Scientific Investigations Report 2010–5175

U.S. Department of the Interior

U.S. Geological Survey

Photo placement

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 Northern 

San Joaquin Basin, 2005: California GAMA Priority Basin Project
Front Cover Map: Groundwater basins categorized by sampling priority. Location of groundwater basin 

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

Cover photographs:

Front cover: Almond orchard in San Joaquin County, California. (Photograph taken by Cathy Munday, U.S. 

Geological Survey)

Back cover: Preparing to sample a well in the Tracy study area, San Joaquin County, California. (Photograph 

taken by Steve Schmitt, U.S. Geological Survey)

Sampling priority

Study area

Low-use basins

Priority basins

Areas that are outside DWR-defined

 groundwater basins
Status and Understanding of Groundwater 

Quality in the Northern San Joaquin Basin, 2005: 

California GAMA Priority Basin Project 

By George L. Bennett, V, Miranda S. Fram, Kenneth Belitz, and Bryant C. Jurgens

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

Scientific Investigations Report 2010–5175

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: 2010

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Suggested citation:

Bennett, G.L., V, Fram, M.S., Belitz, Kenneth, and Jurgens, B.C., 2010, Status and understanding of groundwater 

quality in the northern San Joaquin Basin, 2005: California GAMA Priority Basin Project: U.S. Geological Survey 

Scientific Investigations Report 2010–5175, 82 p.
iii

Contents

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

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

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

Hydrogeologic Setting...................................................................................................................................5

Methods ........................................................................................................................................................10

Design of Sampling Networks ..........................................................................................................10

Identification of CDPH Data for Status Assessment.....................................................................14

Historically and Currently High Constituents ........................................................................14

Selection of CDPH Data for the Grid-Based Approach................................................................14

Status Assessment Methods............................................................................................................15

Relative-Concentrations...........................................................................................................15

Estimation of Aquifer-Scale Proportions ...............................................................................17

Understanding Assessment Methods.............................................................................................19

Statistical Analysis ....................................................................................................................19

Potential Explanatory Factors....................................................................................................................20

Land Use...............................................................................................................................................20

Well Depth and Depth to Top-of-Perforation .................................................................................22

Normalized Lateral Position..............................................................................................................22

Geochemical Conditions.............................................................................................................................26

Correlations Between Explanatory Factors ...................................................................................28

Status and Understanding of Water Quality ...........................................................................................29

Inorganic Constituents.......................................................................................................................36

Trace Elements...........................................................................................................................36

Factors Affecting Arsenic ...............................................................................................36

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

Radioactive Constituents..........................................................................................................44

Nutrients......................................................................................................................................45

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

Trace Elements and Major and Minor Ions ..........................................................................46

Factors Affecting Manganese and Iron........................................................................46

Factors Affecting Total Dissolved Solids ......................................................................47

Organic Constituents..........................................................................................................................48

Trihalomethanes.........................................................................................................................52

Factors Affecting Trihalomethanes................................................................................52

Solvents................................................................................................................................................52

Factors Affecting Solvents..............................................................................................58

Other VOCs...........................................................................................................................................58

Fumigants....................................................................................................................................58

Factors Affecting Fumigants ..........................................................................................59

Pesticides.............................................................................................................................................60

Factors Affecting Pesticides ..........................................................................................60

Special-Interest Constituents...........................................................................................................60
iv

Contents—Continued

Figures

Figure 1. Map showing location of Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit and California hydrogeologic

provinces .................................................................................... 4

Figure 2. Map showing geographic features and study areas of the Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 6

Figure 3. Map showing geologic formations, and areal distribution of grid and

understanding wells, in the Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit ....................................... 7

Figure 4. Conceptual model of the aquifer system for the Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit ............ 8

Figure 5. Map showing land use of the Northern San Joaquin Basin Groundwater

Ambient Monitoring and Assessment (GAMA) study unit .............................. 9

Figure 6. Map showing study area grids, grid and understanding wells sampled, and

California Department of Public Health wells used for supplemental data on

inorganic constituents, Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit ....................................... 11

Figure 7. Ternary diagrams showing proportions of urban, agricultural, and natural land

use, Northern San Joaquin Groundwater Ambient Monitoring and Assessment

(GAMA) study unit ........................................................................... 21

Figure 8. Boxplots showing well depths, depths to top-of-perforation, and perforation

lengths for grid and understanding wells, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit ............ 22

Figure 9. Map and graph showing distribution of wells plotted on visualization of

normalized lateral positions and across the range of normalized lateral

positions, Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 23

Figure 10. Boxplots of classified groundwater age related to depth to top-of-perforation,

well depth, and normalized lateral position, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit ............ 24

Figure 11. Profile showing relation of classified groundwater age to normalized lateral

position and depth of perforated interval of wells, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit ............ 26

Summary .......................................................................................................................................................61

Acknowledgments.......................................................................................................................................63

References Cited..........................................................................................................................................63

Appendix A. Selection of CDPH Well Data.............................................................................................69

Appendix B. Ancillary Datasets ...............................................................................................................72

Appendix C. Calculating Total Dissolved Solids.....................................................................................80

Appendix D. Comparison of CDPH and USGS-GAMA Data.................................................................81
v

Figures—Continued

Figure 12. Map of redox conditions in grid and understanding wells, Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 27

Figure 13. Boxplot of classified redox category to normalized lateral position, Northern

San Joaquin Basin Groundwater Ambient Monitoring and Assessment

(GAMA) study unit ........................................................................ 28

Figure 14. Graph showing maximum relative-concentration in grid wells for constituents

detected by type of constituent, Northern San Joaquin Basin Groundwater

Ambient Monitoring and Assessment (GAMA) study unit .............................. 30

Figure 15. Graphs showing relative-concentrations of trace elements and nutrients with

health-based benchmarks and maximum relative-concentrations greater than

0.5 in grid wells, and major and minor elements with aesthetic benchmarks

and maximum relative-concentrations greater than 0.5 in grid wells, Northern

San Joaquin Basin Groundwater Ambient Monitoring and Assessment

(GAMA) study unit ........................................................................... 37

Figure 16. Maps showing relative-concentrations of selected inorganic constituents for

USGS-grid and USGS-understanding wells, and relative-concentrations from

the most recent inorganic constituent data point from the “prior period”

(January 1, 2001–April 21, 2004) for CDPH wells, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit. ............ 38

Figure 17. Graphs showing relation of arsenic to well depth and pH and redox condition,

Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 44

Figure 18. Boxplots showing nitrate plus nitrite concentrations grouped by geochemical

(redox) conditions, Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit ....................................... 45

Figure 19. Boxplots showing manganese concentrations grouped by geochemical

(redox) conditions, Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit ....................................... 47

Figure 20. Graphs showing relation of total dissolved solids concentrations to normalized

lateral position within the Cosumnes, Eastern San Joaquin, Upland, and Tracy

study areas, Northern San Joaquin Basin Groundwater Ambient Monitoring

and Assessment (GAMA) study unit ...................................................... 48

Figure 21. Graph showing detection frequency and maximum relative-concentration for

organic and special-interest constituents detected in grid wells, Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 49

Figure 22. Graphs showing relative-concentrations and detection frequencies of

selected VOC and special-interest constituents in grid wells, Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 50

Figure 23. Graphs showing relative-concentrations and detection frequencies of

selected fumigant and pesticide constituents in grid wells, Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 51
vi

Figures—Continued

Figure 24. Maps showing relative-concentrations of selected organic constituents for

USGS-grid and USGS-understanding wells representative of the primary

aquifer and from the most recent analysis (January 1, 2001–April 21, 2004) for

CDPH wells, Northern San Joaquin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 53

Figure 25. Graph showing pesticide, solvent, total trihalomethane (TTHM), and fumigant

detection frequencies grouped by groundwater-age classification, Northern

San Joaquin Basin Groundwater Ambient Monitoring and Assessment

(GAMA) study unit ........................................................................... 57

Figure 26. Graph showing relation between detections of total trihalomethanes (TTHMs)

and depth to top-of-perforation and percentage of urban land use in 500-m

buffer areas surrounding wells for USGS-grid and USGS-understanding well

samples, Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 57

Figure 27. Graph showing relation between detections of solvents and depth to

top-of-perforation and percentage of urban land use in 500-m buffer areas

surrounding wells for USGS-grid and USGS-understanding well samples,

Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 58

Figure 28. Graph showing relation between detections of fumigants and depth to

top-of-perforation and percentage of agricultural land use in 500-meter buffer

areas surrounding wells for USGS-grid and USGS-understanding well

samples, Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 59

Figure 29. Graph showing relation between detection of pesticides and depth to

top-of-perforation and percentage of agricultural land use in 500-m buffer

areas surrounding wells for USGS-grid and USGS-understanding well

samples, Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 61

Appendix Figures

Figure A1. Map showing identifiers and locations of grid and understanding wells

sampled during December 2004–February 2005, and grid wells at which data

for inorganic constituents from the California Department of Public Health

were used, Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 71

Figure D1. Graph of paired inorganic concentrations from wells sampled by the

Groundwater Ambient Monitoring and Assessment (GAMA) Program between

December 2004 and February 2005 and the most recent available analysis in

the California Department of Health Services for the same wells, Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 81

Figure D2. Trilinear diagram comparing water types in 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, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit ............ 82
vii

Tables

Table 1. Analytes and numbers of wells sampled for different analytical schedules,

Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 13

Table 2. 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 Northern San Joaquin Basin Groundwater Ambient Monitoring and

Assessment (GAMA) study unit............................................................ 14

Table 3. Constituents in California Department of Public Health (CDPH) wells at high

concentrations from January 10, 1984–April 21, 2004 (the period of record prior

to the 3-year period used in the status assessment), Northern San Joaquin

Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit ...... 15

Table 4. Inorganic constituents (including the special-interest constituent perchlorate),

benchmarks, and number of grid wells per constituent, Northern San Joaquin

Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit ...... 16

Table 5. Benchmark type and value for constituents included in the assessment of

status of groundwater quality in the Northern San Joaquin Basin Groundwater

Ambient Monitoring and Assessment (GAMA) study unit .............................. 18

Table 6. Results of Wilcoxon tests for differences in values of selected water-quality

constituents and potential explanatory factors between young and old

groundwater-age classification, wells with depths to top-of-perforation less

than 200 feet and those equal to or greater than 200 feet, and wells with oxic

conditions versus those with anoxic conditions, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit ............ 25

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

grid wells between selected potential explanatory factors, Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 29

Table 8. Number of constituents analyzed and detected by health-based benchmark

type and constituent type, Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit ....................................... 31

Table 9. Current aquifer-scale proportions computed using raw detection frequency,

spatially weighted, and grid-based approaches for constituents detected at

concentrations greater than water-quality benchmarks during the period of

available data (January 10, 1984–April 21, 2004) from the California Department

of Health Services database or detected in greater than 10 percent of the

wells sampled, Northern San Joaquin Basin Groundwater Ambient Monitoring

and Assessment (GAMA) study unit ...................................................... 32

Table 10. Aquifer-scale proportions for constituent classes, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit ............ 34

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

water-quality constituents and potential explanatory factors, Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit .................................................................................... 35
viii

Conversion Factors, Datum, and Abbreviations and Acronyms

Conversion Factors

Multiply By To obtain

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

square mile (mi2) 2.590 square kilometer (km2) 

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

SI to Inch/Pound

Multiply By To obtain

meter (m) 3.281 foot (ft) 

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

°F=(1.8×°C)+32.

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

°C=(°F-32)/1.8.

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

25 °C).

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

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

1 microgram per liter is equivalent to 1 part per billion (ppb); 1 nanogram per liter (ng/L) is 

equivalent to 1 part per trillion (ppt); 1 per mil (%) is equivalent to 1 part per thousand. 

Appendix Tables

Table A1. Nomenclature for wells sampled by USGS or selected from the CDPH

database for the Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit. ....................................... 70

Table B1. Land-use classification for wells sampled by USGS or selected from CDPH

database for the Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit. ....................................... 73

Table B2. Well construction and normalized lateral position information for wells

sampled by USGS or selected from the CDPH database for the Northern San

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA)

study unit. .................................................................................... 74

Table B3. Summary of tritium data from samples of unfiltered water and classification of

samples into young and old categories, Northern San Joaquin Basin

Groundwater Ambient Monitoring and Assessment (GAMA) study unit. ............ 76

Table B4. Concentrations of constituents used to classify geochemical (redox)

conditions in groundwater, Northern San Joaquin Basin Groundwater Ambient

Monitoring and Assessment (GAMA) study unit. ....................................... 77
ix

Conversion Factors and Datum—Continued

Datum

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

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

(NAVD 88).

Abbreviations and Acronyms

AB Assembly Bill (through the California State Assembly)

BLS below land surface

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

COS Cosumnes study area grid well

DO dissolved oxygen

E estimated or having a higher degree of uncertainty

ESJ Eastern San Joaquin study area grid well

ESJFP Eastern San Joaquin study area flow-path well

GAMA Groundwater Ambient Monitoring and Assessment Program

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

LRL laboratory reporting 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

NSJ-QPC Quaternary/Plio-Pleistocene age semiconsolidated deposits

NL notification level

REDOX reduction-oxidation

RPD relative percent difference

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

SMCL secondary maximum contaminant level

TEAP terminal electron acceptor processes

TRCY Tracy study area grid well

TRCYFP Tracy study area flow-path well

TU tritium unit

US United States
x

Conversion Factors and Datum—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 (USGS)

SWRCB State Water Resources Control Board (California)

TDS total dissolved solids

USEPA U.S. Environmental Protection Agency

USGS U. S. Geological Survey

Selected Chemical Names

DBCP 1,2-dibromo-3-chloropropane

EDB 1,2-dibromomethane

3

H tritium

HTO tritiated water

MTBE methyl-tert-butyl-ether

NDMA N-nitrosodimethylamine

PCE tetrachloroethene

1,2,3-TCP 1,2,3-trichloropropane

TDS total dissolved solids

THM trihalomethane

TTHM total trihalomethanes

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

Northern San Joaquin Basin, 2005: California GAMA 

Priority Basin Project 

By George L. Bennett V, Miranda S. Fram, Kenneth Belitz, and Bryant C. Jurgens

Abstract

Groundwater quality in the 2,079 square mile Northern 

San Joaquin Basin (Northern San Joaquin) study unit was 

investigated from December 2004 through February 2005 

as part of the Priority Basin Project of the Groundwater 

Ambient Monitoring and Assessment (GAMA) Program. The 

GAMA Priority Basin Project was developed in response to 

the Groundwater Quality Monitoring Act of 2001 that was 

passed by the State of California and is being conducted 

by the California State Water Resources Control Board 

in collaboration with the U.S. Geological Survey and the 

Lawrence Livermore National Laboratory. 

The Northern San Joaquin study unit was the third study 

unit to be designed and sampled as part of the Priority Basin 

Project. Results of the study provide a spatially unbiased 

assessment of the quality of raw (untreated) groundwater, as 

well as a statistically consistent basis for comparing water 

quality throughout California. Samples were collected from 

61 wells in parts of Alameda, Amador, Calaveras, Contra 

Costa, San Joaquin, and Stanislaus Counties; 51 of the wells 

were selected using a spatially distributed, randomized 

grid‐based approach to provide statistical representation of 

the study area (grid wells), and 10 of the wells were sampled 

to increase spatial density and provide additional information 

for the evaluation of water chemistry in the study unit 

(understanding/flowpath wells). 

The primary aquifer systems (hereinafter, primary 

aquifers) assessed in this study are defined by the depth 

intervals of the wells in the California Department of 

Public Health database for each study unit. The quality 

of groundwater in shallow or deep water-bearing zones 

may differ from quality of groundwater in the primary 

aquifers; shallow groundwater may be more vulnerable to 

contamination from the surface. Two types of assessments 

were made: (1) status, assessment of the current quality of the 

groundwater resource; and (2) understanding, identification of 

the natural and human factors affecting groundwater quality.

Relative-concentrations (sample concentrations divided 

by benchmark concentrations) were used for evaluating 

groundwater quality for those constituents that have Federal 

or California regulatory or non-regulatory benchmarks 

for drinking-water quality. Benchmarks used in this study 

were either health-based (regulatory and non-regulatory) or 

aesthetic based (non-regulatory). For inorganic constituents, 

relative-concentrations were classified as high (equal to or 

greater than 1.0), indicating relative-concentrations greater 

than benchmarks; moderate (equal to or greater than 0.5, and 

less than 1.0); or, low (less than 0.5). For organic and specialinterest constituents [1,2,3-trichloropropane (1,2,3-TCP), 

N-nitrosodimethylamine (NDMA), and perchlorate], relativeconcentrations were classified as high (equal to or greater than 

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

or, low (less than 0.1). 

Aquifer-scale proportion was used as the primary metric 

in the status assessment for groundwater quality. High aquiferscale proportion is defined as the percentage of the primary 

aquifer with relative-concentrations greater than 1.0; moderate 

and low aquifer-scale proportions are defined as the percentage 

of the primary aquifer with moderate and low relativeconcentrations, respectively. The methods used to calculate 

aquifer-scale proportions are based on an equal-area grid; 

thus, the proportions are areal rather than volumetric. Two 

statistical approaches—grid-based, which used one value per 

grid cell, and spatially weighted, which used the full dataset—

were used to calculate aquifer-scale proportions for individual 

constituents and classes of constituents. The spatially weighted 

estimates of high aquifer-scale proportions were within the 

90-percent confidence intervals of the grid‐based estimates 

in all cases. The understanding assessment used statistical 

correlations between constituent relative‐concentrations 

and values of selected explanatory factors to identify those 

factors potentially affecting constituent relative‐concentrations 

and occurrence. Individual constituents detected at 

high relative‐concentrations or those detected at low 

relative‐concentrations in substantial proportions of the 

aquifer (greater than 10 percent) were evaluated statistically 

in relation to selected explanatory factors. Explanatory factors 

evaluated in this report were land use, well depth, depth to 

top-of-perforation, lateral and vertical position within the flow 

system, groundwater age, and geochemical conditions.
2 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

The status assessment for inorganic constituents showed 

that relative-concentrations (one or more) were high, relative 

to health-based benchmarks, in 13 percent of the primary 

aquifer, moderate in 29 percent, and low in 58 percent. 

High relative-concentrations of inorganic constituents in the 

primary aquifer reflected high proportions of arsenic (high 

relative-concentrations in 9.4 percent of the aquifer) and boron 

(7.6 percent). Inorganic constituents with aesthetic-based 

benchmarks [non-regulatory secondary maximum contaminant 

levels (SMCLs)], manganese, iron, total dissolved solids 

(TDS), chloride, and sulfate, were detected at high relativeconcentrations in 34, 11, 5.8, 3.9, and 2 percent of the primary 

aquifer, respectively. SMCLs are benchmarks given to 

constituents with technical properties that can make drinking 

water undesirable with respect to taste, staining, or scaling at 

high relative-concentrations. 

The status assessment for organic constituents showed 

that relative-concentrations (one or more) were high in 

2.7 percent, moderate in 6.9 percent, and low in 90 percent 

of the primary aquifer of the Northern San Joaquin study 

unit. High relative-concentrations of organic constituents in 

the primary aquifer reflected high relative-concentrations of 

the discontinued soil fumigant 1,2-dibromo-3-chloropropane 

(DBCP, 2.7 percent of the primary aquifer). Maximum 

relative-concentrations were equal to or greater than 0.1 and 

(or) a detection frequency greater than 10 percent for five 

organic constituents: chloroform, DBCP, methyl tert-butyl 

ether (MTBE), simazine, and tetrachloroethylene (PCE).

The understanding assessment for inorganic constituents 

showed that groundwater age, normalized lateral position, 

and redox conditions were the most significant explanatory 

factors related to inorganic constituent relative-concentrations. 

Groundwater age was shown to be associated with 

relative‐concentrations of arsenic, gross alpha radioactivity, 

and total dissolved solid (TDS). High and moderate relativeconcentrations of arsenic, iron, and manganese primarily 

were associated with geochemical conditions. Relativeconcentrations of arsenic were high in oxygen-rich high-pH 

waters and in anoxic waters. High relative-concentrations of 

iron and manganese were most often associated with lowoxygen anoxic waters. Normalized lateral position, a sampled 

well’s position in the Northern San Joaquin study unit relative 

to the basin center and basin edge, was shown to be associated 

with arsenic, nitrate, and TDS relative-concentrations. High 

and moderate relative‐concentrations of arsenic and TDS 

were found more frequently closer to the valley trough (basin 

center), where relative-concentrations of nitrate tended to 

decrease, than in wells near the valley margins (basin edges).

The understanding assessment for organic constituents 

showed that groundwater age, well depth, and land use within 

500 meters of the sampled well were the most significant 

factors affecting organic constituent relative-concentrations. 

Trihalomethanes, fumigants, pesticides, and solvents were 

all shown to have higher relative-concentrations in young 

groundwater than in old groundwater. Fumigant and pesticide 

relative-concentrations were related to well perforation 

depth, with wells with shallow depths to top-of-perforation 

having higher constituent relative-concentrations than those 

with deeper depths to top-of-perforation. Detections of 

trihalomethanes and solvents were positively associated with 

urban land use and negatively associated with agricultural 

land use. Fumigant detections were strongly correlated with a 

specific agricultural land use—orchards and vineyards.

Introduction 

Groundwater comprises nearly 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 

public and domestic drinking-water supply and to establish a 

baseline groundwater quality-monitoring program, the 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 (http://www.waterboards.ca.gov/gama). The GAMA 

Program currently consists of three projects: GAMA Priority 

Basin Project, conducted by the USGS (http://ca.water.usgs.

gov/gama/); GAMA Domestic Well Project, conducted by the 

SWRCB; and GAMA Special Studies, conducted by LLNL. 

On a statewide basis, the Priority Basins Project focused 

primarily on the deep portion of the groundwater resource and 

the SWRCB Domestic Well Project generally focused on the 

shallow aquifer systems.

The SWRCB initiated the GAMA Program in response 

to Legislative mandates (Supplemental Report of the 1999 

Budget Act 1999-00 Fiscal Year; and the GAMA Priority 

Basin project in response to the Groundwater Quality 

Monitoring Act of 2001 {Sections 10780-10782.3 of the 

California Water Code, Assembly Bill 599}. 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 Priority Basin Project, the 

USGS, in collaboration with the SWRCB, developed a 

monitoring plan to assess groundwater basins through the 

direct sampling of groundwater and other statistically reliable 

sampling approaches (Belitz and others, 2003; California State 

Water Resources Control Board, 2003). Additional partners 

in the Priority Basin Assessment include the California 

Department of Public Health (CDPH), California Department 

of Pesticide Regulation (CDPR), 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 and subbasins 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 hydrologic provinces. Groundwater basins and 

subbasins were prioritized for sampling on the basis of the 

number of drinking-water wells, with secondary consideration 

given to municipal groundwater use, agricultural pumping, 

the number of historic leaking underground fuel tanks, 

and registered pesticide applications (Belitz, and others, 

2003). The 116 priority basins, plus additional areas outside 

defined groundwater basins, include about 95 percent of 

drinking‐water wells in California. 

Purpose and Scope

The GAMA Priority Basins Project is comprised of 

three types of water-quality assessments in each study 

unit—(1) Status: assessment of the current quality of the 

groundwater resource, (2) Understanding: identification 

of the natural and human factors affecting groundwater 

quality, and (3) Trends: detection of changes in groundwater 

quality (Kulongoski and Belitz, 2004). The purpose of the 

GAMA status assessments is to provide a statistically robust 

characterization of raw (untreated) groundwater quality in 

the part of the aquifer system used for drinking-water supply 

(Belitz and others, 2003). The statistically robust design also 

allows for comparison between basins and for synthesis of 

results at regional and statewide scales.

The purpose of this report is to present results of the 

status and understanding assessments done in the Northern 

San Joaquin Basin study unit. This report is one in a series of 

published and planned USGS Scientific Investigation Reports 

presenting the status and understanding of current waterquality conditions in GAMA study units. Tabulated USGS data 

from the initial USGS-GAMA groundwater sampling efforts 

in the Northern San Joaquin Basin study unit are available 

in Bennett and others, 2006. Tabulated USGS data from 

several other GAMA study units and are available as USGS 

Data Series Reports. Planned subsequent reports will address 

changes or trends in water-quality across time.

The status assessment in this report includes 

interpretation of water-quality data from 51 wells in the 

Northern San Joaquin Basin selected for sampling by USGS 

within spatially distributed grid cells across the study units; 

hereinafter, referred to as USGS-grid wells. Eighty-two 

percent of these wells are CDPH drinking-water wells, and 

perforation intervals in the remaining wells are similar to 

those in drinking-water wells in the area. Samples were 

collected from wells for analysis of dissolved anthropogenic 

constituents, such as volatile organic compounds (VOCs) and 

pesticides, and naturally occurring dissolved constituents, 

such as major ions and trace elements. Water-quality data 

from the CDPH database also were used to supplement data 

collected by USGS for the GAMA Program. The primary 

aquifer systems (hereinafter, primary aquifers) are defined by 

the depth intervals of the wells listed in the CDPH database 

for each study unit. The quality of groundwater in shallow or 

deep water-bearing zones may differ from that in the primary 

aquifers; shallow groundwater may be more vulnerable to 

contamination from the surface.

For the purposes of providing context, the water-quality 

data discussed in this report are compared to California 

and Federal drinking-water regulatory and non-regulatory 

benchmarks for treated drinking water. The assessments in 

this report are intended to characterize the quality of raw 

groundwater resources in the study unit, not the treated 

drinking water delivered to consumers by water purveyors. 

This study does not attempt to evaluate the quality of water 

delivered to consumers; after withdrawal from the ground, 

water typically is treated, disinfected, and (or) blended with 

other waters to maintain acceptable water quality. Regulatory 

benchmarks apply to treated water that is delivered to the 

consumer, not to raw groundwater.

The understanding assessment in this report was based 

on data from the 51 USGS-grid wells and 10 additional 

wells sampled by USGS for the purpose of understanding 

(hereinafter, referred to as USGS-understanding wells). 

Potential explanatory factors examined included land use, 

well depth, depth to top-of-perforation, position in the 

groundwater-flow system, groundwater age, and geochemical 

condition. This list of potential explanatory factors is not 

exhaustive; however, a comprehensive analysis of all possible 

explanatory factors is beyond the scope of this report.

Data for all constituents in samples collected by the 

USGS for the GAMA Program (hereinafter, USGS-GAMA 

sampling) in the Northern San Joaquin Basin study unit, 

details of sample collection, analysis, and quality-assurance 

procedures for the Northern San Joaquin Basin study unit are 

described by Bennett and others (2006). This report describes 

methods used in design of the sampling network, identification 

of CDPH data for use in the status assessment, estimation 

of aquifer proportions, analysis of ancillary datasets, 

classification of groundwater-age, and statistical and graphical 

approaches used in assessment of water-quality data.
4 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 1. Location of Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) 

study unit and California hydrogeologic provinces (modified from Belitz and others, 2003).

sac09-0345_fig 01

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)

Northern Valley

San Joaquin

Basin Study Unit

Klamath

Mountains

Cascades and

Modoc Plateau

Transverse Ranges and

selected Peninsular Ranges

Southern Coast

Ranges

Sierra

Nevada

Northern

Coast

Ranges

Desert

Central

Basin and Range

San Diego

Drainages

OREGON

NEVADA

MEXICO

ARIZONA

0 200 MILES

0 200 KILOMETERS

40

42 124 122 120 118 116 114

38

36

34

PA C I F I C O C E A N

Bakersfield

Los Angeles

San Diego

Redding

Sacramento

San

Francisco
Hydrogeologic Setting 5

Hydrogeologic Setting

The Northern San Joaquin Basin study unit lies within 

the Central Valley hydrogeologic province described by 

Belitz and others (2003) and contains three CDWR San 

Joaquin Valley groundwater subbasins: Cosumnes, Eastern 

San Joaquin, and Tracy (California Department of Water 

Resources, 2003) (fig. 2). The study unit is bounded by the 

Coast Ranges to the west, the Sierra Nevada to the east, the 

Cosumnes, Mokelumne, and San Joaquin Rivers to the north, 

and Stanislaus County to the south.

The Northern San Joaquin Basin study unit is divided 

into four separate study areas: the Cosumnes (COS), Eastern 

San Joaquin (ESJ), and Uplands (NSJ-QPC) study areas on 

the east side of the San Joaquin Valley, and the Tracy study 

area (TRCY) on the west side (fig. 2). The exterior boundaries 

of the Cosumnes, Eastern San Joaquin, and Tracy study 

areas correspond to the CDWR groundwater subbasins of the 

same names. However, the Quaternary/Plio-Pleistocene‐age 

semiconsolidated (QPC) deposits (Jennings, 1977) in upland 

areas (east of these subbasins) differ from the CDWR 

subbasins, so the study area was designated as the Uplands 

study area. 

The Northern San Joaquin Basin study unit has a 

Mediterranean climate, with summers that are hot and dry, and 

winters that are cool and moist. Average rainfall across the 

study unit ranges from 11 in. in the southern and western parts 

of the study unit to 15 in. in the eastern to northeastern parts 

of the study unit (California Department of Water Resources, 

2006a, 2006b, and 2006c).

Runoff from the Sierra Nevada sustains flow in rivers and 

streams that drain the east side of the Northern San Joaquin 

Basin study unit. The Stanislaus, Calaveras, Mokelumne, and 

Cosumnes Rivers and several smaller streams flow across 

the Uplands, Cosumnes, and Eastern San Joaquin study areas 

into the San Joaquin River (fig. 2), which flows north into the 

Sacramento-San Joaquin Delta and San Francisco Bay estuary. 

Much of the Tracy study area north of the city of Tracy and 

the Eastern San Joaquin study area northwest of the city of 

Stockton are within the boundaries of the Delta. Flows in the 

rivers and sloughs in the Delta are strongly affected by the 

tidal prism flowing through the San Francisco Bay. Flows also 

are affected by pumping of water from the southern end of the 

Delta, just northwest of Tracy, into the California Aqueduct 

and the Delta-Mendota Canal. 

Aquifers within the study unit tapped for drinking-water 

supply are composed of a mix of unconsolidated alluvial-fan 

deposits, deeper consolidated volcanic deposits, and peat. 

Alluvial-fan deposits range in age from late-Pliocene to 

Quaternary and are composed of multiple sediment sequences 

linked to repeated episodes of glaciation in the Sierra Nevada 

(Davis and Hall, 1959; Weissmann and others, 2002, 2004, 

2005; Burow and others, 2004) (fig. 3). Alluvial-fan deposits 

contain interlayered lenses of gravel, sand, silt, and clay 

deposited by rivers draining the Sierra Nevada. A volcanic 

deposit, the Mehrten Formation, located about 1,000 ft below 

land surface in the Stockton area and at shallower depths 

to the east, separates overlying alluvial-fan deposits from 

deeper marine deposits that contain saline water in most 

parts of the study unit (Marchand and Allwardt, 1981; Page, 

1986; Izbicki and others, 2008). The high percentage of black 

andesitic fragments in the Mehrten Formation deposits often 

help distinguish it from overlying formations. Within the 

boundaries of the Sacramento-San Joaquin Delta, sediments 

from the Sierra Nevada and Coast Ranges that accumulated 

within marsh stands that contain a large volume of organic 

matter have formed thick peat soils. These organic soils, as 

much as 60-ft thick in some areas, are highly productive in 

terms of agriculture but are prone to subsidence (California 

Department of Water Resources, 1993; Ingebritsen and others, 

2000). Beneath the organic soil of the Delta lies the Tulare 

Formation, an alluvial deposit derived during the Pleistocene 

from the unglaciated Coast Ranges (Hotchkiss and Balding, 

1971; Bertoldi and others, 1991; California Department of 

Water Resources, 2006c). The source materials within the 

Coast Ranges for the sediments that make up the Tulare 

Formation include the Franciscan Formation and Cretaceous 

and Tertiary age marine deposits (Hotchkiss and Balding, 

1971).

Primary sources of groundwater recharge in the study 

unit are percolation of precipitation, irrigation and urban 

return flows, reservoirs, and rivers (Burow and others, 2004; 

Phillips and others, 2007). Surface water draining from the 

Sierra Nevada, which is stored in reservoirs and diverted 

for irrigation and water supply, is the largest source of 

groundwater recharge to the study unit (Phillips and others, 

2007). Primary sources of discharge are pumping withdrawals 

for irrigation and municipal water supply, discharge to 

streams, and the combination of evaporation from areas with a 

shallow depth to water and transpiration of water from plants 

(evapotranspiration).

The conceptual model of groundwater flow in the 

Northern San Joaquin Basin study unit (fig. 4) is based on and 

modified from previous investigations by Burow and others 

(2004) and Phillips and others (2007) in the Modesto area 

that focused on regional flow patterns east of the San Joaquin 

River. The models are similar in that within the eastern part 

of the Northern San Joaquin Basin study unit, regional lateral 

flow of groundwater is from northeast to southwest. 
6 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 2. Geographic features and study areas of the Northern San Joaquin Basin Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 02

EL DORADO CO

YOLO CO SACRAMENTO CO

AMADOR CO

SOLANO CO

SAN JOAQUIN CO

CALAVERAS CO

CONTRA COSTA CO

ALAMEDA CO

SANTA CLARA CO MERCED CO

STANISLAUS CO

Tuolumne River

Stanislaus River

Littlejohns Creek

Calaveras River

Delta Mendota Canal

California Aqueduct

Mokelumne River

Bear Creek

Mokelumne River

Mokelumne River

Middle River

South

Laguna Creek

Dry Creek

Sacramento River Deep

Water Ship

Channel

Cosumnes River

Rock Creek

Mokelumne River

San Joaquin River

San Joaquin River

Simmons

Creek

Simmons

Creek

Galt

Lodi

Stockton

Tracy

Study areas

Cosumnes (COS)

Eastern San Joaquin (ESJ)

Uplands (NSJ-QPC)

Tracy (TRCY)

EXPLANATION

Water bodies

Swamp or marsh

Inundation area

Boundary of Sacramento-San Joaquin Delta

Boundary of Northern San Joaquin Basin

Boundary of subbasin of the Northern

San Joaquin Basin

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Sierra Nevada

Coast Ranges

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)
Hydrogeologic Setting 7

Figure 3. Geologic formations, and areal distribution of grid and understanding wells, in the Northern San Joaquin Basin Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 03

Geologic units

Study areas

Grid wells (USGS and CDPH)

Understanding wells

CDPH wells only

Cosumnes (COS)

Eastern San Joaquin (ESJ) 

Uplands (NSJ-QPC) 

Tracy (TRCY)

Corcoran Clay (boundary of 

 subsurface occurrence)

Boundary of SacramentoSan Joaquin Delta

Boundary of Northern 

San Joaquin Basin

Boundary of subbasin

of the North San 

Joaquin Basin

Quaternary alluvium (Q)

Quaternary/Plio-Pleistocene 

semi-consolidated (QPc)

Miocene nonmarine consolidated (Mc)

Eocene nonmarine consolidated (Ec)

EXPLANATION

Water bodies

Swamp or marsh

Inundation area

COS

NSJ-QPC

TRCY ESJ

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

San Joaquin River

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

0 5 10 20 MILES

0 10 20 5 KILOMETERS
8 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

This regional flow is driven by an elevation gradient, discharge 

of water to the San Joaquin River, and evapotranspiration in 

the western part of the flow system. Within the eastern part of 

the Northern San Joaquin Basin study unit, return flows are 

a major source of recharge and withdrawals for irrigation are 

a major source of discharge, thereby inducing a component 

of downward vertical flow (Burow and others, 2007). These 

vertical-flow components enhance downward movement 

of water from recharge areas to the perforated intervals of 

withdrawal wells within shallow to intermediate depths in the 

system. These processes may occur in both agricultural and 

urban land-use areas. Groundwater age is vertically stratified, 

with water less than 50 years old in the upper parts of the 

system and water that may be tens of thousands of years old 

at depth (Burow and others, 2008). The Tracy study area, 

west of the San Joaquin River, contains a large part of the 

Sacramento-San Joaquin Delta. Historically, the Delta has 

served as a major discharge area for regional groundwater 

flow originating in the Sacramento and San Joaquin Valleys. 

Groundwater extractions have depressed groundwater levels 

along the perimeter of the Delta, thereby altering these 

historical flow patterns. Drawdown of groundwater levels on 

the Delta periphery has resulted in the interception, through 

the extractons, of regional discharge, including regional 

discharge from the nearby mountain fronts, and major streams 

and rivers (Izbicki and others, 2006).

Land use in the Northern San Joaquin Basin study unit is 

about 57 percent agricultural, 36 percent natural, and 7 percent 

urban, on the basis of classification of USGS National 

Land Cover Data (Nakagaki and others, 2007) (fig. 5). The 

natural land-use areas are mostly grasslands. Natural land 

use is dominant in the eastern parts of the study unit (fig. 5), 

particularly in the Upland study area. Land use in the Uplands 

study area is about 85 percent natural, 13 percent agricultural, 

and 2 percent urban. The Eastern San Joaquin study area is the 

most urbanized in the study unit, with about 12 percent urban 

land use. The city of Stockton, the largest urban area in the 

Northern San Joaquin Basin study unit, is in the Eastern San 

Joaquin study area (figs. 2 and 5).

Figure 4. The aquifer system for the Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit.

sac09-0345_fig 04

COS

NSJ-QPC

ESJ

ESJ

TRCY

Sacramento River

San Joaquin River

San Joaquin Valley

well

well well

Stockton

San Francisco 

Bay

Sacramento

Lodi

Tracy

Galt

EXPLANATION

Cosumnes (COS)

Eastern San Joaquin (ESJ)

Uplands (NSJ-QPC)

Tracy (TRCY)

Study Areas

Saline water in delta deposits

Unsaturated deposits

Freshwater aquifers

Underlying saline aquifers

Bedrock

Marine deposits

Boundary of Sacramento-San Joaquin Delta

Direction of groundwater flow

Corcoran Clay
Hydrogeologic Setting 9

Figure 5. Land use of the Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit.

sac09-0345_fig 05

Stockton

Lodi

Tracy

Galt

0 5 10 20 MILES

0 10 20 5 KILOMETERS

TRCY ESJ

COS

NSJ-QPC

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

Land use

Water bodies

Swamp or marsh

Inundation area

Grid wells (USGS and CDPH)

Understanding wells

Boundary of SacramentoSan Joaquin Delta

Boundary of Northern 

San Joaquin Basin

Boundary of subbasin

Urban

Agricultural

Natural

EXPLANATION

Study areas

Cosumnes (COS)

Eastern San Joaquin (ESJ)

Uplands (NSJ-QPC)

Tracy (TRCY)

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°
10 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Methods 

Methods used for the USGS-GAMA Program were 

selected to achieve the following objectives: (1) design 

a sampling plan to obtain data suitable for statistical 

analysis; (2) combine CDPH data with data collected by the 

USGS‐GAMA Program to assess water quality; (3) quantify 

the proportions of the primary aquifer with high, moderate, 

and low relative-concentrations for constituents of interest 

and constituent classes relative to water-quality benchmarks; 

(4) identify, on the basis of objective criteria, constituents 

of interest to be discussed; (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. 

In brief, GAMA status assessments are based on equalarea grids drawn over an areal extent of the study unit. Cells 

within unique study areas are of equal size; however, cell 

sizes may differ between study areas. For example, for the 

Northern San Joaquin Basin study unit, four unique equal-area 

grids were drawn over four different study areas and although 

generally similar cell sizes are not exactly the same between 

the study areas. Two approaches, a grid-based approach and 

a spatially weighted approach, are used to evaluate waterquality data from drinking-water wells in the grid area. In 

the grid-based approach, the study unit is represented by one 

well per grid cell; water-quality data primarily are from one 

well per grid cell that was sampled by the USGS, but may 

be supplemented with data from the CDPH database for the 

same well or for nearby wells to fill gaps (either spatial or for 

selected constituents) in the USGS dataset. In the spatially 

weighted approach, data for all wells sampled by the USGS 

and for all wells in the CDPH database are used, and results 

at each well are weighted such that each grid cell contributes 

equally to the representation of the study unit. The two 

methods used to calculate aquifer-scale proportions are based 

on equal-area grids; thus, the proportions are areal rather than 

volumetric. Aquifer-scale proportions are defined as fractions 

(sum of all fractions equal to 1.0) of the area of the aquifer 

used for drinking-water supply with concentrations greater 

than or less than specified thresholds relative to regulatory or 

aesthetic benchmarks. 

GAMA understanding assessments are designed to 

evaluate the natural and human factors that affect groundwater 

quality in study units. The methods used are robust enough to 

be applied at regional to statewide scales. The understanding 

assessments rely on the use of multiple lines of evidence to 

investigate the relations between observed water quality and 

potential explanatory factors. 

Design of Sampling Networks

Wells selected for sampling by the USGS-GAMA 

Program reflect the combination of two well selection 

strategies. The first strategy was to randomly select wells 

to provide a statistically unbiased, spatially distributed 

assessment of the quality of groundwater resources used 

for drinking-water supply (USGS-grid wells) (Scott, 1990). 

Water-chemistry data from the randomly selected USGS-grid 

wells were combined with data from selected wells from 

the CDPH database (CDPH-grid wells) to provide complete 

spatial grid coverage, including data for constituents not 

measured at every USGS-grid well, to assess proportions of 

the primary aquifer with high, moderate, and low relativeconcentrations. The second strategy consisted of selecting and 

sampling additional wells that could be used to provide greater 

sample density in several areas—specifically, along regional 

groundwater flowpaths. A total of 10 wells were selected for 

this purpose (USGS-understanding wells).

The USGS-grid wells were selected using a randomized 

grid-based method with the intended purpose of obtained a 

statistically unbiased population of wells for analysis (Scott, 

1990). This was accomplished by subdividing the Cosumnes, 

Eastern San Joaquin, Upland, and Tracy study areas into 

equal-area grid cells of 13, 38, 40, and 35 mi2, respectively, for 

a total of 65 grid cells (fig. 6). From that subdivision, wells to 

be sampled were randomly selected from statewide databases 

maintained by the CDPH and USGS (Bennett and others, 

2006). If a grid cell did not contain accessible drinking water 

wells, then commercial, irrigation, or domestic wells were 

considered for sampling. One USGS-grid well was sampled 

in 51 of the 65 grid cells for the Northern San Joaquin Basin 

study unit; 10 of the 15 grid cells in the Cosumnes study area, 

19 of the 20 grid cells in the Eastern San Joaquin study area, 

11 of the 15 grid cells in the Tracy study area, and 11 of the 

15 grid cells in the Upland study area (fig. 6). Grid cells where 

samples were not collected had no wells suitable for sampling, 

or permission to sample a suitable well in those cells could not 

be obtained. The wells were numbered in the order of sample 

collection with a prefix identifying each study area: Eastern 

San Joaquin, Cosumnes, Tracy, Uplands (see appendix A for a 

detailed description of the well-selection process and naming 

conventions used in this report).

The Cosumnes study area is much smaller than the other 

three study areas (fig. 6) highlighting the need to apply an 

area-weighted correction to avoid over or under representing 

any individual study area when reporting results at the study 

unit scale. The grid-based primary aquifer proportions for 

the study unit were determined by calculating the aquifer 

proportions in each study area separately, and then calculating 

the area-weighted sum: 
Methods 11

Figure 6. Study area grids, grid and understanding wells sampled, and California Department of Public 

Health wells used for supplemental data on inorganic constituents, Northern San Joaquin Basin Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 06

EL DORADO CO

YOLO CO SACRAMENTO CO

AMADOR CO

SOLANO CO

S

CALAVERAS CO

AN JOAQUIN CO SAN JOAQUIN CO

CONTRA COSTA CO

ALAMEDA CO

SANTA CLARA CO MERCED CO

STANISLAUS CO

Lodi

Stockton Stockton

Tracy

Galt

Study areas

Cosumnes (COS)

Eastern San Joaquin (ESJ)

Uplands (NSJ-QPC)

Tracy (TRCY)

EXPLANATION

Grid wells (USGS and CDPH)

Understanding wells

CDPH wells only

Boundary of Northern 

San Joaquin Basin

Boundary of subbasin

of Northern San 

Joaquin Basin

Grid cell boundary

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Sierra Nevada

Coast Ranges

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)
12 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

,

where

is the aquifer proportion for the study unit;

is the aquifer proportion for a study area; 

and

is the fraction of the total study unit area 

occupied by the study area.

SU SA SA

SU

SA

SA

AQP AQP F

AQP

AQP

F

= ∑ (1)

The same weighting was applied to calculations of 

study‐unit detection frequencies. Study-unit detection 

frequency is not the same as detection frequency in the grid 

wells. Detection frequencies for organic constituents discussed 

in this report might differ from those reported in Bennett and 

others (2006) because in the earlier report, the area-weighted 

correction was not applied.

Ten additional wells were sampled to provide additional 

data for the understanding assessment (“understanding” 

wells). Understanding wells were not included in the 

grid‐based characterization of water quality in the Northern 

San Joaquin Basin study unit, because the inclusion of these 

wells could have caused overrepresentation of certain cells.

All wells (USGS-grid and understanding) were sampled 

using a tiered analytical approach (Bennett and others, 2006). 

These tiers are described briefly here. All wells in the Northern 

San Joaquin Basin study unit were sampled for a standard 

set of constituents: temperature and specific conductance, 

low-level VOCs, low-level 1,2-dibromo-3-chloropropane 

(DBCP) and 1,2-dibromomethane (EDB), pesticides and 

pesticide degradates, pharmaceuticals, stable isotopes of 

hydrogen and oxygen in water, tritium and dissolved noble 

gases (table 1). This set of constituents was termed the “fast” 

schedule because three or four wells could be sampled in 

1 day, given the limited number of constituents analyzed. 

Wells on the “intermediate” schedule were sampled for all 

constituents on the fast schedule, plus gasoline oxygenates, 

perchlorate, N-nitrosodimethylamine (NDMA), low-level 

1,2,3-trichloropropane (1,2,3-TCP), major and minor ions and 

trace elements, noble gases, and the abundance and speciation 

of trace elements arsenic, chromium, and iron (table 1); 

however, no more than two wells could be sampled in 1 day 

with this schedule. Wells on the “slow” schedule were sampled 

for all constituents on the fast and intermediate schedules, plus 

alkalinity, dissolved oxygen, pH, turbidity, polar pesticides 

and degradates, carbon isotopes, and radioactive and microbial 

constituents (table 1); but only one well could be sampled per 

day with this schedule. The methods of collection, analysis, 

and quality assurance for the analytes listed in table 1 are 

described in Bennett and others (2006).

Assembly Bill 599, which aims to improve statewide 

comprehensive groundwater monitoring and increase 

availability of information about groundwater quality to 

the public, directs the GAMA Program to integrate existing 

monitoring data and to collect new data as necessary to 

achieve a comprehensive groundwater-monitoring program. 

The statewide CDPH database contains data for constituents 

with water-quality benchmarks regulated as part of Title 22 

(division 2, chapter 3), a California Code of Regulation that 

enforces the Safe Drinking Water and Toxic Enforcement 

Act of 1986 (California Office of Administrative Law, 2009) 

Although water-quality data have been collected by other 

organizations for local studies and specific purposes, the 

CDPH database is the only California statewide database of 

water-chemistry data from drinking water wells available for 

comprehensive analysis. 

Data for some constituents, specifically VOCs, pesticides, 

inorganic constituents, and radioactive constituents, are 

available from both the USGS-GAMA Program and the 

CDPH database (table 2), but, more VOCs and pesticides were 

measured as part of USGS-GAMA sampling than are available 

from CDPH. In addition, laboratory reporting levels (LRLs) 

for USGS-GAMA analyses typically were one to two orders 

of magnitude less than those available from CDPH. Thus, 

the USGS-GAMA sampling was designed to complement 

the CDPH data by providing a larger number of analytes 

and lower analytical levels at the wells sampled compared to 

the CDPH data available at a larger number of wells. Both 

datasets are used in the status and understanding assessments.

New data collected by USGS-GAMA included data for 

hydrologic tracers and geochemical indicators (for example, 

noble gases and carbon-14) that are not regulated water-quality 

constituents with benchmarks, but that are of importance 

for understanding groundwater quality. These constituents 

primarily are discussed in the understanding assessment.
Methods 13

Table 1. Analytes and numbers of wells sampled for different analytical schedules, Northern San Joaquin 

Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[Fast, intermediate, and slow analyte lists refer to the relative amount of time required to collect samples from a well; 1,2,3-TCP, 

1,2,3-trichlorpropane; NDMA, N-nitrosodimethylamine; USGS, U.S. Geological Survey; LRL, laboratory reporting level]

Analyte Lists

Fast Intermediate Slow 

Total number of wells 31 20 10

Number of grid wells sampled 30 16 5

Number of understanding wells sampled 1 4 5

Analyte Groups Number of constituents

Specific conductance, temperature 2 2 2

Volatile organic compounds (VOCs) 1 85 85 85

Pesticides and degradates 63 63 63

Noble gases and tritium 2 7 7 7

Stable isotopes of hydrogen and oxygen in water 2 2 2

Gasoline oxygenates 3 3 3

Major and minor ions, and trace elements 37 37

Arsenic, chromium, and iron species 6 6

Perchlorate, NDMA and low-level 123-TCP 4 3 3

Tritium 5 1 1

Alkalinity, dissolved oxygen, pH, and turbidity 4

Carbon isotopes 2

Dissolved organic carbon 1

Nutrients 5

Polar pesticides and degradates 6 58

Radon-222 1

Radium isotopes 2

Gross alpha and beta radioactivity 4

Microbial constituents 4

Sum: 159 209 290

1

 Includes low-level 1,2,-dibromo-3-chloropropane (DBCP) and 1,2-dibromomethane (EDB), and eight other constituents 

classified as fumigants or fumigant synthesis by-products.

2

 Analyzed at Lawrence Livermore National Laboratory, Livermore, California.

3

 Does not include five constituents in common with VOCs.

4

 Includes one analyte, 123-TCP, in common with VOC analyses. However, the LRL for the low-level analysis is 0.005 mg/L 

compared to 0.18 mg/L for the VOC analysis. Therefore, the low-level analysis is counted as a separate analysis.

5

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

6

 Does not include four constituents in common with pesticides and degradates.
14 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Identification of CDPH Data for Status 

Assessment

The CDPH database contains historical records from 

more than 25,000 wells, necessitating targeted retrievals to 

effectively access water-quality data. For example, for the area 

representing the Northern San Joaquin Basin study unit, the 

historical CDPH database contains more than 300,000 records 

from more than 900 wells. CDPH data were used in two ways 

for the grid-based status assessment. First, the CDPH data 

were used, along with USGS-grid data, to identify constituents 

that have ever been measured in the Northern San Joaquin 

Basin study unit at concentrations greater than water-quality 

benchmarks, so that these constituents could be included in the 

status assessment. Second, for assessing aquifer proportions, 

the CDPH database was used as a supplemental source of 

inorganic constituent data for grid cells where a complete 

suite of inorganic constituent data was not collected by 

USGS-GAMA. 

Historically and Currently High Constituents

Constituent concentrations were identified as historically 

high if (1) concentrations were high (greater than benchmarks) 

at any time during the full period of record of CDPH data 

in the Northern San Joaquin Basin study unit (January 10, 

1984–April 21, 2004), and (2) concentrations were not high 

in the 3-year period (January 1, 2001–April 21, 2004) prior 

to the USGS-GAMA grid-well sampling (December, 2004–

February, 2005) in the study unit, or in USGS-grid data that 

resulted from this sampling (table 3). These constituents do 

not reflect current conditions on which the status assessment is 

based. Constituent concentrations were identified as currently 

high if (1) concentrations were high during the 3-year period 

(January 1, 2001–April 21, 2004, hereinafter referred to 

as prior period) prior to the USGS-GAMA well sampling 

or (2) concentrations were high in the USGS-grid data 

(December 2004–February 2005, hereinafter referred to as the 

most recent). For each constituent with a high concentration 

(prior period and most recent), spatially weighted aquifer-scale 

proportions and detection frequencies in the primary aquifer 

were computed using the most recent concentration available 

within the prior period (January 1, 2001–April 21, 2004).

Selection of CDPH Data for the Grid-Based 

Approach

CDPH data were used in two ways: (1) the CDPH data 

and USGS-GAMA data were combined and used to identify 

constituents that had concentrations greater than water-quality 

Table 2. 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 Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[MDL, method detection level; LRL, laboratory reporting level; VOC, volatile organic compound; mg/L, milligrams per liter; pCi/L, 

picocuries per liter; μg/L, micrograms per liter; nc, not collected]

Constituent type

CDPH GAMA

Concentration or 

Number of activity units

compounds

Median

MDL

Number of 

compounds

Median

LRL

Inorganic constituents with health-based benchmarks

Trace elements 20 8 25 0.12 μg/L

Radioactive constituents 5 1 7 1 0.54 pCi/L

Nutrients, major and minor ions 4 0.4 17 0.06 mg/L

Organic constituents with health-based benchmarks

VOCs (including fumigants) plus gasoline 

oxygenates

61 0.5 88 0.06 μg/L

Pesticides plus degradates 27 2 115 0.019 μg/L

Special-interest constituents with health-based benchmarks

N-Nitrosodimethylamine (NDMA) nc nc 1 0.002 μg/L

1

 Value reported for the median LRL is a median sample-specific critical level (ssLc) for six radioactive constituents collected and analyzed 

by USGS-GAMA.
Methods 15

Table 3. Constituents in California Department of Public Health (CDPH) wells at high concentrations from January 10, 1984–April 21, 

2004 (the period of record prior to the 3-year period used in the status assessment), Northern San Joaquin Basin Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit.

[A high analysis is defined as a concentration or activity that is higher than the benchmark for that constituent]

Constituent Number of wells 

with analyses

Total number 

of analyses

Total number of 

analyses above 

threshold

Number of wells 

with at least one 

high analysis

Date of most 

recent high 

analysis

Inorganic constituents with health-based benchmarks

Cadmium 495 1,644 1 1 03/10/88

Gross beta radioactivity 104 446 2 2 12/03/91

Uranium 179 758 20 9 12/27/89

Organic constituents with health-based benchmarks

Chloroform 520 2,965 1 1 03/01/94

1,1,2,2-Tetrachloroethane 521 2,925 2 1 10/17/86

benchmarks in the Northern San Joaquin Basin study unit; and 

(2) the CDPH database was used as a supplemental source 

of inorganic constituent data for grid cells where inorganic 

constituent data were not collected by USGS-GAMA. 

Procedures for selecting the wells from the CDPH database to 

supplement the grid are described in appendix A. 

Of the 65 grid cells in the study unit, 14 cells did not 

have a USGS-grid well, 30 cells had a USGS-grid well but 

no USGS data for major ions, trace elements, nutrients, and 

radiochemical constituents, and 16 cells had a USGS-grid well 

but incomplete USGS data for radiochemical constituents. 

The CDPH database was queried specifically to provide these 

missing data (table 4). The decision tree used to identify 

suitable CDPH wells is described in appendix A. Selected 

CDPH wells used to supplement the grid data are hereafter 

referred to as CDPH-grid wells. All others will be referred to 

as USGS-grid wells.

CDPH data were not used to supplement USGS-grid well 

data for VOCs or pesticides for the status assessment. Data 

for these constituents were collected by USGS-GAMA at all 

51 USGS-grid wells sampled.

Status Assessment Methods

Status assessment—an assessment of the current quality 

of groundwater resources—is based on computation of 

the relative-concentration for each constituent, where the 

relative-concentration is the concentration of the compound 

normalized to its benchmark (Toccalino and others, 2004; 

Toccalino and Norman, 2006). The proportions of the aquifer 

with high, moderate, and low relative-concentrations (defined 

in the subsequent section) of that compound were determined 

using both the grid-based and the spatially weighted 

approaches.

Relative-Concentrations

Relative-concentrations provide a means to relate 

concentrations of constituents in groundwater samples to 

water-quality benchmarks: 

‐ . sample concentration Relative concentration =

benchmark concentration

Toccalino and others (2004), Toccalino and Norman 

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

of measured concentration to a benchmark [either Maximum 

Contaminant Level (MCL) or Health-Based Screening Level 

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

Relative-concentrations used in this report are equivalent 

to the Benchmark Quotient of Toccalino and others (2004). 

However, HBSLs were not used in this report, as they are 

not currently used as benchmarks by California drinkingwater regulatory agencies. Relative-concentrations less 

than 1 (<1.0) indicate a sample concentration less than the 

benchmark, and relative-concentrations greater than 1 (>1.0) 

indicate a sample concentration greater than the benchmark. 

Relative‐concentrations can be computed only for compounds 

with water-quality benchmarks.
16 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Table 4. Inorganic constituents (including the special-interest constituent perchlorate), benchmarks, and 

number of grid wells per constituent, Northern San Joaquin Basin Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit.

[CDPH, California Department of Public Health; AL-US, U.S. Environmental Protection Agency (USEPA) action level; HAL-US, 

USEPA lifetime health advisory level; MCL-CA, CDPH Maximum Contaminant Level; MCL-US, USEPA Maximum Contaminant 

Level; NL-CA, CDPH Notification Level; SMCL-CA, CDPH Secondary Maximum Contaminant Level; mg/L, milligrams per liter; 

μg/L, micrograms per liter]

Constituent Benchmark type Benchmark 

value Units

Number of 

grid wells 

sampled by 

GAMA

Number of 

grid wells 

selected 

from CDPH

Trace elements with health-based benchmarks

Aluminum MCL-CA 1,000 μg/L 21 27

Antimony MCL-US 6 μg/L 21 26

Arsenic MCL-US 10 μg/L 21 27

Barium MCL-CA 1,000 μg/L 29 20

Beryllium MCL-US 4 μg/L 21 25

Boron NL-CA 1,000 μg/L 29 16

Cadmium MCL-US 5 μg/L 21 26

Chromium MCL-CA 50 μg/L 21 26

Copper AL-US 1,300 μg/L 21 26

Fluoride MCL-CA 2 mg/L 21 22

Lead AL-US 15 μg/L 21 25

Mercury MCL-US 2 μg/L 5 33

Molybdenum HAL-US 40 μg/L 21 0

Nickel MCL-CA 100 μg/L 21 26

Perchlorate 1 MCL-CA 6 μg/L 21 14

Selenium MCL-US 50 μg/L 21 26

Strontium HAL-US 4,000 μg/L 21 0

Thallium MCL-US 2 μg/L 21 26

Vanadium NL-CA 50 μg/L 21 18

Radioactive constituents with health-based benchmarks

Gross alpha radioactivity MCL-US 15 pCi/L 5 21

Gross beta radioactivity MCL-CA 50 pCi/L 5 3

Radon-222 MCL-US 2 4,000 pCi/L 4 0

Radium-226, -228 MCL-US 5 pCi/L 5 4

Uranium MCL-US 20 pCi/L 21 5

Nutrients with health-based benchmarks

Nitrite as nitrogen MCL-US 1 mg/L 5 39

Nitrate plus nitrite, as nitrogen MCL-US 10 mg/L 5 44

Major ions with SMCLs

Chloride SMCL-CA 500 mg/L 29 18

Sulfate SMCL-CA 500 mg/L 21 23

Total dissolved solids SMCL-CA 1,000 mg/L 21 23

Trace elements with SMCLs

Iron SMCL-CA 300 μg/L 21 26

Manganese SMCL-CA 50 μg/L 21 26

Silver SMCL-CA 100 μg/L 21 27

Zinc SMCL-CA 5,000 μg/L 21 26

1

 Special-Interest constituent.

2

 Proposed maximum contaminant level.
Methods 17

In this report, relative-concentrations of constituents 

measured in the raw groundwater were compared with 

human‐health benchmarks established by the U.S. 

Environmental Protection Agency (USEPA) and CDPH, 

specifically, MCLs, notification levels (NLs), health advisory 

levels (HALs), action levels (ALs), and risk-specific 

dose (1 in 100,000) (RSD5s). When both a USEPA and a 

California benchmark existed for a compound, the lower 

benchmark of the two was used. Some constituents in this 

report are compared to benchmarks that apply to the sum 

of a class of constituents, specifically, trihalomethanes 

(THMs). Total trihalomethane (TTHM) concentration is 

therefore used in statistical tests. Benchmarks established for 

aesthetic concerns [secondary maximum contaminant levels, 

(SMCL-CA; SMCL-US)] also were used for comparisons 

(U.S. Environmental Protection Agency, 2006; California 

Department of Health Services, 2007). A summary of 

benchmark types and concentrations for all constituents 

included in the status assessment is presented in table 5. 

Additional information on the types of benchmarks and 

the benchmarks for all constituents analyzed is provided 

by Bennett and others (2006). For ease of discussion, 

relative‐concentrations were classified into high, moderate, 

and low categories. 

Category

Relativeconcentrations for 

organic constituents

Relativeconcentrations for 

inorganic constituents

High > 1 > 1

Moderate > 0.1 and ≤ 1 > 0.5 and ≤ 1

Low ≤ 0.1 ≤ 0.5

Organic and special-interest constituents tend to 

be less prevalent and have smaller maximum relativeconcentrations than inorganic constituents. Therefore, a 

relative-concentration of 0.1 was used as a threshold to 

distinguish between low and moderate relative-concentrations 

of organic and special‐interest constituents, compared 

with a relative‐concentration of 0.5 used as a threshold to 

distinguish between low and moderate relative-concentrations 

for inorganic constituents. As an example, the USEPA has 

previously used a relative-concentration of 0.1 as a threshold 

equal to or greater than which the USEPA wants to be 

informed of the presence of a pesticide in surface water or 

groundwater (U.S. Environmental Protection Agency, 1998).

Estimation of Aquifer-Scale Proportions

Two statistical approaches, grid-based and spatially 

weighted, were selected to evaluate the proportions of the 

primary aquifer in the Northern San Joaquin Basin study 

unit with high, moderate, or low relative-concentrations of 

constituents relative to benchmarks. Detection frequencies 

also were calculated for individual constituents without any 

areal or spatial correction for comparison (hereafter referred to 

as a raw detection frequency), but were not used for estimating 

aquifer-scale proportions because this method creates spatial 

bias towards areas with large numbers of wells.

1. Grid-based: One value per grid cell was used to 

represent the primary aquifer. Data were from the 

USGS-grid and CDPH-grid wells discussed in section, 

“Selection of CDPH Data for the Grid-Based Approach.” 

The proportion of the primary aquifer with high 

relative‐concentrations was computed by dividing the 

number of cells represented by a high concentration for 

that constituent by the total number of grid cells with 

data for that constituent. Proportions of moderate and 

low relative-concentrations were calculated similarly. 

Due to disproportionate study area size, proportions are 

areally corrected using the approach described in section, 

“Design of Sampling Networks.” For constituent classes, 

grid cells were assigned a high or moderate classification 

based on individual members in the constituent 

class with high or moderate relative‐concentrations. 

Confidence intervals for grid-based aquifer proportions 

were computed using the Jeffreys interval for the 

binomial distribution (Brown and others, 2001). 

Although the grid-based estimate is spatially unbiased, 

it may not detect constituents that are present at high 

relative‐concentrations in small proportions of the 

primary aquifer. 

2. Spatially weighted: All CDPH wells in the study 

unit (most recent analysis from each well with data 

for a constituent during the prior period (January 1, 

2001–April 21, 2004), USGS-grid wells, and 

USGS‐understanding wells were used to represent 

the primary aquifer. Using the spatially weighted 

approach, the proportion of high relative-concentrations 

for the primary aquifer for each constituent was 

computed by (a) computing the proportion of wells 

with high relative‐concentrations in each grid-cell; and 

(b) averaging together the grid-cell proportions computed 

in step (a) (Isaaks and Srivastava, 1989). Similar 

procedures were used to calculate the proportions of the 

aquifer with 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 were calculated using the 

same data that were used for the spatially weighted approach. 

However, raw detection frequencies are not spatially unbiased 

because the wells in the CDPH database are not uniformly 

distributed. Consequently, high relative‐concentrations in an 

area representing a small part of the primary aquifer might 

give that area a disproportionately high weight compared to 

spatially unbiased methods. Raw detection frequencies of high 

relative-concentrations are provided for reference in this report 

but were not used to determine aquifer-scale proportions. 
18 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Table 5. Benchmark type and value for constituents included in the assessment of status of groundwater quality in the Northern San 

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[Benchmark type: MCL-US, U.S. Environmental Protection Agency (USEPA) maximum contaminant level; MCL-CA, California Department of Public Health 

(CDPH) maximum contaminant level; HAL-US, USEPA lifetime health advisory level; NL-CA, CDPH notification level. Benchmark units: mg/L, milligrams 

per liter; μg/L, micrograms per liter; μS/cm, microsiemens per centimeter; pCi/L, picocuries per liter]

Constituent Typical use or source

Benchmarks

Type Concentration/

activity Units

Inorganic constituents

Trace elements with health-based benchmarks

Aluminum 1 naturally occurring MCL-CA 1,000 μg/L

Antimony 1 naturally occurring MCL-US 6 μg/L

Arsenic naturally occurring MCL-US 10 μg/L

Barium naturally occurring MCL-CA 1,000 μg/L

Boron naturally occurring NL-CA 1,000 μg/L

Fluoride 1 naturally occurring MCL-CA 2,000 μg/L

Lead 1 naturally occurring MCL-US 15 μg/L

Mercury 1 naturally occurring MCL-US 2 μg/L

Strontium naturally occurring HAL-US 4,000 μg/L

Vanadium naturally occurring NL-CA 50 μg/L

Radioactive constituents

Gross alpha radioactivity 1 naturally occurring MCL-US 15 pCi/L

Uranium 1 naturally occurring MCL-CA 20 pCi/L

Nutrients

Nitrate plus nitrite, as nitrogen natural, fertilizer, sewage MCL-US 10 mg/L

Nitrite, as nitrogen natural, fertilizer, sewage MCL-US 1 mg/L

Major and minor ions, and trace elements with SMCLs

Chloride naturally occurring SMCL-CA 500 mg/L

Iron naturally occurring SMCL-CA 300 μg/L

Manganese naturally occurring SMCL-CA 50 μg/L

Specific conductance naturally occurring SMCL-CA 1,600 μS/cm

Sulfate naturally occurring SMCL-CA 500 mg/L

Total dissolved solids (TDS) naturally occurring SMCL-CA 1,000 mg/L

Organic and special-interest constituents

Volatile organic compounds (VOCs)

Chloroform disinfection by-product MCL-US 80 μg/L

1,1-Dichloroethene 1 solvent MCL-CA 6 μg/L

1,2-Dichloroethane 2 solvent, fumigant, plastics MCL-CA 0.5 μg/L

cis-1,2-Dichloroethene 1 solvent MCL-CA 6 μg/L

Methyl-tert-butyl-ether (MTBE) gasoline oxygenate MCL-CA 13 μg/L

Tetrachloroethene (PCE) dry-cleaning, metal degreasing MCL-US 5 μg/L

Trichloroethene (TCE) 1 dry-cleaning, metal degreasing MCL-US 5 μg/L

Vinyl chloride 2 organic systhesis MCL-CA 0.5 μg/L

Fumigants

1,2-Dibromo-3-chloropropane (DBCP) fumigant MCL-US 0.2 μg/L

1,2-Dibromoethane (EDB) 1 fumigant MCL-US 0.05 μg/L

1,2-Dichloropropane (1,2-DCP) 1 industrial, fumigant MCL-US 5 μg/L

1,4-Dichlorobenzene 2 fumigant MCL-CA 5 μg/L

Pesticides

Simazine herbicide MCL-US 4 μg/L

Special-interest constituents

Perchlorate natural, rocket fuel, flares MCL-CA 6 μg/L

1

 Included on the basis of concentrations reported in CDPH database. Constituent also detected by USGS-GAMA at low relative-concentrations.

2

 Included on the basis of concentrations reported in CDPH database. Constituent not detected by USGS-GAMA
Methods 19

The grid-based and spatially weighted estimates of 

aquifer-scale proportions, based on a spatially distributed 

grid‐cell network across the study unit, are intended to 

characterize the water quality of the aquifer at depths 

that typically are used for drinking-water supply. These 

approaches assign weights to wells based on a single well per 

cell (grid‐based) or the number of wells per cells (spatially 

weighted). Another possible approach would have been to 

assign weights to wells on the basis of water use (withdrawal 

rate). However, water-use data for drinking water and other 

wells generally are not available. Moreover, this approach, 

even if withdrawal data were available for all wells, would 

characterize the volume of groundwater currently used for 

drinking-water supply, which would likely be weighted 

towards fewer wells and smaller areas than the approaches 

used, which were based on spatially distributed grid cells 

across the study unit. 

Understanding Assessment Methods

The understanding assessment—where natural and 

anthropogenic factors are related to changes in groundwater 

quality—is dependent on statistical testing of both the 

water-quality constituents and a set of explanatory factors. 

Constituents of interest were analyzed in relation to 

explanatory factors, specifically, land use, well depth, depth to 

top-of-perforation, normalized lateral position, groundwater 

age, and geochemical conditions, in order to establish context 

for physical and chemical processes. Statistical tests were used 

to identify significant correlations between the constituents of 

interest and the potential explanatory factors. The strongest 

correlations for factors influencing water quality are shown 

graphically. 

A subset of the constituents examined in the status 

assessment, plus selected classes of constituents, were 

examined in the understanding assessment: 

• Constituents with high relative-concentrations in 

greater than 2 percent of the aquifer. These constituents 

were selected to focus the understanding assessment

on those constituents that have the widest effect on raw 

water quality in the primary aquifer.

• Classes of constituents that had no individual 

constituents with high relative-concentrations in 

greater than 2 percent of the aquifer, but had individual 

constituents with high relative-concentrations in a 

small proportion of the aquifer (less than 2 percent) 

or moderate relative-concentrations in greater than 

2 percent of the aquifer. 

• Classes of organic constituents that included one or 

more constituents detected in more than 10 percent of 

USGS-grid wells, regardless of concentration. 

Grid wells (USGS and CDPH) and understanding 

wells were used for the understanding assessment. 

Correlations between water-quality variables and potential 

explanatory factors were tested using either the set of grid 

and understanding wells combined or grid wells only. 

Understanding wells were included in analyses of relations 

between constituents and the vertically distributed explanatory 

factors well depth, depth to top-of-perforation, groundwater 

age, and geochemical conditions, to aid in the identification of 

relationships. However, because the understanding wells were 

not randomly selected on a spatially distributed grid, they were 

excluded from analyses of relations of water quality to areally 

distributed variables (land use and normalized lateral position) 

to avoid areal-clustering bias. Classes of organic constituents, 

specifically, TTHMs, solvents, fumigants, and pesticides 

were compared to explanatory factors as sums of raw 

concentrations, meaning, if multiple constituent concentrations 

within a class were detected in the same well they were added 

together prior to analysis.

Statistical Analysis

Nonparametric statistical methods were used to test the 

significance of correlations between water-quality variables 

and potential explanatory factors. Nonparametric statistics 

are robust techniques that 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) used for 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). Correlations were investigated using Spearman’s 

method to calculate the rank-order correlation coefficient, 

rho, between continuous variables. The values of rho can 

range from +1.0 (perfect positive correlation), through 

0.0 (no correlation), to -1.0 (perfect negative correlation). 

For potential explanatory factors that were classified into 

categories (for example, groundwater-age categories of young 

and old), the values of water-quality parameters between the 

categories were compared using the Wilcoxon rank-sum test. 

Raw concentrations for classes of compounds, for example 

solvents, were summed prior to grouping and testing. The 

Wilcoxon rank-sum test is a median test statistic that compares 

two independent data groups (categories) to determine whether 

one group contains larger values than the other (Helsel and 

Hirsch, 2002). The null hypothesis for the Wilcoxon rank‐sum 

test is that there is no significant difference between the 

observations of the two independent data groups being tested, 

therefore significant differences between groups are noted 

by small p-values (< 0.05). All statistical analyses were done 

using TIBCO Spotfire S+® 8.1 for Windows.
20 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Potential Explanatory Factors

The potential explanatory factors land use, well depth, 

depth to top-of-perforation, normalized lateral position, 

groundwater age, and geochemical conditions are described 

briefly in this section. Correlations between explanatory 

factors that might affect apparent relations between 

explanatory factors and water quality also are described. The 

data sources and methodology used for assigning values for 

potential explanatory factors are described in appendix B: 

Ancillary Data Sets.

Land Use

Land use in this study is reported as the relative 

percentage of three major land-use categories (agricultural, 

urban, and natural) calculated at the scale of the study unit, 

individual study areas, and within 500-m buffers surrounding 

grid wells (USGS-grid wells supplemented with additional 

CDPH grid wells) and all wells in the CDPH database 

(appendix B). Land use based on the study unit as a whole (all 

land within the study unit boundaries) primarily is agricultural 

((57 percent; see in-text table). Land use with respect to 

priority constituents was evaluated as percentages of land use 

within 500-m buffers surrounding sampled wells. The 500-m 

buffer represents a contributing area as defined and evaluated 

by Johnson and Belitz (2009). Land use within 500-m buffers 

of all grid wells also primarily was agricultural (53 percent 

agricultural, 24 percent urban, and 23 percent natural). 

The more urbanized average land use for the buffer areas 

surrounding grid wells (24 percent) compared to the study 

unit as a whole (7 percent) shows that drinking water wells 

commonly are located in or near communities, which may 

result in a bias toward urbanized areas in the results. Urban 

land-use percentages for the 500-m buffer areas surrounding 

grid and CDPH wells are as much as four times greater than 

urban land-use percentages in the study unit as a whole (see in 

text table). The natural-land-use category is most prevalent in 

the Upland study area where, relative to the other study areas, 

few grid wells were sampled (fig. 5). The differences between 

average land use surrounding the grid and CDPH wells 

reflect differences in the spatial distributions of each dataset, 

with grid wells being more evenly distributed due to the grid 

approach and CDPH wells being more clustered in urban 

environments closer to population centers.

When examining the predominant land use (greater than 

50 percent) with respect to the buffered area surrounding 

grid wells, the Cosumnes, Eastern San Joaquin, and Tracy 

study areas primarily are agricultural; however, natural land 

use is dominant in the Uplands study area. When examining 

the buffered area surrounding CDPH wells, the pattern of 

dominant land use changes from agricultural to mixed (no land 

use greater than 50 percent) in the Cosumnes and Eastern San 

Joaquin study areas, but remains agricultural and natural in 

Tracy and Uplands study areas, respectively (fig. 7A). 

Understanding wells were nearly equally distributed 

between predominantly agricultural and predominantly urban 

areas (fig. 7B). One of the understanding wells was located 

in an area with greater than 50 percent natural land use. 

Four understanding wells were located in areas with greater 

than 70 percent urban land use within the 500-m buffers, 

and the remaining wells were located in areas with less than 

45 percent urban land use within the 500-m buffer.

An additional subcategory of agricultural land use, 

orchard/vineyard, was included in the statistical analysis of 

fumigant concentrations. Orchard and vineyard land use, 

which occurs in all study areas, previously was found to be 

related to concentrations of nitrate and some pesticides in parts 

of the eastern San Joaquin Valley (Domagalski, 1997; Burow 

and others, 1998a). For grid wells, the percentage of orchard/

vineyard land use ranged from 0 to 100, with a median of 

31 . For the understanding wells, the percentage of orchard/

vineyard land use ranged from 0 to 84, with a median of 48. 

Land-use category

Agricultural

(percent)

Urban

(percent)

Natural

(percent) Classification

Study unit as a whole 57 7 36 Agricultural

Study unit based on buffer areas 

surrounding grid wells

53 24 23 Agricultural

Study unit based on buffer areas 

surrounding CDPH wells

41 29 29 Mixed
Potential Explanatory Factors 21

Figure 7. Proportions of urban, agricultural, and natural land use, Northern San Joaquin Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit.

sac09-345fig 07

PERCENT AGRICULTURE

PERCENT NATURAL

PERCENT URBAN

PERCENT NATURAL

PERCENT URBAN

A. Individual study areas and study areas based on a 500-m

 buffer surrounding grid and CDPH wells

B. Grid and understanding wells in each study area based on

 land use within a 500-meter buffer surrounding the wells.

100

0

0

20

40

60

80

100

0

20

40

60

80

100

20

40

60

80

100

0

20

40

60

80

100

80 60 40 20 0

PERCENT AGRICULTURE

100 80 60 40 20 0

Study area land use (Cosumnes used as example) Wells

Cosumnes grid

Eastern San Joaquin grid

Uplands grid

Tracy grid

Individual study areas (filled symbols)

Based on 500-meter buffer surrounding grid wells (open symbols)

Based on 500-meter buffer surrounding CDPH wells in study area (dashed open symbols)

EXPLANATION

Understanding
22 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Well Depth and Depth to Top-of-Perforation

Grid wells primarily are used for drinking-water supply. 

Well depths ranged from 83 to 930 ft below land surface 

(BLS), with a median of 360 ft BLS (fig. 8). Depths to the 

top‐of-perforation ranged from 23 to 750 ft BLS, with a 

median of 180 ft BLS. The perforation length was as much 

as 470 ft, with a median of 177 ft. These values represent 

different sets of wells because more well depths were known 

than depths to top-of-perforation.

The understanding wells generally were deeper (although 

not statistically significantly deeper) and screens were longer 

than the depths and screens in the grid wells (fig. 8). The 

median well depth, median depth to top-of-perforation, and 

median perforation length for understanding wells were 460, 

200, and 268 ft, respectively.

Normalized Lateral Position

The normalized lateral position of wells (figs. 9A and 9B) 

serves as a proxy for the horizontal position in the regional 

groundwater-flow system and is calculated as the ratio of the 

distance from the well to the upgradient edge of the regional 

groundwater-flow system (valley margin) to the total distance 

from the center of the valley (valley trough) to the valley 

margin (appendix B). The valley margins (eastern and western 

edges of the San Joaquin Valley) in this description represent 

the upgradient ends of the regional-flow system and high 

values of normalized lateral position. The valley trough (basin 

center) represents the downgradient end of the regional-flow 

system and low values of normalized lateral position. Grid 

wells were distributed across the entire range of normalized 

lateral positions (fig. 9A). Wells with a range of normalized 

lateral positions of 0.20–0.39, 0.40–0.59, and 0.60–0.79, 

made up 20, 27, and 14 percent of the grid wells, respectively 

(fig. 9A). Wells with a normalized lateral position of 0.0 to 

less than 0.20 (near valley trough) and greater than 0.80 (near 

valley margin) made up 22 and 17 percent of the grid wells, 

respectively (fig. 9B). 

Figure 8. Well depths, depths to top-of-perforation, and perforation lengths for grid and understanding wells, Northern 

San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

Well depth Perforation length Well depth Perforation length

Depth to Top-of-Perforation Depth to Top-of-Perforation

GRID WELLS UNDERSTANDING WELLS

800

1,000

1,200

600

400

200

0

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

43 33 31 10 8 8

30 Number of values

Value outside of 1.5 times the 

interquartile range

75th percentile

Median

25th percentile

EXPLANATION

Highest value within 1.5 times

the interquartile range
Potential Explanatory Factors 23

Figure 9. Distribution of wells plotted on visualization of normalized lateral 

positions and across the range of normalized lateral positions, Northern San 

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit. sac09-0345_fig 09ab

0 10 5 20 MILES

0 10 205 KILOMETERS

Grid wells (USGS and CDPH)

Understanding wells

Boundary of Sacramento-San Joaquin Delta

Study area outline

EXPLANATION

Normalized Lateral 

Position Ranges

0.00 – 0.19

0.20 – 0.39

0.40 – 0.59

0.60 – 0.79

0.80 – 1.00

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

A. Distribution of wells plotted on visualization of normalized lateral positions.

B. Grid and understanding wells in each study area based on land use within a

 500-meter buffer surrounding the wells.

Rivers

30

NORMALIZED LATERAL POSITION

25

20

15

10

5

0

0.00–0.19

Downgradient Upgradient

0.20–0.39 0.40–0.59 0.60–0.79 0.80–1.00

PERCENTAGE OF WELLS

San Joaquin River
24 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Groundwater Age

Groundwater age of the 61 groundwater samples obtained 

by USGS-GAMA in the Northern San Joaquin Basin study 

unit were classified by using tritium concentrations. Tritium 

was the only age tracer collected at all wells; the combination 

of tritium, carbon-14 (14C), and noble-gas data were available 

for a subset of wells. Tritium concentrations alone can not 

be used to accurately define groundwater age; however, the 

relative abundance of tritium is a useful indicator of the 

presence of young water. Wells with tritium concentrations 

less than 1 pCi/L were classified as old (recharged prior to 

1950) and those with tritium concentrations equal to or greater 

than 1 pCi/L were classified as young (recharged after 1950) 

(Michel, 1989; Michel and Schroeder, 1994; Solomon and 

Cook, 2000) (appendix B).

Groundwater classified as old occurred significantly 

more often in wells with deeper depths to top-of-perforation 

(fig. 10A, table 6); however, groundwater age was not 

correlated with well depth alone (fig. 10B, table 6). 

Figure 10. Classified groundwater age related to depth to top-of-perforation, well depth, and normalized lateral position, 

Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

n = 16 n = 24

Old Young

GROUNDWATER AGE CLASSIFICATION

600

400

200

DEPTH TO TOP-OF-PERFORATION,

IN FEET BELOW LAND SURFACE

0

800

p = 0.006

A

B C

n = 23 n = 28 n = 39 n = 22

Old Young

GROUNDWATER AGE CLASSIFICATION

Old Young

GROUNDWATER AGE CLASSIFICATION

1,000

800

600

400

200

0

1,200

p = 0.378 p = 0.006

0.0

0.2

0.4

0.6

0.8

1.0

Valley Margin

Valley Trough

 WELL DEPTH,

IN FEET BELOW LAND SURFACE

 NORMALIZED LATERAL POSITION,

UNITLESS

30

EXPLANATION

Number of values

Value outside of 1.5 times the 

interquartile range

75th percentile

Median

25th percentile

Highest value within 1.5 times

the interquartile range
Potential Explanatory Factors 25

Tritium concentrations were significantly higher (indicating 

younger water) in those wells with depths to top-of-perforation 

of less than 200 ft (table 6). Groundwater classified as young 

was most often located closer to the valley trough than 

Table 6. Results of Wilcoxon tests for differences in values of selected water-quality constituents and potential explanatory factors 

between young and old groundwater-age classification, wells with depths to top-of-perforation less than 200 feet and those equal to 

or greater than 200 feet, and wells with oxic conditions versus those with anoxic conditions, Northern San Joaquin Basin Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit.

[p-values calculated using the Wilcoxon rank-sum tests, using exact distribution, with continuity correction. p-values less than 0.05 are defined as significant. 

Young defined as a well with a tritium concentration greater than or equal to 1 tritium unit (TU) and old defined as a well with a tritium concentration less 

than 1 TU. Shallow defined as wells with depths to top-of-perforation less than 200 feet below land surface and deep defined as wells with depths to top-ofperforation greater than or equal to 200 feet. Eastern Study Areas include the Cosumnes, Eastern San Joaquin, and Upland study areas of the Northern San 

Joaquin Basin study unit; nc, count can not be calculated; SMCL, secondary maximum contaminant level; bold text, significant correlation (p-value less than 

0.05); < less than; > greater than]

p-values from Wilcoxon rank-sum tests and significant differences

Groundwater age 

(young, old)

Depth to top-ofperforation 

(shallow, deep)

Redox condition 

(oxic, anoxic)

Potential explanatory factors

Tritium nc 0.002 shallow>deep 0.944

pH 0.857 0.319 0.375

Depth to top-of-perforations 0.006 young<old nc 0.522

Well depth 0.378 nc 0.638

Inorganic constituents with health-based benchmarks

Arsenic 0.038 young>old 0.003 shallow>deep 0.160

Arsenic - Eastern Study Areas 0.086 0.032 shallow>deep 0.241

Boron 0.055 0.057 0.062

Nitrate plus nitrite, as nitrogen 0.558 0.643 <0.001 oxic>anoxic

Gross alpha radioactivity 1 0.008 young>old 0.419 0.207

Inorganic constituents with non-health-based benchmarks (SMCLs)

Chloride 0.638 0.453 0.009 oxic<anoxic

Iron 0.822 0.753 < 0.001 oxic<anoxic

Manganese 0.462 0.767 < 0.001 oxic<anoxic

Sulfate 0.539 0.197 0.140

Total dissolved solids 0.739 0.389 0.950

Total dissolved solids - Eastern Study Areas 0.005 young>old 0.093 0.257

Organic constituent classes with health-based benchmarks

Sum of total trihalomethane (TTHM) concentrations 1 <0.001 young>old nc 0.080

Sum of solvent concentrations 1 <0.001 young>old nc nc

Sum of fumigant concentrations 1 0.001 young>old nc nc

Sum of pesticide concentrations 1 <0.001 young>old nc 0.963

1

 Grid wells only. Variables not footnoted include grid plus understanding wells.

groundwater classified as old (fig. 10C, table 6), possibly 

because of infiltration of young irrigation water in the San 

Joaquin Valley and because wells closer to the valley trough 

tend to be shallower than those in the upland areas (fig. 11).
26 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Geochemical Conditions

Groundwater samples were classified as oxic (oxygen 

reducing) or anoxic (nitrate, manganese, iron, or sulfate 

reducing) based on the available data. The classification of 

groundwater samples is based on the relative-concentrations 

of redox-sensitive constituents (appendix B). Groundwater 

in the Northern San Joaquin Basin study unit primarily was 

classified as oxic (54 percent of wells). Anoxic conditions 

occurred in 20 of the 61 wells evaluated (33 percent). Anoxic 

conditions primarily occurred in the central and western parts 

Figure 11. Relation of classified groundwater age to normalized 

lateral position and depth of perforated interval of wells, Northern 

San Joaquin Basin Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit.

of the study unit (valley trough and deltaic locations) (fig. 12). 

Previous investigations have noted that groundwater typically 

becomes more reducing towards the trough of the San Joaquin 

Valley (Davis and others, 1959; Bertoldi and others 1991; 

Dubrovsky and others, 1993; Chapelle and others, 1995; 

Burow and others, 1998b). Analysis of the relation between 

redox conditions and normalized lateral position of the wells 

indicates that waters classified as anoxic tend to occur closer 

to the valley trough, and wells classified as oxic tend to occur 

closer to the valley margin; however, the relation was not 

statistically significant (fig. 13, table 6).

sac09-0345_fig 11

0 0.20 0.40 0.60 0.80 1.0

NORMALIZED LATERAL POSITION, IN DIMENSIONLESS

1,000

800

600

400

200

DEPTH BELOW LAND SURFACE, IN FEET

1,200

0

Valley trough

(downgradient)

Valley margins

(upgradient)

Top

Bottom

Perforated

Interval

EXPLANATION

Groundwater Age

Young Old
Geochemical Conditions 27

Figure 12. Redox conditions in grid and understanding wells, Northern San Joaquin Basin Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit. Classification of redox category based on framework 

developed by McMahon and Chapelle (2008), which uses measurements of redox-sensitive constituents (dissolved 

oxygen, nitrate, iron, manganese, and sulfate) to determine redox conditions. Wells labeled as indeterminate had 

insufficient data for classification. 

sac09-0345_fig 12

Water bodies

Swamp or marsh

Inundation area

Indeterminate

Anoxic

Oxic

EXPLANATION

Redox Category

Study areas

Cosumnes (COS)

Eastern San Joaquin (ESJ)

Uplands (NSJ-QPC)

Tracy (TRCY)

Boundary of SacramentoSan Joaquin Delta

Boundary of Northern 

San Joaquin Basin

Boundary of subbasin

of Northern San 

Joaquin Basin

Geologic units

Quaternary alluvium (Q)

Quaternary/Plio-Pleistocene 

semi-consolidated (QPc)

Miocene nonmarine consolidated (Mc)

Eocene nonmarine consolidated (Ec)

COS

TRCY ESJ

NSJ-QPC

0 5 10 20 MILES

0 10 20 5 KILOMETERS

37°

30 ́

38°

30 ́

1218 309 1218

38°

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

San Joaquin River
28 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Correlations Between Explanatory Factors

If two explanatory factors are statistically related, that 

relation can result in a spurious relation between one of those 

explanatory factors and a dependent variable. Identification 

of such correlations between explanatory factors therefore is 

important for the correct interpretation of statistical relations 

between explanatory factors and the occurrence or distribution 

of water-quality constituents. Using the grid well dataset, 

significant correlations between explanatory factors were 

seen in 9 of 21 comparisons made and are noted below with 

the exception of the relation between groundwater age and 

depth, which was discussed earlier (table 7). Implications 

of correlations between explanatory factors are discussed in 

section, “Status and Understanding of Water Quality,” as part 

of the analysis of factors affecting individual constituents. 

Because the three land-use types necessarily sum to 

100 percent, the land-use types are expected to be inversely 

correlated and this is seen in the significant negative 

correlations between urban and agricultural land use and 

agricultural and natural land use (table 7). The percentages 

of urban and agricultural land use were significantly 

negatively correlated to normalized lateral position from 

the valley trough, although the percentage of natural land 

use was significantly positively correlated to normalized 

lateral position (table 7). The correlation between urban and 

agricultural land use and normalized lateral position is related 

to the fact that urban and agricultural land uses are located 

closer to the center of the basin, with the largest urban area 

(the city of Stockton) situated along the San Joaquin River 

in the center of the basin and surrounded by agricultural land 

use (fig. 6). Additionally, the percentage of urban land use 

was significantly positively correlated to pH and the depth 

of wells below land surface. Normalized lateral position 

was significantly negatively correlated with pH. Depth to 

top-of-perforation and well depth are auto-correlated as was 

expected.

Figure 13. Classified redox category to normalized lateral position, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 13

n = 12 n = 30

Anoxic Oxic

REDOX CATEGORY

p = 0.097

Valley Margin

Valley Trough

0.0

0.2

0.4

0.6

0.8

1.0

1.2

NORMALIZED LATERAL POSITION, UNITLESS

30

EXPLANATION

Number of values

Value outside of 1.5 times the 

interquartile range

75th percentile

Median

25th percentile

Highest value within 1.5 times

the interquartile range
Status and Understanding of Water Quality 29

Status and Understanding of 

Water Quality 

As a starting point for summarizing the results of 

about 11,000 individual analytical results for the Northern 

San Joaquin Basin study unit, the maximum relativeconcentrations of the individual constituents and constituent 

groups were compared to each other (fig. 14). All constituents 

shown have health-based benchmarks, except for those in 

the group inorganic-SMCL, which have non-health-based 

aesthetic benchmarks. Constituents with moderate or high 

maximum relative-concentrations are discussed individually. 

Aquifer proportions calculated by the grid-based approach 

were considered the most reliable and are used in the 

subsequent discussions, except where otherwise noted. In 

some instances, the spatially weighted approach identified 

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

explanatory factors, Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[Spearman’s rho test used and rho values shown. Significance level (p<0.05); Correlations with rho > 0.2 and p-values < 0.05 are shown in bold; 

<, less than; >, greater than]

rho

Percentage of 

urban 

land use

Percentage of 

agricultural 

land use

Percentage of 

natural 

land use

Normalized 

lateral 

position

Depth to topof-perforation Well depth pH

Percentage of 

urban 

land use

–0.409 –0.118 –0.206 0.124 0.322 0.376

Percentage of 

agricultural 

land use

–0.720 –0.291 0.133 –0.181 0.025

Percentage of 

natural 

land use

0.481 0.067 0.159 –0.160

Normalized 

lateral 

position

0.027 0.039 –0.483

Depth to topof-perforation 0.642 –0.152

Well depth –0.186

pH

constituents that could be present at moderate or high 

relative‐concentrations in small proportions of the primary 

aquifer that were not identified using the grid-based approach. 

Results from the spatially weighted approach were only used 

in cases for which the grid-based approach was found to 

have this limitation. Non-significant relations generally are 

not discussed; selected significant correlations are shown 

graphically. 

Most of the detected organic and special-interest 

constituents (32 of 40; 80 percent) have some type of 

health‐based benchmark (table 8). Three of the detected 

constituents (deethylatrazine, 2,6-diethylaniline, 

3,4-dichloroanaline) on the pesticide schedules and for which 

no health-based benchmarks exist are degradates of pesticides 

that do have health-based benchmarks (atrazine, alachlor, and 

propanil, respectively).
30 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 14. Maximum relative-concentration in grid wells for constituents detected by type of constituent, Northern 

San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 14

Perchlorate

1,2-DCP

Simazine

Atrazine

PCE

TCE

Benzene

Boron

Arsenic

Zinc

Iron

Manganese

TDS (upper)

Chloride(upper)

Sulfate(upper)

1,1-Dichloroethane

1,1-Dichloroethene

Toluene Carbon disulfide

Chloroform

cis-1,2-Dichloroethene

MTBE

Dibromochloromethane

CFC-11 Bromoform

Aluminum

Antimony

Cadmium

Chromium, Lead

Copper

Fluoride

Mercury

Nitrate plus nitrite

Nitrite as nitrogen

Selenium

Vanadium

Barium

Uranium

**Strontium

Gross-alpha

**Gross-beta

**Radium-226

**Radium-228

**Radon-222

VOCs*

Fumigants

Pesticides*

Special-Interest

Nutrients

Trace Elements

SMCL Constituents

Radioactive

0.001

0.01

0.1

1

10

100

1,000

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Organics and Special-Interest Constituents Inorganic Constituents

Moderate

Low

Moderate

High

DBCP

PCE Constituents with analyses in >20 grid wells and wells are spatially representativeName and center of symbol is location of data unless indicated by following location line: 

Constituents with analyses in <20 grid wells and wells are not spatially representativeName and center of symbol is location of data unless indicated by following location line:

Strontium

VOC, volatile organic compound; TCE, trichloroethene; PCE, tetrachloroethene; DBCP, 1,2-dibromo-3-chloropropane 1,2-DCP, 

1,2-dichloropropane; SMCL, secondary maximum contaminant level; MTBE, methyl tert-butyl ether; TDS, total dissolved solids; upper, 

upper water-quality benchmark where multiple benchmarks exist.

* Seven VOCs and four pesticides were detected at relative-concentrations less than 0.001 and are not shown on the figure.

**Constituents with less than 20 sample results.

Relative-Concentration

High

Moderate

Low

EXPLANATION

Abbreviations
Status and Understanding of Water Quality 31

In contrast to organic and specialinterest constituents, almost all inorganic 

and radioactive constituents were detected 

at least once (44 of 48; 91 percent) (table 8). 

Health-based or aesthetic (those with 

SMCLs) benchmarks are not available for 

about one-third of the measured inorganic 

and radioactive constituents (13 of 48; 

27 percent). Most of the constituents without 

benchmarks are major or minor ions that are 

naturally present in groundwater. 

Aquifer-scale proportions were 

computed using spatially weighted and 

grid-based approaches (table 9). Constituents 

of interest were those with (1) high relativeconcentrations during the full period of 

record of the Northern San Joaquin Basin 

CDPH database (January 10, 1984–April 

21, 2004), and (2) constituents selected in 

the grid-based assessment on the basis of 

moderate to high relative-concentrations 

or, for organic constituents, detection 

frequencies greater than 10 percent (table 9). 

Raw detection frequencies also are provided 

for comparison. Five constituents—

chloroform, 1,1,2,2-tetrachloroethane, 

cadmium, gross beta radioactivity, and 

uranium (table 3)—were detected in CDPH 

wells at high relative-concentrations prior 

to the 3-year period used for the status 

assessment (historically high constituents). 

Individual constituents were grouped 

into constituent classes—specifically, 

inorganic constituents with health-based 

benchmarks, inorganic constituents with 

aesthetic [secondary maximum contaminant 

level (SMCL)] benchmarks, and organic 

constituents with health-based benchmarks 

(table 10). For each constituent or 

constituent class meeting the criteria for 

analysis of explanatory factors, relations 

to explanatory factors are described in 

subsections for the constituent class 

(table 11). 

Table 8. Number of constituents analyzed and detected by health-based 

benchmark type and constituent type, Northern San Joaquin Basin Groundwater 

Ambient Monitoring and Assessment (GAMA) study unit.

[Health-based benchmarks include U.S. Environmental Protection Agency (USEPA) and California 

Department of Public Health (CDPH) maximum contaminant levels, USEPA lifetime health 

advisory levels and risk-specific dose level at 10-5 lifetime cancer risk, and CDPH notification 

level. Abbreviations: SMCL, secondary maximum contaminant level (non-health based); VOC, 

volatile organic compound]

Benchmark type

Number of 

constituents 

analyzed

Number of 

constituents 

detected

VOCs plus gasoline oxygenates

Health-based benchmarks 50 20

No benchmark 25 2

Total: 75 22

Fumigants

Health-based benchmarks 9 2

No benchmark 1 0

Total: 10 2

Pesticides and degradates

Health-based benchmarks 40 9

No benchmark 85 6

Total: 125 15

Special-interest constituents

Health-based benchmarks 3 1

No benchmark 0 0

Total: 3 1

Sum of organic and special-interest constituents (all categories above)

Health-based benchmarks 102 32

No benchmark 111 8

Total: 213 40

Sum of inorganic and radioactive constituents

Health-based benchmarks 29 27

SMCL 6 5

No benchmark 13 12

Total: 48 44
32 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project Table 9. Current aquifer-scale proportions computed using raw detection frequency, spatially weighted, and grid-based approaches for constituents detected at concentrations greater than water-quality benchmarks during the period of available data (January 10, 1984–April 21, 2004) from the California Department of Health Services database or detected in greater than 10 percent of the wells sampled, Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit. [Constituents evaluated include those with one or more currently or historically high concentrations or detection frequency greater than 10 percent on the basis of California Department of Public Health (CDPH) data collected from January 10, 1984–April 21, 2004 and USGS grid-well data collected by the U.S. Geological Survey from December 2004–February 2005. Raw detection frequencies are provided for comparison, but can be spatially biased. High concentrations are those that are greater than a water-quality benchmark. Moderate concentrations are less than a benchmark but are greater than or equal to 0.1 of benchmark (for organic and special-interest constituents) or 0.5 of benchmark (for inorganic constituents), low concentrations are less than 0.1 of benchmark (for organic and special-interest constituents) or 0.5 of benchmark (for inorganic constituents). MCL-US, U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; mg/L, milligrams per liter; μg/L, micrograms per liter; NA, not applicable] Constituent Raw detection frequency 1 Spatially weighted aquifer-scale proportions 1 Grid-based aquifer-scale proportion 90-percent confidence interval for grid-based high proportion 2 Number of wells Moderate values (percent) High values (percent) Number of cells Moderate values (percent) High values (percent) Number of wells Moderate values (percent) High values (percent) Lower limit (percent) Upper limit (percent) Trace elements Aluminum 399 0.3 0 53 0.2 0 48 0 0 0 2.8 Antimony 391 0.3 0.3 53 <0.1 <0.1 47 0 0 0 2.8 Arsenic 406 18 16 54 13 9.6 48 19 9.4 4.9 19.4 Barium 399 1.0 0 53 2.3 0 49 2.1 0 0 2.7 Boron 386 4.9 9.8 48 8.4 9.8 45 9.4 7.6 2.4 14.9 Fluoride 366 0 0.3 48 0 0.3 43 0 0 0 3.1 Lead 391 1.0 1.3 52 0.8 0.6 46 0 0 0 2.9 Mercury 377 0 0.3 48 0 0.1 38 0 0 0 3.5 Strontium 3 NA NA NA NA NA NA 21 6.4 0 0 6.2 Vanadium 390 27 1.5 46 18 0.6 39 16 0 0 3.4 Radioactive constituents Gross alpha radioactivity 278 10 2.5 40 6.4 2.1 27 0 0 0 4.8 Uranium 125 8.0 0 32 5.3 0 27 0 0 0 4.8 Nutrients Nitrate plus nitrite, as nitrogen 647 17 3.1 55 13 2.1 50 9.0 0 0 2.7 Nitrite as nitrogen 449 0 0.2 53 0 0.1 45 0 0 0 2.9 Major and minor ions, and trace elements with SMCLs Chloride 347 4.9 2.0 49 4.5 5.1 47 3.9 3.9 1.2 11.3 Iron 355 6.5 9.6 51 8.6 16.7 47 9.9 11 5.0 19.8 Manganese 347 4.3 26 50 5.4 32.4 44 5.4 34 25.3 48.7 Sulfate 342 4.7 0.6 48 5.9 1.0 44 3.9 2.0 0.4 8.5 Total dissolved solids (TDS) 340 23 4.7 48 20 7.7 44 16 5.8 2.5 15.2 Specific conductance, field 368 22 5.4 56 16 7.5 56 11 9.1 4.2 16.7
Status and Understanding of Water Quality 33

Constituent

Raw detection frequency 1 Spatially weighted aquifer-scale proportions 1 Grid-based aquifer-scale proportion 90-percent confidence interval for grid-based high proportion 2

Number of 

wells

Moderate 

values 

(percent)

High

values

(percent)

Number of 

cells

Moderate 

values 

(percent)

High

values 

(percent)

Number of 

wells

Moderate 

values 

(percent)

High 

values 

(percent)

Lower 

limit 

(percent)

Upper 

limit 

(percent)

Trihalomethanes (THMs)Chloroform 4 356 0 0 56 0 0 51 0 0 0 2.6

Solvents1,1-Dichloroethene 366 0.5 0 56 0.2 0 51 0 0 0 2.61,2-Dichloroethane 367 0 0.3 56 0 <0.1 51 0 0 0 2.6cis-1,2-Dichloroethene 364 1.4 0 56 0.4 0 51 0 0 0 2.6Tetrachloroethene (PCE) 4 355 3.5 0.3 56 1.6 0.1 51 1.8 0 0 2.6Trichloroethene (TCE) 364 1.1 0.3 56 0.3 <0.1 51 0 0 0 2.6Vinyl chloride 367 0 0.3 55 0 0.1 51 0 0 0 2.6Other VOCsMethyl-tert-butyl-ether (MTBE) 498 0.2 0 56 0 0 51 2.1 0 0 2.6Fumigants1,2-Dibromo-3-chloropropane (DBCP) 392 5.9 5.1 56 2.6 3.7 51 2.7 2.7 1.1 10.41,2-Dibromoethane (EDB) 381 0 0.3 56 0 <0.1 51 0 0 0 2.61,2-Dichloropropane (1,2-DCP) 348 0.3 0 56 0.1 0 51 0 0 0 2.61,4-Dichlorobenzene 348 0.3 0 56 0.6 0 51 0 0 0 2.6PesticidesSimazine 4 239 0 0 51 0 0 51 0 0 0 2.6Special-interest constituentsPerchlorate 241 0.8 0 36 0.1 0 29 2.6 0 0 4.4

1 Based on most recent CDPH analysis during January 1, 2001–April 21, 2004 (most recent data available at time of analysis) combined with grid-based data.2 Based on the Jeffrey’s interval for the binomial distribution (Brown and others, 2001).3 Strontium not analyzed as part of the CDPH Title 22 California Code of Regulations.4 Constituent detected in greater than 10 percent of grid wells.

Table 9. Current aquifer-scale proportions computed using raw detection frequency, spatially weighted, and grid-based approaches for constituents detected at concentrations greater than water-quality benchmarks during the period of available data (January 10, 1984–April 21, 2004) from the California Department of Health Services database or detected in greater than 10 percent of the wells sampled, Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.—Continued[Constituents evaluated include those with one or more currently or historically high concentrations or detection frequency greater than 10 percent on the basis of California Department of Public Health (CDPH) data collected from January 10, 1984–April 21, 2004 and USGS grid-well data collected by the U.S. Geological Survey from December 2004–February 2005. Raw detection frequencies are provided for comparison, but can be spatially biased. High concentrations are those that are greater than a water-quality benchmark. Moderate concentrations are less than a benchmark but are greater than or equal to 0.1 of benchmark (for organic and special-interest constituents) or 0.5 of benchmark (for inorganic constituents), low concentrations are less than 0.1 of benchmark (for organic and special-interest constituents) or 0.5 of benchmark (for inorganic constituents). MCL-US, U.S. Environmental Protection Agency maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; mg/L, milligrams per liter; μg/L, micrograms per liter; NA, not applicable]
34 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Table 10. Aquifer-scale proportions for constituent classes, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[SMCL, secondary maximum contaminant level; VOCs, volatile organic compounds; 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; rounding of 

values may result in proportions for a class not adding up to 100 percent]

Aquifer-scale proportion (percent) 1

Low 

values 

Moderate 

values

High

values

Inorganic constituent classes with health-based benchmarks

Trace elements 62 25 13

Nutrients 89 9 2 2.1

Radioactive constituents 98 0 2 2.1

Any inorganic with health-based benchmarks 58 29 13

Inorganic constituents with SMCLs

Any inorganic with aesthetic benchmarks 33 31 36

Organic constituent classes with health-based benchmarks

Total trihalomethanes (TTHMs) 100 0 0

Solvents 98 1.8 2 0.1

Other VOCs 98 2.1 2 0.1

Fumigants 95 2.7 2.7

Pesticides 100 0 0

Any organic with health-based benchmarks 90 6.9 2.7

1

 Aquifer-scale proportions by class calculated using the grid-based approach unless othewise noted, all grid-based 

results are area weighted.

2

 High values based on the spatially weighted approach.
Status and Understanding of Water Quality 

35

Table 11. Summary of Spearman’s rho correlation analysis between selected water-quality constituents and potential explanatory factors, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[Sum of organic constituent classes calculated using raw concentrations with non-detects treated as zeros. rho, Spearman’s correlation statistic; Significant correlations (p<0.05) are shown in bold]

Benchmark 

type

Aquifer-scale 

proportion 

with high 

values 

(percent)

Grid and understanding 

wells combined Grid wells

Depth to 

top-ofperforation Well depth pH

Normalized 

lateral 

position

Percent 

urban land 

use 1

Percent 

agricultural 

land use 1

Percent 

natural land 

use 1

Percent 

orchard/ 

vineyard 

land use 1

rho rho rho rho rho rho rho rho

Inorganic constituents with health-based benchmarksArsenic MCL-US 9.4 –0.439 –0.346 0.178 –0.695 –0.014 0.275 –0.368 0.064

Boron NL-CA 7.6 0.234 0.272 –0.046 0.051 –0.104 0.061 0.048 –0.039Nitrate MCL-US 2.1 0.010 0.159 –0.200 0.357 –0.212 0.161 –0.057 0.403Gross alpha radioactivity MCL-US 2.1 –0.608 –0.516 0.320 –0.231 –0.086 0.186 –0.201 –0.027Inorganic constituents with SMCLsChloride SMCL-CA 3.9 0.050 –0.037 0.267 –0.327 0.022 –0.052 0.110 –0.084Iron SMCL-CA 11 –0.072 0.045 –0.225 0.047 –0.061 –0.028 0.118 –0.122

Manganese SMCL-CA 34 –0.106 –0.132 –0.006 –0.208 –0.064 0.075 –0.053 –0.141

Sulfate SMCL-CA 2.0 0.183 0.209 0.286 –0.162 0.180 –0.093 –0.102 –0.052Total dissolved solids (TDS) SMCL-CA 5.8 0.042 –0.101 0.319 –0.282 0.157 –0.001 –0.022 0.041Organic constituent classes with health-based benchmarksSum of total trihalomethane (TTHM) 

concentrations

MCL-US 0 0.065 0.263 0.075 –0.313 0.601 –0.267 –0.277 –0.004

Sum of solvent concentrations variable 0 –0.182 0.009 –0.061 –0.044 0.347 –0.236 –0.146 0.053

Sum of fumigant concentrations MCL-US 2.7 –0.222 –0.187 –0.129 –0.162 –0.004 0.113 –0.136 0.408

Sum of pesticide concentrations variable 0 –0.300 –0.133 –0.025 –0.352 0.147 –0.101 –0.111 –0.051

1 Land-use percentages are within a circle with a radius of 500-meters centered around each well included in analysis. 
36 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Inorganic Constituents

Inorganic constituents as a group were the largest 

contributor to high and moderate relative-concentrations 

throughout the Northern San Joaquin Basin study unit. 

Relative-concentrations for 11 inorganic constituents were 

greater than 0.5 based on the grid-based approach (fig.14)—

the trace elements arsenic, barium, boron, strontium, and 

vanadium; the nutrient nitrate; the major and minor elements 

chloride, iron, and manganese; sulfate; and total dissolved 

solids (TDS) [measured directly or calculated from specific 

conductance (see appendix C)]. In addition, relativeconcentrations of gross alpha radioactivity were greater than 

2 percent based on the spatially weighted approach (table 9). 

Analysis of potential explanatory factors is included for those 

constituents with relative-concentrations greater than 1—

specifically, arsenic, boron, gross alpha radioactivity, nitrate, 

chloride, iron, manganese, sulfate, and TDS.

 Inorganic constituents with health-based benchmarks 

(nutrients, trace elements, and radioactive constituents) 

were assessed by using the grid-based approach. Relativeconcentrations of at least one constituent were high for 

13‐percent, moderate for 29 percent, and low for 58 percent 

of the primary aquifer (table 10). The proportion of the 

primary aquifer with high relative-concentrations of inorganic 

constituents as a class was equivalent to the proportion of 

the primary aquifer with high relative-concentrations of trace 

elements because no nutrient or radioactive constituent was 

detected at high relative-concentrations.

Trace Elements

Trace elements as a group were the largest contributor to 

high relative-concentrations in the inorganic constituent class. 

Relative-concentrations of at least one trace element with 

health-based benchmarks were high for 13 percent, moderate 

for 25 percent, and low for 62 percent of the primary aquifer 

(table 10).

The trace elements mercury, fluoride, lead, and vanadium 

were detected at high relative-concentrations in the primary 

aquifer using the spatially weighted approach but were not 

high using the grid-based approach; however, proportions 

of the primary aquifer with high relative-concentrations of 

mercury, fluoride, lead, and vanadium were 0.1, 0.3, 0.6, and 

0.6 percent, respectively (table 9). Relative-concentrations 

for each of these trace elements were high in at least one 

well in the CDPH data for January 1, 2001–April 21, 2004. 

These detections reflect high relative-concentrations of these 

constituents in a very small proportion of the primary aquifer.

Arsenic relative-concentrations were high in 9.4 percent 

and moderate in 19 percent of the primary aquifer (table 9, 

fig. 15A). Arsenic relative-concentrations were high and 

moderate in the Cosumnes, Eastern San Joaquin, and Tracy 

study areas, but not in the Upland study area (figs. 15A and 

16A). Arsenic is a naturally occurring semi-metallic trace 

element commonly associated with sulfide minerals and 

deposits of metal ores, such as copper and gold (Welch and 

others, 2006). Industrially, arsenic is most commonly used 

as a wood preservative, but also can be used in paints, dyes, 

metals, drugs, soaps, semi-conductors, and in the mining of 

copper and gold (U.S. Environmental Protection Agency, 

2009). 

Boron relative-concentrations were high in 7.6 percent 

and moderate in 9.4 percent of the primary aquifer (table 9). 

Boron relative-concentrations were high in grid wells in 

the western part of the Tracy study area (fig. 16B). Boron 

relative‐concentrations were moderate primarily in the 

Tracy study area, with one well with a moderate relativeconcentration in the Eastern San Joaquin study area (fig. 16B). 

Boron is a trace element that occurs in many minerals, 

principally borax; it is mined principally in California and 

Turkey. Boron is an essential plant nutrient in small amounts; 

however, large amounts can be harmful or even toxic to some 

plants (Hem, 1992). Boron has numerous uses, including glass 

and silicate production, fire retardants, laundry and cleaning 

products, and insecticides.

Factors Affecting Arsenic

Arsenic concentrations in the Northern San Joaquin 

Basin study unit are related to geochemical conditions in the 

primary aquifer, which vary with depth, groundwater age, and 

normalized lateral position. Arsenic concentrations in samples 

with a groundwater age classified as young and collected from 

wells with a depth to the top-of-perforation of less than 200 ft 

were significantly higher than samples with a groundwater age 

classified as old based on tritium concentrations or samples 

from wells with a depth to top-of-perforation of greater than 

200 ft (table 6). Arsenic concentrations also were correlated 

(negatively) with normalized lateral position and percentage 

of natural land use (table 11), indicating an increase in arsenic 

concentrations with increased proximity to the valley trough. 
Status and Understanding of Water Quality 37

Figure 15. Relative-concentrations of trace elements and nutrients with health-based benchmarks and maximum relativeconcentrations greater than 0.5 in grid wells, and major and minor elements with aesthetic benchmarks and maximum 

relative-concentrations greater than 0.5 in grid wells, Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit.

Results from this study can be compared to results of 

work conducted by Izbicki and others (2008). In their study 

of the sources and distribution of arsenic in the Eastern San 

Joaquin Groundwater Basin, arsenic concentrations were not 

uniformly distributed areally or with depth, but generally 

were low (less than the maximum contaminant level of 

10 μg/L) in the eastern part of the study area near recharge 

areas and were high (greater than 10 μg/L) near the margin 

of the Sacramento-San Joaquin Delta. Results reported by 

Izbicki and others (2008) indicate that arsenic concentrations 

generally increase with well depth (opposite of what was 

observed in this report), but that arsenic concentrations in 

samples from wells within the high-arsenic areas near the 

margin of the Delta commonly were greater than 10 μg/L 

regardless of well depth, and that arsenic in water from wells 

in the eastern part of the study area were not correlated with 

depth. In the study presented here, an inverse correlation 

with depth can be attributed to low arsenic concentrations 

in deep (> 700 ft) wells sampled in the southern part of the 

Tracy study area (fig. 17A). When arsenic concentrations are 

compared to well depths, excluding the results from the Tracy 

study area, there is no significant correlation. 

Previous investigations of arsenic in the San Joaquin 

Valley (Belitz and others, 2003; Welch and others, 2006; 

Izbicki and others, 2008) and literature reviews (Stollenwerk, 

2003; Welch and others, 2006) have attributed elevated arsenic 

in groundwater in the eastern San Joaquin Valley primarily to 

two mechanisms. One is the release of arsenic resulting from 

reductive dissolution of iron or manganese oxyhydroxides 

under iron- or manganese-reducing conditions. Dissolved 

arsenic also can increase from pH-dependent desorption 

of arsenic from aquifer sediments under oxic conditions. 

This tends to occur in groundwater with pH greater than 7.5 

(Stollenwerk, 2003). 

sac09-0345_fig 15

Low

Moderate

High

0.001

0.01

0.1

1

10

100

1,000

0.001

0.01

0.1

1

10

100

1,000

ARSENIC BARIUM BORON NITRATE 

PLUS 

NITRITE

VANADIUM STRONTIUM

RELATIVE-CONCENTRATION, DIMENSIONLESSRELATIVE-CONCENTRATION, DIMENSIONLESS

CHLORIDE

(upper)

IRON MANGANESE S ULFATE

(upper)

TOTAL DISSOLVED

SOLIDS

(upper)

A. B.

EXPLANATION

Relative-Concentration

High

Moderate

Low

Study Areas

Cosumnes

Eastern San Joaquin

Uplands

Tracy

(upper), upper water-quality 

benchmark where multiple 

benchmarks exist
38 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 16. Relative-concentrations of selected inorganic constituents for USGS-grid and USGS-understanding wells, and relativeconcentrations from the most recent inorganic constituent data point from the “prior period” (January 1, 2001–April 21, 2004) for CDPH 

wells, Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit. sac09-0345_fig 16a

Relative-Concentration

Water bodies

Swamp or marsh

Inundation area

Low

Moderate

High

Low

Moderate

High

EXPLANATION

Grid and Understanding 

 Wells

Boundary of Sacramento- CDPH Wells

San Joaquin Delta

Boundary of Northern 

San Joaquin Basin

Boundary of subbasin

of Northern San Joaquin

Basin Study areas

Cosumnes (COS)

Eastern San Joaquin (ESJ)

Uplands (NSJ-QPC)

Tracy (TRCY)

Geologic units

Quaternary alluvium (Q)

Quaternary/Plio-Pleistocene 

semi-consolidated (QPc)

Miocene nonmarine consolidated (Mc)

Eocene nonmarine consolidated (Ec)

0 5 10 20 MILES

0 10 20 5 KILOMETERS

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

COS

NSJ-QPC

TRCY ESJ

A. Arsenic

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

San Joaquin River San Joaquin River
Status and Understanding of Water Quality 39

Figure 16.—Continued

sac09-0345_fig 16b

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

COS

TRCY ESJ

NSJ-QPC

San Joaquin River 0 5 10 20 MILES

0 10 20 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

B. Boron
40 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 16.—Continued

sac09-0345_fig 16c

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

C. Nitrate

COS

TRCY ESJ

NSJ-QPC

San Joaquin River

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

Land use

Urban

Agricultural

Natural

EXPLANATION
Status and Understanding of Water Quality 41

Figure 16.—Continued

sac09-0345_fig 16d

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

D. Manganese

COS

TRCY ESJ

NSJ-QPC

San Joaquin River

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°
42 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 16.—Continued

sac09-0345_fig 16e

COS

TRCY ESJ

NSJ-QPC

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

0 5 10 20 MILES

0 9 18 4.5 KILOMETERS

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

E. Iron

San Joaquin River
Status and Understanding of Water Quality 43

 Figure 16.—Continued 

sac09-0345_fig 16f

0 5 10 20 MILES

0 10 20 5 KILOMETERS

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°

COS

TRCY ESJ

NSJ-QPC

F. Total dissolved solids

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

San Joaquin River
44    Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Elevated concentrations of arsenic in the Northern San 

Joaquin Basin study unit can be attributed to both processes 

discussed in the previous studies. Concentrations of arsenic 

generally increase, although not significantly, as pH increases 

(p=0.2, rho=0.17, table 11). Four of the five high relativeconcentrations of arsenic occur equal to or greater than a pH 

of 7.5 (fig. 17B). Reductive dissolution of arsenic also can be 

inferred in that for any given pH, arsenic concentrations are 

higher in anoxic wells than in oxic wells (fig. 17B).

Factors Affecting Boron

Concentrations of boron are highest in the westernmost 

parts of the Tracy study area (fig.16B). Boron concentrations 

were not significantly correlated with any of the explanatory 

factors explored in this study (table 11) and there was no 

significant differences in boron concentrations between waters 

with different age or redox categories (table 6).

Because high and moderate relative-concentrations 

of boron were limited to the western part of the study unit, 

elevated concentrations of boron relative to the rest of the 

study unit appear to be associated with sediments in the 

aquifer derived from marine deposits, which are naturally high 

in boron, and which are contained within the Coast Ranges 

on the western flank of the study unit. Saline waters, which 

also contain relatively high concentrations of boron (Hem, 

1992), and which once permeated these marine sediments 

have migrated into adjacent and overlying continental deposits 

(Page, 1986). Boron concentrations were significantly 

positively correlated with concentrations of TDS (p = <0.01, 

rho = 0.54).

Radioactive Constituents

Constituents in the radioactive category in this study were 

gross alpha and beta radioactivity, radium isotopes, radon222, and tritium. The only radioactive constituent detected at 

a high relative-concentration in any proportion of the primary 

aquifer was gross alpha radioactivity. Relative-concentrations 

of gross alpha radioactivity were high in 2.1 percent of the 

primary aquifer, as determined using the spatially weighted 

approach (table 9). Gross alpha radioactivity was not 

detected at high or moderate relative-concentrations using 

the grid‐based approach. “However, samples from a limited 

number of wells in the grid-based approach were analyzed 

for gross alpha radioactivity (27 wells), which results in 

a 90-percent upper confidence level of 4.8 percent for 

gross alpha radioactivity for the grid-based approach. In other 

words, the grid-based approach has a reasonable chance of 

Figure 17. Relation of arsenic to well depth and pH and redox condition, Northern San Joaquin Basin Groundwater Ambient 

Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 17ab

1,000

800

600

400

200

0

1,200

WELL DEPTH, IN FEET BELOW LAND SURFACE

pH, STANDARD UNITS

0 5 10 15 20 25 0 5 10 15 20 25

ARSENIC, IN MICROGRAMS PER LITER ARSENIC, IN MICROGRAMS PER LITER

5.5

6.0

6.5

7.0

7.5

8.0

8.5

A. Well depth B. pH and redox condition

CORRELATION COEFFICIENT (rho) = -0.346 CORRELATION COEFFICIENT (rho) = 0.178

Relative-Concentration

High

Moderate

Low

Study Areas Anoxic Oxic

Cosumnes

Eastern San Joaquin

Uplands

Tracy

Cosumnes

Eastern San Joaquin

Uplands

Tracy

Maximum contaminant level (MCL)

0.5 * MCL

EXPLANATION
Status and Understanding of Water Quality 45

failing to detect high relative-concentrations of gross alpha 

radioactivity if relative-concentrations were high in less 

than 4.8 percent of the primary aquifer. None of the other 

radioactive constituents were detected at moderate or high 

relative-concentrations. Activities of gross alpha radioactivity 

were negatively correlated to depths to top-of-perforation 

and well depth (table 11). Gross alpha radioactivity also was 

positively correlated with pH (table 11).

Nutrients

Nutrients measured in this study were ammonia, 

nitrite plus nitrate (hereinafter referred to as nitrate), 

nitrite, total nitrogen, and orthophosphate. Ammonia, 

nitrate, and nitrite are the only nutrients measured that have 

health-based benchmarks, and they are grouped together 

as the nutrient class in this report. As a class, nutrient 

relative-concentrations were high in the primary aquifer 

equivalent to the proportion of high relative-concentrations 

of nitrate. Relative-concentrations of nitrate were high 

in 2.1 percent of the primary aquifer computed using the 

spatially weighted approach (tables 9 and 10). The spatially 

weighted result for the high proportion of nitrate in the 

primary aquifer is presented because grid-based results 

for high relative‐concentrations of nitrate were zero. The 

aquifer proportion for high relative-concentrations of nitrate 

calculated using the spatially weighted approach were within 

the 90- percent confidence interval for the grid-based high 

aquifer proportion (table 9).The moderate proportion of 

nitrate relative-concentrations determined from the grid-based 

approach was 9.0 percent of the primary aquifer (tables 9 and 

10). 

Relative-concentrations of nitrate were high primarily 

in the central part of the Eastern San Joaquin and southern 

part of the Tracy study area (fig. 16C). Moderate relativeconcentrations were measured in the Eastern San Joaquin, 

Upland, and Tracy study areas (figure 15A and fig. 16C). The 

most significant source of nitrate to the environment is the 

application of fertilizers.

Factors Affecting Nitrate 

Nitrate concentrations were significantly different 

when comparing samples in oxic and anoxic geochemical 

conditions, with concentrations significantly higher in 

oxic samples (table 6, fig. 18). Nitrate concentrations 

were positively correlated to normalized lateral position 

(table 11). Nitrate concentrations were not significantly 

correlated with percentage of agricultural land use; however, 

nitrate concentrations were correlated to the more specific 

agricultural land-use category defined by orchard and vineyard 

land use (table 11).

Figure 18. Nitrate plus nitrite concentrations grouped by geochemical (redox) conditions, Northern San Joaquin 

Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit. The U.S. Environmental Protection 

Agency Maximum Contaminant Level for nitrate plus nitrite is 10 milligrams per liter.

n = 18 n = 26

Anoxic Oxic

REDOX CETOGORY

NITRATE AS NITROGEN,

IN MILLIGRAMS PER LITER

0

1

2

3

4

5

6

p < 0.001

30

EXPLANATION

Number of values

Value outside of 1.5 times the 

interquartile range

75th percentile

Median

25th percentile

Highest value within 1.5 times

the interquartile range
46 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Nitrate has been extensively studied in groundwater 

in the eastern San Joaquin Valley (for example, Dubrovsky 

and others, 1998; Burow and others, 2007). Results of 

previous investigations of nitrate in the Central Valley have 

shown positive correlations between nitrate concentrations 

in relatively shallow parts of the aquifer and percentage of 

agricultural land use (Burow and others, 1998a, 1998b, 2007). 

The results of this study do not show an inverse relation 

between nitrate and depth but do show a relation to a specific 

type of agricultural land use (percentage of orchard and 

vineyard land use); however, it is unclear if this is an artifact 

of well selection or a difference in the amount of nitrate 

applied to this specific land-use type. The lack of a relation 

between nitrate concentrations and shallow groundwater may 

result from the fact that in this study, the relation between well 

depth and nitrate was tested using only grid wells perforated 

at depths where young recharge affected by agricultural land 

use can be mixed with deep, older water unaffected by recent 

land‐use practices. This mixing with deeper water may be 

diluting the nitrate contributed to groundwater at shallow 

depths. The relation of nitrate to normalized lateral position 

and redox is due to the fact that nitrate is a redox-sensitive 

constituent that is removed from groundwater in a reducing 

environment. Reducing conditions exist more often toward 

the center of the basin, at the low end of normalized lateral 

position values.

Trace Elements and Major and Minor Ions 

Concentrations of some inorganic constituents can 

affect aesthetic properties of water, such as taste, color, 

and odor, and technical properties, such as scaling and 

staining. Although there are no adverse health effects 

associated with these properties, they may reduce consumer 

satisfaction with the water or have economic impacts. For 

some constituents, CDPH has established non-enforceable 

benchmarks (SMCL‐CAs) that are based on aesthetic or 

technical properties rather than on health-based concerns. For 

TDS and the major ions chloride and sulfate, CDPH defines 

a “recommended” and an “upper” SMCL-CA. In this report, 

the “upper” SMCL-CA benchmarks were used to compute 

relative-concentrations; a relative-concentration of 0.5 thus 

corresponds to a concentration equal to the “recommended” 

SMCL-CA for these constituents. In all these cases, the 

“upper” SMCL-CA is twice the “recommended” SMCL-CA. 

The minor elements manganese and iron also have SMCL-CA 

benchmarks and had maximum relative-concentrations greater 

than 0.5 (fig. 15).

Manganese relative-concentrations were high in 

34 percent and moderate in 5.4 percent of the primary aquifer 

(table 9). Wells with moderate or high relative‐concentrations 

of manganese primarily were located in the central 

and western parts of the study unit (fig. 16D). Iron 

relative‐concentrations were high in 11 percent of the primary 

aquifer (table 9). The distributions of wells with high and 

moderate relative-concentrations of manganese and iron were 

very similar (figs. 16D and 16E).

Chloride, sulfate, and TDS relative-concentrations 

were high in 3.9, 2.0, and 5.8 percent of the primary aquifer, 

respectively (table 9). Chloride, sulfate, and TDS relativeconcentrations were moderate in 3.9, 3.9, and 16 percent 

of the primary aquifer, respectively (table 9). Samples 

with high and moderate relative-concentrations of TDS 

were collected from wells primarily located in the Eastern 

San Joaquin and Tracy study areas (figs.15 and 16F). The 

dominant anion in most groundwater in the study unit was 

bicarbonate (appendix D), thus high and moderate relativeconcentrations of TDS need not necessarily be accompanied 

by high or moderate relative‐concentrations of chloride or 

sulfate given the abundance of bicarbonate. Of the samples 

with high TDS, chloride and (or) sulfate were high in 

33 percent and moderate in 53 percent of the samples. Of the 

samples with moderate TDS, chloride and sulfate relativeconcentrations were moderate in 10 percent of the samples 

Sulfate relative‐concentrations were high and moderate only 

in the Tracy study area; chloride relative-concentrations were 

high and moderate in the Eastern San Joaquin and Tracy study 

areas.

Factors Affecting Manganese and Iron

Manganese and iron concentrations were correlated with 

redox conditions, with higher concentrations of each in anoxic 

conditions than in oxic conditions (table 6, fig. 19). Like 

nitrate, manganese and iron are redox sensitive constituents. 

When concentrations of manganese or iron were compared 

to classifications of groundwater age or depths to top-ofperforation, neither was shown to be significantly different in 

terms of age or depth (table 6). Manganese and iron were not 

correlated to any of the explanatory factors (table 11).

Because manganese and iron are redox-sensitive 

constituents, geochemical conditions appear to be the primary 

control affecting manganese and iron concentrations in the 

Northern San Joaquin Basin study unit. Generally, oxic 

conditions can result in the precipitation of manganese as 

a mineral coating, or encrustation, on aquifer sediments. 

Conversely, under anoxic conditions, these mineral coatings 

tend to dissolve, releasing manganese back into the water 

(Stollenwerk, 2003). Similar reactions also are exhibited by 

arsenic and iron, which tend to co-precipitate with manganese 

under oxic conditions. As a result, there is a strong positive 

correlation (p <0.05) between concentrations of arsenic and 

manganese and between iron and manganese as determined 

using the Spearman’s rank correlation test (rho=0.476 

and rho=0.618, respectively). Manganese concentrations 

(as well as all other major ions and TDS) were highest in 

the Tracy study area (fig.15), which contains a large part 

of the Sacramento-San Joaquin Delta, where much of the 

groundwater is anoxic (fig. 12).
Status and Understanding of Water Quality 47

Figure 19. Manganese concentrations grouped by geochemical (redox) conditions, Northern San 

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

Factors Affecting Total Dissolved Solids

TDS was significantly positively correlated with pH 

and significantly negatively correlated with normalized 

lateral position from the valley trough (table 11). Within the 

three study areas east of the San Joaquin River (Cosumnes, 

Eastern San Joaquin, and the Uplands), concentrations of TDS 

increase from east to west across the study unit (figs. 16F

and 20A). This increase had been noted previously (Bertoldi 

and others, 1991) and is ascribed to a combination of 

low‐TDS water derived from Sierra Nevada recharge and the 

effect of regional flow toward the center of the valley. TDS 

concentrations generally increase with residence time and 

distance from point of infiltration by interaction between the 

groundwater and the aquifer sediments. This pattern explains 

the significant correlation between TDS and normalized lateral 

position in the study areas east of the San Joaquin River 

(p< 0.001) (fig. 20A).

Concentrations of TDS for wells in the Tracy study area 

are significantly elevated relative to concentrations for wells in 

the other study areas and also are correlated with normalized 

lateral position; however, concentrations of TDS increase 

toward the valley margin rather than toward the valley trough 

(fig. 20B). Sources of TDS in the Tracy study area primarily 

are associated with the proximity of the study area to the 

Sacramento-San Joaquin Delta, and regional groundwater‐flow 

patterns of the San Joaquin Valley. Historically, the 

Sacramento-San Joaquin Delta was periodically inundated 

by saline water, which saturated delta deposits and increased 

TDS concentrations (Piper and others, 1939). Additionally, 

the Coast Ranges are composed of easily weathered marine 

deposits, which results in an elevated TDS in recharge water 

on the western side of the valley.

sac09-0345_fig 19

n = 20 n = 32

Anoxic Oxic

REDOX CETOGORY

MANGANESE, IN MICROGRAMS PER LITER

p < 0.001

17,100

0

500

1,000

1,500

2,000

2,500

Manganese seconday maximum 

contaminant level (SMCL) 30

EXPLANATION

Number of values

Value outside of 1.5 times the 

interquartile range

75th percentile

Median

25th percentile

Highest value within 1.5 times

the interquartile range
48 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 20. Relation of total dissolved solids concentrations to 

normalized lateral position within the Cosumnes, Eastern San 

Joaquin, Upland, and Tracy study areas, Northern San Joaquin 

Basin Groundwater Ambient Monitoring and Assessment 

(GAMA) study unit.

Organic Constituents

The organic compounds are organized by constituent 

class, specifically, four classes of VOCs and one class 

consisting of pesticides. VOCs may be present in paints, 

solvents, fuels, refrigerants, can be by-products of water 

disinfection, and are characterized by their tendency 

to evaporate. In this report, VOCs are classified as 

trihalomethanes, solvents, other VOCs (such as gasoline 

additives), or fumigants. Pesticides, which include 

herbicides and insecticides, are used to control weeds, 

insects, or fungi in agricultural, urban, and suburban 

settings. 

Maximum relative-concentration, and the detection 

frequency at any concentration, were used as selection 

criteria for organic and special-interest constituents 

(fig. 21). Figure 21 shows organic and specialinterest constituents; the y-axis indicates maximum 

relative‐concentrations on a log scale. 

Maximum relative-concentrations were greater than 

0.1 or detection frequency was greater than 10 percent 

for five organic constituents that were sampled for in all 

grid wells (fig. 21). Detection frequencies and relativeconcentrations by study area for all grid wells with 

detections of any VOC (fig. 22) and pesticides (fig. 23) 

meeting the selection criteria indicate that nearly all 

detections are at low relative-concentrations and that 

individual constituent detection frequencies generally were 

less than 20 percent.

Concentrations of all organic constituents detected, 

with the exception of DBCP, in USGS-grid wells from 

the Northern San Joaquin Basin study unit were less 

than health-based benchmarks. One or more VOCs were 

detected in about 45 percent of the 51 USGS-grid wells 

sampled.

Of the 63 pesticides and pesticide degradates 

sampled at all grid wells, 10 were detected. Of these 10, 

6 were parent pesticide compounds with benchmarks, 

1 was a parent compound without a benchmark, and 3 

were degradates (2 of which were degradates of parent 

compounds with benchmarks) not having a benchmark. 

The constituents that were not detected and the wells 

sampled for additional pesticides and pesticide degradates 

are listed in Bennett and others (2006). 

For all organic constituents combined, 

relative‐concentration of at least one organic constituent 

(table 10) was 2.7 percent of the primary aquifer. 

This proportion is based exclusively on high relativeconcentrations of the fumigant DBCP (2.7 percent). The 

proportion of the primary aquifer with moderate relativeconcentrations of organic constituents was 6.9 percent.

sac09-0345_fig 20

0.0 0.2 0.4 0.6 0.8 1.0

100

0

200

300

400

TOTAL DISSOLVED SOLIDS, IN MILLIGRAMS PER LITER

500

1,000

0

2,000

3,000

4,000

5,000

Valley

trough

Valley

margin

20,210

p = 0.027

p < 0.001

NORMALIZED LATERAL POSITION, DIMENSIONLESS

0.0 0.2 0.4 0.6 0.8 1.0

NORMALIZED LATERAL POSITION, DIMENSIONLESS

EXPLANATION

Relative-Concentration

High

Moderate

Low

Study Areas

Cosumnes

Eastern San Joaquin

Uplands

Tracy

B. Tracy study area

A. Cosumnes, Eastern San Joaquin, and Uplands study areas
Status and Understanding of Water Quality 49

Figure 21. Detection frequency and maximum relative-concentration for organic and special-interest constituents 

detected in grid wells, Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study 

unit.

sac09-0345_fig 21

0 5 10 15 20 25

DETECTION FREQUENCY AT ANY CONCENTRATION, IN PERCENT

1e-005

0.0001

0.001

0.01

0.1

1

10

MAXIMUM RELATIVE-CONCENTRATIONS, DIMENSIONLESS

DBCP

PCE

Atrazine

TCE

1,2-DCP

MTBE

Benzene

Toluene

CFC-11

Metolachlor

CFC-12

Bromoform

Dibromochloromethane

Carbon disulfide

cis-1,2-Dichloroethene

1,1-Dichloroethene

1,1-Dichloroethane

Perchlorate

o-Xylene

Tebuthiuron

m- and p-Xylene

Ethylbenzene Styrene

Hexazinone

Trifluralin

Bromodichloromethane

1,2,4-Trimethylbenzene

Chloroform

Simazine

Moderate

Low

Moderate

High

TCE, trichloroethene; PCE, tetrachloroethene; DBCP, 1,2-dibromo-3-chloropropane 1,2-DCP, 1,2-dichloropropane; 

CFC-11, trichlorofluoromethane; CFC-12, dichlorodifluoromethane; MTBE, methyl tert-butyl ether.

Relative-Concentration

High

Moderate

Low

EXPLANATION

Abbreviations

Although most relative-concentrations of organic 

constituents were low or moderate, detection of these 

constituents can serve as an indication of groundwater 

affected by young recharge and recent land-use activities. 

Their relation to potential explanatory factors might assist 

in identifying factors affecting those constituents with high 

relative-concentrations.
50 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 22. Relative-concentrations and detection frequencies of selected VOC 

and special-interest constituents in grid wells, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 22

0.0001

0.001

0.01

0.1

1

10

RELATIVE-CONCENTRATION, DIMENSIONLESS

C hloroform T T H M* P C E M T B E P ERCHLORATE

C hloroform T T H M* P C E M T B E P ERCHLORATE

0

20

40

60

80

DETECTION FREQUENCY, IN PERCENT

MTBE, methyl tert-butyl ether; PCE, tetrachloroethene; TTHM, total trihalomethanes

*Chloroform is the most frequently detected trihalomethane in the Northern San

Joaquin study unit and is included in the TTHM category included on these plots.

100

EXPLANATION

Study Areas

Cosumnes

Eastern San Joaquin

Uplands

Tracy

Relative-Concentration

High

Moderate

Low

Moderate

Low

Moderate

High
Status and Understanding of Water Quality 51

Figure 23. Relative-concentrations and detection frequencies of selected 

fumigant and pesticide constituents in grid wells, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

sac09-0345_fig 23 RELATIVE-CONCENTRATION, DIMENSIONLESS

DBCP SIMAZINE

0

20

40

60

80

DETECTION FREQUENCY, IN PERCENT

100

EXPLANATION

Study Areas

Cosumnes

Eastern San Joaquin

Uplands*

Tracy*

Relative-Concentration

High

Moderate

Low

DBCP SIMAZINE

0.001

0.01

0.1

1

10

* Study area did not have a detection of DBCP or simazine

Moderate

Low

Moderate

High
52 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Trihalomethanes

An MCL-US has been established for total 

trihalomethanes (TTHM), which applies to the sum of 

the four trihalomethane (THM) compounds—chloroform, 

bromodichloromethane, dibromochloromethane, and 

bromoform. Chloroform, however, was the only THM 

detected in more than 10 percent of the wells sampled, and 

was detected in 9 of the 51 grid wells. The other three THM 

compounds were detected in one of those nine wells (an 

Eastern San Joaquin grid well), and bromoform was detected 

in the absence of the other THMs in another Eastern San 

Joaquin grid well (Bennett and others, 2006). Total THM 

(TTHM) concentration, however, rather than the concentration 

of chloroform alone, was used in the statistical tests for the 

understanding assessment. Non-detections of TTHMs were set 

to zero for the summation of TTHM concentrations. TTHMs 

were detected in 20 percent of the primary aquifer (fig. 22); 

there were no high or moderate relative-concentrations. 

TTHMs were more prevalent in the Eastern San Joaquin study 

area [detection frequency 42 percent (8 of 19 wells)] than 

in the other study areas (detection frequencies ≤ 10 percent) 

(fig. 24A; Bennett and others, 2006). Chloroform was the 

VOC most frequently detected (just greater than 20 percent) in 

U.S. aquifers sampled for national assessments by the USGS 

National Water Quality Assessment (NAWQA) Program 

(Zogorski and others, 2006). 

Primary aquifer proportion for TTHMs as a class was 

equivalent to that of chloroform (tables 9 and 10). TTHMs 

were not detected in high relative-concentrations in wells 

from the CDPH database based on the most recent CDPH data 

(January 1, 2001–April 21, 2004).

Factors Affecting Trihalomethanes

TTHM concentrations were significantly correlated with 

groundwater age, land use, normalized lateral position, and 

well depth (tables 6 and 11). TTHM concentrations in wells 

with groundwater classified as young age based on tritium 

concentrations were significantly higher and frequency of 

detection was greater than those wells with groundwater 

classified as old (table 6, fig. 25). TTHM concentrations were 

positively correlated with well depth (rho = 0.263) and amount 

of urban land use (rho = 0.601) (table 11), and negatively 

correlated with the amount of agricultural and natural land 

use. Nationally, TTHM concentrations have been reported 

to be correlated with percentage of urban land use (Zogorski 

and others, 2006). Potential urban sources of TTHMs include 

recharge from landscape irrigation with disinfected water, 

leakage from distribution or sewer systems, and industrial 

and commercial sources (Ivahnenko and Barbash, 2004). The 

negative correlation between TTHMs and normalized lateral 

position likely is a spurious correlation resulting from the 

negative correlation between urban land use and normalized 

lateral position (table 6). The most urbanized area in the 

study unit, the city of Stockton, is located on the east bank 

of the San Joaquin River in the center of the valley (fig. 6). 

Of the 15 wells with detections of TTH-Ms, depths to topof‐perforation were less than 200 ft for 11 of the 15 wells 

(fig. 26). Of the 11 wells with detections of TTHM with depths 

to top-of-perforation less than 200 ft, 7 wells (64 percent) 

were surrounded by greater than 60 percent urban land use.

Solvents

Chlorinated solvents have various industrial, commercial, 

and domestic uses, including as drain and pipe cleaners, 

dry cleaning fluids, oven cleaners, shoe polish, household 

degreasers, deodorizers, leather dyes, photographic supplies, 

tar remover, waxes, and pesticides (Zogorski and others, 

2006). Although chloroform can be used as a solvent, it was 

not counted in this class because it was already considered 

as a TTHM. Because many of the factors associated with the 

occurrences of individual chlorinated solvents were similar 

in the national NAWQA groundwater studies (Zogorski and 

others, 2006), solvents were considered as a class rather than 

as individual constituents for the understanding assessment

presented here. Total solvent concentration was defined as the 

sum of the concentrations of all detected chlorinated solvents, 

with nondetections assumed to have a concentration of zero. 

As a class, concentrations of solvents were high in 0.1 percent 

and moderate in 1.8 percent of the primary aquifer (table 10). 

Grid cells were assigned a high or moderate classification 

based on individual constituents within the class with high 

or moderate relative-concentrations. PCE was the primary 

constituent accounting for the high and moderate relativeconcentrations of solvents (table 9). Most of the other solvents 

detected in the grid wells or reported in the CDPH database 

were compounds that can be degradation products of PCE 

[trichloroethene (TCE), dichloroethene (DCE), and vinyl 

chloride). 

PCE was detected in 13 percent of the primary aquifer, 

and all but one of the detections were at low relativeconcentrations (fig. 22). As with THMs, the detection 

frequency of PCE was highest in the Eastern San Joaquin 

study area (26 percent; fig. 24B; Bennett and others, 2006). 

The detection frequency in the Upland study area was 18 

percent and PCE was not detected in the Cosumnes and 

Tracy study areas (fig. 24B). All wells with detections of 

PCE or other solvents reported in the CDPH database were 

in the Eastern San Joaquin study area (fig. 24B). Nationally, 

PCE was the solvent most frequently detected in aquifers, 

on the basis of assessments by the USGS NAWQA Program 

(Zogorski and others, 2006). 
Status and Understanding of Water Quality 53

Figure 24. Relative-concentrations of selected organic constituents for USGS-grid and USGS-understanding wells 

representative of the primary aquifer and from the most recent analysis (January 1, 2001–April 21, 2004) for CDPH wells, Northern 

San Joaquin Groundwater Ambient Monitoring and Assessment (GAMA) study unit. sac09-0345_fig 24a

COS

TRCY ESJ

NSJ-QPC

A. Sum of total trihalomethanes (TTHMs)

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

San Joaquin River

Water bodies

Swamp or marsh

Inundation area

EXPLANATION

Relative-Concentration

Not detected

Low

Moderate

High

Not detected

Low

Moderate

High

Land use

Urban

Agricultural

Natural

Orchards and vineyards

Grid and Understanding 

 Wells

Boundary of Sacramento- CDPH Wells

San Joaquin Delta

Boundary of Northern 

San Joaquin Basin

Boundary of subbasin of

Norhern San Joaquin Basin

Study areas

Cosumnes (COS)

Eastern San Joaquin (ESJ)

Uplands (NSJ-QPC)

Tracy (TRCY)

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°
54 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 24.—Continued

sac09-0345_fig 24b

COS

TRCY ESJ

NSJ-QPC

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

B. Sum of solvents

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°
Status and Understanding of Water Quality 55

Figure 24.—Continued

sac09-0345_fig 24c

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

C. DBCP

COS

TRCY ESJ

NSJ-QPC

San Joaquin River

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°
56 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Figure 24.—Continued

sac09-0345_fig 24d

0 5 10 20 MILES

0 10 20 5 KILOMETERS

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

COS

TRCY ESJ

NSJ-QPC

D. Sum of pesticides

San Joaquin River

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°
Status and Understanding of Water Quality 57

Figure 25. Pesticide, solvent, total 

trihalomethane (TTHM), and fumigant 

detection frequencies grouped by 

groundwater-age classification, Northern 

San Joaquin Basin Groundwater Ambient 

Monitoring and Assessment (GAMA) 

study unit.

Figure 26. Relation between 

detections of total trihalomethanes 

(TTHMs) and depth to top-ofperforation and percentage of urban 

land use in 500-m buffer areas 

surrounding wells for USGS-grid and 

USGS-understanding well samples, 

Northern San Joaquin Basin 

Groundwater Ambient Monitoring 

and Assessment (GAMA) study unit.

0345_fig 25

PESTICIDES SOLVENTS TTHM FUMIGANTS

0

20

40

60

80

DETECTION FREQUENCY, IN PERCENT

100

Old

Young

0 20 40 60 80 100

URBAN LAND USE, IN PERCENT

600

400

200

DEPTH TO TOP-OF-PERFORATION, IN FEET

800

0

Detected

Not detected

TTHMs
58 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Factors Affecting Solvents

Solvent concentrations in groundwater samples classified 

as young were significantly higher than concentrations in 

groundwater samples classified as old, and the frequency 

of solvent detections was greater in samples classified as 

young than those classified as old (table 6, fig. 25). Solvent 

concentrations were positively correlated with urban land 

use and negatively correlated with agricultural land use 

(table 11, fig. 24B). Although solvent concentrations were 

not significantly correlated with either well depth or depth 

to top-of-perforation, of the eight wells with detections only 

one had a depth to the top-of-perforations of greater than 

200 ft. (fig. 27). Six of the remaining seven wells with solvent 

detections were surrounded by greater than 60 percent urban 

land use. These correlations suggest that occurrences of 

solvents and THMs in the primary aquifer of the Northern 

San Joaquin Basin study unit are related to the presence of 

young groundwater recharging beneath urban areas, and that 

this young groundwater has infiltrated to depths of as much as 

200ft below land surface.

Other VOCs

VOCs that can not be classified as a THM, solvent, or 

fumigant in this study were placed in the “other VOC” class. 

Only one constituent was placed in this class, methyl tert-butyl 

ether (MTBE), a gasoline oxygenate used to raise the octane 

number and promote more complete fuel combustion. MTBE 

was detected at moderate relative-concentrations in 2.1 percent 

of the primary aquifer (table 9). In a national assessment of 

VOCs in groundwater in the U.S., MTBE was the second most 

frequently detected VOC (at a 0.2 μg/L assessment level) in 

samples from domestic and drinking-water wells (Zogorski 

and others, 2006). Because MTBE was not detected at high 

relative-concentrations or in greater than 10 percent of the 

wells sampled, no statistical tests were done in relation to the 

explanatory factors discussed in this report.

Fumigants

Fumigants are gaseous forms of pesticides applied to 

soils, grain, produce, and structures for the purpose of pest 

control. As a form of pesticide, fumigants are grouped with 

pesticides on some graphs presented in this report. Those 

VOCs primarily are used as fumigants to control pests in 

agriculture and in households and synthesis by-products 

included in fumigant mixtures were grouped into their own 

constituent class for the purpose of this report (fumigants). 

The classification of these constituents as fumigants was 

initially determined by the USGS National Water Quality 

Assessment Program (Zogorski and others, 2006). 

One fumigant, DBCP, was detected at high 

relative‐concentrations in the primary aquifer (figs. 21 and 

23). DBCP relative-concentrations were high in 2.7 percent 

of the primary aquifer; the highest proportion of any organic 

constituent (table 9). DBCP relative-concentrations were 

high and moderate only in the Cosumnes and Eastern San 

Figure 27. Relation between 

detections of solvents and 

depth to top-of-perforation and 

percentage of urban land use in 

500-m buffer areas surrounding 

wells for USGS-grid and 

USGS-understanding well samples, 

Northern San Joaquin Basin 

Groundwater Ambient Monitoring 

and Assessment (GAMA) study 

unit. 

0 20 40 60 80 100

URBAN LAND USE, IN PERCENT

600

400

200

DEPTH TO TOP-OF-PERFORATION, IN FEET

800

0

Detected

Not detected

Solvent
Status and Understanding of Water Quality 59

Joaquin study areas (fig. 24C). DBCP was the only fumigant 

detected in grid wells; therefore, the fumigant class in this 

study is equivalent to relative-concentrations and detections 

of DBCP. DBCP was used as a soil fumigant to control 

nematodes, primarily on orchards and vineyards but also on 

some row crops, between about 1955 and 1977 in California 

(Peoples and others, 1980; Domagalski, 1997). Use of 

DBCP was discontinued by the California Department of 

Food and Agriculture in 1977 because of its detection in 

groundwater and its toxicity (U.S. Environmental Protection 

Agency, 2000). Additional fumigants—1,2-dibromoethane, 

1,2-dichloropropane (1,2-DCP), and 1,4-dichlorobenzene—

were detected using the spatially weighted approach but 

were not detected using the grid-based approach. Relativeconcentrations of 1,2-dibromoethanwere high in less than 

0.1 percent of the primary aquifer; and relative-concentrations 

of 1,2-dichloropropane (1,2-DCP) and 1,4-dichlorobenzene 

were moderate in 0.1 and 0.6 percent of the primary aquifer, 

respectively (table 9). One fumigant, 1,2-dibromoethane 

(EDB), was detected at a high concentration in one of 

the understanding wells and thus is not represented in the 

grid‐based assessment. 

Factors Affecting Fumigants 

Fumigant concentrations (equivalent to concentrations 

of DBCP) were (1) significantly negatively correlated with 

depths to top-of-perforation (table 11), (2) significantly lower 

in those water samples categorized as old as compared to 

those categorized as young (table 6), and (3) significantly 

positively correlated with percentage of orchard/vineyard 

land use (table 11). The fumigant DBCP was the most 

frequently detected fumigant or pesticide in groundwater 

samples collected from the San Joaquin Valley during 

1971–88 (Domagalski, 1997) and in California as a whole 

up to 1999 (Troiano and others, 2001). Detection frequencies 

of DBCP in groundwater in the Central Valley have been 

higher than in most other parts of the country because of 

historical use of this compound as an agricultural fumigant 

on orchards and vineyards (Dubrovsky and others, 1998; 

Zogorski and others, 2006). As with solvents and TTHMs, 

fumigants in this study were most frequently detected in 

younger groundwater (fig. 25). Fumigants were detected at 

depths to top-of-perforation of 200 ft or less (fig. 28). DBCPs 

physical and chemical properties (low vapor pressure and 

moderate solubility) contribute to its ability to be transported 

into the aquifer (Burlinson and others, 1982). Ideal conditions 

for DBCP transport into groundwater exist in the eastern 

San Joaquin Valley. Tests on DBCPs ability to be retained 

or sorbed to aquifer sediments or biologically transformed 

indicate that in conditions with relatively low organic content 

in soils, DBCP is only weakly sorbed, and in well-oxygenated 

groundwater, DBCP is resistant to biological transformation 

(Bloom and Alexander, 1990; Deeley and others, 1991; Burow 

and others, 1999). Organic content in aquifer materials in the 

eastern study areas (Cosumnes, East San Joaquin, and Upland) 

are minor in comparison to the Sacramento-San Joaquin Delta 

and redox conditions in these study areas are predominantly 

oxic (fig. 12). 

Figure 28. Relation between 

detections of fumigants and depth to 

top-of-perforation and percentage 

of agricultural land use in 500-meter 

buffer areas surrounding wells for 

USGS-grid and USGS-understanding 

well samples, Northern San Joaquin 

Basin Groundwater Ambient 

Monitoring and Assessment (GAMA) 

study unit.

Detected

Not detected

Fumigant

0 20 40 60 80 100

AGRICULTURAL LAND USE, IN PERCENT

600

400

200

DEPTH TO TOP-OF-PERFORATION, IN FEET

800

0
60 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Pesticides

All detections of pesticides in samples from the 

Northern San Joaquin Basin study unit were at low 

relative‐concentrations (figs. 14, 21, and 23). At least one 

pesticide was detected in water from 22 percent of grid wells 

sampled (11 of 50). The maximum relative-concentration 

(0.08) for any pesticide was for atrazine, and the only 

pesticide detected in more than 10 percent of the grid wells 

was simazine. The study-unit detection frequency for simazine 

was 16 percent, but the detections were not evenly distributed 

geographically; the detection frequencies in the four study 

areas were 32 percent in East San Joaquin, 20 percent in 

Cosumnes, and 0 percent in Tracy and Upland.

Simazine is most commonly applied to vineyards and 

orchards in the study unit, but it also is used on roadways 

for weed control (Domagalski and Dubrovsky, 1991). 

Simazine was the triazine pesticide most frequently detected 

in groundwater collected in a study by the California 

Department of Pesticide Regulation summarizing the 

presence of pesticides in California as of December 1999 

(693 of 10,403 wells) (Troiano and others, 2001) and in 

183 wells in the San Joaquin Valley in 1985–87 (Domagalski 

and Dubrovsky, 1991). Simazine also was among the most 

frequently detected pesticides or pesticide degradates in 

groundwater in major aquifers across the United States 

(Gilliom and others, 2006).

Detections of individual pesticides were grouped together 

and are discussed here as a class of pesticide constituents. 

Pesticides were not detected at moderate or high relativeconcentrations in the grid wells (table 10). Similarly, there 

were no current high relative-concentrations of pesticides 

in the CDPH database. For the understanding assessment, 

pesticide concentrations were summed with nondetections 

treated as zeros. In addition to simazine, five other pesticides 

with health-based benchmarks, and three degradates of 

pesticides with health-based benchmarks were detected in 

grid wells—atrazine, deethylatrazine (a degradate of atrazine), 

3,4-dichloroanaline (a degradate of diuron), 2,6-diethylaniline 

(a degradate of alachlor), hexazinone, metolachlor, 

tebuthiuron, and trifluralin. All detections were at low relativeconcentrations, and only deethylatrazine was detected in more 

than 10 percent of the grid wells (Bennett and others, 2006). 

The concentrations of these nine pesticides were summed 

and this summation was used for the statistical tests on the 

pesticide class. The distribution of pesticide detections was 

similar to that of simazine detections; most of the detections 

were in the Eastern San Joaquin study area (fig. 24D).

Factors Affecting Pesticides 

Pesticide concentrations were significantly related to 

groundwater age, depth to top-of-perforation, and normalized 

lateral position (tables 6 and 11). Groundwater classified 

as young had a significantly higher detection frequency of 

pesticides as a class (fig. 25) and contained significantly 

higher pesticide concentrations than groundwater classified as 

old (fig. 25, table 6). With the exception of one understanding 

well screened at 337 ft below land surface, all pesticides were 

detected in wells with a depth to top-of-perforation of less 

than 200 ft (fig. 29; table 11). As with the solvents, TTHMs, 

and fumigants, the correlation with age and depth suggests 

that pesticides in the primary aquifer of the Northern San 

Joaquin Basin study unit are related to young groundwater 

and that this young groundwater has infiltrated to depths 

of as much as 200 ft below land surface (fig. 29). Pesticide 

concentrations were negatively correlated to normalized 

lateral position, indicating increasing pesticide concentration 

with decreasing distance to the center of the basin. Most 

pesticides were detected in wells along the western side of 

the East San Joaquin study area (fig. 24D; table 11). Pesticide 

concentrations were not correlated with any of the land‐use 

categories. This might be because simazine and some of 

the other pesticides detected have both agricultural and 

non-agricultural uses (Gilliom and others, 2006). Pesticides 

commonly are used on rights-of-way and in landscaping, and 

thus may be associated with urban land use. 

Special-Interest Constituents

Special-interest constituents measured in the Northern 

San Joaquin Basin study unit were N-nitrosodimethylamine 

(NDMA), 1,2,3-trichloropropane (1,2,3-TCP), and 

perchlorate. These constituents were selected for analysis 

because they have been detected recently in, or are 

considered to have the potential to reach, water resources 

used for drinking-water supply (California Department of 

Public Health, 2009a, 2009b, 2010). NDMA was found in 

California drinking-water wells in 1998, and is a by-product 

of rocket fuel production and is used in a number of industrial 

processes. Additionally, it has been shown to be a by-product 

of drinking‐water disinfection. Small concentrations of 

NDMA have been shown to be harmful to humans; therefore. 

it has an established benchmark notification level of 10 ng/L, 

which is equal to 0.010 μg/L (California Department of Public 

Health, 2009a). In 2001, California required the monitoring 

of 1,2,3-TCP in groundwater to gather information about its 

presence in drinking-water sources. 1,2,3-TCP has a number 

of uses, including as a paint remover, cleaning agent, solvent, 

chemical intermediate, and pesticide (California Department 

of Public Health, 2009b). Perchlorate became a regulated 

constituent in drinking water in California in October 2007. 

Perchlorate has been shown to interfere with the thyroid gland, 

which can affect pre- and post-natal development and mental 

development in adults (California Department of Public 

Health, 2010). Perchlorate is most commonly associated with 

the production of rockets, missiles, and fireworks, but it also 

has natural sources, including fertilizer from Chile (Urbansky 

and others, 2001; Dasgupta and others, 2006). 
Summary 61

NDMA and 1,2,3-TCP were not detected in any of the 

21 grid wells sampled. Perchlorate was detected in one well in 

the Northern San Joaquin Basin study unit at a concentration 

of 1 μg/L (Bennett and others, 2006).Perchlorate was detected 

in the Eastern San Joaquin study area and the relativeconcentration was 0.16 (fig. 22). Perchlorate was detected 

at a high concentration in water from a well in the current 

CDPH database. The perchlorate detection in this well was a 

high relative-concentration within the prior period (January 1, 

2001–April 21, 2004); however, the most recent (post-priorperiod) sample from that well was a nondetection (table 9). 

Because perchlorate was not detected at a high concentration 

or in greater than 10 percent of the sampled wells, no 

statistical analysis was done. 

Summary 

Groundwater quality in the 2,079-square mile Northern 

San Joaquin Basin (Northern San Joaquin) study unit was 

investigated as part of the Priority Basin Project of the 

Groundwater Ambient Monitoring and Assessment (GAMA) 

Program. Samples were collected during January 2004 through 

February 2005 from 61 wells.

The USGS-GAMA Priority Basin Project was designed 

to provide a statistically robust characterization of the quality 

of untreated (raw) groundwater in the Northern San Joaquin 

Basin study unit. Fifty-one grid wells were randomly selected 

by the USGS within spatially distributed grid cells, with no 

more than one well per cell. Samples were collected from 

these USGS-grid wells and from 10 additional wells for 

analysis of 175 to 306 constituents. Additional data were 

selected from the CDPH database for the most recent 3-year 

period available at the time of analysis (January 1, 2001–

April 21, 2004) to supplement data for USGS-grid wells, as 

inorganic constituents were not measured by the USGS in 

some grid cells (CDPH-grid wells). Raw detection frequencies 

were calculated using all available CDPH and GAMA data; 

these frequencies are provided for comparison, but potentially 

are spatially biased because the wells are not uniformly 

distributed geographically. Unbiased, grid-based and spatially 

weighted approaches were used to determine aquifer-scale 

proportions of constituents measured at high, moderate, and 

low relative-concentrations in the primary aquifer.

Relative-concentrations were used as the primary metric 

for comparing concentrations of constituents. Constituents 

with high or moderate relative-concentrations or with 

detection frequencies greater than 10 percent were selected 

to focus the understanding assessment on those constituents 

that have the greatest effect on water quality. The relativeconcentration threshold for classifying a constituent as 

moderate was 0.5 for inorganic constituents and 0.1 for 

organic constituents. The lower threshold value for organic 

constituents was selected because these constituents are 

anthropogenic in origin, generally less prevalent, and have 

lower relative-concentrations than naturally occurring 

inorganic constituents.

Figure 29. Relation between 

detection of pesticides and 

depth to top-of-perforation and 

percentage of agricultural land use 

in 500-m buffer areas surrounding 

wells for USGS-grid and 

USGS-understanding well samples, 

Northern San Joaquin Basin 

Groundwater Ambient Monitoring 

and Assessment (GAMA) study unit.

9-0345_fig 29

600

400

200

DEPTH TO TOP-OF-PERFORATION, IN FEET

800

0

0 20 40 60 80 100

AGRICULTURAL LAND USE, IN PERCENT

Detected

Not detected

Pesticide
62 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Of the inorganic constituents with a health-based 

benchmark, relative-concentrations of at least one constituent 

were high for 13 percent, moderate for 29 percent, and low for 

58 percent of the primary aquifer. High relative-concentrations 

of inorganic constituents with health-based benchmarks 

primarily resulted from high relative-concentrations of trace 

elements (13 percent of the primary aquifer), and to a lesser 

extent from high relative-concentrations of radioactive 

constituents (2.1 percent of the primary aquifer) and nutrients 

(2.1 percent of the primary aquifer). Inorganic constituents 

with high relative-concentrations for the grid wells were 

arsenic (9.4 percent of the primary aquifer) and boron 

(7.6 percent of the primary aquifer). Inorganic constituents 

with moderate relative-concentrations for the grid wells 

were arsenic (19 percent of the primary aquifer), barium 

(2.1 percent of the primary aquifer), boron (9.4 percent of the 

primary aquifer), nitrate (9.0 percent of the primary aquifer), 

strontium (6.4 percent of the primary aquifer), and vanadium 

(16 percent of the primary aquifer). Spatially weighted 

aquifer-scale proportions of constituents with high relativeconcentrations always fell within the 90-percent confidence 

intervals of the grid-based aquifer-scale proportions for 

constituents with high relative-concentrations, indicating that 

the grid-based approach yielded results that were statistically 

equivalent to those obtained using the spatially weighted 

approach and incorporating CDPH data.

Of the inorganic constituents with a non-healthbased benchmark, relative-concentrations of at least one 

constituent were high for 36 percent of the primary aquifer, 

moderate for 31 percent, and low for 33 percent. High 

relative‐concentrations of inorganic constituents with 

non‐health-based benchmarks primarily resulted from high 

relative-concentrations of manganese (34 percent of the 

primary aquifer), and to a lesser extent iron (11 percent 

of the primary aquifer), TDS (5.8 percent of the primary 

aquifer), chloride (3.9 percent of the primary aquifer), and 

sulfate (2.0 percent of the primary aquifer). Twenty-two of 

the 75 measured VOCs (not including fumigants) in grid 

wells were detected. All VOCs detected in grid-well samples 

from the Northern San Joaquin Basin study unit were at 

concentrations less than health-based benchmarks. The 

maximum relative-concentrations of 20 of 22 (91 percent) 

detected VOCs were low. The detected VOCs at moderate 

relative-concentrations in grid wells were MTBE and PCE. 

When data from the most recent analysis in the CDPH 

database during the current period were included in the 

analysis, relative-concentrations of two VOCs – PCE and 

vinyl chloride – were high in small proportions of the primary 

aquifer (0.1 percent each). Detection frequencies were less 

than 10 percent for 20 of 22 VOCs (91 percent). Chloroform 

and PCE were the two VOCs with detection frequencies 

greater than 10 percent in the grid wells.

Relative-concentrations for the soil fumigant DBCP 

were high in 2.7 percent of the primary aquifer; the highest 

primary-aquifer proportion of any organic constituent, even 

though its use was discontinued in California in 1977. DBCP 

relative-concentrations were moderate in another 2.7 percent 

of the primary aquifer. DBCP remains one of the most 

frequently detected fumigants in groundwater due to its use as 

a soil fumigant on orchards and vineyards throughout the San 

Joaquin Valley up until the late 1970s, and due to its physical 

and chemical properties (low vapor pressure and moderate 

solubility) that contribute to its persistence in groundwater.

Of the 63 pesticide and pesticide degradates analyzed 

for all grid wells, nine (18 percent) were detected. Of the 

nine pesticides and pesticide degradates detected, six were 

parent compounds with benchmarks and three were degradates 

without a benchmark. Relative-concentrations for all six of 

the parent compounds with benchmarks were low. Only the 

pesticide simazine and pesticide degradate deethylatrazine 

were detected in more than 10 percent of the grid wells.

One special-interest constituent, perchlorate was 

detected at a moderate relative-concentration in one grid well. 

Perchlorate was not detected at high relative-concentrations in 

any wells. 

Relations between water quality and five explanatory 

factors (land use, depth, normalized lateral position, 

groundwater age, and geochemical conditions) were evaluated 

statistically. Land use represented by the wells (defined as 

the percentages of urban, agricultural, and natural land use 

within a 500-meter buffer surrounding the well) was more 

urban than was land use for the study unit as a whole, likely 

a result of the tendency to locate drinking-water wells near 

population centers. Well depths ranged from 83 to 930 feet 

with a median depth of 360 feet. Normalized lateral position, 

a measure of the location of a well with respect to distance 

from the valley trough and normalized to the distance between 

the valley margin and the trough, was determined for all 

wells. Grid wells were distributed across the entire range of 

normalized lateral positions. Groundwater ages were classified 

as young or old based on 3H (tritium) concentrations. Thirtysix of 61 wells were classified as young and 25 were classified 

as old. Redox conditions were assigned based on relativeconcentrations of redox-sensitive constituents for the wells 

sampled that had redox information; groundwater in the 

Northern San Joaquin Basin study unit primarily was oxic (33 

of 61 wells; 54 percent). 

Groundwater age, normalized lateral position, and redox 

conditions were the most significant explanatory factors 

related to inorganic constituent concentrations. Groundwater 

age was shown to be associated with concentrations of 

arsenic, gross alpha radioactivity, and total dissolved solid 

(TDS). High and moderate relative-concentrations of 

arsenic, iron, and manganese primarily were associated with 
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geochemical conditions. Arsenic relative-concentrations 

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most commonly associated with anoxic waters. Normalized 

lateral position was shown to be associated with arsenic, 

nitrate, and TDS concentrations. High and moderate 

relative‐concentrations of arsenic and total dissolved solids 

were more often closer to the basin center (valley trough), 

whereas concentrations of nitrate tended to decrease towards 

the basin center.

Groundwater age, well depth, and land use within 

500 meters of each sampled well were the most significant 

factors affecting organic constituent concentrations. 

Trihalomethanes, fumigants, pesticides, and solvents 

were all shown to have higher concentrations in young 

groundwater than in old groundwater. Fumigant and pesticide 

concentrations were related to well perforation depth, with 

shallower perforation depths having higher constituent 

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well depths, and urban land use are commonly related to the 

occurrence of organic constituents in the Northern San Joaquin 

Basin study unit. 

Acknowledgments

The authors thank the following cooperators for their 

support: the California State Water Resources Control 

Board, California Department of Public Health, California 

Department of Water Resources, and Lawrence Livermore 

National Laboratory. We especially thank the well owners and 

water purveyors for their generosity in allowing the USGS to 

collect samples from their wells. Funding for this work was 

provided by State bonds authorized by Proposition 50 and 

administered by the SWRCB. 

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68 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

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Appendix A 69

Appendix A. Selection of CDPH Well Data

For the Northern San Joaquin Basin study unit, the 

historical CDPH database (January 10, 1984–April 21, 2004) 

contains more than 300,000 records containing information 

from more than 900 wells, requiring targeted retrievals 

to manageably use the data to assess water quality. The 

paragraphs below summarize the selection process for wells 

and data from the CDPH database for use in the grid-based 

status assessment.

For the status assessment, additional CDPH inorganic 

constituent data were needed to supplement the grid in areas 

where the USGS did not have inorganic constituent data. 

This involved prioritizing data from different sources. The 

first choice was to select CDPH inorganic constituent data 

for a grid well where the USGS measured other constituents, 

provided that 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 balance commonly is used as a quality-assurance check 

for water sample analysis (Hem, 1992). An imbalance of 

equal to or greater than 10 percent indicates uncertainty in 

the quality of the data. The most recent CDPH data from the 

USGS-grid well were evaluated to determine whether the 

CDPH data had a cation/anion imbalance less than 10 percent. 

If so, those data were selected for use as grid-well data for 

inorganic constituents. It was assumed that analyses with 

high-quality major-ion data also had high-quality data for 

trace elements, nutrients, and radiochemical constituents. For 

identification purposes, data from the CDPH for these grid 

wells were assigned identification numbers equivalent to those 

of the USGS-grid well but with ‘D’ inserted between the study 

area prefix and sequence number (for example, CDPH-grid 

well COS-D-01 is the same well as USGS-grid well COS-01, 

table A1).

If the first step did not yield acceptable CDPH inorganic 

constituent data for a grid cell, the second step was to 

rank the CDPH wells for the cell randomly and choose the 

highest ranked well with a cation/anion imbalance less than 

10 percent. These second-choice CDPH-grid wells were not 

co-located with the USGS-grid wells. If no CDPH wells in 

a grid cell met the charge-balance criterion or there were 

insufficient data to evaluate charge balance, the third choice 

for the CDPH-grid well was to select the highest randomly 

ranked CDPH well, for the cell, that had any of the needed 

inorganic constituent data, which could include USGSgrid wells that did not meet the criteria in the first step. For 

identification purposes, data from the CDPH for these grid 

wells were assigned identifications numbers equivalent to the 

USGS- grid well but with ‘DG’ inserted between the study 

area prefix and sequence number (for example, CDPH-grid 

well COS-DG-06 is in the same cell but is not the same well 

as USGS-grid well COS-06, table A1).

Empty cells, that is, those that were not sampled by the 

USGS, were checked for CDPH wells that could be added to 

the grid using the steps described above. For identification 

purposes, data from CDPH for these selected grid wells were 

assigned identification numbers equivalent to those of the 

USGS wells but with ‘DHS’ inserted between the study area 

prefix and sequence number (for example, CDPH-grid well 

COS-DHS-11 is in a grid cell where there are no USGS wells, 

table A1). CDPH-grid wells added to previously unsampled 

cells were given the next available sequence number.

Analysis of the combined datasets to evaluate the 

occurrence of high or moderate relative-concentrations was 

not affected by differences in reporting levels (LRLs or 

MDLs) between USGS-GAMA and CDPH data because 

concentrations greater than one-half of water-quality 

benchmarks were substantially higher than the highest LRLs 

or MDLs.

The locations and identification numbers of grid and 

understanding wells are shown in figure A1. Several types 

of comparisons between USGS-collected and CDPH data 

are described in appendix D: Comparison of CDPH and 

USGS‐GAMA data.
70 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Grid or understanding 

well sampled by GAMA

Grid supplemented by CDPH data from a

USGS-grid well Different well

Northern San Joaquin grid wells

COS-01 COS-D-01 –

COS-02 – –

COS-03 COS-D-03 –

COS-04 COS-D-04 –

COS-05 – –

COS-06 – COS-DG-06

COS-07 – –

COS-08 COS-D-08 –

COS-09 – COS-DG-09

COS-10 COS-D-10 –

– – COS-DHS-11

– – COS-DHS-12

– – COS-DHS-13

ESJ-01 ESJ-D-01 –

ESJ-02 ESJ-D-02 –

ESJ-03 ESJ-D-03 –

ESJ-04 – –

ESJ-05 ESJ-D-05 –

ESJ-06 ESJ-D-06 –

ESJ-07 ESJ-D-07 –

ESJ-08 ESJ-D-08 –

ESJ-09 ESJ-D-09 –

ESJ-10 – –

ESJ-11 ESJ-D-11 –

ESJ-12 – –

ESJ-13 ESJ-D-13 –

ESJ-14 – –

ESJ-15 ESJ-D-15 –

ESJ-16 ESJ-D-16 –

ESJ-17 – ESJ-DG-17

ESJ-18 ESJ-D-18 –

ESJ-19 – –

– – ESJ-DHS-20

NSJ-QPC-01 – –

NSJ-QPC-02 NSJ-QPC-D-02 –

Grid or understanding 

well sampled by GAMA

Grid supplemented by CDPH data from a

USGS-grid well Different well

Northern San Joaquin grid wells—Continued

NSJ-QPC-03 NSJ-QPC-D-03 –

NSJ-QPC-04 NSJ-QPC-D-04 –

NSJ-QPC-05 – NSJ-QPC-DG-05

NSJ-QPC-06 – –

NSJ-QPC-07 NSJ-QPC-D-07 –

NSJ-QPC-08 NSJ-QPC-D-08 –

NSJ-QPC-09 NSJ-QPC-D-09 –

NSJ-QPC-10 NSJ-QPC-D-10 –

NSJ-QPC-11 – NSJ-QPC-DG-11

TRCY-01 TRCY-D-01 –

TRCY-02 – TRCY-DG-02

TRCY-03 – –

TRCY-04 TRCY-D-04 –

TRCY-05 – TRCY-DG-05

TRCY-06 – TRCY-DG-06

TRCY-07 – –

TRCY-08 – TRCY-DG-08

TRCY-09 TRCY-D-09 –

TRCY-10 – –

TRCY-11 TRCY-D-11 –

– – TRCY-DHS-12

– – TRCY-DHS-13

– – TRCY-DHS-14

Northern San Joaquin understanding wells

ESJFP-07 – –

ESJFP-08 – –

ESJFP-09 – –

ESJFP-10 – –

TRCYFP-01 – –

TRCYFP-02 – –

TRCYFP-03 – –

TRCYFP-04 – –

TRCYFP-05 – –

TRCYFP-06 – –

Table A1. Nomenclature for wells sampled by USGS or selected from the CDPH database for the Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[COS, Cosumnes study area grid well; ESJ, Eastern San Joaquin study area grid well; NSJ-QPC, Upland study area grid well; TRCY, Tracy study area grid well; 

USGS, U.S. Geological Survey: Abbreviations associated with well names: D, USGS sampled well with supplemental CDPH data; DG, non-USGS sampled 

CDPH well selected to supplement USGS data in the same grid cell; DHS, non-USGS sampled CDPH well selected in a cell with no USGS data; FP, flow-path 

well]
Appendix A 71

Figure A1. Map showing identifiers and locations of grid and understanding wells sampled during December 2004–February 2005, 

and grid wells at which data for inorganic constituents from the California Department of Public Health were used, Northern San 

Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit

sac09-0345_fig A1

EL DORADO CO YOLO CO SACRAMENTO CO

AMADOR CO

SOLANO CO

CALAVERAS CO

SAN JOAQUIN CO

CONTRA COSTA CO

ALAMEDA CO

SANTA CLARA CO MERCED CO

STANISLAUS CO

NSJQPC-05

NSJ-QPC-05

NSJ-QPC-D-05

NSJ-QPC-04

NSJ-QPC-D-04

NSJ-QPC-07

NSJ-QPC-D-07

NSJ-QPC-09

NSJ-QPC-D-09

NSJ-QPC-03

QPC-D-03

COS-08

COS-D-08

COS-10

COS-D-10

NSJ-QPC-DG-11 COS-DHS-11

COS-D-01

COS-10

COS-D-10

ESJ-06

ESJ-D-06 NSJ-QPC-08

NSJ-QPC-D-08

COS-03

COS-D-03

COS-07

ESJ-11

ESJ-05

ESJ-19

TRCY-10

ESJ-04

TRCY-08

TRCYFP-05

TRCYFP-04

TRCY-DG-08

COS-06

ESJ-14

TRCY-05

COS-04

COS-D-04

NSJ-QPC-11

COS-DHS-13

ESJ-DHS-20

TRCY-DG-06

TRCY-DG-02

TRCY-DG-12

TRCY-DHS-13

TRCYFP-03

TRCYFP-06

ESJFP-08

TRCY-02

TRCY-03

TRCY-04

TRCY-D-04

COS-DHS-12

COS-DG-06

COS-DG-09

NSJ-QPC-06

NSJ-QPC-01

ESJ-13

ESJ-D-13

ESJ-16

ESJ-D-16

ESJ-09

ESJ-D-09

TRCY-01

TRCY-D-01

COS-02

COS-09

ESJ-12

ESJFP-09

TRCYFP-02

TRCY-DHS-14

ESJFP-10

TRCYFP-01

TRCY-DG-05

ESJ-10

ESJ-17

ESJ-15

ESJ-D-15

TRCY-09

TRCY-D-09

ESJFP-07

TRCY-11

TRCY-D-11

ESJ-01

ESJ-D-01

ESJ-18

ESJ-D-18

COS-05

TRCY-06

TRCY-07

ESJ-02

ESJ-D-02

ESJ-03

ESJ-D-03 ESJ-08

ESJ-D-08

NSJ-QPC-10

NSJ-QPC-D-10

NSJ-QPC-02

NSJ-QPC-D-02

ESJ-07

ESJ-D-07

ESJ-DG-17

Study areas

Cosumnes Basin (COS)

Eastern San Joaquin Basin (ESJ)

Uplands Basin (NSJ-QPC)

Tracy Basin (TRCY)

Grid well (USGS)

Understanding well (USGS)

Grid Well (CDPH)

EXPLANATION

Water bodies

Swamp or marsh

Inundation area

Boundary of SacramentoSan Joaquin Delta

Boundary of Northern 

San Joaquin Basin

Boundary of subbasin of

Northern San Joaquin Basin

Lodi

Stockton tockton

Tracy

Galt

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Delta Boundary — Section 12220 of the Water Code

North American Datum of 1983 (NAD 83)

Sierra Nevada

Coast Ranges

37°

30 ́

38°

30 ́

121° 30 ́ 121°

38°
72 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Appendix B. Ancillary Datasets 

Land Use

Land use was classified using 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 data represent land use 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 three general 

classifications for the purpose of general categorization of 

principal land use in the study unit: urban, agricultural, and 

natural. Land-use statistics for the study unit, the study areas, 

and a circular buffer of a 500-m radius around each well were 

computed for datasets using ArcGIS (version 9.2) (table B1) 

(Johnson and Belitz, 2009).

Well Depth and Depth to Top-of-Perforation

Well depth and depth to top-of-perforation are used 

as explanatory factors in the understanding assessment. 

Well‐construction data were determined primarily from 

drillers’ logs. More rarely, well-construction data were 

obtained from ancillary records of well owners or the 

USGS National Water Information System database. Well 

construction information is shown in table B2. 

Normalized Lateral Position

The normalized lateral position of wells serves as a 

proxy for the horizontal position in the regional groundwater 

flow system. Regionally, the direction of groundwater flow 

primarily is from the eastern and western margins of the 

valley deposits along the Sierra Mountain front and the Coast 

Ranges, respectively, towards the San Joaquin River (fig. 6). 

The groundwater flow system has vertical and horizontal flow 

components that deviate from the general direction in response 

to groundwater withdrawals and recharge (Phillips and others, 

2007). Nevertheless, because the predominant pattern of 

regional groundwater flow is from the valley margins towards 

the San Joaquin River, normalized lateral position serves as 

an approximate indicator of relative position of a well within 

the regional flow system. The normalized lateral position of 

each well was calculated as the ratio of the distance from the 

well to the upgradient edge of the regional groundwater flow 

system (valley margin) to the total distance from the center of 

the valley (valley trough) to the valley margin. The east and 

west edges of the valley (valley margins) are represented by 

the boundaries of the valley fill deposits, and wells located 

here were assigned a value of 1 and represent the upgradient 

end of regional flow. In the Northern San Joaquin, the center 

of the valley (valley trough) was represented by the position 

of the San Joaquin River, and wells located here were assigned 

a value of 0 and represent the downgradient end of regional 

flow. Both boundaries were represented as approximate line 

segments and normalized lateral position was calculated 

along lines perpendicular to both boundaries (fig. 9). The 

normalized lateral position was calculated for all 30 by 30-m 

wide cells in the San Joaquin Valley as part of a regional 

groundwater flow modeling study (Faunt, 2009). ArcGIS 

(version 9.2) was used to assign normalized lateral position 

values to all wells residing within those cells (table B2). 

Higher values of normalized lateral position indicate locations 

closer to the valley margin or in the upgradient (proximal) 

part of the flow system and lower values of normalized lateral 

position indicate locations closer to the valley trough or the 

downgradient (distal) part of the flow system.

Groundwater Age

Groundwater age-dating techniques provide a measure 

of the time since the groundwater was last in contact 

with the atmosphere. Techniques aimed at estimating 

groundwater residence time or “age” in this study was based 

on concentrations of tritium (3H) (Tolstikhin and Kamensky, 

1969; Torgersen and others, 1979; Solomon and others, 1992). 

3H is a short-lived radioactive isotope of hydrogen with 

a half-life of 12.32 years (Lucas and Unterweger, 2000). 3H 

is produced naturally in the atmosphere from the interaction 

of cosmogenic radiation with nitrogen (Craig and Lal, 1961), 

by above-ground nuclear explosions, and by the operation of 

nuclear reactors. Tritium enters the hydrologic cycle following 

oxidation to tritiated water (HTO). Consequently, the presence 

of 3H in groundwater is used to identify water that has 

exchanged with the atmosphere in the past 50 years. 

In this study, groundwater with tritium activities less 

than 1 tritium unit (TU) were categorized as old (table B3). 

Old groundwater is defined as having recharged prior to 

about 1950. The category old could include groundwater 

that recharged from 56 to tens of thousands of years ago. 

Groundwater with tritium activities greater than 1 TU 

was categorized as young. Young groundwater is defined 

as having recharged after about 1950. In reality, old 

groundwater could contain small fractions of young water, 

and young groundwater could contain small fractions of old 

water. Previous investigations have used a range of tritium 

concentrations from 0.3 to 1.0 TU as thresholds for this 

distinction (Michel, 1989; Plummer and others, 1993, p. 260; 

Michel and Schroeder, 1994; Clark and Fritz, 1997, p. 185). 

By using a tritium concentration of 1.0 TU, at the upper end of 

the range used in the literature, for the threshold in this study, 

the age classification scheme allows a slightly larger fraction 

of young water to be present in an age distribution categorized 

as old than if a lower threshold were used.
Appendix B 73

Well 

identification 

No.

Land-use classification

Land use within 500-m of the well (percent)

Agricultural Natural Urban

Northern San Joaquin grid wells

COS-01 34 60 6

COS-02 1 62 37

COS-03 52 8 40

COS-04 15 59 26

COS-05 100 0 0

COS-06 91 9 0

COS-07 64 34 2

COS-08 36 47 17

COS-09 87 13 0

COS-10 86 4 9

ESJ-01 38 47 16

ESJ-02 100 0 0

ESJ-03 90 2 7

ESJ-04 44 56 0

ESJ-05 23 60 18

ESJ-06 51 7 42

ESJ-07 62 36 2

ESJ-08 83 9 8

ESJ-09 0 16 84

ESJ-10 99 0 1

ESJ-11 41 7 51

ESJ-12 85 1 14

ESJ-13 61 3 36

ESJ-14 100 0 0

ESJ-15 0 3 97

ESJ-16 25 1 74

ESJ-17 99 0 1

ESJ-18 98 0 2

ESJ-19 0 0 100

NSJ-QPC-01 72 28 0

NSJ-QPC-02 38 0 62

NSJ-QPC-03 0 100 0

NSJ-QPC-04 10 88 2

NSJ-QPC-05 0 100 0

NSJ-QPC-06 16 81 2

NSJ-QPC-07 0 98 2

NSJ-QPC-08 50 25 25

NSJ-QPC-09 0 66 34

NSJ-QPC-10 84 3 13

NSJ-QPC-11 14 86 0

Table B1. Land-use classification for wells sampled by USGS or selected from CDPH database for the Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[Land-use classification is based on 500-meter buffer from Johnson and Belitz (2009). COS, Cosumnes study area grid well; ESJ, Eastern San Joaquin study 

area grid well; NSJ-QPC, Upland study area grid well; TRCY, Tracy study area grid well; USGS, U.S. Geological Survey: Abbreviations associated with well 

names: D, USGS sampled well with supplemental CDPH data; DG, non-USGS sampled CDPH well selected to supplement USGS data in the same grid cell; 

DHS, non-USGS sampled CDPH well selected in a cell with no USGS data; FP, flow-path well]

Well 

identification 

No.

Land-use classification

Land use within 500-m of the well (percent)

Agricultural Natural Urban

Northern San Joaquin grid wells—Continued

TRCY-01 83 8 9

TRCY-02 3 96 1

TRCY-03 21 62 17

TRCY-04 45 27 28

TRCY-05 33 49 18

TRCY-06 84 16 0

TRCY-07 98 2 0

TRCY-08 89 3 8

TRCY-09 90 10 0

TRCY-10 77 23 0

TRCY-11 59 11 30

Northern San Joaquin additional CDPH wells

COS-DG-06 54 5 41

COS-DG-09 0 100 0

COS-DHS-11 9 82 9

COS-DHS-12 83 0 17

COS-DHS-13 27 67 6

ESJ-DG-17 0 100 0

ESJ-DHS-20 70 28 3

NSJ-QPC-DG-05 0 100 0

NSJ-QPC-DG-11 66 29 5

TRCY-DG-02 37 41 22

TRCY-DG-05 14 47 39

TRCY-DG-06 56 30 13

TRCY-DG-08 19 64 18

TRCY-DHS-12 100 0 0

TRCY-DHS-13 33 15 52

TRCY-DHS-14 67 33 0

Northern San Joaquin understanding wells

ESJFP-07 94 6 0

ESJFP-08 17 3 79

ESJFP-09 26 4 70

ESJFP-10 97 2 1

TRCYFP-01 96 1 3

TRCYFP-02 1 5 94

TRCYFP-03 67 2 31

TRCYFP-04 0 55 45

TRCYFP-05 1 4 96

TRCYFP-06 100 0 0
74 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Table B2. Well construction and normalized lateral position information for wells sampled by USGS or selected from the CDPH 

database for the Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[Normalized lateral position: Values of normalized lateral position range from 0 to 1. Values of 0 represent the valley trough (basin center), and values of 1 

represent the valley margins (basin edges). COS, Cosumnes study area grid well; ESJ, Eastern San Joaquin study area grid well; NSJ-QPC, Upland study area 

grid well; TRCY, Tracy study area grid well; LSD, land-surface datum; USGS, U.S. Geological Survey: Abbreviations associated with well names: D, USGS 

sampled well with supplemental CDPH data; DG, non-USGS sampled CDPH well selected to supplement USGS data in the same grid cell; DHS, non-USGS 

sampled CDPH well selected in a cell with no USGS data; FP, flow-path well; —, not available]

Well

identification 

No.

Construction information

Normalized 

lateral position 

(dimensionless) Well depth 

(feet below LSD)

Depth to top-ofperforation 

(feet below LSD)

Depth to bottom-ofperforation 

(feet below LSD)

Perforation

length 

(feet)

Northern San Joaquin grid wells

COS-01 360 300 360 60 0.61

COS-02 930 750 910 180 0.38

COS-03 470 250 460 220 0.33

COS-04 660 180 650 480 0.36

COS-05 256 – – – 0.49

COS-06 117 117 – 0 0.27

COS-07 160 – – – 0.49

COS-08 575 178 570 397 0.33

COS-09 – – – – 0.55

COS-10 268 – – – 0.45

ESJ-01 270 180 265 90 0.07

ESJ-02 340 138 340 202 0.13

ESJ-03 360 100 350 260 0.20

ESJ-04 – – – – 0.09

ESJ-05 535 – – – 0.56

ESJ-06 403 200 395 203 0.27

ESJ-07 – – – – 0.43

ESJ-08 610 280 600 330 0.54

ESJ-09 365 130 345 235 0.25

ESJ-10 364 156 354 208 0.13

ESJ-11 245 135 – 110 0.12

ESJ-12 270 190 270 80 0.49

ESJ-13 619 – – – 0.53

ESJ-14 212 – – – 0.15

ESJ-15 268 170 236 98 0.12

ESJ-16 427 196 419 231 0.23

ESJ-17 390 290 390 100 0.49

ESJ-18 – – – – 0.62

ESJ-19 519 195 495 324 0.11

NSJ-QPC-01 – – – – 0.87

NSJ-QPC-02 175 130 175 45 0.67

NSJ-QPC-03 – – – – 0.89

NSJ-QPC-04 520 235 512 285 0.76

NSJ-QPC-05 200 160 200 40 1

NSJ-QPC-06 208 – – – 0.66

NSJ-QPC-07 310 100 310 210 1

NSJ-QPC-08 164 – – – 0.52

NSJ-QPC-09 366 150 350 216 0.94

NSJ-QPC-10 – – – – 0.73

NSJ-QPC-11 – – – – 0.65

TRCY-01 502 384 480 118 0.12

TRCY-02 200 50 200 150 1

TRCY-03 900 420 890 480 0.79

TRCY-04 325 225 315 100 0.86
Appendix B 75

Well

identification 

No.

Construction information

Normalized 

lateral position 

(dimensionless) Well depth 

(feet below LSD)

Depth to top-ofperforation 

(feet below LSD)

Depth to bottom-ofperforation 

(feet below LSD)

Perforation

length 

(feet)

Northern San Joaquin grid wells—Continued

TRCY-05 380 – – – 0.34

TRCY-06 – – – – 0.48

TRCY-07 83 23 73 60 0.41

TRCY-08 340 320 340 20 0.37

TRCY-09 – – – – 0.15

TRCY-10 185 175 185 10 0.43

TRCY-11 400 310 400 90 0.32

Northern San Joaquin additional CDPH grid wells

COS-DG-06 – – – – –

COS-DG-09 – – – – –

COS-DHS-11 – – – – 0.57

COS-DHS-12 – – – – 0.33

COS-DHS-13 450 160 – 290 0.48

ESJ-DG-17 – – – – –

ESJ-DHS-20 – – – – 0.10

NSJ-QPC-DG-05 – – – – –

NSJ-QPC-DG-11 240 – – – –

TRCY-DG-02 – – – – –

TRCY-DG-05 – – – – –

TRCY-DG-06 – – – – –

TRCY-DG-08 730 180 690 550 –

TRCY-DHS-12 72 – – – 0.97

TRCY-DHS-13 95 75 95 20 0.83

TRCY-DHS-14 235 – – – 0.14

Northern San Joaquin understanding wells

ESJFP-07 466 – – – 0.33

ESJFP-08 425 199 415 226 0.14

ESJFP-09 300 162 288 138 0.27

ESJFP-10 540 200 520 340 0.18

TRCYFP-01 130 90 130 40 0.07

TRCYFP-02 1,148 337 1,128 811 0.56

TRCYFP-03 400 – – – 0.66

TRCYFP-04 860 420 850 440 0.77

TRCYFP-05 990 490 980 500 0.56

TRCYFP-06 148 128 148 20 0.10

Table B2. Well construction and normalized lateral position information for wells sampled by USGS or selected from the CDPH 

database for the Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.—Continued

[COS, Cosumnes study area grid well; ESJ, Eastern San Joaquin study area grid well; NSJ-QPC, Upland study area grid well; TRCY, Tracy study area grid 

well; ft, feet; m, meter; LSD, land surface datum; USGS, U.S. Geological Survey: Abbreviations associated with well names: D, USGS sampled well with 

supplemental CDPH data; DG, non-USGS sampled CDPH well selected to supplement USGS data in the same grid cell; DHS, non-USGS sampled CDPH well 

selected in a cell with no USGS data; FP, flow-path well; —, not available]
76 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

 GAMA_ID Tritium 

(tritium units)

Tritium 2-sigma 

combined 

standard 

uncertainty 

(tritium units)

Age 

classification

Northern San Joaquin grid wells

COS-01 -0.1 ± 0.2 old

COS-02 0.2 ± 0.2 old

COS-03 0.4 ± 0.2 old

COS-04 1.0 ± 0.2 young

COS-05 8.7 ± 0.6 young

COS-06 4.1 ± 0.4 young

COS-07 0.8 ± 0.2 old

COS-08 -0.1 ± 0.2 old

COS-09 0.4 ± 0.2 old

COS-10 0.6 ± 0.2 old

ESJ-01 6.4 ± 0.4 young

ESJ-02 0.3 ± 0.2 old

ESJ-03 3.8 ± 0.3 young

ESJ-04 4.4 ± 0.3 young

ESJ-05 0.0 ± 0.2 old

ESJ-06 2.3 ± 0.3 young

ESJ-07 8.3 ± 0.6 young

ESJ-08 0.3 ± 0.2 old

ESJ-09 1.1 ± 0.2 young

ESJ-10 1.0 ± 0.2 young

ESJ-11 0.5 ± 0.2 old

ESJ-12 0.0 ± 0.2 old

ESJ-13 0.1 ± 0.2 old

ESJ-14 0.4 ± 0.2 old

ESJ-15 1.2 ± 0.3 young

ESJ-16 5.1 ± 0.4 young

ESJ-17 0.0 ± 0.2 old

ESJ-18 -0.1 ± 0.3 old

ESJ-19 4.8 ± 0.4 young

NSJ-QPC-01 4.4 ± 0.3 young

NSJ-QPC-02 11.7 ± 0.6 young

NSJ-QPC-03 0.1 ± 0.2 old

Table B3. Summary of tritium data from samples of unfiltered water and classification of samples into young and old categories, 

Northern San Joaquin Basin Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[COS, Cosumnes study area grid well; ESJ, Eastern San Joaquin study area grid well; NSJ-QPC, Upland study area grid well; TRCY, Tracy study area grid well; 

FP, flow-path well]

 GAMA_ID Tritium 

(tritium units)

Tritium 2-sigma 

combined 

standard 

uncertainty 

(tritium units)

Age 

classification

Northern San Joaquin grid wells—Continued

NSJ-QPC-04 1.7 ± 0.2 young

NSJ-QPC-05 0.0 ± 0.2 old

NSJ-QPC-06 0.0 ± 0.2 old

NSJ-QPC-07 0.9 ± 0.2 old

NSJ-QPC-08 -0.1 ± 0.3 old

NSJ-QPC-09 0.0 ± 0.3 old

NSJ-QPC-10 1.0 ± 0.2 young

NSJ-QPC-11 0.0 ± 0.2 old

TRCY-01 0.1 ± 0.2 old

TRCY-02 0.7 ± 0.2 old

TRCY-03 0.1 ± 0.2 old

TRCY-04 0.1 ± 0.2 old

TRCY-05 0.1 ± 0.2 old

TRCY-06 0.2 ± 0.2 old

TRCY-07 2.8 ± 0.3 young

TRCY-08 0.1 ± 0.3 old

TRCY-09 3.8 ± 0.4 young

TRCY-10 0.1 ± 0.2 old

TRCY-11 0.0 ± 0.2 old

Northern San Joaquin understanding wells

ESJFP-07 0.1 ± 0.2 old

ESJFP-08 3.1 ± 0.3 young

ESJFP-09 10.6 ± 0.6 young

ESJFP-10 2.7 ± 0.3 young

TRCYFP-01 0.0 ± 0.2 old

TRCYFP-02 0.6 ± 0.2 old

TRCYFP-03 0.7 ± 0.2 old

TRCYFP-04 0.4 ± 0.2 old

TRCYFP-05 0.4 ± 0.2 old

TRCYFP-06 0.1 ± 0.2 old
Appendix B 77

Geochemical Conditions

Geochemical conditions investigated as potential 

explanatory factors were oxidation-reduction characteristics 

(redox) and pH. Redox conditions influence the transport 

of many organic and inorganic constituents by controlling 

whether the constituent is in a dissolved or adsorbed oxidation 

state (McMahon and Chapelle, 2008). Redox conditions 

along groundwater flow paths commonly proceed along a 

well‐documented sequence of terminal electron-accepting 

processes (TEAP), in which a single TEAP typically 

dominates at a particular time and location within the aquifer 

(Chapelle and others, 1995; Chapelle, 2001). The predominant 

TEAPs are oxygen-reducing (oxic), nitrate-reducing, 

manganese-reducing, iron-reducing, sulfate-reducing, and 

methanogenic.

Classifications of redox condition were made by using 

the framework of McMahon and Chapelle (2008), for grid 

and understanding wells with available measurements of 

redox-sensitive constituents (dissolved oxygen, nitrate, iron, 

manganese, and sulfate). An automated workbook program 

was used to assign the redox classification to each sample 

(Jurgens and others, 2009). Of the 61 wells with analyses 

of one or more redox-sensitive constituents, only 53 had 

information sufficient to make a determination of redox 

condition (table B4). In some cases, determinations were 

made on as few as two constituents. A key component to 

the accurate classification of redox conditions using the 

McMahon and Chapelle (2008) framework is availability of 

dissolved oxygen (DO) data, which were lacking in many 

wells sampled in the Northern San Joaquin Basin study unit. 

Using the McMahon and Chapelle (2008) framework, wells 

with fewer than four redox indicators often were categorized 

as belonging to a “mixed” redox category, a rare occurrence 

where both oxic and anoxic conditions are indicated by the 

sample, but which can occur in wells that have long screens, 

which can integrate water from multiple depths of the aquifer. 

Samples assigned to mixed categories were recategorized 

as either oxic or anoxic, based on the available redox 

information. For example, samples having only measurements 

of iron, manganese, and sulfate were evaluated based on the 

relative abundance of each constituent in the sample. Higher 

concentrations of these constituents (greater than the threshold 

value used by the McMahon and Chapelle (2008) redox 

framework) generally indicate reducing conditions.

Table B4. Concentrations of constituents used to classify geochemical (redox) conditions in groundwater, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.

[O2, oxygen reducing; redox, oxidation-reduction; mg/L, milligram per liter; μg/L, microgram per liter; oxic, dissolved oxygen > 0.5; anoxic, dissolved oxygen 

< 0.5 but no other redox indicators available; suboxic, dissolved oxygen < 0.5 but redox status can not be further discerned because multiple indicators are below 

thresholds; NO3, nitrate reducing; Mn(IV), manganese reducing; Fe(III), iron reducing; ?, insufficient data to determine whether process is occurring or not; 

/, and(or); -, and; –, not analyzed/not available; >, greater than; <, less than]

Redox constituents

General redox 

category Redox assignment Dissolved oxygen

Nitrate 

plus nitrite, 

nitrogen

Manganese Iron Sulfate

Redox threshold values >0.5 >0.5 >50 > 100 >0.5

Possible redox if concentration 

greater than threshold

O2

(mg/L)

NO3

(mg/L)

Mn

(μg/L)

Fe

(μg/L)

SO4

(mg/L) Redox classification

Sample ID 1

COS-D-01 – 0.61 0 0 – Indeterminate –

COS-D-03 – 0.45 200.4 201.8 5.9 Anoxic Fe(III)/SO4-O2?

COS-D-04 – 0.15 179.1 215.8 17.2 Anoxic Fe(III)/SO4-O2?

COS-DG-06 – 0.68 10 50 6 Oxic O2?OrNO3

COS-D-08 – 0.47 – – – Indeterminate –

COS-D-10 – 3.28 – – – Indeterminate –
78 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Redox constituents

General redox 

category Redox assignment Dissolved oxygen

Nitrate 

plus nitrite, 

nitrogen

Manganese Iron Sulfate

Redox threshold values >0.5 >0.5 > 50 >100 >0.5

Possible redox if concentration 

greater than threshold

O2

(mg/L)

NO3

(mg/L)

Mn

(μg/L)

Fe

(μg/L)

SO4

(mg/L) Redox classification

Sample ID 1

ESJ-D-01 – 4.29 0 0 20 Oxic O2?OrNO3

ESJ-D-02 – 1.42 40 0 12 Oxic O2?OrNO3

ESJ-D-03 – 5.87 0 0 19 Oxic O2?OrNO3

ESJ-D-05 – 1.20 20 50 1.9 Oxic O2?OrNO3

ESJ-06, ESJ-D-06 – 3.39 0.2 6 13.6 Oxic O2?OrNO3

ESJ-D-07 – 1.49 – – – Indeterminate –

ESJ-08, ESJ-D-08 – 3.93 0.4 6 2.93 Oxic O2?OrNO3

ESJ-D-09 – 1.25 0 0 30.6 Oxic O2?OrNO3

ESJ-10 – – 66.2 17 4.16 Oxic –

ESJ-D-11 – 0.09 100 50 1.00 Anoxic Mn(IV)-O2?

ESJ-12 5.8 0.91 0.4 6 3.63 Oxic O2

ESJ-13, ESJ-D-13 – 1.31 1.2 14 3.61 Oxic O2?OrNO3

ESJ-14 – – 187 65 6.34 Anoxic –

ESJ-15, ESJ-D-15 – 0.45 19 6 30.9 Oxic O2?OrSuboxic

ESJ-16, ESJ-D-16 – 0.68 6.5 13 12.1 Oxic O2?OrNO3

ESJ-17 – – 2.2 6 11.2 Oxic –

ESJ-18, ESJ-D-18 – 3.07 0.2 6 7.91 Oxic O2?OrNO3

ESJ-19 3.4 0.05 0.9 4 24.7 Oxic O2

NSJ-QPC-01 5.6 3.31 0.2 6 27.9 Oxic O2

NSJ-QPC-D-02 – 7.16 – – – Indeterminate –

NSJ-QPC-D-03 – 2.01 20 50 2.6 Oxic O2?OrNO3

NSJ-QPC-04, NSJ-QPC-D-04 – 0.72 54.8 150 2.33 Anoxic Fe(III)/SO4-O2?OrNO3

NSJ-QPC-DG-05 – 2.01 30 350 11 Anoxic Fe(III)/SO4-O2?OrNO3

NSJ-QPC-06 – – 2.6 3 1.4 Oxic –

NSJ-QPC-07, NSJ-QPC-D-07 – 2.48 0.4 5 10.8 Oxic O2?OrNO3

NSJ-QPC-08, NSJ-QPC-D-08 – 0.95 0.2 3 5.72 Oxic O2?OrNO3

NSJ-QPC-D-09 – 0.45 89.4 173 43 Anoxic Fe(III)/SO4-O2?

NSJ-QPC-10, NSJ-QPC-D-10 – 2.01 0.3 4 5.07 Oxic O2?OrNO3

NSJ-QPC-DG-11 – 0.59 0 110 – Indeterminate –

TRCY-D-01 – 0 360 100 200 Anoxic Fe(III)/SO4-O2?

TRCY-DG-02 – 0 57 380 100 Anoxic Fe(III)/SO4-O2?

TRCY-03 6.2 2.3 0.2 4 248 Oxic O2

TRCY-D-04 – 1.02 10 50 217 Oxic O2?OrNO3

TRCY-DG-05 – 0 140 0 130 Anoxic Mn(IV)-O2?

TRCY-DG-06 – 0 220 0 140 Anoxic Mn(IV)-O2?

TRCY-07 0.1 0.06 17,100 24,500 750 Anoxic Fe(III)/SO4

TRCY-DG-08 – 0 170 260 7.9 Anoxic Fe(III)/SO4-O2?

TRCY-09, TRCY-D-09 – 0.09 519 4,180 34 Anoxic Fe(III)/SO4-O2?

TRCY-11, TRCY-D-11 – 0.09 194 8 191 Anoxic Mn(IV)-O2?

Table B4. Concentrations of constituents used to classify geochemical (redox) conditions in groundwater, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.—Continued

[O2, oxygen reducing; redox, oxidation-reduction; mg/L, milligram per liter; μg/L, microgram per liter; oxic, dissolved oxygen > 0.5; anoxic, dissolved oxygen 

< 0.5 but no other redox indicators available; suboxic, dissolved oxygen < 0.5 but redox status can not be further discerned because multiple indicators are below 

thresholds; NO3, nitrate reducing; Mn(IV), manganese reducing; Fe(III), iron reducing; ?, insufficient data to determine whether process is occurring or not; /, 

and(or); -, and; –, not analyzed/not available; >, greater than; <, less than]
Appendix B 79

Redox constituents

General redox 

category Redox assignment Dissolved oxygen

Nitrate 

plus nitrite, 

nitrogen

Manganese Iron Sulfate

Redox threshold values >0.5 >0.5 >50 > 100 >0.5

Possible redox if concentration 

greater than threshold

O2

(mg/L)

NO3

(mg/L)

Mn

(μg/L)

Fe

(μg/L)

SO4

(mg/L) Redox classification

Sample ID 1

COS-DHS-11 – 0.45 – – – Indeterminate –

COS-DHS-12 – 0.38 90 50 7 Anoxic Mn(IV)-O2?

COS-DHS-13 – 1.83 0 0 – Indeterminate –

ESJ-DHS-20 – 5.20 18 50 18 Oxic O2?OrNO3

TRCY-DHS-12 – 0 0 17 7.45 Oxic O2?OrSuboxic

TRCY-DHS-13 – 10 200 370 8.1 Anoxic Fe(III)/SO4-O2?OrNO3

TRCY-DHS-14 – 880 950 8 6.9 Anoxic Mn(IV)-O2?OrNO3

ESJFP-07 – 0.54 0.8 4 6.05 Oxic O2?OrNO3

ESJFP-08 – – 87.9 13 18.8 Anoxic –

ESJFP-09 – – 0.4 5 18.7 Oxic –

ESJFP-10 – 1.37 0.2 6 16.1 Oxic O2?OrNO3

TRCYFP-01 – 0.06 2,480 1,240 62.9 Anoxic Fe(III)/SO4-O2?

TRCYFP-02 – 1.69 0.2 3 252 Oxic O2?OrNO3

TRCYFP-03 – – 1.9 9 223 Oxic –

TRCYFP-04 – 1.86 1.5 15 309 Oxic O2?OrNO3

TRCYFP-05 – – 2.1 6 244 Oxic –

1

Samples with two sample identification numbers identify those samples for which concentrations of nitrate plus nitrite (as nitrogen) were obtained from the 

California Department of Public Health database and all other results are from USGS-GAMA.

Table B4. Concentrations of constituents used to classify geochemical (redox) conditions in groundwater, Northern San Joaquin Basin 

Groundwater Ambient Monitoring and Assessment (GAMA) study unit.—Continued

[O2, oxygen reducingredox, oxidation-reduction; mg/L, milligram per liter; μg/L, microgram per liter; oxic, dissolved oxygen > 0.5; anoxic, dissolved oxygen < 

0.5 but no other redox indicators available; suboxic, dissolved oxygen < 0.5 but redox status can not be further discerned because multiple indicators are below 

thresholds; NO3, nitrate reducing; Mn(IV), manganese reducing; Fe(III), iron reducing; ?, insufficient data to determine whether process is occurring or not; /, 

and(or); -, and; –, not analyzed/not available; >, greater than; <, less than]
80 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Appendix C. Calculating Total Dissolved Solids

Data for specific conductance (SC), an electrical measure 

of total dissolved solids (TDS), were available for all 61 

USGS-grid and understanding wells, whereas measured TDS 

data were available only for 33 USGS-grid and understanding 

wells. For wells that had no measured TDS, TDS was 

estimated from SC values using a linear regression equation 

(TDS = 0.6973*SC -9.87) derived from the comparison of 

TDS and SC values obtained from the 33 USGS-GAMA wells 

having SC and TDS data in the Northern San Joaquin Basin 

study unit (method described by Hem, 1992). The resulting 

regression equation had a coefficient of determination (r2) 

value of 0.989. 
Appendix D 81

Appendix D. Comparison of CDPH and USGS-GAMA Data

CDPH and USGS-GAMA data were compared to assess 

the validity of using data from these different sources in 

combination. Because laboratory reporting levels (LRLs or 

MDLs), generally defined as the level at which a constituent 

can be reliably be detected, for most organic constituents 

and trace elements were substantially lower for USGSGAMA data than for CDPH data (table 2), it was not possible 

to directly compare concentrations of these constituent 

types in individual wells in any meaningful way. However, 

concentrations of major ions and nitrate, which generally 

are prevalent and have concentrations well above reporting 

levels, could be compared for those wells with data from both 

sources. 

Seventeen wells had some data for major ions and 

nitrate in common between the datasets. The small number 

of wells prevented a statistically robust analysis for each 

Figure D1. Graph of paired 

inorganic concentrations 

from wells sampled by the 

Groundwater Ambient Monitoring 

and Assessment (GAMA) 

Program between December 2004 

and February 2005 and the most 

recent available analysis in the 

California Department of Health 

Services for the same wells, 

Northern San Joaquin Basin 

Groundwater Ambient Monitoring 

and Assessment (GAMA) study 

unit.

individual constituent. However, the paired analyses for 

eight constituents (calcium, magnesium, sodium, alkalinity, 

chloride, sulfate, TDS, nitrate-N) with concentrations greater 

than the reporting levels in both data bases were combined 

into one dataset so that there was a sufficiently large dataset 

(126 pairs) for meaningful statistical comparison (fig. D1). 

The Wilcoxon rank-sum test indicated no significant 

differences between the paired datasets (z = 0.05, p = 0.95). 

Although differences between the paired data sets occurred 

for a few wells, most sample pairs plotted close to a 1 to 1 line 

(fig. D1). The relative percent difference (RPD) was calculated 

for each data pair. The median RPD was 6.8 percent and 

nearly 80 percent of the RPD values were less than 20 percent. 

These direct comparisons indicated that the USGS-GAMA 

and CDPH inorganic constituent data were not significantly 

different. 

0.01 0.1 1 10 100 1,000 10,000

CONCENTRATION, USGS-GAMA DATA, IN MILLIGRAMS PER LITER

0.01

0.1

1

10

100

1,000

10,000

CONCENTRATION, CDPH DATA, IN MILLIGRAMS PER LITER

Alkalinity

Calcium

Chloride

Magnesium

Nitrate

Sodium

Sulfate

TDS
82 Status and Understanding of Groundwater Quality, Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project

Trilinear diagrams commonly are used to define 

groundwater type (Hem, 1992). Trilinear diagrams illustrate 

the relative contributions of major cations and anions (on 

a charge-equivalent basis) as a percentage of the total ion 

content of the water (fig. D2). Combined USGS-GAMA and 

CDPH major ion data for grid wells were plotted on trilinear 

diagrams (Piper, 1944) along with all CDPH major ion data. 

This was done to determine whether the groundwater types 

observed in grid wells were similar to groundwater types 

observed historically in the study unit. All recent (January 1, 

2001–April 21, 2004) CDPH data having cation/anion data 

and a cation/anion balance within 10 percent were retrieved 

and plotted on the trilinear diagrams for comparison with 

grid‐well data.

PERCENTAGE OF TOTAL, IN MILLIEQUVALENTS PER LITER

CALCIUM

 0 

 20 

 40 

 60 

 80 

 100 

MAGNESIUM

 0 

 20 

 40 

 60 

 80 

 100 

SODIUM PLUS POTASSIUM

CHLORIDE, NITRITE PLUS NITRATE

 100 

 80 

 60 

 40 

 20 

 0 

BICARBONATE

 100 

 80 

 60 

 40 

 20 

 0 

SULFATE

 20 

 40 

 60 

 80 

SULFATE PLUS CHLORIDE 20 

 40 

 60 

 80 

CALCIUM PLUS MAGNESIUM 

 80 

 60 

 40 

 20 

 80 

 60 

 40 

 20 

PERCENT

PERCENT

100

100 80 60 40 20 0 0 20 40 60 80 100

100

0 0

Grid well

CDPH well

EXPLANATION

Figure D2. Trilinear diagram comparing water types in 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, Northern San Joaquin Basin Groundwater Ambient Monitoring and 

Assessment (GAMA) study unit.

Data from grid wells and recent CDPH data indicate that 

a similar range of water types was sampled (fig. D2). At most 

wells, no single cation accounted for more than 60 percent 

of the total cations, and bicarbonate accounted for more than 

60 percent of the total anions; these samples are described as 

mixed cation-bicarbonate type waters. At many other wells, 

no single cation and no single anion accounted for more than 

60 percent of the total; these samples are described as mixed 

cation-mixed anion type waters. In a minority of wells, sodium 

and chloride accounted for more than 60 percent of the total 

cations and anions, respectively; these samples are classified 

as sodium-chloride type waters. The similarity of the range of 

relative abundance of major cations and anions in grid wells to 

those in the set of all CDPH wells indicates that the grid wells 

represent the distribution and diversity of water types present 

in the Northern San Joaquin Basin study unit.
Publishing support provided by the U.S. Geological Survey

Publishing Network, Sacramento and Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

 Director, California Water Science Center

U.S. Geological Survey

6000 J Street

Sacramento, California 95819

http://ca.water.usgs.gov
Bennett and others—Status and Understanding of Groundwater Quality in the Northern San Joaquin Basin, 2005: California GAMA Priority Basin Project—SIR 2010--5175

Photo placement