U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the California State Water Resources Control Board

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

Status and Understanding of Groundwater Quality in the Two Southern 

San Joaquin Valley Study Units, 2005–2006: California GAMA Priority

Basin Project

Photo placement

Scientific Investigations Report 2011–5218
Front Cover Map: Groundwater basins categorized by sampling priority. Location of groundwater basin 

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

Cover photographs:

Front cover: Cotton fields near Fresno, California. (Photograph taken by Justin Kulongski,

U.S. Geological Survey.)

Back cover: Irrigation well in Kern County, California. (Photograph taken by Michael T. Wright,

U.S. Geological Survey.) 

Sampling priority

Study area

Low-use basins

Priority basins

Areas that are outside CDWR-defined

 groundwater basins
Status and Understanding of Groundwater 

Quality in the Two Southern San Joaquin 

Valley Study Units, 2005–2006: California 

GAMA Priority Basin Project

By Carmen A. Burton, Jennifer L. Shelton, and Kenneth Belitz

A product of the California Groundwater Ambient Monitoring and Assessment 

(GAMA) Program

Prepared in cooperation with the California State Water Resources Control Board

Scientific Investigations Report 2011–5218

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: 2012

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Burton, C.A., Shelton, J.L., and Belitz, Kenneth, 2012, Status and understanding of groundwater quality in the two 

southern San Joaquin Valley study units, 2005–2006—California GAMA Priority Basin Project: U.S. Geological Survey 

Scientific Investigations Report 2011–5218, 150 p.
iii

Contents

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

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

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

Description of the Study Units.....................................................................................................................6

Hydrogeologic Setting .........................................................................................................................6

Southeast San Joaquin Valley Study Unit ...............................................................................6

Kings Study Area ................................................................................................................9

Kaweah Study Area............................................................................................................9

Tule Study Area...................................................................................................................9

Tulare Lake Study Area......................................................................................................9

Kern County Subbasin Study Unit ...........................................................................................11

Methods.........................................................................................................................................................11

Status Assessment Methods............................................................................................................12

Relative-Concentrations and Water-Quality Benchmarks .................................................12

Design of Sampling Networks for Status Assessment........................................................13

California Department of Public Health Grid Well Selection..............................................13

Selection of Constituents for Additional Evaluation ............................................................17

Calculation of Aquifer-Scale Proportions..............................................................................19

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

U.S. Geological Survey Understanding Wells.......................................................................20

Statistical Analysis ....................................................................................................................20

Potential Explanatory Factors....................................................................................................................21

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

Depth ..................................................................................................................................................21

Lateral Position in the Flow System.................................................................................................23

Septic-System Density.......................................................................................................................23

Formerly Leaking Underground Fuel Tanks ....................................................................................23

Groundwater Age................................................................................................................................23

Geochemical Conditions....................................................................................................................29

Correlations Between Explanatory Variables ................................................................................33

Status and Understanding of Water Quality............................................................................................38

Inorganic Constituents ......................................................................................................................46

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

Arsenic................................................................................................................................46

Antimony.............................................................................................................................62

Boron ................................................................................................................................67

Vanadium............................................................................................................................67

Fluoride...............................................................................................................................68

Uranium and Radioactive Constituents..................................................................................68

Uranium and Gross Alpha Radioactivity .......................................................................70

Nutrients......................................................................................................................................70

Nitrate ................................................................................................................................70
iv

Status and Understanding of Water Quality—Continued

Inorganic Constituents—Continued

Inorganics with Aesthetic Benchmarks ................................................................................73

Manganese and Iron........................................................................................................73

Total Dissolved Solids ......................................................................................................74

Sulfate ................................................................................................................................74

Chloride...............................................................................................................................75

Organic Constituents..........................................................................................................................75

Volatile Organic Compounds....................................................................................................82

Trihalomethanes................................................................................................................82

Solvents..............................................................................................................................89

Other Volatile Organic Compounds................................................................................90

Fumigants....................................................................................................................................91

DBCP ................................................................................................................................91

Pesticides....................................................................................................................................93

Constituents of Special Interest.......................................................................................................95

Perchlorate .................................................................................................................................95

Summary........................................................................................................................................................96

Acknowledgments.......................................................................................................................................99

References Cited..........................................................................................................................................99

Appendix A: Selection of California Department of Public Health Grid Wells................................107

Appendix B: Comparison of CDPH and GAMA Priority Basin Data..................................................119

Appendix C: Calculation of Aquifer-Scale Proportions ......................................................................124

Appendix D: Attribution of Potential Explanatory Factors .................................................................126

Contents—Continued
v

Figures

Figure 1. Map showing location of the two southern San Joaquin Valley study

units—Kern County Subbasin and Southeast San Joaquin Valley—and

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

California GAMA Priority Basin Project................................................... 4

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

Joaquin Valley study units (Southeast San Joaquin Valley and Kern County

Subbasin), California GAMA Priority Basin Project .................................... 5

Figure 3. Illustration showing ternary diagram with proportions of urban, agricultural,

and natural land use for wells and study units, study areas, and domestic

wells, Southeast San Joaquin Valley and Kern County Subbasin study units,

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

Figure 4. Map showing land use in the Southeast San Joaquin Valley and Kern County

Subbasin study units, California GAMA Priority Basin Project ........................ 8

Figure 5. Map showing geology and generalized regional groundwater flow system in

the two southern San Joaquin Valley study units, California GAMA Priority

Basin Project................................................................................. 10

Figure 6. Map showing locations of grid cells, USGS-grid, and USGS-understanding

wells sampled during October 2005–March 2006, and California Department of

Public Health (CDPH)-grid wells for inorganic constituents, for the two

southern San Joaquin Valley study units, California GAMA Priority Basin Project 14

Figure 7. Graphs showing boxplots of construction characteristics for USGS-grid and

USGS-understanding wells, and wells from the Domestic Well Project

sampling in the Southeast San Joaquin Valley study unit and the Kern County

Subbasin study unit, California GAMA Priority Basin Project ........................ 22

Figure 8. Map showing normalized lateral position (distance from valley trough to valley

margins) and wells, Southern San Joaquin Valley study units, California GAMA

Priority Basin Project........................................................................ 24

Figure 9. Graph showing bar charts of distribution of USGS- and CDPH-grid wells and

domestic wells for normalized lateral position, Southeast San Joaquin Valley

study unit, Kern County Subbasin study unit, and Domestic Well Project wells,

California GAMA Priority Basin Project................................................... 25

Figure 10. Graphs showing boxplots and bar charts showing the relation of groundwater

age classification to depth to top-of-perforations and numbers of wells with

each groundwater age class in each of the three depth categories, Southeast

San Joaquin Valley study unit, California GAMA Priority Basin Project ............ 28

Figure 11. Graphs showing boxplots and bar charts showing the relation of groundwater

age classification to depth to top-of-perforations and numbers of wells with

each groundwater age class in each of the three depth categories, Kern

County Subbasin study unit, California GAMA Priority Basin Project ............... 30

Figure 12. Graph showing relation of oxidation-reduction category to lateral position and

depths of perforated interval of wells in the Kings study area of the Southeast

San Joaquin Valley study unit, California GAMA Priority Basin Project ............ 31

Figure 13. Graph showing relation of oxidation-reduction category to lateral position and

depth of perforated interval of wells located adjacent to or north of the Kern

River, Kern County Subbasin study unit, California GAMA Priority Basin Project ... 32
vi

Figure 14. Graphs showing relation of pH and well depth between the two southern San

Joaquin Valley study units, California GAMA Priority Basin Project, and the

domestic wells from the GAMA Domestic Well Project .............................. 34

Figure 15. Maximum relative-concentration in USGS- and CDPH-grid wells for

constituents detected by type of constituent in the Southeast San Joaquin

Valley study unit, and Kern County Subbasin study unit, California GAMA

Priority Basin Project........................................................................ 40

Figure 16. Graphs showing relative-concentrations of inorganic constituents with

human-health-based or aesthetic benchmarks with high maximum

relative-concentrations in USGS- and CDPH-grid wells in the two southern

San Joaquin Valley study units, California GAMA Priority Basin Project ............ 48

Figure 17. Maps showing relative-concentrations of selected inorganic constituents with

human-health-based and aesthetic benchmarks in USGS-grid, CDPH-grid,

USGS-understanding wells, and CDPH-other wells in the two southern San

Joaquin Valley study units, California GAMA Priority Basin Project ............... 51

Figure 18. Graphs showing relation of arsenic concentrations to well depth and pH in the

two southern San Joaquin Valley study units, California GAMA Priority Basin

Project, and the Domestic Well Project................................................... 66

Figure 19. Graph showing relation of fluoride concentrations to well depth and pH in the

two southern San Joaquin Valley study units, California GAMA Priority Basin

Project ....................................................................................... 69

Figure 20. Graphs showing relation of uranium concentrations to well depth,

groundwater age classification, and alkalinity in the two southern San Joaquin

Valley study units, California GAMA Priority Basin Project ........................... 71

Figure 21. Graphs showing relation of nitrate to well depth and percentage of

orchard and vineyard land use in the two southern San Joaquin Valley study

units, California GAMA Priority Basin Project .......................................... 72

Figure 22. Graphs showing detection frequency and maximum relative-concentration for

organic and special-interest constituents detected in USGS-grid wells in the

Southeast San Joaquin Valley study unit and the Kern County Subbasin study

unit., California GAMA Priority Basin Project .......................................... 77

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

selected organic and special-interest constituents in USGS-grid wells in the

two southern San Joaquin Valley study units, California GAMA Priority Basin

Project ....................................................................................... 79

Figure 24. Maps showing number of volatile organic compound (VOC) detections,

relative-concentrations of DBCP, number of pesticide detections, and

relative-concentration of perchlorate in USGS-grid wells,

USGS-understanding wells, and CDPH-other wells in the two southern San

Joaquin Valley study units, California GAMA Priority Basin Project .................. 85

Figure 25. Graph showing the relation of detections of total trihalomethanes (THMs) to

depth to top-of-perforations and dissolved oxygen for USGS-grid and

USGS-understanding wells in the Southern San Joaquin Valley study units,

California GAMA Priority Basin Project................................................... 89

Figure 26. Graph showing the relation of solvent detection with depth to

top-of-perforation and percentage of urban land use for USGS-grid and

USGS-understanding well samples in the two southern San Joaquin Valley

study units, California GAMA Priority Basin Project .................................... 90

Figures—Continued
vii

Figure 27. Bar graph showing relation of DBCP detection frequency to percentage of

orchard and(or) vineyard land use and dissolved oxygen, Southeast San

Joaquin Valley study unit, California GAMA Priority Basin Project .................. 92

Figure 28. Graphs showing relation of number of pesticide detections to depth to

top-of-perforations and groundwater age in the two southern San Joaquin

Valley study units, California GAMA Priority Basin Project ........................... 94

Figure 29. Graph showing relation of perchlorate concentration to dissolved oxygen in

the two southern San Joaquin Valley study units, California GAMA Priority

Basin Project................................................................................. 96

Figures—Continued

Tables

Table 1. Analytes and wells sampled for each analytical schedule for the two southern

San Joaquin Valley study units, California GAMA Priority Basin Project,

October 2005–March 2006.................................................................. 15

Table 2. Inorganic constituents, associated benchmark information, and number of grid

wells per constituent for the two southern San Joaquin Valley study units,

California GAMA Priority Basin Project ................................................ 16

Table 3. Comparison of the number of compounds and median method detection limits

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

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

collected by the U.S. Geological Survey in the two southern San Joaquin

Valley study units, California GAMA Priority Basin Project, October

2005–March 2006 ........................................................................... 17

Table 4. Constituents in CDPH wells with historically high concentrations but not during

the current period (January 1, 2003, to December 31, 2005) in the two southern

San Joaquin Valley study units, California GAMA Priority Basin Project ............ 18

Table 5. Results of statistical tests (Kruskal-Wallis or Wilcoxon) for differences

between selected potential categorical and continuous explanatory factors

and differences between categorical explanatory factors and selected

water-quality constituents for the two southern San Joaquin Valley study

units, California GAMA Priority Basin Project, and the GAMA Domestic Well

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

Table 6A. Results of nonparametric (Spearman’s method) analysis of correlations in grid

and understanding wells between selected potential explanatory factors, Kern

County Subbasin study unit, California GAMA Priority Basin Project ............... 35

Table 6B. Results of nonparametric (Spearman’s method) analysis of correlations in grid

and understanding wells between selected potential explanatory factors,

Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project 36

Table 6C. Results of nonparametric (Spearman’s method) analysis of correlations in 

domestic wells between selected potential explanatory factors,

California GAMA Domestic Well Project ................................................ 37

Table 7. Number of constituents analyzed and detected in USGS-grid wells by

human-health-based or aesthetic benchmark and constituent type in the two

southern San Joaquin Valley study units, California GAMA Priority Basin Project 39
viii

Tables—Continued

Table 8. Raw detection frequencies and aquifer-scale proportions using spatially

weighted and grid-based methods for inorganic constituents detected at

concentrations greater than water-quality benchmarks from data available

during the most recent 3 years (January 1, 2003, through December 31, 2005) in

the California Department of Public Health (CDPH) database, or detected at

high or at moderate relative-concentrations in samples collected from

USGS-grid wells (October 2005–March 2006) in the two southern San Joaquin

Valley study units, California GAMA Priority Basin Project ........................... 42

Table 9. Raw detection frequencies and aquifer-scale proportions using spatially

weighted and grid-based methods for organic constituents and constituents

of special interest detected at concentrations greater than water-quality

benchmarks from data available during the most recent 3 years (January 1,

2003, through December 31, 2005) in the California Department of Public Health

(CDPH) database, or detected at high or at moderate relative-concentrations,

or detected at detection frequencies greater than 10 percent in samples

collected from USGS-grid wells (October 2005–March 2006) in the two

southern San Joaquin Valley study units, California GAMA Priority Basin Project 44

Table 10. Aquifer-scale proportions for inorganic constituent classes for the two

southern San Joaquin Valley study units, California GAMA Priority Basin Project 47

Table 11. Results of non-parametric (Spearman’s method) correlation analysis between

selected inorganic water-quality constituents and potential explanatory

factors in the two southern San Joaquin Valley study units, California GAMA

Priority Basin Project........................................................................ 63

Table 12. Aquifer-scale proportions for organic constituent classes and constituents of

special interest for the two southern San Joaquin Valley study units, California

GAMA Priority Basin Project............................................................... 76

Table 13. Results of non-parametric correlation analysis (Spearman’s method) between

selected organic and special-interest water-quality constituents and potential

explanatory factors in the two southern San Joaquin Valley study units,

California GAMA Priority Basin Project................................................... 83
ix

Abbreviations and Acronyms

AB Assembly Bill (through the California State Assembly)

AL action level

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

GAMA Groundwater Ambient Monitoring and Assessment Program

HAL lifetime health advisory level

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

HBSL health-based screening levels

Hwy99T Highway 99 understanding well

KERN Kern County subbasin study unit 

KING Kings study area 

KINGFP Kings study area understanding well

KWH Kaweah study area 

Conversion Factors, Datums, and Abbreviations and 

Acronyms

Conversion Factors

Inch/foot/mile to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square foot (ft2) 0.09290 square meter (m2)

square mile (mi2) 2.590 square kilometer (km2) 

Radioactivity

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

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

°F =(1.8×°C)+ 32

Datums

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

(NAVD 88).

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

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).
x

Abbreviations and Acronyms—Continued

LRL laboratory reporting level

LSD land-surface datum

LUFT leaking underground fuel tanks

MCL maximum contaminant 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

NL notification level

NL-CA California Department of Public Health notification level 

QC quality control

RPD relative percent difference

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

SC specific conductance

SESJ Southeast San Joaquin Valley study unit

SMCL secondary maximum contaminant level

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

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

TEAP terminal electron acceptor process

TLR Tulare Lake study area 

TT-US USEPA treatment technique

TULE Tule study area 

Organizations

CDPH California Department of Public Health (Department of Health Services prior to July 1, 2007)

CDPR California Department of Pesticide Regulation

CDWR California Department of Water Resources

LLNL Lawrence Livermore National Laboratory

NAWQA National Water-Quality Assessment Program (USGS)

SWRCB State Water Resources Control Board (California)

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
xi

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued

Selected chemical names

Ammonia ammonia as nitrogen

CaCO3 calcium carbonate

DBCP 1,2-dibromo-3-chloropropane

DO dissolved oxygen

EDB 1,2-dibromoethane (ethylene dibromide)

NDMA N-nitrosodimethylamine

Nitrate nitrate as nitrogen

Nitrite nitrite as nitrogen

PCE tetrachloroethene (perchloroethene)

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

TCE trichloroethene

TDS total dissolved solids

THM trihalomethane

VOC volatile organic compound

Units of Measure

cm centimeter

δi

E delta notation; the ratio of the heavier isotope (i) to the more common lighter isotope of an element (E), 

relative to a standard reference material, expressed as per mil

ft foot (feet)

in. inch

km kilometer

km2 square kilometer

L liter

m meter

mg/L milligrams per liter (parts per million)

mi mile

mi2 square mile

μg/L micrograms per liter (parts per billion)

μS/cm microsiemens per centimeter

pCi/L picocuries per liter

per mil parts per thousand

pmc percent modern carbon

TU tritium unit
xii

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Abstract 

Groundwater quality in the southern San Joaquin Valley 

was investigated from October 2005 through March 2006 as 

part of the Priority Basin Project of the Groundwater Ambient 

Monitoring and Assessment (GAMA) Program. The GAMA 

Priority Basin Project is conducted by the U.S. Geological 

Survey (USGS) in collaboration with the California State 

Water Resources Control Board and the Lawrence Livermore 

National Laboratory. There are two study units located in the 

southern San Joaquin Valley: the Southeast San Joaquin Valley 

(SESJ) study unit and the Kern County Subbasin (KERN) 

study unit.

The GAMA Priority Basin Project in the SESJ and 

KERN study units was designed to provide a statistically 

unbiased, spatially distributed assessment of untreated 

groundwater quality within the primary aquifers. The status 

assessment is based on water-quality and ancillary data 

collected in 2005 and 2006 by the USGS from 130 wells on 

a spatially distributed grid, and water-quality data from the 

California Department of Public Health (CDPH) database. 

Data was collected from an additional 19 wells for the 

understanding assessment. The aquifer systems (hereinafter 

referred to as primary aquifers) were defined as that part of the 

aquifer corresponding to the perforation interval of wells listed 

in the CDPH database for the SESJ and KERN study units.

The status assessment of groundwater quality used data 

from samples analyzed for anthropogenic constituents such 

as volatile organic compounds (VOCs) and pesticides, as well 

as naturally occurring inorganic constituents such as major 

ions and trace elements. The status assessment is intended to 

characterize the quality of untreated groundwater resources 

within the primary aquifers in the SESJ and KERN study 

units, not the quality of drinking water delivered to consumers.

Although the status assessment applies to untreated 

groundwater, Federal and California regulatory and 

non-regulatory water-quality benchmarks that apply to 

drinking water are used to provide context for the results. 

Relative-concentrations (sample concentration divided 

by benchmark concentration) were used for evaluating 

groundwater. A relative-concentration greater than 1.0 

indicates a concentration greater than the benchmark and is 

classified as high. The relative-concentration threshold for 

classifying inorganic constituents as moderate or low was 0.5; 

for organic constituents the threshold between moderate and 

low was 0.1.

Aquifer-scale proportion was used as the primary 

metric for assessing the quality of untreated groundwater 

for the study units. High aquifer-scale proportion is defined 

as the areal percentage of the primary aquifers with a high 

relative-concentration for a particular constituent or class 

of constituents. Moderate and low aquifer-scale proportions 

were defined as the areal percentage of the primary aquifers 

with moderate and low relative-concentrations, respectively. 

Two statistical approaches—grid-based and spatially 

weighted—were used to evaluate aquifer-scale proportions for 

individual constituents and classes of constituents. Grid-based 

and spatially weighted estimates were comparable for the 

two study units in the southern San Joaquin Valley (within 

90 percent confidence intervals).

The status assessment showed that inorganic constituents 

were more prevalent than organic constituents and that 

relative-concentrations were higher for inorganic constituents 

than for organic constituents. For inorganic constituents 

with human-health benchmarks, the relative-concentration 

of at least one constituent in the SESJ study unit was high in 

30 percent of the primary aquifers. In the KERN study unit, 

the relative-concentration of at least one constituent was high 

in 23 percent of the primary aquifers. In the SESJ and KERN 

study units, the inorganic constituents with human-health 

benchmarks detected at high relative-concentrations in more 

than 2 percent of the primary aquifers were arsenic, boron, 

vanadium, nitrate, uranium, and gross alpha radioactivity. 

Additional constituents with human-health benchmarks—

antimony, radium, and fluoride—were detected at high 

relative-concentrations in the KERN study unit.

Status and Understanding of Groundwater Quality in the 

Two Southern San Joaquin Valley Study Units, 2005–2006: 

California GAMA Priority Basin Project

By Carmen A. Burton, Jennifer L. Shelton, and Kenneth Belitz
2 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

For inorganic constituents with aesthetic benchmarks 

(secondary maximum contaminant levels, SMCLs), the 

relative-concentration of at least one constituent in the SESJ 

study unit was high in 6.6 percent of the primary aquifers. In 

the KERN study unit, the relative-concentration of at least one 

constituent was high in 22 percent of the primary aquifers. 

Inorganic constituents with aesthetic benchmarks detected at 

high relative-concentrations in the primary aquifers in the SESJ 

and KERN study units were iron and manganese. Additional 

constituents with aesthetic benchmarks—total dissolved 

solids (TDS), sulfate, and chloride—were detected at high 

relative-concentrations in the KERN study unit.

In contrast, the status assessment for organic 

constituents with human-health benchmarks showed that 

relative-concentrations were high in 4.8 percent and 2.1 percent 

of the primary aquifers in the SESJ and KERN study units, 

respectively. The special-interest constituent, perchlorate, was 

detected at high relative-concentrations in 1.2 percent of the 

primary aquifers in the SESJ study unit.

Twenty-eight of the 78 VOCs (not including fumigants) 

analyzed were detected. Of these 28 VOCs, benzene had 

high relative-concentrations in the SESJ study unit, and 

relative-concentrations for the other 27 VOCs were moderate 

and low. Five of the 10 fumigants were detected; 1,2-dibromo3-chloropropane (DBCP) was the only fumigant with high 

relative-concentrations in the SESJ and KERN study units. 

Of the 136 pesticides and pesticide degradates analyzed, 

33 were detected. Human-health benchmarks were established 

for eighteen of the detected pesticides. Dieldrin was detected 

at moderate relative-concentrations in the SESJ and KERN 

study units. All other pesticides detected with human-health 

benchmarks were present at low relative-concentrations. The 

detection frequencies for two of these pesticides—simazine and 

atrazine—were greater than or equal to 10 percent in the SESJ 

and KERN study units.

The understanding assessment of groundwater quality 

included an analysis of correlations of selected water-quality 

constituents or classes of constituents with potential explanatory 

factors. The understanding assessment indicated that the 

concentrations of many trace elements and major ions were 

correlated to well depth, groundwater age, and/or geochemical 

conditions. Many trace elements were positively correlated with 

depth. Arsenic, boron, vanadium, fluoride, manganese, and iron 

concentrations increased with well depth or depth to top-ofperforations. The concentrations for these trace elements also 

were higher in older (pre-modern) groundwater. In contrast, 

uranium concentrations decreased with increasing depth and 

groundwater age.

Most trace elements were correlated to geochemical 

conditions. Arsenic, antimony, boron, fluoride, manganese, and 

iron concentrations generally were higher wherever the pH of 

the groundwater was greater than 7.6. Concentrations for these 

constituents generally were higher at low concentrations of 

dissolved oxygen (DO). Uranium was the exception; uranium 

concentrations generally were lower at high pH and at high 

concentrations of DO.

Nitrate concentrations generally were lower in 

deeper wells. Nitrate concentrations also were higher in 

groundwater with higher DO.

Total dissolved solids, sulfate, and chloride 

concentrations were higher in the KERN study unit than in 

the SESJ study unit. Total dissolved solids were negatively 

correlated with pH in the KERN study unit. Total dissolved 

solids and sulfate were higher in areas with more agricultural 

land use. Chloride concentrations increased with depth to 

top-of-perforations in the KERN study unit.

Organic constituents and constituents of special 

interest, like many inorganic constituents, were correlated 

with well depth, groundwater age, and DO. Unlike most 

trace elements, however, solvent and pesticide detections, 

and total trihalomethanes (THM), DBCP, and perchlorate 

concentrations decreased with increasing well depth. Volatile 

organic compound, solvent, and pesticide detections, and 

THM concentrations also were lower in older (pre-modern) 

groundwater than in modern-age groundwater. Solvent 

detections and total THM, DBCP, and perchlorate 

concentrations increased with increasing DO concentrations.

Introduction

Groundwater composes 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 drinking-water supply and to establish 

a baseline groundwater quality monitoring program, the 

California State Water Resources Control Board (SWRCB), 

in collaboration with the U.S. Geological Survey (USGS) 

and Lawrence Livermore National Laboratory (LLNL), 

implemented the Groundwater Ambient Monitoring and 

Assessment (GAMA) Program (California State Water 

Resources Control Board, 2011, website at http://www.

waterboards.ca.gov/water_issues/programs/gama). The 

statewide GAMA Program consists of three projects: (1) 

the Priority Basin Project, conducted by the USGS (U.S. 

Geological Survey, 2011, website at http://ca.water.usgs.

gov/gama); (2) the Domestic Well Project, conducted by the 

SWRCB; and (3) the Special Studies, conducted by LLNL. 

On a statewide basis, the GAMA Priority Basin Project 

primarily focused on the deep part of the groundwater 

resource (primary aquifers), and the SWRCB Domestic Well 

Project generally focused on the shallow aquifer systems. 

The primary aquifers may be at less risk of contamination 

than the shallow wells, such as private domestic or 

environmental monitoring wells, that are closer to surficial 

sources of contaminants. As a result, concentrations of 

contaminants, such as volatile organic compounds (VOCs) 

and nitrate, in wells screened in the deep primary aquifers 

may be lower than concentrations of contaminants in shallow 

wells (Nolan and Hitt, 2006; Landon and others, 2010).
Introduction 3

The SWRCB initiated the GAMA Program in 2000 in 

response to Legislative mandates (State of California, 1999, 

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

Fiscal Year). The GAMA Priority Basins Project was initiated 

in response to the Groundwater Quality Monitoring Act of 

2001 (State of California, 2001b, Section 10780-10782.3 

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

and monitor the quality of groundwater in California. The 

GAMA Priority Basin Project is a comprehensive assessment 

of statewide groundwater quality designed to improve 

understanding of and to identify risks to groundwater 

resources, and to increase the availability of information about 

groundwater quality to the public. For the GAMA Priority 

Basin Project, the USGS, in collaboration with the SWRCB, 

developed the monitoring plan to assess groundwater basins 

through direct and other statistically reliable sampling 

approaches (Belitz and others, 2003; California State Water 

Resources Control Board, 2003). Additional partners in the 

GAMA Priority Basin Project are the California Department of 

Public Health (CDPH), the California Department of Pesticide 

Regulation (CDPR), the California Department of Water 

Resources (CDWR), local water agencies, and well owners 

(Kulongoski and Belitz, 2004).

The range of hydrologic, geologic, and climatic 

conditions 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). These hydrogeologic provinces include groundwater 

basins designated by the CDWR (California Department of 

Water Resources, 2003). Groundwater basins generally consist 

of relatively permeable, unconsolidated deposits of alluvial or 

volcanic origin (California Department of Water Resources, 

2003). 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. Some 

groundwater basins, such as the San Joaquin Valley basin, 

cover large areas and are further divided into groundwater 

subbasins by the CDWR. Groundwater basins and subbasins 

were prioritized for sampling on the basis of the number 

of CDPH wells in the basin or subbasin, with secondary 

consideration given to municipal groundwater use, agricultural 

pumping, the number of historical leaking underground 

fuel tanks, and registered pesticide applications (Belitz and 

others, 2003). Of the 472 basins and subbasins designated by 

the CDWR, 116 basins, as well as additional areas outside 

defined groundwater basins, were grouped into 35 study units, 

which include approximately 95 percent of CDPH wells in 

California. 

The two GAMA Priority Basin Project study units (fig. 1) 

located in the southern San Joaquin Valley are the Southeast 

San Joaquin Valley study unit (hereinafter referred to as the 

SESJ study unit) and the Kern County Subbasin study unit 

(hereinafter referred to as the KERN study unit). The SESJ 

study unit is composed of four CDWR groundwater subbasins 

(Kings, Kaweah, Tule, and Tulare Lake, fig. 2). KERN is 

composed of one groundwater subbasin (Kern County, fig. 2). 

Purpose and Scope

This report is one of a series of GAMA Priority 

Basin Project assessment reports presenting the status and 

understanding of current water-quality conditions in GAMA 

Priority Basin Project study units. Tabulated USGS data are 

available from several study units and are available as data 

series reports (for example, Burton and Belitz, 2008; Shelton 

and others, 2008), and planned subsequent reports will address 

changes or trends in water quality across time.

The status and understanding assessments of the two 

southern San Joaquin Valley study units are presented in this 

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

unit description: brief description of the hydrogeologic setting 

of the study units; (2) a status assessment: an assessment 

of the current status of untreated-groundwater quality in 

the primary aquifers; and (3) an understanding assessment: 

an identification of the natural and human factors affecting 

groundwater quality and an explanation of the relations 

between water quality and selected potential explanatory 

factors. An explanation of the causative factors of any 

relations between water quality and explanatory factors is 

beyond the scope of this report. 

The status assessment in this report includes analysis 

of water-quality data from 130 wells selected for sampling 

by the USGS within spatially distributed grid cells across 

the two study units (hereinafter referred to as USGS-grid 

wells). Eighty-two percent of the USGS-grid wells were 

wells listed in the CDPH database, and the remainder had 

perforation intervals similar to CDPH wells in each study unit. 

Samples were collected from USGS-grid wells for analysis of 

anthropogenic constituents such as volatile organic compounds 

(VOCs) and pesticides, as well as naturally occurring 

constituents such as major ions, nutrients, and trace elements. 

Water-quality data from the CDPH database also were used 

to supplement data collected by the USGS for the GAMA 

Priority Basin Project. The resulting set of water-quality data 

from USGS-grid wells and supplemental CDPH data were 

considered to be representative of the primary aquifers in the 

southern San Joaquin Valley study units. 

To provide context, the water-quality data discussed in 

this report were compared to California and Federal regulatory 

and non-regulatory benchmarks for drinking water. The 

assessments in this report are intended to characterize the 

quality of untreated groundwater resources of the primary 

aquifers in the study units, not the drinking water delivered to 

consumers by water purveyors. This study does not attempt to 

evaluate the quality of water delivered to consumers.
4 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Basin and Range

Klamath

Mountains

Desert

Cascades and

Modoc Plateau

Southern 

Coast

 Ranges

Sierra

Nevada

Central

Valley

Northern

Coast

Ranges

San Diego

Drainages

Transverse Ranges and

Selected Peninsular Ranges

Bakersfield

San Francisco

OREGON

NEVADA

MEXICO

ARIZONA

Redding

Los Angeles

San Diego

Pacific Ocean

0 200 MILES

0 200 KILOMETERS

40

42 124 122 120 118 116 114

38

36

34

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Provinces from Belitz and others, 2003

Sacramento

Kern County

Subbasin study unit

Southeast San Joaquin 

Valley study unit

IP004884_Figure 1. Location in California

Figure 1. Location of the two southern San Joaquin Valley study units—Kern County Subbasin and Southeast 

San Joaquin Valley—and California hydrogeologic provinces (modified from Belitz and others, 2003), California 

GAMA Priority Basin Project.
Introduction 5

1

33

46

65

41

198

180

58

101

99

99

395

5

5

Bakersfield

Delano

Fresno

Tulare

Visalia

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant-Kern Canal

Lake

Isabella

0 10 20 40 Miles

0 10 20 40 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Kaweah

SOUTHEAST SAN JOAQUIN VALLEY

Study

area

SOUTHERN SAN JOAQUIN VALLEY STUDY UNITS 

KERN COUNTY SUBBASIN

Kern

Kings

Tulare lakebed

County line

Tulare Lake

Tule

River or stream

Canal

Aqueduct

Pacific Ocean

MADERA

CO

FRESNO

CO

INYO

CO SAN

BENITO

CO

MONTEREY CO

TULARE

KINGS CO

CO

KERN

CO

SAN LUIS

OBISPO

CO

SANTA

BARBARA

CO Tehachapi Mts

S i e r r a N e v a d a

S o u t h C o a s t R a n g e s

San Emigdio Mts

Kettleman Hills

Westside groundwater subbasin

IP004884_Figure 02

Southeast San Joaquin Valley

 study unit boundary

Kern County Subbasin

 study unit boundary

Figure 2. Study areas and geographic features of the two southern San Joaquin Valley study units (Southeast San Joaquin 

Valley and Kern County Subbasin), California GAMA Priority Basin Project.
6 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

The understanding assessment for the SESJ and the 

KERN study units includes data from 19 wells sampled 

by USGS for the purpose of understanding (hereinafter 

referred to as USGS-understanding wells). Some of the 

USGS-understanding wells represent shallow zones above 

the primary aquifers. Potential explanatory factors examined 

included land-use, well-depth and perforation information, 

position in the groundwater flow system, groundwater age, 

density of septic systems and historically leaking underground 

fuel tanks, and geochemical-condition indicators. A 

comprehensive analysis of all possible explanatory factors is 

beyond the scope of this report.

Water-quality data for samples collected by the USGS 

for the GAMA Priority Basin Project in the SESJ and KERN 

study units and details of sample collection, analysis, and 

quality-assurance procedures were reported by Burton and 

Belitz (2008) and Shelton and others (2008). Using these 

data and data from the CDPH database, this report describes 

methods used in designing the sampling network, identifying 

CDPH data for use in the status assessment, estimating 

aquifer-scale proportions, analyzing ancillary datasets, 

classifying groundwater age, and assessing the status and 

understanding of groundwater quality and its relation to 

selected explanatory factors. 

Description of the Study Units

The southern San Joaquin Valley lies in the Central 

Valley Hydrogeologic Province described by Belitz and others 

(2003) and includes two study units of the GAMA Priority 

Basin Project—the Southeast San Joaquin Valley (SESJ) and 

the Kern County Subbasin (KERN) study units. Together, 

these two study units cover about 6,780 square miles (mi2) 

[17,560 square kilometers (km2)] in Fresno, Kings, Tulare, and 

Kern Counties. 

The SESJ study unit covers about 3,780 mi2 (9,790 km2) 

and is bounded by the San Joaquin River on the north, the 

Sierra Nevada Mountains to the east, Kern County to the 

south, and the Kettleman Hills and the Westside subbasin 

to the west (fig. 2). The SESJ study unit includes four 

groundwater subbasins as defined by the CDWR—Kings, 

Kaweah, Tule, and Tulare Lake (California Department of 

Water Resources, 2003). 

The KERN study unit is at the southern boundary of 

the Central Valley hydrogeologic province, and covers about 

3,000 mi2 (7,770 km2) in Kern County (fig. 1). The study unit 

is bounded by the Kern County line to the north, the granitic 

bedrock of the Sierra Nevada and Tehachapi Mountains 

to the east and southeast, and the marine sediments of the 

San Emigdio Mountains and South Coast Ranges to the 

southwest and west (fig. 2) (California Department of Water 

Resources, 2006e). The KERN study unit is composed of 

one groundwater subbasin as defined by the CDWR—Kern 

County (California Department of Water Resources, 2003). 

The land use in the southern San Joaquin Valley primarily 

is agricultural, with most of the irrigated acreage used for 

field crops and fruit and nut orchards. Land use in the SESJ 

study unit is 85 percent agricultural, 9 percent natural, and 

6 percent urban based on the classification of USGS National 

Land Cover Data (Nakagaki and others, 2007; figs. 3 and 4). 

Land use in the KERN study unit is 66 percent agricultural, 

31 percent natural, and 3 percent urban. Urban land use in 

the SESJ and KERN study units is relatively low; however, 

the rates of increase in population in Fresno and Bakersfield 

from 1990 to 2000, the largest cities in the SESJ and Kern 

study units, respectively, are the highest in the San Joaquin 

Valley and two of the highest in the State (U.S. Census 

Bureau, 2000). 

Hydrogeologic Setting

The northwest-trending, asymmetrical structural trough 

of the San Joaquin Valley occupies the southern two-thirds of 

the Central Valley and is filled with marine and continental 

sediments of Tertiary and Quaternary age. The sediment 

thickness increases from the valley margins toward the axis 

of the trough, and from the north toward the south, up to a 

total thickness of about 30,000 feet (ft) in the south-central 

area of Kern County (Page, 1986). Freshwater occurs in the 

uppermost 3,000 ft, with brackish water beneath (Page, 1973).

Southeast San Joaquin Valley Study Unit

The climate for the SESJ study unit is Mediterranean 

with hot, dry summers and cool, moist winters. Average 

rainfall across the study unit ranges from 7 inches (in.) 

[18 centimeters (cm)] in the western part of the study unit to 

13 in. (33 cm) in the eastern part of the study unit (California 

Department of Water Resources, 2006a, b, c, d). More than 

90 percent of the precipitation falls between October and 

April. Several creeks and rivers drain the study unit. The San 

Joaquin, Kings, Kaweah, and Tule Rivers are the primary 

streams draining the study unit; most of their flow originates 

in the Sierra Nevada Mountains to the east. The San Joaquin 

River flows north and empties into the Sacramento–San 

Joaquin Delta. The Kings River is diverted to the San Joaquin 

River through the Fresno Slough. The Kaweah and Tule 

Rivers flow south or west toward the location of the Tulare 

Lake bed, which generally has been dry since 1919 as a result 

of long-term climate changes and stream diversions (Lofgren 

and Klausing, 1969; Faunt, 2009).
Description of the Study Units 7

SOUTHEAST

SAN JOAQUIN

STUDY UNIT

KERN

STUDY UNIT

STUDY AREA STUDY AREA

SOUTHEAST

SAN JOAQUIN

STUDY UNIT

KERN

STUDY UNIT

Data point:

Land use

based on

satellite

imagery

Data point:

Land use

based on a

500-meter-radius

area around

grid wells

A

B

Understanding well

King Kern

Kaweah

Tule

Tulare Lake

20

40

60

80

100

0

PERCENT URBAN

20

40

60

80

100

0

PERCENT URBAN

100 80 60 40 20 0

PERCENT AGRICULTURE

100 80 60 40 20 0

PERCENT AGRICULTURE

100

80

60

40

20

0

PERCENT NATURAL

100

80

60

40

20

0

PERCENT NATURAL

STUDY AREA

King

Kaweah

Tule

Tulare Lake

All CDPH wells

All domestic

wells

Domestic well

IP004884_Figure 03ab

Figure 3. Ternary diagram with proportions of urban, agricultural, and natural land use for (A) wells and (B) study units, 

study areas, and domestic wells, Southeast San Joaquin Valley and Kern County Subbasin study units, California GAMA 

Priority Basin Project.
8 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Figure 4. Land use in the Southeast San Joaquin Valley and Kern County Subbasin study units, California GAMA Priority Basin 

Project.

IP004884_Figure 04

Bakersfield

Delano

Fresno

Tulare

Visalia

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

Urban River or stream

LAND USE

Agricultural

Natural

Pacific Ocean

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

Tulare lakebed

Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

S i e r r a N e v a d a

S o u t h C o a s t R a n g e s
Description of the Study Units 9

The primary source of recharge is runoff from the nearby 

Sierra Nevada Mountains (Faunt, 2009). Other sources of 

recharge include irrigation, seepage from rivers, streams, and 

irrigation canals, percolation of precipitation, urban runoff, 

and recharge (California Department of Water Resources, 

2003; Wright and others, 2004). Discharge from the aquifer 

primarily is from groundwater pumping for irrigation and 

public water supply. About 40 percent of the water used in the 

study unit comes from groundwater (California Department 

of Water Resources, 2003). Most municipalities in the study 

unit, such as Visalia and Tulare, use groundwater as their sole 

source of public supply (Water Education Foundation, 2006).

The SESJ study unit is divided into four study areas—the 

Kings, Kaweah, Tule, and Tulare Lake study areas—which 

coincide with the groundwater subbasins, of the same names, 

that were defined by the CDWR. 

Kings Study Area

The Kings study area includes 1,530 mi2 (3,960 km2) 

of Fresno County and the northern part of Kings and Tulare 

Counties. It is bounded to the north by the San Joaquin River, 

to the east by the Sierra Nevada Mountains, to the south by 

the Kaweah and Tulare Lake groundwater subbasins, and to 

the west by the Delta-Mendota and Westside groundwater 

subbasins. The San Joaquin and Kings Rivers are the two 

major rivers within or bordering the subbasin (California 

Department of Water Reources, 2006a) (fig. 2). 

The aquifer system in the Kings study area consists of 

unconsolidated marine and continental deposits of the Tertiary 

and Quaternary age overlain by younger alluvial deposits 

of Quaternary age. The eastern two-thirds of the study area 

consist of the quaternary alluvial fans of the Kings River, with 

compound alluvial fans of intermittent streams to the north 

and south (Burow and others, 1997). These deposits generally 

are highly permeable. The western one-third of the study area, 

near the center of the valley, includes less permeable deposits 

in the basin geomorphic province (Davis and others, 1959; 

Faunt, 2009). Vertical flow is restricted by discontinuous silt 

and clay layers to an increasing degree toward the valley 

center. The Corcoran Clay member of the Tulare Formation, 

ranging in depths of about 250–550 ft (75–170 m), is the 

most extensive clay layer and forms a regional confining unit 

(California Department of Water Resources, 2006a) (fig. 5).

Kaweah Study Area

The Kaweah study area includes 700 mi2 (1,810 km2) 

of Tulare County. It is bounded by the Kings subbasin to 

the north, the Sierra Nevada Mountains to the east, the 

Tule subbasin to the south, and the Tulare Lake subbasin to 

the west. The Kaweah and Saint Johns Rivers are the two 

major rivers within the subbasin. The primary sources of 

groundwater recharge are recharge from the landscape (mostly 

irrigation return flow) and the Kaweah River (California 

Department of Water Resources, 2006b; Faunt, 2009) (fig. 2). 

The aquifer system in the Kaweah subbasin consists of 

unconsolidated marine and continental deposits of Pliocene, 

Pleistocene, and Holocene age (California Department of 

Water Resources, 2006b). The eastern part of the subbasin 

consists of three stratigraphic layers: continental deposits, 

older alluvium, and younger alluvium. The deeper continental 

deposits are from the Pliocene and Pleistocene age and 

are poorly permeable. The older alluvium, consisting 

of quaternary alluvial deposits, is moderately-to-highly 

permeable and is the major aquifer in the subbasin. Also, 

the younger alluvium, consisting of quaternary alluvial fan 

deposits, is moderately-to-highly permeable. The alluvial fan 

deposits interfinger with the less permeable deposits in the 

basin geomorphic province in the western part of the subbasin. 

The groundwater aquifer is confined by the Corcoran Clay 

in the western part of the subbasin (fig. 5) (California 

Department of Water Resources, 2006b; Faunt, 2009).

Tule Study Area

The Tule study area includes 730 mi2 (1,890 km2) of 

Tulare County. The study area is bounded by the Kaweah 

subbasin to the north, the Sierra Nevada Mountains to the east, 

Kern County to the south, and the Tulare Lake subbasin and 

Kings County to the west. Tule River and White River are 

the major rivers within the subbasin, and they flow westward 

toward the Tulare Lake bed (California Department of Water 

Resources, 2006c) (fig. 2). 

The aquifer system in the Tule subbasin consists of 

continental deposits of Pliocene, Pleistocene, and Holocene 

age (California Department of Water Resources, 2006c). The 

basin and lake deposits in the western part of the subbasin 

consist mainly of silt and clay and are relatively impermeable. 

The Corcoran Clay of the Tulare Formation in the western 

part of the subbasin (fig. 5) is a confining layer for underlying 

groundwater. The older alluvium underlies the alluvial fans, 

is very permeable, and is a major aquifer in the subbasin. 

The younger alluvial fans are very permeable but contain 

little water. The continental deposits in the eastern part of the 

subbasin are poorly sorted deposits of clay, silt, sand, and 

gravel from the Sierra Nevada Mountains and are a major 

source of groundwater (California Department of Water 

Resources, 2006c).

Tulare Lake Study Area

The Tulare Lake study area includes 820 mi2 (2,120 km2) 

of Kings County. It is bounded by the Kings subbasin to the 

north, the Kaweah and Tule subbasins to the east, Kern County 

to the south, and the Kettleman Hills and Westside subbasin 

to the west. The southern part of the subbasin is located in 

the former Tulare Lake bed (California Department of Water 

Resources, 2006d) (fig. 2).
10 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Figure 5. Geology and generalized regional groundwater flow system in the two southern San Joaquin Valley study units, 

California GAMA Priority Basin Project. IP004884_Figure 05

Bakersfield

Fresno

0 10 20 40 Miles

0 10 20 40 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Geology from Jennings, C.W., 1977, and

Saucedo, G.J. and others, 2000

EXPLANATION

River or

 stream

Canal

Aqueduct

Direction of

 groundwater

 flow

Corcoran

clay

Pacific Ocean

GEOLOGIC UNIT

Cenozoic Quaternary-Sedimentary rocks

Recent

 alluvium

Basin

 deposits

Alluvial fan

Pleistocene

 nonmarine

Lake

 deposits

Plio-Pleistocene

 nonmarine 

Tulare

 lakebed

Stream

 channel

 deposits

Qal Sand dune

Qb

Qc

Qf

Ql

Qp

Qs

Qsc

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

S i e r r a N e v a d a

S o u t h C o a s t R a n g e s
Methods 11

The aquifer system of the Tulare Lake subbasin consists 

of older and younger alluvium, and continental deposits 

(California Department of Water Resources, 2006d). 

Continental deposits are poorly sorted deposits of clay, sand, 

and gravel; their permeability is low-to-moderate. Older 

alluvium is poorly sorted deposits of clay, sand, silt, and 

gravel. The older alluvium is moderately to highly permeable 

and is a major aquifer in the subbasin. The younger alluvium 

consists of sorted and unsorted beds of clay, sand, silt, and 

gravel. The younger alluvium is very permeable but lies above 

the water table. Flood-basin and lake deposits, which include 

the Corcoran Clay, are not an important source of groundwater 

in the subbasin (California Department of Water Resources, 

2006d) (fig. 5).

In general, regional groundwater flow is from the east 

toward the west. North of the Kings River, groundwater 

generally flows westward toward the valley axis at the 

western boundary of the study unit. South of the Kings River, 

groundwater generally flows west or southwest toward the 

Tulare Lake bed (fig. 5).

Kern County Subbasin Study Unit

The hot Mediterranean climate of the KERN study unit 

is well suited to farming, with long, hot summer days and 

cool nights, and mild, damp winters with dense fog. The 

average annual precipitation, on the basis of a 56-year record 

at the California Irrigation Management Information System 

stations in Bakersfield and Shafter, located about 30 miles 

(78 km) west of Bakersfield, is 6 in. (15 cm), which occurs as 

rain primarily between November and February (California 

Irrigation Management Information System, 2006). 

The Kern River is the primary stream draining into the 

study unit (fig. 2); however, the primary sources of recharge 

are from artificial recharge at groundwater banking facilities 

(Tom Haslebacher, Kern County Water Agency, written 

commun., June 15, 2007). Locations of groundwater banking 

facilities are given in Shelton and others (2008). Secondary 

sources of recharge include return flows from agricultural and 

municipal irrigation and infiltration of flows from intermittent 

streams along the margin of the subbasin (California 

Department of Water Resources, 2006e).

The primary geologic formations that compose the 

aquifer system are the Plio-Pleistocene-age Tulare and Kern 

River Formations and overlying alluvium and terrace deposits 

(California Department of Water Resources, 2006e). The 

aquifer system is unconfined in the eastern part and above the 

Corcoran Clay, where it is present, in the central and western 

parts of the study unit. Aquifers below the Corcoran Clay 

are confined (Dale and others, 1966). The aquifer is thickest 

along the eastern margin of the study unit and ranges from 

about 175 to 2,900 ft (320 to 880 m) with an average thickness 

of about 600 ft (183 m), according to estimates from the 

California Department of Water Resources (2006e). 

In general, groundwater flows westward out of the Sierra 

Nevada and Tehachapi Mountains toward the center of the 

valley. Groundwater north of the Kern River flows west then 

flows north toward the Tulare Lakebed. Groundwater to the 

south of the Kern River flows toward southwest (fig. 5).

Methods

The status assessment provides a spatially unbiased 

assessment of groundwater quality in the primary aquifers, 

whereas the understanding assessment was designed to 

evaluate the natural and human factors that affect groundwater 

quality in the southern San Joaquin Valley study units. 

This section describes the methods used for: (1) defining 

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

status assessment, (3) determining which constituents warrant 

assessment, (4) calculating aquifer-scale proportions, and 

(5) analyzing statistics for the understanding assessment. 

Methods used for compilation of data on potential explanatory 

factors are described in appendix A.

The primary metric for defining groundwater quality 

is relative-concentration, which compares concentrations 

of constituents measured in groundwater to regulatory and 

non-regulatory benchmarks used to evaluate drinking-water 

quality. Constituents were selected for additional evaluation 

in the assessment based on objective criteria by using their 

relative-concentrations. Groundwater-quality data collected by 

GAMA Priority Basin Project and data compiled in the CDPH 

database are used in the status assessment. Two statistical 

methods based on spatially unbiased equal-area grids are used 

to calculate aquifer-scale proportions of low, moderate, or 

high relative-concentrations: the “grid-based” method uses 

one value per cell to represent groundwater quality, and the 

“spatially weighted” method uses many values per cell (Belitz 

and others, 2010).

Priority Basin Project understanding assessments are 

designed for the evaluation of the natural and human factors 

that affect groundwater quality at the study-unit level. The 

understanding assessments can be compared with other study 

units at regional and statewide scales. A finite set of potential 

explanatory factors was analyzed in relation to constituents of 

interest to place the observed water quality within the context 

of physical and chemical processes. Statistical tests were used 

to identify significant correlations between the constituents of 

interest and potential explanatory factors.
12 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Status Assessment Methods

The status assessment included the following two 

steps. (1) Water-quality data were normalized to their 

respective water-quality benchmarks by calculating their 

relative-concentrations (Toccalino and others, 2004; Toccalino 

and Norman, 2006). (2) Aquifer-scale proportions were 

determined for categories of “high,” “moderate,” and “low” 

relative-concentrations using two methods: grid-based and 

spatially weighted. Results for the two approaches were 

compared, and results from the preferred approach were used 

to identify constituents of interest for further discussion.

Relative-Concentrations and 

Water-Quality Benchmarks

Concentrations of constituents are presented as 

relative-concentrations in the status assessment section of 

this report:

Sample concentration Relative-concentration . Water-quality benchmark concentration =

Relative-concentrations provide context for the measured 

concentrations in the sample: relative-concentrations less than 

1.0 indicate sample concentrations less than the benchmark, 

and values greater than 1.0 indicate sample concentrations 

greater than the benchmark. The use of relative-concentrations 

permits comparison of a wide range of concentrations for 

different constituents on a single scale.

Toccalino and others (2004), Toccalino and Norman 

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

measured concentration to a benchmark (either maximum 

contaminant level [MCLs] or health-based screening levels 

[HBSL]) and defined this ratio as the benchmark quotient. 

Relative-concentrations used in this report are equivalent 

to the benchmark quotient reported by Toccalino and others 

(2004) for constituents with water-quality benchmarks. 

Relative-concentrations were computed only for compounds 

with water-quality benchmarks; therefore, constituents without 

water-quality benchmarks were not included in the status 

assessment. HBSLs were not used in this report because 

HBSLs are not currently used as benchmarks by California 

drinking-water regulatory agencies. 

Regulatory and non-regulatory benchmarks apply 

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

groundwater. However, to provide some context for the 

water-quality results, concentrations of constituents measured 

in the untreated groundwater were compared with regulatory 

and non-regulatory health-based benchmarks established 

by the U.S. Environmental Protection Agency (USEPA) 

and CDPH (U.S. Environmental Protection Agency, 2006; 

California Department of Health Services, 2007). The 

health-based benchmarks used for each constituent were 

selected in the following order of priority:

1. Regulatory, in the order of health-based USEPA and 

CDPH maximum contaminant levels (MCL-US and 

MCL-CA), USEPA action levels (AL-US), and USEPA 

treatment technique levels (TT-US). Federal benchmarks 

were used unless the California levels were lower.

2. Non-regulatory USEPA and CDPH secondary maximum 

contaminant levels (SMCL-US and SMCL-CA). For 

constituents with recommended and upper SMCL-CA 

levels, the values for the upper levels were used.

3. Non-regulatory, in the order of health-based CDPH 

notification levels (NL-CA), USEPA lifetime health 

advisory levels (HAL-US) and USEPA risk-specific dose 

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

Note that for constituents with multiple types of benchmarks, 

this hierarchy may not result in selection of the benchmark 

with the lowest concentration. Additional information on the 

types of benchmarks and the benchmarks for all constituents 

analyzed is provided by Burton and Belitz (2008) and Shelton 

and others (2008).

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

A relative-concentration greater than 1.0 is classified 

as high. A relative-concentration of 0.1 was used as a 

boundary between moderate and low values of organic and 

special-interest constituents for consistency with other studies 

and reporting requirements (Toccalino and others, 2004). The 

USEPA also established a relative-concentration of 0.1 of the 

regulatory benchmark as a threshold concentration so that 

the agency would be notified if the presence of a pesticide 

in surface water or groundwater is greater than or equal 

to that threshold (U.S. Environmental Protection Agency, 

1997). In addition, organic and special-interest constituents, 

which generally are anthropogenic, usually are less prevalent 

and have smaller maximum relative-concentrations than 

inorganic constituents. In contrast, inorganic constituents 

are typically naturally occurring at concentrations that 

could be greater than 0.1 of regulatory benchmarks; 

consequently, it would be difficult to identify inorganic 

constituents that may have elevated concentrations greater 

than background levels if a relative-concentration of 0.1 

was used as the threshold between moderate and low 

relative-concentrations. Therefore, the boundary between 

moderate and low relative-concentrations was set at 0.5 of the 

regulatory benchmark. 
Methods 13

Design of Sampling Networks for 

Status Assessment

The wells selected for sampling by the USGS in the SESJ 

and KERN study units were selected to provide a statistically 

unbiased, spatially distributed set of wells for the assessment 

of the quality of groundwater in the primary aquifers 

(USGS-grid wells). Water-quality data from the USGS-grid 

wells were supplemented with data from selected wells from 

the CDPH database (CDPH-grid wells, and discussed in 

more detail in the “California Department of Public Health 

Grid Well Selection” section) to obtain more complete grid 

coverage and to include constituents that were not analyzed 

for in every USGS-grid well. These data were used to assess 

proportions of the primary aquifers with high, moderate, and 

low relative-concentrations. 

The primary data used for the grid-based calculations 

of aquifer-scale proportions were data from wells sampled 

by the GAMA Priority Basin Project. Detailed descriptions 

of the methods used to identify wells for sampling are given 

in Burton and Belitz (2008) and Shelton and others (2008). 

USGS-grid wells (83 wells in the SESJ study unit and 

47 wells in the KERN study unit) were selected to provide 

a statistically unbiased, spatially distributed set of wells for 

the assessment of the quality of groundwater in the primary 

aquifers (Scott, 1990). The objective of the grid design was 

to sample one CDPH well in each cell. If a grid cell did not 

contain accessible CDPH wells, then commercial, irrigation, 

or domestic wells were considered for sampling. The 

USGS-grid wells were sampled by the USGS for the GAMA 

Priority Basin Project, but are owned by other organizations 

or individuals.

One USGS-grid well was sampled in 83 of the 102 grid 

cells in the SESJ study unit, including 39 of the 40 grid cells 

in the Kings study area, 18 of the 20 grid cells in the Kaweah 

study area, 17 of the 20 grid cells in the Tule study area, and 9 

of the 22 grid cells in the Tulare Lake study area (fig. 6). One 

USGS-grid well was sampled in 47 of the 122 grid cells in the 

KERN study unit (fig. 6). The grid cells from which samples 

were not collected had few, if any, wells, or permission 

to sample was not granted for wells that did exist in those 

cells. The 130 USGS-grid wells sampled in the southern 

San Joaquin Valley included 108 CDPH wells, 6 domestic 

wells, 15 irrigation wells, and 1 fire-protection well. The 

CDPH wells, irrigation wells, and the fire-protection well 

are considered production wells for this report. USGS-grid 

wells in the SESJ study unit were numbered in the order 

of sample collection with the prefix varying by study area: 

Kings study area (KING), Kaweah study area (KWH), Tule 

study area (TULE), Tulare Lake study area (TLR) (fig. A1A, 

appendix A). USGS-grid wells in the KERN study unit were 

numbered in the order of sample collection with the prefix 

KERN (fig. A2A, appendix A).

Samples collected from USGS-grid wells were analyzed 

for 180 to 345 constituents (table 1). VOCs, pesticides, 

perchlorate, noble gases, tritium, and stable isotopes of 

hydrogen and oxygen were analyzed in water samples from 

all wells. Additional pesticides, N-nitrosodimethylamine 

(NDMA), major and minor ions, trace elements, nutrients, 

isotopes of nitrogen and oxygen in nitrate, uranium 

isotopes, and redox species were analyzed in samples 

from 42 USGS-grid wells. Gasoline oxygenates, dissolved 

organic carbon, additional radiochemical constituents, 

carbon isotopes, and microbial constituents were analyzed in 

samples from 19 USGS-grid wells. The collection, analysis, 

and quality-control data for the analytes listed in table 1

are described by Burton and Belitz (2008) and Shelton and 

others (2008).

California Department of Public Health Grid 

Well Selection

Data for VOCs, pesticides, and perchlorate were 

collected at all 130 USGS-grid wells. The USGS-grid-well 

data included more VOC and pesticide constituents, and 

reporting levels were lower than reporting levels from the 

CDPH database. Therefore, CDPH data for these constituents 

were not used to supplement USGS-grid-well data for the 

status assessment. 

Samples for analysis of inorganic constituents were 

collected from 42 of 130 USGS-grid wells (28 in the SESJ 

study unit, and 14 in the KERN study unit). Because the 

GAMA Priority Basin Project did not collect a complete 

suite of inorganic constituents for all grid cells, the CDPH 

database was used to provide data for inorganic constituents 

for the cells without this data (table 2). In addition, the GAMA 

Priority Basin Project was not able to sample wells in six 

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

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

selected to supplement USGS-grid wells are referred to as 

“CDPH-grid” wells. The approach used to identify suitable 

CDPH wells is described in appendix A. Briefly, the first 

choice was to use CDPH data from the same well as the 

USGS-grid well (“DG” CDPH-grid wells; tables A1 and A2). 

If the DG well did not have all needed data, a second well 

was randomly selected from the subset of CDPH wells in 

the same cell with data (“DPH” CDPH-grid wells; tables A1

and A2). Combining data from CDPH-grid wells with data 

from USGS-grid wells produced inorganic data for 146 cells. 

All other CDPH wells with data from the current period 

(January 1, 2003, through December 31, 2005) not selected to 

be CDPH-grid wells are referred to as “CDPH-other” wells. 

Comparisons of data from USGS and CDPH wells to assess 

the validity of using these different sources in combination are 

presented in appendix B. 
14 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

1

33

46

65

41

198

180

58

101

99

99

395

5

5

Bakersfield

Delano

Fresno

Tulare

Visalia

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Tulare lakebed

River or stream

Canal

Aqueduct

Pacific Ocean

USGS-grid well (GAMA data only)

CPDH-grid well (CDPH data only)

USGS-grid well (GAMA and supplemental

 CDPH data only) 

USGS-understanding well

Grid cell All other CDPH wells

0 10 20 40 Miles

0 10 20 40 Kilometers

S i e r r a N e v a d a

S o u t h C o a s t R a n g e s

Kaweah

SOUTHEAST SAN JOAQUIN VALLEY

Study

area

SOUTHERN SAN JOAQUIN VALLEY STUDY UNITS 

KERN COUNTY SUBBASIN

Kern

Kings

Tulare Lake

Tule

Southeast San Joaquin Valley

 study unit boundary

Kern County Subbasin

 study unit boundary

IP004884_Figure 06_Wells Figure 6. Locations of grid cells, USGS-grid, and USGS-understanding wells sampled during October 2005–March 2006, and 

California Department of Public Health (CDPH)-grid wells for inorganic constituents, for the two southern San Joaquin Valley study 

units, California GAMA Priority Basin Project.
Methods 15

Table 1. Analytes and wells sampled for each analytical schedule for the two southern San Joaquin Valley study units, California 

GAMA Priority Basin Project, October 2005–March 2006.

[Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; VOC, volatile organic compound; NDMA, N-nitrosodimethylamine; 

1,2,3-TCP, 1,2,3-trichloropropane; μg/L, microgram per liter]

Study unit

Southeast San Joaquin Valley Kern County Subbasin

Sampling schedule

Fast Intermediate Slow Fast Intermediate Slow

Well summary Number of wells

Total number of wells 55 20 24 33 6 11

Number of grid wells sampled 55 19 9 33 4 10

Number of understanding wells sampled 0 1 15 0 2 1

Analyte group Number of 

constituents

Schedule

Fast Intermediate Slow

Dissolved oxygen, specific conductance, temperature 3 X X X

Volatile organic compounds (VOCs) 1 85 X X X

Pesticides and degradates 82 X X X

Perchlorate 1 X X X

Noble gases & tritium 2 7 X X X

Stable isotopes of water 2 X X X

Pharmaceuticals 3 12 X11 X X

Laboratory alkalinity and pH 2 X X

Polar pesticides and degradates 4 54 X X

NDMA and low-level 1,2,3-TCP 5 2 X X

Nutrients 5 X X

Nitrogen and oxygen isotopes of nitrate 2 X X

Major, minor, and trace elements 6 36 X X

Arsenic and iron speciation 4 X X

Uranium isotopes 3 X X

Tritium 7 1 X X

Gasoline oxygenates 8 3 X

Dissolved organic carbon 1 X

Field alkalinity and pH 2 X

Carbon isotopes 2 X

Radon-222 1 X

Radium isotopes 2 X

Gross alpha and beta radioactivity9 4 X

Microbial constituents 4 X

Low-level halogenated VOCs (chlorofluorocarbons) 3,10 25 X

Total number of constituents analyzed 180 301 345

1

 Includes 10 constituents classified as fumigants or fumigant synthesis byproducts.

2

 Analyzed at Lawrence Livermore National Laboratory, Livermore, California.

3

 Not discussed in this report.

4

 Does not include four constituents in common with pesticides and degradates.

5

 Includes one analyte, 1,2,3-TCP, in common with VOC analyses. However, the laboratory reporting level for the low-level analysis is 0.005 μg/L compared 

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

6

 Includes one constituent, uranium, classified as a radioactive constituent later in this report.

7

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

8

 Does not include five constituents in common with VOCs.

9

 Both gross alpha and gross beta particle activities were measured after 72-hour and 30-day holding times; the 72-hour results are used in this report.

10 Includes 22 analytes in common with VOC analyses. However, the laboratory reporting levels for the low-level analyses are two or three orders of 

magnitude lower than for the VOC analyses. Therefore, the low-level analyses are counted as separate analyses.

11

 Collected on the fast schedule only in the Kern County Subbasin study unit.
16 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table 2. Inorganic constituents, associated benchmark information, and number of grid wells per constituent for the two southern San 

Joaquin Valley study units, California GAMA Priority Basin Project.

[Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of Public Health; HAL-US, USEPA 

lifetime health advisory level; MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; NL-CA, CDPH notification level; 

AL-US, USEPA action level; SMCL-CA, CDPH secondary maximum contaminant level; SMCL-US, USEPA secondary maximum contaminant level; USEPA, 

U.S. Environmental Protection Agency]

Constituent Benchmark 

type

Study unit

Southeast San Joaquin Valley Kern County Subbasin

Number of grid 

wells sampled 

by GAMA

Number of grid 

wells selected 

from CDPH

Number of grid 

wells sampled 

by GAMA

Number of grid 

wells selected 

from CDPH

Nutrients with health-based benchmarks

Ammonia, as nitrogen HAL-US 28 0 14 0

Nitrite, as nitrogen MCL-US 28 38 14 46

Nitrate, as nitrogen MCL-US 28 51 14 53

Trace elements and minor ions with health-based benchmarks

Aluminum MCL-CA 28 44 14 42

Antimony MCL-US 28 40 14 42

Arsenic MCL-US 28 45 14 42

Barium MCL-CA 28 44 14 42

Beryllium MCL-US 28 44 14 42

Boron NL-CA 28 18 14 18

Cadmium MCL-US 28 44 14 42

Chromium MCL-CA 28 43 14 40

Copper AL-US 28 35 14 37

Lead AL-US 28 41 14 39

Mercury MCL-US 28 40 14 42

Molybdenum HAL-US 28 0 14 0

Nickel MCL-CA 28 44 14 42

Selenium MCL-US 28 40 14 42

Strontium HAL-US 28 0 14 0

Thallium MCL-US 28 40 14 42

Vanadium MCL-US 28 21 14 15

Fluoride MCL-CA 28 39 14 43

Trace elements and major ions with secondary maximum contaminant levels

Iron SMCL-CA 28 33 14 39

Manganese SMCL-CA 28 33 14 39

Silver SMCL-CA 28 42 14 37

Zinc SMCL-US 28 33 14 36

Chloride SMCL-CA 28 33 14 34

Sulfate SMCL-CA 28 33 14 34

Total dissolved solids SMCL-US 51 33 24 34

Radioactive constituents with health-based benchmarks

Gross alpha MCL-US 9 44 10 28

Gross beta MCL-US 9 1 10 3

Radon-222 MCL-US 9 0 10 1

Radium-226 + -228 MCL-US 9 24 9 9

Uranium MCL-US 28 12 14 6
Methods 17

Selection of Constituents for 

Additional Evaluation

The GAMA Priority Basin Project used available 

monitoring data along with newly collected data for 

characterization of the groundwater resource. The statewide 

CDPH database contains data for regulated constituents with 

water-quality benchmarks. Although other organizations 

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

statewide database of public-supply well data available for 

comprehensive analysis. Data for some constituents, including 

VOCs, pesticides, inorganic constituents, and radioactive 

constituents, are available from the GAMA Priority Basin 

Project and the CDPH databases. However, more VOCs 

and pesticides are analyzed by the GAMA Priority Basin 

Project than were available in the CDPH database (table 3). 

In addition, laboratory reporting levels (LRLs) for GAMA 

Priority Basin Project data typically were one or two orders of 

magnitude less than the method detection levels (MDLs) used 

for analyses compiled by CDPH (table 3). Thus, the GAMA 

Priority Basin Project data was selected to enhance the CDPH 

database by providing a larger number of analytes and lower 

reporting levels than are found in the CDPH database. Both 

datasets are used in the status and understanding assessments.

The CDPH database contains more than 1,800,000 

records from more than 2,200 wells in the SESJ and KERN 

study units, necessitating targeted retrievals to access 

water-quality data effectively. CDPH data were used with 

USGS-grid data to identify constituents in the study units 

at concentrations greater than water-quality benchmarks 

at any time during the period of record (March 28, 1980 

through December 31, 2005, for the SESJ study unit and 

April 26, 1978 through December 31, 2005, for the KERN 

study unit). These constituents were included in the status 

assessment. Constituent concentrations retrieved from the 

CDPH database for samples in the study units were identified 

as “historically high” (table 4) if (1) concentrations were high 

(greater than benchmarks) at any time during the period of 

record and (2) concentrations were not high in the most recent 

3-year period (January 1, 2003 through December 31, 2005, 

hereinafter referred to as current period) or in USGS-grid data. 

These constituents do not reflect current conditions on which 

the status assessment is based. 

Table 3. Comparison of the number of compounds and median method detection limits or median laboratory reporting levels by 

type of constituent for data stored in the California Department of Public Health (CDPH) database and data collected by the U.S. 

Geological Survey in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project, October 2005–

March 2006.

[Median MDL and LRL units: VOCs, pesticides, pharmaceutical constituents, NDMA, perchlorate, and trace elements, micrograms per liter (μg/L); 

radioactive constituents, picocuries per liter (pCi/L); nutrients and major and minor ions, milligrams per liter (mg/L). Abbreviations: CDPH, California 

Department of Public Health; GAMA, Groundwater Ambient Monitoring and Assessment Program; MDL, method detection limit; LRL, laboratory 

reporting level; VOC, volatile organic compound; ssLc, sample-specific critical level; nc, not collected]

Constituent

CDPH GAMA

Number of 

compounds

Median 

MDL

Number of 

compounds

Median 

LRL

Organic constituents

Volatile organic compounds (VOCs) plus gasoline oxygenates 

(including fumigants)

68 0.5 88 0.06

Pesticides plus degradates 68 1 136 0.016

Inorganic constituents

Pharmaceutical constituents nc nc 14 1 0.024

Trace elements 19 10 25 0.12

Radioactive constituents (ssLc

) 6 2 11 2 0.66

Major and minor ions 10 unknown 11 0.10

Nutrients, dissolved organic carbon 6 0.4 6 0.05

Constituents of special interest

Perchlorate 1 4 1 0.5

N-Nitrosodimethylamine (NDMA) nc nc 1 0.002

1

 Value reported is a median MDL. 

2

 Value reported is a median sample-specific critical level (ssLc) for 11 radioactive constituents collected and analyzed by GAMA.
18 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table 4. Constituents in CDPH wells with historically high concentrations but not during the current period (January 1, 2003, to 

December 31, 2005) in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.

[Benchmark value units: trace elements, pesticides, fumigants, solvents, other organics, and constituents of interest, micrograms per liter (μg/L); 

radioactive constituents, picocuries per liter (pCi/L). A high analysis is defined as a concentration that is greater than the human-health benchmark for 

that constituent. Abbreviations: CDPH, California Department of Public Health; GAMA, Groundwater Ambient Monitoring and Assessment Program; 

MCL-US, USEPA maximum contaminant level; MCL-CA, CDPH maximum contaminant level; HAL-US, USEPA health advisory level; RSD5-US, 

USEPA risk-specific dose at a risk factor of 10–5; USEPA, U.S. Environmental Protection Agency]

Constituent Number of wells 

with analyses

Benchmark 

type

Benchmark 

value

Date of most 

recent high value

Number of wells 

with at least one 

historically high value

Southeast San Joaquin Valley study unit (March 28, 1980, to December 31, 2002)

Trace elements

Antimony 950 MCL-US 6 01-24-1994 1

Beryllium 962 MCL-US 4 06-01-2000 1

Cadmium 1,075 MCL-US 5 07-02-2002 9

Chromium 1,068 MCL-CA 50 08-28-2002 2

Mercury 1,062 MCL-US 2 05-12-1989 4

Molybdenum 6 HAL-US 40 07-24-2001 1

Radioactive constituents

Radium-226 163 MCL-US 5 03-16-1995 2

Radium-228 275 MCL-US 5 07-01-1994 1

Pesticides

Diazinon 844 HAL-US 1 06-17-1996 1

Dieldrin 514 RSD5-US 0.02 02-03-1982 1

Fumigants

1,2-Dichloropropane 1,095 MCL-US 5 01-05-1994 2

1,4-Dichlorobenzene 1,144 MCL-CA 5 01-05-1994 1

Solvents

1,1-Dichloroethane 1,144 MCL-CA 5 01-05-1994 1

1,2-Dichloroethane 1,144 MCL-CA 0.5 01-05-1994 1

trans-1,2-Dichloroethene 1,142 MCL-CA 10 07-05-1988 1

1,1,2,2-Tetrachloroethane 1,143 MCL-CA 1 01-05-1994 2

1,2,4-Trichlorobenzene 1,001 MCL-CA 5 01-05-1994 1

1,1,2-Trichloroethane 1,143 MCL-US 5 01-05-1994 1

Other organics

Vinyl chloride 1,143 MCL-CA 0.5 01-05-1994 1

Kern County Subbasin study unit (April 26, 1978, to December 31, 2002)

Trace Elements

Aluminum 488 MCL-CA 1,000 05-01-2000 4

Barium 498 MCL-CA 1,000 12-19-2001 3

Chromium 498 MCL-CA 50 03-08-1993 2

Mercury 499 MCL-US 2 10-20-1999 5

Radioactive constituents

Radium-226 119 MCL-US 5 11-05-1993 1

Gross beta radioactivity 56 MCL-CA 50 07-10-2002 2

Pesticides

Atrazine 472 MCL-CA 1 08-04-1998 3

Solvents

1,2-Dichloroethane 503 MCL-CA 0.5 07-16-2002 12

Constituent of special interest

Perchlorate 251 MCL-CA 6 05-13-2001 1
Methods 19

More than 340 constituents were analyzed in the southern 

San Joaquin Valley study units; however, only a subset of 

these constituents is selected for additional evaluation in this 

report. Three criteria were used to identify constituents for 

additional evaluation:

1. Constituents with concentrations at high or at moderate 

relative-concentrations in the CDPH database 

during the current 3-year period (January 1, 2003, to 

December 31, 2005),

2. Constituents with concentrations at high or at moderate 

relative-concentrations in the USGS-grid wells or 

USGS-understanding wells, or

3. Organic constituents that were detected in more than 

10 percent in the USGS-grid-well dataset, even if 

relative-concentrations were low.

The relative-concentrations for constituents discussed 

in the understanding assessment were high in more than 

2 percent of the primary aquifers, or constituents were 

detected in more than 10 percent of the USGS-grid well 

dataset. A complete list of the constituents investigated by 

GAMA Priority Basin Project in the southern San Joaquin 

Valley study units may be found in the data reports for the 

study unit (Burton and Belitz, 2008; Shelton and others, 2008).

Calculation of Aquifer-Scale Proportions

The status assessment is intended to characterize the 

quality of groundwater resources in the primary aquifers of 

the two southern San Joaquin Valley study units. The primary 

aquifers are defined by the depth intervals over which wells 

listed in the CDPH database are perforated. The use of the 

term “primary aquifers” does not imply that there is a discrete 

aquifer unit. In most groundwater basins, municipal and 

community supply wells generally are perforated at greater 

depths than are domestic wells. Most of the wells used in the 

status assessment are listed in the CDPH databases. Thus, 

because domestic wells are not listed in the CDPH database, 

the primary aquifers generally correspond to the part of the 

aquifer system tapped by municipal and community supply 

wells. 

Water quality in the primary aquifers can differ from 

water quality in shallow or deep parts of the aquifer system. 

Previous investigations in the study unit have shown that 

groundwater in shallow parts of the aquifer generally is of 

poorer quality than groundwater at greater depths in the 

aquifer (Burow and others, 1998a, 1998b, 2007, 2008b). 

Similarly, water quality at greater depths than those typically 

used for public supply can be of different quality, particularly 

with respect to dissolved solids (Page, 1973). The proportions 

for the primary aquifers discussed in this report do not 

characterize the shallow or deep parts of the aquifer system.

Two statistical methods—grid-based and spatially 

weighted—were applied to evaluate the proportions of the 

primary aquifers in the southern San Joaquin Valley study 

units with high, moderate, and low relative-concentrations 

of constituents. For ease of discussion, these proportions are 

referred to as “high,” “moderate,” and “low” aquifer-scale 

proportions. Calculations of aquifer-scale proportions were 

made for individual constituents meeting the criteria for 

additional evaluation in the status assessment, and for classes 

of constituents. Classes of constituents with health-based 

benchmarks included trihalomethanes (THMs), solvents, 

other VOCs, fumigants, pesticides, trace elements and minor 

ions, uranium and radioactive constituents, and nutrients. 

Among constituents with aesthetic benchmarks (SMCLs), 

aquifer-scale proportions were calculated for major ions 

(total dissolved solids, chloride, and sulfate) in addition to 

manganese and iron.

The grid-based calculation uses the grid-well dataset 

assembled from the USGS- and CDPH-grid wells (Belitz and 

others, 2010). The proportion of the primary aquifers with 

high relative-concentrations of a constituent was calculated by 

dividing the number of grid cells represented by a high value 

for that constituent by the total number of grid cells with data 

for that constituent (see appendix C for details of methods). 

Proportions of moderate and low relative-concentrations 

were calculated similarly. Confidence intervals for grid‐based 

detection frequencies of high relative-concentrations were 

computed using the Jeffreys interval for the binomial 

distribution (Brown and others, 2001). Although the 

grid-based estimate is spatially unbiased, the grid-based 

approach may not detect constituents that are present at high 

concentrations in small proportions of the primary aquifers. 

For calculation of high aquifer-scale proportion for a class of 

constituents, cells were considered high if the value for any of 

the constituents was high. Cells were considered moderate if 

the value for any of the constituents was moderate, but none of 

the values were high.

The spatially weighted calculation used all available 

data from the following sources to calculate the aquifer-scale 

proportions—(1) all CDPH wells in each study unit (most 

recent analysis from each well with data for the constituent 

during the current period, January 1, 2003, to December 31, 

2005), (2) USGS-grid wells, and (3) USGS-understanding 

wells with perforation intervals representative of the primary 

aquifers (discussed in the section Understanding Assessment 

Methods). USGS-understanding wells that were monitoring 

wells were excluded because these wells were perforated 

at shallower depths than is typical for wells in the CDPH 

database. For the spatially weighted approach, proportions are 

computed on a cell-by-cell basis (Isaaks and Srivistava, 1989; 

Belitz and others, 2010), rather than as an average of all 

wells. The proportion of high relative-concentrations for 

each constituent for the primary aquifers was computed 

(1) by computing the proportion of wells with high 

relative-concentrations in each grid cell and (2) by averaging 

the grid-cell proportions computed in step (1) (see appendix C

for details of methods). Similar procedures were used to 

calculate the aquifer-scale proportions of moderate and low 

relative-concentrations. The resulting proportions are spatially 

unbiased (Isaaks and Srivastava, 1989).
20 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Detection frequencies of wells with high 

relative-concentrations for constituents calculated using the 

same data that was used for the spatially weighted approach 

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

to assess aquifer-scale proportions. Detection frequencies 

are not spatially unbiased because the wells in the CDPH 

database are not uniformly distributed. Consequently, 

high relative-concentrations in spatially clustered wells in 

a particular area representing a small part of the primary 

aquifers could be given a disproportionately high weight 

compared to spatially unbiased methods.

The grid-based aquifer-scale proportions were used 

to represent proportions in the primary aquifers unless the 

spatially weighted proportions were significantly different 

than the grid-based values. Significantly different results were 

defined as follows:

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

and spatially weighted aquifer-scale proportion was 

non-zero, then the spatially weighted result was used. This 

situation can arise when the concentration of a constituent 

is high in a small fraction of the aquifer.

2. If the grid-based high aquifer-scale proportion was 

non‐zero, then the 90 percent confidence interval (based 

on the Jeffreys interval for the binomial distribution, 

Brown and others, 2001) was used to evaluate the 

difference. If the spatially weighted proportion was 

outside the 90 percent confidence interval, then the 

spatially weighted proportion was used.

The grid-based moderate and low proportions were used 

in most cases because the reporting limits for many organic 

constituents and some inorganic constituents in the CDPH 

database were higher than the boundary between the moderate 

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

proportion was zero and the spatially weighted proportion 

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

estimate for the moderate proportion. 

Understanding Assessment Methods

The potential explanatory factors—land use, well depth, 

depth to the top-of-perforations, normalized position of wells 

along flow paths, classified groundwater age, and geochemical 

condition (see appendix D for more details)—were analyzed 

in relation to constituents selected for additional evaluation 

for the understanding assessment in order to establish context 

for physical and chemical processes within the groundwater 

system. Statistical tests were used to identify significant 

correlations between the constituents of interest and potential 

explanatory factors. Graphs, bar charts, and maps were used 

to improve the understanding of factors affecting water quality 

for selected correlations. 

The wells selected for the understanding assessment 

were USGS- and CDPH-grid wells and USGS-understanding 

wells. CDPH-other wells were not used in the understanding 

assessment because carbon isotope, tritium, dissolved oxygen, 

and some well construction data were not available. 

U.S. Geological Survey Understanding Wells

Nineteen wells (16 wells in the SESJ study unit and 

3 wells in the KERN study unit) were sampled to improve 

understanding of factors and processes that affect groundwater 

quality. The USGS-understanding wells sampled in the 

study units were numbered in the order of collection with a 

prefix modified from those used for the USGS-grid wells. 

USGS-understanding wells sampled in the SESJ study unit 

were designated as either flow-path (for example, KING-FP) 

or transect wells (HWY99T). All three USGS-understanding 

wells in the KERN study unit were designated as flow-path 

wells. The understanding wells included 11 monitoring 

and 8 CDPH wells. The USGS-understanding wells were 

selected (1) to assess changes in water quality along regional 

groundwater flow paths from east to west across the Kings 

and Kern study areas; (2) to compare water quality at depths 

less than 250 ft where most of the USGS-understanding 

wells are perforated with water quality at depths greater 

than 250 ft where CDPH wells generally are perforated 

[previous investigations have identified that vertical changes 

in water chemistry occur, primarily within relatively shallow 

to intermediate depths, with more uniform water quality at 

greater depths in the aquifer system (Burow and others, 2007, 

2008b; Jurgens and others, 2008)]; or (3) to assess differences 

in water quality along an approximate regional transect across 

various alluvial fans in the southern San Joaquin Valley, 

roughly paralleling Highway 99.

Statistical Analysis

Nonparametric statistical methods were used to test the 

significance of correlations between water-quality parameters 

and potential explanatory variables. 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). Two 

different types of statistical tests were used because the set 

of potential explanatory factors included both continuous 

and categorical variables. Relations between categorical 

variables (for example, classified groundwater age or land-use 

class) and water-quality variables were evaluated using the 

Wilcoxon rank-sum or Kruskal-Wallis nonparametric tests. 

Correlations between continuous variables were evaluated 

using Spearman’s method. Correlations between potential 

explanatory factors, between water-quality parameters, and 
Potential Explanatory Factors 21

between potential explanatory factors and water-quality 

constituents were tested for significance. Correlations of total 

THMs were performed on the sum of THM concentrations. 

For example, the total THM concentrations for KWH-17 

is 1.36 micrograms per liter (μg/L) [0.42 (chloroform) + 

0.39 (bromodichloromethane) + 0.15 (bromoform) + 0.4 

(dibromochloromethane)].

Correlations between explanatory factors and 

groundwater constituents were tested using either the set of 

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

wells primarily represented relatively shallow groundwater 

in agricultural areas that were not randomly selected on a 

spatially distributed grid, they were excluded from analysis 

of relations between water quality and areally distributed 

explanatory variables (land use and lateral position) to avoid 

areal-clustering bias. However, USGS-understanding wells 

were included in analysis of relations between water-quality 

constituents and vertically distributed explanatory factors 

(depth, classified groundwater age, and oxidation-reduction 

characteristics). In addition, wells located in the SESJ study 

unit that were sampled as part of the Domestic Well Program 

conducted by SWRCB were included in the analysis where 

applicable.

Potential Explanatory Factors

A brief description of potential explanatory factors 

including land use, well depth, normalized lateral position 

in the flow system, septic-system density, formerly leaking 

underground fuel tanks (LUFTs), groundwater age, and 

geochemical conditions are described in this section. The 

data sources and methodology used for assigning values for 

potential explanatory factors are described in appendix D.

Land Use

In the southern San Joaquin Valley, land use is a 

combination of agricultural, urban, and natural; however, land 

use in the areas surrounding the southern San Joaquin Valley 

primarily is natural (fig. 4). Land use in the SESJ study unit is 

85 percent agricultural, 6 percent urban, and 9 percent natural 

(fig. 3B). Within the 500-meter (m) (1,640-ft) radius around 

each USGS- and CDPH-grid well, average land use was 

57 percent agricultural (lower than for the SESJ study unit) 

and urban and natural average land use were higher than for 

the study unit at 31 and 12 percent, respectively. In contrast, 

within the 500-m (1,640-ft) radius around each CDPH well 

(CDPH-grid and CDPH-other wells), the average land use 

was 40 percent agricultural (lower than for the SESJ study 

unit or the grid wells), and urban average land use was higher 

than for the study area or grid wells at 48 percent (fig. 3B). In 

general, the land use around the grid wells in the SESJ study 

unit over-represent the urban land use and under-represent the 

agricultural land use but are closer to the overall land use of 

the study unit than the land use around the CDPH wells.

Land use in the KERN study unit is 66 percent 

agricultural, 3 percent urban, and 31 percent natural. 

Within the 500-m (1,640-ft) radius around each USGS- and 

CDPH-grid well, average land use was 58 percent agricultural 

(lower than for the KERN study unit), and natural and urban 

average land use was higher at 23 and 19 percent, respectively. 

In contrast, within the 500-m (1,640-ft) radius around each 

CDPH well (CDPH-grid and CDPH-other wells), the average 

land use was 37 percent agricultural (lower than for the KERN 

study unit or the grid wells), and urban average land use 

was higher than for the study unit or grid wells at 44 percent 

(fig. 3B). Similar to the SESJ study unit, the grid wells in the 

KERN study unit over-represent the urban land use and 

under-represent the agricultural land use but are closer to the 

overall land use of the study unit than the land use around the 

CDPH wells.

Average land use within 500-m (1,640-ft) radius around 

each domestic well that was sampled by SWRCB as part 

of the Domestic Well Project was 80 percent agricultural, 

5 percent urban, and 15 percent natural. These percentages 

were very similar to the land-use percentages in the Kaweah 

and Tule study areas where most of the domestic wells 

sampled were located (fig. A3). Land use in the Kaweah 

study area was 82 percent agricultural, 5 percent urban, and 

13 percent natural, whereas land use in the Tule study area was 

88 percent agricultural, 3 percent urban, and 9 percent natural.

The percentage of agricultural land use was adequate for 

correlation with most water-quality constituents. However, 

in some cases, constituents needed to be correlated with 

the percentage of orchard or vineyard land use (a subset of 

agricultural land use) to improve understanding of the relation 

between water quality and explanatory factors. The percentage 

of orchard or vineyard land use within the 500-m radius 

around wells is presented in tables D2, D3, and D4.

Depth

The depth of USGS- and CDPH-grid wells varied 

between the two study units. The median well depth for 

SESJ grid wells was 515 ft (157 m) below land surface; well 

depths ranged from 76 to 1,641 ft (23–500 m; fig. 7A). The 

median well depth for KERN grid wells was 719 ft (219 m) 

below land surface; well depths ranged from 400 to 1,496 ft 

(122–456 m; fig. 7B). A similar pattern was observed for 

depth to the top-of-perforations. The median depth to topof-perforations for SESJ grid wells was 245 ft (75 m), and 

the median depth to the top-of-perforations for KERN grid 

wells was 395 ft (120 m). These values represent a subset 

of the grid wells because well depth and depth to the topof-perforations were not known for several wells. Only 

wells with construction information available for the study 

units (tables A1 and A2) were included in the analyses of 

explanatory variables involving depth.
22 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

IP004884_Figure 7ab. 

A

Well

depth

Domestic wells

Well

depth

Top-ofperforationsPerforation

length

Well

depth

Top-ofperforationsPerforation

length

Grid wells Understanding wells

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

76 67 67 16 16 16 99

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

B

Well

depth

Top-ofperforationsPerforation

length

Well

depth

Top-ofperforationsPerforation

length

Grid wells Understanding wells

DEPTH BELOW LAND SURFACE OR LENGTH, IN FEET

56 48 48 3 3 3

76 Number of wells

 with data

75th percentile

Median

25th percentile

10th percentile

90th percentile

Value greater than

90th percentile

Value less than

10th percentile

EXPLANATION

Figure 7. Boxplots of construction characteristics for USGS-grid and USGS-understanding wells, and 

wells from the Domestic Well Project sampling in (A) the Southeast San Joaquin Valley study unit and 

(B) the Kern County Subbasin study unit, California GAMA Priority Basin Project.
Potential Explanatory Factors 23

The median depth of USGS-understanding wells (190 ft, 

58 m) in the SESJ study unit was shallower than the median 

depth for grid wells (fig. 7A). This was expected because 

many of the understanding wells were monitoring wells that 

tap shallow groundwater. In contrast, the median depth of 

USGS-understanding wells (700 ft, 213 m) in the KERN study 

unit was similar to the median depth for grid wells (fig. 7B). 

This was expected because these understanding wells were 

additional CDPH wells.

The median depth of the 143 domestic wells (168 ft, 

51 m) sampled for the Domestic Well Project located in the 

SESJ study unit was much shallower than the median depth 

of the SESJ grid wells (515 ft, 157 m) but is similar to the 

median depth for the understanding wells in the SESJ study 

unit (fig. 7A). The maximum depth of the domestic wells 

(900 ft, 274 m) was not as deep as the maximum depth of the 

SESJ grid wells (1,641 ft, 500 m) but was deeper than any of 

the understanding wells. The Domestic Well Project did not 

sample wells in the KERN study unit.

Lateral Position in the Flow System

USGS- and CDPH-grid wells were distributed across the 

entire range of normalized lateral positions (figs. 8 and 9A and 

B). Wells in the SESJ study unit with lateral positions of 0.0 

to 0.20 (distal or western) and greater than 0.80 (proximal or 

eastern) made up 20 percent and 28 percent of the total grid 

wells, respectively (fig. 9A). USGS- and CDPH-grid wells 

in the KERN study unit with lateral positions of 0.0 to 0.20 

(distal or western) and greater than 0.80 (proximal or eastern) 

made up 25 percent and 6 percent of the total grid wells, 

respectively (fig. 9B). The KERN study unit has fewer USGSand CDPH-grid wells in the eastern part than the SESJ study 

unit. Lateral position for each grid well in the study units can 

be found in tables D2 and D3.

Domestic wells were not distributed across the entire 

range of lateral positions (figs. 8 and 9C). The majority of 

the domestic wells (57 percent) were located near the eastern 

boundary (lateral position greater than 0.80) of the SESJ study 

unit. Less than 1 percent had lateral position of 0.0 to 0.20.

Septic-System Density

The number of septic tanks or cesspools in the 500-m 

(1,640-ft) radius around each USGS-grid and understanding 

well in the SESJ study unit ranged from 0 to 71 septic tanks, 

with a median of 5 septic tanks. Septic-system density greater 

than the median value occurred in a larger fraction of grid 

wells in the Kaweah study area (15 of 26, 58 percent) than in 

the other study areas (34 of 81, 42 percent) (table D2). The 

number of septic tanks or cesspools in the 500-m (1,640-ft) 

radius around each USGS-grid and understanding well in the 

KERN study unit ranged from 0 to 218 septic tanks, with a 

median of 1 septic tank (table D3). The number of septic tanks 

or cesspools in the 500-m radius around each domestic well 

sampled for the Domestic Well Project ranged from 0.2 to 

101 septic tanks, with a median of 5.8 septic tanks (table D4).

Formerly Leaking Underground Fuel Tanks

The density of LUFTs located within the Thiessen 

polygon (a description of a Thiessen polygon can be 

found in appendix D in the section Formerly Leaking 

Underground Fuel Tanks) around each USGS- and CDPH-grid 

and USGS-understanding well in the SESJ study unit 

ranged from 0.01 to 9.23 tanks per square kilometer (tanks/

km2), with a median of 0.05 tanks/km2 (table D2). The density 

of LUFTs around grid and understanding wells in the KERN 

study unit ranged from 0.004 to 4.17 tanks/km2, with a median 

of 0.02 tanks/km2 (table D3). The LUFT density for most of 

the wells in both study units was very low, usually less than 

0.1 tank/km2.

Groundwater Age

Groundwater samples were assigned age classifications 

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

of the samples (appendix D). Of the 99 USGS-grid and 

understanding wells in the SESJ study unit sampled by the 

Priority Basin Project, groundwater samples were classified 

as modern age in 38 wells, mixed age in 23 wells (evidence 

of both modern and pre-modern groundwater in the same 

sample), and pre-modern age in 36 wells (table D5). Samples 

from two wells could not be classified because the age-tracer 

data were incomplete.

The median depth of USGS-grid and -understanding 

wells in the SESJ study unit classified as pre-modern age was 

deeper than the depths of wells classified as modern or mixed 

ages (table 5). The median depth to the top-of-perforations of 

wells classified as pre-modern age also was deeper than the 

depth to the top-of-perforations of wells classified modern or 

mixed ages (fig. 10A, table 5). Well depths or depths to top-ofperforations in samples with modern and mixed ages were not 

significantly different.

Groundwater ages for nearly all SESJ wells perforated 

entirely at depths less than 250 ft (76 m) below land surface 

(19 of 20 wells) were modern or mixed (fig. 10B). Likewise, 

groundwater ages for most wells perforated entirely at 

depths greater than or equal to 250 ft (23 of 29 wells) were 

pre-modern. Groundwater ages for most of the wells with 

the top-of-perforation less than 250 ft but with the bottomof-perforation greater than or equal to 250 ft were modern or 

mixed (18 of 22 wells).
24 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

IP004884_Figure 08

101

99

99

395

5

5

Bakersfield

Delano

Fresno

Tulare

Visalia

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection EXPLANATION

Normalized

lateral position

River or stream

Canal

Aqueduct

0.0 to <0.2

0.2 to <0.4

0.4 to <0.6

0.6 to <0.8

0.8 to 1.0

Pacific Ocean

S i e r r a N e v a d a

Domestic well

San Joaquin Valley

 basin trough

Tulare lakebed

0 10 20 40 Miles

0 10 20 40 Kilometers

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

USGS-grid well (GAMA data only)

USGS-grid well (GAMA and supplemental

 California Department of Public Health

 data only) 

USGS-understanding well

Figure 8. Normalized lateral position (distance from valley trough to valley margins) and wells, Southern San Joaquin Valley 

study units, California GAMA Priority Basin Project.
Potential Explanatory Factors 25

NUMBER OF WELLS, IN PERCENT

0

5

10

15

20

25

30

35

0

5

10

15

20

25

30

35

< 0.20 0.20 − 0.39 0.40 − 0.59 0.60 − 0.79 > 0.80

IP004884_Figure 09abc Lateral position of wells

A Southeast San Joaquin

B Kern

C Domestic wells

NORMALIZED LATERAL POSITION

0

10

20

30

40

50

60

Downgradient Upgradient

Figure 9. Bar charts of distribution of USGS- and CDPH-grid wells and 

domestic wells for normalized lateral position, (A) Southeast San Joaquin 

Valley study unit, (B) Kern County Subbasin study unit, and (C) Domestic 

Well Project wells, California GAMA Priority Basin Project.
26 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table 5. Results of statistical tests (Kruskal-Wallis or Wilcoxon) for differences between selected potential categorical and 

continuous explanatory factors and differences between categorical explanatory factors and selected water-quality constituents for 

the two southern San Joaquin Valley study units, California GAMA Priority Basin Project, and the GAMA Domestic Well Project.

[Groundwater age class: Mod, modern; Mix, mixture of modern and pre-modern; preM, pre-modern (see appendix D for explanation of groundwater age 

classes). p-values less than 0.05 calculated using the Kruskal-Wallis (for comparing three or more groups) or Wilcoxon (for comparing two groups). Nonparametric tests indicate significant differences. Abbreviations: SESJ, Southeast San Joaquin Valley study unit; KERN, Kern County Subbasin study unit; 

DomWell, Domestic Well Project; Kings, Kings study area; KWH, Kaweah study area; TLR, Tulare Lake study area; Tule, Tule study area; Kern, Kern study 

area; VOC, volatile organic compounds; DBCP, 1,2-dibromo-3-chloropropane; ns, not significant; <, less than; >, greater than]

Potential explanatory 

factors

Groundwater age class1

(Mod, Mix, preM)

p-value

significant differences2

Study unit

(SESJ, KERN, DomWell)

p-value

significant differences2

Study area

(Kings, KWH, TLR, TULE, Kern)

p-value

significant differences2

SESJ KERN

Potential explanatory factors

Well depth <0.001 

preM>Mod,Mix

0.033

preM>Mod

<0.001

KERN>SESJ>DomWell

<0.001

Kern,Tule,TLR>Kings,KWH

Depth to top-of-perforations1 <0.001 

preM>Mod,Mix

0.016

preM>Mod

<0.001

KERN>SESJ

<0.001

Kern,TLR>Kings,KWH

Orchard or vineyard land use 

(percent)

ns 0.006

preM>Mod

<0.001

DomWell>SESJ,KERN

0.022

KWH>TLR,Kern

Agricultural land use (percent)3 ns 0.032

preM>Mod

<0.001

DomWell>SESJ,KERN

ns

Urban land use (percent)3 ns ns <0.001

SESJ>KERN>DomWell

0.029

Kings>Kern

Natural land use (percent)3 ns ns ns ns

Normalized lateral position from 

valley trough

ns ns <0.001

DomWell>SESJ>KERN

<0.001

Kings, KWH>TLR,Kern;

Tule>TLR

Number of septic tanks or cesspools ns ns <0.001

SESJ,DomWell>KERN

<0.001

Kings,KWH>Kern

Number of formerly leaking 

underground fuel tanks1

ns ns ns 0.024

Kings,KWH>Tule,Kern

Dissolved oxygen (mg/L)1 <0.001

Mod>preM

0.009

Mod>preM

0.039

SESJ>KERN

<0.001

KWH>TLR,Kern

pH 0.041

preM>Mod

ns <0.001

SESJ,KERN>DomWell

<0.001

TLR>Kings

Selected inorganic water-quality constituents

Arsenic 0.007

preM>Mod

ns <0.001

SESJ,KERN>DomWell

<0.001

TLR>Kings,KWH,Kern

Antimony ns ns <0.001

KERN>DomWell

0.004

Kern>Kings

Boron 0.039

preM>Mix

ns <0.001

SESJ,KERN>DomWell

0.003

TLR,Kern>Kings

Vanadium ns ns <0.001

SESJ,DomWell>KERN

<0.001

Kings>TLR,Kern

Fluoride 0.003

preM>Mod

0.002

preM>Mod

<0.001

SESJ,KERN>DomWell

<0.001

TLR>Kings

Uranium 0.001

Mod,Mix>preM

ns ns ns

Nitrate ns ns <0.001

DomWell>SESJ,KERN

<0.001

KWH>Tule>TLR,Kern

Manganese 0.006

preM>Mix,Mod

ns <0.001

DomWell>SESJ,KERN

ns
Potential Explanatory Factors 27

Potential explanatory 

factors

Groundwater age class1

(Mod, Mix, preM)

p-value

significant differences2

Study unit

(SESJ, KERN, DomWell)

p-value

significant differences2

Study area

(Kings, KWH, TLR, TULE, Kern)

p-value

significant differences2

SESJ KERN

Selected inorganic water-quality constituents—Continued

Iron 0.048

PreM>Mod

0.022

PreM>Mod

ns 0.049

TLR>KWH,Kings

Total dissolved solids (TDS) ns ns <0.001

KERN,DomWell>SESJ

0.013

Kern>Kings,KWH,Tule

Sulfate 0.024

Mix>PreM

ns <0.001

KERN>DomWell>SESJ

<0.001

Kern>Kings,KWH

Chloride ns ns <0.001

KERN>SESJ,DomWell

<0.001

Kern,TLR>Kings,KWH,Tule

Selected organic and special-interest water-quality constituents

Number of VOC detections 4 <0.001

Mod>PreM

ns <0.001

SESJ,KERN>DomWell

<0.001

KWH>Kings,Kern

Total trihalomethane (THM) 

concentration

0.006

Mod>PreM

ns ns <0.001

KWH>Kings,Tule

Number of solvent detections 0.028

Mod>PreM

ns <0.001

SESJ>DomWell

ns

Number of other VOC detections ns 0.024

PreM>Mod

<0.001

SESJ>DomWell

ns

DBCP ns ns 0.042

SESJ>KERN

ns

Number of pesticide detections 1 <0.001

Mod,Mix>PreM

0.012

Mod>PreM

ns 0.024

KWH>TLR

Perchlorate ns ns <0.001

DomWell>KERN

<0.001

KWH>TLR

1

 Data were not available for the domestic wells sampled as part of the Domestic Well Project.

2

 Only significant differences are shown. For example, PreM>Mod for well depth means that wells with pre-modern age water are significantly deeper than 

wells with modern age water, but wells with mixed age water are not significantly different from wells with either pre-modern or modern age water.

3

 Grid wells only.

4

 Does not include VOCs classified as fumigants.

Table 5. Results of statistical tests (Kruskal-Wallis or Wilcoxon) for differences between selected potential categorical and 

continuous explanatory factors and differences between categorical explanatory factors and selected water-quality constituents for 

the two southern San Joaquin Valley study units, California GAMA Priority Basin Project, and the GAMA Domestic Well Project.—

Continued

[Groundwater age class: Mod, modern; Mix, mixture of modern and pre-modern; preM, pre-modern (see appendix D for explanation of groundwater age 

classes). p-values less than 0.05 calculated using the Kruskal-Wallis (for comparing three or more groups) or Wilcoxon (for comparing two groups). Nonparametric tests indicate significant differences. Abbreviations: SESJ, Southeast San Joaquin Valley study unit; KERN, Kern County Subbasin study unit; 

DomWell, Domestic Well Project; Kings, Kings study area; KWH, Kaweah study area; TLR, Tulare Lake study area; Tule, Tule study area; Kern, Kern study 

area; VOC, volatile organic compounds; DBCP, 1,2-dibromo-3-chloropropane; ns, not significant; <, less than; >, greater than]
28 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project DEPTH TO TOP-OF-PERFORATION, IN FEET

0

200

400

600

800

1,000

1,200

26 17 28

NUMBER OF WELLS

GROUNDWATER AGE CLASSIFICATION

Modern Mixed Pre-modern

26 Number of wells

 with data

75th percentile

Median

25th percentile

10th percentile

90th percentile

Value greater than

90th percentile

Value less than

10th percentile

EXPLANATION

DEPTH OF PERFORATED INTERVAL BELOW LAND SURFACE

Entire perforated

interval less than

250 feet 

Entire perforated

interval greater

than or equal to

250 feet 

Top of perforated

interval less than

250 feet; Bottom of

perforated interval

greater than or

equal to 250 feet 

Modern

GROUNDWATER AGE

 CLASSIFICATION

Mixed

Pre-modern

EXPLANATION

0 10 20 30

IP004884_Figure 10ab GW age SESJ

A

B

Figure 10. Boxplots and bar charts showing the relation of groundwater age classification to 

(A) depth to top-of-perforations and (B) numbers of wells with each groundwater age class in 

each of the three depth categories, Southeast San Joaquin Valley study unit, California GAMA 

Priority Basin Project.
Potential Explanatory Factors 29

In each of the three depth categories, ages for some 

wells are modern, mixed, and pre-modern. The presence of 

one pre-modern sample from one well less than 250 ft (76 m) 

deep and three modern samples from wells greater than or 

equal to 250 ft deep (fig. 10B) indicates that there are local 

variations in the general groundwater age-depth relations. 

These variations may indicate the position of the well relative 

to regional recharge and discharge areas. 

Of the 50 USGS-grid and -understanding wells in the 

KERN study unit sampled by the Priority Basin Project, 

groundwater ages were classified as modern in 16 wells, 

mixed in 16 wells, and pre-modern in 14 wells (table D6). 

Samples from four wells could not be classified because 

the age-tracer data were incomplete or did not meet all 

quality-assurance checks.

Similar to the SESJ study unit, the median depth of 

USGS-grid and -understanding wells in the KERN study unit 

classified as pre-modern age was deeper than the median 

depths of wells classified as modern age (table 5). Depths of 

USGS-grid and -understanding wells classified as mixed age 

were not significantly different than depths of wells classified 

as modern or pre-modern (table 5). The median depth to the 

top-of-perforations of wells classified as pre-modern age also 

was deeper than the median depth to the top-of-perforations of 

wells classified as modern age (fig. 11A; table 5). 

The water table below land surface in the KERN study 

unit is deeper than the water table in the SESJ study unit 

(Faunt, 2009). As a result, wells in the Kern study unit 

generally are deeper than in the SESJ study unit (fig.8); 

therefore, a depth below land surface threshold of 500 ft 

(152 m) was used to categorize well depth and groundwater 

age in the Kern study unit rather than the depth of 250 ft 

(76 m) used in the SESJ study unit. Groundwater age for most 

of the wells perforated entirely between land surface and 500 

ft below land surface (5 of 7 wells) was modern or mixed 

(fig. 11B). Likewise, nearly all of the wells perforated entirely 

at depths greater than or equal to 500 ft (6 of 7 wells) were 

of pre-modern age. The groundwater ages of most wells with 

the top-of-perforations less than 500 ft but with bottom-ofperforations greater than or equal to 500 ft were modern or 

mixed ages (17 of 22 wells).

Geochemical Conditions

Geochemical conditions investigated as potential 

explanatory factors in this report include oxidation-reduction 

characteristics and pH. An abridged classification of 

oxidation-reduction (redox) conditions adapted from the 

framework presented by McMahon and Chapelle (2008) for 

USGS-grid and USGS-understanding wells in the southern 

San Joaquin Valley is given in appendix D (table D7

and D8). DO and pH were measured at USGS-grid and 

USGS-understanding wells. pH measurements for many of the 

CDPH-grid wells were available in the CDPH database, but no 

DO concentration data were available.

Groundwater in the SESJ study unit was oxic (redox 

category oxic or DO above 2.0 mg/L) in 73 percent of 

USGS-grid wells and 88 percent of USGS-understanding 

wells, but becomes more reducing (DO less than 2.0 mg/L) 

near the western (downgradient) area of the study unit 

(fig. 12). The lateral position and depth of wells having DO 

of 0.5 to 2.0 mg/L is consistent with general transitions from 

east to west and with increasing depth. Figure 12 shows the 

USGS-grid and -understanding wells with well-construction 

and DO data from the Kings study area on a single composite 

cross-section.

Groundwater in the KERN study unit was oxic (redox 

category oxic or DO above 2.0 mg/L) in 64 percent of 

the USGS-grid wells. In contrast to the SESJ study unit, 

measurements indicated reducing conditions (DO less than 

2.0 mg/L) in groundwater in the KERN study unit in the 

eastern (upgradient) part of the study unit as well in the 

western (downgradient) part of the flow system (fig. 13). 

Reducing conditions (table D8) also were indicated in 

USGS-grid wells (KERN-32, -40, -43, and -46; fig. A2A) in 

the southern part of the KERN study unit, near the Tehachapi 

Mountains. High DO concentrations in the central part of 

the KERN study unit may indicate infiltration from the 

Kern River and groundwater banking facilities. Figure 13

shows the USGS-grid and USGS-understanding wells with 

well-construction and DO data which are adjacent to or north 

of the Kern River, on a single composite cross-section. Redox 

conditions for the CDPH-grid wells and the domestic wells 

from the Domestic Well Project were not categorized because 

DO data were not available for the wells.

Although the redox classification for the study units is 

valuable for characterizing the range and spatial distribution 

of redox conditions, hereinafter DO concentrations are used 

as the factor for evaluating relations of redox conditions 

with concentrations of water-quality constituents. DO was 

used as a redox indicator because (1) data were available 

for all USGS-grid and USGS-understanding wells, whereas 

other redox indicators were available for only 79 of the 

130 USGS-grid wells, and (2) most groundwater samples in 

the southern San Joaquin Valley study units were classified as 

oxic, so the number of wells within the various reducing redox 

processes generally were too small for meaningful statistical 

analysis. 
30 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project DEPTH TO TOP-OF-PERFORATION, IN FEET

0

200

400

600

800

1,000

1,200

14 11 13

NUMBER OF WELLS

GROUNDWATER AGE CLASSIFICATION

Modern Mixed Pre-modern

DEPTH OF PERFORATED INTERVAL BELOW LAND SURFACE

Entire perforated

interval less than

500 feet 

Entire perforated

interval greater

than or equal to

500 feet 

Top of perforated

interval less than

500 feet; Bottom of

perforated interval

greater than or

equal to 500 feet 

Modern

GROUNDWATER AGE

 CLASSIFICATION

Mixed

Pre-modern

EXPLANATION

0 4 8 12

IP004884_Figure 11ab GW age Kern

A

B

13 Number of wells

 with data

75th percentile

Median

25th percentile

10th percentile

90th percentile

Value greater than

90th percentile

Value less than

10th percentile

EXPLANATION

Figure 11. Boxplots and bar charts showing the relation of groundwater age classification to 

(A) depth to top-of-perforations and (B) numbers of wells with each groundwater age class in 

each of the three depth categories, Kern County Subbasin study unit, California GAMA Priority 

Basin Project.
Potential Explanatory Factors 31

ALTITUDE, IN FEET

Downgradient Upgradient

Land surface

Top

Bottom

Perforated interval

Dissolved oxygen concentration,

in milligrams per liter

EXPLANATION

0

-600

-400

-200

0

200

400

600

0.2 0.4 0.6 0.8 1.0

NORMALIZED LATERAL POSITION FROM VALLEY TROUGH

IP004884_Figure 12 Xsec SESJ

> 2.0 0.5 − 2.0 < 0.5

Figure 12. Relation of oxidation-reduction category to lateral position and depths of perforated interval of wells in the Kings 

study area of the Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project.
32 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project ALTITUDE, IN FEET

Downgradient Upgradient

Land surface

Top

Bottom

Perforated interval

Dissolved oxygen concentration,

in milligrams per liter

EXPLANATION

0

-800

-600

-1,000

-400

-200

0

200

400

600

800

0.2 0.4 0.6 0.8 1.0

NORMALIZED LATERAL POSITION FROM VALLEY TROUGH

IP004884_Figure 13 Xsec Kern

> 2.0 0.5 − 2.0 < 0.5

Figure 13. Relation of oxidation-reduction category to lateral position and depth of perforated interval of wells located adjacent to 

or north of the Kern River, Kern County Subbasin study unit, California GAMA Priority Basin Project.
Potential Explanatory Factors 33

Other studies have shown that pH is correlated to several 

inorganic constituents in the San Joaquin Valley (Belitz and 

others, 2003; Welch and others, 2006; Izbicki and others, 

2008; Landon and others, 2010a). pH ranged from 6.6 to 9.8 

in 80 USGS- and CDPH-grid and USGS-understanding wells 

in the SESJ study unit (table D7; fig. 14A) with a median 

pH of 7.9. Median pH in the Kings study area was 7.7, in 

the Kaweah study area 8.1, in the Tule study area 8.0, and in 

the Tulare Lake study area 8.5. pH ranged from 5.5 to 9.6 in 

54 USGS- and CDPH-grid and USGS-understanding wells in 

the KERN study unit (table D8; fig. 14A) with a median pH 

of 8.1. Data for pH also were available for the domestic wells 

sampled as part of the Domestic Well Project. The pH for the 

domestic wells was significantly lower than the pH for USGSand CDPH-grid and USGS-understanding wells in either study 

unit (table 5; fig. 14A). The pH for domestic wells ranged from 

6.3 to 8.4 in 143 domestic wells (table D9; fig. 14A) with a 

median pH of 7.2.

Correlations Between Explanatory Variables

Significant correlations between explanatory variables are 

important to identify because apparent correlations between 

an explanatory variable and a water-quality constituent could 

indicate relations between two explanatory variables and 

not between an explanatory variable and a water-quality 

constituent. Significant correlations using the Wilcoxon 

or Kruskal-Wallis statistical tests between categorical 

explanatory variables are given in table 5 for the southern San 

Joaquin Valley study units and domestic wells. Significant 

correlations using the Spearman’s method between continuous 

explanatory variables are given in tables 6A, 6B, and 6C. 

In addition to the relations of groundwater age and depth 

already discussed, well depth varied by study unit. Wells in 

the KERN study unit were deeper than wells in the SESJ study 

unit. In turn, the wells sampled for the Priority Basin Project 

were deeper than the wells sampled for the Domestic Well 

Project (table 5).

Well depth and depth to the top-of-perforations were 

positively correlated with urban land use in the KERN study 

unit (table 6A). In general, wells were deeper in urban areas 

than in agricultural land-use areas. Well depth and depth to 

top-of-perforations were not correlated with land use in the 

SESJ study unit or in the Domestic Well Project (tables 6B and 

6C). Another study in the Central Eastside San Joaquin Valley 

(Landon and others, 2010a) also did not observe correlations 

of well depth with land use.

Groundwater age was related to other explanatory factors 

in ways that varied by study unit. In the KERN study unit, 

the percentage of agricultural land use was higher in wells 

with groundwater classified as pre-modern than in wells with 

groundwater classified as modern (table 5). This correlation 

was not observed for the SESJ study unit. In contrast, pH was 

significantly higher in wells with pre-modern age groundwater 

than in wells with modern age groundwater in the SESJ study 

unit but not in the KERN study unit. However, if the wells 

located on the western side of the valley trough in KERN 

are not included in the correlation, pH is higher in wells with 

pre-modern age groundwater than in wells with modern age 

groundwater (p=0.013). DO generally was higher in modern 

age groundwater than in pre-modern groundwater for both 

study units.

Correlations between lateral position and depth vary 

between study units. Grid wells in the SESJ study unit have 

a negative correlation with lateral position for well depth 

and depth to top-of-perforations (table 6B). This correlation 

indicates that wells generally are deeper in the distal part of 

the study unit than in the proximal part of the study unit. In 

contrast, lateral position is positively correlated with depth 

to top-of-perforations in the KERN study unit, implying that 

depth to top-of-perforations is greater near the boundary of the 

valley than in the center of the valley (table 6A).

Lateral position is correlated negatively with agricultural 

land use in both study units, and positively with urban land use 

in the KERN study unit and natural land use in the SESJ study 

unit. These correlations were expected because agricultural 

land use is more prevalent in the distal part of the study units, 

and urban land use in the KERN study unit and natural land 

use in the SESJ study unit are more prevalent along the eastern 

boundary (fig. 4).

Concentrations of DO were correlated significantly 

(positively) with lateral position in the SESJ study unit 

(table 6B), and negatively with well depth and depth to the 

top-of-perforations (fig. 12). Wells in the eastern (upgradient) 

part of the study unit have higher DO concentrations than 

wells in the western part. The correlation of DO with 

well depth may partially indicate the relation of DO with 

lateral position because the deep wells generally are in the 

western part of the SESJ study unit (table 6B). DO in the 

KERN study unit also was negatively correlated with depth 

to top-of-perforations. However, DO was not correlated 

significantly with normalized lateral position in the KERN 

study unit (table 6A). This was not unexpected because DO 

concentrations are low at the eastern boundary and near the 

valley trough of the study unit (fig. 13). Many of the wells in 

the eastern part of KERN have the deepest depth from land 

surface to the top-of-perforations.
34 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Figure 14. Relation of (A) pH and (B) well depth between the two southern San Joaquin Valley 

study units, California GAMA Priority Basin Project, and the domestic wells from the GAMA 

Domestic Well Project.

WELL DEPTH, IN FEET BELOW LAND SURFACE

200

400

600

800

1,000

1,200

1,400

1,600

Domestic Well Project

EXPLANATION

Kern County Subbasin

Southeast San Joaquin 

pH, IN STANDARD UNITS

pH, IN STANDARD UNITS

5

6

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

7

8

9

10

11

Kern County Subbasin

study unit 

Southeast San Joaquin

study unit

Domestic Well

Project 

A

B

54 80 143

IP004884_Figure 14

54 Number of wells

 with data

75th percentile

Median

25th percentile

10th percentile

90th percentile

Value greater than

90th percentile

Value less than

10th percentile

EXPLANATION
Potential Explanatory Factors 35

Table 6A. Results of nonparametric (Spearman’s method) analysis of correlations in grid and understanding wells between selected 

potential explanatory factors, Kern County Subbasin study unit, California GAMA Priority Basin Project.

[Spearmans’s rho (ρ) values and p-values are shown when correlations between selected potential explanatory factors are significant (p < 0.05). Number of 

septic tanks or cesspools in 500-meter radius around each well (U.S. Census Bureau, 1990). Number of formerly leaking underground fuel tanks within a 

Thiessen polygon in square kilometers, data from Geographic Information Management System GeoTracker (California Environmental Protection Agency, 

2001). Abbreviations: ρ, Spearman’s correlation statistic; p, significance level of Spearman’s test based on a threshold value (α) of 0.05; mg/L, milligrams per 

liter; ns, not significant; <, less than]

Type 

of well 

analyzed

Explanatory factor

ρ :Spearman’s correlation statistic/p: significance level

Depth 

of 

well

Depth to 

top-ofperforations

Normalized 

lateral 

position from 

valley trough

Dissolved 

oxygen 

(mg/L)

pH

Number 

of septic 

tanks or 

cesspools

Number of 

formerly 

leaking 

underground 

fuel tanks

Grid wells

Orchard or vineyard land use (percent) ns 0.428

0.006 ns ns ns ns ns

Agricultural land use (percent) ns ns –0.323

0.027 ns ns –0.3310.019

–0.383

0.006

Natural land use (percent) ns ns ns ns ns ns ns

Urban land use (percent) 0.294

0.042

0.345

0.029

0.295

0.044 ns ns 0.3720.008

0.541

<0.001

Grid and 

understanding wells

Depth of well 0.565

<0.001 ns ns ns ns ns

Depth to top-of-perforations 0.483

0.002

–0.354

0.025 ns ns ns

Normalized lateral position from valley trough ns ns ns 0.301

0.034

Dissolved oxygen (mg/L) ns ns ns

pH ns ns

Number of septic tanks or cesspools 0.681

<0.001
36 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table 6B. Results of nonparametric (Spearman’s method) analysis of correlations in grid and understanding wells between selected 

potential explanatory factors, Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project.

[Spearmans’s rho (ρ) values and p-values are shown when correlations between selected potential explanatory factors are significant (p < 0.05). Number of 

septic tanks or cesspools in 500-meter radius around each well (U.S. Census Bureau, 1990). Number of formerly leaking underground fuel tanks within a 

Thiessen polygon in square kilometers, data from Geographic Information Management System GeoTracker (California Environmental Protection Agency, 

2001). Abbreviations: ρ, Spearman’s correlation statistic, p, significance level of Spearman’s test based on a threshold value (α) of 0.05; mg/L, milligrams per 

liter; ns, not significant; <, less than]

Type 

of well 

analyzed

Explanatory factor

ρ :Spearman’s correlation statistic/p: significance level

Depth 

of 

well

Depth to 

top-ofperforations

Normalized 

lateral 

position from 

valley trough

Dissolved 

oxygen 

(mg/L)

pH

Number 

of septic 

tanks or 

cesspools

Number of 

formerly 

leaking 

underground 

fuel tanks

Grid wells

Orchard or vineyard land use (percent) ns ns 0.274

0.012

0.203

0.045

–0.342

0.005

0.244

0.015 ns

Agricultural land use (percent) ns ns –0.285

0.009 ns ns ns –0.480<0.001

Natural land use (percent) ns ns 0.259

0.018 ns ns ns 0.2050.042

Urban land use (percent) ns ns ns ns ns 0.231

0.022

0.479

<0.001

Grid and 

understanding wells

Depth of well 0.748

<0.001

–0.267

0.0154

–0.347

0.002

0.626

<0.001 ns ns

Depth to top-of-perforations –0.423

<0.001

–0.389

<0.001

0.621

<0.001

–0.293

0.012 ns

Normalized lateral position from valley trough 0.547

<0.001

–0.604

<0.001

0.434

<0.001

0.432

<0.001

Dissolved oxygen (mg/L) –0.320

0.009

0.244

0.0155

0.349

<0.001

pH ns ns

Number of septic tanks or cesspools 0.564

<0.001
Potential Explanatory Factors 37

Table 6C. Results of nonparametric (Spearman’s method) analysis of correlations in 

domestic wells between selected potential explanatory factors, California GAMA Domestic 

Well Project.

[Spearmans’s rho (ρ) values and p-values are shown when correlations between selected potential 

explanatory factors are significant (p < 0.05). Number of septic tanks or cesspools in 500-meter radius 

around each well (U.S. Census Bureau, 1990). Abbreviations: ρ, Spearman’s correlation statistic; p, 

significance level of Spearman’s test based on a threshold value (α) of 0.05; ns, not significant; <, less than]

Explanatory factor

ρ :Spearman’s correlation statistic/

p: significance level

Depth of 

well

Normalized 

lateral 

position from 

valley trough

pH

Number 

of septic 

tanks or 

cesspools

Orchard or vineyard land use (percent) –0.349

<0.001

0.296

<0.001

–0.200

0.017

0.316

<0.001

Agricultural land use (percent) ns –0.422

<0.001

0.205

0.014 ns

Natural land use (percent) ns 0.515

<0.001

–0.289

<0.001 ns

Urban land use (percent) ns ns ns 0.401

<0.001

Depth of well ns 0.223

0.029

–0.321

0.001

Normalized lateral position from valley trough –0.478

<0.001 ns

pH ns

pH was positively correlated with well depth and depth 

to the top of the perforations in the SESJ study unit; pH 

was higher for deep wells than for shallow wells (table 6B; 

fig. 14B). pH was negatively correlated to lateral position and 

DO; pH was higher in the distal part of the SESJ study unit 

where DO concentrations are lower. The correlations of pH 

with lateral position and DO may indicate the relation of pH 

to well depth. pH also was significantly lower in wells with 

modern age groundwater than in wells with pre-modern age 

groundwater but not in wells with mixed age groundwater 

(table 5). Similar to other explanatory variables, correlations 

with pH vary between the two study units. pH was not 

correlated with any explanatory variables in the KERN study 

unit (table 6A). However, if the wells on the western side of 

the valley trough are not included in the correlations, pH is 

positively correlated with well depth (rho=0.569, p=0.001) 

and depth to top-of-perforations (rho=0.506; p=0.010). 

Implications of correlations between explanatory factors 

are discussed later in the report as part of analysis of factors 

affecting individual constituents.
38 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Status and Understanding of 

Water Quality

The status assessment was designed to identify the 

constituents or classes of constituents most likely to be 

water-quality concerns because of high relative-concentrations 

or prevalence. The assessment applies only to constituents 

with regulatory (MCL and AL) or non-regulatory (HAL, 

RSD5-US, or NL) human-health benchmarks or aesthetic 

benchmarks (SMCL) established by the USEPA or the CDPH 

(U.S. Environmental Protection Agency, 2008a, 2008b; 

California Department of Public Health, 2008a). The spatially 

distributed, randomized approach to well selection and data 

analysis yields a view of groundwater quality in which all 

areas of the primary aquifers are weighted equally.

The understanding assessment was designed to help 

answer the question of why specific constituents are, or are 

not, detected in groundwater. The understanding assessment 

addresses a subset of the constituents discussed in the status 

assessment and is based on statistical correlations between 

water quality and a finite set of potential explanatory factors. 

This assessment may improve our understanding of how 

human and natural sources of contaminants affect groundwater 

quality in the southern San Joaquin Valley; however, it was not 

designed to identify specific sources of constituents to specific 

wells. 

In USGS-grid wells, less than one-third of organic 

and special-interest constituents analyzed for were detected 

(68 of 226). Human-health benchmarks are established 

for about two-thirds of the organic and special-interest 

constituents (46 of 68) detected (table 7). Twenty-eight 

VOCs, including gasoline oxygenates, were detected; 

human‐health benchmarks are established for all but five 

VOCs. Human-health benchmarks were established for all 

five fumigants detected. Thirty-three pesticides were detected; 

human-health benchmarks were established for 18 of the 

33 pesticides (Burton and Belitz, 2008; Shelton and others, 

2008). Eight of the detected pesticides (de-ethylatrazine, 

de-ethyl-deisopropylatrazine, de-isopropylatrazine,1-napthol, 

3,4-dichloroanaline, 3,5-dichloroaniline, desulfinyl fipronil, 

and fipronil sulfide) with no benchmarks are pesticide 

degradates; human-health benchmarks are established for three 

(atrazine, carbaryl, and diuron) of the five parent compounds 

of these degradates. Human-health benchmarks have not been 

established for the parent compounds (fipronil and iprodione) 

of the remaining degradates that were detected. Human-health 

benchmarks are established for both constituents of special 

interest detected. Thus, the organic and special-interest 

constituents that are regulated include most of these 

constituents that were detected in groundwater in the southern 

San Joaquin Valley study units.

In contrast to organic constituents, inorganic constituents 

nearly always were detected (52 of 53, table 7) in USGS- and 

CDPH-grid wells. Human-health or aesthetic benchmarks 

have been established for almost three-quarters of inorganic 

constituents detected (38 of 52). Most of the constituents 

without benchmarks are major or minor ions that are naturally 

present in nearly all groundwater.

The maximum relative-concentration for each constituent 

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

figure 15. In the SESJ study unit, nine inorganic constituents 

(including radioactive constituents) were detected at high 

relative-concentrations in one or more grid wells, and six 

additional inorganic constituents were detected at moderate 

relative-concentrations (fig. 15A). In contrast, three of the 

organic and special-interest constituents were detected 

at high relative-concentrations in grid wells, and three 

additional organic and special-interest constituents were 

detected at moderate relative-concentrations. In the KERN 

study unit, ten inorganic constituents were detected at 

high relative-concentrations in one or more grid wells, and 

seven additional inorganic constituents were detected at 

moderate relative-concentrations (fig. 15B). Only one organic 

constituent was detected at high relative-concentrations, and 

six additional organic and special-interest constituents were 

detected at moderate relative-concentrations.

Aquifer-scale proportions were calculated for each 

inorganic and organic constituent detected at high or at 

moderate relative-concentrations and for each organic and 

special-interest constituent detected in more than 10 percent 

of the grid wells (tables 8 and 9). Spatially weighted 

high aquifer-scale proportions were within the 90 percent 

confidence intervals for their respective grid-based aquifer 

high proportions for all constituents (see tables 8 and 9) 

except for unadjusted gross alpha radioactivity from KERN, 

providing evidence that the grid-based approach yields 

statistically equivalent results to the spatially weighted 

approach. 
Status and Understanding of Water Quality 39

Table 7. Number of constituents analyzed and detected in 

USGS-grid wells by human-health-based or aesthetic benchmark 

and constituent type in the two southern San Joaquin Valley study 

units, California GAMA Priority Basin Project.

[Regulatory human-health benchmarks include U.S. Environmental Protection 

Agency (USEPA) and California Department of Public Health (CDPH) 

maximum contaminant levels. Non-regulatory human-health benchmarks 

include USEPA lifetime health advisory levels and risk-specific dose level at 

10–5 lifetime cancer risk and CDPH notification level. Abbreviations: SESJ, 

Southeast San Joaquin Valley study unit; KERN, Kern County Subbasin study 

unit; VOCs, volatile organic compounds; NWQL, USGS National Water 

Quality Laboratory; HHB, human-health-based benchmark; SMCL, USEPA 

or CDPH Secondary Maximum Contaminant Level (aesthetic based); USGS, 

U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and 

Assessment Program]

Benchmark 

type

Number of 

constituents 

analyzed

Number of constituents detected

SESJ KERN SESJ and KERN

VOCs + Gasoline oxygenates (excluding fumigants)

Regulatory HHB 29 19 11 19

Non-regulatory HHB 22 4 3 4

None 27 5 0 5

Total 78 28 14 28

Fumigants

Regulatory HHB 4 3 3 4

Non-regulatory HHB 4 1 1 1

None 2 0 0 0

Total 10 4 4 5

Pesticides and degradates (NWQL Schedule 2033)

Regulatory HHB 5 3 2 3

Non-regulatory HHB 18 5 6 7

None 59 9 4 12

Total 82 17 12 22

Polar pesticides and degradates (NWQL Schedule 2060)

Regulatory HHB 7 3 1 3

Non-regulatory HHB 9 2 3 3

None 38 5 2 5

Total 54 10 6 11

Special interest

Regulatory HHB 1 1 1 1

Non-regulatory HHB 1 1 0 1

Total 2 2 1 2

Sum of inorganic and radioactive constituents

Regulatory HHB 25 24 24 24

Non-regulatory HHB 9 8 8 9

Aesthetic - SMCL 5 5 5 5

None 14 14 14 14

Total 53 51 51 52
40 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

0.001

0.01

0.1

1

10

100

A

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

 Unless indicated by following location line: (greater than or equal to 25 grid wells sampled) 

EXPLANATION

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

 Unless indicated by following location line: (less than 25 grid wells sampled) 

Radon-222

Moderate

Low

Moderate

Low

Moderate

High High

Organic and special

interest constituents

Inorganic

constituents

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Volatile

organic

compounds

Fumigants Pesticides Special

interest

Nutrients Trace

elements

Major and

minor ions

and trace

elementsSMCL

Radioactive

constituents

Number of 

compounds

detected

< 0.001

4 0 5 0 0 0 0 0

IP004884_Figure 15a

Abbreviations: N, nitrogen; TDS, total dissolved solids

Simazine

Perchlorate

N-Nitrosodimethylamine

(NDMA)

Benzene

Tetrachloroethene (PCE)

1,2-Dichloroethene

CFC-11

Carbon disulfide

Ethylbenzene

Carbon tetrachloride

Total trihalomethane

Bromodichloromethane

Dibromochloromethane

Bromochloromethane

Bromoform

Chloroform

Methyl tert-butyl ether

Methylene chloride Dieldrin

Atrazine

Diazinon

Diuron

Alachlor

Nitrite-N

Ammonia-N

Nitrate-N

Manganese

Iron

TDS

Zinc

Sulfate

Chloride

Fluoride

Molybdenum

Boron

Lead

Copper

Radon-222

Gross alpha

Uranium

Gross beta

Radium

Vanadium

Cadmium

Chromium

Antimony

Selenium

Beryllium

Arsenic

Mercury

Aluminum

Barium

Nickel

Thallium

Strontium

Trichloroethene (TCE)

1,1-Dichloroethene

1,1-Dichloroethane

1,2,3-Trichloropropane

1,2-Dichloropropane

1,2-Dibromo-3-chloropropane

(DBCP)

Figure 15. Graphs showing maximum relative-concentration in USGS- and CDPH-grid wells for 

constituents detected by type of constituent in the (A) Southeast San Joaquin Valley study unit, 

and (B) Kern County Subbasin study unit, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 41

Figure 15.—Continued

0.001

0.01

0.1

1

10

100

B

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

 Unless indicated by following location line: (greater than or equal to 25 grid wells sampled) 

EXPLANATION

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

 Unless indicated by following location line: (less than 25 grid wells sampled) 

Radon-222

Moderate

Low

Moderate

Low

Moderate

High High

Organic and special

interest constituents

Inorganic

constituents

MAXIMUM RELATIVE-CONCENTRATION, DIMENSIONLESS

Volatile

organic

compounds

Fumigants Pesticides Special

interest

Nutrients Trace

elements

Major and

minor ions

and trace

elementsSMCL

Radioactive

constituents

Number of 

compounds

detected

< 0.001

3 0 6 0 0 0 0 0

Zinc

IP004884_Figure 15b

Simazine

Perchlorate

Benzene

Tetrachloroethene

(PCE)

1,2-Dichloroethane

Total trihalomethane

1,2-Dichloroethene

1,2-Dichloroethane

Chloroform

Dieldrin

Atrazine

Diuron

Dinoseb

Chlorpyrifos

Nitrite-N

Ammonia-N

Nitrate-N Manganese

Iron

TDS

Zinc

Sulfate

Chloride

Fluoride

Molybdenum

Strontium

Boron

Lead

Copper

Nickel Radon-222

Gross alpha

Uranium

Gross beta

Radium Vanadium

Cadmium

Chromium

Antimony

Selenium

Beryllium

Arsenic

Mercury

Aluminum

Barium

Trichloroethene

(TCE)

1,1-Dichloroethene

1,2,3-Trichloropropane

1,2-Dichloropropane

1,2-Dibromoethane

(EDB)

1,2-Dibromo-3-chloropropane

(DBCP)

Abbreviations: N, nitrogen; TDS, total dissolved solids
42 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project Table 8. Raw detection frequencies and aquifer-scale proportions using spatially weighted and grid-based methods for inorganic constituents detected at concentrations greater than water-quality benchmarks from data available during the most recent 3 years (January 1, 2003, through December 31, 2005) in the California Department of Public Health (CDPH) database, or detected at high or at moderate relative-concentrations in samples collected from USGS-grid wells (October 2005–March 2006) in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project. [Grid-based aquifer-scale proportions for inorganic constituents are based on samples collected by the U.S. Geological Survey from grid wells during October 2005–March 2006 and supplemented with additional data from selected wells in the CDPH data base (January 1, 2003, through December 31, 2005). High, concentrations greater than water-quality benchmark; moderate, concentrations less than or equal to benchmark but greater than 0.5 of benchmark; low, concentrations less than 0.5 of benchmark. Constituents are ordered by class and high aquifer-scale proportion. Abbreviation: nc, not collected; USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program] Constituent Raw detection frequency1 Spatially weighted aquifer-scale proportion1 Grid-based aquifer-scale proportion 90 percent confidence interval for grid-based high aquifer- scale proportion2 Number of wells Moderate values (percent) High values (percent) Number of cells Moderate values (percent) High values (percent) Number of cells Moderate values (percent) High values (percent) Lower limit (percent) Upper limit (percent) Southeast San Joaquin Valley study unit Trace and minor elements Arsenic 752 5.5 9.2 76 11 21 73 12 19 13 28 Boron 290 2.1 1.0 57 4.5 3.9 46 6.5 6.5 2.4 15 Vanadium 333 23 4.5 60 27 5.6 49 25 6.1 2.2 14 Lead 741 1.2 0.3 73 1.1 1.5 69 0.0 1.4 0.3 5.5 Fluoride 723 3.3 0.0 71 5.5 0.0 67 10 0.0 0.0 2.8 Molybdenum nc nc nc nc nc nc 28 3.6 0.0 0.0 4.7 Chromium 693 0.4 0.0 75 0.6 0.0 71 1.4 0.0 0.0 1.9 Aluminum 743 1.1 0.4 75 1.7 0.5 72 0.0 0.0 0.0 1.9 Barium 742 0.0 0.1 75 0.0 0.3 72 0.0 0.0 0.0 1.9 Radioactive constituents Uranium3 156 10 3.2 51 11 6.9 40 5.0 5.0 1.4 13 Gross alpha radioactivity 628 10 3.2 62 11 4.7 53 7.5 3.8 1.1 10 Radon-222 nc nc nc nc nc nc 9 11 0.0 0.0 14 Nutrients Nitrate plus nitrite 975 18 5.9 81 17 9.0 79 16 6.3 2.9 12 Inorganics with aesthetic benchmarks (SMCLs) Manganese 682 1.0 3.8 65 2.7 8.5 61 1.6 4.9 1.8 11 Iron 684 3.8 4.5 65 6.5 8.4 61 4.9 3.3 0.9 8.8 Total dissolved solids (TDS) 679 3.5 0.3 65 7.5 0.4 84 13 0.0 0.0 1.6 Sulfate 680 0.3 0.0 65 0.8 0.0 61 1.7 0.0 0.0 2.2 Chloride 679 0.1 0.1 65 0.2 0.3 61 0.0 0.0 0.0 2.2
Status and Understanding of Water Quality 43

Table 8. Raw detection frequencies and aquifer-scale proportions using spatially weighted and grid-based methods for inorganic constituents detected at concentrations greater than water-quality benchmarks from data available during the most recent 3 years (January 1, 2003, through December 31, 2005) in the California Department of Public Health (CDPH) database, or detected at high or at moderate relative-concentrations in samples collected from USGS-grid wells (October 2005–March 2006) in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.—Continued[Grid-based aquifer-scale proportions for inorganic constituents are based on samples collected by the U.S. Geological Survey from grid wells during October 2005–March 2006 and supplemented with additional data from selected wells in the CDPH data base (January 1, 2003, through December 31, 2005). High, concentrations greater than water-quality benchmark; moderate, concentrations less than or equal to benchmark but greater than 0.5 of benchmark; low, concentrations less than 0.5 of benchmark. Constituents are ordered by class and high aquifer-scale proportion. Abbreviation: nc, not collected; USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program]

Constituent

Raw detection frequency1 Spatially weighted aquifer-scale proportion1 Grid-based aquifer-scale proportion 90 percent confidence interval for grid-based high aquifer- scale proportion2

Number 

of wells

Moderate 

values 

(percent)

High 

values 

(percent)

Number 

of cells

Moderate 

values 

(percent)

High 

values 

(percent)

Number 

of cells

Moderate 

values 

(percent)

High 

values 

(percent)

Lower 

limit 

(percent)

Upper 

limit

 (percent)

Kern County Subbasin study unit

Trace and minor elementsArsenic 353 19 12 57 21 19 56 21 20 12 29

Antimony 346 1 2.0 57 1.8 4.7 56 1.8 3.6 1.0 9.5

Fluoride 349 0.6 0.6 58 2.0 3.4 57 1.8 3.5 1.0 9.4

Vanadium 188 3.2 2.1 57 0.0 1.1 29 0.0 3.4 2.0 18

Boron 161 1.9 1.2 36 4.9 2.1 32 9.4 0.0 0.0 4.1

Molybdenum 20 5.0 0.0 17 5.9 0.0 14 7.1 0.0 0.0 9.1

Strontium nc nc nc nc nc nc 14 7.1 0.0 0.0 9.1

Lead 335 0.9 1.8 55 0.7 1.2 53 1.9 0.0 0.0 2.5

Thallium 346 0.0 0.9 57 0.0 1.1 56 0.0 0.0 0.0 2.4

Selenium 346 0.0 0.3 57 0.0 0.3 56 0.0 0.0 0.0 2.4

Radioactive constituentsGross alpha radioactivity 257 8.2 3.1 42 12 4.0 38 13 0.0 0.0 3.5

Radium 123 0.8 1.6 24 4 4.5 18 5.6 0.0 0.0 10

Uranium 83 4.8 4.8 28 2.5 6.1 14 0.0 0.0 0.0 9.1

NutrientsNitrate plus nitrite 391 13 4.9 67 15 5.6 67 12 4.5 1.6 10

Nitrite 360 0.3 0.0 61 0.8 0.0 60 1.7 0.0 0.0 2.2

Inorganics with aesthetic benchmarks (SMCLs)Total dissolved solids (TDS) 336 9.2 6.0 58 16 16 58 17 14 7.7 23

Sulfate 316 3.5 1.9 48 6.1 8.4 48 6.2 8.3 3.5 17

Chloride 316 2.5 0.3 48 3.8 1.0 48 4.2 2.1 0.4 7.9

Iron 329 3.0 6.1 53 5.0 9.5 53 3.8 9.4 4.4 18

Manganese 327 1.2 3.4 53 1.0 5.4 53 1.9 5.7 2.1 13

1 Based on most recent analysis for each CDPH well during January 1, 2003–December 31, 2005, combined with GAMA grid-based data.2 Based on the Jeffreys interval for the binomial distribution (Brown and others, 2001).3 Gross alpha activities were not adjusted for uranium activity. The MCL-US for gross alpha activity applies to adjusted gross alpha activity.
44 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project Table 9. Raw detection frequencies and aquifer-scale proportions using spatially weighted and grid-based methods for organic constituents and constituents of special interest detected at concentrations greater than water-quality benchmarks from data available during the most recent 3 years (January 1, 2003, through December 31, 2005) in the California Department of Public Health (CDPH) database, or detected at high or at moderate relative-concentrations, or detected at detection frequencies greater than 10 percent in samples collected from USGS-grid wells (October 2005–March 2006) in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project. [Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from grid wells during October 2005–March 2006. High, concentrations greater than water-quality benchmark; moderate, concentrations less than or equal to benchmark but greater than 0.1 of benchmark; low, concentrations less than 0.1 of benchmark. Constituents are ordered by class and high aquifer-scale proportion. Abbreviation: nc, not collected] Constituent Raw detection frequency1 Spatially weighted aquifer-scale proportion1 Grid-based aquifer-scale proportion 90 percent confidence interval for grid-based high aquifer- scale proportion2 Number of wells Moderate values (percent) High values (percent) Number of cells Moderate values (percent) High values (percent) Number of cells Moderate values (percent) High values (percent) Lower limit (percent) Upper limit (percent) Southeast San Joaquin Valley study unit Trihalomethanes (THMs)3 Total THMs 773 0.8 0.0 83 2.5 0.0 83 1.2 0.0 0.0 1.6 Chloroform 773 0.1 0.0 85 0.1 0.0 83 0.0 0.0 0.0 1.6 Solvents Dichloromethane (methylene chloride) 771 0.3 0.0 85 1.3 0.0 83 1.2 0.0 0.0 1.6 Carbon tetrachloride 771 0.0 0.4 85 0.0 0.3 83 0.0 0.0 0.0 1.6 Tetrachloroethene (PCE) 771 2.7 0.4 85 0.8 0.3 83 0.0 0.0 0.0 1.6 Trichloroethene (TCE) 772 0.3 0.5 85 0.0 0.1 83 0.0 0.0 0.0 1.6 Other volatile organic compounds (VOCs) Benzene 773 0.0 0.4 85 0.0 1.2 83 0.0 1.2 0.2 4.6 Fumigants 1,2-Dibromo-3-chloropropane (DBCP) 835 15 5.6 84 12 3.8 83 9.6 3.6 1.3 8.2 1,2-Dibromoethane (EDB) 825 0.1 0.2 84 0.1 0.04 83 0.0 0.0 0.0 1.6 Pesticides Dieldrin 272 0.7 0.0 83 0.7 0.0 83 2.4 0.0 0.0 1.6 Atrazine 765 0.0 0.0 84 0.0 0.0 83 0.0 0.0 0.0 1.6 Simazine 765 0.0 0.0 84 0.0 0.0 83 0.0 0.0 0.0 1.6 Bromacil 693 0.0 0.0 65 0.0 0.0 28 0.0 0.0 0.0 4.7 Constituents of special interest Perchlorate 297 8.0 0.7 84 13 1.2 83 18 1.2 0.2 4.6

N-Nitrosodimethylamine nc nc nc nc nc nc 29 3.4 0.0 0.0 4.5
Status and Understanding of Water Quality 45

Table 9. Raw detection frequencies and aquifer-scale proportions using spatially weighted and grid-based methods for organic constituents and constituents of special interest detected at concentrations greater than water-quality benchmarks from data available during the most recent 3 years (January 1, 2003, through December 31, 2005) in the California Department of Public Health (CDPH) database, or detected at high or at moderate relative-concentrations, or detected at detection frequencies greater than 10 percent in samples collected from USGS-grid wells (October 2005–March 2006) in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.—

Continued

[Grid-based aquifer-scale proportions for organic constituents are based on samples collected by the U.S. Geological Survey from grid wells during October 2005–March 2006. High, concentrations greater than water-quality benchmark; moderate, concentrations less than or equal to benchmark but greater than 0.1 of benchmark; low, concentrations less than 0.1 of benchmark. Constituents are ordered by class and high aquifer-scale proportion. Abbreviation: nc, not collected]

Constituent

Raw detection frequency1 Spatially weighted aquifer-scale proportion1 Grid-based aquifer-scale proportion 90 percent confidence interval for grid-based high aquifer- scale proportion2

Number 

of wells

Moderate 

values 

(percent)

High 

values 

(percent)

Number 

of cells

Moderate 

values 

(percent)

High 

values 

(percent)

Number 

of cells

Moderate 

values 

(percent)

High 

values 

(percent)

Lower 

limit 

(percent)

Upper 

limit 

(percent)

Kern County Subbasin study unit

Trihalomethanes (THMs)3

Total THMs 355 1.1 0.0 60 4.2 0.0 47 0.0 0.0 0.0 2.8

Chloroform 355 0.3 0.0 60 1.7 0.0 47 0.0 0.0 0.0 2.8

SolventsTetrachloroethene (PCE) 353 3.7 0.0 60 1.3 0.0 47 2.1 0.0 0.0 2.8

1,2-Dichloroethane (1,2-DCA) 352 0.3 0.0 60 0.8 0.0 47 2.1 0.0 0.0 2.8

Carbon tetrachloride 352 0.0 0.3 60 0.0 0.1 47 0.0 0.0 0.0 2.8

Trichloroethene (TCE) 353 0.6 0.3 60 0.0 0.04 47 0.0 0.0 0.0 2.8

Other volatile organic compounds (VOCs)Benzene 354 0.3 0.0 60 1.7 0.0 47 2.1 0.0 0.0 2.8

Fumigants1,2-Dibromo-3-chloropropane (DBCP) 378 4.8 1.1 61 5.0 1.2 47 2.1 2.1 0.4 8.0

1,2-Dibromoethane (EDB) 378 2.1 0.0 61 3.4 0.0 47 2.1 0.0 0.0 2.8

1,2-Dichhloropropane (1,2-DCP) 352 0.9 0.0 60 1.2 0.0 47 0.0 0.0 0.0 2.8

1,2,3-Trichloropropane (1,2,3-TCP) 327 0.0 0.0 59 0.0 0.0 47 0.0 0.0 0.0 2.8

PesticidesDieldrin 97 2.1 0.0 49 1.2 0.0 46 2.2 0.0 0.0 2.9

Atrazine 328 0.0 0.0 61 0.0 0.0 46 0.0 0.0 0.0 2.9

Simazine 328 0.0 0.0 61 0.0 0.0 46 0.0 0.0 0.0 2.9

Dinoseb 53 0.0 0.0 25 0.0 0.0 14 0.0 0.0 0.0 9.1

Constituents of special interestPerchlorate 149 2.0 0.0 49 5.1 0.0 47 6.4 0.0 0.0 2.8

1 Based on most recent analysis for each CDPH well during January 1, 2003–December 31, 2005, combined with GAMA grid-based data.2 Based on the Jeffreys interval for the binomial distribution (Brown and others, 2001).3 The MCL-US (U.S. Environmental Protection Agency maximum contaminant level) for trihalomethanes is the sum of chloroform, bromoform, bromodichloromethane, and dibromochloromethane.
46 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Inorganic Constituents 

Inorganic constituents generally occur naturally in 

groundwater, although these concentrations may be affected by 

human as well as natural factors. In the study units, inorganic 

constituents with human-health benchmarks that were detected 

at high relative-concentrations in more than 2 percent of the 

primary aquifers were arsenic, boron, vanadium, nitrate, 

uranium, and gross alpha radioactivity. Inorganic constituents 

with aesthetic benchmarks that were detected at high relativeconcentrations in the study units were iron and manganese. 

Additional constituents with human-health benchmarks—

antimony, radium, and fluoride—and constituents with 

aesthetic benchmarks—total dissolved solids (TDS), sulfate, 

and chloride—were detected at high relative-concentrations 

in more than 2 percent of the primary aquifer in the KERN 

study unit but not in SESJ. Lead, aluminum, barium, thallium, 

and selenium were detected at high relative-concentrations in 

one or both study units in less than 2 percent of the primary 

aquifers (table 8). All detections are in untreated groundwater 

samples from the primary aquifers and not from drinking 

water, which is frequently treated before it is delivered to 

consumers by water purveyors.

Inorganic constituents in the SESJ study units with 

human-health benchmarks, as a group (trace elements 

and minor ions, uranium and radioactive constituents, and 

nutrients), were detected at high relative-concentrations 

in 30 percent, at moderate relative-concentrations in 

30 percent, and at low relative-concentrations or not 

detected in 39 percent of the primary aquifers (table 10). 

Inorganic constituents with aesthetic benchmarks, as a 

group, were detected at high relative-concentrations in 

6.6 percent, at moderate relative-concentrations in 13 

percent, and at low relative-concentrations or not detected 

in 81 percent of the primary aquifers. In contrast, inorganic 

constituents in the KERN study unit with human-health 

benchmarks, as a group (trace elements and minor ions, 

uranium and radioactive constituents, and nutrients), were 

detected at high relative-concentrations in 23 percent, at 

moderate relative-concentrations in 29 percent, and at 

low relative-concentrations or not detected in 48 percent 

of the primary aquifers (table 10). Inorganic constituents 

with aesthetic benchmarks, as a group, were detected at 

high relative-concentrations in 22 percent, at moderate 

relative-concentrations in 17 percent, and at low relativeconcentrations or not detected in 61 percent of the primary 

aquifers (table 10).

Trace Elements and Minor Ions

In the study units, the aquifer-scale proportions 

of one or more constituents for trace elements and 

minor ions with human-health benchmarks, as a class, 

were high. Trace elements in the SESJ study unit were 

detected at high relative-concentrations in 24 percent, at 

moderate relative-concentrations in 26 percent, and at low 

relative-concentrations or not detected in 50 percent of the 

primary aquifers (table 10). The aquifer-scale proportions for 

trace elements detected in the KERN study unit were similar 

to those detected in the SESJ study unit; trace elements were 

detected at high relative-concentrations in 20 percent, at 

moderate relative-concentrations in 27 percent, and at low 

relative-concentrations or not detected in 54 percent of the 

primary aquifers (table 10). Only constituents detected at high 

relative-concentrations in more than 2 percent of the primary 

aquifers in the study units are discussed further in this report.

Arsenic

Arsenic is a naturally occurring semi-metallic trace 

element. The most common source of arsenic is from aquifer 

materials in the southern San Joaquin Valley including 

dissolution of arsenic-rich minerals such as arsenopyrite, 

a common constituent of shales, and apatite, a common 

constituent of phosphorites. Anthropogenic sources of arsenic 

are from uses, for example, as a wood preservative, in paints 

and dyes, in drugs, and in the mining of copper and gold 

(Welch and others, 2000).

Arsenic was detected at high relative-concentrations in 

19 and 20 percent of the primary aquifers in the SESJ and 

KERN study units, respectively (table 8). Arsenic was detected 

at high relative-concentrations in USGS- and CDPH-grid 

wells in the Kings, Tule, and Tulare Lake study areas in the 

SESJ study unit and in the KERN study unit (fig.16A). High 

relative-concentrations of arsenic also were detected in some 

CDPH-other wells in the Kaweah study area (fig. 17A). Most 

arsenic detections at high relative-concentrations were in the 

western part of the SESJ study unit. There also were several 

arsenic detections of high relative-concentrations near the city 

of Delano and to the south of the city of Bakersfield.
Status and Understanding of Water Quality 47

Table 10. Aquifer-scale proportions for inorganic constituent classes for the two southern San Joaquin Valley study 

units, California GAMA Priority Basin Project.

[Aquifer-scale proportions are given in percentage of area of the primary aquifer. All values greater than 10 percent are rounded to the 

nearest 1 percent, values less than 10 percent are rounded to the nearest 0.1 percent, values may not add up to 100 percent because of 

rounding. Abbreviations: SMCL, secondary maximum contaminant level; SO4, sulfate; Cl, chloride; TDS, total dissolved solids]

Constituent

Aquifer-scale proportion (percent)

High Moderate Low or not 

detected 

Southeast San Joaquin Valley study unit

Inorganics with human-health benchmark

Trace elements and minor ions 24 26 50

Uranium and radioactive constituents 6.9 6.9 86

Nutrients 6.3 19 75

Any inorganic with human-health benchmarks 30 30 39

Inorganics with aesthetic benchmark (SMCLs)

Major ions (TDS, SO4, Cl) 0.4 13 87

Manganese and (or) iron 6.6 4.9 88

Any inorganic with an SMCL 6.6 13 81

Kern County Subbasin study unit

Inorganics with human-health benchmark

Trace elements and minor ions 20 27 54

Uranium and radioactive constituents 6.1 13 81

Nutrients 4.5 13 82

Any inorganic with human-health benchmarks 23 29 48

Inorganics with aesthetic benchmark (SMCLs)

Major ions (TDS, SO4, Cl) 14 17 69

Manganese and (or) iron 13 5.7 82

Any inorganic with an SMCL 22 17 61
48 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

0.001

0.01

0.1

1

10

RELATIVE-CONCENTRATION, DIMENSIONLESS

Antimony Fluoride

TRACE ELEMENTS

Arsenic Boron Vanadium

A

High

Moderate

Low

STUDY UNIT RELATIVE-CONCENTRATION

SOUTHEAST

SAN JOAQUIN

KERN COUNTY

SUBBASIN

Kings

Kaweah

Tulare Lake

Tule

Kern

High

Moderate

Low

IP004884_Figure 16a

Figure 16. (A–C) Relative-concentrations of inorganic constituents with human-health-based 

or aesthetic benchmarks with high maximum relative-concentrations in USGS- and CDPHgrid wells in the two southern San Joaquin Valley study units, California GAMA Priority Basin 

Project.
Status and Understanding of Water Quality 49

0.001

0.01

0.1

1

10

100

RELATIVE-CONCENTRATION, DIMENSIONLESS

Uranium Nitrate as Nitrogen

IP008444_Figure 16b

Gross Alpha

CONSTITUENTS THAT HAVE HEALTH-BASED THRESHOLDS

(MAXIMUM CONTAMINANT LEVEL, HEALTH

ADVISORY LEVEL, NOTIFICATION LEVEL) 

0.0001

B

STUDY UNIT

SOUTHEAST

SAN JOAQUIN

KERN COUNTY

SUBBASIN

Kings

Kaweah

Tulare Lake

Tule

Kern

RELATIVE-CONCENTRATION

High

Moderate

Low

Figure 16.—Continued
50 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

STUDY UNIT RELATIVE-CONCENTRATION

SOUTHEAST

SAN JOAQUIN

KERN COUNTY

SUBBASIN

0.001

0.0001

0.01

0.1

1

10

100

RELATIVE-CONCENTRATION, DIMENSIONLESS

Total Dissolved Solids Manganese

CONSTITUENTS THAT HAVE SECONDARY MAXIMUM CONTAMINANT LEVELS

Chloride Iron Sulfate

Kings

Kaweah

Tulare Lake

Tule

Kern

High

Moderate

Low

C

IP004884_Figure 16c Figure 16.—Continued
Status and Understanding of Water Quality 51

IP004884_Figure 17a

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

CDPHother

well

USGSunderstanding

well

0-5

5.1-10

> 10

ARSENIC, IN MICROGRAMS PER LITER

0-5

5.1-10

> 10

Low

Moderate

High

(> is greater than)

USGS- and

CDPHgrid well

0-5

5.1-10

> 10

A. Arsenic

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17. (A–K) Relative-concentrations of selected inorganic constituents with human-health-based and aesthetic 

benchmarks in USGS-grid, CDPH-grid, USGS-understanding wells, and CDPH-other wells in the two southern San Joaquin Valley 

study units, California GAMA Priority Basin Project.
52 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

IP004884_Figure 17b

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

ANTIMONY, IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

B. Antimony

0-3.0

3.1-6.0

> 6.0

0-3.0

3.1-6.0

> 6.0

0-3.0

3.1-6.0

> 6.0

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
Status and Understanding of Water Quality 53

IP004884_Figure 17c

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

BORON, IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

C. Boron

0-500

501-1,000

> 1,000

0-500

501-1,000

> 1,000

0-500

501-1,000

> 1,000

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
54 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

IP004884_Figure 17d

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

VANADIUM, IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

D. Vanadium

0-25

25.1-50

> 50

0-25

25.1-50

> 50

0-25

25.1-50

> 50

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
Status and Understanding of Water Quality 55

IP004884_Figure 17e

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule

River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

FLUORIDE, IN MILLIGRAMS PER LITER

Low

Moderate

High

(> is greater than)

E. Fluoride

0-1.0

>1-2.0

> 2.0

0-1.0

>1-2.0

> 2.0

0-1.0

>1-2.0

> 2.0

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
56 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

IP004884_Figure 17f

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

URANIUM, IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

F. Uranium

0-15

15.1-30

> 30

0-15

15.1-30

> 30

0-15

15.1-30

> 30

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
Status and Understanding of Water Quality 57

IP004884_Figure 17g

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

NITRATE AS NITROGEN, IN MILLIGRAMS PER LITER

Low

Moderate

High

(> is greater than)

G. Nitrate as nitrogen

0-5

5.1-10

> 10

0-5

5.1-10

> 10

0-5

5.1-10

> 10

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
58 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

IP004884_Figure 17h

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

MANGANESE, IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

H. Manganese

0-25

25.1-50

> 50

0-25

25.1-50

> 50

0-25

25.1-50

> 50

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
Status and Understanding of Water Quality 59

IP004884_Figure 17i

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

IRON, IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

I. Iron

0-150

151-300

> 300

0-150

151-300

> 300

0-150

151-300

> 300

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
60 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

IP004884_Figure 17j

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

TOTAL DISSOLVED SOLIDS (TDS), IN MILLIGRAMS PER LITER

Low

Moderate

High

(> is greater than)

J. Total dissolved solids

0-500

501-1,000

> 1,000

0-500

501-1,000

> 1,000

0-500

501-1,000

> 1,000

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
Status and Understanding of Water Quality 61

IP004884_Figure 17k

Kings River

South 

North

Fork

Fork

Kern 

Fresno Slough

Poso Creek

Owens

River

Tule River

Chowchilla 

Kings

River

Deer Creek

Aqueduct

Fowler 

Switch Canal

Canal

Canal

Main

Santa Maria 

River

Cuyama

River

Tulare

Lakeland

Los

Angeles

Aqueduct

Madera

Canal

Kern

River

Cross

Creek

People's

Ditch

Kern

Channel

Saint

White

Kaweah River

San Joaquin River

Outlet Canal

Canal

Canal

California 

Caliente Creek

River

Kern 

River

River

River 

Lake

Johns River

Friant Canal

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

Land use from Nakagaki and others, 2007

EXPLANATION

River or stream

Urban

LAND-USE CLASSIFICATION

Agricultural

Natural

Pacific Ocean

Tulare lakebed Canal

Aqueduct

0 10 20 40 Miles

0 10 20 40 Kilometers

SULFATE, IN MILLIGRAMS PER LITER

Low

Moderate

High

(> is greater than)

K. Sulfate

0-250

251-500

> 500

0-250

251-500

> 500

0-250

251-500

> 500

Southeast San Joaquin Valley

 study unit boundary

Southeast San Joaquin Valley

 study area boundary

Kern County Subbasin

 study unit boundary

CDPHother

well

USGSunderstanding

well

USGS- and

CDPHgrid well

Bakersfield

Delano

Fresno

Tulare

Visalia

Figure 17.—Continued
62 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Arsenic concentrations were significantly higher in older 

and deeper groundwater in the SESJ study unit (tables 6 and 

12). However, arsenic concentrations were not higher in 

the older and deeper groundwater in the KERN study unit. 

Arsenic concentrations in the SESJ study unit were positively 

correlated with the depth to the top of the perforated interval 

(table 11). When the data from the Domestic Well Project 

is combined with the Priority Basin Project data, arsenic 

concentrations are higher in deep wells than in shallow wells 

(table 11, fig. 18A). Almost all detections with moderate or 

high relative-concentrations were in wells deeper than 250 ft 

(fig. 18A). This correlation also was detected in other Priority 

Basin Project study units in the San Joaquin Valley (Bennett 

and others, 2010; Landon and others, 2010a). 

Arsenic concentrations were negatively correlated with 

normalized lateral position in the study units (table 11); 

this correlation shows that most of the high concentrations 

of arsenic were located in the distal part of the study units 

(fig. 17A). The correlation of arsenic with lateral position 

also may indicate that lateral position is correlated with well 

depth and(or) depth to top-of-perforations in the study units 

(tables 6A and 6B). 

Arsenic also was negatively correlated with the number 

of septic systems located within the 500-m buffer around the 

well and positively correlated with percentage of agricultural 

land use in the SESJ study unit (table 11). The correlation with 

septic systems likely is a result of the correlation of septic 

systems to normalized lateral position and depth to top-ofperforations. Arsenic also was positively correlated with 

the percentage of agricultural land use in the KERN study 

unit (table 11). This result probably indicates that most high 

arsenic concentrations occur in the distal part of the study 

units where land use predominantly is agricultural (fig. 4) 

rather than agricultural land use being the cause of high 

arsenic concentrations. 

Previous investigations of arsenic in the San Joaquin 

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

Izbicki and others, 2008) and literature reviews (Welch 

and other, 2000; Stollenwerk, 2003) have indicated two 

mechanisms for elevated arsenic. 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 above 

7.5 (Stollenwerk, 2003).

Evidence for pH-dependent desorption of arsenic from 

aquifer sediments in the SESJ study unit is the positive 

correlation of arsenic concentrations with pH (table 11). 

Almost all arsenic detections with moderate or high 

relative-concentrations were in samples with pH values greater 

than 7.6 (fig. 18B). This correlation still exists even when 

the shallow domestic wells are included. Arsenic may not be 

correlated with pH in the KERN study unit because there were 

very few wells with pH less than 7.6 (fig. 18B). Mobilization 

of arsenic from sediments by reductive dissolution also 

may occur; evidence for this mechanism is supported by 

the presence of high concentrations of arsenic where DO 

concentrations were low (table 11). However, manganeseand iron-reducing conditions were not commonly found in 

the study units (tables D7, D8, and D9), so the correlation 

of arsenic with DO may indicate relations of both of these 

constituents to depth or pH. These observations suggest that 

mobilization of arsenic as a result of reducing conditions 

may not be as widespread as pH-dependent desorption in the 

southern San Joaquin Valley.

Groundwater samples in the SESJ study unit with 

groundwater age classified as modern contained significantly 

lower arsenic concentrations than groundwater samples 

classified as pre-modern. Other studies in the San Joaquin 

Valley also determined that high arsenic concentrations 

were associated with old groundwater (Bennett and others, 

2010; Izbicki and others, 2008; Landon and others, 2010a). 

Groundwater age is correlated with pH in the SESJ study unit 

(table 5) which further supports the idea that pH-dependent 

desorption may be the major mechanism for the mobilization 

of arsenic. Arsenic may not be correlated with groundwater 

age in the KERN study unit because the pH for almost all 

of the wells in the KERN study unit was greater than 7.5 

regardless of groundwater age (fig. 18A and 18B).

The correlations of arsenic concentrations with DO, pH, 

and well depth would explain why the highest concentrations 

of arsenic are in the Tulare Lake study area where wells 

generally are deeper, pH generally is higher, and DO generally 

is lower (table 5) than in the other study areas. This suggests 

that arsenic in SESJ and KERN primarily are derived from 

natural sources and not anthropogenic sources.

Antimony

Antimony is a naturally occurring semi-metallic trace 

element that easily combines with other elements, particularly 

sulfur, to form a variety of minerals. Antimony also is found in 

trace amounts in silver, copper, and lead ores. Anthropogenic 

uses of antimony include use as a flame retardant in clothes 

and toys, use in metal alloys, particularly in lead-acid batteries 

and other metallic products, and use for the clarification of 

specialty glasses (Carlin, 2006). It also is used in paints, 

ceramics, and fireworks, and as enamels for plastics, metal, 

and glass (Agency for Toxic Substances and Disease 

Registry, 1995).

Antimony was detected at high relative-concentrations 

in 3.6 percent and at moderate relative-concentrations in 

1.8 percent of the primary aquifers in the KERN study unit 

(table 8; fig. 16A). Antimony was not detected at high or at 

moderate relative-concentrations in the SESJ study unit. The 

high relative-concentration of antimony occurred in CDPHgrid and CDPH-other wells in the area south of the city of 

Bakersfield (fig. 17B).
Status and Understanding of Water Quality 63

Table 11. Results of non-parametric (Spearman’s method) correlation analysis between selected inorganic water-quality constituents and potential explanatory factors in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.[Aquifer-scale proportions are from the grid-based method unless otherwise stated. Spearman’s correlation statistic (ρ) for a significant correlation on the basis of significance level (p) less than threshold value 

(α) of 0.05. Abbreviations: TDS, total dissolved solids; MCL-US, USEPA maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; ns, no significant correlation; <, less than; GAMA, Groundwater Ambient Monitoring and Assessment Program; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health]

Constituent Benchmark type High aquifer proportion (percent)

Grid and understanding wells used in analysis Grid wells used in analysis

Depth to 

top-ofperforations Well depth pH Dissolved oxygen Number of septic tanks or cesspools1 Orchard and vineyard land use1 Agricultural land use1 (percent) Urban land use1 (percent) Natural land use1 (percent) Normalized lateral position from valley trough

ρ: Spearman’s correlation statistic/p: significance level

Southeast San Joaquin Valley study unit

Arsenic 2 MCL-US 19.0 0.341

0.006

ns 0.488

<0.001

–0.550

<0.001

–0.338

0.002

ns 0.311

0.009

ns -0.256

0.032

–0.636

<0.001

Antimony MCL-US 0.0 ns ns 0.284

0.018

ns –0.227

0.048

ns ns ns ns ns

Boron 2 NL-CA 6.5 0.285

0.045

ns 0.366

0.006

–0.408

0.002

–0.496

<0.001

-0.250

0.050

ns ns ns –0.530

<0.001

Vanadium 2 NL-CA 6.1 ns ns ns 0.328

0.011

ns ns ns ns ns 0.331

0.020

Fluoride MCL-US 0.0 0.286

0.022

0.3700.002

0.509

<0.001

–0.448

<0.001

ns ns 0.253

0.039

ns ns –0.404

<0.001

Uranium 2 MCL-US 5.0 –0.620

<0.001

–0.483

<0.001

–0.361

0.007

0.3190.024

0.2700.044

ns ns ns ns ns

Nitrate 2 MCL-US 6.3 –0.308

0.010

–0.244

0.033

–0.346

0.003

0.642

<0.001

0.3090.002

0.3410.001

ns ns ns 0.533

<0.001

Manganese 2 SMCL-CA 4.9 ns ns ns –0.314

0.011

ns ns ns ns ns ns

Iron 2 SMCL-CA 3.3 ns ns 0.330

0.004

–0.355

0.004

ns ns ns ns ns –0.395

0.002

TDS SMCL-CA 3 0.4 ns ns –0.300

0.007

ns ns ns ns ns ns ns

Sulfate SMCL-CA 0.0 –0.463

<0.001

–0.372

0.002

ns ns ns ns ns ns ns ns

Chloride SMCL-CA 3 0.3 ns ns ns ns ns ns ns ns ns –0.288

0.025
64 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project Table 11. Results of non-parametric (Spearman’s method) correlation analysis between selected inorganic water-quality constituents and potential explanatory factors in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.—Continued [Aquifer-scale proportions are from the grid-based method unless otherwise stated. Spearman’s correlation statistic (ρ) for a significant correlation on the basis of significance level (p) less than threshold value (α) of 0.05. Abbreviations: TDS, total dissolved solids; MCL-US, USEPA maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; ns, no significant correlation; <, less than; GAMA, Groundwater Ambient Monitoring and Assessment Program; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health] Constituent Benchmark type High aquifer proportion (percent) Grid and understanding wells used in analysis Grid wells used in analysis Depth to top-of- perforations Well depth pH Dissolved oxygen Number of septic tanks or cesspools1 Orchard and vineyard land use1 Agricultural land use1 (percent) Urban land use1 (percent) Natural land use1 (percent) Normalized lateral position from valley trough

ρ: Spearman’s correlation statistic/p: significance level

Kern County Subbasin study unit

Arsenic 2 MCL-US 20.0 ns ns ns ns ns ns 0.266

0.050

ns –0.282

0.037

–0.423

0.001

Antimony 2 MCL-US 4.7 ns ns ns 0.380

0.046

ns ns ns –0.322

0.023

ns ns

Boron 2 NL-CA 3 2.1 ns ns –0.535

0.001

ns –0.431

0.010

ns ns ns ns ns

Vanadium 2 NL-CA 3.4 ns ns ns ns ns 0.369

0.037

ns ns ns ns

Fluoride 2 MCL-US 3.5 0.377

0.033

0.3800.017

ns –0.607

<0.001

–0.450

<0.001

ns 0.275

0.039

ns ns ns

Uranium 2 MCL-US 3 6.1 ns ns –0.426

0.042

ns –0.464

0.026

ns ns ns ns ns

Nitrate 2 MCL-US 4.5 ns 0.361

0.014

ns 0.482

0.003

0.2970.013

0.2890.015

ns ns ns 0.268

0.028

Manganese 2 SMCL-CA 5.7 0.365

0.043

ns ns –0.502

0.004

–0.362

0.006

ns ns ns ns ns

Iron 2 SMCL-CA 9.4 ns 0.332

0.042

ns –0.387

0.029

ns ns ns ns ns ns

TDS 2 SMCL-CA 14 ns ns –0.299

0.031

ns –0.336

0.008

ns 0.334

0.010

ns ns ns

Sulfate 2 SMCL-CA 8.3 ns ns ns ns ns ns 0.476

0.001

ns –0.301

0.032

ns

Chloride 2 SMCL-CA 2.1 0.454

0.014

ns ns ns ns 0.422

0.002

ns ns ns ns
Status and Understanding of Water Quality 65

Table 11. Results of non-parametric (Spearman’s method) correlation analysis between selected inorganic water-quality constituents and potential explanatory factors in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.—Continued[Aquifer-scale proportions are from the grid-based method unless otherwise stated. Spearman’s correlation statistic (ρ) for a significant correlation on the basis of significance level (p) less than threshold value 

(α) of 0.05. Abbreviations: TDS, total dissolved solids; MCL-US, USEPA maximum contaminant level; NL-CA, CDPH notification level; SMCL-CA, CDPH secondary maximum contaminant level; ns, no significant correlation; <, less than; GAMA, Groundwater Ambient Monitoring and Assessment Program; USEPA, U.S. Environmental Protection Agency; CDPH, California Department of Public Health]

Constituent Benchmark type High aquifer proportion (percent)

Grid and understanding wells used in analysis Grid wells used in analysis

Depth to 

top-ofperforations Well depth pH Dissolved oxygen Number of septic tanks or cesspools1 Orchard and vineyard land use1 Agricultural land use1 (percent) Urban land use1 (percent) Natural land use1 (percent) Normalized lateral position from valley trough

ρ: Spearman’s correlation statistic/p: significance level

Southeast San Joaquin Valley study unit with Domestic Wells included

Arsenic MCL-US 0.185

0.017

0.298

<0.001

ns ns

Antimony MCL-US 0.214

0.006

0.260

<0.001

ns –0.181

0.007

Boron NL-CA 0.334

<0.001

0.330

<0.001

–0.159

0.023

–0.196

0.005

Vanadium NL-CA ns ns –0.146

0.036

0.1870.007

Fluoride MCL-US 0.383

<0.001

0.423

<0.001

ns –0.203

0.002

Uranium MCL-US –0.476

<0.001

–0.354

0.006

ns ns

Nitrate MCL-US –0.306

<0.001

–0.403

<0.001

ns 0.332

<0.001

Manganese SMCL-CA –0.256

0.001

–0.250

<0.001

ns ns

Iron SMCL-CA ns ns ns ns

TDS SMCL-CA –0.173

0.022

–0.426

<0.001

ns 0.264

<0.001

Sulfate SMCL-CA –0.293

<0.001

–0.391

<0.001

ns 0.330

<0.001

Chloride SMCL-CA ns ns –0.156

0.21

ns

1 Land-use percentages and number of septic tanks are within a circle with a radius of 500 meters around each well included in analysis.2 Constituents with greater than 2 percent high aquifer proportion.3 Based on the spatially weighted approach.
66 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

0 5 10 15 20 25 30

WELL DEPTH, IN FEET BELOW LAND SURFACE pH, IN STANDARD UNITS

0

200

400

600

800

1,000

1,200

1,400

1,600

10.0

9.5

9.0

8.5

8.0

7.5

7.0

6.5

6.0

Kern County Subbasin study unit

Southeast San Joaquin Valley study unit

Domestic wells

A

B

Maximum contaminant level

EXPLANATION

0.5 * Maximum contaminant level

Kern County Subbasin study unit

Southeast San Joaquin Valley study unit

Domestic wells

Maximum contaminant level

EXPLANATION

0.5 * Maximum contaminant level

Low Moderate High

Low Moderate High

ARSENIC CONCENTRATION, IN MICROGRAMS PER LITER

0 5 10 15 20 25 30

ARSENIC CONCENTRATION, IN MICROGRAMS PER LITER

IP004884_FIGURE 18 As distrib

Figure 18. Relation of arsenic concentrations to (A) well depth and (B) pH in the two 

southern San Joaquin Valley study units, California GAMA Priority Basin Project, and the 

Domestic Well Project.
Status and Understanding of Water Quality 67

Antimony concentrations, similar to arsenic, significantly 

increase as pH increases in the SESJ study unit. This 

correlation was expected as many of the chemical traits of 

antimony are similar to arsenic (Hem, 1985). Antimony 

concentrations generally increased with well depth in the SESJ 

study unit when data from the Domestic Well Project was 

included in the analysis (table 11). The correlations with depth 

and pH suggest that the primary source of antimony is natural. 

The lack of a correlation of antimony with pH or depth in the 

KERN study unit (table 11), where the high concentrations of 

antimony occurred, may be a result of pH values greater than 

7.5 for almost all wells in the KERN study unit and of wells 

in the KERN study unit that are deeper than wells in the SESJ 

study unit.

Boron

Boron is a trace element that occurs in many minerals. 

Natural sources of boron include igneous rocks such as granite 

and pegmatite (as the mineral tourmaline), and evaporite 

minerals such as kernite and colemanite (Hem, 1970; Reimann 

and de Caritat, 1998). Borax, a boron-containing evaporate 

mineral that is mined in California, is used as a cleaning agent 

and therefore may be present in sewage and industrial wastes. 

Seawater contains 4.5 mg/L of boron (Burton, 1996).

Boron was detected at high relative-concentrations in 

6.5 percent and at moderate concentrations in 6.5 percent 

of the primary aquifers in the SESJ study unit (table 8; 

fig. 16A). Boron was detected at high relative-concentrations 

in 2.1 percent and at moderate relative-concentrations in 

9.4 percent of the primary aquifers in the KERN study unit 

(table 8). The few detections of boron at high and at moderate 

relative-concentrations in the SESJ study unit mainly occurred 

in the western part of the Kings study area and northern part of 

the Tulare Lake study area (fig. 17C). The detections of boron 

in the KERN study unit were in the southern part of the study 

unit near the Tehachapi Mountains.

Boron concentrations increase significantly with 

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

increasing pH and low DO concentrations in the SESJ study 

unit (tables 5 and 11); in contrast, boron concentrations 

decrease with increasing pH in the KERN study unit 

(table 11). The correlations of pH and groundwater age with 

boron in the SESJ study unit were expected because pH and 

age are correlated to well depth (tables 6 and 7). The opposite 

relation in the KERN study unit, a negative correlation of 

boron with pH, may reflect that most wells in the KERN 

study unit are deep and do not span the range of depths and 

pHs as the wells in the SESJ study unit. Boron concentrations 

decrease significantly with the number of septic systems 

located within the 500-m buffer around wells in the study 

units (table 11). The negative correlation with septic systems 

suggests that boron in cleaning agents is not a major source 

of boron in groundwater. Boron concentrations also decrease 

significantly with increasing percentage of vineyard or orchard 

land use in the SESJ study unit which is most likely a result of 

the correlation of boron with the deep wells with high pH in 

the western part of the SESJ study unit rather than land use as 

a causative relation.

Because high and moderate relative concentrations of 

boron were limited to the western part of the SESJ study 

unit and the southern part of the KERN study unit (fig. 17C), 

elevated concentrations of boron relative to the rest of the 

study units may be associated with sediments in the aquifer 

derived from marine deposits derived from the Coast Ranges 

and San Emigdio Mountains to the west and southwest of the 

study units (Page, 1986) and not from anthropogenic sources. 

These marine deposits are naturally high in boron. Saline 

waters, which also contain relatively high concentrations of 

boron (Hem, 1985) and underlie the freshwater aquifer (Page, 

1986), also could potentially cause high boron concentrations 

by moving into the overlying continental deposits.

Vanadium

Vanadium potentially is released to groundwater from 

both natural and anthropogenic sources. Natural sources can 

be attributed to the dissolution of vanadium-rich rocks, which 

include mafic rocks such as basalts and gabbros (Nriagu, 1998, 

as cited by Wright and Belitz, 2010), and sedimentary rocks 

such as shale (Vine and Tourtelot, 1970, as cited by Wright 

and Belitz, 2010; McKelvey and others, 1986, as cited by 

Wright and Belitz, 2010). Vanadium has been known to be 

mobilized under oxic, alkaline conditions (Wright and Belitz, 

2010). Anthropogenic sources of vanadium can come from 

waste streams associated with the ferrous metallurgy industry 

(International Programme on Chemical Safety, 1988, as cited 

by Wright and Belitz, 2010) and through the combustion 

of vanadium-enriched fossil fuels, primarily in the form of 

residual crude oil and coal (Duce and Hoffman, 1976, as cited 

by Wright and Belitz, 2010; Hope, 1997, as cited by Wright 

and Belitz, 2010). Atmospheric vanadium can be deposited 

to the land surface through wet and dry deposition and 

transported to the subsurface by infiltrating surface water.

Vanadium was detected at high relative-concentrations 

in 6.1 and 3.4 percent of the primary aquifers in the 

SESJ and KERN study units, respectively (table 8). High 

relative-concentrations of vanadium occurred in the Kings 

study area and in the KERN study unit (fig. 16A). The high 

and moderate relative-concentrations of vanadium primarily 

are located in the middle and eastern part of the SESJ 

study unit, especially in the Kings and Kaweah study areas 

(fig. 17D). The high and moderate relative-concentrations 

of vanadium in the KERN study unit are near the city of 

Delano (fig. 17D).
68 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Vanadium concentrations increase with increasing DO 

and in the eastern part of the SESJ study unit (table 11). 

Vanadium concentrations increase with increasing percentage 

of vineyards or orchards in the KERN study unit (table 11). 

This correlation is expected as the vineyards and orchards are 

located on the eastern boundary of the study units at the foot 

of the Sierra Nevada Mountains where mafic-rich source rock 

can be found. The correlation with DO was expected because 

high concentrations of vanadium are frequently associated 

with oxic conditions (Wright and Belitz, 2010). Vanadium also 

was found to be associated with high DO in another Priority 

Basin Project study unit in the San Joaquin Valley north 

of the SESJ study unit (Landon and others, 2010a). These 

correlations indicate that vanadium primarily is from natural 

sources. Vanadium did not have a significant correlation with 

pH, although vanadium is frequently associated with alkaline 

conditions (Wright and others, 2010); this suggests that the 

deep aquifer sediments in the distal part of the study units 

where pH is high may not contain vanadium-rich rocks or that 

conditions are anoxic. 

Fluoride

Potential sources of fluoride to groundwater are both 

natural and anthropogenic. Fluoride minerals can be found 

in igneous and sedimentary rocks. Fluoride frequently is 

associated with volcanic gases, and, in some areas, this may 

be an important source to groundwater (Hem, 1985). Fluoride 

often is added to drinking-water systems and toothpaste for the 

prevention of dental decay (Centers for Disease Control and 

Prevention, 2001).

Fluoride was detected at high relative-concentrations 

in 3.5 percent and at moderate relative-concentrations in 

1.8 percent of the primary aquifers in the KERN study 

unit (table 8; fig. 16A). Fluoride was not detected at high 

relative-concentrations in the SESJ study unit but was detected 

at moderate relative-concentrations in 10 percent of the 

primary aquifers (table 8). The high relative-concentrations 

of fluoride in Kern were located southwest of the City of 

Bakersfield (fig. 17E). A cluster of detections of fluoride at 

moderate relative-concentrations also is located in or near the 

northern part of the Tulare Lake study area (fig. 17E).

Fluoride concentrations generally increase with 

increasing well depth, increasing depth to top-of-perforations, 

older groundwater age, increasing pH, and increasing 

percentage of agricultural land use in the study units (tables 6

and 11). The correlation of fluoride with agriculture is most 

likely a result of the correlation of agricultural land use 

with lateral position because most of the high and moderate 

values of fluoride occur in the distal part of the basin where 

the primary land use is agricultural. Fluoride concentration 

was higher in pre-modern than in modern-age groundwater. 

Groundwater age may serve as a surrogate for well depth 

because groundwater age and well depth are correlated 

(table 5). These correlations suggest high and moderate 

fluoride concentrations are naturally occurring and not related 

to human activities.

Fluoride often occurs as mineral complexes with calcium 

(Hem, 1985). Substitution of fluoride with hydroxide ions at 

mineral surfaces may occur at high pH values, and fluoride 

ions are more likely to adsorb to sediment surfaces at low pH 

(Hem, 1985). The presence of calcium complexes may limit 

the solubility of fluoride. Therefore, fluoride concentration 

often is elevated in high pH, low calcium waters. Fluoride 

concentrations increased with increasing pH in the SESJ 

study unit. Fluoride was not correlated with pH in the KERN 

study unit, but this may be because the pH for very few wells 

sampled in the KERN study unit was below 7.6 (fig. 19). 

Fluoride is negatively correlated with calcium in the SESJ 

study unit (rho = –0.324, p = 0.013) but not in the KERN 

study unit. The moderate relative-concentrations of fluoride 

in the SESJ study unit were in samples where the calcium 

concentration was low (less than 10 mg/L) (Burton and Belitz, 

2008). The pH was greater than 7.6 and the well depths were 

greater than 300 ft for all the wells with moderate or high 

relative-concentrations of fluoride (fig. 19). The calcium 

concentration in wells with high relative-concentrations 

of fluoride was not low, but the pH was greater than 8.1, 

suggesting that calcium complexes are not limiting fluoride 

concentrations in the KERN study unit. The correlations of 

fluoride with lateral position and DO may be the result of 

the correlations of lateral position and DO with pH and well 

depth (table 11).

Uranium and Radioactive Constituents

Uranium-238, thorium-232, and uranium-235 are the 

main sources of natural radioactivity in groundwater (Hem, 

1985). Uranium-238 is the most common. Gross alpha 

radioactivity usually consists of isotopes of radium and radon 

which are part of the uranium and thorium radioactive decay 

series (Hem, 1985).

The MCL-US (15 pCi/L) for gross alpha particle activity 

applies to adjusted gross alpha activity, which is equal to the 

measured gross alpha activity minus uranium activity (U.S. 

Environmental Protection Agency, 2000). Data collected by 

USGS-GAMA and data compiled in the CDPH database are 

reported as gross alpha activity without correction for uranium 

activity. Gross alpha is used as a screening tool to determine 

whether other radioactive constituents must be analyzed 

(California Department of Public Health, 2012). For regulatory 

purposes, analysis of uranium is only required if gross alpha 

activity is greater than 15 pCi/L; therefore, the CDPH database 

contains more data for gross alpha activity than for uranium. 

As a result, it is not always possible to calculate adjusted 

gross alpha activity. For this reason, gross alpha data without 

correction for uranium are the primary data used in the status 

assessments made by USGS-GAMA for Priority Basin Project 

study units. Examination of data from samples having USGSGAMA data for uranium and gross alpha indicated that, in 

the absence of data for uranium, uncorrected gross alpha data 

likely provide a more accurate estimate of the aquifer-scale 

proportions for uranium and radioactive constituents as a 
Status and Understanding of Water Quality 69

Southeast San Joaquin Valley study unit, pH less than 7.6

Southeast San Joaquin Valley study unit, pH equal to or greater than 7.6

Southeast San Joaquin Valley study unit, pH unknown

Kern County Subbasin study unit, pH less than 7.6

Kern County Subbasin study unit, pH equal to or greater than 7.6

Kern County Subbasin study unit, pH unknown

Maximum contaminant level

EXPLANATION

0.5 * Maximum contaminant level

Low Moderate High

0 1 2 3 4

WELL DEPTH, IN FEET BELOW LAND SURFACE

0

200

400

600

800

1,000

1,200

1,400

1,600

FLUORIDE CONCENTRATION, IN MILLIGRAMS PER LITER

Figure 19. Relation of fluoride concentrations to well depth and pH in the two southern San 

Joaquin Valley study units, California GAMA Priority Basin Project.

class than does adjusted gross alpha (Miranda Fram, USGS 

California Water Science Center, written commun., 2012).

Most data for uranium in the CDPH database are reported 

as activities in units of picocuries per liter, and the majority 

of uranium data gathered by USGS-GAMA are reported as 

concentrations in units of micrograms per liter. The factor used 

to convert uranium mass concentration to uranium activity 

depends on the isotopic composition of the uranium (U.S. 

Environmental Protection Agency, 2000). This report uses a 

conversion factor of 0.79.

Radioactive constituents were detected at high 

relative-concentrations in 6.9 percent, at moderate 

relative-concentrations in 6.9 percent, and at low 

relative-concentrations or not detected in about 86 percent 

of the primary aquifers in the SESJ study unit (table 10). The 

radioactive constituents with high relative-concentrations were 

uranium and unadjusted gross alpha radioactivity (table 8; 

fig. 16B). Radioactive constituents were detected at high 

relative-concentrations in 6.1 percent, moderate in 13 percent, 

and low or not detected in 81 percent of the primary 

aquifers in the KERN study unit (table 10). The radioactive 

constituents with high relative-concentrations in the KERN 

study unit were radium, uranium, and unadjusted gross alpha 

radioactivity (table 8).
70 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Uranium and Gross Alpha Radioactivity

Unadjusted gross alpha radioactivity was detected 

at high relative-concentrations in 3.8 percent and at 

moderate relative-concentrations in 7.5 percent of the 

primary aquifers in the SESJ study unit (table 8). The high 

relative-concentrations of gross alpha radioactivity were in 

the Kings study area (fig. 16B). Gross alpha radioactivity was 

detected at high relative-concentrations in 4.0 percent of the 

primary aquifers in the KERN study unit and at moderate 

relative-concentrations in 13 percent of the primary aquifers 

(table 8).

Gross alpha radioactivity was positively correlated 

with uranium (rho = 0.716, p = < 0.001). This result suggests 

gross alpha radioactivity measurements may be considered a 

surrogate for uranium concentrations in the study units. For 

this reason, gross alpha radioactivity is not discussed further in 

this report.

Uranium was detected at high relative-concentrations 

in 5.0 percent and at moderate relative-concentrations in 

5.0 percent of the primary aquifers in the SESJ study unit 

(table 8). The uranium detections at high and at moderate 

relative-concentrations were detected in grid wells in the 

Tulare Lake and Tule study areas (fig. 16B); additional 

uranium detections at moderate relative-concentrations were 

in the Kings study area. High relative-concentrations also were 

detected in CDPH-other wells and in one USGS-understanding 

well in the Kings study area and in a CDPH-other well in the 

Kaweah study area (fig. 17F). 

Uranium was detected at high relative-concentrations 

in 6.1 percent of the primary aquifers in the KERN study 

unit. Uranium detections in CDPH-other wells at high or 

at moderate relative-concentrations are around the city of 

Bakersfield (fig. 17F).

Uranium concentrations were negatively correlated 

with pH (table 11), and positively correlated with calcium 

(SESJ study unit, rho=0.678, p=< 0.001; KERN study unit, 

not significant) and alkalinity (SESJ study unit, rho=0.414, 

p=0.001; KERN study unit, rho=0.693, p=< 0.001, fig. 20B). 

The results for the SESJ and KERN study units mirror the 

results of a local-scale investigation in an area to the north 

near the city of Modesto (Jurgens and others, 2008), a regional 

investigation in the eastern San Joaquin Valley (Jurgens and 

others, 2010), and another Priority Basin Project study unit 

(Landon and others, 2010a). Elevated uranium in shallow 

groundwater was attributed by Jurgens and others (2008, 

2010) to the enhanced desorption of uranium from sediments 

by irrigation and urban recharge having high bicarbonate 

(alkalinity) concentrations. 

Uranium concentrations were significantly greater in 

modern- and mixed-age groundwater than pre-modern-age 

groundwater (table 5), and were negatively correlated with 

well depths and depth to the top-of-perforations (table 11; 

fig. 20A) in the SESJ study unit. The association of high 

uranium with modern and mixed ages is consistent with the 

mobilization of naturally occurring uranium by irrigation and 

urban recharge in the shallow part of the aquifer. The lack of 

correlation with well depth (or depth to top-of-perforations) 

in the KERN study unit may be because very few wells with 

uranium data had a well depth less than 400 ft (fig. 20A).

Nutrients

Nutrients with human-health benchmarks, as a class, 

were detected at high relative-concentrations (for one or more 

constituents) in the study units. Nutrients were detected at high 

relative-concentrations in both SESJ and KERN (table 10). 

The only nutrient detected at high relative-concentrations in 

the study units was nitrate plus nitrite (hereinafter referred to 

as nitrate) (table 8).

Nitrate

Nitrogen in groundwater occurs in the forms of dissolved 

nitrate, nitrite, or ammonia. Certain bacteria and algae 

naturally convert nitrogen from the atmosphere to nitrate, 

which is an important nutrient for plants. Nitrate also is 

present in precipitation (Hem, 1970). Anthropogenic sources 

of nitrate include its application as a fertilizer for agriculture 

and production by livestock of nitrogenous waste that can 

leach to groundwater when animals are present in concentrated 

numbers (Hem, 1985). Septic systems also contain nitrogenous 

waste that may leach into groundwater.

Nitrate was detected at high relative-concentrations 

in 6.3 and 4.5 percent of the primary aquifers in the 

SESJ and KERN study units, respectively (table 8). High 

relative-concentrations of nitrate occurred in grid wells in the 

Kaweah and Tule study areas and in the KERN study unit 

(fig. 16B). High relative-concentrations of nitrate also were 

detected in some CDPH-other wells. Most high and moderate 

relative-concentrations of nitrate were detected in the eastern 

part of the study units (fig. 17G). 

Nitrate was positively correlated with dissolved oxygen, 

lateral position, orchard and vineyard land use, and septic 

systems (table 11) in the study units. The correlation of nitrate 

to septic systems was not unexpected because septic systems 

can be a source of nitrate. The relation of nitrate to normalized 

lateral position and DO partially may indicate that nitrate is a 

redox-sensitive constituent that is removed from groundwater 

in a reducing environment, and this relation also may occur 

because wells become deeper toward the valley trough. 

Reducing conditions mostly exist toward the distal part of the 

southern San Joaquin Valley, at the low end of normalized 

lateral position values. 

Nitrate concentration was negatively correlated with 

well depth (fig. 21A), depth to the top-of-perforations, and 

pH (table 11) in the SESJ study unit. High and moderate 

relative-concentrations of nitrate were detected in the shallow 

USGS-understanding wells in the Kings study area (fig. 17G). 
Status and Understanding of Water Quality 71

ALKALINITY, IN MILLIGRAMS PER LITER AS CACO3

500

400

300

200

100

0

B

Low

IP004884_FIGURE 20 U distrib

A

Mod High

Low Mod High

EXPLANATION

0.01 0.1 1 10 100

URANIUM CONCENTRATION, IN MICROGRAMS PER LITER

0.01 0.1 1 10 100

URANIUM CONCENTRATION, IN MICROGRAMS PER LITER

WELL DEPTH, IN FEET BELOW LAND SURFACE

0

200

400

600

800

1,000

1,200

1,400

1,600

Modern

GROUNDWATER AGE

CLASSIFICATION

Mixed

Pre-modern

EXPLANATION

Kern County Subbasin

 study unit

Southeast San Joaquin

 Valley study unit

Domestic wells

Maximum

 contaminant level

0.5 * Maximum

 contaminant level

Kern County Subbasin

 study unit

Southeast San Joaquin

 Valley study unit

Domestic wells

Maximum

 contaminant level

0.5 * Maximum

 contaminant level

Figure 20. Relation of uranium concentrations to (A) well depth, groundwater age classification, and (B) alkalinity in the 

two southern San Joaquin Valley study units, California GAMA Priority Basin Project.
72 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

0 10 20 30 40 50 60

0 10 20 30 40 50 60

WELL DEPTH, IN FEET BELOW LAND SURFACE ORCHARD AND VINEYARD LAND USE, IN PERCENT

0

200

400

600

800

1,000

1,200

1,400

1,600

100

0.1

1

10

Kern County Subbasin study unit

Southeast San Joaquin Valley study unit

Domestic wells

A

B

Maximum contaminant level

EXPLANATION

0.5 * Maximum contaminant level

Kern County Subbasin study unit

Southeast San Joaquin Valley study unit

Domestic wells

Maximum contaminant level

EXPLANATION

0.5 * Maximum contaminant level

Low Mod High

NITRATE AS NITROGEN CONCENTRATION, IN MILLIGRAMS PER LITER

NITRATE AS NITROGEN CONCENTRATION, IN MILLIGRAMS PER LITER

Low Mod High

IP004884_FIGURE 21 NO3 distrib

Figure 21. Relation of nitrate to (A) well depth and (B) percentage of orchard and vineyard 

land use in the two southern San Joaquin Valley study units, California GAMA Priority Basin 

Project.
Status and Understanding of Water Quality 73

Nitrate in groundwater has been studied extensively 

in the eastern San Joaquin Valley (for example, Dubrovsky 

and others, 1998; Burow and others, 2008b). Results of 

these investigations of nitrate in the San Joaquin 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). 

This study, similar to other Priority Basin Project study units 

in the northern part of the San Joaquin Valley (Bennett and 

others, 2010; Landon and others, 2010a), did not show a direct 

correlation of nitrate concentrations with agricultural land use. 

However, in this study and that of Landon and others (2010a), 

nitrate is correlated with a specific type of agricultural land 

use, percentage of orchard and vineyard land use (fig. 21B; 

table 11). Percentage of orchard and vineyard land use is 

positively correlated with normalized lateral position and is 

most common in the eastern part of the study units. 

Nitrate concentrations are significantly higher in shallow 

wells than in deep wells in the SESJ study unit (table 11). 

More evidence for the negative relation of nitrate with well 

depth is supplied by comparing the data from the Domestic 

Well Project and the grid wells from the study units. The 

domestic wells were significantly shallower than the grid wells 

from either study unit (fig. 7; table 5), and the concentration 

of nitrate is higher in domestic wells than in grid wells from 

the study units (table 5; fig. 21A). Nitrate concentrations in 

the KERN study unit generally are higher in some of the deep 

wells than in the SESJ study unit and in the domestic wells 

(table 11; fig. 21B). This result may be affected by the lack of 

wells less than 400 ft below land surface where most of the 

high concentrations of nitrate were found in other parts of the 

San Joaquin Valley. Nitrate concentration was not significantly 

correlated with groundwater age in the study units; this was 

unexpected because well depth and groundwater age are 

significantly correlated (table 5). The correlations of nitrate 

with well depth, septic systems, and orchard and vineyard 

land use suggest elevated nitrate concentrations are related to 

human activities.

Some of the explanatory factors related to nitrate are 

themselves related—DO with depth to the top-of-perforations 

(tables 6A,B), and lateral position and pH (table 6B). The 

correlations between explanatory factors could affect the 

correlations between nitrate and the explanatory factor. For 

example, the correlations of nitrate concentrations with well 

depth may be strengthened by the correlation of well depth 

with DO. Also, the relation of nitrate to lateral position may 

be strengthened by the correlation of nitrate with orchard and 

vineyard land use which is located on the eastern margin on 

the study units where DO is higher.

Inorganics with Aesthetic Benchmarks

As a class, inorganics with aesthetic benchmarks 

(SMCLs) were detected at high relative-concentrations (for 

one or more constituents) in the study units. Inorganics 

with SMCLs were detected at high relative-concentrations 

in 6.6 percent, at moderate relative-concentrations in 

13 percent, and at low relative-concentrations or not detected 

in 81 percent of the primary aquifers in the SESJ study unit 

(table 10). The constituents with high relative-concentrations 

in the SESJ study unit were manganese and iron (table 8). 

Relative-concentrations of inorganic constituents with SMCLs 

were high in 22 percent, moderate in 17 percent, and low in 

61 percent of the primary aquifers in the KERN study unit 

(table 10). The constituents with high relative-concentrations 

in the KERN study unit were manganese, iron, TDS, sulfate, 

and chloride (table 8).

For USGS- and CDPH-grid wells without a measured 

TDS value, TDS was calculated from specific conductance 

(SC) using a linear regression equation. Two linear regression 

equations, one for each study unit, were developed from 

USGS-grid and understanding wells having measured SC and 

TDS data. SC, an electrical measure of TDS, was available 

in all 130 USGS-grid and 19 USGS-understanding wells, 

whereas laboratory-measured TDS data (as residue on 

evaporation) were available for only 61 of these wells. The 

linear regression equation (TDS = (0.657*SC)–8.503) was 

developed from data for 44 USGS-grid and understanding 

wells in the SESJ study unit. The linear regression equation 

(TDS = (0.699*SC) –28.59) was developed from data for 

17 USGS-grid and understanding wells in the KERN study 

unit. The predicted TDS using the regression equations closely 

matched measured TDS (r2> 0.98) for both study units. 

Manganese and Iron

Potential natural sources of manganese and iron 

to groundwater include the dissolution of igneous and 

metamorphic rocks as well as various secondary minerals 

(Hem, 1970) which can be mobilized under reducing or low 

pH conditions. Potential anthropogenic sources of these 

constituents to groundwater include effluents associated with 

the steel and mining industries (Reimann and de Caritat, 

1998), and soil amendments, in the form of manganese and 

iron sulfates, that are added to deficient soils in order to 

stimulate crop growth.

Manganese was detected at high relative-concentrations 

in 4.9 percent and 5.7 percent of the primary aquifers in the 

SESJ and KERN study units, respectively (table 8). High 

relative-concentrations of manganese occurred in USGSand CDPH-grid wells in the Tulare Lake and Tule study 
74 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

areas in the SESJ and in KERN study units (fig. 16C). High 

relative-concentrations of manganese also were detected 

in some CDPH-other wells in the Kings and Kaweah study 

areas and one USGS-understanding well in the Kings study 

area. Most of the high and moderate relative-concentrations 

of manganese were near the eastern boundary of the SESJ 

and KERN study units, the southern boundary of the KERN 

study unit, or near the valley center in the Kings study area 

(fig. 17H).

Iron was detected at high relative-concentrations in 

3.3 percent and 9.4 percent of the primary aquifers in the 

SESJ and KERN study units, respectively (table 8). High 

relative-concentrations of iron occurred in USGS- and CDPHgrid wells in the Tule study area and in the KERN study unit 

(fig. 16C). Distributions of high relative-concentrations of iron 

were similar to high relative-concentrations of manganese. In 

addition, high relative-concentrations of iron were detected 

in CDPH-other wells in the northern part of the Tulare Lake 

study area and south of the city of Delano in the KERN study 

area (fig. 17I).

Manganese and iron were negatively correlated to DO 

in the SESJ and KERN study units (table 11). Concentrations 

of manganese were higher in pre-modern-age groundwater 

than in mixed or modern-age groundwater in the SESJ study 

unit; concentrations of iron were higher in pre-modern-age 

groundwater than in modern-age groundwater in the SESJ 

and KERN study units (table 5). The higher concentrations of 

manganese and iron in pre-modern-age groundwater suggest 

that the primary source of manganese and iron is natural. Most 

of the remaining significant correlations of manganese (depth 

to top-of-perforations) or iron (normalized lateral position, 

wells depth, pH) with other explanatory factors observed in 

the KERN and SESJ study units (table 11) may be affected 

by their relations with DO (table 6A and B). The DO in deep 

wells in the SESJ study unit generally was low, and pH was 

high. Unexpectedly, manganese was negatively correlated 

to septic system density in the KERN study unit (table 11); 

however, this apparent correlation likely indicates relations 

of manganese and septic system density to other explanatory 

factors rather than a direct relation.

Total Dissolved Solids

Natural sources of TDS include (1) mixing of 

groundwater with deep saline groundwater that is affected 

by interactions with deep marine or lacustrine sediments, 

(2) concentration by evaporation in discharge areas, and(or) 

(3) rock/water interaction. Potential anthropogenic sources of 

TDS to groundwater include agricultural and urban irrigation, 

evaporation, disposal of wastewater and industrial effluent, 

and leaking water and sewer pipes. 

TDS was detected at high relative-concentrations 

in 14 percent and at moderate relative-concentrations 

in 17 percent of the primary aquifers in the KERN 

study unit (table 8; fig. 16C). TDS was detected at high 

relative-concentrations in 0.4 percent and at moderate 

relative-concentrations in 13 percent of the primary aquifers 

in the SESJ study unit (table 8). The high and moderate 

relative-concentrations of TDS occurred in CDPH-grid and 

CDPH-other wells in the area south of the city of Bakersfield 

(fig. 17J), near the Tehachapi and San Emigdio Mountains, 

and in the distal part of the KERN study unit. Moderate 

relative-concentrations of TDS were detected on the eastern 

and western boundaries of the SESJ study unit (fig. 17J).

In general, TDS was significantly higher in the KERN 

study unit than in the SESJ study unit (table 5). The higher 

concentrations of TDS in the KERN study unit may be 

affected by sediments from the Tehachapi and San Emigdio 

Mountains and the Coast Ranges which contain marine 

deposits. TDS was negatively correlated with pH in the study 

units (table 11). This relation also was found in two GAMA 

study units in the northern part of the San Joaquin Valley 

(Bennett and others, 2010; Landon and others, 2010a). TDS 

also was positively correlated to agricultural land use in the 

KERN study unit. TDS was correlated in orchard and vineyard 

land use (a subset of agricultural land use) and well depth 

in the SESJ study unit if the domestic wells are included in 

the analysis. Agricultural irrigation is a potential source of 

TDS. TDS also was negatively correlated to the number of 

septic systems in the KERN study unit. On the basis of these 

relations, higher concentrations of TDS in SESJ primarily may 

be a result of human activities, while higher concentrations of 

TDS in KERN primarily may be from natural sources as well 

as human activities.

Sulfate

Sulfur occurs naturally in both igneous and sedimentary 

rocks as metallic sulfides. Pyrite crystals that occur in many 

sedimentary rocks are a major source of both ferrous iron and 

sulfate in groundwater (Hem, 1985). Sulfate also occurs in 

evaporate sediments such as gypsum (calcium sulfate). Sulfur 

also is applied as an agricultural fertilizer on parts of the San 

Joaquin Valley (Jurgens and others, 2008). 

Sulfate was detected at high relative-concentrations 

in 8.3 percent and at moderate relative-concentrations in 

6.2 percent of the primary aquifers in the KERN study 

unit (table 8; fig. 16C). Sulfate was detected at moderate 

relative-concentrations in 1.7 percent of the primary aquifers 

in the SESJ unit (table 8). The high relative-concentrations 

of sulfate primarily occurred in CDPH-grid and CDPH-other 

wells in the area south of the city of Bakersfield (fig. 17K) 

near the Tehachapi and San Emigdio Mountains.
Status and Understanding of Water Quality 75

Similar to TDS, sulfate concentrations were higher in 

the KERN study unit than in the SESJ study unit (table 5). 

High concentrations of sulfate may be from sediments from 

the Tehachapi and San Emigdio Mountains. Sulfate was 

positively correlated to agricultural land use in the KERN 

study unit but not in the SESJ study unit (table 11). However, 

sulfate was correlated with orchard and vineyard land use 

(a subset of agricultural land-use) in the SESJ study unit 

if the domestic wells are included in the analysis. Sulfate 

concentrations also decreased with well depth in the SESJ 

study unit. Sulfate was significantly higher in mixed-age 

groundwater than in pre-modern-age groundwater in the SESJ 

study unit, but sulfate concentrations were not significantly 

different between modern-age water and mixed or pre-modern 

age groundwater. This relation may be affected by well depth. 

These relations suggest elevated sulfate concentrations in 

SESJ primarily are from anthropogenic sources, while the high 

sulfate concentrations in the southern part of KERN are from 

natural sources.

Chloride

Chloride most commonly is associated with sedimentary 

rocks, particularly evaporates. Where porous rocks are 

submerged by the sea, soluble salts infiltrate the rock. 

Fine-grained marine shales might retain chloride for 

long periods of time (Hem, 1985). Chloride is present in 

precipitation as a result of entrainment of marine salts into 

the air at the ocean’s surface. Human activities also may be a 

source of chloride in some areas.

Chloride was detected at high relative-concentrations 

in 2.1 percent and at moderate relative-concentrations in 

4.2 percent of the primary aquifers in the KERN study 

unit (table 8; fig. 16C). Chloride was detected in high 

relative-concentrations in 0.3 percent in the SESJ study unit 

(table 8).

Similar to TDS, chloride concentration was higher in 

the KERN study unit than in the SESJ study unit (table 5). 

Chloride was positively correlated with depth to top-ofperforations and orchard and vineyard land use in the KERN 

study unit (table 11). Chloride concentration was significantly 

higher in the distal part of the SESJ study unit than in the 

eastern part. These relations suggest chloride concentrations 

are affected by both human activities and natural sources 

similar to sulfate.

Organic Constituents

Organic and special-interest constituents, unlike 

inorganic constituents, usually are of anthropogenic origin. 

VOCs may be present in paints, solvents, fuels, refrigerants, 

can be byproducts of water disinfection, and are characterized 

by their tendency to evaporate. In this report, VOCs are 

classified as THMs, solvents, and other VOCs. Pesticides are 

used to control weeds, insects, or fungi in agricultural, urban, 

and suburban settings. Pesticides are classified as fumigants 

and pesticides. 

Organic constituents with human-health benchmarks 

were detected at high relative-concentrations in 4.8 percent 

and 2.1 percent of the primary aquifers in the SESJ and 

KERN study units, respectively (table 12). Benzene, 

1,2-dibromo-3-chloropropane (DBCP), carbon tetrachloride, 

tetrachloroethene (PCE), and trichloroethene (TCE) were the 

organic constituents detected at high relative-concentrations in 

grid wells (table 9). Organic constituents with human-health 

benchmarks were detected at moderate relative-concentrations 

(greater than 0.1 but less than or equal to 1.0) in 11 percent 

and 8.5 percent of the primary aquifers in the SESJ and KERN 

study units, respectively (table 12). Organic constituents 

with moderate relative-concentrations in either the SESJ or 

KERN study units were the solvents dichloromethane and 

1,2-dichloroethane (1,2-DCA), the pesticide dieldrin, and 

the fumigant 1,2-dibromoethane (EDB). All of the other 

organic constituents with human-health benchmarks were 

detected at low relative-concentrations (less than or equal 

to 0.1) or were not detected (fig. 22). In addition, several 

organic constituents—the THM chloroform, the pesticides 

atrazine, simazine, dinoseb, and bromacil, the fumigants 

1,2,3-trichloropropane (1,2,3-TCP) and 1,2-dichloropropane 

(1,2-DCP)—were prevalent (detection frequency greater 

than 10 percent in USGS-grid wells) in the primary aquifers 

(fig. 22). The relative-concentrations of selected organic 

compounds are shown in figure 23 in relations to the study 

areas of the SESJ study unit or KERN study unit in which they 

are detected.
76 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table 12. Aquifer-scale proportions for organic constituent classes and constituents of special interest for the two 

southern San Joaquin Valley study units, California GAMA Priority Basin Project.

[Aquifer-scale proportions are given in percentage of area of the primary aquifer. All values greater than 10 percent are rounded to the 

nearest 1 percent; values less than 10 percent are rounded to the nearest 0.1 percent; values may not add up to 100 percent because of 

rounding. THMs, trihalomethanes; VOCs, volatile organic compounds; NDMA, N-nitrosodimethylamine]

Constituent

Aquifer-scale proportion (percent)

High Moderate Low or not 

detected 

Southeast San Joaquin Valley study unit

Organics with human-health benchmarks

THMs 0.0 1.2 99

Solvents 0.7 1.2 88

Other VOCs 1.2 0.0 99

Fumigants 3.6 9.6 86

Pesticides 0.0 2.4 98

Any organic constituent 4.8 11 84

Constituents of special interest

Perchlorate, NDMA 1.2 19 80

Kern County Subbasin study unit

Organics with human-health benchmarks

THMs 0.0 4.2 96

Solvents 0.1 4.3 95

Other VOCs 0.0 2.1 98

Fumigants 2.1 4.3 93

Pesticides 0.0 0.0 100

Any organic constituent 2.1 8.5 89

Constituents of special interest

Perchlorate 0.0 6.4 94
Status and Understanding of Water Quality 77

RELATIVE-CONCENTRATION, DIMENSIONLESS

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

A 100

Moderate

High

Low

DETECTION FREQUENCY AT ANY CONCENTRATION, IN PERCENT

1 10 100

N-Nitrosodimethylamine

(NDMA)

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

 Unless indicated by following location line: (greater than or equal to 25 grid wells sampled) 

EXPLANATION

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

 Unless indicated by following location line: (less than 25 grid wells sampled) 

Diuron

IP004884_Figure 22a

Benzene

Methylene chloride

Carbon tetrachloride

Bromochloromethane

Methyl tert-butyl ether

1,2-Dichloroethene

1,2-Dichloroethane

Diazinon

Alachlor

Metolachlor

Isopropylbenzene

Dieldrin

Ethylbenzene

Carbon disulfide

CFC-113

o-Xylene

Hexazinone

Simazine Atrazine

Chloroform

1,2-Dibromo-3-chloropropane

(DBCP)

Tetrachloroethene (PCE)

m-,p-Xylene

Bromacil

Bromodichloromethane

Prometon

1,2-Dichloropropane

Bromoform

1,2,3-Trichloropropane

1,1-Dichloroethene

Dibromochloromethane

Trichloroethene (TCE)

CFC-11

Dinoseb

Perchlorate

Total trihalomethane

Diuron

Figure 22. Detection frequency and maximum relative-concentration for organic and specialinterest constituents detected in USGS-grid wells in (A) the Southeast San Joaquin Valley study 

unit and (B) the Kern County Subbasin study unit., California GAMA Priority Basin Project.
78 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

DETECTION FREQUENCY AT ANY CONCENTRATION, IN PERCENT

RELATIVE-CONCENTRATION, DIMENSIONLESS

0.00001

1 10 100

0.0001

0.001

0.01

0.1

1

10

1,2-Dichloropropane

Simazine

Atrazine

Total trihalomethane

Chloroform

Dinoseb

1,2,3-Trichloropropane

Bromacil

Diphenamid

Diuron

Benzene

1,2-Dibromo-3-chloropropane

(DBCP)

Hexazinone

Prometon

Metolachlor

Isopropylbenzene

Perchlorate

B

High

Moderate

Low

Chlorpyrifos

Dieldrin

Metribuzin

Bromodichloromethane

Ethylbenzene

Trichloroethene (TCE)

1,2-Dichloroethene

1,2-Dichloroethane

1,1-Dichloroethene

1,1-Dichloroethane

1,2-Dibromoethane

Tetrachloroethene (PCE)

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

 Unless indicated by following location line: (greater than or equal to 25 grid wells sampled) 

EXPLANATION

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

 Unless indicated by following location line: (less than 25 grid wells sampled) 

Prometon

Diuron

IP004884_Figure 22b

Figure 22.—Continued
Status and Understanding of Water Quality 79

Benzene

Chloroform

Total trihalomethane

Dichloromethane

Tetrachloroethene

(PCE)

m,p-Xylene

1,2-Dichloroethane

5

5

13

2

3

11

2

3

4

2

1

2

13

14

4

5

2

2

1

3

1

1

STUDY UNIT RELATIVE-CONCENTRATION

SOUTHEAST SAN JOAQUIN KERN COUNTY SUBBASIN

Kings

Kaweah

Tulare Lake

Tule

Kern High

Moderate

Low

RELATIVE-CONCENTRATION, DIMENSIONLESS

NUMBER OF GRID

WELLS CONTAINING

THE CONSTITUENT

0.00001 0.0001 0.001 0.01 0.1 1 10 100

STUDY UNIT DETECTION FREQUENCY,

IN PERCENT

0 5 10 15 20 25 30 35

Southeast San Joaquin

 Valley study unit

Kern County Subbasin

 study unit

10% detection

IP004884_Figure 23ab VOC

A B

Figure 23. (A–H) Detection frequencies and relative-concentrations of selected organic and special-interest constituents in 

USGS-grid wells in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.
80 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Bromacil

Atrazine

Dinoseb

Simazine

Dieldrin

11

10

1

2

14

1

1

1

1

15

17

6

17

1

1

1

1

1

3

STUDY UNIT RELATIVE-CONCENTRATION

SOUTHEAST SAN JOAQUIN KERN COUNTY SUBBASIN

Kings

Kaweah

Tulare Lake

Tule

Kern High

Moderate

Low

RELATIVE-CONCENTRATION, DIMENSIONLESS

NUMBER OF

WELLS CONTAINING

THE CONSTITUENT

0.0001 0.001 0.01 0.1 1 10

STUDY UNIT

DETECTION FREQUENCY,

IN PERCENT

0 5040302010

Southeast San Joaquin

 Valley study unit

Kern County Subbasin

 study unit

10% detection

IP004884_Figure 23cd Pesticides

C D

Figure 23.—Continued
Status and Understanding of Water Quality 81

1,2-Dichloropropane

1,2,3-Trichloropropane

1,2-Dibromoethane

(EDB)

1,2-Dibromo-3-

chloropropane

(DBCP)

2

1

14

2

1

19

3

4

1

10

1

Perchlorate

N-Nitrosodimethylamine

(NDMA)

1

6

2

8

3

STUDY UNIT RELATIVE-CONCENTRATION

SOUTHEAST SAN JOAQUIN KERN COUNTY SUBBASIN

Kings

Kaweah

Tulare Lake

Tule

Kern High

Moderate

Low

RELATIVE-CONCENTRATION, DIMENSIONLESS

NUMBER OF

WELLS CONTAINING

THE CONSTITUENT

0.0001 0.001 0.01 0.1 1 10

STUDY UNIT

DETECTION FREQUENCY,

IN PERCENT

0 5040302010

Southeast San Joaquin

 Valley study unit

Kern County Subbasin

 study unit

10% detection

IP004884_Figure 23efgh Fumigants and Special interest

E F

0.001 0.01 0.1 1 10 0 252015105

10%

detection

G H

Figure 23.—Continued
82 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Volatile Organic Compounds

VOCs discussed in this report are classified as THMs, 

solvents, and other VOCs. More than one VOC was detected 

in almost one-half of the USGS-grid wells with VOC 

detections. Figure 24A shows the number of VOC detections 

in USGS-grid and USGS-understanding wells and CDPH 

wells. Wells with more than one VOC generally were located 

in the eastern part of the study units. The number of VOC 

detections decreased with well depth and depth to top-ofperforations in the SESJ study unit (table 13). The number 

of VOC detections was higher in modern-age groundwater 

than in groundwater of pre-modern-age in the SESJ study 

unit. These relations were not observed in the KERN study 

unit. The lack of a relation with depth in the KERN study unit 

may be because most wells in the KERN study unit are deep 

and do not span the range of depths as the wells in the SESJ 

study unit do (table 5). The correlation of the number of VOCs 

detected with DO and normalized lateral position was positive 

in the SESJ study unit (table 13). The relation with lateral 

position may be affected by the correlation of lateral position 

with DO and well depth (table 6B).

Trihalomethanes

Water used for drinking water and other household 

uses in both domestic and municipal systems commonly is 

disinfected with hypochlorite solutions (bleach). As a side 

effect of disinfection, the hypochlorite reacts with organic 

matter to produce THMs and other chlorinated and/or 

brominated disinfection byproducts. The THMs analyzed 

in this study were chloroform, bromodichloromethane, 

dibromochloromethane, and bromoform. THMs, as a class, 

were not detected at high relative-concentrations in the 

SESJ or KERN study units but were detected at moderate 

relative-concentrations in 1.2 and 4.2 percent of the primary 

aquifers in the SESJ and KERN study units, respectively 

(table 12). Chloroform was the only THM detected in 

10 percent or more of the primary aquifers (fig. 22). 

Comparison of the relative-concentrations for total THMs 

and chloroform shows that chloroform accounted for most 

of the total THMs in almost all the samples (22 of 23 in the 

SESJ study unit; 14 of 14 in the KERN study unit; Burton 

and others, 2008; Shelton and others, 2008). Chloroform 

was detected in more than 25 percent of the samples and was 

detected in all four study areas of the SESJ study unit and in 

the KERN study unit at low relative-concentrations (figs. 23A

and B). Nationally, chloroform was the most frequently 

detected VOC in aquifers in studies conducted by the USGS 

National Water-Quality Assessment (NAWQA) Program 

(Zogorski and others, 2006).

Correlations were done using total THMs as the 

water-quality variable because the MCL (80 μg/L) is for 

total THMs and not for the individual THM compounds. 

In the SESJ study unit, THMs were positively correlated 

with DO, lateral position, number of septic systems, and 

LUFT density; negatively correlated with depth to top-ofperforations; and higher in modern-age groundwater than in 

pre-modern-age groundwater (tables 6 and 13). Total THMs 

were negatively correlated to natural land use in the KERN 

study unit (table 13). These correlations are consistent with an 

anthropogenic origin for the THMs detected in groundwater in 

these areas.

THM concentrations were low with low concentrations 

of DO (less than 4 mg/L) in the SESJ study unit (table 13; 

fig. 25). The relation between THMs and DO could indicate 

the degradation of THMs in increasingly anoxic groundwater 

(Pavelic and others, 2006) or simply may indicate that both 

constituents decrease with depth. Positive correlations of 

THMs and DO also have been noted in the Priority Basin 

Project Central Eastside study unit north of the SESJ study 

unit (Landon and others, 2010a) and in nationwide analysis 

(Squillace and others, 2004; Zogorski and others, 2006). The 

correlation of THMs with lateral position could be affected 

by the relation between DO and lateral position as well as by 

the greater density of potential sources of THMs, including 

septic systems, in the eastern part of the study unit. Although 

THMs were not directly positively correlated with urban land 

use, as was found by Bennett and others (2010) and Landon 

and others (2010a) farther north in the San Joaquin Valley, 

the increase in septic system and LUFT density in the eastern 

part of the SESJ study unit may represent greater urban 

activities and concentration of potential sources of THMs 

in that area (table 5). However, the negative correlation of 

THMs with natural land use in the KERN study unit indicates 

anthropogenic sources of THMs.

THM concentrations decreased as depths to the topof-perforations in wells increased in the SESJ study unit 

(fig. 25). THM concentrations also decreased in older 

groundwater (table 5). THM concentrations in samples having 

a groundwater age classified as modern were significantly 

higher than in groundwater classified as pre-modern in the 

SESJ study unit. The lack of correlation between THMs and 

depth or age in the KERN study unit may be affected by 

the lack of relatively shallow wells sampled in the KERN 

study unit.
Status and Understanding of Water Quality 83

Table 13. Results of non-parametric correlation analysis (Spearman’s method) between selected organic and special-interest water-quality constituents and potential explanatory factors in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project.[Aquifer-scale proportions are from the grid-based method unless otherwise stated. Spearman’s correlation statistic (ρ) for a significant correlation on the basis of significance level (p) less than threshold value (α) of 0.05. Abbreviations: LUFTs, leaking underground fuel tanks; VOC, volatile organic conpound; THMs, trihalomethanes; DBCP, 1,2-Dibromo-3-chloropropane; MCL-US, USEPA maximum contaminant level; ns, no significant correlation; <, less than; GAMA, Groundwater Ambient Monitoring and Assessment Program; USEPA, U.S. Environmental Protection Agency]

Constituent, constituent class, or 

special-interest constituent

Benchmark 

type

High aquifer 

proportion 

(percent)

Grid and understanding wells used in analysis Grid wells used in analysis

Depth to 

top-ofperforations Well depth pH Dissolved oxygen LUFTs Number of septic tanks or cesspools1 Orchard and vineyard land use1 Agricultural land use1 (percent) Urban land use1 (percent) Natural land use1 (percent) Normalized lateral position from valley trough

ρ: Spearman’s correlation statistic/p: significance level

Southeast San Joaquin study unit

Number of VOC detections 2 variable 2.4 –0.312

0.007

–0.234

0.034

ns 0.460

<0.001

ns ns ns ns ns ns 0.302

0.006

THMs, total 3 MCL-US 0.0 –0.263

0.025

ns ns 0.479

<0.001

0.2130.034

0.2080.039

ns ns ns ns 0.259

0.018

Number of solvent detections 3 variable 0.7 –0.301

0.010

ns ns 0.271

0.007

ns 0.275

0.006

ns –0.287

0.009

0.2870.009

ns 0.255

0.020

Number of other VOC detections 3 variable 1.2 ns ns –0.206

0.041

ns ns ns ns ns ns ns ns

DBCP MCL-US 3.6 ns ns –0.349

0.004

0.400

<0.001

ns ns ns ns ns ns 0.280

0.010

Number of pesticide detections 3 variable 0.0 –0.521

<0.001

–0.474

<0.001

–0.360

0.003

0.408

<0.001

ns 0.249

0.013

ns –0.232

0.035

0.2460.025

ns 0.475

<0.001

Perchlorate MCL-US 1.2 –0.366

0.002

–0.377

0.001

–0.335

0.006

0.505

<0.001

0.2830.005

0.2820.005

ns ns ns ns 0.389

<0.001

Kern County Subbasin study unit

Number of VOC detections 2 variable 0.1 ns ns ns ns ns ns ns ns –0.318

0.030

ns ns

THMs, total 3 MCL-US 0.0 ns ns ns ns ns ns ns ns ns –0.311

0.0338

ns

Number of solvent detections variable 0.1 ns ns ns ns ns 0.305

0.031

-0.290

0.041

ns ns ns ns

Number of other VOC detections variable 0.0 ns ns ns ns -0.360

0.011

ns ns ns ns ns ns

DBCP MCL-US 2.1 ns ns ns ns ns ns ns ns ns ns ns

Number of pesticide detections 3 variable 0.0 ns –0.300

0.039

ns ns ns ns ns ns ns ns ns

Perchlorate MCL-US 0.0 ns ns ns 0.410

0.007

ns ns ns ns ns ns ns
84 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project Table 13. Results of non-parametric correlation analysis (Spearman’s method) between selected organic and special interest water-quality constituents and potential explanatory factors in the two southern San Joaquin Valley study units, California GAMA Priority Basin Project. [Aquifer-scale proportions are from the grid-based method unless otherwise stated. Spearman’s correlation statistic (ρ) for a significant correlation on the basis of significance level (p) less than threshold value (α) of 0.05. Abbreviations: LUFTs, leaky underground fuel tanks; VOC, volatile organic conpound; THMs, trihalomethanes; DBCP, 1,2-Dibromo-3-chloropropane; MCL-US, USEPA maximum contaminant level; ns, no significant correlation; <, less than; GAMA, Groundwater Ambient Monitoring and Assessment Program; USEPA, U.S. Environmental Protection Agency] Constituent, constituent class, or special-interest constituent Benchmark type High aquifer proportion (percent) Grid and understanding wells used in analysis Grid wells used in analysis Depth to top-of- perforations Well depth pH Dissolved oxygen LUFTs Number of septic tanks or cesspools1 Orchard and vineyard land use1 Agricultural land use1 (percent) Urban land use1 (percent) Natural land use1 (percent) Normalized lateral position from valley trough

ρ: Spearman’s correlation statistic/p: significance level

Southeast San Joaquin Valley study unit with Domestic Wells included

DBCP MCL-US ns -0.349

0.004

ns 0.155

0.016

Perchlorate MCL-US -0.387

<0.001

-0.335

0.006

ns ns

1 Land use percentages and number of septic tanks are within a radius of 500 meters around each well included in analysis.2 Does not include VOCs classified as fumigants.3 Classes of compounds that include constituents with high values less than 2 percent, moderate values, or detection frequencies at any concentration greater than 10 percent.
Status and Understanding of Water Quality 85

101

99

99

395

5

5

0 10 20 40 Miles

0 10 20 40 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Pacific Ocean

S i e r r a N e v a d a

CDPHother wellUSGSunderstanding

well

VOLATILE ORGANIC COMPOUND DETECTIONS

USGS- and CDPHgrid well

0

1

2-4

5-11

0

1

2

0

1

2-4

5

IP004884_Figure 24a

A

Southeast

San Joaquin

Kern

Tulare lakebed

River or stream

Canal

Aqueduct

STUDY UNIT

Urban

LAND USE

Agricultural

Natural

Land use from Nakagaki and others, 2007

Bakersfield

Delano

Fresno

Tulare

Visalia

 

Figure 24. (A) Number of volatile organic compound (VOC) detections, (B) relative-concentrations of DBCP, (C) number of pesticide 

detections, and (D) relative-concentration of perchlorate in USGS-grid wells, USGS-understanding wells, and CDPH-other wells in the two 

southern San Joaquin Valley study units, California GAMA Priority Basin Project.
86 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

101

99

99

395

5

5

0 10 20 40 Miles

0 10 20 40 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Pacific Ocean

S i e r r a N e v a d a

IP004884_Figure 24b

B

CDPHother wellUSGSunderstanding

well

1,2-DIBROMO-3-CHLOROPROPANE (DBCP),

IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

USGS- and CDPHgrid well

0-0.02

0.021-0.20

> 0.20

0-0.02

0.021-0.20

> 0.20

0-0.02

0.021-0.20

> 0.20

Southeast

San Joaquin

Kern

Tulare lakebed

River or stream

Canal

Aqueduct

STUDY UNIT

Urban

LAND USE

Agricultural

Natural

Land use from Nakagaki and others, 2007

Bakersfield

Delano

Fresno

Tulare

Visalia

 

Figure 24.—Continued
Status and Understanding of Water Quality 87

101

99

99

395

5

5

0 10 20 40 Miles

0 10 20 40 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Pacific Ocean

S i e r r a N e v a d a

CDPHother wellUSGSunderstanding

well

PESTICIDE DETECTIONS

USGS- and CDPHgrid well

0

1

2-4

5 or more

2-4

5 or more

0

1

0

1

2-4

IP004884_Figure 24c

C

Southeast

San Joaquin

Kern

Tulare lakebed

River or stream

Canal

Aqueduct

STUDY UNIT

Urban

LAND USE

Agricultural

Natural

Land use from Nakagaki and others, 2007

Bakersfield

Delano

Fresno

Tulare

Visalia

 

Figure 24.—Continued
88 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

101

99

99

395

5

5

0 10 20 40 Miles

0 10 20 40 Kilometers

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Pacific Ocean

S i e r r a N e v a d a

IP004884_Figure 24d

D

CDPHother wellUSGSunderstanding

well

PERCHLORATE,

IN MICROGRAMS PER LITER

Low

Moderate

High

(> is greater than)

USGS- and CDPHgrid well

0-0.60

0.61-6.00

> 6.0

0-0.60

0.61-6.00

> 6.0

0-0.60

0.61-6.00

> 6.0

Not

Not shown

shown

Southeast

San Joaquin

Kern

Tulare lakebed

River or stream

Canal

Aqueduct

STUDY UNIT

Urban

LAND USE

Agricultural

Natural

Land use from Nakagaki and others, 2007

Bakersfield

Delano

Fresno

Tulare

Visalia

 

Figure 24.—Continued
Status and Understanding of Water Quality 89

DISSOLVED OXYGEN CONCENTRATION, IN MILLIGRAMS PER LITER

DEPTH TO TOP-OF-PERFORATIONS, IN FEET BELOW LAND SURFACE

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

0 2 4 6 8 10 12

Not detected

Detected

IP004884_Figure 25 THM detects

Figure 25. Relation of detections of total trihalomethanes (THMs) to depth to top-ofperforations and dissolved oxygen for USGS-grid and USGS-understanding wells in the 

Southern San Joaquin Valley study units, California GAMA Priority Basin Project.

Solvents

Solvents are used for various industrial, commercial, and 

domestic purposes. Solvents, as a class, were detected at high 

relative-concentrations in 0.7 and 0.1 percent, at moderate 

relative-concentrations in 1.2 and 4.3 percent, and at low 

relative-concentrations or not detected in 88 and 95 percent 

of the primary aquifers in the SESJ and KERN study units, 

respectively (table 12). Carbon tetrachloride, TCE, and PCE 

were detected at high relative-concentrations in less than 

1 percent of the primary aquifers in the SESJ study unit 

(table 9). Carbon tetrachloride and TCE were detected at high 

relative-concentrations in less than 1 percent of the primary 

aquifers in the KERN study unit (table 9). Dichloromethane 

was detected at moderate relative-concentrations in the SESJ 

study unit; PCE and 1,2-DCA were each detected at moderate 

relative-concentrations in the KERN study unit (fig. 23B). 

The number of solvent detections was positively 

correlated with percentage of urban land use, DO, and number 

of septic systems in the SESJ study unit (table 13). The 

number of solvent detections also was higher in the eastern 

part of the SESJ study unit where most of the urban land use 

and septic systems are located than in the western part of the 

SESJ study unit. The number of solvent detections also was 

positively correlated to septic systems in the KERN study unit. 

The number of solvent detections was negatively correlated 

to agricultural land use and to depth to the top-of-perforations 

(table 5), and solvent detections were significantly greater in 

modern than in pre-modern aged groundwater in the SESJ 

study unit (table 5). Solvent detections were negatively 

correlated with orchard and vineyard land use in the KERN 

study unit (table 13). 

The number of solvents detected was greater in wells 

when the percentage of urban land use was greater than 

40 percent (7 out of 9 wells with detections, fig. 26) in the 

SESJ study unit. Correlations of solvents with urban land 

use also were found in two other GAMA study units in the 

San Joaquin Valley (Bennett and others, 2010; Landon and 

others, 2010a). Nationally, solvent concentrations also have 

been correlated strongly with percentage of urban land use 

because most solvents are of anthropogenic origin (Zogorski 

and others, 2006; Moran and others, 2007). A previous 

investigation in the Fresno area showed that urban land use 

was the best predictor for the detection of VOCs (Wright and 

others, 2004). The correlation of solvents with the density 

of septic systems likely is a result of the positive correlation 

of urban land use with septic systems (table 6B). The low 

number of solvent detections in the KERN study unit makes 

it difficult to identify relations; the negative correlation of 

solvent detections with orchard and vineyard land use may 

indicate other factors. 
90 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Solvent detections decreased as depth to the top-ofperforation in wells increased in the southern San Joaquin 

Valley. Almost all detections (14 of 17) were in wells where 

the depth to top-of-perforations was less than 300 ft (fig. 26). 

Solvent detections also decreased in older groundwater 

(table 5). These relations are consistent with the results of two 

other GAMA study units in the San Joaquin Valley (Bennett 

and others, 2010; Landon and others, 2010a).

Other Volatile Organic Compounds

Other VOCs, as a class, were detected at high 

relative-concentrations in 1.2 percent of the primary 

aquifers in the SESJ study unit and not in the KERN study 

unit (table 12). Other VOCs were detected at moderate 

relative-concentrations in 2.1 percent of the primary aquifers 

in the KERN study unit, and at low relative-concentrations 

or not detected in 99 percent and 98 percent of the primary 

aquifers in the SESJ and KERN study units, respectively. 

Benzene, was detected at high relative-concentrations in one 

well in the Tulare Lake study area (fig. 23B). Benzene was 

detected at moderate and at low relative-concentrations in 

three wells in KERN. 

Detections of other VOCs consisted of gasoline 

hydrocarbons, compounds used for organic synthesis, and 

two refrigerants (Burton and others, 2008; Shelton and others, 

2008). Detections of other VOCs were negatively correlated 

with DO in the SESJ study unit and with LUFTs in the 

KERN study (table 13). Other VOC detections were greater 

in pre-modern than in modern-age groundwater in the KERN 

study area (table 6). 

The negative correlation with LUFTs was unexpected 

because most of the detections in the KERN study unit 

were gasoline hydrocarbons. However, the higher detection 

frequency in pre-modern-age groundwater than in modern-age 

groundwater suggests the source of the gasoline hydrocarbons 

is natural from deep in the aquifer. There are several oil and 

gas fields located near the detections of gasoline hydrocarbons. 

It is uncertain if the correlation of other VOCs with DO in 

the SESJ study unit is an explanatory factor. Almost all of the 

detections of other VOCs in the SESJ study unit are in the 

eastern half of the study unit where DO is higher. Because 

detection frequencies of these compounds are low, conclusions 

based on these data are uncertain. Other VOC detections 

were not correlated to any other explanatory factor in either 

study unit.

URBAN LAND USE, IN PERCENT

DEPTH TO TOP-OF-PERFORATIONS, IN FEET BELOW LAND SURFACE

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

0 20 40 60 80 100 120

Not detected

Detected

SOUTHEAST SAN JOAQUIN

VALLEY STUDY UNIT

KERN COUNTY SUBBASIN

STUDY UNIT

Not detected

Detected

IP004884_Figure 26 VOC detects

Figure 26. Relation of solvent detection with depth to top-of-perforation and percentage of 

urban land use for USGS-grid and USGS-understanding well samples in the two southern 

San Joaquin Valley study units, California GAMA Priority Basin Project.
Status and Understanding of Water Quality 91

Fumigants

Ten VOCs used primarily as fumigants to control pests 

in agriculture and in households, or synthesis byproducts 

included in fumigant mixtures, were grouped into the 

constituent class of fumigants. The classification of nine 

of these constituents as fumigants was determined by the 

USGS NAWQA Program (Zogorski and others, 2006). 

1,2,3-trichloropropane (1,2,3-TCP) is classified as having 

a primary use as a solvent and in the synthesis of some 

organic compounds (Zogorski and others, 2006), but 

1,2,3-trichloropropane (1,2,3-TCP) also was a synthesis 

byproduct in fumigant mixtures in use from the 1950s until the 

early 1980s (Oki and Giambelluca, 1987; Zebarth and others, 

1998), including use in the San Joaquin Valley (Domagalski 

and Dubrovsky, 1991). 1,2,3-TCP has been detected in 

groundwater in areas where fumigants have been used 

(Zogorski and others, 2006). Consequently, 1,2,3-TCP was 

included in the fumigants category in this report, but actually 

represents a fumigant synthesis byproduct. 

Fumigants, as a class, were detected at high 

relative-concentrations in 3.6 and 2.1 percent of the primary 

aquifers in the SESJ and the KERN study units, respectively 

(table 12). Four fumigants—DBCP, EDB, 1,2-DCP, and 

1,2,3-TCP—were detected in USGS-grid wells in either the 

SESJ or the KERN study units (Burton and Belitz, 2008; 

Shelton and others, 2008). Only DBCP and 1,2-dibromoethane 

(EDB) were detected at high or at moderate relativeconcentrations (table 10, fig. 23F). In general, fumigants were 

detected more frequently in the KERN study unit than in the 

SESJ study unit except for DBCP (fig. 23E); most of the high 

detections of DBCP in either USGS-grid wells or in CDPHother wells were in the Kings study area (fig. 24B). DBCP also 

was detected at high relative-concentrations near the city of 

Delano and in two locations in the KERN study unit. 

DBCP

Historically, DBCP was used as a soil fumigant to control 

nematodes. Between 1955 and 1977, DBCP primarily was 

used on orchards and on vineyards but also on some row 

crops in California, including the Fresno area (Peoples and 

others, 1980; Domagalski, 1997; Burow and others, 1999). 

Use of DBCP was discontinued by the California Department 

of Food and Agriculture in 1977 in response to concern about 

the potential hazardous effects of DBCP on human health 

(Domalgoski, 1997; Burow and others, 1999).

DBCP concentrations were positively correlated to lateral 

position and DO and negatively correlated to pH in the SESJ 

study unit (table 13). When the data from the Domestic Well 

Project was included, DBCP significantly correlated with 

orchard and vineyard land use (table 13). DBCP was not 

correlated to any of the explanatory factors in the KERN study 

unit. The absence of correlations of DBCP with explanatory 

variables may be a result of the low detection frequency for 

DBCP in the KERN study unit, making it difficult to identify 

relations.

DBCP concentration data from the Priority Basin Project 

was not correlated with orchard and vineyard land use without 

the data from the GAMA Domestic Well Project. This is in 

contrast to two Priority Basin Project study units in the San 

Joaquin Valley (Bennett and others, 2010; Landon and others, 

2010a) which found significant positive correlations with 

orchard and vineyard land use in the Priority Basin Project 

data. However, the detection frequency of DBCP in the SESJ 

study-unit grid wells (33 percent) was significantly higher 

when the orchard and vineyard land use was greater than 

40 percent than in the SESJ study-unit grid wells (15 percent) 

when orchard and vineyard land use was less than 40 percent 

(fig. 27); this indicates that orchard and vineyard land use may 

have some effect on the presence of DBCP.

The detection frequency of DBCP in wells where DO 

was greater than 2.0 mg/L was significantly higher than 

in wells where the DO was less than 2.0 mg/L (fig. 27). 

The higher detection frequency of DBCP with higher DO 

concentrations may partially be because DBCP is resistant 

to biological transformation in oxic conditions (Bloom and 

Alexander, 1990; Burow and others, 1999). Not only are DO 

and lateral position correlated with each other, but they are 

correlated to orchard and vineyard land use. This provides 

additional indirect evidence that orchard and vineyard land 

use may affect where DBCP may be detected. DBCP also 

was correlated with pH, but this relation may result from the 

correlation of pH with DO and orchard and vineyard land 

use (table 6B).

DBCP concentrations were not correlated with well 

depth, depth to top-of-perforations, or groundwater age. 

However, detection frequencies of DBCP in modern-age 

groundwater (31 percent) were greater than in pre-modern-age 

groundwater (10 percent). The presence of DBCP in older 

groundwater may indicate the ability of DBCP to persist in 

the aquifer as a result of the low organic content of the aquifer 

materials (Burow and others, 1999). Another explanation for 

the occurrence of DBCP in pre-modern water is the presence 

of short-circuit mechanisms as a result of well construction 

or well operation practices that allow modern contaminants 

to mix with deeper, pre-modern water, as has been found for 

other constituents (Jurgens and others, 2008; Landon and 

others, 2010b).
92 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

DBCP DETECTION FREQUENCY, IN PERCENT

0 10 20 30 40 50

Orchard and vineyard land use

greater than 40 percent and

dissolved oxygen concentration

greater than 2 milligrams per liter

Orchard and vineyard land use

greater than 40 percent and

dissolved oxygen concentration

less than 2 milligrams per liter

Orchard and vineyard land use

less than 40 percent and

dissolved oxygen concentration

greater than 2 milligrams per liter

Orchard and vineyard land use

less than 40 percent and

dissolved oxygen concentration

less than 2 milligrams per liter

Number of wells

 sampled

11

3

40

22

40

EXPLANATION

IP004884_Figure 27 DBCP vs DO

Figure 27. Relation of DBCP detection frequency to percentage of orchard and(or) vineyard 

land use and dissolved oxygen, Southeast San Joaquin Valley study unit, California GAMA 

Priority Basin Project.
Status and Understanding of Water Quality 93

Pesticides

Pesticides are used in agricultural and urban 

settings. Pesticides, as a class, were not detected at high 

relative-concentrations in the study units but were detected 

at moderate relative-concentrations in 2.4 percent of the 

primary aquifers in the SESJ study unit (table 12). The 

herbicides atrazine and simizine were detected at low 

relative-concentrations in more than 10 percent of the primary 

aquifers in both study units (fig. 23C). Bromacil and dinoseb 

were detected in more than 10 percent of the primary aquifers 

in SESJ and KERN, respectively. Atrazine and simazine were 

the most commonly detected, with detection frequencies over 

30 percent in the study units. Atrazine and simazine were 

among the most commonly detected herbicides in groundwater 

in major aquifers across the United States (Gilliom and others, 

2006). Historically, simazine is most commonly used on 

vineyards and orchards in the study units, but also is used on 

rights-of-way for weed control (Domagalski and Dubrovsky, 

1991). Dieldrin, an insecticide, was detected at moderate 

concentrations in more than 2 percent of the primary aquifers 

in both study units (table 9; figs. 23C and 23D). Dieldrin 

still persists in the environment even though its use was 

discontinued in California in 1987 (Barbash and Resek, 1996).

Similar to VOCs, more than one pesticide usually was 

detected in wells with pesticides (fig. 24C). The number of 

pesticides detected in a well was correlated with well depth 

and groundwater age in both study units (tables 5 and 13). The 

number of pesticides in a well was positively correlated with 

DO, percentage of urban land use, lateral position, and septic 

system density in the SESJ study unit (table 13). The number 

of pesticides was negatively correlated with depth to top-ofperforations, pH, and percentage of agricultural land use in the 

SESJ study unit (table 13). 

The number of pesticides detected was significantly 

lower in pre-modern-age groundwater than in modern-age 

groundwater for the study units (fig. 28; table 5). However, 

pesticides were detected in 42 and 50 percent of the wells with 

pre-modern-age groundwater in the SESJ and the KERN study 

units, respectively. This indicates that some pesticides may 

persist in groundwater for long periods of time.

Pesticides in the SESJ study unit were detected more 

frequently in wells with depths to the top-of-perforations 

less than 250 ft (detection frequency 83 percent compared 

to 45 percent). However, pesticides were detected in some 

wells with depths to top-of-perforations as great as 1,000 ft 

(fig. 28A). Pesticides in the KERN study unit were detected 

more frequently in wells with depths to top-of-perforations 

less than 400 ft (detection frequency 86 percent compared to 

63 percent). However, pesticides were detected in a few wells 

with depths to top-of-perforations as great as 640 ft (fig. 28B). 

The presence of pesticides in deep wells could indicate 

the effects of short-circuit mechanisms because of well 

construction or well operation practices that allow modern 

contaminants to mix with pre-modern water, as has been found 

for other constituents (Jurgens and others, 2008; Landon and 

others, 2010b)

The correlation of DO with pesticides in the SESJ study 

unit likely is a result of the correlation of DO with well depth 

(table 6B). The positive correlation of pesticide detections 

with lateral position indicates that the number of pesticides 

detected increases eastward through the study unit. A majority 

of the detections were in the eastern part of the SESJ study 

unit (fig. 24C).

The number of pesticides detected was positively 

correlated with urban land use in the SESJ study unit. 

Herbicide concentrations also were found to be positively 

correlated to urban land use in the Priority Basin Project 

Central Eastside study unit (Landon and others, 2010a). In 

contrast, the number of pesticides detected was negatively 

correlated with agricultural land use. This surprising 

correlation most likely is a result of the correlation of number 

of pesticides detected with depth to top-of-perforations. The 

wells in the SESJ study unit with agricultural land use are 

deeper than wells with urban land use. In the SESJ study unit, 

changes in land use from east to west across the study unit 

make it feasible that historical pesticide use patterns have not 

been uniform across the study unit. In addition, the Corcoran 

clay layer located in the western part of the SESJ study unit 

can act as a barrier to the downward migration of pesticides 

into the deeper aquifer used by many CDPH and irrigation 

wells.
94 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

NUMBER OF PESTICIDE DETECTIONS

DEPTH TO TOP-OF-PERFORATIONS, IN FEET BELOW LAND SURFACE

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

0 2 4 6 8 10 12

DEPTH TO TOP-OF-PERFORATIONS, IN FEET BELOW LAND SURFACE

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

0 2 4 6 8 10 12

A

B

Modern

GROUNDWATER AGE CLASSIFICATION

Mixed Pre-modern

EXPLANATION

KERN COUNTY SUBBASIN

 STUDY UNIT

SOUTHEAST SAN JOAQUIN

 VALLEY STUDY UNIT

IP004884_Figure 28 Pest detect by GW age Figure 28. (A–B) Relation of number of pesticide detections to depth to top-of-perforations and 

groundwater age in the two southern San Joaquin Valley study units, California GAMA Priority 

Basin Project.
Status and Understanding of Water Quality 95

Constituents of Special Interest

Special-interest constituents, similar to organic 

constituents, usually are anthropogenic in origin. The 

special-interest constituents analyzed by the Priority Basin 

Project in the southern San Joaquin Valley are perchlorate and 

N-nitrosodimethylamine (NDMA). Possible anthropogenic 

sources of perchlorate could include nitrate fertilizers mined 

from the Atacama Desert of Chile that have been used 

historically on some orchard crops (Dasgupta and others, 

2006), or industrial, manufacturing, or commercial uses such 

as explosives, road flares, rocket fuel, and other products 

(California Department of Public Health, 2008c; Parker and 

others, 2008). Perchlorate can occur under natural conditions 

in a variety of climatic conditions (Fram and Belitz, 2011), 

and not just arid climates (Dasgupta and others, 2005; 

Plummer and others, 2006). However, perchlorate is more 

likely to occur naturally in arid environments such as the 

arid and semi-arid environments found in the southwestern 

United States (Fram and Belitz, 2011). Perchlorate and 

NDMA have been detected recently in, or are considered to 

have the potential to reach, water resources used for drinkingwater supplies (California Department of Public Health, 

2008b, 2008c). 

Constituents of special interest, as a class, were 

detected at high relative-concentrations in 1.2 percent of the 

primary aquifers in the SESJ study unit, and at moderate 

relative-concentrations in 19 and 6.4 percent in the SESJ and 

the KERN study units, respectively (table 12). Perchlorate 

was detected at high relative-concentrations (1.2 percent) 

in the SESJ study unit but not in the KERN study unit. 

Perchlorate was detected at moderate relative-concentrations 

in 18 and 6.4 percent in the SESJ and the KERN study units, 

respectively (table 9; figs. 23G and 23H). NDMA was detected 

at moderate relative-concentrations in 3.4 percent of the 

primary aquifers in the SESJ study unit (table 9; fig. 23H). 

Detection frequencies of perchlorate were almost 20 percent 

in the SESJ study unit with detections located in the Kings, 

Kaweah, and Tule study areas (figs. 23E and 24D).

Perchlorate

Perchlorate was positively correlated to DO in the study 

units (table 13). Perchlorate was not detected in any samples 

where the DO was less than 3.0 mg/L (fig. 29). Perchlorate 

also was positively correlated to density of LUFTs and septic 

systems around the wells and lateral position, and negatively 

correlated to well depth, depth to top-of-perforations, and pH 

in the SESJ study unit (table 13). Perchlorate concentrations 

were not significantly different between groundwater age 

categories (table 5).

Perchlorate concentrations decreased as well depth 

increased in the SESJ study unit. In contrast, perchlorate 

concentrations were not correlated with well depth in 

the Central Eastside study unit which also is the Priority 

Basin Project study unit in the San Joaquin Valley (Landon 

and others, 2010a). However, the positive correlations of 

perchlorate to DO in the study units were similar in the Central 

Eastside study unit. The positive correlation of perchlorate 

and DO could be affected by perchlorate biodegradation 

under anoxic conditions (Sturchio and others, 2007) or may 

simply indicate that concentrations for both constituents 

typically are high in shallow groundwater. The correlation 

with lateral position may be influenced by the correlation of 

lateral position with DO in the SESJ study unit. Perchlorate 

was not correlated to orchard and vineyard land use. This lack 

of correlation was not expected because Chilean fertilizer, a 

source of perchlorate, was used historically on orchards. The 

correlations of LUFTs and septic systems with perchlorate 

(table 6B) likely indicate unknown variations in sources of 

perchlorate across the study unit. 

The predicted probability of detecting naturally occurring 

perchlorate at a concentration greater than 0.5 μg/L is 10 

to 20 percent on the basis of the logistic regression model 

developed by Fram and Belitz (2011) in the SESJ and 

KERN study units. The predicted probability of detecting 

naturally occurring perchlorate at a concentration greater than 

1 μg/L is only 1 to 5 percent. All the perchlorate detections 

were greater than 0.5 μg/L, and more than one-third of the 

perchlorate detections were greater than 1 μg/L. This indicates 

that anthropogenic sources have contributed perchlorate to 

groundwater in the study units.
96 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Summary

Groundwater quality in the southern San Joaquin Valley 

was investigated as part of the Priority Basin Project of the 

Groundwater Ambient Monitoring and Assessment (GAMA) 

Program. The GAMA Priority Basin Project is conducted by 

the U.S. Geological Survey (USGS) in collaboration with 

the California State Water Resources Control Board and the 

Lawrence Livermore National Laboratory. Two Priority Basin 

Project study units are located in the southern San Joaquin 

Valley—Southeast San Joaquin Valley (SESJ) and Kern 

County Subbasin (KERN). 

The GAMA Priority Basin Project is designed to provide 

a statistically unbiased, spatially distributed assessment of 

untreated groundwater quality in the primary aquifers at 

the basin-scale. The aquifer systems (hereinafter referred 

to as primary aquifers) were defined as that part of the 

aquifer corresponding to the perforation interval of wells 

listed in the CDPH database for the study units. Wells were 

randomly selected within spatially distributed grid cells 

across the study units to assess the quality of the groundwater. 

Samples were collected by the USGS from October 2005 

through March 2006 from 130 grid wells (83 in the SESJ 

study unit and 47 in the KERN study unit) which included 

108 CDPH wells, 15 irrigation wells, 6 domestic wells, 

and 1 fire protection well. An additional 19 wells (8 CDPH 

wells and 11 monitoring wells) were sampled to improve the 

understanding of the relation of water quality to explanatory 

factors. Samples from USGS-grid and USGS-understanding 

wells were analyzed for up to 345 constituents. CDPH 

inorganic data from the 3-year period (January 1, 2003 to 

December 31, 2005) were used to complement USGS-grid 

well data and provide additional information about 

groundwater quality. 

Relative-concentrations (sample concentration divided 

by water-quality benchmark concentration) were used 

for evaluating groundwater quality. Selected constituents 

with high relative-concentrations or detection frequencies 

greater than or equal to 10 percent were selected to focus the 

understanding assessment on those constituents that have the 

greatest effect on water quality. The relative-concentration 

threshold for classifying inorganic constituents as moderate 

was 0.5, whereas for organic constituents it was 0.1. A 

relative-concentration of 0.1 was used as a boundary between 

low and moderate values of organic and special-interest 

constituents for consistency with other studies and 

reporting requirements.

Aquifer-scale proportion was used as a metric for 

assessing the quality of untreated groundwater for the 

study units. High aquifer-scale proportion is defined 

as the areal percentage of the primary aquifers with a 

relative-concentration greater than 1.0. Moderate and 

DISSOLVED OXYGEN CONCENTRATION, IN MILLIGRAMS PER LITER

PERCHLORATE CONCENTRATION, IN MICROGRAMS PER LITER

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

0 2 4 6 8 10 12

KERN COUNTY SUBBASIN

 STUDY UNIT

SOUTHEAST SAN JOAQUIN

 VALLEY STUDY UNIT

IP004884_Figure 30 Perchlorate vs DO

Figure 29. Relation of perchlorate concentration to dissolved oxygen in the two southern 

San Joaquin Valley study units, California GAMA Priority Basin Project.
Summary 97

low aquifer-scale proportions were defined as the areal 

percentage of the primary aquifers with moderate and 

low relative-concentrations, respectively. Grid-based and 

spatially weighted statistical approaches were used to assess 

aquifer-scale proportions of constituents at high, moderate, 

and low relative-concentrations in the primary aquifers. 

Inorganic constituents were more prevalent and 

relative-concentrations for inorganic constituents 

generally were higher than for organic constituents. For 

inorganic constituents with human-health benchmarks, 

relative-concentrations for 30 and 23 percent of the primary 

aquifers were high for at least one constituent in the SESJ 

and the KERN study units, respectively. In the study units, 

the inorganic constituents with human-health benchmarks 

that were detected at high relative-concentrations in more 

than 2 percent of the primary aquifers were arsenic, boron, 

vanadium, nitrate, uranium, and unadjusted gross alpha 

radioactivity. Additional constituents with human-health 

benchmarks—antimony, radium, and fluoride—were detected 

at high relative-concentrations in the KERN study unit.

For inorganic constituents with aesthetic benchmarks 

(SMCL), relative-concentrations for 6.6 and 22 percent of 

the primary aquifers were high for at least one constituent in 

the SESJ and the KERN study units, respectively. Inorganic 

constituents with aesthetic benchmarks that were detected 

at high relative-concentrations in the study units were iron 

and manganese. Additional constituents with aesthetic 

benchmarks—total dissolved solids (TDS), sulfate, and 

chloride—were detected at high relative-concentrations 

in KERN. 

In contrast to inorganic constituents, organic 

constituents with human-health benchmarks were detected 

at high relative-concentrations in 4.8 and 2.1 percent of 

the primary aquifers in the SESJ and KERN study units, 

respectively. Of the 78 VOCs analyzed (not including 

fumigants), 28 were detected—23 with human-health 

benchmarks. Benzene, carbon tetrachloride, tetrachloroethene 

(PCE), and trichloroethene (TCE) were the only VOCs 

detected at high relative-concentrations. Dichloromethane 

was detected at moderate relative-concentrations in the 

SESJ study unit, and 1,2-dichloroethane was detected 

at moderate relative-concentrations in the KERN study 

unit. Human-health benchmarks were established for all 

five fumigants detected. Fumigants were detected at high 

relative-concentrations in 3.6 percent and 2.1 percent of the 

SESJ and KERN study units, respectively. The fumigant, 

1,2-dibromo-3-chloropropane (DBCP), was detected at 

high relative-concentrations in both study units. Of the 

136 pesticides and polar pesticides analyzed, 33 pesticides 

were detected—18 with and 15 without human-health 

benchmarks. All pesticides were detected at low relativeconcentrations or were not detected except dieldrin. Dieldrin 

was detected at moderate relative-concentrations in both study 

units. The detection frequencies for two pesticides, simazine 

and atrazine, were greater than or equal to 10 percent in the 

study units. The special-interest constituent, perchlorate, was 

detected at high and moderate relative-concentrations in the 

SESJ study unit and at moderate relative-concentrations in the 

KERN study unit.

The understanding assessment used statistical correlations 

between concentrations of constituents and values of selected 

potential explanatory factors to identify the factors potentially 

affecting the concentrations and occurrences of inorganic or 

organic constituents detected at high relative-concentrations or 

for organic constituents with detection frequencies greater than 

10 percent. The potential explanatory factors evaluated were 

land use, depth, lateral position in flow path, septic system 

density, formerly leaking underground fuel tanks (LUFTs), 

groundwater age, and geochemical conditions [dissolved 

oxygen (DO) and pH]. The datasets from the two study units 

were treated separately.

Well depth, depth to top-of-perforations, groundwater 

age, DO, and pH were the explanatory factors that most 

frequently were correlated with inorganic constituents. Depth, 

groundwater age, land use, and DO were the explanatory 

factors that most frequently were correlated to organic and 

special-interest constituents. 

Arsenic concentrations were significantly high in deep 

and old (pre-modern) groundwater in the SESJ study unit, 

but not in the KERN study unit. Arsenic also was correlated 

with geochemical conditions. Arsenic concentrations tended 

to increase when pH was above 7.6 or when DO decreased 

in the SESJ study unit. Antimony concentrations also 

increased with pH in the SESJ study unit. Similar to arsenic, 

boron concentrations were significantly higher in old, deep 

groundwater in the SESJ study unit. Boron concentrations 

also increased as pH increased in the SESJ study unit. 

Vanadium concentrations also were positively correlated 

to DO in the SESJ study unit, but were not correlated to 

depth or groundwater age. Similarly, fluoride concentrations 

generally increased in deep or old groundwater, with high 

pH, and with low DO in the study units. Manganese and 

iron concentrations, like the trace elements, increased with 

increasing well depth and decreasing DO. In contrast to most 

of the other trace elements, uranium concentrations were high 

in shallow wells with modern-age groundwater and low pH. 

The source of all these constituents are attributed to natural 

sources. However, elevated uranium concentrations in shallow 

groundwater was attributed to the enhanced desorption of 

uranium from sediments by irrigation and urban recharge with 

high bicarbonate concentrations. 

Nitrate concentrations, in contrast to most of the trace 

elements, were high in shallow wells with modern-age 

groundwater. Nitrate concentrations increased with increasing 

DO. Nitrate was not correlated with agricultural land use as 

in some other studies, but nitrate was positively correlated 

with orchard and vineyard land use (a subset of agricultural 

land use). Nitrate occurs naturally in the study units, but 

concentrations are elevated by human activities.
98 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

In general, concentrations of TDS, sulfate, and chloride 

were significantly higher in the KERN study unit than in the 

SESJ study unit. TDS was negatively correlated with pH in 

the study units. TDS and sulfate were positively correlated 

to agricultural land use in the KERN study unit, but not in 

the SESJ study unit. Sulfate concentrations were high in 

shallow wells in the primary aquifers in the SESJ study unit 

whereas chloride concentrations were high in deep wells in the 

KERN study unit. These correlations suggest TDS and sulfate 

concentrations in SESJ primarily are influenced by human 

factors while TDS and sulfate concentration in KERN study 

unit primarily are influenced by natural sources.

Organic compounds usually were detected at low 

relative-concentrations; therefore, statistical analyses of 

relations to explanatory factors usually were done for classes 

of constituents. Classes of organic compounds consisted of 

VOCs—which were further subdivided into trihalomethanes 

(THMs), solvents, and other VOCs—fumigants, and 

pesticides. VOCs were detected more frequently in shallow 

wells with modern-age groundwater and high DO in the SESJ 

study unit. These relations were not observed in the KERN 

study unit. 

Total THM concentration and solvent detection frequency 

decreased in deep and old (pre-modern) groundwater in the 

SESJ study unit. Statistical relations with depth were absent 

in the KERN study unit possibly because relatively few 

shallow wells were sampled in the KERN study unit. THM 

concentrations and solvent detection frequency were positively 

correlated with DO in the SESJ study unit. Solvent detection 

frequency in the SESJ study unit was positively correlated 

with urban land use, whereas THM concentration was not 

correlated with urban land use as has been observed in other 

Priority Basin Project study units. 

High relative-concentrations of DBCP accounted for the 

large majority of fumigant detections; therefore, statistical 

analysis was performed on DBCP concentrations to represent 

fumigants as a class. DBCP was positively correlated to DO 

in the SESJ study unit. DBCP was not correlated to orchard 

and vineyard land use as observed in two other Priority Basin 

Project study units; however, the detection frequency of DBCP 

was significantly higher when the orchard and vineyard land 

use was greater than 40 percent compared to when orchard 

and vineyard land use was less than 40 percent. DBCP, unlike 

many other organic compounds, was not correlated with depth.

Pesticide detection frequency, similar to other organic 

constituents, decreased in deep or old (pre-modern) 

groundwater in the SESJ study unit. Pesticide detections also 

decreased with increasing well depth in the KERN study unit. 

Pesticide detections in the SESJ study unit were positively 

correlated with urban land use and negative correlated with 

agricultural land use.

Similar to organic constituents, perchlorate 

concentrations decreased with increasing well depth in the 

SESJ study unit but were not correlated to groundwater age. 

Perchlorate concentrations generally were high in groundwater 

with high DO conditions. Anthropogenic sources have 

contributed perchlorate to groundwater in the study units 

although low levels of perchlorate may occur naturally.

There are many similarities and differences between 

the SESJ and the KERN study units. In the study units, 

relative-concentrations of arsenic, vanadium, boron, uranium, 

gross alpha radioactivity, nitrate, manganese, and iron were 

high in more than 2 percent of the primary aquifers. However, 

vanadium concentration was significantly higher in the 

SESJ study unit than in the KERN study unit. In addition, in 

the KERN study unit, relative-concentrations of antimony, 

fluoride, TDS, sulfate, and chloride were high in more than 

2 percent of the primary aquifers. In the KERN study unit, 

concentrations of TDS, sulfate, and chloride were significantly 

higher than in the SESJ study unit, although concentrations of 

TDS and chloride in the SESJ study unit were high in less than 

1 percent of the primary aquifers. 

The organic constituents, carbon tetrachloride, TCE, and 

DBCP, were detected at high relative-concentrations in the 

study units. However, DBCP concentrations were significantly 

higher in the SESJ study unit than in the KERN study unit. 

In addition, benzene, PCE, 1,2-dibromoethane (EDB), and 

perchlorate were detected at high relative-concentrations 

in the SESJ study unit but not in the KERN study unit. 

Although simazine and atrazine were detected at low 

relative-concentrations in more than 10 percent of the primary 

aquifers in the study units, bromacil was detected in more than 

10 percent of the primary aquifers in the SESJ study unit, and 

dinoseb was detected in more than 10 percent of the primary 

aquifers in the KERN study unit.

Some of the explanatory factors were significantly 

different between the two study units. Well depth and depth 

to top-of-perforations were significantly deeper in the KERN 

study unit than in the SESJ study unit. In contrast, DO and 

percentage of urban land use were significantly higher in the 

SESJ study unit than in the KERN study unit. pH was not 

significantly different between the KERN study unit and the 

SESJ study unit, but the pH values in the KERN study unit are 

skewed toward higher pH values than in the SESJ study unit. 

These differences in explanatory factors may explain some of 

the dissimilarity in relations between water-quality variables 

and explanatory factors observed between the two study units.

VOCs, pesticides, and perchlorate may be used as 

tracers of groundwater that has recharged over the decades 

because these compounds began to be used for industrial and 

commercial purposes because of the low concentration at 

which these compounds were detected. Low-level analyses 

provide an early awareness of constituents whose presence in 

groundwater at low concentrations may be important for the 

prioritization of monitoring water quality in the future.
References Cited 99

Acknowledgments

The authors thank the following cooperators for their 

support: the California State Water Resources Control 

Board, Lawrence Livermore National Laboratory, California 

Department of Public Health, and California Department 

of Water Resources. We especially thank the well owners 

and water purveyors for their cooperation and generosity 

in allowing the USGS to collect samples from their wells. 

Funding for this work was provided by State of California 

bonds authorized by Proposition 50 and administered by the 

State Water Resources Control Board. 

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106 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

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Appendix A 107

California requires samples to be collected regularly from 

public-supply wells under Title 22 (California Department 

of Health Services, 2007). Historical data derived from these 

samples are available from the California Department of 

Public Health (CDPH) database. Assembly Bill (AB) 599 

directs the Groundwater Ambient Monitoring and Assessment 

(GAMA) Program to use available data and to collect new 

data as needed. The GAMA Priority Basin Project uses this 

existing monitoring data along with newly collected data to 

characterize the water quality of the primary aquifers. The 

CDPH database provided additional water-quality data for the 

spatially weighted and grid-based approaches to estimating 

aquifer-scale proportions for a wide range of constituents. 

CDPH data were not used to provide data for grid wells for 

VOCs, pesticides, or perchlorate because reporting limits for 

these constituents in the CDPH database generally were not 

sufficiently low enough to differentiate between “low” and 

“moderate” concentrations. 

Three approaches were used to select CDPH inorganic 

constituent data for each grid cell where the USGS did not 

sample for inorganic constituents. The first approach was 

to identify CDPH data collected during the current period 

for the USGS-grid well (a well which was not sampled for 

inorganic constituents by the USGS). Analytical results were 

reviewed to determine if they met quality-control criteria to 

minimize the selection of poor-quality data from the CDPH 

database. Cation-anion balance was used as the quality-control 

assessment metric for selecting a CDPH-grid well. Because 

water is electrically neutral, the total positive charge on 

dissolved cation species in a water sample must equal the total 

negative charge on dissolved anion species; the cation/anion 

imbalance commonly is used as a quality-control criterion for 

water sample analysis (Hem, 1985). Cation-anion imbalance 

was calculated as the difference between the total cations and 

total anions divided by the sum, expressed as a percentage:

*100

where

is the sum of calcium, magnesium, 

sodium,and potassium in

milliequivalents per liter (meq/L), 

and

is the sum of chlorid

cations anions

percent difference cations anions

cations

anions

  − =  

+  

∑ ∑

∑ ∑

∑

∑ e, sulfate, fluoride,

nitrate, and bicarbonate in meq/L.

An imbalance, or percentage difference, greater than or 

equal to 10 percent indicates uncertainty in the quality of the 

data. The most recent CDPH data from the current period 

(January 1, 2003 to December 31, 2005) for the USGS-grid 

wells with missing data were evaluated to determine whether 

the cation/anion imbalance for CDPH data was less than 

10 percent. If so, the CDPH inorganic data for the well 

was selected for use as the grid well data for inorganic 

constituents. It was assumed that if analyses met highquality-control criteria for major-ion data, then the data quality 

for the analyses at these wells also would be acceptable for 

trace elements, nutrients, and radiochemical constituents. 

This approach resulted in the selection of inorganic data from 

CDPH at 30 USGS-grid wells in the SESJ study unit and 

15 wells in the KERN study unit. For identification purposes, 

data from the CDPH for these grid wells were assigned 

GAMA identification numbers equivalent to the GAMA 

USGS-grid well number but with DG inserted between 

the study area prefix and sequence number (for example, 

CDPH-grid well KWH-DG-11 is the same well as USGS-grid 

well KWH-11, tables A1 and A2).

If the first approach did not yield CDPH inorganic data 

for a grid cell, the second approach was to search the CDPH 

database to identify the highest ranked well within that cell 

with a cation/anion imbalance of less than 10 percent. This 

approach resulted in selecting CDPH inorganic data for wells 

not sampled by USGS in 14 grid cells in the SESJ study unit 

and 21 grid cells in the KERN study unit. These 36 CDPHgrid wells were located within the same cell as the USGS-grid 

well but not necessarily right next to the USGS-grid well or 

in a grid cell not sampled by USGS-GAMA. To identify these 

new CDPH-grid wells, a well ID was created that added DPH 

after the study area prefix. If a USGS-grid well was sampled 

in that cell, the DPH was inserted between the study unit 

prefix and the sequence number (for example, CDPH-grid 

well KERN-DPH-05 is in the same cell as USGS-grid well 

KERN-05). If the CDPH-grid well was in a cell not sampled 

by USGS-GAMA then the DPH was followed by the next 

incremental number not used by the USGS-grid wells in that 

study area (for example KING-DPH-40). 

If the cation-anion imbalance for data from the well in 

the CDPH database in a grid cell was not less than 10 percent, 

the third approach was to select the highest ranked well in 

the CDPH database with any of the needed inorganic data. 

This approach resulted in the selection of 15 USGS-grid 

wells in the SESJ study unit and 7 USGS-grid wells in the 

KERN study unit from which some CDPH inorganic data 

(usually nutrient data) were available. Because the wells were 

USGS-grid wells, a well ID was created that added DG to the 

GAMA ID (for example, well KWH-DG-13). In addition, this 

Appendix A: Selection of California Department of Public Health Grid Wells
108 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

approach resulted in the selection of 9 CDPH-grid wells in the 

SESJ study unit and 14 CDPH-grid wells in the KERN study 

unit. The well ID for these CDPH-grid wells was assigned in 

the same manner as the other CDPH-grid wells.

The result of these approaches was one grid well per 

cell with data from the USGS database, the CDPH database, 

or both databases. Inorganic data for 128 CDPH-grid wells 

(68 in the SESJ study unit and 60 in the KERN study unit) 

in the CDPH database were used (fig. A1 and A2). Data 

were not available for all inorganic constituents from all 

128 CDPH-grid wells. Table 4 in the report shows the number 

of USGS- and CDPH-grid wells with data for each inorganic 

constituent. In combination with USGS-grid-well inorganic 

data (42 wells), inorganic data was available for 148 grid cells. 

Most of the cells without a grid well were located in the area 

of the Tulare Lake bed or in the western part of the KERN 

study unit.

A larger error is associated with the 90 percent confidence 

intervals for estimates of aquifer-scale proportion for 

constituents made on the basis of a smaller number of wells 

(based on the Jeffreys interval for the binomial distribution, 

Brown and others, 2001). Analysis of the combined datasets 

to evaluate the occurrence of relatively high or moderate 

concentrations for inorganic constituents was not affected 

by differences in reporting levels between GAMA-collected 

and CDPH data because concentrations greater than one-half 

of water-quality benchmarks generally were substantially 

higher than the highest reporting levels. Comparisons between 

USGS-collected and CDPH data are described in appendix B.

Wells sampled by the Domestic Well Project are 

included in some of the analyses. Aquifer proportions were 

not calculated for these wells because they are not spatially 

distributed. Location and well identification are shown 

in figure A3.
Appendix A 109

Table A1. Nomenclature and construction information for USGS-grid and USGS-understanding wells, and CDPH-grid wells, 

Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project.

[Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of 

Public Health; ft, foot; LSD, land-surface datum; KING, Kings study area well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, 

Tule study area well; FP, flow-path well; HWY99T, transect well; DG, designates CDPH data from same well as USGS-grid well; DPH, designates well 

selected from subset of CDPH wells; na, not available]

USGS GAMA 

well 

identification 

number

CDPH 

GAMA well 

identification 

number

Cell

number

Well construction information

Well type Well depth, 

(ft below LSD)

Top of perforation 

(ft below LSD)

Bottom of 

perforation 

(ft below LSD)

Length of 

perforated 

interval

(ft)

Grid wells

KING-01 KING-DG-01 1 Production 610 580 600 20

KING-02 KING-DG-02 13 Production na na na na

KING-03 KING-DG-03 15 Production 380 145 358 213

KING-04 3 Production 500 240 500 260

KING-05 KING-DG-05 12 Production 555 505 545 40

KING-06 16 Production 705 345 695 350

KING-07 KING-DG-07 27 Production 410 194 410 216

KING-08 KING-DG-08 30 Production 540 287 540 253

KING-09 KING-DG-09 25 Production 582 202 380 178

KING-10 29 Production 228 228 228 0

KING-11 KING-DG-11 38 Production 540 280 520 240

KING-12 KING-DG-12 36 Production 246 126 246 120

KING-13 KING-DG-13 37 Production 420 260 400 140

KING-14 KING-DG-14 6 Production 800 640 780 140

KING-15 KING-DG-15 19 Production 440 270 440 170

KING-16 KING-DG-16 20 Production na na na na

KING-17 18 Production 650 320 640 320

KING-18 KING-DG-18 31 Production 370 160 360 200

KING-19 KING-DG-19 39 Production 260 218 260 42

KING-20 32 Production 124 na na na

KING-21 KING-DG-21 14 Production 210 150 210 60

KING-22 KING-DG-22 22 Production 510 280 500 220

KING-23 KING-DG-23 21 Production 540 150 510 360

KING-24 KING-DG-24 23 Production 236 140 236 96

KING-25 KING-DG-25 28 Production 384 168 384 216

KING-26 4 Production 480 240 480 240

KING-27 KING-DG-27 7 Production 409 na na na

KING-28 KING-DG-28 34 Production 76 40 74 34

KING-29 KING-DG-29 33 Production 390 120 390 270

KING-30 35 Production 490 330 470 140

KING-31 2 Production 520 280 520 240

KING-32 KING-DG-32 17 Production 445 370 445 75

KING-33 KING-DG-33 40 Production 675 175 655 480

KING-34 KING-DG-34 11 Production na na na na

KING-35 KING-DG-35 9 Production na na na na

KING-36 KING-DG-36 8 Production 452 na na na

KING-37 26 Production na na na na

KING-38 24 Production 700 na na na

KING-39 5 Production na na na na

KWH-01 KWH-DG-01 13 Production 640 245 620 375

KWH-02 KWH-DG-02 14 Production 225 116 225 109

KWH-03 KWH-DG-03 8 Production 310 na na na

KWH-04 KWH-DG-04 16 Production 205 80 200 120
110 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

USGS GAMA 

well 

identification 

number

CDPH 

GAMA well 

identification 

number

Cell

number

Well construction information

Well type Well depth, 

(ft below LSD)

Top of perforation 

(ft below LSD)

Bottom of 

perforation 

(ft below LSD)

Length of 

perforated 

interval

(ft)

Grid wells—Continued

KWH-05 KWH-DG-05 15 Production na na na na

KWH-06 KWH-DG-06 19 Production 580 300 580 280

KWH-07 KWH-DG-07 17 Production na na na na

KWH-08 KWH-DG-08 18 Production na na na na

KWH-09 KWH-DG-09 12 Production na na na na

KWH-10 KWH-DG-10 20 Production 323 na na na

KWH-11 KWH-DG-11 10 Production 700 300 700 400

KWH-12 9 Production 404 205 381 176

KWH-13 KWH-DG-13 2 Production 220 na na na

KWH-14 7 Production 400 175 390 215

KWH-15 KWH-DG-15 3 Production na na na na

KWH-16 11 Domestic 240 200 240 40

KWH-17 KWH-DG-17 1 Production 350 225 338 113

KWH-18 6 Production 165 95 155 60

TLR-01 TLR-DG-01 21 Production na na na na

TLR-02 TLR-DG-02 13 Production 1,330 1,000 1,330 330

TLR-03 TLR-DG-03 15 Production 1,420 1,067 1,395 328

TLR-04 14 Production 1,320 980 1,300 320

TLR-05 5 Production 561 311 561 250

TLR-06 TLR-DG-06 6 Production na na na na

TLR-07 TLR-DG-07 2 Production 570 210 545 335

TLR-08 22 Production na na na na

TLR-09 4 Production 1,200 na na na

TULE-01 9 Production 800 400 800 400

TULE-02 TULE-DG-02 15 Production na na na na

TULE-03 TULE-DG-03 6 Production 280 169 270 101

TULE-04 TULE-DG-04 20 Production 820 550 800 250

TULE-05 TULE-DG-05 19 Production 1,368 930 1,348 418

TULE-06 18 Production 1,641 702 1,641 939

TULE-07 TULE-DG-07 7 Production 600 na na na

TULE-08 8 Production 810 340 810 470

TULE-09 TULE-DG-09 16 Production 587 160 587 427

TULE-10 TULE-DG-10 17 Production 965 201 965 764

TULE-11 1 Production 1,150 600 1,150 550

TULE-12 3 Production na na na na

TULE-13 TULE-DG-13 14 Production 400 120 400 280

TULE-14 TULE-DG-14 13 Production 600 280 600 320

TULE-15 2 Production 1,330 970 1,265 295

TULE-16 11 Domestic na na na na

TULE-17 TULE-DG-17 10 Production 245 185 240 55

KING-DPH-06 16 Production na na na na

KING-DPH-08 30 Production na na na na

Table A1. Nomenclature and construction information for USGS-grid and USGS-understanding wells, and CDPH-grid wells, 

Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project.—Continued

[Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of 

Public Health; ft, foot; LSD, land-surface datum; KING, Kings study area well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, 

Tule study area well; FP, flow-path well; HWY99T, transect well; DG, designates CDPH data from same well as USGS-grid well; DPH, designates well 

selected from subset of CDPH wells; na, not available]
Appendix A 111

USGS GAMA 

well 

identification 

number

CDPH 

GAMA well 

identification 

number

Cell

number

Well construction information

Well type Well depth, 

(ft below LSD)

Top of perforation 

(ft below LSD)

Bottom of 

perforation 

(ft below LSD)

Length of 

perforated 

interval

(ft)

Grid wells—Continued

KING-DPH-10 29 Production na na na na

KING-DPH-14 6 Production na na na na

KING-DPH-27 7 Production na na na na

KING-DPH-28 34 Production 400 40 400 360

KING-DPH-32 17 Production na na na na

KING-DPH-38 24 Production na na na na

KING-DPH-39 5 Production 580 310 570 260

KING-DPH-40 2 Production na na na na

KWH-DPH-07 17 Production na na na na

KWH-DPH-08 18 Production na na na na

KWH-DPH-09 12 Production 350 254 350 96

KWH-DPH-10 20 Production na na na na

KWH-DPH-17 1 Production 1,150 520 1,130 610

KWH-DPH-19 4 Production 690 230 690 460

KWH-DPH-20 5 Production na na na na

KWH-DPH-21 11 Production na na na na

TLR-DPH-08 22 Production na na na na

TLR-DPH-09 4 Production na na na na

TULE-DPH-06 18 Production 600 200 590 390

TULE-DPH-10 17 Production 200 30 115 85

TULE-DPH-16 11 Production 800 700 800 100

TULE-DPH-18 4 Production 1,210 560 1,210 650

Understanding wells

KINGFP-01 23 Production 320 160 310 150

KINGFP-02 23 Production 620 410 610 200

KINGFP-03 23 Production 550 445 540 95

KINGFP-04 23 Production 420 150 240 90

KINGFP-05 23 Monitoring well 172 162 167 5

KINGFP-06 23 Monitoring well 265 255 260 5

KINGFP-07 23 Monitoring well 70 60 65 5

KINGFP-08 32 Monitoring well 81 71 76 5

KINGFP-09 32 Monitoring well 168 158 163 5

KINGFP-10 32 Monitoring well 268 258 263 5

KINGFP-11 23 Monitoring well 158 148 153 5

KINGFP-12 23 Monitoring well 80 70 75 5

KINGFP-13 18 Monitoring well 148 108 138 30

KINGFP-14 9 Monitoring well 140 110 130 20

KINGFP-15 9 Monitoring well 208 178 198 20

HWY99T-01 20 Production 681 213 670 457

Table A1. Nomenclature and construction information for USGS-grid and USGS-understanding wells, and CDPH-grid wells, 

Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project.—Continued

[Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of 

Public Health; ft, foot; LSD, land-surface datum; KING, Kings study area well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, 

Tule study area well; FP, flow-path well; HWY99T, transect well; DG, designates CDPH data from same well as USGS-grid well; DPH, designates well 

selected from subset of CDPH wells; na, not available]
112 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table A2. Nomenclature and construction information for USGS-grid and USGS-understanding wells, and CDPH-grid wells, Kern 

County Subbasin study unit, California GAMA Priority Basin Project.

[Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of 

Public Health; No., number; ft, foot; LSD, land-surface datum; KERN, Kern study unit well; FP, flow-path well; DG, designates well with CDPH data from 

same well as USGS-grid well; DPH, designates well selected from subset of CDPH wells; na, not available]

USGS GAMA 

well 

identification 

number

CDPH 

GAMA well 

identification 

number

Cell No.

Well construction information

Well type Well depth, 

(ft below LSD)

Top of perforation 

(ft below LSD)

Bottom of 

perforation 

(ft below LSD)

Length of 

perforated 

interval

(ft)

Grid wells

KERN-01 89 Production 560 220 560 340

KERN-02 117 Production 702 402 702 300

KERN-03 KERN-DG-03 104 Production 755 400 na 355

KERN-04 KERN-DG-04 101 Production 637 na na na

KERN-05 27 Domestic 522 396 522 126

KERN-06 KERN-DG-06 36 Production 700 400 700 300

KERN-07 KERN-DG-07 78 Production 1,000 400 1,000 600

KERN-08 KERN-DG-08 50 Production 430 290 410 120

KERN-09 KERN-DG-09 48 Production 400 150 400 250

KERN-10 KERN-DG-10 87 Production 1,007 119 1,007 888

KERN-11 KERN-DG-11 81 Production 785 440 785 345

KERN-12 KERN-DG-12 40 Production 500 200 500 300

KERN-13 KERN-DG-13 52 Production 790 380 780 400

KERN-14 KERN-DG-14 82 Production 1,023 630 1,008 378

KERN-15 KERN-DG-15 103 Production 724 340 724 384

KERN-16 42 Production 460 160 460 300

KERN-17 KERN-DG-17 96 Production 627 na na na

KERN-18 KERN-DG-18 85 Production 740 na na na

KERN-19 KERN-DG-19 98 Production 700 260 680 420

KERN-20 70 Production 1,000 350 1,000 650

KERN-21 41 Production 455 125 455 330

KERN-22 KERN-DG-22 99 Production 970 502 970 468

KERN-23 KERN-DG-23 25 Production na na na na

KERN-24 39 Domestic 500 400 500 100

KERN-25 118 Domestic 900 639 900 261

KERN-26 KERN-DG-26 94 Production 400 320 400 80

KERN-27 51 Production 600 na na na

KERN-28 KERN-DG-28 83 Production 763 482 763 281

KERN-29 KERN-DG-29 79 Production 500 320 500 180

KERN-30 KERN-DG-30 77 Production 1,008 294 1,008 714

KERN-31 KERN-DG-31 102 Production 590 300 590 290

KERN-32 KERN-DG-32 90 Production 730 590 730 140

KERN-33 120 Production 793 395 793 398

KERN-34 62 Production 765 106 762 656

KERN-35 84 Production 680 240 na na

KERN-36 97 Production 720 400 720 320

KERN-37 100 Production 500 na na na

KERN-38 116 Production na na na na

KERN-39 59 Domestic 694 494 694 200

KERN-40 110 Production 1,496 1,048 1,400 352

KERN-41 56 Production 1,390 955 1,370 415

KERN-42 95 Production 718 na na na

KERN-43 KERN-DG-43 108 Production 880 730 870 140

KERN-44 58 Production 709 300 709 409
Appendix A 113

USGS GAMA 

well 

identification 

number

CDPH 

GAMA well 

identification 

number

Cell No.

Well construction information

Well type Well depth, 

(ft below LSD)

Top of perforation 

(ft below LSD)

Bottom of 

perforation 

(ft below LSD)

Length of 

perforated 

interval

(ft)

Grid wells—Continued

KERN-45 60 Production 600 na na na

KERN-46 107 Production 1,028 483 1,028 545

KERN-47 KERN-DG-47 73 Production 810 na na na

KERN-DPH-01 89 Production na na na na

KERN-DPH-02 117 Production 698 438 na na

KERN-DPH-05 27 Production 640 565 640 75

KERN-DPH-16 42 Production na na na na

KERN-DPH-20 70 Production na na na na

KERN-DPH-21 41 Production na na na na

KERN-DPH-24 39 Production 503 403 na na

KERN-DPH-25 118 Production na na na na

KERN-DPH-27 51 Production na na na na

KERN-DPH-28 83 Production na na na na

KERN-DPH-30 77 Production na na na na

KERN-DPH-31 102 Production 601 192 na na

KERN-DPH-33 120 Production na na na na

KERN-DPH-46 107 Production na na na na

KERN-DPH-48 38 Production na na na na

KERN-DPH-49 49 Production na na na na

KERN-DPH-50 53 Production 925 427 na na

KERN-DPH-51 57 Production na na na na

KERN-DPH-52 61 Production na na na na

KERN-DPH-53 63 Production 790 160 na na

KERN-DPH-54 69 Production 400 300 400 100

KERN-DPH-55 72 Production na na na na

KERN-DPH-56 74 Production 801 500 801 301

KERN-DPH-57 75 Production na na na na

KERN-DPH-58 76 Production 802 300 na na

KERN-DPH-59 80 Production 800 250 na na

KERN-DPH-60 91 Production na na na na

KERN-DPH-61 93 Production na na na na

KERN-DPH-62 105 Production na na na na

KERN-DPH-63 106 Production na na na na

KERN-DPH-64 111 Production na na na na

KERN-DPH-65 112 Production 1,447 767 na na

KERN-DPH-66 114 Production na na na na

KERN-DPH-67 119 Production na na na na

KERN-DPH-68 121 Production na na na na

Understanding wells

KERNFP-01 101 Production 750 400 740 340

KERNFP-02 97 Production 720 420 720 300

KERNFP-03 97 Production 477 220 457 237

Table A2. Nomenclature and construction information for USGS-grid and USGS-understanding wells, and CDPH-grid wells, Kern 

County Subbasin study unit, California GAMA Priority Basin Project.—Continued

[Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of 

Public Health; No., number; ft, foot; LSD, land-surface datum; KERN, Kern study unit well; FP, flow-path well; DG, designates well with CDPH data from 

same well as USGS-grid well; DPH, designates well selected from subset of CDPH wells; na, not available]
114 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Figure A1. Identifiers and locations of (A) USGS-grid and USGS-understanding wells sampled during October 2005 through 

February 2006 and (B) CDPH-grid wells using data for inorganic constituents from the California Department of Public Health 

(CDPH), Southeast San Joaquin Valley study unit, southern San Joaquin Valley, California GAMA Priority Basin Project.

33 46

65

41

198

180

99

99

5

Delano

Fresno

Tulare

Visalia

Kings River

Fresno

Slough

Tule

River

Chowchilla 

Kings

River

Deer 

Creek

Aqueduct

Fowler 

Switch 

Canal

Canal

Tulare

Lakeland

Cross

Creek

People's

Ditch

Saint

White

Kaweah River

San Joaquin River

Canal

Canal

California 

River

Lake

Johns

River

Friant Canal

0 10 20 Miles

0 10 20 Kilometers

MADERA

CO

FRESNO

CO

TULARE

CO KINGS

CO

KERN

CO

S i e r r a N e v a d a

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Kaweah

SOUTHEAST SAN JOAQUIN STUDY UNIT

Kings

Tulare Lake

Tule

River or stream

USGS-grid well and identifier

USGS-understanding well and identifier

Tulare

 lakebed Canal

Aqueduct

TLR-09

TLR-08

TLR-07

TLR-06

TLR-05

TLR-04

TLR-03

TLR-02

TLR-01

KWH-18

KWH-17

KWH-16

KWH-15

KWH-14

KWH-13

KWH-12

KWH-11

KWH-10

KWH-09

KWH-08

KWH-07

KWH-06

KWH-05

KWH-04

KWH-03

KWH-02

KWH-01

TULE-17

TULE-16

TULE-15

TULE-14

TULE-13

TULE-12

TULE-11

TULE-11

TULE-10

TULE-09

TULE-08

TULE-07

TULE-06

TULE-05

TULE-04

TULE-03 TULE-02

TULE-01

KING-39

KING-38

KING-37

KING-36

KING-35

KING-34

KING-33

KING-31 KING-32

KING-30

KING-29 KING-28

KING-27

KING-26

KING-25

KING-24

KING-23

KING-22

KING-21

KING-20

KING-19

KING-18

KING-16

KING-15

KING-14

KING-13

KING-12

KING-11

KING-10

KING-09

KING-07

KING-06

KING-05

KING-04

KING-03

KING-02

KING-01

KING-17

KING-08

KINGFP-14,15

KINGFP-13

KINGFP-04

HWY99T-01

KINGFP-11,12

KINGFP-08,09,10

KINGFP-05,06,07

KINGFP-03

KINGFP-03

KINGFP-01,02

A
Appendix A 115

Figure A1—Continued

33 46

65

41

198

180

99

99

5

Delano

Fresno

Tulare

Visalia

Kings River

Fresno

Slough

Tule

River

Chowchilla 

Kings

River

Deer 

Creek

Aqueduct

Fowler 

Switch 

Canal

Canal

Tulare

Lakeland

Cross

Creek

People's

Ditch

Saint

White

Kaweah River

San Joaquin River

Canal

Canal

California 

River

Lake

Johns

River

Friant Canal

0 10 20 Miles

0 10 20 Kilometers

MADERA

CO

FRESNO

CO

TULARE

CO KINGS

CO

KERN

CO

S i e r r a N e v a d a

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

CDPH-grid well and identifier

CDPH-other well and identifier

TULE-DPH-16

River or stream KING-DG-21 Tulare

 lakebed Canal

Aqueduct

Kaweah

SOUTHEAST SAN JOAQUIN STUDY UNIT

Kings

Tulare Lake

Tule

KWH-DPH-21

KWH-DPH-10

KWH-DPH-08

KWH-DPH-09

KWH-DPH-07

KWH-DPH-17

KWH-DPH-19

TLR-DPH-08

TLR-DPH-09

KWH-DPH-20

TULE-DPH-06

TULE-DPH-10 TULE-DPH-18

TULE-DPH-16

KING-DPH-10

KING-DPH-27

KING-DPH-38

KING-DPH-39

KING-DPH-28

KING-DPH-06

KING-DPH-32

KING-DPH-40

KING-DPH-14

KING-DPH-08

TLR-DG-07

TLR-DG-06

TLR-DG-03 TLR-DG-02

KWH-DG-17

KWH-DG-15

KWH-DG-13

KWH-DG-11

KWH-DG-09

KWH-DG-06

KWH-DG-05

KWH-DG-04

KWH-DG-02

KWH-DG-01

TULE-DG-17

TULE-DG-14

TULE-DG-13

TULE-DG-10

TULE-DG-09

TULE-DG-07

TULE-DG-05

TULE-DG-04

TULE-DG-03

TULE-DG-02

KING-DG-36

KING-DG-35

KING-DG-34

KING-DG-33

KING-DG-32

KING-DG-29

KING-DG-28

KING-DG-27

KING-DG-25

KING-DG-24

KING-DG-23

KING-DG-22

KING-DG-21

KING-DG-19

KING-DG-18

KING-DG-13

KING-DG-12

KING-DG-11

KING-DG-09

KING-DG-07

KING-DG-05

KING-DG-03 KING-DG-02

KING-DG-01

TLR-DG-01

KWH-DG-10

KWH-DG-08 KWH-DG-03

KING-DG-16

KING-DG-15

KING-DG-14

KING-DG-08

B
116 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Figure A2. Identifiers and locations of (A) USGS-grid and USGS-understanding wells sampled during January through March 2006 

and (B) CDPH-grid wells using data for inorganic constituents from the California Department of Public Health (CDPH), Kern County 

Subbasin study unit, southern San Joaquin Valley, California GAMA Priority Basin Project.

33

46

65

41

58

101

99

5

Poso 

Creek

Deer 

Creek

Aqueduct

California

Santa Maria 

River

Cuyama

River

Lakeland

Friant-Kern

Kern

River

Kern

Channel

White

Outlet

Canal

Canal

Canal

Caliente 

Creek

River

River 

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

TULARE

CO

KINGS

CO

KERN

CO

SAN LUIS

OBISPO

CO

SANTA

BARBARA

CO

VENTURA CO

Tehachapi Mts

S i e r r a N e v a d a

San Emigdio Mts

0 10 20 Miles

0 10 20 Kilometers

USGS-grid well and identifier

USGS-understanding well and identifier

River or stream KERN-11

Canal

Aqueduct

KERN COUNTY SUBBASIN

STUDY UNIT 

San Joaquin Valley

 basin trough

Bakersfield

Delano

KERN-47

KERN-46

KERN-45

KERN-44

KERN-43

KERN-42

KERN-41

KERN-40

KERN-39

KERN-38

KERN-37

KERN-36

KERN-35

KERN-34 KERN-33

KERN-32

KERN-31

KERN-30

KERN-29

KERN-28

KERN-27

KERN-26

KERN-25

KERN-24

KERN-23

KERN-22

KERN-21

KERN-20

KERN-19

KERN-18

KERN-17

KERN-16

KERN-15

KERN-14

KERN-12

KERN-11

KERN-10

KERN-09

KERN-08

KERN-07

KERN-06

KERN-05

KERN-04

KERN-03 KERN-02

KERN-01

KERN-13

KERNFP-03

KERNFP-02 KERNFP-01

KERNFP-03

A
Appendix A 117

Figure A2—Continued

33

46

65

41

58

101

99

5

Poso 

Creek

Deer 

Creek

Aqueduct

California

Santa Maria 

River

Cuyama

River

Lakeland

Friant-Kern

Kern

River

Kern

Channel

White

Outlet

Canal

Canal

Canal

Caliente 

Creek

River

River 

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

TULARE

CO

KINGS

CO

KERN

CO

SAN LUIS

OBISPO

CO

SANTA

BARBARA

CO

VENTURA CO

Tehachapi Mts

S i e r r a N e v a d a

San Emigdio Mts

0 10 20 Miles

0 10 20 Kilometers

CDPH-grid well and identifier

CDPH-other well and identifier

KERN-DPH-16

KERN-DG-21

KERN COUNTY SUBBASIN

STUDY UNIT River or stream

Canal

Aqueduct

San Joaquin Valley

 basin trough

Bakersfield

Delano

KERN-DPH-48

KERN-DPH-53

KERN-DPH-50

KERN-DPH-56

KERN-DPH-30

KERN-DPH-58

KERN-DPH-21

KERN-DPH-31

KERN-DPH-64

KERN-DPH-52

KERN-DPH-57

KERN-DPH-25

KERN-DPH-63

KERN-DPH-01

KERN-DPH-54

KERN-DPH-51

KERN-DPH-66

KERN-DPH-27

KERN-DPH-61

KERN-DPH-46

KERN-DPH-60

KERN-DPH-20

KERN-DPH-55

KERN-DPH-65

KERN-DPH-05

KERN-DPH-68

KERN-DPH-62

KERN-DPH-33

KERN-DPH-16

KERN-DPH-24

KERN-DPH-59

KERN-DPH-28

KERN-DPH-67

KERN-DPH-49

KERN-DPH-02

KERN-DG-47

KERN-DG-43

KERN-DG-31

KERN-DG-29

KERN-DG-28

KERN-DG-23

KERN-DG-22

KERN-DG-19

KERN-DG-15

KERN-DG-14

KERN-DG-12

KERN-DG-09

KERN-DG-07

KERN-DG-06

KERN-DG-02

KERN-DG-32

KERN-DG-30

KERN-DG-26

KERN-DG-18

KERN-DG-17

KERN-DG-13

KERN-DG-11

KERN-DG-10

KERN-DG-08

KERN-DG-04

KERN-DG-03

B
118 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

65

41

198

99

5

Tule

River

Kings

River

Deer 

Creek

Aqueduct

Fowler Switch 

Canal

Tulare

Lakeland

Cross

Creek

People's

Ditch

Saint

White

Kaweah River

Canal

Canal

California 

River

Lake

Johns

River

Friant-Kern Canal

0 10 20 Miles

0 10 20 Kilometers

FRESNO

CO

TULARE

CO

KINGS

CO

KERN CO

S i e r r a N e v a d a

119°45' 119°

36°

30'

36°

Shaded relief derived from U.S. Geological Survey 

National Elevation Dataset, 2006, 

Albers Equal Area Conic Projection

EXPLANATION

Domestic Well Project sampled well

 and identifier

Kaweah

SOUTHEAST SAN JOAQUIN STUDY UNIT

Kings

Tulare Lake

Tule

Tulare River or stream

 lakebed Canal

Aqueduct

TUL999

TUL997

TUL996

TUL992

TUL991

TUL990

TUL988

TUL987

TUL986 TUL978

TUL976

TUL975

TUL974

TUL973

TUL972

TUL970

TUL969

TUL967

TUL961

TUL960

TUL952

TUL952

TUL951

TUL950

TUL949

TUL946

TUL943

TUL942

TUL941

TUL939

TUL937

TUL936 TUL935

TUL934

TUL933

TUL932

TUL929

TUL928

TUL925

TUL924

TUL923

TUL922

TUL919

TUL917 TUL916

TUL914

TUL913

TUL912

TUL907

TUL906

TUL905

TUL904

TUL903

TUL902

TUL1505

TUL1111

TUL1106

TUL1105

TUL1101

TUL1098

TUL1094

TUL1092

TUL1088

TUL1084

TUL1071

TUL1065

TUL1064

TUL1062

TUL1060

TUL1057

TUL1054

TUL1044

TUL1043

TUL1041

TUL1036

TUL1034

TUL1033

TUL1031

TUL1029

TUL1028 TUL1022

TUL1021

TUL1017

TUL1013

TUL1007

TUL1003

TUL1000

TUL979

TUL971

TUL959 TUL955, 958

TUL948

TUL947

TUL945

TUL944

TUL940

TUL938

TUL931

TUL930

TUL926

TUL915

TUL911 TUL910

TUL909

TUL908

TUL901

TUL1107

TUL1103

TUL1093

TUL1091

TUL1090

TUL1089

TUL968,1085

TUL1083

TUL1082

TUL1081

TUL1080

TUL1076

TUL1075

TUL1074 TUL1073

TUL1072

TUL1070

TUL1066

TUL1061

TUL1059

TUL1058

TUL1056

TUL1055

TUL1042

TUL1035

TUL1032

TUL1027

TUL1026

TUL1025

TUL1020

TUL1016

TUL1015

TUL1014

TUL1012

TUL1011

TUL1010

TUL1008

TUL1005 TUL1006

Delano

Tulare

Visalia

Figure A3. Identifiers and locations of domestic wells sampled by the California GAMA Domestic Well Project in the Southeast 

San Joaquin Valley study unit, southern San Joaquin Valley, California, 2006.
Appendix B 119

CDPH and USGS-GAMA data were compared to 

assess the validity of combining data from these different 

sources. Because laboratory reporting levels for most 

organic constituents and trace elements were substantially 

lower for GAMA Priority Basin Project data than for CDPH 

data (table 2), it was not possible to directly compare 

concentrations of many constituents in individual wells in any 

meaningful way. However, concentrations of major ions and 

nitrate, which generally are prevalent and have concentrations 

substantially above reporting levels, could be compared for 

each well using data from both sources. 

Comparisons were made for wells that were analyzed 

by USGS-GAMA Priority Basin Project for inorganic and 

radiochemical constituents and for which CDPH data were 

available within the most recent 3-year interval. Major ion, 

nitrate, and trace element data were available for 25 wells 

from the SESJ study unit and 14 wells from the KERN 

study unit in the USGS and the CDPH databases. Compared 

in the SESJ study unit were 22 to 25 pairs of data for nine 

constituents (calcium, magnesium, sodium, alkalinity, 

chloride, sulfate, total dissolved solids, nitrate, and arsenic). 

Three additional constituents were compared although there 

were fewer than 20 paired results for analysis (potassium, 

17 pairs; fluoride, 15 pairs; and vanadium, 7 pairs). 

Compared in the KERN study unit were 10 to 14 pairs of 

data for 10 constituents (the same 9 as listed for the SESJ 

study unit plus fluoride). The dataset was large enough for 

meaningful statistical comparison for each constituent. Two 

additional constituents were compared although there were 

fewer than 10 paired results for analysis (potassium, 9 pairs; 

and vanadium, 6 pairs). Comparison tests performed on the 

combined datasets for the study units produced similar results 

to the individual study units.

A non-parametric signed-rank test indicated no significant 

differences between the paired USGS-GAMA and CDPH data 

for either the SESJ study unit (p-values ranging from 0.409 for 

alkalinity to 1.000 for magnesium) or the KERN study unit 

(p-values ranging from 0.507 for sodium to 0.982 for nitrate). 

Although differences between the paired datasets occurred for 

a few wells, most sample pairs plotted close to a 1-to-1 line 

(fig. B1). The relative percent difference (absolute difference 

of the two values divided by the average of the two values, 

RPD) was calculated for each data pair. The median RPD was 

less than 20 percent for all constituents except for vanadium 

in both study units. The median RPD for vanadium was 

24 percent in the KERN study unit. These direct comparisons 

indicated that the GAMA and CDPH inorganic data were not 

significantly different.

Piper diagrams show the relative abundance of major 

cations and anions (on a charge equivalent basis) as a 

percentage of the total ion content of the water (fig. B2). 

Piper diagrams often are used to define groundwater type 

(Hem, 1985). Combined GAMA Priority Basin Project and 

CDPH major-ion data for grid wells were plotted on Piper 

diagrams (Piper, 1944) along with all CDPH major-ion data 

from the current period to determine whether the groundwater 

types in grid wells were similar to groundwater types 

observed historically in the study unit. All cation/anion data 

in the CDPH database with a cation/anion balance less than 

10 percent were retrieved and plotted on these Piper diagrams 

for comparison with grid well data.

The range of water types for grid wells and other wells 

from the CDPH database for the current period were similar 

(fig. B2A and B2B). Most wells in the SESJ study unit were 

classified as mixed cation-bicarbonate type waters, indicating 

that 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. Many of the wells were 

classified as mixed cation-mixed anion type waters, indicating 

that no single cation accounted for more than 60 percent of 

the total cations, and no single anion accounted for more than 

60 percent of the total anions. The most common cations were 

calcium and sodium, although some samples also contained a 

high percentage of magnesium. Bicarbonate and chloride were 

the dominant anions in these waters.

About one-third of the wells in the KERN study unit were 

classified as mixed cation-bicarbonate, where the dominant 

cations are calcium and sodium. The remaining wells were 

classified as mixed cation-mixed anion. In comparison with the 

SESJ study unit, sulfate was the dominant cation in more wells 

in the KERN study unit (fig. B2). 

The determination that the range of relative abundance of 

major cations and anions in grid wells (83 in the SESJ study 

unit and 47 in the KERN study unit) is similar to the range of 

those in the selected CDPH-other wells (578 wells in the SESJ 

study unit and 263 in the KERN study unit) indicates that the 

grid wells represent the diversity of water types present within 

the southern San Joaquin Valley. 

Appendix B: Comparison of CDPH and GAMA Priority Basin Data
120 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

CONCENTRATION OF DATA COLLECTED BY USGS

EXPLANATION

CONCENTRATION OF DATA FROM CALIFORNIA DEPART

MENT OF PUBLIC HEALTH DATABASE 

0.01

0.01 0.1 1 10 100 1,000

0.1

1

10

100

1,000

Calcium

Magnesium

Potassium

Sodium

Alkalinity

Chloride

Fluoride

Sulfate

TDS

Nitrate

Arsenic

Vanadium

1:1 Correlation line

A

IP004884_Figure 01Ba

Figure B1. Paired inorganic concentrations from wells sampled in the southern San Joaquin 

Valley (October 2005 to March 2006) and the most recent available analysis in the California 

Department of Health Services (January 1, 2003, to December 31, 2005) in the (A) Southeast San 

Joaquin Valley study unit and the (B) Kern County Subbasin study unit, southern San Joaquin Valley, 

California GAMA Priority Basin Project.
Appendix B 121

EXPLANATION

Calcium

Magnesium

Potassium

Sodium

Alkalinity

Chloride

Fluoride

Sulfate

TDS

Nitrate

Arsenic

Vanadium

CONCENTRATION OF DATA COLLECTED BY USGS

0.01 0.1 1 10 100 1,000

CONCENTRATION OF DATA FROM CALIFORNIA DEPART

MENT OF PUBLIC HEALTH DATABASE 

0.01

0.1

1

10

100

1,000

B

1:1 Correlation line

IP004884_Figure 01Bb

Figure B1—Continued
122 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Percent

Calcium

 100 

 80 

 60 

 40 

 20  0 

 0 

 20 

 40 

 60 

 80 

 100 

Magnesium

 0 

 20 

 40 

 60 

 80 

 100 

Sodium plus Potassium

Chloride, Fluoride, Nitrite plus Nitrate

 0 

 20 

 40 

 60 

 80 

 100 

 100 

 80 

 60 

 40 

 20 

 0 

Bicarbonate

 100 

 80 

 60 

 40 

 20 

 0 

Sulfate

 0 

 20 

 40 

 60 

 80 

 100 

Sulfate plus Chloride

 0 

 20 

 40 

 60 

 80 

 100 

Calcium plus Magnesium

 100 

 80 

 60 

 40 

 20 

 0 

 100 

 80 

 60 

 40 

 20 

 0 

Percent

Percent

EXPLANATION

USGS-grid well

CDPH well

IP004884_Figure B2a Grid wells_SESJ

A

Figure B2. Piper diagram showing USGS- and CDPH-grid wells and all other wells in the 

California Department of Public Health database with a charge imbalance of less than 10 percent 

in the (A) Southeast San Joaquin Valley study unit and in the (B) Kern County Subbasin study unit, 

southern San Joaquin Valley, California GAMA Priority Basin Project.
Appendix B 123

Percent

Calcium

 100 

 80 

 60 

 40 

 20  0 

 0 

 20 

 40 

 60 

 80 

 100 

Magnesium

 0 

 20 

 40 

 60 

 80 

 100 

Sodium plus Potassium

Chloride, Fluoride, Nitrite plus Nitrate

 0 

 20 

 40 

 60 

 80 

 100 

 100 

 80 

 60 

 40 

 20 

 0 

Bicarbonate

 100 

 80 

 60 

 40 

 20 

 0 

Sulfate

 0 

 20 

 40 

 60 

 80 

 100 

Sulfate plus Chloride

 0 

 20 

 40 

 60 

 80 

 100 

Calcium plus Magnesium

 100 

 80 

 60 

 40 

 20 

 0 

 100 

 80 

 60 

 40 

 20 

 0 

Percent

Percent

EXPLANATION

USGS-grid well

CDPH well

IP004884_Figure B2b Grid wells_Kern

B

Figure B2—Continued
124 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Two statistical approaches—grid-based and spatially 

weighted—were selected to evaluate the aquifer-scale 

proportions of the primary aquifers in the southern San 

Joaquin Valley study units with high, moderate, or low 

relative-concentrations (concentration relative to its 

water-quality benchmark) of constituents. Raw detection 

frequencies also were calculated for individual constituents, 

but were not used for estimating aquifer-scale proportions 

because this method creates spatial bias toward regions with 

large numbers of wells. 

Grid-Based Calculation

One well in each grid cell, a “grid well,” was 

used to represent the primary aquifers. Most grid wells 

sampled for this study were USGS-grid wells. The 

relative-concentration for each constituent (concentration 

relative to its water-quality benchmark) was then evaluated 

for each grid well. The proportion of the primary aquifers 

with high relative-concentrations was calculated by dividing 

the number of cells with concentrations greater than the 

benchmark (relative-concentration greater than 1) by the 

total number of grid wells in each study unit (Belitz and 

others, 2010). Proportions containing moderate and low 

relative‐concentrations were calculated similarly. Confidence 

intervals for grid-based aquifer-scale proportions were 

computed using the Jeffreys interval for the binomial 

distribution (Brown and others, 2001). The grid-based 

estimate is spatially unbiased. However, the grid-based 

approach may not detect constituents that are present at high 

relative-concentrations in small proportions of the primary 

aquifers.

The grid-based aquifer-scale proportions for constituent 

classes also are calculated on a one-value-per-cell basis. A 

cell with a high relative-concentration for any constituent 

in the class is defined as a high cell, and the high proportion 

is the number of high cells divided by the number of cells 

with data for any of the constituents in that class. The 

moderate proportion for the constituent class is calculated 

similarly, except that a cell already defined as high cannot 

also be defined as moderate. A cell with a moderate 

relative-concentration for any constituent in the class without 

a high value for any constituent in the class is defined as 

moderate. The grid-based aquifer-scale proportion for the 

low category was calculated similarly, such that a cell could 

only be low if the relative-concentration was neither moderate 

nor high for any constituent in the class. The proportions 

for the high, moderate, and low categories were expected to 

total 100 percent, except for small differences as a result of 

rounding.

Spatially Weighted Calculation

The spatially weighted calculation of aquifer-scale 

proportions uses the most recent value for a constituent 

from all wells in the CDPH database with data in the 3-year 

interval prior to USGS-GAMA sampling (January 1, 2003, to 

December 31, 2005) in the study units, from all USGS-grid 

well data, and from selected USGS-understanding well data. 

The spatially weighted approach computes the aquifer-scale 

proportion using the percentage of wells with high relative 

concentrations from all of the wells in each cell, instead of 

using data from only one well (Belitz and others, 2010). 

For each constituent, the high aquifer-scale proportion was 

computed by calculating the proportion of wells with high 

relative-concentrations in each grid-cell and dividing by the 

number of cells (Belitz and others, 2010):

1

where

is the proportion of wells in the th cell with

high relative-concentrations,

is the number of wells in the th cell with data

for the constituent,

is the numbe

high

i

total

n

i

i

SU

i

total

high

W

P

W

P

P

n

P i

W i

W

=

=

=

∑

r of wells in the th cell with

high relative-concentratons,

is the aquifer-scale proportion for the study

unit, and

is the number of cells with data for the 

constituent.

SU

i

P

n

Appendix C: Calculation of Aquifer-Scale Proportions
Appendix C 125

Similar procedures were used to calculate the proportions 

of moderate and low relative-concentrations. The resulting 

proportions are spatially unbiased (Isaaks and Srivastava, 

1989). 

Raw Detection Frequencies

The raw detection frequencies of wells with high 

relative-concentrations for constituents were calculated using 

the same data used for the spatially weighted approach. Raw 

detection frequency is the percentage (frequency) of wells in 

the study unit with high relative-concentrations. However, 

raw detection frequencies are not spatially unbiased because 

the wells in the CDPH database and USGS-understanding 

wells are not uniformly distributed. Consequently, high 

relative-concentrations for wells clustered in a particular area 

represent a small part of the primary aquifers and could be 

given a disproportionately high weight compared to that given 

by spatially unbiased approaches. Raw detection frequencies 

of high relative-concentrations are provided for reference 

in this report but were not used to assess aquifer-scale 

proportions.
126 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Well Construction Information

Well construction data were from driller’s logs or from 

information provided by the well owner. Well identification 

verification procedures are described by Burton and Belitz 

(2008) and Shelton and others (2008). Well depths and depths 

to the top and bottom of the perforated interval for USGS-grid, 

USGS-understanding, and CDPH-grid wells are listed for the 

SESJ study unit in table A1 and for the KERN study unit in 

table A2. Wells were classified as production wells, monitoring 

wells, or domestic wells. Production wells pump groundwater 

from the aquifer to a distribution system. Monitoring wells 

are short-screened wells installed exclusively for monitoring 

purposes. Domestic wells pump groundwater from the aquifer 

for home use. Well construction data for the domestic wells 

collected by SWRCB as part of GAMA’s Domestic Well 

Project were obtained from the well owners and are shown in 

table D1.

Land-Use Classification

Land use was classified using an enhanced version of the 

satellite-derived (30-m pixel resolution) nationwide USGS 

National Land Cover Dataset (Nakagaki and others, 2007). 

This dataset has been used in previous national and regional 

studies relating land use to water quality (Gilliom and others, 

2006; Zogorski and others, 2006). The dataset characterizes 

land cover during the early 1990s. The imagery is classified 

into 25 land-cover classifications (Nakagaki and Wolock, 

2005). These 25 land-cover classifications were assigned to 

3 general land-use classifications—urban, agricultural, and 

natural. Land-use statistics for each study unit and for circles 

with a radius of 500 m around each well in the SESJ study 

unit (table D2) and in the KERN study unit (table D3) and 

the wells from the Domestic Well Project (table D4) were 

assigned using USGS National Land Cover Dataset (Johnson 

and Belitz, 2009).

Lateral Position 

The lateral position of wells within the valley serves 

as a proxy for the horizontal position in the regional 

groundwater‐flow system. Regionally, groundwater primarily 

flows from the eastern boundary of the valley-fill deposits 

along the Sierra Nevada mountain front toward the southwest 

to the western boundary of the flow system (fig. 5). The 

groundwater-flow system has vertical flow components as 

well as horizontal flow components that deviate from the 

regional northeast-to-southwest flow direction in response to 

withdrawals and recharge related to groundwater development 

for irrigation since the early to mid-1900s (Burow and others, 

2008a, 2008b; California Department of Water Resources, 

2008). These vertical and nonparallel horizontal flow 

components are superimposed on the topographically driven 

regional flow system. The aquifer system also contains large 

quantities of groundwater that was recharged before the 

modern flow system developed; under predominantly natural 

conditions, groundwater primarily had moved from northeast 

to southwest. 

The normalized lateral position (hereinafter, lateral 

position) was calculated as part of a regional groundwater‐flow 

modeling study for a set of 30 × 30-m-wide cells in the San 

Joaquin Valley (Faunt and others, 2009). Lateral positions 

were assigned to wells residing in those cells using ArcGIS 

(version 9.2). The lateral position of each well was calculated 

as the ratio of (1) the distance from the well to the San Joaquin 

Valley trough and (2) the total distance from the San Joaquin 

Valley trough to the eastern boundary of the valley. The 

eastern boundary of the valley was represented by the eastern 

boundary of the valley fill deposits and was assigned a value 

of 1. The valley trough was assigned a value of 0. Because 

the Tulare Lake bed and other dry lake beds in the southern 

KERN study unit are topographic lows covering large 

areas, the approximate boundary of these lake beds, where 

present, were used to represent the location of the valley 

trough (fig. 8). The San Joaquin Valley trough and the eastern 

boundary were represented as approximate line segments, and 

lateral position was calculated along lines perpendicular to 

both bounding lines. High values of lateral position indicate 

locations in the upgradient or proximal portion of the flow 

system, and lower values of lateral position indicate locations 

in the downgradient or distal portion of the flow system. 

Plotting of data, with respect to lateral position, also allows 

for aggregation of areally distributed data into a single, 

diagrammatic cross section across the study unit. Values for 

lateral position for each USGS-grid and USGS-understanding 

well, CDPH-grid well, and Domestic Well Project well are 

given in tables D2, D3, and D4, respectively.

Septic Systems

Septic tank density was determined from housing 

characteristics data from the 1990 U.S. Census (U.S. Census 

Bureau, 1990, ftp://ftp2.census.gov/census_1990). The density 

of septic tanks in each housing census block was calculated 

from the number of tanks and block area. The density of septic 

tanks around each well was calculated from the area-weighted 

mean of the block densities for blocks within a 500-m buffer 

around the well location (Tyler Johnson, U.S. Geological 

Survey, written commun., 2009) (tables D2, D3, and D4).

Appendix D: Attribution of Potential Explanatory Factors
Appendix D 127

Formerly Leaking Underground Fuel Tanks

Density for formerly leaking underground fuel tanks 

(LUFTs) was determined from data obtained from the 

Geographic Information Management System GeoTracker 

(California Environmental Protection Agency, 2001). The 

density is a measure of the number of tanks in a Thiessen 

polygon in square kilometers (km2). The boundaries of the 

Thiessen polygons are created by bisecting the distance 

between all surrounding LUFTs. For instance, if a tank 

is surrounded by four tanks each 1,000 m away, then the 

Thiessen polygon will be drawn exactly one-half of the 

distance to each tank (500 m), resulting in a polygon that is 

relatively small and therefore of high density. The density is 

calculated by dividing the number of tanks at a single location 

(usually one) and dividing it by the total area of the polygon. If 

the nearest tanks are many miles away, then the polygon will 

be large, and therefore the density will be relatively low. This 

measure was added because two wells could be each 100 m 

away from a LUFT, but one could be surrounded by 10 nearby 

tanks and the other secluded without another tank for 100 mi. 

The Thiessen polygon method is a non-interpolated measure 

of density that has the added value of being able to handle 

extreme high and low densities equally well. LUFT density 

data for each USGS-grid and USGS-understanding well and 

CDPH-grid well are in tables D2 and D3.

Groundwater Age Classification

Groundwater dating techniques indicate the time after 

the groundwater was last in contact with the atmosphere 

(residence time). Techniques used to estimate groundwater 

residence times or ‘age’ include those based on tritium (3H; 

Tolstikhin and Kamensky, 1969; Torgersen and others, 1979) 

and 3H in combination with its decay product helium-3 (3He) 

(Schlosser and others, 1988), carbon-14 (14C) activities (Vogel 

and Ehhalt, 1963; Plummer and others, 1993; Kalin, 2000), 

and dissolved noble gases, particularly helium-4 accumulation 

(Davis and DeWiest, 1966; Andrews and Lee, 1979; Cey and 

others, 2008; Kulongoski and others, 2008). 

Tritium (3H) is a short-lived radioactive isotope 

of hydrogen with a half-life of 12.32 years (Lucas and 

Unterweger, 2000). Tritium is produced naturally in the 

atmosphere from the interaction of cosmogenic radiation 

with nitrogen (Craig and Lal, 1961), by above-ground 

nuclear explosions, and by the operation of nuclear reactors. 

Above-ground nuclear-bomb testing between 1951 and 1980 

(peak production in 1963) introduced much larger quantities of 

3H than natural production into the atmosphere (Michel, 1989; 

Solomon and Cook, 2000). Tritium enters the hydrologic 

cycle as precipitation following oxidation to tritiated water. 

Consequently, the presence of 3H in groundwater may be used 

to identify water that has exchanged with the atmosphere in 

the past 50 years. By determining the ratio of 3H to its decay 

product 3He, the time that the water has resided in the aquifer 

can be calculated more precisely than using 3H alone (Takaoka 

and Mizutani, 1987; Poreda and others, 1988). Tritium activity 

and tritium-helium age of the water samples are shown in 

tables D5 and D6. 14C is a radioactive isotope of carbon, with a half-life 

of 5,730 years, that is formed naturally in the atmosphere by 

the interaction of cosmic-ray neutrons with nitrogen, and to 

a lesser degree, interaction with oxygen and carbon. 14C is 

incorporated into carbon dioxide which is mixed throughout 

the atmosphere. Carbon dioxide is dissolved in precipitation 

and incorporated into the hydrologic cycle. 14C activity in 

groundwater, expressed as percent modern carbon (pmc), 

indicates exposure to the atmospheric 14C source. 14C can 

be used to estimate groundwater ages ranging from 1,000 

to less than 30,000 years before present (Clark and Fritz, 

1997). Calculated 14C ages (tables D5 and D6) in this study 

are referred to as “uncorrected” because they have not been 

adjusted to consider exchanges with sedimentary sources of 

carbon (Fontes and Garnier, 1979; Kalin, 2000).The 14C age 

(residence time) is calculated based on the decrease in 14C 

activity as a result of radioactive decay after groundwater 

recharge, relative to an assumed initial 14C concentration 

(Clark and Fritz, 1997). A mean initial 14C activity of 99 pmc 

was assumed for this study, with estimated errors on calculated 

groundwater ages as great as ±20 percent.

Helium (He) is a naturally occurring inert gas produced 

by the radioactive decay of lithium, thorium, and uranium in 

the Earth. Measured He concentrations in groundwater is the 

sum of air-equilibrated He, He from dissolved-air bubbles, 

terrigenic He, and tritiogenic 3He. The helium (3He and 4He 

isotopes) concentrations in groundwater often exceed the 

expected solubility equilibrium values, which are a function 

of the temperature of the water, as a result of subsurface 

production of both isotopes, and their subsequent release into 

the groundwater (Morrison and Pine, 1955; Andrews and 

Lee, 1979; Torgersen, 1980; Andrews, 1985; Torgersen and 

Clark, 1985). The presence of terrigenic He in groundwater, 

from its production in aquifer material or deeper in the 

crust, is indicative of long groundwater residence times. The 

amount of terrigenic He is defined as the concentration of 

the total measured He, minus He from air equilibration and 

dissolved‐air bubbles. Percentage of terrigenic He is defined 

as the concentration of terrigenic He (as defined previously) 

divided by the total measured He in the sample (corrected 

for air-bubble entrainment). Samples in which more than 

5 percent of the total He is terrigenic He (percentage of 

terrigenic He) indicate groundwater has a residence time of 

more than 100 years. 
128 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Recharge temperatures for 138 samples were determined 

from dissolved neon, argon, krypton, and xenon data using 

methods described by Aeschbach-Hertig and others (1999). 

The only modeled recharge temperatures accepted were those 

for which the probability was greater than 1 percent that the 

sum of the squared deviations between the modeled and the 

measured concentrations (weighted with the experimental 

1-sigma errors) was equal to or greater than the observed value 

(Aeschbach-Hertig and others, 2000). 

The groundwater age was computed using 3H /3He 

as described by Poreda and others (1988). The 3He/4He 

of samples was determined by the linear regression of the 

percentage of terrigenic He and δ3He ([δ3He = Rmeas/Ratm

–1] × 100) of samples with less than 1 tritium unit (TU). 

Rmeas is the ratio of 3He/4He in the measured sample; Ratm is 

the ratio of 3He/4He in the atmosphere. Calculations of the 

recharge temperature using noble gases and 3He/4He are useful 

because they can be used to constrain He-based groundwater 

ages further.

In this study, the ages of samples are classified as 

pre-modern, modern, and mixed (tables D5 and D6). 

Groundwater with 3H activity less than 1 TU, percentage of 

terrigenic He greater than 5 percent, and 14C less than 90 pmc 

was designated as pre-modern. Pre-modern groundwater is 

defined as having been recharged before 1952. Groundwater 

with 3H greater than 1 TU, percentage of terrigenic He less 

than 5 percent, and 14C greater than 90 pmc is designated 

as modern. Modern groundwater is defined as having 

been recharged after 1952. Samples with pre-modern and 

modern components are designated as mixed groundwater. 

In reality, pre-modern groundwater could contain small 

fractions of modern groundwater, and modern groundwater 

could contain small fractions of pre-modern groundwater. 

Previous investigations have used a range of tritium values 

from 0.3 to 1.0 TU as thresholds for distinguishing pre-1950 

from post-1950 groundwater (Michel, 1989; Plummer and 

others, 1993, p. 260; Michel and Schroeder, 1994; Clark and 

Fritz, 1997, p. 185; Manning and others, 2005). By using a 

tritium value of 1.0 TU for the threshold in this study, the 

age classification scheme allows a larger fraction of modern 

groundwater to be classified as pre-modern than if a lower 

threshold was used. A lower threshold for tritium would 

result in more samples classified as mixed age rather than 

pre-modern age, when other tracers—14C and terrigenic He—

indicated that the samples primarily were pre-modern age. 

This high threshold was considered more appropriate for this 

study because many of the wells were production wells with 

long screens and mixing water of pre-modern and modern age 

likely occurs. 

Geochemical Conditions

Geochemical conditions investigated as potential 

explanatory variables in this report include oxidation-reduction 

characteristics, dissolved oxygen concentrations, and pH 

(tables D7, D8, D9). Oxidation-reduction (redox) conditions 

and pH influence the mobility of many organic and inorganic 

constituents (Hem, 1985; McMahon and Chapelle, 2008). 

Along groundwater flow paths, redox conditions commonly 

proceed along a well-documented sequence of Terminal 

Electron Acceptor Processes (TEAP); one TEAP typically 

dominates at a particular time and aquifer location (Chapelle 

and others, 1995; Chapelle, 2001). The predominant 

TEAPs are oxygen-reducing (oxic), nitrate-reducing, 

manganese-reducing, iron-reducing, sulfate-reducing, and 

methanogenesis. The presence of redox-sensitive chemical 

species indicating more than one TEAP may indicate (1) the 

discharge from the well includes mixed waters from different 

redox zones upgradient of the well, (2) the well is screened 

across more than one redox zone, or (3) there is spatial 

heterogeneity in microbial activity in the aquifer. In addition, 

different redox couples often are not consistent, indicating 

electrochemical disequilibrium in groundwater (Lindburg 

and Runnels, 1984) complicating the assessments of redox 

conditions. 

In this report, redox conditions were represented in two 

ways: dissolved oxygen concentration and classified redox 

state. Dissolved oxygen concentrations were measured at 

USGS-grid and USGS-understanding wells (Burton and 

Belitz, 2008; Shelton and others, 2008), but are not reported 

in the CDPH database (tables D7 and D8). Redox conditions 

were classified based on dissolved oxygen, nitrate, manganese, 

iron, and sulfate concentrations using the classification scheme 

of McMahon and Chapelle (2008) (tables D7 and D8). An 

automated workbook program was used to assign the redox 

classification to each sample (Jurgens and others, 2009). For 

wells without USGS inorganic constituent data, the most 

recent data within the previous 3 years (January 1, 2003, to 

December 31, 2005) for that well in the CDPH database were 

used.

Redox conditions for wells in the Domestic Well Project 

are included in table D9. However, dissolved oxygen data was 

not collected as part of this project.
Appendix D 129

Table D1. Nomenclature and well-depth information from domestic wells sampled in Tulare County and located in the Southeast San 

Joaquin Valley study unit, California GAMA Domestic Well Project, 2006.

[Domestic well is well primarily used for home use. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; TUL, Tulare County 

domestic well; ft, feet; LSD, land surface datum; na, not available]

Well identification 

number

Groundwater 

subbasin

Well depth

(ft below LSD)

TUL901 Kaweah 250

TUL902 Kaweah na

TUL903 Kaweah 110

TUL904 Kaweah 130

TUL905 Kaweah 80

TUL906 Kaweah na

TUL907 Kaweah na

TUL908 Kaweah na

TUL909 Kaweah 98

TUL910 Kaweah na

TUL911 Kaweah 150

TUL912 Kaweah 200

TUL913 Kaweah 25

TUL914 Kaweah 90

TUL915 Kaweah 325

TUL916 Kaweah 192

TUL917 Kaweah 150

TUL922 Kaweah 200

TUL923 Kaweah 208

TUL924 Kaweah 150

TUL925 Kaweah 230

TUL926 Kaweah 100

TUL928 Kings 138

TUL929 Kaweah 134

TUL930 Kaweah 192

TUL931 Kaweah na

TUL932 Kaweah na

TUL933 Kaweah 320

TUL934 Kaweah na

TUL935 Kaweah 130

TUL936 Kaweah 212

TUL937 Kaweah 275

TUL938 Kaweah na

TUL939 Kaweah 189

TUL940 Tule 120

TUL941 Kaweah 312

TUL942 Tule na

TUL943 Tule na

TUL944 Tule na

TUL945 Tule 320

TUL946 Tule 216

TUL947 Tule na

TUL948 Tule na

TUL949 Kaweah na

TUL950 Tule 250

TUL951 Tule na

TUL952 Tule 900

TUL955 Kaweah 257

TUL958 Tule 300

TUL959 Tule 148

Well identification 

number

Groundwater 

subbasin

Well depth

(ft below LSD)

TUL960 Tule na

TUL961 Tule 320

TUL967 Kaweah na

TUL968 Kings 190

TUL969 Kings na

TUL970 Kings 160

TUL971 Kings 100

TUL972 Kings na

TUL973 Kings 115

TUL974 Kaweah 220

TUL976 Kaweah 179

TUL978 Tule 380

TUL979 Tule na

TUL986 Tule na

TUL987 Tule na

TUL988 Kaweah 300

TUL990 Tule 320

TUL991 Kaweah na

TUL992 Tule 280

TUL996 Kaweah 160

TUL997 Kaweah 250

TUL999 Tule 380

TUL1000 Tule 365

TUL1003 Kaweah 276

TUL1005 Tule 200

TUL1006 Tule na

TUL1007 Kings 80

TUL1008 Kaweah na

TUL1010 Kings 200

TUL1011 Tule 300

TUL1012 Tule 420

TUL1013 Kaweah na

TUL1014 Kings 100

TUL1015 Kings 90

TUL1016 Kings na

TUL1017 Kaweah 143

TUL1020 Kaweah na

TUL1021 Kings na

TUL1022 Tule 420

TUL1025 Kings 120

TUL1026 Kaweah 120

TUL1027 Kaweah 200

TUL1028 Tule na

TUL1029 Tule 510

TUL1031 Kings 90

TUL1032 Kings 200

TUL1033 Kaweah 250

TUL1034 Tule 80

TUL1035 Kaweah 154

TUL1036 Kaweah 120

TUL1041 Tule 490
130 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table D1. Nomenclature and well-depth information from domestic wells sampled in Tulare County and located in the Southeast San 

Joaquin Valley study unit, California GAMA Domestic Well Project, 2006.—Continued

[Domestic well is well primarily used for home use. Abbreviations: GAMA, Groundwater Ambient Monitoring and Assessment Program; TUL, Tulare County 

domestic well; ft, feet; LSD, land surface datum; na, not available]

Well identification 

number

Groundwater 

subbasin

Well depth

(ft below LSD)

TUL1042 Kaweah 53

TUL1043 Kings 170

TUL1044 Kaweah na

TUL1054 Tule 50

TUL1055 Kaweah na

TUL1056 Kaweah 300

TUL1057 Kings na

TUL1058 Kaweah 95

TUL1059 Kaweah 152

TUL1060 Kaweah 200

TUL1061 Kaweah 300

TUL1062 Tule 68

TUL1064 Tule 160

TUL1065 Kaweah 440

TUL1066 Kaweah 200

TUL1070 Kings 65

TUL1071 Kaweah 315

TUL1072 Kings 75

TUL1073 Kings 140

TUL1074 Kings na

TUL1075 Kaweah 150

Well identification 

number

Groundwater 

subbasin

Well depth

(ft below LSD)

TUL1076 Kaweah 130

TUL1080 Kaweah 130

TUL1081 Kaweah na

TUL1082 Kaweah na

TUL1083 Kaweah 165

TUL1084 Kings na

TUL1085 Kings 100

TUL1088 Kaweah na

TUL1089 Kings na

TUL1091 Kaweah na

TUL1092 Kings na

TUL1093 Kings 100

TUL1094 Kaweah 150

TUL1098 Kaweah na

TUL1101 Tule na

TUL1103 Kaweah na

TUL1105 Kings 165

TUL1106 Kaweah 100

TUL1107 Tule na

TUL1111 Kings na

TUL1505 Kaweah 163
Appendix D 131

Table D2. Land-use classification, normalized lateral position, septic systems, and formerly leaking underground fuel tank information 

for USGS-grid and USGS-understanding wells, and CDPH-grid wells for inorganic constituents, Southeast San Joaquin Valley study unit, 

California GAMA Priority Basin Project.

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks or cesspools in 500-meter radius 

around each well (U.S. Census Bureau, 1990). Number of leaking underground fuel tanks within a Thiessen polygon in square kilometers, data from Geographic 

Information Management System GeoTracker (California Environmental Protection Agency, 2001). Abbreviations: USGS, U.S. Geological Survey; GAMA, 

Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of Public Health; KING, Kings study area well; KWH, Kaweah 

study area well; TLR, Tulare Lake study area well; TULE, Tule study area well; FP, flow-path well; Hwy99T, transect well; DG, designates well with CDPH 

data from same well as USGS-grid well; DPH, designates well selected from subset of CDPH wells; km2, square kilometer]

USGS 

GAMA well 

identification 

number

CDPH 

GAMA well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral position 

from valley 

trough

Number 

of septic 

tanks or 

cesspools

Density of formerly 

leaking underground 

fuel tanks

(number of tanks/km2

)

Orchards or 

vineyards1 Agricultural Natural Urban

Grid wells

KING-01 KING-DG-01 1.4 75.7 6.8 17.5 Agricultural 0.04 0.7 0.02

KING-02 2.4 89.6 0.0 10.4 Agricultural 0.10 2.3 0.01

KING-03 KING-DG-03 2.1 30.8 0.7 68.5 Urban 0.18 2.4 0.04

KING-04 16.0 88.3 2.1 9.6 Agricultural 0.20 1.3 0.01

KING-05 7.4 91.5 7.2 1.3 Agricultural 0.12 1.1 0.01

KING-06 26.9 30.0 12.4 57.6 Urban 0.35 15.4 0.07

KING-07 KING-DG-07 4.1 36.3 8.8 54.9 Urban 0.36 7.3 0.02

KING-08 49.9 55.0 2.6 42.4 Agricultural 0.76 4.2 0.34

KING-09 KING-DG-09 27.0 55.2 11.1 33.7 Agricultural 0.61 18.3 0.31

KING-10 6.8 10.3 5.4 84.3 Urban 0.60 9.6 0.43

KING-11 KING-DG-11 5.0 5.6 0.9 93.5 Urban 0.83 4.3 0.25

KING-12 KING-DG-12 3.7 7.4 2.3 90.3 Urban 0.85 32.7 1.02

KING-13 KING-DG-13 39.5 60.1 30.6 9.3 Agricultural 0.80 3.0 1.32

KING-14 KING-DG-14 11.8 67.6 0.8 31.6 Agricultural 0.34 6.8 0.21

KING-15 KING-DG-15 0.0 0.0 18.6 81.4 Urban 0.65 15.8 1.00

KING-16 KING-DG-16 0.0 0.0 2.5 97.5 Urban 0.70 0.0 0.65

KING-17 2.3 39.5 16.4 44.1 Mixed 0.63 15.6 0.42

KING-18 KING-DG-18 0.1 0.1 4.7 95.2 Urban 0.86 3.5 0.72

KING-19 KING-DG-19 25.7 33.8 3.8 62.4 Urban 0.66 3.2 0.02

KING-20 19.6 85.5 10.2 4.4 Agricultural 0.96 7.3 0.07

KING-21 KING-DG-21 4.7 98.9 0.0 1.1 Agricultural 0.11 8.2 0.02

KING-22 KING-DG-22 0.0 7.2 47.9 44.9 Mixed 0.83 2.8 0.65

KING-23 KING-DG-23 0.0 12.8 7.9 79.3 Urban 0.89 9.9 0.15

KING-24 KING-DG-24 0.0 1.1 24.2 74.7 Urban 0.78 37.3 1.39

KING-25 KING-DG-25 21.1 44.4 12.1 43.4 Mixed 0.59 2.7 0.10

KING-26 18.4 67.2 32.8 0.0 Agricultural 0.06 1.2 0.03

KING-27 98.7 98.7 0.0 1.3 Agricultural 0.36 6.8 0.03

KING-28 0.0 0.1 91.2 8.7 Natural 1.00 0.7 0.02

KING-29 KING-DG-29 3.2 44.3 54.6 1.0 Natural 0.96 16.7 0.09

KING-30 54.0 83.3 15.3 1.4 Agricultural 0.91 4.7 0.03

KING-31 0.0 88.3 7.0 4.7 Agricultural 0.03 0.9 0.02

KING-32 86.9 89.0 6.6 4.4 Agricultural 0.41 7.7 0.03

KING-33 0.9 65.3 8.1 26.6 Agricultural 0.80 1.5 0.03

KING-34 KING-DG-34 60.1 75.1 7.3 17.5 Agricultural 0.37 5.1 0.03

KING-35 KING-DG-35 60.7 81.0 1.8 17.2 Agricultural 0.52 5.3 0.08

KING-36 KING-DG-36 71.9 74.5 8.2 17.3 Agricultural 0.48 5.8 0.03

KING-37 36.0 96.9 2.7 0.3 Agricultural 0.47 5.7 0.02

KING-38 76.6 99.0 1.0 0.0 Agricultural 0.66 7.1 0.28

KING-39 6.0 17.4 27.4 55.2 Urban 0.09 2.0 0.01

KWH-01 KWH-DG-01 28.8 91.3 6.6 2.1 Agricultural 0.80 35.9 0.36

KWH-02 KWH-DG-02 22.0 47.4 7.0 45.6 Mixed 0.85 18.9 0.05

KWH-03 KWH-DG-03 31.4 65.3 8.9 25.8 Agricultural 0.65 9.7 0.06
132 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table D2. Land-use classification, normalized lateral position, septic systems, and formerly leaking underground fuel tank information 

for USGS-grid and USGS-understanding wells, and CDPH-grid wells for inorganic constituents, Southeast San Joaquin Valley study unit, 

California GAMA Priority Basin Project.—Continued

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks or cesspools in 500-meter radius 

around each well (U.S. Census Bureau, 1990). Number of leaking underground fuel tanks within a Thiessen polygon in square kilometers, data from Geographic 

Information Management System GeoTracker (California Environmental Protection Agency, 2001). Abbreviations: USGS, U.S. Geological Survey; GAMA, 

Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of Public Health; KING, Kings study area well; KWH, Kaweah 

study area well; TLR, Tulare Lake study area well; TULE, Tule study area well; FP, flow-path well; Hwy99T, transect well; DG, designates well with CDPH 

data from same well as USGS-grid well; DPH, designates well selected from subset of CDPH wells; km2, square kilometer]

USGS 

GAMA well 

identification 

number

CDPH 

GAMA well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral position 

from valley 

trough

Number 

of septic 

tanks or 

cesspools

Density of formerly 

leaking underground 

fuel tanks

(number of tanks/km2

)

Orchards or 

vineyards1 Agricultural Natural Urban

Grid wells—Continued

KWH-04 KWH-DG-04 28.4 70.7 25.2 4.1 Agricultural 1.00 4.9 0.13

KWH-05 KWH-DG-05 74.1 89.7 8.8 1.5 Agricultural 1.00 3.2 0.04

KWH-06 KWH-DG-06 15.8 44.9 11.7 43.4 Mixed 0.92 8.7 1.04

KWH-07 57.6 73.4 18.8 7.8 Agricultural 1.00 7.5 0.06

KWH-08 KWH-DG-08 49.3 77.0 22.8 0.2 Agricultural 1.00 5.6 0.04

KWH-09 24.2 85.7 3.3 11.0 Agricultural 0.72 7.9 0.05

KWH-10 4.1 6.5 14.8 78.7 Urban 0.98 28.1 1.07

KWH-11 KWH-DG-11 0.1 66.1 9.0 24.9 Agricultural 0.46 9.5 0.32

KWH-12 0.0 0.0 17.0 83.0 Urban 0.53 1.3 3.60

KWH-13 KWH-DG-13 1.6 83.4 16.6 0.0 Agricultural 0.35 2.5 0.05

KWH-14 0.1 14.9 34.8 50.3 Urban 0.56 4.3 0.06

KWH-15 KWH-DG-15 0.0 98.6 1.4 0.0 Agricultural 0.11 2.3 0.06

KWH-16 23.8 89.3 3.6 7.1 Agricultural 0.63 7.3 0.03

KWH-17 16.8 79.7 12.9 7.3 Agricultural 0.18 2.5 0.01

KWH-18 15.7 75.7 4.9 19.4 Agricultural 0.78 19.3 0.03

TLR-01 TLR-DG-01 0.9 77.8 16.0 6.2 Agricultural 0.10 2.4 0.07

TLR-02 8.1 47.7 3.9 48.5 Mixed 0.14 4.9 0.22

TLR-03 TLR-DG-03 3.3 63.0 6.9 30.1 Agricultural 0.18 13.3 0.08

TLR-04 4.4 76.7 21.1 2.2 Agricultural 0.26 4.2 0.04

TLR-05 0.5 99.2 0.8 0.0 Agricultural 0.20 3.1 0.02

TLR-06 TLR-DG-06 2.4 59.9 4.4 35.7 Agricultural 0.00 5.8 0.19

TLR-07 TLR-DG-07 0.0 4.1 48.1 47.8 Mixed 0.18 0.7 0.03

TLR-08 0.2 44.4 5.6 49.9 Mixed 0.26 6.8 0.43

TLR-09 0.0 90.4 2.6 7.0 Agricultural 0.00 0.2 0.00

TULE-01 7.0 16.2 7.0 76.9 Urban 0.44 3.1 0.19

TULE-02 TULE-DG-02 6.9 40.9 3.1 56.0 Urban 0.66 2.8 0.02

TULE-03 TULE-DG-03 1.5 73.2 16.6 10.2 Agricultural 0.42 2.0 0.02

TULE-04 TULE-DG-04 40.9 61.5 12.8 25.7 Agricultural 0.71 0.3 0.03

TULE-05 TULE-DG-05 8.4 64.1 6.3 29.6 Agricultural 0.85 3.3 0.01

TULE-06 69.9 99.9 0.1 0.0 Agricultural 0.80 5.2 0.05

TULE-07 TULE-DG-07 0.0 12.5 6.6 80.9 Urban 0.42 0.1 0.03

TULE-08 0.2 38.9 0.7 60.4 Urban 0.45 6.2 0.14

TULE-09 TULE-DG-09 0.0 5.0 8.6 86.4 Urban 0.98 71.2 3.57

TULE-10 51.7 86.3 13.2 0.6 Agricultural 0.83 5.4 0.05

TULE-11 0.0 100.0 0.0 0.0 Agricultural 0.18 1.1 0.01

TULE-12 0.1 26.8 63.7 9.5 Natural 0.18 1.1 0.01

TULE-13 TULE-DG-13 14.7 39.6 1.5 58.9 Urban 0.76 55.5 0.03

TULE-14 TULE-DG-14 25.4 93.1 6.6 0.2 Agricultural 0.65 1.2 0.01

TULE-15 0.0 99.3 0.6 0.1 Agricultural 0.13 1.1 0.01

TULE-16 86.3 99.3 0.5 0.2 Agricultural 0.55 0.6 0.02

TULE-17 TULE-DG-17 0.0 100.0 0.0 0.0 Agricultural 0.36 0.9 0.01

KING-DPH-06 37.3 92.0 2.9 5.2 Agricultural 0.33 4.5 0.03
Appendix D 133

Table D2. Land-use classification, normalized lateral position, septic systems, and formerly leaking underground fuel tank information 

for USGS-grid and USGS-understanding wells, and CDPH-grid wells for inorganic constituents, Southeast San Joaquin Valley study unit, 

California GAMA Priority Basin Project.—Continued

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks or cesspools in 500-meter radius 

around each well (U.S. Census Bureau, 1990). Number of leaking underground fuel tanks within a Thiessen polygon in square kilometers, data from Geographic 

Information Management System GeoTracker (California Environmental Protection Agency, 2001). Abbreviations: USGS, U.S. Geological Survey; GAMA, 

Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of Public Health; KING, Kings study area well; KWH, Kaweah 

study area well; TLR, Tulare Lake study area well; TULE, Tule study area well; FP, flow-path well; Hwy99T, transect well; DG, designates well with CDPH 

data from same well as USGS-grid well; DPH, designates well selected from subset of CDPH wells; km2, square kilometer]

USGS 

GAMA well 

identification 

number

CDPH 

GAMA well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral position 

from valley 

trough

Number 

of septic 

tanks or 

cesspools

Density of formerly 

leaking underground 

fuel tanks

(number of tanks/km2

)

Orchards or 

vineyards1 Agricultural Natural Urban

Grid wells—Continued

KING-DPH-08 76.7 83.2 6.6 10.2 Agricultural 0.66 12.7 0.06

KING-DPH-10 53.7 83.7 11.5 4.8 Agricultural 0.66 10.0 0.13

KING-DPH-14 12.5 77.3 8.9 13.7 Agricultural 0.08 1.4 0.01

KING-DPH-27 67.2 68.2 0.7 31.2 Agricultural 0.50 4.5 0.03

KING-DPH-28 0.0 0.0 100.0 0.0 Natural 1.00 0.7 0.02

KING-DPH-32 95.2 96.0 1.0 3.0 Agricultural 0.47 10.5 0.06

KING-DPH-38 20.5 26.7 19.2 54.1 Urban 0.65 8.1 9.23

KING-DPH-39 25.0 76.7 0.7 22.6 Agricultural 0.30 2.8 0.19

KING-DPH-40 0.0 64.9 4.8 30.2 Agricultural 0.04 0.9 0.02

KWH-DPH-07 33.1 39.3 39.3 21.4 Mixed 1.00 6.4 0.01

KWH-DPH-08 30.7 62.0 20.0 18.0 Agricultural 0.94 8.9 0.27

KWH-DPH-09 34.4 89.0 9.6 1.4 Agricultural 0.82 4.6 0.03

KWH-DPH-10 7.4 66.2 7.4 26.3 Agricultural 0.83 9.8 0.03

KWH-DPH-17 7.8 82.2 17.8 0.0 Agricultural 0.12 0.8 0.01

KWH-DPH-19 71.8 97.5 1.7 0.8 Agricultural 0.37 2.5 0.03

KWH-DPH-20 11.2 93.8 3.2 3.0 Agricultural 0.42 2.7 0.03

KWH-DPH-21 26.7 58.2 3.8 38.0 Agricultural 0.61 7.3 0.03

TLR-DPH-08 1.6 33.7 5.5 60.8 Urban 0.26 6.8 1.62

TLR-DPH-09 2.3 86.5 9.3 4.2 Agricultural 0.00 0.1 0.00

TULE-DPH-06 4.9 58.6 4.2 37.1 Agricultural 0.88 27.8 0.45

TULE-DPH-10 0.0 4.1 95.9 0.0 Natural 1.00 0.2 0.05

TULE-DPH-16 37.5 94.4 3.9 1.7 Agricultural 0.54 0.8 0.02

TULE-DPH-18 0.0 93.1 6.9 0.0 Agricultural 0.06 0.9 0.01

Understanding wells

KINGFP-01 21.3 80.5 14.7 4.8 Agricultural 0.72 17.4 0.29

KINGFP-02 22.7 79.8 15.0 5.2 Agricultural 0.72 17.4 0.29

KINGFP-03 5.2 23.4 12.6 64.0 Urban 0.70 13.7 0.80

KINGFP-04 0.0 2.5 23.5 74.0 Urban 0.70 9.1 1.43

KINGFP-05 91.1 95.4 3.0 1.6 Agricultural 0.81 6.1 0.06

KINGFP-06 91.1 95.4 3.0 1.6 Agricultural 0.81 6.1 0.06

KINGFP-07 91.1 95.4 3.0 1.6 Agricultural 0.81 6.1 0.06

KINGFP-08 72.2 80.0 3.0 17.1 Agricultural 0.85 7.7 0.06

KINGFP-09 72.2 80.0 3.0 17.1 Agricultural 0.85 7.7 0.06

KINGFP-10 72.2 80.0 3.0 17.1 Agricultural 0.85 7.7 0.06

KINGFP-11 75.5 90.1 5.3 4.6 Agricultural 0.79 9.6 0.14

KINGFP-12 75.5 90.1 5.3 4.6 Agricultural 0.79 9.6 0.14

KINGFP-13 2.2 40.5 16.5 43.0 Mixed 0.63 15.9 0.42

KINGFP-14 46.7 48.6 36.0 15.5 Mixed 0.47 5.1 0.02

KINGFP-15 46.7 48.6 36.0 15.5 Mixed 0.47 5.1 0.02

HWY99T-01 4.9 45.7 19.2 35.1 Mixed 0.67 6.4 0.12

1

 Orchard and vineyard land use is a subset of agricultural land use.
134 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table D3. Land-use classification, normalized lateral position, septic systems, and formerly leaking underground fuel tank information 

for USGS-grid and USGS-understanding wells, and CDPH-grid wells for inorganic constituents, Kern County Subbasin study unit, 

California GAMA Priority Basin Project.

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks or cesspools in 500-meter radius 

around each well (U.S. Census Bureau, 1990). Number of leaking underground fuel tanks within a Thiessen polygon in square kilometers, data from Geographic 

Information Management System GeoTracker (California Environmental Protection Agency, 2001). Abbreviations: USGS, U.S. Geological Survey; GAMA, 

Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of Public Health; km2, square kilometer; KERN, Kern study area 

well; FP, flow-path well; DG, designates well with CDPH data from same well as USGS-grid well; DPH, designates well selected from subset of CDPH wells; 

na, not available]

USGS 

GAMA well 

identification 

number

CDPH 

GAMA well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral 

position from 

valley trough

Number 

of septic 

tanks or 

cesspools

Density of formerly 

leaking underground 

fuel tanks

(number of tanks/km2

)

Orchards or 

vineyards Agricultural Natural Urban

Grid wells

KERN-01 0.0 5.6 94.3 0.1 Natural 0.99 0.61 0.004

KERN-02 41.9 49.9 13.2 36.9 Mixed 0.73 5.76 0.02

KERN-03 KERN-DG-03 3.4 47.1 2.5 50.4 Urban 0.40 1.79 0.08

KERN-04 KERN-DG-04 0.0 0.0 11.3 88.7 Urban 0.59 13.18 4.17

KERN-05 4.2 91.2 7.8 1.0 Agricultural 0.00 0.36 0.004

KERN-06 KERN-DG-06 0.8 100.0 0.0 0.0 Agricultural 0.31 0.13 0.01

KERN-07 KERN-DG-07 9.6 64.5 8.4 27.1 Agricultural 0.51 2.75 0.56

KERN-08 KERN-DG-08 21.5 71.8 11.8 16.4 Agricultural 0.08 0.74 0.04

KERN-09 KERN-DG-09 0.0 53.3 46.6 0.1 Agricultural 0.04 0.74 0.02

KERN-10 KERN-DG-10 0.0 93.4 4.0 2.6 Agricultural 0.00 0.32 0.004

KERN-11 KERN-DG-11 45.1 71.7 14.2 14.1 Agricultural 0.72 0.39 0.02

KERN-12 KERN-DG-12 2.7 96.2 3.8 0.0 Agricultural 0.01 0.74 0.004

KERN-13 KERN-DG-13 0.2 71.2 2.3 26.5 Agricultural 0.31 1.42 0.01

KERN-14 KERN-DG-14 28.5 45.7 29.6 24.7 Mixed 0.68 0.47 0.01

KERN-15 KERN-DG-15 0.5 59.5 10.4 30.1 Agricultural 0.34 76.27 0.88

KERN-16 0.3 96.0 4.0 0.0 Agricultural 0.00 0.74 0.01

KERN-17 KERN-DG-17 0.0 11.7 15.7 72.6 Urban 0.48 28.94 0.69

KERN-18 KERN-DG-18 0.2 86.1 13.2 0.7 Agricultural 0.40 0.60 0.12

KERN-19 KERN-DG-19 0.1 84.2 6.9 8.9 Agricultural 0.61 83.72 0.25

KERN-20 0.0 13.9 77.8 8.4 Natural 0.00 0.32 0.01

KERN-21 1.1 97.7 2.3 0.0 Agricultural 0.14 1.61 0.16

KERN-22 KERN-DG-22 1.5 9.5 49.6 40.9 Mixed 0.77 2.41 0.11

KERN-23 KERN-DG-23 0.0 0.1 98.3 1.6 Natural 0.36 0.32 0.004

KERN-24 0.2 93.1 2.7 4.1 Agricultural 0.22 0.13 0.01

KERN-25 32.4 67.7 9.7 22.6 Agricultural 0.63 1.01 0.03

KERN-26 KERN-DG-26 0.9 91.5 8.5 0.0 Agricultural 0.14 1.13 0.01

KERN-27 24.1 93.5 5.6 0.9 Agricultural 0.36 1.61 0.01

KERN-28 KERN-DG-28 20.5 75.6 6.4 18.0 Agricultural 0.60 0.76 0.11

KERN-29 KERN-DG-29 49.5 55.9 44.1 0.0 Agricultural 0.65 0.60 0.01

KERN-30 KERN-DG-30 69.1 93.0 4.5 2.5 Agricultural 0.61 0.39 0.08

KERN-31 KERN-DG-31 0.0 3.2 0.8 96.0 Urban 0.46 218.07 0.82

KERN-32 KERN-DG-32 0.1 96.8 1.0 2.2 Agricultural 0.43 0.60 0.01

KERN-33 16.7 84.8 11.1 4.1 Agricultural 0.49 22.87 0.37

KERN-34 0.0 41.4 58.6 0.0 Natural 0.06 0.74 0.01

KERN-35 0.1 23.9 76.1 0.0 Natural 0.33 1.13 0.03

KERN-36 0.0 41.2 58.8 0.0 Natural 0.53 36.86 0.09

KERN-37 0.0 0.6 99.2 0.2 Natural 0.95 1.74 0.02

KERN-38 0.6 99.4 0.6 0.0 Agricultural 0.91 0.31 0.01

KERN-39 1.4 84.1 12.6 3.3 Agricultural 0.45 1.14 0.05

KERN-40 19.9 54.6 23.3 22.1 Agricultural 0.77 0.42 0.01

KERN-41 3.8 99.3 0.0 0.7 Agricultural 0.44 1.68 0.02

KERN-42 0.0 16.7 40.4 42.8 Mixed 0.38 0.00 0.18

KERN-43 KERN-DG-43 1.0 69.8 9.7 20.5 Agricultural 0.39 0.39 0.11

KERN-44 1.3 44.0 4.8 51.2 Urban 0.43 10.68 0.07
Appendix D 135

USGS 

GAMA well 

identification 

number

CDPH 

GAMA well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral 

position from 

valley trough

Number 

of septic 

tanks or 

cesspools

Density of formerly 

leaking underground 

fuel tanks

(number of tanks/km2

)

Orchards or 

vineyards Agricultural Natural Urban

Grid wells—Continued

KERN-45 12.4 84.0 14.3 1.7 Agricultural 0.36 11.46 0.27

KERN-46 37.9 88.8 10.2 1.0 Agricultural 0.34 0.59 0.01

KERN-47 KERN-DG-47 18.3 75.6 5.2 19.2 Agricultural 0.48 2.31 0.02

KERN-DPH-01 56.0 56.0 43.4 0.6 Agricultural 0.67 0.61 0.004

KERN-DPH-02 71.9 83.3 8.9 7.8 Agricultural 0.70 5.77 0.02

KERN-DPH-05 2.6 98.1 1.8 0.1 Agricultural 0.00 0.39 0.004

KERN-DPH-16 0.2 97.6 2.4 0.0 Agricultural 0.00 0.74 na

KERN-DPH-20 0.2 64.0 33.2 2.7 Agricultural 0.00 0.44 0.01

KERN-DPH-21 0.9 40.0 6.8 53.3 Urban 0.00 0.91 2.60

KERN-DPH-24 26.7 98.6 0.6 0.8 Agricultural 0.17 0.13 0.01

KERN-DPH-25 68.6 96.0 4.0 0.0 Agricultural 0.55 1.01 0.03

KERN-DPH-27 56.2 90.5 8.7 0.8 Agricultural 0.34 1.61 0.01

KERN-DPH-28 41.2 84.8 15.2 0.0 Agricultural 0.53 0.76 0.04

 KERN-DPH-30 5.7 66.8 6.4 26.8 Agricultural 0.58 2.88 0.07

KERN-DPH-31 0.0 0.0 2.6 97.4 Urban 0.54 0.91 1.66

KERN-DPH-33 10.2 46.5 14.4 39.1 Mixed 0.54 23.75 0.39

KERN-DPH-46 41.1 87.2 8.2 4.6 Agricultural 0.19 0.89 0.01

 KERN-DPH-48 0.0 93.2 6.0 0.8 Agricultural 0.18 0.13 0.01

KERN-DPH-49 0.5 21.1 56.8 22.1 Natural 0.11 0.74 0.06

 KERN-DPH-50 3.4 61.9 3.1 35.1 Agricultural 0.42 1.22 2.06

KERN-DPH-51 38.3 75.3 6.4 18.3 Agricultural 0.45 1.14 0.01

KERN-DPH-52 0.0 3.8 96.2 0.0 Natural 0.21 0.74 0.02

 KERN-DPH-53 0.0 21.4 78.6 0.0 Natural 0.05 0.74 0.01

KERN-DPH-54 0.0 80.9 10.2 8.9 Agricultural 0.05 0.32 0.004

KERN-DPH-55 8.1 83.5 5.7 10.8 Agricultural 0.36 11.26 0.03

KERN-DPH-56 36.1 60.1 13.1 26.8 Agricultural 0.43 10.01 0.37

KERN-DPH-57 16.7 68.4 7.0 24.6 Agricultural 0.61 0.39 0.02

KERN-DPH-58 5.8 42.5 7.0 50.5 Urban 0.58 7.70 0.21

KERN-DPH-59 13.2 26.2 1.9 71.8 Urban 0.60 3.00 0.05

KERN-DPH-60 0.0 80.5 7.4 12.0 Agricultural 0.24 0.63 0.004

KERN-DPH-61 0.3 94.7 1.3 4.0 Agricultural 0.10 1.13 0.01

KERN-DPH-62 1.9 94.8 2.6 2.5 Agricultural 0.11 0.88 0.02

KERN-DPH-63 18.9 54.0 43.9 2.2 Agricultural 0.36 0.59 0.02

KERN-DPH-64 20.0 72.7 26.8 0.5 Agricultural 0.87 0.31 0.01

KERN-DPH-65 48.5 94.4 1.8 3.8 Agricultural 0.90 0.31 0.01

KERN-DPH-66 90.4 92.2 0.0 7.8 Agricultural 0.54 0.59 0.01

KERN-DPH-67 3.7 77.9 7.4 14.7 Agricultural 0.68 1.01 0.02

KERN-DPH-68 0.7 96.0 1.1 2.9 Agricultural 0.65 11.37 0.11

Understanding wells

KERNFP-01 0.0 0.0 6.0 94.0 Urban 0.79 1.33 1.15

KERNFP-02 0.0 0.6 77.0 22.4 Natural 0.69 9.77 1.91

KERNFP-03 0.0 0.0 30.7 69.3 Urban 0.60 34.18 0.56

1

 Orchard and vineyard land use is a subset of agricultural land use.

Table D3. Land-use classification, normalized lateral position, septic systems, and formerly leaking underground fuel tank information 

for USGS-grid and USGS-understanding wells, and CDPH-grid wells for inorganic constituents, Kern County Subbasin study unit, 

California GAMA Priority Basin Project.—Continued

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks or cesspools in 500-meter radius 

around each well (U.S. Census Bureau, 1990). Number of leaking underground fuel tanks within a Thiessen polygon in square kilometers, data from Geographic 

Information Management System GeoTracker (California Environmental Protection Agency, 2001). Abbreviations: USGS, U.S. Geological Survey; GAMA, 

Groundwater Ambient Monitoring and Assessment Program; CDPH, California Department of Public Health; km2, square kilometer; KERN, Kern study area 

well; FP, flow-path well; DG, designates well with CDPH data from same well as USGS-grid well; DPH, designates well selected from subset of CDPH wells; 

na, not available]
136 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table D4. Land-use classification, normalized lateral position, and septic system information for 

domestic wells sampled in Tulare County and located in the Southeast San Joaquin Valley study unit, 

California GAMA Domestic Well Project, 2006.

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks 

or cesspools in 500-meter radius around each well (U.S. Census Bureau, 1990). Abbreviations: GAMA, Groundwater 

Ambient Monitoring and Assessment Progam; TUL, Tulare County well]

Domestic well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral position 

from valley 

trough

Number 

of septic 

tanks or 

cesspools

Orchards or 

vineyards1 Agricultural Natural Urban

TUL901 63.1 92.2 7.3 0.5 Agricultural 0.77 12.2

TUL902 30.0 93.7 5.7 0.6 Agricultural 0.81 8.0

TUL903 26.8 82.9 14.3 2.9 Agricultural 0.91 10.2

TUL904 87.0 95.4 4.6 0.0 Agricultural 0.98 3.4

TUL905 54.9 81.5 18.5 0.0 Agricultural 0.94 3.8

TUL906 40.0 84.7 9.8 5.5 Agricultural 0.91 14.4

TUL907 87.1 94.9 4.5 0.7 Agricultural 0.95 5.4

TUL908 59.1 91.9 7.9 0.2 Agricultural 0.97 5.5

TUL909 67.8 88.5 10.5 1.0 Agricultural 0.98 7.5

TUL910 39.4 76.3 23.7 0.0 Agricultural 1.00 7.2

TUL911 34.7 93.3 6.7 0.0 Agricultural 1.00 4.6

TUL912 62.4 86.6 13.4 0.0 Agricultural 1.00 7.2

TUL913 28.8 43.2 49.3 7.5 Mixed 1.00 5.7

TUL914 75.6 79.1 16.8 4.1 Agricultural 1.00 4.0

TUL915 1.8 6.5 93.3 0.2 Natural 1.00 1.0

TUL916 0.0 0.0 99.9 0.1 Natural 1.00 1.0

TUL917 75.8 79.7 20.3 0.0 Agricultural 1.00 7.5

TUL922 89.5 95.9 4.1 0.0 Agricultural 1.00 7.5

TUL923 34.2 60.5 39.2 0.3 Agricultural 1.00 0.9

TUL924 34.0 49.8 23.9 26.4 Mixed 1.00 5.8

TUL925 51.1 95.4 3.8 0.8 Agricultural 1.00 4.9

TUL926 41.1 77.6 7.2 15.2 Agricultural 0.81 10.6

TUL928 22.5 85.7 13.9 0.3 Agricultural 0.99 3.2

TUL929 60.4 94.9 5.1 0.0 Agricultural 0.73 6.1

TUL930 8.9 90.4 6.4 3.2 Agricultural 0.70 6.4

TUL931 56.7 88.7 11.2 0.1 Agricultural 0.93 8.7

TUL932 82.2 89.5 7.5 3.0 Agricultural 0.94 8.8

TUL933 35.4 80.6 19.4 0.0 Agricultural 1.00 0.9

TUL934 31.7 95.0 4.8 0.2 Agricultural 0.79 4.8

TUL935 80.9 90.0 10.0 0.0 Agricultural 0.93 6.1

TUL936 0.6 90.5 9.5 0.0 Agricultural 0.36 2.6

TUL937 15.2 96.7 3.2 0.1 Agricultural 0.30 2.5

TUL938 16.4 77.7 14.6 7.8 Agricultural 0.49 12.1

TUL939 13.6 71.0 22.1 6.8 Agricultural 0.49 15.0

TUL940 61.6 88.2 11.8 0.0 Agricultural 0.81 8.5

TUL941 12.5 55.4 3.4 41.1 Agricultural 0.61 7.3

TUL942 40.5 93.9 4.3 1.7 Agricultural 0.70 3.0

TUL943 49.7 92.2 6.3 1.5 Agricultural 0.83 7.4

TUL944 0.0 0.0 9.4 90.6 Urban 0.99 59.6

TUL945 11.6 64.8 28.8 6.4 Agricultural 1.00 23.6

TUL946 1.5 1.8 9.2 88.9 Urban 0.98 100.8

TUL947 23.0 85.6 13.3 1.1 Agricultural 0.46 1.1

TUL948 29.5 75.3 24.5 0.2 Agricultural 0.49 1.2

TUL949 11.9 93.6 5.8 0.6 Agricultural 0.40 9.5

TUL950 72.8 99.2 0.8 0.0 Agricultural 0.47 0.4

TUL951 83.2 100.0 0.0 0.0 Agricultural 0.50 0.9

TUL952 16.2 99.1 0.9 0.0 Agricultural 0.80 0.6

TUL955 50.7 93.2 6.8 0.0 Agricultural 1.00 2.2

TUL958 50.7 93.2 6.8 0.0 Agricultural 1.00 2.2
Appendix D 137

Domestic well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral position 

from valley 

trough

Number 

of septic 

tanks or 

cesspools

Orchards or 

vineyards1 Agricultural Natural Urban

TUL959 33.2 93.3 3.1 3.7 Agricultural 0.96 12.1

TUL960 34.1 93.8 2.4 3.8 Agricultural 0.92 5.1

TUL961 1.4 75.0 25.0 0.0 Agricultural 1.00 1.0

TUL967 19.2 69.7 10.3 20.0 Agricultural 0.79 12.5

TUL968 15.5 34.7 65.3 0.0 Natural 1.00 0.8

TUL969 1.1 88.0 10.6 1.4 Agricultural 0.77 1.9

TUL970 71.2 92.1 7.8 0.1 Agricultural 1.00 8.6

TUL971 68.9 87.8 12.0 0.2 Agricultural 1.00 8.6

TUL972 61.2 85.7 11.4 2.9 Agricultural 0.91 4.7

TUL973 53.5 88.2 10.4 1.4 Agricultural 0.90 5.7

TUL974 49.1 73.4 26.6 0.0 Agricultural 1.00 0.9

TUL976 39.8 97.0 2.9 0.1 Agricultural 0.76 6.9

TUL978 1.3 6.6 93.2 0.2 Natural 1.00 0.2

TUL979 43.6 82.2 17.8 0.0 Agricultural 1.00 2.2

TUL986 1.0 99.4 0.0 0.6 Agricultural 0.56 1.2

TUL987 5.6 100.0 0.0 0.0 Agricultural 0.40 0.9

TUL988 10.7 87.2 8.3 4.5 Agricultural 0.79 5.6

TUL990 7.8 22.0 76.6 1.4 Natural 1.00 2.9

TUL991 71.9 93.0 6.7 0.2 Agricultural 1.00 5.9

TUL992 1.7 93.8 6.2 0.0 Agricultural 0.57 2.6

TUL996 2.2 21.3 30.7 47.9 Mixed 0.61 3.7

TUL997 70.9 92.5 7.5 0.0 Agricultural 1.00 8.9

TUL999 1.4 84.3 15.1 0.6 Agricultural 0.45 1.2

TUL1000 24.3 90.9 1.5 7.7 Agricultural 0.43 4.4

TUL1003 44.1 88.2 4.1 7.7 Agricultural 0.66 6.3

TUL1005 25.3 98.1 1.9 0.0 Agricultural 0.79 7.4

TUL1006 31.2 83.4 3.7 12.9 Agricultural 0.94 11.6

TUL1007 75.8 89.2 10.8 0.0 Agricultural 0.73 9.8

TUL1008 62.7 87.6 12.3 0.1 Agricultural 1.00 7.5

TUL1010 67.8 78.2 17.9 3.9 Agricultural 0.65 9.8

TUL1011 0.2 86.4 13.5 0.1 Agricultural 0.45 1.2

TUL1012 4.0 99.9 0.0 0.1 Agricultural 0.49 1.2

TUL1013 1.5 98.3 1.7 0.0 Agricultural 0.48 4.4

TUL1014 74.5 89.1 1.1 9.8 Agricultural 0.71 10.7

TUL1015 45.1 68.9 31.1 0.0 Agricultural 1.00 0.9

TUL1016 60.8 68.8 6.7 24.5 Agricultural 0.98 9.9

TUL1017 6.5 96.7 2.5 0.8 Agricultural 0.42 9.5

TUL1020 44.4 96.2 3.8 0.0 Agricultural 0.67 3.4

TUL1021 58.6 93.6 6.1 0.3 Agricultural 1.00 0.9

TUL1022 1.0 95.7 2.9 1.5 Agricultural 0.00 0.8

TUL1025 72.1 82.7 11.7 5.6 Agricultural 0.88 6.7

TUL1026 30.0 57.1 42.9 0.0 Agricultural 1.00 0.9

TUL1027 35.0 86.4 10.5 3.1 Agricultural 0.76 15.4

TUL1028 4.0 99.7 0.3 0.0 Agricultural 0.48 1.3

TUL1029 1.3 86.2 13.8 0.0 Agricultural 0.59 2.0

TUL1031 46.0 70.4 26.3 3.3 Agricultural 0.66 9.8

TUL1032 84.5 94.6 5.3 0.1 Agricultural 0.83 6.8

TUL1033 0.5 65.0 4.7 30.3 Agricultural 0.48 4.4

Table D4. Land-use classification, normalized lateral position, and septic system information for 

domestic wells sampled in Tulare County and located in the Southeast San Joaquin Valley study unit, 

California GAMA Domestic Well Project, 2006.—Continued

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks 

or cesspools in 500-meter radius around each well (U.S. Census Bureau, 1990). Abbreviations: GAMA, Groundwater 

Ambient Monitoring and Assessment Progam; TUL, Tulare County well]
138 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Domestic well 

identification 

number

Land use (in percent)

Land-use 

classification

Normalized 

lateral position 

from valley 

trough

Number 

of septic 

tanks or 

cesspools

Orchards or 

vineyards1 Agricultural Natural Urban

TUL1034 63.2 92.4 7.4 0.2 Agricultural 0.90 4.8

TUL1035 45.4 87.5 12.3 0.1 Agricultural 0.72 10.6

TUL1036 1.1 80.5 19.5 0.0 Agricultural 0.67 8.9

TUL1041 4.6 99.8 0.0 0.2 Agricultural 0.50 1.2

TUL1042 52.9 91.9 8.0 0.1 Agricultural 0.91 10.2

TUL1043 76.4 95.9 4.1 0.0 Agricultural 0.76 8.4

TUL1044 36.2 80.0 20.0 0.0 Agricultural 1.00 7.2

TUL1054 0.0 99.3 0.6 0.1 Agricultural 0.34 0.5

TUL1055 10.1 69.1 11.2 19.7 Agricultural 0.31 2.6

TUL1056 24.2 64.0 5.3 30.7 Agricultural 0.87 10.1

TUL1057 60.5 71.6 17.1 11.3 Agricultural 0.85 13.1

TUL1058 38.6 56.4 17.6 26.0 Agricultural 1.00 7.5

TUL1059 42.1 59.0 17.6 23.4 Agricultural 1.00 7.5

TUL1060 75.5 93.3 6.7 0.0 Agricultural 0.91 8.1

TUL1061 54.8 72.9 20.2 6.8 Agricultural 1.00 7.5

TUL1062 0.0 15.8 14.2 70.1 Urban 1.00 25.0

TUL1064 6.3 91.7 8.0 0.3 Agricultural 0.50 2.6

TUL1065 1.1 95.8 4.1 0.1 Agricultural 0.23 2.5

TUL1066 11.2 73.3 11.1 15.6 Agricultural 0.31 2.6

TUL1070 68.5 79.8 19.7 0.6 Agricultural 0.82 6.7

TUL1071 9.6 96.0 4.0 0.0 Agricultural 0.36 2.5

TUL1072 21.2 81.0 17.4 1.6 Agricultural 0.81 1.5

TUL1073 84.2 90.9 8.7 0.5 Agricultural 0.87 6.7

TUL1074 82.3 92.2 7.7 0.1 Agricultural 0.87 6.7

TUL1075 64.3 89.2 10.8 0.0 Agricultural 0.94 9.5

TUL1076 47.4 83.2 11.2 5.6 Agricultural 0.90 7.2

TUL1080 9.9 97.4 2.1 0.6 Agricultural 0.81 12.1

TUL1081 72.1 93.3 5.9 0.8 Agricultural 0.73 6.1

TUL1082 27.7 93.9 4.2 1.8 Agricultural 0.69 6.1

TUL1083 71.1 94.3 5.5 0.2 Agricultural 0.76 12.2

TUL1084 83.9 92.8 2.9 4.3 Agricultural 0.62 9.7

TUL1085 15.5 34.7 65.3 0.0 Natural 1.00 0.8

TUL1088 9.1 65.8 34.2 0.0 Agricultural 1.00 0.9

TUL1089 87.8 96.2 3.8 0.0 Agricultural 1.00 8.4

TUL1091 0.0 0.0 99.9 0.1 Natural 1.00 1.0

TUL1092 70.4 96.8 3.2 0.0 Agricultural 0.72 2.0

TUL1093 4.7 97.6 2.4 0.0 Agricultural 0.79 1.5

TUL1094 4.3 40.3 58.4 1.3 Natural 1.00 0.9

TUL1098 3.3 71.4 7.2 21.4 Agricultural 0.68 3.4

TUL1101 58.4 77.9 22.1 0.0 Agricultural 1.00 2.2

TUL1103 36.8 83.4 14.3 2.3 Agricultural 0.80 18.8

TUL1105 61.6 93.5 6.5 0.0 Agricultural 0.88 5.9

TUL1106 20.8 92.7 6.9 0.5 Agricultural 0.78 18.9

TUL1107 46.1 83.0 15.3 1.7 Agricultural 1.00 2.2

TUL1111 56.2 86.9 13.1 0.0 Agricultural 1.00 8.6

TUL1505 56.6 92.0 8.0 0.0 Agricultural 0.93 3.2

1

 Orchard and vineyard land use is a subset of agricultural land use.

Table D4. Land-use classification, normalized lateral position, and septic system information for 

domestic wells sampled in Tulare County and located in the Southeast San Joaquin Valley study unit, 

California GAMA Domestic Well Project, 2006.—Continued

[Land-use classification based on 500-meter radius around each well (Johnson and Belitz, 2009). Number of septic tanks 

or cesspools in 500-meter radius around each well (U.S. Census Bureau, 1990). Abbreviations: GAMA, Groundwater 

Ambient Monitoring and Assessment Progam; TUL, Tulare County well]
Appendix D 139

USGS 

GAMA well 

identification 

number

Noble-gas 

based 

recharge 

temperature 

(°C)

Tritium 

activity 

(TU)

Terrigenic 

helium, 

(percent 

of total 

helium)

Modern 

carbon-14

(percent)

Groundwater 

age 

classification

Grid wells

KING-01 14 <1 82.1 nc Pre-Modern

KING-02 18 1.2 92.3 nc Mixed

KING-03 na 4.9 88.5 nc Mixed

KING-04 20 2.4 82.6 60.5 Mixed

KING-05 15 <1 89.9 nc Pre-Modern

KING-06 16 <1 84.0 nc Pre-Modern

KING-07 17 7.4 0.0 nc Modern

KING-08 15 <1 0.0 nc Mixed

KING-09 16 1.6 0.0 nc Modern

KING-10 17 5.0 0.0 nc Modern

KING-11 22 <1 72.3 nc Pre-Modern

KING-12 20 3.4 0.0 nc Modern

KING-13 21 <1 64.4 nc Pre-Modern

KING-14 18 <1 55.3 nc Pre-Modern

KING-15 20 <1 30.4 nc Pre-Modern

KING-16 18 1.3 0.4 nc Modern

KING-17 20 <1 54.3 66.9 Pre-Modern

KING-18 15 7.6 0.1 nc Modern

KING-19 20 1.1 28.2 nc Mixed

KING-20 15 4.5 0.0 116 Modern

KING-21 17 1.7 68.6 nc Mixed

KING-22 21 4.5 0.0 nc Modern

KING-23 20 8.1 0.0 nc Modern

KING-24 18 6.6 0.0 nc Modern

KING-25 21 <1 77.0 nc Pre-Modern

KING-26 16 <1 94.2 nc Pre-Modern

KING-27 25 <1 42.0 nc Pre-Modern

KING-28 16 3.7 0.0 nc Modern

KING-29 19 4.3 77.1 nc Mixed

KING-30 19 <1 77.8 nc Pre-Modern

KING-31 40 <1 0.0 nc Mixed

KING-32 15 <1 9.0 nc Pre-Modern

KING-33 18 <1 99.1 nc Pre-Modern

KING-34 22 9.8 4.9 nc Modern

KING-35 21 2.8 3.1 nc Modern

KING-36 17 <1 0.0 nc Mixed

KING-37 21 5.4 0.0 nc Modern

KING-38 21 <1 82.9 nc Pre-Modern

KING-39 17 <1 82.4 nc Pre-Modern

KWH-01 17 7.0 7.9 nc Mixed

KWH-02 17 4.5 0.0 nc Modern

KWH-03 19 8.9 0.0 nc Modern

Table D5. Groundwater age classification information for USGS-grid and USGS-understanding wells sampled October 2005 through 

February 2006, Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project.

[Samples classified as pre-modern if recharged berfore 1952. Samples classified as modern if recharged after 1952. Samples classified as mixed if sample 

contains both modern and pre-modern water. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment 

Program; °C, degrees Celsius; TU, tritium units; KING, Kings study area well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, Tule 

study area well; FP, flow-path well; HWY99T, transect well; na, not available; <, less than; nc, not collected]

USGS 

GAMA well 

identification 

number

Noble-gas 

based 

recharge 

temperature 

(°C)

Tritium 

activity 

(TU)

Terrigenic 

helium, 

(percent 

of total 

helium)

Modern 

carbon-14

(percent)

Groundwater 

age 

classification

Grid wells—Continued

KWH-04 17 4.4 75.7 nc Mixed

KWH-05 20 7.6 0.0 nc Modern

KWH-06 20 <1 74.8 nc Pre-Modern

KWH-07 20 5.0 94.4 nc Mixed

KWH-08 20 3.1 93.2 nc Mixed

KWH-09 18 9.4 0.0 nc Modern

KWH-10 21 3.2 52.5 nc Mixed

KWH-11 15 <1 92.1 nc Pre-Modern

KWH-12 17 5.0 0.0 85.7 Mixed

KWH-13 19 5.2 0.0 nc Modern

KWH-14 15 4.3 0.0 105 Modern

KWH-15 17 8.9 43.2 nc Mixed

KWH-16 18 8.9 0.0 nc Modern

KWH-17 21 11.0 0.0 nc Modern

KWH-18 19 5.9 3.9 nc Modern

TLR-01 nc nc nc nc Not Datable

TLR-02 16 <1 85.6 nc Pre-Modern

TLR-03 15 3.5 96.9 nc Mixed

TLR-04 17 <1 98.9 0.73Pre-Modern

TLR-05 15 <1 94.0 7.66Pre-Modern

TLR-06 14 <1 75.0 nc Pre-Modern

TLR-07 14 <1 34.4 nc Pre-Modern

TLR-08 17 <1 82.2 nc Pre-Modern

TLR-09 na <1 89.1 nc Pre-Modern

TULE-01 17 5.4 4.2 42.5 Modern

TULE-02 18 7.3 0.0 nc Modern

TULE-03 16 6.3 0.0 nc Modern

TULE-04 20 <1 25.4 nc Pre-Modern

TULE-05 18 <1 98.9 nc Pre-Modern

TULE-06 15 <1 96.5 nc Pre-Modern

TULE-07 16 3.7 4.7 nc Modern

TULE-08 17 <1 21.6 42.1 Pre-Modern

TULE-09 16 3.6 0.0 nc Modern

TULE-10 19 1.9 0.0 nc Modern

TULE-11 13 <1 49.5 nc Pre-Modern

TULE-12 na <1 38.6 nc Pre-Modern

TULE-13 17 4.4 0.0 nc Modern

TULE-14 18 2.1 0.0 nc Modern

TULE-15 nc nc nc nc Not Datable

TULE-16 21 2.5 0.0 nc Modern

TULE-17 19 <1 1.0 nc Mixed
140 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

USGS 

GAMA well 

identification 

number

Noble-gas 

based 

recharge 

temperature 

(°C)

Tritium 

activity 

(TU)

Terrigenic 

helium, 

(percent 

of total 

helium)

Modern 

carbon-14

(percent)

Groundwater 

age 

classification

Understanding wells

KINGFP-01 20 1.6 33.0 105 Mixed

KINGFP-02 22 <1 85.5 47.9 Pre-Modern

KINGFP-03 21 <1 80.8 42.8 Pre-Modern

KINGFP-04 20 2.4 5.9 102 Mixed

KINGFP-05 20 3.2 11.0 nc Mixed

KINGFP-06 20 <1 78.3 79.2 Pre-Modern

KINGFP-07 26 3.9 0.0 114 Modern

KINGFP-08 27 6.8 0.0 118 Modern

Table D5. Groundwater age classification information for USGS-grid and USGS-understanding wells sampled October 2005 through 

February 2006, Southeast San Joaquin Valley study unit, California GAMA Priority Basin Project.—Continued

[Samples classified as pre-modern if recharged berfore 1952. Samples classified as modern if recharged after 1952. Samples classified as mixed if sample 

contains both modern and pre-modern water. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment 

Program; °C, degrees Celsius; TU, tritium units; KING, Kings study area well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, Tule 

study area well; FP, flow-path well; HWY99T, transect well; na, not available; <, less than; nc, not collected]

USGS 

GAMA well 

identification 

number

Noble-gas 

based 

recharge 

temperature 

(°C)

Tritium 

activity 

(TU)

Terrigenic 

helium, 

(percent 

of total 

helium)

Modern 

carbon-14

(percent)

Groundwater 

age 

classification

Understanding wells—Continued

KINGFP-09 22 <1 33.3 93.0 Pre-Modern

KINGFP-10 19 <1 75.8 nc Pre-Modern

KINGFP-11 21 2.2 9.4 121 Mixed

KINGFP-12 20 2.6 0.0 124 Modern

KINGFP-13 20 1.7 0.0 91.6 Modern

KINGFP-14 29 3.6 0.0 97.3 Modern

KINGFP-15 19 3.9 0.0 96.7 Modern

HWY99T-01 16 5.1 27.6 nc Mixed
Appendix D 141

Table D6. Groundwater age classification information for USGS-grid and 

USGS-understanding wells sampled January through March 2006, Kern County 

Subbasin study unit, California GAMA Priority Basin Project.

[Samples classified as pre-modern if recharged before 1953. Samples classified as modern if recharged after 

1953. Samples classified as mixed if sample contains both modern and pre-modern water. Abbreviations: 

USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and Assessment Program; °C, 

degrees Celsius; TU, tritium units; KERN, Kern County Subbasin well; FP, flow-path well; nc, not collected; 

na, not available; <, less than]

USGS 

GAMA well 

identification 

number

Noble-gas 

based recharge 

temperature 

(°C)

Tritium 

activity 

(TU)

Terrigenic 

helium, 

(percent 

of total 

helium)

Modern 

carbon-14

(percent)

Groundwater age 

classification

Grid wells

KERN-01 18.9 2.6 0.0 nc Modern

KERN-02 nc nc nc nc nc

KERN-03 19.3 1.3 66.6 nc Mixed

KERN-04 16.4 <1 0.0 nc Mixed

KERN-05 13.7 <1 86.3 nc Pre-Modern

KERN-06 18.3 <1 14.9 nc Pre-Modern

KERN-07 22.1 <1 0.0 nc Mixed

KERN-08 19.5 <1 90.7 nc Pre-Modern

KERN-09 16.8 4.0 0.0 nc Modern

KERN-10 na <1 73.8 nc Pre-Modern

KERN-11 21.4 <1 0.0 nc Mixed

KERN-12 19.4 <1 36.5 nc Pre-Modern

KERN-13 17.4 <1 0.0 nc Mixed

KERN-14 15.3 <1 75.9 nc Pre-Modern

KERN-15 16.7 2.9 0.0 nc Modern

KERN-16 19.3 3.7 22.9 nc Mixed

KERN-17 17.9 6.5 0.0 nc Modern

KERN-18 15.5 7.1 0.0 nc Modern

KERN-19 na 1.0 0.0 nc Modern

KERN-20 nc nc nc nc nc

KERN-21 17.2 <1 0.0 nc Mixed

KERN-22 16.7 <1 16.0 nc Pre-Modern

KERN-23 21.8 1.5 44.0 nc Mixed

KERN-24 23.5 2.9 0.0 nc Modern

KERN-25 13.9 3.3 11.0 nc Mixed

KERN-26 16.8 1.4 2.7 nc Modern

KERN-27 nc nc nc nc nc

KERN-28 19.8 <1 27.6 nc Pre-Modern

KERN-29 20.3 8.0 0.0 nc Modern

KERN-30 22.0 <1 0.9 nc Mixed

KERN-31 20.6 5.0 0.0 nc Modern

KERN-32 17.2 <1 26.6 nc Pre-Modern

KERN-33 16.5 <1 45.4 nc Pre-Modern

KERN-34 17.0 2.7 0.0 90.3 Modern

KERN-35 19.9 3.4 0.0 104 Modern

KERN-36 17.4 6.7 20.8 62.9 Mixed

KERN-37 55.8 3.4 0.0 90.0 Mixed

KERN-38 22.2 1.0 0.0 71.3 Mixed

KERN-39 19.9 4.7 0.0 98.4 Modern

KERN-40 16.9 <1 38.3 nc Pre-Modern

KERN-41 16.3 <1 66.3 1.75 Pre-Modern

KERN-42 15.7 <1 0.0 83.2 Mixed
142 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table D6. Groundwater age classification information for USGS-grid and 

USGS-understanding wells sampled January through March 2006, Kern County 

Subbasin study unit, California GAMA Priority Basin Project.—Continued

[Samples classified as pre-modern if recharged before 1953. Samples classified as modern if recharged 

after 1953. Samples classified as mixed if sample contains both modern and pre-modern water. 

Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient Monitoring and 

Assessment Program; °C, degrees Celsius; TU, tritium units; KERN, Kern County Subbasin well; FP, 

flow-path well; nc, not collected; na, not available; <, less than]

USGS 

GAMA well 

identification 

number

Noble-gas 

based recharge 

temperature 

(°C)

Tritium 

activity 

(TU)

Terrigenic 

helium, 

(percent 

of total 

helium)

Modern 

carbon-14

(percent)

Groundwater age 

classification

Grid wells—Continued

KERN-43 19.5 <1 48.9 nc Pre-Modern

KERN-44 19.6 1.5 0.0 89.4 Mixed

KERN-45 19.6 <1 18.6 64.0 Pre-Modern

KERN-46 18.0 <1 1.7 nc Mixed

KERN-47 nc nc nc nc nc

Understanding wells

KERNFP-01 17.9 7.7 0.0 nc Modern

KERNFP-02 17.4 9.2 0.0 90.5 Modern

KERNFP-03 17.4 4.8 0.0 nc Modern
Appendix D 143

Table D7. Oxidation-reduction classification and pH for USGS-grid and USGS-understanding wells, Southeast San Joaquin Valley 

study unit, California GAMA Priority Basin Project.

[Redox category and redox process determined using the algorithm of McMahon and Chapelle (2008) implemented by Jurgens and others (2009b) except 

for samples with incomplete redox data, which were excluded from the analysis. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater 

Ambient Monitoring and Assessment Program; mg/L, milligram per liter; μg/L, microgram per liter; redox, oxidation-reduction; KING, Kings study area 

well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, Tule study area well; FP, flow-path well; HWY99T, transect well; nc, not 

collected; –, not detected; <, less than; ≥, greater than or equal to; anoxic-NO3, nitrate reducing; oxic, dissolved oxygen ≥ 0.5; suboxic, dissolved oxygen 

< 0.5; anoxic-Mn, manganese reducing; anoxic-Fe, iron reducing; O2, oxygen; Fe(III), iron oxide; SO4, sulfate; Mn(IV), manganese oxide]

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Redox category Redox process Dissolved 

oxygen 

(mg/L)

Nitrate plus 

nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

Grid wells

KING-01 nc 0.1 – nc nc nc O2 < 0.5 mg/L Unknown

KING-02 nc 0.2 – nc nc nc O2 < 0.5 mg/L Unknown

KING-03 8.7 0.1 – – 180 19.0 Anoxic Fe(III)

KING-04 8.2 2 2.53 0.3 5 22.4 Oxic O2

KING-05 nc 0.1 – nc nc nc O2 < 0.5 mg/L Unknown

KING-06 nc 0.1 nc nc nc nc O2 < 0.5 mg/L Unknown

KING-07 8.1 1.1 0.68 – – 14.0 Oxic O2

KING-08 7.9 4.4 0.90 – – 5.0 Oxic O2

KING-09 8.2 4.1 1.79 0.1 – 8.5 Oxic O2

KING-10 7.8 5.3 1.69 – – 8.8 Oxic O2

KING-11 7.9 5.7 4.09 1.5 7 7.6 Oxic O2

KING-12 7.9 7.9 8.24 0.1 – 34.6 Oxic O2

KING-13 8 6.9 3.21 0.1 – 5.3 Oxic O2

KING-14 8.7 3.2 2.03 – – 4.0 Oxic O2

KING-15 7.9 6.5 6.95 0.2 – 7.5 Oxic O2

KING-16 7.7 8.7 2.09 0.2 – 4.1 Oxic O2

KING-17 7.9 4.7 4.80 0.1 – 4.3 Oxic O2

KING-18 7.2 6.6 0.84 – – 4.8 Oxic O2

KING-19 7.7 4.3 3.57 – – 10.3 Oxic O2

KING-20 7.4 4.5 1.01 – 3 7.9 Oxic O2

KING-21 nc 0.3 – nc nc nc O2 < 0.5 mg/L Unknown

KING-22 8.0 6.6 0.90 – – 0.0 Oxic O2

KING-23 7.9 9.8 3.39 – – 14.0 Oxic O2

KING-24 7.6 5.9 5.33 – – 27.0 Oxic O2

KING-25 7.5 3.1 5.14 0.3 4 13.3 Oxic O2

KING-26 nc 0.3 nc nc nc nc O2 < 0.5 mg/L Unknown

KING-27 nc 6.1 3.25 nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-28 nc 2.2 0.56 nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-29 7.0 3.10 1.49 – – 4.5 Oxic O2

KING-30 7.1 5.1 2.75 – – 7.0 Oxic O2

KING-31 nc 1.3 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-32 nc 1.0 – nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-33 nc 1.9 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-34 nc 4.6 5.35 nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-35 nc 6.7 3.68 nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-36 8.1 4.2 1.58 nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-37 nc 7.0 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KING-38 7.9 3.2 2.45 0.2 6 6.1 Oxic O2

KING-39 nc 0.1 nc nc nc nc O2 < 0.5 mg/L Unknown

KWH-01 8.0 3.1 4.47 – – 17.0 Oxic O2

KWH-02 7.8 6.3 1.36 – – 5.0 Oxic O2

KWH-03 8.1 6.7 2.65 – 4 13.1 Oxic O2

KWH-04 8.0 3.7 1.81 – – 9.0 Oxic O2

KWH-05 nc 7.0 21.69 nc nc nc O2 ≥ 0.5 mg/L Unknown
144 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table D7. Oxidation-reduction classification and pH for USGS-grid and USGS-understanding wells, Southeast San Joaquin Valley 

study unit, California GAMA Priority Basin Project.—Continued

[Redox category and redox process determined using the algorithm of McMahon and Chapelle (2008) implemented by Jurgens and others (2009b) except 

for samples with incomplete redox data, which were excluded from the analysis. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater 

Ambient Monitoring and Assessment Program; mg/L, milligram per liter; μg/L, microgram per liter; redox, oxidation-reduction; KING, Kings study area 

well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, Tule study area well; FP, flow-path well; HWY99T, transect well; nc, not 

collected; –, not detected; <, less than; ≥, greater than or equal to; anoxic-NO3, nitrate reducing; oxic, dissolved oxygen ≥ 0.5; suboxic, dissolved oxygen 

< 0.5; anoxic-Mn, manganese reducing; anoxic-Fe, iron reducing; O2, oxygen; Fe(III), iron oxide; SO4, sulfate; Mn(IV), manganese oxide]

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Redox category Redox process Dissolved 

oxygen 

(mg/L)

Nitrate plus 

nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

Grid wells—Continued

KWH-06 8.1 8.8 9.13 0.5 22 28.3 Oxic O2

KWH-07 nc 2.1 10.84 nc nc nc O2 ≥ 0.5 mg/L Unknown

KWH-08 nc 7.9 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KWH-09 nc 8.3 5.65 nc nc nc O2 ≥ 0.5 mg/L Unknown

KWH-10 8.2 11 14.46 – – 37.0 Oxic O2

KWH-11 9.0 0.4 – 2.3 15 6.7 Suboxic Suboxic

KWH-12 8.5 5.8 3.26 0.4 9 12.9 Oxic O2

KWH-13 nc 3.9 7.12 nc nc nc O2 ≥ 0.5 mg/L Unknown

KWH-14 7.9 7.9 2.71 – – 10.5 Oxic O2

KWH-15 nc 4.1 – nc nc nc O2 ≥ 0.5 mg/L Unknown

KWH-16 nc 9.5 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KWH-17 nc 5.2 13.10 nc nc nc O2 ≥ 0.5 mg/L Unknown

KWH-18 nc 7.8 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

TLR-01 8.2 10.9 2.71 – 120 69.0 Mixed (oxic-anoxic) O2-Fe(III)/SO4

TLR-02 8.8 1.0 – 2.3 20 0.3 Oxic O2

TLR-03 8.4 0.1 – – 45.4 15.7 Suboxic Suboxic

TLR-04 8.8 0.1 – 16.9 6 0.2 Suboxic Suboxic

TLR-05 9.4 0.2 – 1 26 1.2 Suboxic Suboxic

TLR-06 8.3 0.9 – 66.2 181 282.0 Mixed (oxic-anoxic) O2-Fe(III)/SO4

TLR-07 7.8 0.2 – 28 – 228.0 Suboxic Suboxic

TLR-08 8.6 1.8 – 6.6 5 13.8 Oxic O2

TLR-09 nc 0.1 nc nc nc nc O2 < 0.5 mg/L Unknown

TULE-01 8.8 4.0 3.37 – 6 18.3 Oxic O2

TULE-02 7.9 7.3 5.42 – – 23.0 Oxic O2

TULE-03 8.6 1.5 2.30 0.1 4 13.5 Oxic O2

TULE-04 8.2 0.6 5.74 20 – 70.0 Oxic O2

TULE-05 nc 0.4 1.13 nc nc nc O2 < 0.5 mg/L Unknown

TULE-06 nc 0.9 nc nc nc nc Indeterminate

TULE-07 8.3 5.5 2.34 – – 12.0 Oxic O2

TULE-08 9.8 1.1 0.43 0.2 6 8.5 Oxic O2

TULE-09 7.3 1.0 1.15 – – 8.0 Oxic O2

TULE-10 nc 7.5 6.78 nc nc nc O2 ≥ 0.5 mg/L Unknown

TULE-11 nc 0.3 nc nc nc nc O2 < 0.5 mg/L Unknown

TULE-12 nc 0.1 nc nc nc nc O2 < 0.5 mg/L Unknown

TULE-13 nc 2.2 3.16 nc nc nc O2 ≥ 0.5 mg/L Unknown

TULE-14 7.6 8.6 7.68 – – 48.0 Oxic O2

TULE-15 nc 0.4 nc nc nc nc O2 < 0.5 mg/L Unknown

TULE-16 nc 6.9 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

TULE-17 8.0 6.8 1.75 – – 13.2 Oxic O2

Understanding wells

KINGFP-01 7.4 6.2 12.1 – 9 64.2 Oxic O2

KINGFP-02 8.0 3.5 2.82 – 5 3.6 Oxic O2

KINGFP-03 8.1 3.5 3.54 – 6 3.6 Oxic O2
Appendix D 145

Table D7. Oxidation-reduction classification and pH for USGS-grid and USGS-understanding wells, Southeast San Joaquin Valley 

study unit, California GAMA Priority Basin Project.—Continued

[Redox category and redox process determined using the algorithm of McMahon and Chapelle (2008) implemented by Jurgens and others (2009b) except 

for samples with incomplete redox data, which were excluded from the analysis. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater 

Ambient Monitoring and Assessment Program; mg/L, milligram per liter; μg/L, microgram per liter; redox, oxidation-reduction; KING, Kings study area 

well; KWH, Kaweah study area well; TLR, Tulare Lake study area well; TULE, Tule study area well; FP, flow-path well; HWY99T, transect well; nc, not 

collected; –, not detected; <, less than; ≥, greater than or equal to; anoxic-NO3, nitrate reducing; oxic, dissolved oxygen ≥ 0.5; suboxic, dissolved oxygen 

< 0.5; anoxic-Mn, manganese reducing; anoxic-Fe, iron reducing; O2, oxygen; Fe(III), iron oxide; SO4, sulfate; Mn(IV), manganese oxide]

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Redox category Redox process Dissolved 

oxygen 

(mg/L)

Nitrate plus 

nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

Understanding wells—Continued

KINGFP-04 7.5 6.5 6.41 0.1 – 23.5 Oxic O2

KINGFP-05 7.1 4.5 7.17 0.5 11 64.8 Oxic O2

KINGFP-06 7.7 5.7 2.38 0.1 5 5.9 Oxic O2

KINGFP-07 6.6 6.1 25.8 0.2 <6 112.0 Oxic O2

KINGFP-08 7.2 8.3 12.8 28.4 4 98.5 Oxic O2

KINGFP-09 7.4 9.1 2.80 0.4 7 11.1 Oxic O2

KINGFP-10 7.6 5.3 2.44 – – 13.1 Oxic O2

KINGFP-11 7.5 6.4 10.3 0.8 5 124.0 Oxic O2

KINGFP-12 7.2 6.1 34.3 0.2 – 360.0 Oxic O2

KINGFP-13 7.5 4.9 10.3 – – 27.0 Oxic O2

KINGFP-14 7.3 0.1 – 2,910 50 14.4 Anoxic Mn(IV)

KINGFP-15 7.3 0.1 – 651 168 9.2 Anoxic SO4

HWY99T-01 7.8 6.8 1.76 – – 8.0 Oxic O2
146 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

Table D8. Oxidation-reduction classification and pH for USGS-grid and USGS-understanding wells, Kern County Subbasin study 

unit, California GAMA Priority Basin Project.

[Redox category and redox process determined using the algorithm of McMahon and Chapelle (2008) implemented by Jurgens and others (2009b) except 

for samples with incomplete redox data, which were excluded from the analysis. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater 

Ambient Monitoring and Assessment Program; mg/L, milligram per liter; μg/L, microgram per liter; redox, oxidation-reduction; KERN, Kern County 

Subbasin well; FP, flow-path well; nc, not collected; <, less than; –, not detected; ≥, greater than or equal to; oxic, dissolved oxygen ≥ 0.5; suboxic, 

dissolved oxygen < 0.5; anoxic-NO3, nitrate reducing; anoxic-Mn, manganese reducing; anoxic-Fe, iron reducing; O2, oxygen; Fe(III), iron oxide; SO4, 

sulfate; NO3, nitrate; Mn(IV), manganese oxide]

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Redox category Redox process Dissolved 

oxygen 

(mg/L)

Nitrate plus 

nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

Grid wells

KERN-01 nc 7.5 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-02 nc 3.9 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-03 8.2 1.9 6.10 – – 100 Oxic O2

KERN-04 8.1 1.1 1.04 – – 20 Oxic O2

KERN-05 nc 1.8 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-06 nc 3.9 4.29 – – O2 ≥ 0.5 mg/L Unknown

KERN-07 8.2 1.4 8.81 – – 63 Oxic O2

KERN-08 nc <0.2 – nc nc nc Suboxic Unknown

KERN-09 6.8 2.1 1.94 – – nc O2 ≥ 0.5 mg/L Unknown

KERN-10 8.13 <0.2 <0.10 12 230 554 Anoxic Fe(III)/SO4

KERN-11 nc 5.6 4.29 nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-12 nc 1.8 1.54 nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-13 8.6 7.7 3.16 – – 62 Oxic O2

KERN-14 8.3 <0.2 – 58 1,800 2.3 Anoxic Fe(III)

KERN-15 7.8 1.7 2.60 10 100 26 Mixed (oxic-anoxic) O2-SO4

KERN-16 nc 0.3 nc nc nc nc Suboxic Unknown

KERN-17 8.0 5.4 0.38 – – 22 Oxic O2

KERN-18 8.2 6.6 0.69 0.19 18.8 11.4 Oxic O2

KERN-19 9.3 <0.2 0.01 – – 64 Suboxic Suboxic

KERN-20 nc 6.4 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-21 nc 5.7 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-22 8.5 1.1 3.73 – – 91 Oxic O2

KERN-23 nc 0.5 – nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-24 nc 3.6 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-25 nc 1.9 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-26 7.7 0.8 – – – 44 Mixed (oxic-anoxic) O2-Fe(III)-SO4

KERN-27 nc 6.9 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-28 nc 0.2 nc nc nc nc Suboxic Unknown

KERN-29 nc 5.0 – nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-30 nc 8.4 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-31 nc 4.9 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-32 8.1 <0.2 – 22 60 426 Suboxic Suboxic

KERN-33 nc 1.1 nc nc nc nc O2 ≥ 0.5 mg/L Unknown

KERN-34 8.0 7.8 1.92 – – 34 Oxic O2

KERN-35 8.1 5.8 1.04 – – 23.7 Oxic O2

KERN-36 8.5 3.0 1.12 0.2 – 38.8 Oxic O2

KERN-37 7.7 2.4 0.36 2.6 8 28.3 Oxic O2

KERN-38 7.7 4.0 13.4 0.7 – 131 Oxic O2

KERN-39 7.9 7.8 12.4 0.3 – 120 Oxic O2

KERN-40 7.8 0.2 2.78 6.3 22 779 Anoxic NO3

KERN-41 9.6 0.5 3.38 0.5 13 39.7 Oxic O2

KERN-42 8.4 1.5 0.55 0.5 – 12.1 Oxic O2

KERN-43 7.7 <0.2 0.54 1.2 – 43.4 Anoxic NO3

KERN-44 8.0 9.0 8.50 – – 30.3 Oxic O2
Appendix D 147

Table D8. Oxidation-reduction classification and pH for USGS-grid and USGS-understanding wells, Kern County Subbasin study 

unit, California GAMA Priority Basin Project.—Continued

[Redox category and redox process determined using the algorithm of McMahon and Chapelle (2008) implemented by Jurgens and others (2009b) except 

for samples with incomplete redox data, which were excluded from the analysis. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater 

Ambient Monitoring and Assessment Program; mg/L, milligram per liter; μg/L, microgram per liter; redox, oxidation-reduction; KERN, Kern County 

Subbasin well; FP, flow-path well; nc, not collected; <, less than; –, not detected; ≥, greater than or equal to; oxic, dissolved oxygen ≥ 0.5; suboxic, 

dissolved oxygen < 0.5; anoxic-NO3, nitrate reducing; anoxic-Mn, manganese reducing; anoxic-Fe, iron reducing; O2, oxygen; Fe(III), iron oxide; SO4, 

sulfate; NO3, nitrate; Mn(IV), manganese oxide]

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Redox category Redox process Dissolved 

oxygen 

(mg/L)

Nitrate plus 

nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

Grid wells—Continued

KERN-45 9.1 3.0 2.04 – 13 95.5 Oxic O2

KERN-46 7.6 <0.2 7.45 69.3 – 579 Mixed (anoxic) NO3-Mn(IV)

KERN-47 8.7 5.4 4.48 0.4 10 161 Oxic O2

Understanding wells

KERNFP-01 8.0 <0.2 – 80.3 31 147 Anoxic Mn(IV)

KERNFP-02 7.9 <0.2 0.07 1.3 7 21.1 Suboxic Suboxic

KERNFP-03 7.1 4.0 0.46 6.9 – 20.3 Oxic O2
148 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Dissolved 

oxygen 

(mg/L)

Nitrate 

plus nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

TUL901 6.9 nc nc 1.72 87.5 nc

TUL902 6.5 nc 3.4 1.86 42.4 15

TUL903 6.5 nc – 10.2 56.4 6.3

TUL904 7.1 nc 21 2.94 115 120

TUL905 6.8 nc 1.6 5.24 – 13

TUL906 7.0 nc 3.6 – 27.1 33

TUL907 6.8 nc 14 1.81 – 69

TUL908 6.8 nc – 2.91 107 2.5

TUL909 6.3 nc 5.2 2.65 46.8 11

TUL910 6.8 nc 6.8 25.8 – 150

TUL911 6.9 nc 0.61 5.14 168 5.7

TUL912 6.6 nc 9.5 – – 77

TUL913 6.3 nc 0.75 – – 3.5

TUL914 7.0 nc 7 – – 67

TUL915 6.3 nc 4.7 1.79 – 6.4

TUL916 6.7 nc 7 – – 31

TUL917 6.6 nc 8.4 – – 50

TUL922 7.1 nc 14 – – 71

TUL923 6.6 nc 1.4 1.33 – 9.4

TUL924 6.8 nc 9 – 23.7 49

TUL925 6.9 nc 4.7 10.4 – 100

TUL926 6.9 nc – 1.26 – 14

TUL928 7.6 nc 1.6 1.8 83.9 35

TUL929 6.8 nc 25 – – 50

TUL930 7.2 nc 21 4.95 68.6 47

TUL931 7.2 nc 13 1.84 – 60

TUL932 7.1 nc 25 2.6 153 120

TUL933 7.2 nc 9.8 3.51 235 79

TUL934 7.6 nc 18 – 219 48

TUL935 7.2 nc 14 0.92 53.4 50

TUL936 7.4 nc 1.5 – – 3.3

TUL937 8.4 nc 7 – – 10

TUL938 8.4 nc 4.3 – – 14

TUL939 7.6 nc 5.2 – – 15

TUL940 7.1 nc 0.15 – – 3.5

TUL941 7.4 nc 19 4.68 – 14

TUL942 7.0 nc 9.2 – – 23

TUL943 6.9 nc 6.3 – – 23

TUL944 7.3 nc 11 – – 19

TUL945 7.5 nc 16 – – 43

TUL946 7.1 nc 8.3 – – 14

TUL947 7.1 nc 4.3 – 68.6 39

TUL948 7.9 nc 1.3 – – 14

TUL949 7.3 nc 8 – – 66

TUL950 7.7 nc 7.3 – 81 32

TUL951 7.5 nc 3.2 – – 44

Table D9. Oxidation-reduction classification and pH for domestic wells sampled in Tulare County and located in the Southeast San 

Joaquin Valley study unit, California GAMA Domestic Well Project.

[Redox classification was not determined because of incomplete data. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient 

Monitoring and Assessment Program; mg/L, milligram per liter; μg/L, microgram per liter; TUL, Tulare County well; nc, not collected; –, not detected]

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Dissolved 

oxygen 

(mg/L)

Nitrate 

plus nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

TUL952 7.1 nc 2.8 – – 18

TUL955 7.5 nc 9.9 9.28 – 41

TUL958 7.0 nc 50 2.49 – 160

TUL959 7.0 nc 11 7.33 – 80

TUL960 7.4 nc 9.2 0.57 – 40

TUL961 7.3 nc 12 1.66 – 34

TUL967 7.3 nc 8.1 3.13 – 32

TUL968 6.5 nc 4.8 1.36 – 18

TUL969 7.0 nc 6.8 172 – 60

TUL970 7.1 nc 16 – 21.1 49

TUL971 7.0 nc 13 2.15 39 33

TUL972 7.3 nc 6.5 1.38 – 28

TUL973 7.2 nc 44 3.43 – 93

TUL974 6.7 nc 21 7.95 162 160

TUL976 7.3 nc 3 1.87 – 9.3

TUL978 7.1 nc 21.9 32.8 – 81

TUL979 7.4 nc 54 0.71 – 52

TUL986 7.6 nc 5.1 0.83 35.3 16

TUL987 7.5 nc 2.7 1.82 – 28

TUL988 7.2 nc 18.3 2.7 – 104

TUL990 7.4 nc 6.3 2.39 – 48

TUL991 7.9 nc 8.4 8.96 189 17

TUL992 7.2 nc 12 1.85 – 28

TUL996 7.6 nc 5.4 1.15 – 16

TUL997 7.4 nc 27 2.78 – 46

TUL999 7.2 nc 7.5 2.89 – 43

TUL1000 7.5 nc 8.4 3.33 – 16

TUL1003 7.7 nc 2.5 1.37 – 5.5

TUL1005 7.8 nc 9.26 1.39 – 31.6

TUL1006 7.4 nc 7.81 2.91 – 19.9

TUL1007 7.5 nc 6.05 1.76 – 18.9

TUL1008 6.9 nc 21.8 0.7 – 122

TUL1010 7.5 nc 0.11 0.72 – 5.7

TUL1011 7.4 nc 14 7.44 650 84

TUL1012 8.1 nc 3.8 0.75 – 16

TUL1013 6.9 nc 41 1.52 – 53

TUL1014 7.1 nc 32 0.88 – 70

TUL1015 6.9 nc 35 2.03 – 110

TUL1016 7.2 nc 11 2.75 – 180

TUL1017 7.5 nc 11 1.38 – 88

TUL1020 7.1 nc 31 0.55 – 42

TUL1021 6.9 nc 31 1.43 – 78

TUL1022 7.4 nc – 30.1 – 110

TUL1025 7.0 nc 28 2.52 – 220

TUL1026 7.0 nc 17 1.04 – 88

TUL1027 7.7 nc 0.42 0.87 – 2.5
Appendix D 149

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Dissolved 

oxygen 

(mg/L)

Nitrate 

plus nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

TUL1072 6.7 nc 4.79 2.78 – 42.3

TUL1073 7.2 nc 12.1 3.11 – 44.7

TUL1074 7.1 nc 11 2.24 – 62.9

TUL1075 7.1 nc 10.1 0.55 – 62.2

TUL1076 6.9 nc 10.9 0.75 – 96.4

TUL1080 7.5 nc 10.1 0.63 – 33.9

TUL1081 7.0 nc 50.8 0.54 – 45.6

TUL1082 7.7 nc 3.86 0.99 – 6.5

TUL1083 7.3 nc 5.33 2.28 – 20

TUL1084 7.3 nc 3.48 16.4 – 44

TUL1085 6.7 nc 31.8 1.3 – 58.6

TUL1088 7.0 nc 4.22 0.82 – 30.3

TUL1089 7.1 nc 22.4 18.3 50.7 87.7

TUL1091 7.0 nc 11 0.88 – 34

TUL1092 7.2 nc 15.7 1.19 – 77.6

TUL1093 7.4 nc 10.5 8.32 – 24

TUL1094 6.9 nc 2.05 – – ND

TUL1098 7.9 nc 0.56 2.88 – 5.2

TUL1101 7.3 nc 43.8 0.11 – 28.4

TUL1103 7.6 nc 2.51 19.7 – 7.1

TUL1105 7.3 nc 11.1 0.33 – 78.6

TUL1106 8.1 nc 2.01 9.43 – 6.1

TUL1107 7.2 nc 18.7 0.13 – 64.6

TUL1111 7.0 nc 50.4 13.4 – 84.3

TUL1505 7.3 nc 20.5 2.9 – 54.5

Table D9. Oxidation-reduction classification and pH for domestic wells sampled in Tulare County and located in the Southeast San 

Joaquin Valley study unit, California GAMA Domestic Well Project.—Continued

[Redox classification was not determined because of incomplete data. Abbreviations: USGS, U.S. Geological Survey; GAMA, Groundwater Ambient 

Monitoring and Assessment Program; mg/L, milligram per liter; μg/L, microgram per liter; TUL, Tulare County well; nc, not collected; –, not detected]

USGS 

GAMA well 

identification 

number

pH

Oxidizing and reducing constituents

Dissolved 

oxygen 

(mg/L)

Nitrate 

plus nitrite

(mg/L)

Manganese

(μg/L)

Iron

(μg/L)

Sulfate

(mg/L)

TUL1028 7.4 nc 7.4 1.68 – 18

TUL1029 7.4 nc 4.9 4.39 125 18

TUL1031 7.7 nc – 2.64 – 3.8

TUL1032 7.1 nc 16 2.99 49.9 64

TUL1033 7.6 nc 7.2 1.13 20.1 30

TUL1034 7.1 nc 0.34 3.52 – 7.5

TUL1035 7.8 nc 6.64 1.62 85.4 55.6

TUL1036 6.6 nc 0.66 0.87 – 4.7

TUL1041 7.2 nc 14 0.86 – 35

TUL1042 6.8 nc 0.43 3.56 42.9 3.9

TUL1043 7.1 nc 9.64 2.34 – 55.4

TUL1044 7.4 nc 2.21 2.15 – 13.5

TUL1054 8.1 nc 4.97 2.2 30.5 29.2

TUL1055 7.4 nc 0.72 2.06 – 3.5

TUL1056 6.8 nc 1.2 15.1 50.8 ND

TUL1057 6.8 nc 21.5 1.22 – 64.9

TUL1058 6.8 nc 28.9 11.6 100 36.7

TUL1059 7.2 nc 20.4 0.88 – 26.9

TUL1060 7.1 nc 4.97 3.69 – 50.8

TUL1061 6.7 nc 10 1.34 – 99.5

TUL1062 7.2 nc 20.7 0.56 101 35.3

TUL1064 7.6 nc 1.49 0.54 – 5.8

TUL1065 6.8 nc 13.1 19.2 – 37.8

TUL1066 7.5 nc 0.56 11.5 57.2 2.5

TUL1070 7.0 nc 24.6 2.36 – 64.8

TUL1071 6.6 nc 6.82 8.1 – 47.2
150 Status and Understanding of Groundwater Quality, Two Southern San Joaquin Valley Study Units, 2005–2006: California GAMA Priority Basin Project

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Burton and others—Status and Understanding of Groundwater Quality in the Two Southern San Joaquin Valley Study Units, 2005–2006:

California GAMA Priority Basin Project—SIR 2011–5218