-DPH-48 Production 302 102 302

USGS-understanding wells

SCRVU-01 – Production 820 400 820

SCRVU-02 – Production 759 459 759

SCRVU-03 – Production 330 100 320

SCRVU-04 – Production 1,483 403 1,463

SCRVU-05 – Production na na na

SCRVU-06 – Monitor 740 720.0 740

SCRVU-07 – Monitor 720 680 720

SCRVU-08 – Monitor 640 600 640

SCRVU-09 – Monitor 970 930 970

SCRVU-10 – Monitor 220 200 220

SCRVU-11 – Production 810 400 810
82 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Table D3. Groundwater age classification information for wells sampled by the USGS for April–June 2007, Santa 

Clara River Valley study unit, California, GAMA Priority Basin Project.

[Samples classified as pre-modern if recharged prior to 1955. Samples classified as modern if recharged after 1955. Samples classified 

as mixed if sample contains both modern and pre-modern water. GAMA, Groundwater Ambient Montoring and Assessment Program; 

SCRV, Santa Clara River Valley USGS-grid well; SCRVU, USGS-understanding well; >, greater than; <, less than]

USGS 

GAMA well 

identification 

No.

Tritium 

activity 

(TU)

Tritium-helium 

age 

(years)

Uncorrected 

carbon-14 age 

(years)

Percentage of 

terrigenic helium 

in total helium

Groundwater age 

classification

USGS-grid wells

SCRV-01 0.00 >50 24.9 Pre-modern

SCRV-02 0.19 >50 70.2 Pre-modern

SCRV-03 0.0 >50 16,400 74.8 Pre-modern

SCRV-04 2.10 10.1 0.0 Modern

SCRV-05 1.82 3.8 0.0 Modern

SCRV-06 2.10 11.9 0.0 Modern

SCRV-07 1.60 Not datable <1,000 0.0 Mixed

SCRV-08 0.00 >50 53.5 Pre-modern

SCRV-09 0.09 >50 0.0 Mixed

SCRV-10 0.09 >50 3,800 0.0 Mixed

SCRV-11 0.50 >50 1.8 Mixed

SCRV-12 0.31 >50 7,600 12.5 Pre-modern

SCRV-13 2.60 16.2 11.9 Mixed

SCRV-14 0.00 >50 47.2 Pre-modern

SCRV-15 1.00 19.2 0.0 Modern

SCRV-16 1.60 8.2 2.2 Modern

SCRV-17 2.10 Not datable 0.0 Modern

SCRV-18 0.09 >50 6.5 Pre-modern

SCRV-19 2.51 9.7 0.0 Modern

SCRV-20 0.00 >50 0.0 Mixed

SCRV-21 0.19 >50 10.3 Pre-modern

SCRV-22 0.09 >50 15.2 Pre-modern

SCRV-23 2.82 5.1 0.0 Modern

SCRV-24 2.10 8.1 0.0 Modern

SCRV-25 2.41 Not datable 2.5 Modern

SCRV-26 3.10 >50 19.5 Mixed

SCRV-27 1.00 23.6 0.0 Modern

SCRV-28 1.10 17.7 0.0 Modern

SCRV-29 3.61 3.8 0.0 Modern

SCRV-30 2.82 >50 9.5 Mixed

SCRV-31 3.20 >50 25.1 Mixed

SCRV-32 1.00 22.3 33.1 Mixed

SCRV-33 1.41 Not datable 4.6 Modern

SCRV-34 0.00 >50 0.0 Mixed

SCRV-35 2.82 Not datable 3,300 3.5 Mixed

SCRV-36 2.60 Not datable 0.0 Modern
Appendix D 83

Table D3. Groundwater age classification information for wells sampled by the USGS for April–June 2007, Santa 

Clara River Valley study unit, California, GAMA Priority Basin Project.—Continued

[Samples classified as pre-modern if recharged prior to 1955. Samples classified as modern if recharged after 1955. Samples classified 

as mixed if sample contains both modern and pre-modern water. GAMA, Groundwater Ambient Montoring and Assessment program; 

SCRV, Santa Clara River Valley USGS-grid well; SCRVU, USGS-understanding well; >, greater than; <, less than]

USGS 

GAMA well 

identification 

No.

Tritium 

activity 

(TU)

Tritium-helium 

age 

(years)

Uncorrected 

carbon-14 age 

(years)

Percentage of 

terrigenic helium 

in total helium

Groundwater age 

classification

USGS-grid wells—Continued

SCRV-38 0.60 >50 0.0 Mixed

SCRV-39 0.00 >50 3.7 Mixed

SCRV-41 2.32 16.1 0.0 Modern

SCRV-42 3.61 3.0 4.9 Modern

USGS-understanding wells

SCRVU-01 2.01 25.1 1,300 0.0 Mixed

SCRVU-02 2.70 >50 3,100 66.5 Mixed

SCRVU-03 1.97 1.3 <1,000 0.0 Mixed

SCRVU-04 0.19 >50 11,300 86.2 Pre-modern

SCRVU-05 3.01 >50 16.7 Mixed

SCRVU-06 0.00 >50 75.0 Pre-modern

SCRVU-10 0.41 >50 64.4 Pre-modern

SCRVU-11 0.00 >50 93.7 Pre-modern

Helium (He) is a naturally occurring inert gas produced 

by the radioactive decay of lithium, thorium, and uranium 

in the earth. Measured He concentrations in groundwater 

are determined as the sum of air-equilibrated He, He from 

dissolved-air bubbles, terrigenic He, and tritiogenic 3He. The 

helium (3He and 4He isotopes) concentrations in groundwater 

commonly exceed the expected solubility equilibrium values, 

which are a function of the temperature of the water, as a 

result of subsurface production of both isotopes, and their 

subsequent release into the groundwater (Morrison and Pine, 

1955; Andrews and Lee, 1979; Torgersen, 1980; Andrews, 

1985; Torgersen and Clark, 1985). The presence of terrigenic 

He in groundwater, from its production in aquifer material 

or deeper in the crust, is indicative of long groundwater 

residence times. The amount of terrigenic He is defined as 

the concentration of the total measured He, minus He from 

air equilibration and dissolved-air bubbles. Percentage of 

terrigenic He is defined as the concentration of terrigenic He 

(as defined previously) divided by the total measured He in 

the sample (corrected for air-bubble entrainment). Samples 

in which more than 5 percent of the total He is terrigenic 

He (percentage of terrigenic He) indicate groundwater has a 

residence time of more than 100 yrs.

Recharge temperatures for 48 samples were determined 

from dissolved neon, argon, krypton, and xenon data using 

methods described by Aeschbach-Hertig and others (1999). 

The only modeled recharge temperatures accepted were those 

for which the probability was greater than 1 percent, and the 

sum of the squared deviations between the modeled and the 

measured concentrations (weighted with the experimental 

1-sigma errors) was equal to or greater than the observed 

value (Aeschbach-Hertig and others, 2000). The recharge 

temperature with the highest probability for each sample was 

used in this report. For samples that had a modeled recharge 

temperature probability of less than 1 percent, the mean 

recharge temperature of all samples was used.

The groundwater age was computed using 3H /3He 

as described by Poreda and others (1988). The 3He/4He 

of samples was determined by the linear regression of the 

percentage of terrigenic He and δ3He ([δ3He = Rmeas/

Ratm–1] × 100 percent) of samples with less than 1 tritium 

unit. Rmeas is the ratio of 3He/4He in the measured sample; 

Ratm is the ratio of 3He/4He in the atmosphere. Calculations 

of the recharge temperature using noble gases and 3He/4He 

are useful because they can be used to constrain helium-based 

groundwater ages further.
84 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

In this study, the ages of samples were classified as 

pre-modern, modern, and mixed (table D3). Groundwater 

with 3H activity less than 1 tritium unit (TU), percentage of 

terrigenic He greater than 5 percent, and 14C less than 90 pmc 

was designated as pre-modern. Pre-modern groundwater was 

defined as having been recharged before 1950. Groundwater 

with 3H greater than 1 TU, percentage of terrigenic He less 

than 5 percent, and 14C greater than 90 pmc was designated 

as modern. Modern groundwater is defined as having been 

recharged after 1950. Samples with pre-modern and modern 

components are designated as mixed groundwater. In reality, 

pre-modern groundwater could contain small fractions 

of modern groundwater and modern groundwater could 

contain small fractions of pre-modern groundwater. Previous 

investigations have used a range of tritium values from 0.3 to 

1.0 TU as thresholds for distinguishing pre-1950 from post1950 groundwater (Michel, 1989; Plummer and others, 1993, 

p. 260; Michel and Schroeder, 1994; Clark and Fritz, 1997, 

p. 185; Manning and others, 2005). By using a tritium value 

of 1.0 TU for the threshold in this study, the age classification 

scheme allows a larger fraction of modern groundwater to be 

classified as pre-modern than if a lower threshold was used. 

A lower threshold for tritium would result in fewer samples 

classified as pre-modern age than as mixed age, when other 

tracers, 14C and terrigenic He, indicated that the samples 

primarily were pre-modern age. This higher threshold was 

considered more appropriate for this study because many of 

the wells were production wells with long screens and mixing 

water of pre-modern and modern age likely occur.

Geochemical Conditions

Geochemical conditions investigated as potential 

explanatory variables in this report include oxidation-reduction 

characteristics, dissolved-oxygen concentrations, and pH 

(table D4). Oxidation-reduction (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 (TEAP); one TEAP typically is predominant at a 

particular time and aquifer location (Chapelle and others, 

1995; Chapelle, 2001). The predominant TEAPs are oxygenreduction (oxic), nitrate-reduction, manganese-reduction, 

iron-reduction, sulfate-reduction, and methanogenesis. The 

presence of redox-sensitive chemical species indicating 

more than one TEAP may indicate (1) the discharge from 

the well includes mixed waters from different redox zones 

upgradient of the well, (2) the well is screened across more 

than one redox zone, or (3) there is spatial heterogeneity in 

microbial activity in the aquifer. In addition, different redox 

couples often are not consistent, indicating electrochemical 

disequilibrium in groundwater (Lindburg and Runnels, 1984) 

complicating the assessments of redox conditions. 

In this report, redox conditions were represented in 

two ways: as dissolved oxygen concentration and as redox 

category based on the predominant TEAP(s). Dissolvedoxygen concentrations were measured at USGS-grid and 

USGS-understanding wells (Montrella and Belitz, 2009), but 

are not reported in the CDPH database (table D4). Redox 

conditions were classified based on dissolved oxygen, 

nitrate, manganese, iron, and sulfate concentrations using 

the classification scheme of McMahon and Chapelle (2008) 

(table D4). An automated workbook program was used to 

assign the redox class to each sample (Jurgens and others, 

2009). For wells without USGS inorganic constituent data, 

the most recent data within the previous 3 years (November 1, 

2003, to October 31, 2006) for that well in the CDPH database 

were used.
Appendix D 85

Table D4. Oxidation-reduction classification and pH for wells sampled by the USGS for April–June 2007, and CDPH-grid wells for 

inorganic constituents, Santa Clara River Valley study unit, California, GAMA Priority Basin Project.

[Redox category and redox process determined using the algorithm of McMahon and Chapelle (2008) implemented by Jurgens and others (2009) except for 

samples with incomplete redox data, which were excluded from the analysis. CDPH, California Department of Public Health; USGS, U.S. Geological Survey; 

SCRV, Santa Clara River Valley USGS-grid well; SCRVU, USGS-understanding well; SCRV-DG, CDPH-grid well with USGS and supplemental CDPH data; 

SCRV-DPH, CDPH-grid well with CDPH data only; redox, oxidation-reduction; mg/L, milligrams per liter; oxic, dissolved oxygen greater than 0.5; anoxic, 

dissolved oxygen less than 0.5; O2, oxygen; NO3, nitrate reducing; Mn, manganese reducing; Fe/SO4, iron and (or) sulfate reducing; >, greater than; nc, not 

collected; na, not able to determine; –, no well]

USGS 

GAMA well 

identification 

No.

CDPH 

GAMA well 

identification 

No.

pH

Oxidizing and reducing constituents 

Redox 

category

Redox 

process

Dissolved 

oxygen 

(mg/L)

Nitrate plus 

nitrite

(mg/L)

Manganese 

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

USGS- and CDPH-grid wells

SCRV-01 – 7.7 <0.2 nc nc nc nc O2 < 0.5 mg/L unknown

SCRV-02 – 7.6 <0.2 nc nc nc nc O2 < 0.5 mg/L unknown

SCRV-03 – 7.7 0.4 <0.06 66.5 76 137 anoxic Mn

SCRV-04 – 7.0 5.0 11.2 0.4 9 225 oxic O2

SCRV-05 SCRV-DG-05 7.0 4.1 1.56 30.0 430 557 Mixed (oxic-anoxic) O2-Fe/SO4

SCRV-06 – 7.2 0.3 12.9 17.5 14 673 anoxic NO3

SCRV-07 – 6.8 5.9 0.94 <0.2 <6 227 oxic O2

SCRV-08 SCRV-DG-08 7.5 <0.2 <0.11 54.0 260 210 anoxic Fe/SO4

SCRV-09 – 7.1 <0.2 <0.06 230 1,420 811 anoxic Fe/SO4

SCRV-10 – 7.6 <0.2 <0.06 174 906 343 anoxic Fe/SO4

SCRV-11 SCRV-DG-11 7.5 <0.2 nc nc nc nc O2 < 0.5 mg/L unknown

SCRV-12 – 7.6 0.3 0.04 188 319 367 anoxic Fe/SO4

SCRV-13 SCRV-DG-13 7.4 2.7 26.9 <10 <50 213 oxic O2

SCRV-14 – 7.4 1.8 nc nc nc nc O2 ≥ 0.5 mg/L unknown

SCRV-15 SCRV-DG-15 7.3 0.4 5.85 120 <50 454 anoxic NO3/Mn

SCRV-16 – 7.5 <0.2 1.86 396 3 448 anoxic NO3/Mn

SCRV-17 – 7.1 2.6 nc nc nc nc O2 ≥ 0.5 mg/L unknown

SCRV-18 SCRV-DG-18 7.2 0.7 <0.41 160 <100 486 Mixed (oxic-anoxic) O2-Mn

SCRV-19 – 7.3 7.3 2.71 0.1 3 387 oxic O2

SCRV-20 SCRV-DG-20 7.8 0.8 0.03 100 160 102 Mixed (oxic-anoxic) O2-Fe/SO4

SCRV-21 SCRV-DG-21 7.6 0.5 0.03 64.0 110 134 Mixed (oxic-anoxic) O2-Fe/SO4

SCRV-22 SCRV-DG-22 7.5 0.5 nc nc nc nc O2 ≥ 0.5 mg/L unknown

SCRV-23 – 7.6 <0.2 nc nc nc nc O2 < 0.5 mg/L unknown

SCRV-24 SCRV-DG-24 7.4 1.1 6.60 80.0 740 545 Mixed (oxic-anoxic) O2-Fe/SO4

SCRV-25 SCRV-DG-25 7.4 3.0 1.11 <20 <100 174 oxic O2

SCRV-26 SCRV-DG-26 7.5 2.2 2.98 <20 <100 237 oxic O2

SCRV-27 – 7.4 0.3 3.09 243 44 572 Mixed (oxic-anoxic) O2-Mn

SCRV-28 – 7.1 3.2 22.2 1.9 30 483 oxic O2

SCRV-29 SCRV-DG-29 7.3 3.4 2.94 1.6 <40 440 oxic O2

SCRV-30 SCRV-DG-30 7.4 3.0 3.37 <20 <100 188 oxic O2

SCRV-31 SCRV-DG-31 7.4 6.1 4.77 <20 <100 183 oxic O2

SCRV-32 – 7.0 <0.2 nc nc nc nc O2 < 0.5 mg/L unknown

SCRV-33 – 7.0 2.6 15.2 11.7 <6 905 oxic O2

SCRV-34 – 7.3 0.7 nc nc nc nc O2 ≥ 0.5 mg/L unknown

SCRV-35 – 7.0 7.7 12.4 <0.2 <6 893 oxic O2
86 Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007: California GAMA Priority Basin Project

Table D4. Oxidation-reduction classification and pH for wells sampled by USGS for April–June 2007, and CDPH-grid wells for inorganic 

constituents, Santa Clara River Valley Groundwater Ambient Monitoring and Assessment (GAMA) study unit, California.—Continued

[Redox category and redox process determined using the algorithm of McMahon and Chapelle (2008) implemented by Jurgens and others (2009) except for 

samples with incomplete redox data, which were excluded from the analysis. CDPH, California Department of Public Health; USGS, U.S. Geological Survey; 

SCRV, Santa Clara River Valley USGS-grid well; SCRVU, USGS-understanding well; SCRV-DG, CDPH-grid well with USGS and supplemental CDPH data; 

SCRV-DPH, CDPH-grid well with CDPH data only redox, oxidation-reduction; mg/L, milligrams per liter; oxic, dissolved oxygen greater than 0.5; anoxic, 

dissolved oxygen less than 0.5; O2, oxygen; NO3, nitrate reducing; Mn, manganese reducing; Fe/SO4, iron and (or) sulfate reducing; >, greater than; nc, not 

collected; na, not able to determine; –, no well]

USGS 

GAMA well 

identification 

No.

CDPH 

GAMA well 

identification 

No.

pH

Oxidizing and reducing constituents 

Redox 

category

Redox 

process

Dissolved 

oxygen 

(mg/L)

Nitrate plus 

nitrite

(mg/L)

Manganese 

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

USGS- and CDPH-grid wells—Continued

SCRV-36 – 7.4 3 nc nc nc nc O2 ≥ 0.5 mg/L unknown

SCRV-37 – 7.5 <0.2 <0.06 176 330 39.5 anoxic Fe/SO4

SCRV-38 – 7.2 0.2 nc nc nc nc O2 < 0.5 mg/L unknown

SCRV-39 – 7.3 <0.2 0.38 253 934 719 anoxic Fe/SO4

SCRV-41 SCRV-DG-41 nc 4.0 4.9 nc nc nc O2 ≥ 0.5 mg/L unknown

SCRV-42 – 7.6 4.6 2.10 0.5 8 268 oxic O2

– SCRV-DPH-5 7.0 nc 0.18 <10 <50 210 na na

– SCRV-DPH-7 7.1 nc 2.55 690 <50 206 na na

– SCRV-DPH-16 7.4 nc <0.09 <10 <50 396 na na

– SCRV-DPH-17 7.4 nc 0.03 20.0 350 191 na na

– SCRV-DPH-21 7.5 nc 0.47 10.0 <40 160 na na

– SCRV-DPH-25 7.1 nc <0.11 240 1,100 880 na na

– SCRV-DPH-26 7.5 nc <0.09 130 120 296 na na

– SCRV-DPH-35 7.6 nc <0.45 <20 <100 124 na na

– SCRV-DPH-38 7.1 nc 2.98 10 530 450 na na

– SCRV-DPH-40 7.1 nc 9.71 <1 <40 620 na na

– SCRV-DPH-42 7.3 nc 2.98 nc nc 133 na na

– SCRV-DPH-43 7.2 nc 3.5 <20 <100 137 na na

– SCRV-DPH-44 7.4 nc 5.74 <20 <100 113 na na

– SCRV-DPH-45 7.3 nc 3.16 <20 120 137 na na

– SCRV-DPH-48 7.8 nc 13.8 nc nc 107 na na

USGS-understanding wells

SCRVU-01 – 7.0 1.1 2.07 5.5 7 461 oxic O2

SCRVU-02 – 7.2 <0.2 <0.06 149 137 586 anoxic Fe/SO4

SCRVU-03 – 7.6 7.9 2.34 0.6 13 518 oxic O2

SCRVU-04 – 7.6 <0.2 <0.06 27.6 128 316 anoxic Fe/SO4

SCRVU-05 – 7.4 5.1 nc nc nc nc O2 ≥ 0.5 mg/L unknown

SCRVU-06 – 7.4 <0.2 <0.06 155 62 214 anoxic Mn

SCRVU-07 – 7.1 <0.2 <0.06 2,370 7,960 2.25 anoxic Fe/SO4

SCRVU-08 – 6.9 <0.2 <0.06 186 <6 635 anoxic Mn

SCRVU-09 – 7.3 <0.2 <0.06 221 <6 330 anoxic Mn

SCRVU-10 – 7.2 0.4 <0.06 784 10,200 2,060 anoxic Fe/SO4

SCRVU-11 – 7.3 nc <0.06 8.9 17 247 na na
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Burton and others—Status and Understanding of Groundwater Quality in the Santa Clara River Valley, 2007:

California GAMA Priority Basin Project—SIR 2011–5052

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