U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2015–5150

Prepared in cooperation with the Borrego Water District

Hydrogeology, Hydrologic Effects of Development, and 

Simulation of Groundwater Flow in the Borrego Valley, 

San Diego County, California

Basement

Lower aquifer

Middle aquifer

Upper aquifer

2010

Current usage trend—2060

1945

Optimized usage trend—2060

Recreational

pumping

Municipal

pumping

Agricultural

pumping

1966: Grape irrigation ends

1978: Citrus expands

1945

2010

Cumulative storage change
Cover.

Background—Cross section showing the simulated groundwater level tables for 1945 and 2010 and for management scenarios 

projected for 2060, Borrego Valley Hydrologic Model, Borrego Valley, California. 

Foreground—Simulated annual groundwater pumpage and climatic patterns from the Borrego Valley Hydrologic Model, Borrego Valley, 

California, 1945–2010, by water use.
Hydrogeology, Hydrologic Effects 

of Development, and Simulation of 

Groundwater Flow in the Borrego Valley, 

San Diego County, California

By Claudia C. Faunt, Christina L. Stamos, Lorraine E. Flint, Michael T. Wright, 

Matthew K. Burgess, Michelle Sneed, Justin Brandt, Peter Martin, and 

Alissa L. Coes

Prepared in cooperation with the Borrego Water District

Scientific Investigations Report 2015–5150

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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

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U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

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

Suggested citation:

Faunt, C.C., Stamos, C.L., Flint, L.E., Wright, M.T., Burgess, M.K., Sneed, Michelle, Brandt, Justin, Martin, Peter, 

and Coes, A.L., 2015, Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the 

Borrego Valley, San Diego County, California: U.S. Geological Survey Scientific Investigations Report 2015–5150, 

135 p., http://dx.doi.org/10.3133/sir20155150.

ISSN 2328-0328 (online)
iii

This project could not have been completed without the help of many individuals and organizations. 

First, the authors acknowledge the Borrego Water District for their support of this study. The work would 

not have been possible without the data, technical input, and collaboration provided by the Borrego 

Water District. In particular, Jerry Rolwing provided invaluable assistance. Lyle Brecht, Board Member, 

Borrego Water/Sewer District, supplied important insight. We are grateful to our U.S. Geological Survey 

colleagues Larry Schneider, illustrator; Steve Predmore, geographic information system specialist; and 

the technical reviewers. Finally, a debt of gratitude is owed to the authors of the previous studies done in 

Borrego Valley.

Acknowledgments
iv

Contents

Executive Summary.......................................................................................................................................1

Introduction.....................................................................................................................................................4

Purpose and Scope ..............................................................................................................................7

Approach................................................................................................................................................7

Accessing Data.....................................................................................................................................7

Description of Study Area ............................................................................................................................8

Previous Studies ............................................................................................................................................8

Hydrologic System.........................................................................................................................................9

Climate....................................................................................................................................................9

Potential Evapotranspiration ..............................................................................................................9

Climatic Trends....................................................................................................................................13

Land Use...............................................................................................................................................13

Hydrogeology................................................................................................................................................26

Geologic Structures............................................................................................................................26

Configuration of Basin .......................................................................................................................26

Geologic Units .....................................................................................................................................26

Aquifers ................................................................................................................................................31

Three-Dimensional Hydrogeologic Framework Model ................................................................36

Selection and Compilation of Existing Well Data .................................................................36

Adjustment of Aquifer Surfaces..............................................................................................36

Texture Model.............................................................................................................................37

Classification of Texture from Drillers’ Logs and Regularization of Well Data.......38

Geostatistical Model of Coarse-Grained Texture........................................................38

Groundwater-Flow and Groundwater-Level Change....................................................................43

Groundwater Recharge .....................................................................................................................43

Sources of Recharge ................................................................................................................43

Transient Estimates of Natural Recharge from the Basin Characterization Model .......48

Groundwater Discharge ....................................................................................................................51

Evapotranspiration ....................................................................................................................55

Underflow Out of the Basin......................................................................................................55

Groundwater Pumping..............................................................................................................55

Agricultural Water Use ....................................................................................................56

Recreational Water Use ..................................................................................................56

Municipal Water Use .......................................................................................................56

Groundwater-Quality Sampling and Wellbore Flow...............................................................................60

Wellbore Flow and Depth-Dependent Water-Quality Sampling .................................................61

Sources of Water-Quality Data.........................................................................................................61

Groundwater Quality and Age ...................................................................................................................63

Changes in Groundwater Quality Compared to Changes in Groundwater Levels...................63

Distribution and Variation of Groundwater Quality .......................................................................65

Distribution of Nitrates and Total Dissolved Solids..............................................................65

Variations in Water Quality with Depth..................................................................................65

Groundwater Age................................................................................................................................68
v

Land Subsidence..........................................................................................................................................70

Global Positioning System ................................................................................................................70

Ellipsoid Heights and Elevations .............................................................................................70

Land Subsidence at Geodetic Monuments ...........................................................................73

GPS Derived Elevations ............................................................................................................73

Interferometric Synthetic Aperture Radar ....................................................................................73

Groundwater-Flow Models ........................................................................................................................77

Wellbore-Groundwater-Flow Model................................................................................................77

Integrated Hydrologic Model............................................................................................................77

Discretization and Boundaries ................................................................................................79

Spatial Discretization .......................................................................................................79

Temporal Discretization...................................................................................................79

Initial Conditions ........................................................................................................................82

Aquifer Type................................................................................................................................82

Aquifer Characteristics.............................................................................................................83

Textural Analysis...............................................................................................................83

Calculation of Hydraulic Properties...............................................................................83

Hydraulic Conductivity of Lithologic End Members....................................................85

Storage Properties ...........................................................................................................86

Unsaturated Hydraulic Properties .................................................................................86

Recharge.....................................................................................................................................88

Water-Balance Subregions .....................................................................................................90

Landscape Water Use...............................................................................................................90

Delivery Requirement.......................................................................................................92

Soils ................................................................................................................................92

Land Use.............................................................................................................................92

Discharge....................................................................................................................................92

Natural Discharge ............................................................................................................92

Groundwater Pumpage....................................................................................................92

Agricultural Pumpage ............................................................................................97

Recreational Pumpage ...........................................................................................97

Municipal Pumpage ................................................................................................97

Groundwater Inflows and Outflows........................................................................................97

Specified (No Flow) Flow Boundaries...........................................................................97

Specified Flow Boundaries .............................................................................................99

Specified (Constant) Head Boundary............................................................................99

Model Calibration................................................................................................................................99

Parameter Data..........................................................................................................................99

Observation Data .....................................................................................................................103

Regularization...........................................................................................................................106

Pumpage Observations..................................................................................................107

Groundwater-Level Maps..............................................................................................108

Calibration Procedure.............................................................................................................108

Contents—Continued
vi

Farm Process Parameters.............................................................................................108

Hydraulic Parameters ....................................................................................................112

Streamflow Properties...................................................................................................112

Sensitivity Analysis...........................................................................................................................112

Model Uncertainty, Limitations, and Improvements ..................................................................113

Hydrologic Flow Analysis ........................................................................................................................115

Future Groundwater-Management Scenarios......................................................................................122

Scenario 1: Status Quo ....................................................................................................................124

Scenarios 2–4: Low, Medium, and High Municipal Growth Over 50 Years .............................124

Scenario 5: Water-Usage Reduction to Avoid Future Groundwater Storage 

Depletion Over 50 Years .....................................................................................................124

Scenario 6: Management Scenario for Rapid Changes Over 20 Years ...................................124

Summary and Conclusions.......................................................................................................................127

References Cited........................................................................................................................................130

Contents—Continued

Figures

1. Map showing location of the Borrego Valley, California .......................................................5

2. Graphs showing water levels in selected wells in the Borrego Valley,

California, 1945–2010....................................................................................................................6

3. Graphs showing A, average annual precipitation and B, cumulative departure

of precipitation for Borrego Valley, California .......................................................................10

4. Maps showing estimated average annual for Borrego Valley, California,

1945–2010, of A, precipitation and B, potential evapotranspiration...................................11

5. Graphs showing trends in climate for the Borrego Valley, California, 1899–2008, 

including A, estimated minimum and maximum annual mean air temperatures; 

B, annual precipitation; and C, estimated potential evapotranspiration, 

Borrego Valley.............................................................................................................................14

6. Maps showing land use in the Borrego Valley, California, during 

A, pre-development; B, 1953; C, 1954; D, 1959; E, 1968; F, 1979; G, 1980; H, 1992; 

I, 1995; J, 2000; and K, 2009........................................................................................................15

7. Maps showing Borrego Valley, California, showing A, geology; B, hydrogeology; 

and C, generalized hydrogeologic cross sections A-A’ and B-B’.......................................27

8. Map showing gridded depth from land surface to basement rock in the

Borrego Valley, California..........................................................................................................30

9. Map showing location of wells with driller’s and (or) geophysical logs used to

develop the hydrogeologic framework model for the Borrego Valley, California............32

10. Maps showing extent and approximate thickness of aquifers in Borrego Valley,

California, A, upper; B, middle; and C, lower..........................................................................33

11. Generalized cross sections of Borrego Valley, California: A, northwest to

southeast, modified from Moyle (1982), and B, southwest to northeast, 

drawn by using data from geophysical logs collected in 2012 ...........................................37

12. Maps showing percentage of coarse-grained deposits in aquifers of Borrego Valley,

California: A, upper; B, middle; and C, lower..........................................................................40
vii

13. Maps showing water-level elevations and direction of groundwater flow in Borrego 

Valley, California, for A, 1945, approximately predevelopment, and B, 2010 ....................44

14. Graphs showing water-level elevations in selected wells in parts of the

Borrego Valley, California, 1950–2010: A, northern; B, west–northern; 

C, middle; and D, southern ........................................................................................................46

15. Graphs showing discharge of creeks in Borrego Valley, California, 1950–2004:

A, Coyote Creek; B, Borrego Palm Creek; and C, San Felipe Creek ...................................47

16. Map showing drainage basin boundaries and geology used in the Basin

Characterization Model to estimate climate-driven natural recharge in the

Borrego Valley, California..........................................................................................................49

17. Graph showing measured annual streamflow at Borrego Palm Creek

streamgage and simulated annual streamflow from the Basin 

Characterization Model for the Borrego Palm Creek drainage watershed, 

Borrego Valley, California..........................................................................................................50

18. Maps showing spatially distributed values for the Borrego Valley, California,

1971–2000, of potential A, runoff and B, recharge ................................................................52

19. Graphs showing simulated values for the Borrego Valley, California, 1940–2007,

of total annual A, potential runoff into the valley; B, potential recharge in

upstream portions of the watershed (potential underflow); and C, recharge

(underflow and runoff) into the valley .....................................................................................54

20. Graph showing annual and cumulative total pumpage, Borrego Valley,

California, 1945–2000..................................................................................................................55

21. Graph showing annual and cumulative municipal pumpage, Borrego Valley,

California, 1945–2010..................................................................................................................59

22. Graph showing average percentage of reported annual pumpage, by month,

for Borrego Water District wells, Borrego Valley, California, 1997–2010 ..........................59

23. Graph showing pumpage by Rams Hill Development and total pumpage by

Borrego Water District, Borrego Valley, California, 1983–2010...........................................60

24. Well construction for well 10S/6E-35Q1, Borrego Valley, California: 

A, wellbore-flow log and B, concentrations of selected dissolved constituents 

by depth, under pumping conditions .......................................................................................62

25. Graphs showing concentrations of total dissolved solids and sulfate, and

groundwater levels for wells A, 10S/6E-18R1, B, 11S/6E-7K3, and C, 11S/6E-23J1

in the Borrego Valley, California, 1980–2010 ..........................................................................64

26. Map showing distribution of nitrate as nitrogen concentrations in the upper,

middle, and lower aquifers, Borrego Valley, California, for the most recent sample......66

27. Map showing distribution of total dissolved solids concentrations in the upper,

middle, and lower aquifers, Borrego Valley, California........................................................67

28. Map showing distribution of tritium values and uncorrected carbon-14 ages for

wells in the Borrego Valley, California ....................................................................................69

29. Map showing location of geodetic monuments used as Global Positioning

System stations, Borrego Valley, California ...........................................................................71

30. Graph showing vertical position of Continuous Global Positioning System site

P486, 2005–10, in the Borrego Valley, California....................................................................74

31. Map showing line and area of equal subsidence as interpreted from Interferometric 

Synthetic Aperture Radar satellite imagery of the Borrego Valley, California,

November 2003–September 2007.............................................................................................76

Figures—Continued
viii

32. Diagram showing simulated lithology, calibrated hydraulic conductivity

distribution, and simulated and measured flow logs from well 10S/6E-35Q1,

Borrego Valley, California..........................................................................................................78

33. Map showing model grid with active model cells for the upper and lower

aquifers in the Borrego Valley Hydrologic Model, Borrego Valley, California .................81

34. Graph showing model grid with geologic types of depositional zones used for

parameter zones of hydraulic properties for the Borrego Valley Hydrologic

Model, Borrego Valley, California ............................................................................................84

35. Schematic diagram showing magnitudes of specific yield for the upper, middle,

and lower aquifers, Borrego Valley, California......................................................................86

36. Map showing model grid with texture zones used to define the distribution of

specific yield for the Borrego Valley Hydrologic Model, Borrego Valley, California.......87

37. Map showing model-grid stream cells used to simulate streamflow in the

Borrego Valley Hydrologic Model, Borrego Valley, California............................................89

38. Map showing model grid with water balance subregions used to account for

water usage in the Borrego Valley, California .......................................................................91

39. Map showing model grid with two types of soil categories used in the Borrego

Valley Hydrologic Model, Borrego Valley, California............................................................96

40. Graphs showing simulated annual groundwater pumpage and climatic patterns

from the Borrego Valley Hydrologic Model, Borrego Valley, California,

1945–2010, by A, aquifer and B, water use.............................................................................98

41. Map showing location of observation wells used in the calibration of the

Borrego Valley Hydrologic Model, Borrego Valley, California..........................................104

42. Hydrographs showing simulated and measured water-level elevations for the

Borrego Valley Hydrologic Model, Borrego Valley, California, 1945–2010......................105

43. Histogram of distribution of groundwater-level residual (observed minus

simulated) values for the Borrego Valley Hydrologic Model, Borrego Valley,

California ....................................................................................................................................107

44. Graph showing simulated water level-elevations in relation to measured

water-level elevations for the Borrego Valley Hydrologic Model, California,

Borrego Valley, California........................................................................................................107

45. Maps showing simulated water levels for the Borrego Valley Hydrologic

Model, Borrego Valley, California, A, in 1945; B, 2010; and C, simulated

drawdown from 1945 to 2010 ..................................................................................................109

46. Graph showing most sensitive parameters for the Borrego Valley Hydrologic

Model, Borrego Valley, California ..........................................................................................113

47. Graphs showing simulated components of the basic groundwater budget by

using climatic patterns A, with no anthropogenic effects and B, with

anthropogenic effects for the Borrego Valley Hydrologic Model, Borrego

Valley, California, 1945–2010 ...................................................................................................116

48. Graphs showing simulated components of the net groundwater budget from

the Borrego Valley Hydrologic Model, Borrego Valley, California, 1945–2010, by

using climatic patterns A, with no anthropogenic effects and B, with

anthropogenic effects..............................................................................................................117

49. Graph showing precipitation, streamflow, and underflow from adjacent

watersheds and basins for the Borrego Valley, California, 1945–2010............................118

Figures—Continued
ix

50. Graphs showing simulated landscape budget with climatic patterns from the

Borrego Valley Hydrologic Model, Borrego Valley, California, 1945–2010, A, with

no anthropogenic effects and B, with anthropogenic effects ..........................................119

51. Graph showing simulated monthly net groundwater budget, Borrego Valley

Hydrologic Model, Borrego Valley, California, 2010............................................................121

52. Graph showing simulated annual and cumulative changes in groundwater

storage by using climatic patterns with no anthropogenic effects and with

anthropogenic effects, Borrego Valley Hydrologic Model, Borrego Valley,

California, 1945–2010................................................................................................................121

53. Graph showing cumulative change in groundwater storage for six

water-management scenarios simulated by using the Borrego Valley

Hydrologic Model, Borrego Valley, California, 1945–2060 .................................................123

54. Cross section showing the simulated groundwater level tables for 1945 and

2010 and for six management scenarios projected for 2060, Borrego Valley

Hydrologic Model, Borrego Valley, California......................................................................123

55. Map showing simulated drawdown projected for scenario 1, or the status quo

scenario, 2060 minus 2010, Borrego Valley Hydrologic model, Borrego Valley,

California ....................................................................................................................................125

56. Map showing simulated drawdown projected for Scenario 6, 2060 minus 2010,

Borrego Valley Hydrologic Model, Borrego Valley, California..........................................126

Figures—Continued

Tables

1. Mean and standard deviation of estimated annual air temperature,

precipitation, and potential evapotranspiration for three periods during

1899–2008 obtained by using the Parameter-Elevation Regressions on

Independent Slopes Model database for the regional Borrego Valley drainage

basin, California .........................................................................................................................13

2. Description of aquifers, Borrego Valley, California...............................................................36

3. Textures used to describe lithology recorded on drillers’ logs, Borrego Valley,

California ......................................................................................................................................38

4. Input for geostatistical model, Borrego Valley, California...................................................39

5. Estimated and reported total municipal pumpage, Borrego Valley, California,

1945–2010 .....................................................................................................................................57

6. Location of geodetic monuments in the land-subsidence monitoring network

with ellipsoid heights for 2009, and elevations for 1969, 1978, 1995, and 2009,

Borrego Valley, California..........................................................................................................72

7. Interferograms processed from the European Space Agency’s satellites for

Borrego Valley, California..........................................................................................................75

8. Details of radial groundwater-flow model construction for well 10S/6E-35Q1,

Borrego Valley, California..........................................................................................................78

9. Summary of One Water Hydrologic Model packages and processes used in the

integrated hydrologic model of Borrego Valley, California..................................................80

10. Summary of groupings of water-balance subregions into regions for the

Borrego Valley Hydrologic Model, Borrego Valley, California............................................82
x

11. Coordinates of the Borrego Valley Hydrologic Model for Borrego Valley, California.....82

12. Summary of hydraulic properties from previous studies and the radial

groundwater-flow model, Borrego Valley, California ...........................................................85

13. Summary of Borrego Valley land-use types in the Borrego Valley Hydrologic

Model, Borrego Valley, California ............................................................................................93

14. Monthly crop coefficients for each land-use type simulated in the Borrego

Valley Hydrologic Model, Borrego Valley, California............................................................93

15. Summary of fractions of transpiration and evaporation, by month, for Borrego

Valley land-use type, or virtual crops, Borrego Valley, California......................................94

16. Monthly efficiency for each land-use type, or virtual crop, simulated in the

Borrego Valley Hydrologic Model, Borrego Valley, California............................................95

17. Percentage of each land-use type, or virtual crop, used in simulations for 

11 periods of mapped land use, Borrego Valley, California.................................................95

18. Parameter values estimated for the Borrego Valley Hydrologic Model, Borrego

Valley, California .......................................................................................................................100

19. Simplified groundwater budget for pre-development and 2010, Borrego Valley

Hydrologic Model, Borrego Valley, California......................................................................118

20. Groundwater budgets for six management scenarios from the Borrego Valley

Hydrologic Model, Borrego Valley, California, 2011–60 .....................................................122

Tables—Continued
xi

14

C carbon-14

2-D two-dimensional

3-D three-dimensional

asl above sea level

BAR Borrego Air Ranch

BCM Basin Characterization Model

bls below land surface

BSPCSD Borrego Springs Park and Community Service District

BVGB Borrego Valley Groundwater Basin

BVHM Borrego Valley Hydrologic Model

BWD Borrego Water District

CA-DPH California Department of Public Health

CA-DWR California Department of Water Resources

CA-MCL California maximum contaminant level

CA-SMCL California secondary maximum contaminant level

CGPS Continuous Global Positioning System

CIR crop irrigation requirement

EM electromagnetic

ERS Earth Remote Sensing

ET evapotranspiration

ETo evapotranspiration rate

FHB1 Flow Head Boundary

FMP Farm Process

GIS Geographic Information System

GPS global positioning system

InSAR Interferometric Synthetic Aperture Radar K hydraulic conductivity

KC crop coefficients

MF2K5 MODFLOW-2005

MNW2 multi-mode wells

MODIS Moderate-Resolution Imaging Spectroradiometer

NO

3

-N nitrate as nitrogen

NWIS National Water Information System

NWISWeb USGS National Water Information System Web page

OWHM One Water Hydrologic Model

PEST parameter estimation software

PET potential evapotranspiration

PRISM Parameter-Elevation Regressions on Independent Slopes Model

Qya older and younger alluvium

RTK real time kinematic

SAR Synthetic Aperture Radar

SFR Streamflow Routing Package

SGMA Sustainable Groundwater Management Act

SOPAC Scripps Orbit and Permanent Array Center

STATSGO State Soil Geographic Database

TDS total dissolved solids

TFDR total farm delivery requirement

TU tritium units

UPW Upstream Weighting Package

USGS U.S. Geological Survey

UZF unsaturated-zone model

WBS water-balance subregions

ybp years before present

Abbreviations
xii

Inch/Pound to International System of Units

Multiply By To obtain

Length

inch (in.) 25.4 millimeter (mm)

inch (in.) 25,400 micrometer (μm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2) 2.590 square kilometer (km2) 

Volume

ounce, fluid (fl. oz) 29.5735 milliliter (mL)

gallon (gal) 3.785 liter (L) 

acre-foot (acre-ft) 1,233 cubic meter (m3)

Flow rate

acre-foot per year (acre-ft/yr) 1,233 cubic meter per year (m3/yr)

foot per day (ft/d) 0.3048 meter per day (m/d)

foot per year (ft/yr) 0.3048 meter per year (m/yr)

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

gallon per day (gal/d) 0.003785 cubic meter per day (m3/d)

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

cubic foot per month (ft3/mo) 0.0009 cubic meter per day (m3/d)

Specific capacity

gallon per minute per foot [(gal/min)/ft)] 0.2070 liter per second per meter [(L/s)/m]

Hydraulic conductivity

foot per day (ft/d) 0.3048 meter per day (m/d)

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

°F = (1.8 × °C) + 32.

Datum

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).

Elevation, as used in this report, refers to distance above the vertical datum.

Supplemental Information

Transmissivity: The standard unit for transmissivity is cubic foot per day per square foot times 

foot of aquifer thickness [(ft3/d)/ft2]ft. In this report, the mathematically reduced form, foot 

squared per day (ft2/d), is used for convenience.

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 in either milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

Conversion Factors
xiii

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xiv

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Hydrogeology, Hydrologic Effects of Development, and 

Simulation of Groundwater Flow in the Borrego Valley, 

San Diego County, California

By Claudia C. Faunt, Christina L. Stamos, Lorraine E. Flint, Michael T. Wright, Matthew K. Burgess, Michelle 

Sneed, Justin Brandt, Peter Martin, and Alissa L. Coes

Executive Summary

The Borrego Valley is a small valley (110 square miles) 

in the northeastern part of San Diego County, California. 

Although the valley is about 60 miles northeast of city of San 

Diego, it is separated from the Pacific Ocean coast by the 

mountains to the west and is mostly within the boundaries of 

Anza-Borrego Desert State Park. From the time the basin was 

first settled, groundwater has been the only source of water to 

the valley. Groundwater is used for agricultural, recreational, 

and municipal purposes. Over time, groundwater withdrawal 

through pumping has exceeded the amount of water that has 

been replenished, causing groundwater-level declines of more 

than 100 feet in some parts of the basin. Continued pumping 

has resulted in an increase in pumping lifts, reduced well 

efficiency, dry wells, changes in water quality, and loss of 

natural groundwater discharge. As a result, the U.S. Geological 

Survey began a cooperative study of the Borrego Valley with 

the Borrego Water District (BWD) in 2009. The purpose 

of the study was to develop a greater understanding of the 

hydrogeology of the Borrego Valley Groundwater Basin 

(BVGB) and to provide tools to help evaluate the potential 

hydrologic effects of future development. The objectives 

of the study were to (1) improve the understanding of 

groundwater conditions and land subsidence, (2) incorporate 

this improved understanding into a model that would assist in 

the management of the groundwater resources in the Borrego 

Valley, and (3) use this model to test several management 

scenarios. This model provides the capability for the BWD 

and regional stakeholders to quantify the relative benefits of 

various options for increasing groundwater storage. The study 

focuses on the period 1945–2010, with scenarios 50 years into 

the future.

This report documents and presents (1) an analysis of 

the conceptual model, (2) a description of the hydrologic 

features, (3) a compilation and analysis of water-quality 

data, (4) the measurement and analysis of land subsidence 

by using geophysical and remote sensing techniques, (5) the 

development and calibration of a two-dimensional boreholegroundwater-flow model to estimate aquifer hydraulic 

conductivities, (6) the development and calibration of a 

three-dimensional (3-D) integrated hydrologic flow model, 

(7) a water-availability analysis with respect to current 

climate variability and land use, and (8) potential future 

management scenarios. The integrated hydrologic model, 

referred to here as the “Borrego Valley Hydrologic Model” 

(BVHM), is a tool that can provide results with the accuracy 

needed for making water-management decisions, although 

potential future refinements and enhancements could further 

improve the level of spatial and temporal resolution and 

model accuracy. Because the model incorporates time-varying 

inflows and outflows, this tool can be used to evaluate the 

effects of temporal changes in recharge and pumping and to 

compare the relative effects of different water-management 

scenarios on the aquifer system. Overall, the development 

of the hydrogeologic and hydrologic models, data networks, 

and hydrologic analysis provides a basis for assessing surface 

and groundwater availability and potential water-resource 

management guidelines.

The groundwater-flow system consists of three aquifers 

within the BVGB: upper, middle, and lower. The three 

aquifers—which were identified on the basis of the hydrologic 

properties, age, and depth of the unconsolidated deposits—

consist of gravel, sand, silt, and clay alluvial deposits and 

clay and silty-clay lacustrine deposits. Recharge is primarily 

the infiltration of runoff from the surrounding mountains. 

Infiltration of return flows from agricultural irrigation is 

an additional source of recharge to the aquifer system. 

Some underflow from the surrounding tributary basins 

also contributes to recharge of the BVGB. Partial barriers 

to horizontal groundwater flow, such as faults, have been 

identified on the eastern edge of BVGB. Prior to groundwater 

development in the BVGB, groundwater flowed from the 

recharge areas, generally near the margins of the basin, to 

discharge areas around the Borrego Sink, where it discharged 

from the aquifer system through evapotranspiration. 

Groundwater-level declines owing to groundwater 

development have eliminated the natural sources of discharge, 

and pumping for agricultural, recreational, and municipal 

uses has become the primary form of discharge from the 

groundwater system. 
2 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

The quality of groundwater in the Borrego Valley is a 

concern because of reliance on groundwater for agricultural, 

recreational, and municipal supply. Groundwater quality 

can be affected by land-use activities occurring at or near 

land surface. These activities include irrigation of vegetated 

landscapes and the use of septic systems to dispose of 

wastewater. Groundwater quality can also be affected by 

declining groundwater levels, because there is the potential for 

a change in the distribution of flow from underlying aquifers 

to wells. Historical and current groundwater-quality data 

were used to determine which constituents were present in 

relatively high concentrations compared to State water-quality 

thresholds and whether these constituent concentrations had 

changed in response to declining groundwater levels. Agedating isotopes (tritium and carbon-14 [14C]) were analyzed to 

determine whether modern (tritium-containing) groundwater 

recharge is occurring in Borrego Valley. Major findings of the 

groundwater-quality part of this study follow.

• Historical water-quality data show that, in the upper 

aquifer, total dissolved solids (TDS) and nitrate (as N) 

exceeded their water-quality thresholds of 500 mg/L 

(secondary recommended California maximum 

contaminant level) and 10 mg/L, respectively. At 

the time of publication, the source of this nitrate is 

unknown.

• TDS and sulfate are the only constituents that show 

increasing concentrations with simultaneous declines 

in groundwater levels.

• TDS and nitrate concentrations were generally highest 

in the upper aquifer and in the northern part of the 

Borrego Valley where agricultural activities are 

primarily concentrated.

• Age-dating isotopes indicate that little natural 

groundwater recharge is occurring under current 

(1900–2000) climatic conditions and that almost all 

of the natural recharge is occurring adjacent to the 

mountain fronts.

The long-term extraction of groundwater causes increases 

in the effective or intergranular stresses in the aquifer-system 

materials; this increased stress can result in irreversible 

compaction of the aquifer system. This compaction results 

in land subsidence in many areas where long-term pumping, 

typically in excess of recharge, has depleted groundwater 

storage. Three methods were employed as part of this study 

to assess the land subsidence in Borrego Valley: Global 

Positioning System (GPS) surveys, continuous GPS (CGPS) 

data collection, and interferometric synthetic aperture radar 

(InSAR) remote sensing techniques. InSAR results, derived 

from synthetic-aperture radar data, provide spatially detailed 

ground deformation maps (interferograms) that can elucidate 

spatially detailed patterns of vertical deformation for specific 

time spans. The InSAR methods complement the GPS surveys 

and CGPS data, which provide time-series data at a series of 

points. The GPS surveys, CGPS data, and InSAR analyses 

show little land subsidence has occurred in the Borrego Valley 

(much less than 1 inch in the last 50 years, 1961–2010). 

Hence, land subsidence attributed to aquifer-system 

compaction is not currently a problem in the Borrego Valley 

and is unlikely to be a significant problem in the future.

The GPS surveys were also used to improve the previous 

crude determinations of elevations for groundwater wells, 

which were derived from topographic maps and from which 

groundwater levels and groundwater-level gradients were 

determined. Historical land-surface elevations were updated 

for 79 groundwater wells. Historical elevations were changed 

by more than 5 feet at 10 wells and by almost 30 feet at 1 well. 

The updated elevations give a better estimate of spatially 

distributed groundwater levels, particularly the locations of 

highs and lows of the groundwater table.

The BVHM was developed on the basis of historical 

conditions (66 years) for the analysis of the use and movement 

of groundwater and surface water throughout the valley and 

to provide a basis for addressing groundwater availability and 

sustainability analyses. The model has a uniform horizontal 

discretization of 92 acres per cell (2,000 ft by 2,000 ft) and 

is oriented subparallel to the tectonic structure and to Coyote 

Creek. Vertically, the model has three layers representing the 

upper, middle, and lower aquifers. The model was calibrated 

by using groundwater-level measurements for 1945–2010 

and simulates conditions during that period. Natural and 

anthropogenic recharge and discharge, and the transient nature 

of these stresses, were simulated. 

The main sources of recharge to the system are runoff 

from creeks and streams draining the surrounding watershed, 

which quickly seeps into the permeable streambeds and 

infiltrates through the unsaturated zone, and groundwater 

underflow from the adjacent basins. Exceptionally large 

and infrequent storms typically contribute the most water 

to recharge. Excess flow sometimes terminates in middle of 

the valley at the Borrego Sink or flows out the southeastern 

end of the valley along San Felipe Wash. Over the 66-year 

study period, on average, the natural recharge that reached 

the saturated groundwater system was approximately 

5,700 acre-feet per year (acre-ft/yr), but natural recharge 

fluctuated in the arid climate from less than 1,000 to more 

than 25,000 acre-ft/yr. On average, of the 5,700 acre-ft/yr, 

about 1,700 acre-ft/yr seeps into the ground during wet years 

and rapidly discharges as evapotranspiration. In addition, 

approximately 1,400 acre-ft/yr enters the basin as underflow 

from adjacent basins. Since agricultural, recreational, and 

municipal land uses have been developed, a relatively small 

amount of recharge also occurs from excess irrigation water 

and septic-tank effluent. Recharge from irrigation return flows, 

as indicated by the model results, was about 10–30 percent 

of agricultural and recreational pumpages. Although a small 

amount of recharge from septic systems occurs and can be 

important locally, it is negligible relative to natural recharge 

and return flow from agricultural and recreational pumpages. 
Executive Summary 3

The BVHM uses a one-dimensional unsaturated-zone model 

to estimate the delay associated with return flow moving 

through the unsaturated zone. Depending on the thickness, 

permeability, and residual moisture content in the relatively 

thick unsaturated zone, it takes tens to hundreds of years for 

the bulk of return flow to reach the water table. In addition, 

not all water that reaches the root zone reaches the water table 

because some water is lost through evapotranspiration or goes 

into storage in the unsaturated zone. Therefore, in many areas, 

water that is applied to previously unirrigated land arrives at 

the underlying water table decades or longer after it is applied. 

Groundwater discharge occurs in three primary forms: 

(1) evapotranspiration from the ground and through the 

direct uptake of plants (mostly in and around the Borrego 

Sink); (2) a small amount of seepage from the southern end 

of the basin; and (3) groundwater pumping for agricultural, 

recreational, and municipal uses. Natural discharge from 

evapotranspiration ranges from approximately 6,500 acre-ft/yr 

prior to development to virtually zero in the last several 

decades (1990–2010), because the groundwater levels in 

the basin dropped below the reach of the mesquite in the 

basin. Underflow out the southern end of the basin was 

small and relatively stable over time, at about 500 acre-ft/yr. 

Groundwater pumpage for agriculture and recreation was 

estimated on the basis of irrigated acreage and consumptiveuse data. Values of pumpage for municipal supply were 

compiled from water-use records. Estimated combined annual 

agricultural, recreational, and municipal pumpage peaked at 

around 19,600 acre-ft from 2005 to 2010. 

Results of the calibrated model simulations indicated 

that simulated groundwater pumpage exceeded simulated 

actual natural recharge in most years, resulting in an estimated 

cumulative depletion of groundwater storage of about 

450,000 acre-ft from 1945 to 2010. Groundwater pumping 

resulted in simulated groundwater-level declines of more 

than 150 ft from 1945 conditions in much of the northern 

portion of the study area. The decline in groundwater levels 

was the result of this depletion of groundwater storage. In 

turn, the simulated decline in groundwater levels resulted 

in the elimination of almost all of natural discharge through 

evapotranspiration from the groundwater basin. Because there 

are few fine-grained, compressible deposits in the aquifer 

system materials, little aquifer-system compaction and land 

subsidence have occurred. 

The calibrated BVHM was used to simulate the 

response of the aquifer system to six future 50-year (2011 to 

2060) pumping scenarios: (1) no change in the agricultural, 

recreational, and municipal pumpage rates (status quo); (2–4) 

various levels of reductions in agricultural and recreational 

pumpage rates, coupled with low to high increases in 

municipal pumping rates; (5) reduction of all groundwater 

pumpage to that needed to avoid future groundwater-storage 

depletion over 50 years; and (6) a less severe, but more rapid, 

reduction in all groundwater usage over 20 years, followed by 

30 years at a constant much lower pumpage rate. 

Results from Scenario 1 (continuation of current, 2010, 

annual pumpage) indicated that the drawdown observed 

since pre-development would continue, with a total depletion 

in groundwater storage of about 1,000,000 acre-ft by 2060. 

Consequently, the water table declines to the middle aquifer 

in some areas. Because of the lower hydraulic conductivity 

and storage properties of the middle aquifer relative to the 

upper aquifer, continued pumping at these rates would result 

in larger, more rapid groundwater-level declines in the future 

and possibly a reduction in groundwater quality. As a result, 

more or deeper wells could be needed to accomplish similar 

pumpage rates. Scenarios 2–4 represent combinations of 

changes in agricultural and recreational pumpages, as well 

as in municipal pumpage. Although less than Scenario 1 

(status quo) pumpage rates, pumpage rates in two of these 

three scenarios exceed the average annual recharge rate, 

groundwater levels continue to decline, and there is continued 

cumulative depletion of groundwater storage. Because more 

water is being extracted from the groundwater basin than is 

being recharged either through natural or induced means, 

groundwater levels continue to decline. As the groundwater 

table is lowered from the relatively storage-rich and permeable 

upper aquifer to the middle and lower aquifers, the rate and 

areal extent at which groundwater levels decline accelerate, 

and the areal extent over which storage changes would be 

affected would be larger in the middle and lower aquifers with 

lower storativities. Furthermore, if the groundwater quality 

is less desirable deeper in the system, as existing information 

indicates, then the water quality of groundwater pumpage 

would deteriorate as deeper sources of water contribute more 

water to supply wells; this water could require more advanced 

water treatment than is used at present (2010) for municipal, 

and potentially, irrigation supply.

The location of the largest drawdown varies with the 

relative contributions of the three water-use categories 

(agricultural, recreational, and municipal) to overall pumpage 

in each scenario. In Scenario 5, water use is reduced in all 

three categories (agricultural, recreational, and municipal) 

to reach a sustainable level over a 50-year time span. The 

California Sustainable Groundwater Management Act 

(SGMA) of 2014 requires basins to reach sustainable yield. 

Scenario 5, with its 50-year time span, covers a longer 

period than is required by the act. The sustainable level for 

the Borrego Valley, assuming no significant degradation 

in groundwater quality, equates to total discharge equaling 

the long-term average recharge to the basin. As human 

activities change the system, the components of the water 

budget (inflows, outflows, and changes in storage) also 

change and must be accounted for in any management 

decision. Because there currently is little effect on captured 

recharge or discharge, in this system, ‘sustainability’ is a 

maximum amount of discharge to avoid future groundwaterstorage depletion and is being simplified and equated to this 

average recharge. As the rate of total groundwater extraction 

approaches the rate of recharge (meaning all inflows—natural 
4 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

and anthropogenic recharge, including induced recharge from 

captured water sources) to the aquifer system, the change in 

groundwater storage, and thus the rate of groundwater storage 

depletion, approaches zero, indicating no additional loss in 

storage. In the long run, the average change in groundwater 

storage would be negligible when the basin is operated at the 

sustainable level; however, groundwater levels and storage 

changes would fluctuate as they have historically with climatic 

variability. For example, during relatively wet years, more 

water could go into storage than is extracted. In turn, during 

moderate and relatively dry years, more water would be 

extracted than goes into storage. 

In order to simulate a realistic approach for meeting 

SGMA requirements on the 20-year SGMA timeline for 

implementation, in Scenario 6, municipal and recreational 

pumpages both were reduced to 50 percent of current (2010) 

rates, and agricultural pumpage was reduced to 40 percent 

of current rates. These reductions were applied linearly over 

20 years and continued for the next 30 years until 2060. With 

these reductions, at 2060, recharge approximates discharge. 

Simulated drawdowns are approximately 50 feet over a 

broad part of the basin. Drawdown and groundwater-storage 

losses continue in areas where agricultural, recreational, and 

municipal pumping occurs. In the long run, groundwater 

levels would stabilize and would not decline as they would 

for the Scenario 1 simulation, which had continued significant 

groundwater level and storage declines. However, changes in 

groundwater storage would fluctuate with climatic variability. 

Because climate models indicated greater variability in 

natural recharge in the future than during historical periods, 

the variability of groundwater-storage changes could also 

increase. Managed artificial recharge through engineered, 

enhanced infiltration of storm water or imported surface water 

is a water-management strategy that could help alleviate the 

demands on the valley’s groundwater system.

Introduction

The Borrego Valley is a small valley in the northeastern 

part of San Diego County, California, about 60 miles northeast 

of San Diego (fig. 1). Native Americans inhabited the valley 

and utilized the springs and surface-water sources from the 

nearby mountain ranges. Cattlemen began homesteading the 

Borrego Valley in about 1875. The first successful modern 

well was dug in 1926, which quickly led to irrigation farming 

(Moyle, 1982). By then, the valley’s population center, the 

small desert community of Borrego Springs, included a post 

office, a small general store, and a gas station. Historically, the 

principal source of water for the valley has been groundwater. 

The Anza-Borrego Desert State Park, which has 600,000 acres 

in and around the Borrego Valley, was established in 1933 

(fig. 1). The park was established to protect this unique desert 

environment. The military presence both of the Army and 

Navy during World War II brought the first paved roads and 

electricity to Borrego Springs. After the war, land developers 

subdivided the area, attempting to create a resort community 

supported by an increase in tourism generated by the 

Anza-Borrego Desert State Park (fig. 1).

The residents of the valley rely on groundwater for 

drinking water and irrigation (Moyle, 1982; Mitten and others, 

1988; California Department of Water Resources, 2003). 

Irrigated agriculture, golf courses, residential and commercial 

uses, and the Anza-Borrego Desert State Park require five 

times more water than is available through natural recharge. 

The imbalance between recharge and discharge, which began 

in the mid-1940s, has caused long-term groundwater-level 

declines. Moyle (1982) estimated that from 1945 to 1980 

about 330,000 acre-feet (acre-ft) of groundwater was pumped 

from the basin in excess of recharge. As a result, by 2010, 

the northern part of the groundwater basin had groundwaterlevel declines of about 120 feet (ft; fig. 2). Therefore, the 

U.S. Geological Survey (USGS), in cooperation with the 

Borrego Water District (BWD), undertook this water-resource 

assessment to understand the hydrologic budget and the limits 

of groundwater availability better in order to avoid future 

groundwater-storage depletion. The purpose of the study 

was to develop a greater understanding of the hydrogeology 

of the Borrego Valley Groundwater Basin (BVGB) and 

provide tools to evaluate the potential hydrologic effects 

of future development. The objectives of the study were to 

(1) improve the understanding of groundwater conditions and 

land subsidence, (2) incorporate this improved understanding 

in an integrated hydrologic model to aid in managing the 

groundwater resources in the Borrego Valley, and (3) apply 

this model to test several management scenarios. An integrated 

hydrologic model can provide the capability for the BWD 

and regional stakeholders to quantify the relative benefits of 

various options for reducing groundwater overdraft.

The California Sustainable Groundwater Management 

Act (SGMA) requires that groundwater basins reach 

sustainable yield. SGMA sets a 20-year timeline for 

implementation. Overdrafted basins must achieve 

groundwater sustainability by 2040 or 2042, predicated on 

the implementation of plans, which are expected to take 5 to 

7 years to complete. The SGMA recognizes that groundwater 

is managed at the local or regional level best and that there are 

geographic, geologic, and hydrologic differences accounting 

for groundwater supply. The goal of this legislation is 

reliable groundwater management, which it defines as “the 

management and use of groundwater in a manner that can 

be maintained during the 5-to-7-year planning period and 

20-year implementation horizon without causing undesirable 

results” (California Department of Water Resources, 2015). 

Undesirable results are defined as any of the following effects:
Introduction 5

Salton Sea

Clark

Lake

(Dry)

?

78

86

10S/5E-36A1

10S/6E-21A1, A2

11S/6E-11D2

0 5 10 MILES

0 5 10 KILOMETERS

SAN DIEGO COUNTY

IMPERIAL COUNTY

Ocotillo

wells

Borrego

Springs

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

EXPLANATION

Desert

Lodge

Anza-Borrego

San Ysidro Mountains

Desert State Park

Vallecito Mountains

Coyote Mountain

Superstition 

Mountain

Santa Rosa Mountains

Superstition Mountain fault

Coyote Creek fault

sac13-0509_Fig01_BorregoV.ai

Borrego

Sink

Borrego

Badlands

Coyote Creek

Borrego 

Palm

Creek

San Felipe Creek

Carrizo

Creek

Fish

Creek

 Mountains

116°15’ 116°

33°

15’

33°

Borrego

Palm

Canyon

Rams Hill

Golf Course I m p e r i a l Va l l e y

P e n i n s u l a r R a n g e s

Borrego

study area

C

ALIFORNIA

Pacific Ocean

San Diego

Cleveland National Forest

Sonoran Desert boundary

Boundary of Borrego Valley 

groundwater basin

Well with hydrograph 

(figure 2) and identifier

Boundary of Anza-Borrego

 Desert State Park

Fault—Dashed where

 approximate, dotted

 where concealed Stream channel

78 Road

10S/6E-11D2

Figure 1. Location of the Borrego Valley, California.
6 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 2. Water levels in selected wells in the Borrego Valley, California, 1945–2010.

sac13-0509_fig02 hydros

37

40

42

45

47

50

52

55

57

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

37

40

42

45

47

50

52

55

57

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

37

40

42

45

47

50

52

55

57

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

5

0

5

0

5

0

5

0

5

5

0

5

0

5

0

5

0

5

5

0

5

0

5

0

5

0

5

Water-level elevation, 

in feet above N

AVD 88

Water-level elevation, 

in feet above N

AVD 88

Water-level elevation, 

in feet above N

AVD 88

10S/5E-36A1

Climatic patterns

Wet

Dry

Average

EXPLANATION

Well

Climatic patterns

Wet

Dry

Average

EXPLANATION

Well

Climatic patterns

Wet

Dry

Average

EXPLANATION

Well

10S/6E-21A1

10S/6E-21A2

11S/6E-11D2

• Chronic lowering of groundwater levels (not including 

overdraft during a drought if a basin is otherwise 

managed).

• Significant and unreasonable reduction of groundwater 

storage.

• Significant and unreasonable seawater intrusion.

• Significant and unreasonable degraded water quality, 

including the migration of contaminant plumes that 

impair water supplies.

• Significant and unreasonable land subsidence that 

substantially interferes with surface land uses.

• Depletions of interconnected surface water that have 

significant and unreasonable adverse effects on 

beneficial uses of the surface water.

Long-term groundwater-level and groundwater-storage 

declines were analyzed in detail in this study. To accomplish 

this, an integrated regional hydrologic model was used 

to simulate the effects of climate variability and changes 

in water demand from 1945 to 2010. Fifty-year scenarios 

were run from 2011 to 2060. The creation of the integrated 

hydrologic model required reanalysis of the existing 

conceptual hydrologic model and hydrogeologic framework 

(Moyle, 1982; Mitten and others, 1988; Henderson, 2001; 
Introduction 7

and Netto, 2001) and estimation of various components of the 

hydrologic cycle. The model was then used to evaluate several 

future water-use scenarios.

In order to examine the potential for land subsidence to 

interfere with land uses in the Borrego Valley, the historical 

subsidence and factors affecting potential future subsidence 

were examined. Long-term pumping and the resulting 

groundwater-level declines in areas where some clay deposits 

are present within the aquifer system—mostly in the middle 

of the basin—can cause compaction and could result in land 

subsidence. To date (2010), minimal subsidence has been 

documented in the Borrego Valley even in the middle of 

the basin, where there are some finer grained deposits, and 

water levels have declined. Although large groundwater-level 

declines make subsidence possible in the future, the geological 

materials constituting the aquifer system in the valley 

(Moyle, 1968) make this unlikely. The potential lowering of 

the water table below the upper part of the aquifer system 

could accelerate the deterioration of groundwater quality 

(predominantly higher total dissolved solids) if water that 

enters wells from deeper sources is of poorer quality. Managed 

artificial recharge through the engineered, enhanced infiltration 

of storm water or imported surface water is one watermanagement strategy that could help mitigate deleterious 

consequences of high demand for the valley’s groundwater 

resources.

Purpose and Scope

This report documents or presents (1) an analysis 

of the hydrologic conceptual model and hydrogeologic 

framework, (2) a description of the hydrologeologic features, 

(3) a compilation and analysis of water-quality data, 

(4) measurement and analysis of land subsidence by using 

geophysical and remote sensing techniques, (5) development 

and calibration of a one-dimensional borehole flow model to 

estimate aquifer hydraulic conductivities, (6) development and 

calibration of a three-dimensional (3-D) integrated hydrologic 

flow model, (7) a water-availability analysis with respect to 

current climate variability and land use, and (8) simulation 

and analysis of potential future water-resources management 

scenarios for the Borrego Valley. The integrated hydrologic 

model, referred to as the “Borrego Valley Hydrologic Model” 

(BVHM), is a tool capable of being accurate at scales 

relevant to water-management decisions. Because the model 

incorporates time-varying inflows and outflows, this tool 

can be used to evaluate the effects of temporal changes in 

recharge and pumping on the hydrologic system. Overall, the 

development and use of hydrogeologic and hydrologic models, 

data networks, and hydrologic analysis described in this report 

provide a basis for assessing water availability and potential 

water-resource management guidelines. 

Approach

The objectives of the study were accomplished by 

collecting and compiling historical hydrogeologic data, 

collecting new data, and converting a previously developed 

USGS finite-element groundwater-flow model (Mitten and 

others, 1988) into a more current and comprehensively 

integrated hydrologic model. The creation of the hydrologic 

model required reanalysis of the conceptual model and 

hydrogeologic framework and estimation of the components 

of the hydrologic cycle. The updated conceptual model was 

revised by using new information about the hydrogeologic 

framework, recharge, land use, and streamflow infiltration. 

Updating the hydrogeologic framework required the 

remapping of geologic surfaces and reconciliation with recent 

geologic information from wells and other investigations.

The BVHM was constructed on the basis of the 

new conceptual model and hydrogeologic framework to 

simulate the flow and use of water during October 1945–

December 2010. The BVHM includes updated layering, 

updated inflows and outflows, and a more detailed 

representation of the current land use and vegetation. This 

valley-wide model includes estimates of runoff from the 

surrounding basins.

Accessing Data

A website was developed as part of this study for 

easy access to the water-quality and other data used for 

this study, which is accessible at http://ca.water.usgs.gov/

projects/borrego/index.html. The website summarizes water 

availability, groundwater quality, and the hydrologic model; 

it also features an interactive map and data files that can be 

downloaded. At this website, one can access relevant waterquality data from the USGS, the Borrego Water District, 

and the California Department of Public Health (CA-DPH). 

Data from the CA-DPH also can be obtained at http://www.

cdph.ca.gov/certlic/drinkingwater/Pages/EDTlibrary.aspx

(California Department of Public Health, 2013).

The USGS data used for this and other studies nationwide 

are stored in the USGS National Water Information System 

(NWIS) and are accessible from NWISWeb at http://

waterdata.usgs.gov/nwis/. NWISWeb serves as an interface to 

NWIS, a database network of site information and real-time 

groundwater, surface-water, and water-quality data collected 

from locations throughout the 50 states and elsewhere. Data 

are updated in the database network on a regular basis. 

Data are retrieved by category and geographic area and 

can be selectively refined by specific location or parameter. 

NWISWeb can output groundwater-level and water-quality 

graphs, site maps, and data tables (in HTML and ASCII 

format), and the user can develop site-selection lists.
8 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Description of Study Area

Borrego Valley is about 110 square miles (mi2) and 

is about 60 miles (mi) northeast of San Diego in the 

northwestern part of the Sonoran Desert Region (fig. 1). The 

valley is bounded on the northeast and east by the Coyote 

Creek fault, which forms Coyote Mountain and the Borrego 

Badlands, on the south by the Vallecito Mountains, and on 

the west and northwest by the San Ysidro Mountains. The 

southeastern boundary is a surface-water divide south of 

Ocotillo Wells (fig. 1). The 915 mi2 Anza-Borrego Desert State 

Park surrounds the valley, which ranges in elevation from 

approximately 1,100 to 1,200 ft above the North American 

Vertical Datum of 1988 (NAVD 88) around the margins to 

approximately 450 ft within the vicinity of Borrego Sink. 

The desert climate is characterized by low precipitation, 

hot summers, and relatively cool winters. Precipitation 

occurs in winter and late summer (Western Region Climate 

Center, http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca0983,

accessed September 29, 2015. Borrego Valley is widely 

acknowledged as the westernmost extent of the great 

southwestern geographical region known as the Sonoran 

Desert (Hunt, 1967). Currently, about 30 percent of the land 

is used for agriculture, about 69 percent is natural vegetation, 

and 1 percent is municipal land use (California Department of 

Water Resources, 1998). The natural vegetation on the valley 

floor is a diverse variety of desert flora. One of the iconic 

species found within the Borrego Valley is Washingtonia 

filifera, the California Fan Palm, which is a lower risk/nearthreatened species and the only palm native to the western 

United States (Hogan, 2009). 

Approximately 400 mi2 of tributary watersheds of 

multiple intermittent creeks and streams drain from the 

surrounding mountains into Borrego Valley. The largest 

surface-water inflow occurs along the Coyote Creek drainage 

area and enters at the northern part of Borrego Valley. Two 

other important watersheds are Borrego Palm Creek and 

San Felipe Creek, where surface water enters the western 

part of the valley. The Borrego Sink, which is in the middle 

of Borrego Basin, is a major collection point for runoff 

in Borrego Valley (fig. 1). In the desert environment, this 

runoff quickly returns to the atmosphere by evaporation or 

is transpired by phreatophytes, long-rooted plants that obtain 

water from the water table or the capillary fringe just above it.

Land use in the study area is primarily agricultural and 

recreational. Residential and commercial development is 

relatively minor; the population of Borrego Springs, which is 

in the middle of the valley, was 3,429 at the 2010 census, up 

from 2,535 at the 2000 census (U.S. Census Bureau, http://

factfinder2.census.gov/main.html, accessed September 29, 

2015). Tourism is a major industry in Borrego Springs, which 

has four public golf courses, a tennis center, and horseback 

riding, among other facilities and attractions available to 

visitors. The village is a popular destination for “snow birds,” 

residents that migrate annually from the colder climates in 

winter to enjoy the sunshine of this desert community. During 

2000–10, the BWD reported an average groundwater use of 

about 4,000 acre-ft/yr for residential and commercial uses 

(Jerry Rolwing, Borrego Water District, written commun., 

2011); groundwater pumping for agricultural and recreational 

uses was estimated to be about 16,000 acre-ft/yr.

Previous Studies

Studies of the Borrego Valley water resources began in 

the early 1900s. Moyle (1982) reported that an unpublished 

map on linen of the wells and springs of the Borrego Valley 

area was compiled in January 1905 from U.S. Surveys and 

personal surveys by C.S. Alverson (civil engineer). The first 

published data were compiled by Mendenhall (1909). Other 

early publications of hydrologic data were produced by the 

USGS (Waring, 1915; Brown, 1923). In the mid-1940s, more 

wells were drilled to support the growing agricultural and 

municipal water demand (Moyle, 1982). Since the mid1950s, various studies have been done to assess groundwater 

supply and quality and to ensure an adequate water supply 

for all uses. In 1954, Burnham (1954) published a study 

that inventoried water-well data and included summaries of 

drillers’ logs. In 1968, Moyle updated Burnham’s work and 

compiled available water well and geologic data during a 

groundwater investigation to support planned development in 

the area. 

In the 1970s, several reports evaluating the water 

resources in southern Borrego Valley in relation to the Rams 

Hill Development (fig. 1) were completed. In addition, water 

use and the adequacy of future water supply were addressed 

briefly by the U.S. Bureau of Reclamation (1968, 1972). 

More recent studies of the Borrego Valley describe the 

water resources and document long-term groundwater-level 

changes resulting from groundwater pumping (Moyle, 1982; 

Mitten and others, 1988; Henderson, 2001; and Netto, 2001). 

In 1982, the USGS, in cooperation with the County of San 

Diego, completed the first phase of an anticipated three-phase 

study to evaluate the water resources of Borrego Valley and 

vicinity. The purpose of the phase-1 study was to define the 

geologic and hydrologic characteristics of the basin to be 

used for the conceptual model for development of a numerical 

groundwater-flow model in phase 2. In a cooperative effort, 

the USGS, the County of San Diego, and the California 

Department of Water Resources (CA-DWR) prepared five 

technical information reports (California Department of Water 

Resources, 1981, 1983a, 1983b, 1983c, and 1984a) focusing 

on recharge rates, future water demand, and alternative 

water supplies for the Borrego Valley; these issues were 

summarized in a final report (California Department of Water 
Hydrologic System 9

Resources, 1984b). In 1988, the USGS completed phase 2 

of the study (Mitten and others, 1988), which consisted of 

developing a numerical groundwater-flow model that was 

based on the conceptualization of the aquifer system described 

by Moyle (1982). 

In 2001, a draft, groundwater-management study report 

of a technical committee to the BWD was completed (Borrego 

Water District, 2000). The technical committee report had 

three primary purposes: (1) to summarize and present findings 

of various existing studies on the aquifer system, (2) to make 

projections regarding the future use of the aquifer system and 

potential related effects, and (3) to evaluate the feasibility 

and effectiveness of various alternatives presented to the 

committee to mitigate overdraft. In 2001, two master’s theses 

were completed that focused on the Borrego Valley water 

resources. In the first, Netto (2001) documented the water 

resources. In the second, Henderson (2001) described the 

hydrogeology and developed a groundwater-flow model of 

the system, which simulated conditions from 1945 to 2000. 

The USGS model mentioned previously in the Phase 2 study 

simulated groundwater conditions from 1945 to 1979 (Mitten 

and others, 1988). More than 25 years have passed since the 

basin was last evaluated by the USGS in 1988.

Hydrologic System

The conceptual model for the hydrologic cycle starts 

with inflows from precipitation and streamflow. Streamflow 

enters Borrego Valley along Coyote Creek and San Felipe 

Creek (fig. 1) and as runoff from other local intermittent 

streams in the surrounding mountains. Infiltration of runoff 

and groundwater enter the basin along the boundaries as 

underflow, and infiltration of precipitation and irrigation 

water contribute to groundwater recharge. Underflow of 

groundwater exits the basin through the southeastern tip of 

the valley as outflow and is lost from the system (fig. 1). 

Discharge (outflow) also occurs as evapotranspiration from 

natural vegetation, municipal landscapes, golf courses, 

and irrigated agriculture. Additional outflow occurs as 

groundwater pumping for agricultural, recreational, and 

residential/municipal uses. These are the natural and manmade inflows and outflows that represent the supply-anddemand components of water use in the hydrologic cycle of 

Borrego Valley.

Climate

The climate of the Borrego Valley is arid with hot 

summers and cool winters. Precipitation occurs in winter 

and late summer and averaged 5.83 in/yr during 1942–2015 

on the valley floor (Western Region Climate Center, http://

www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca0983, accessed 

September 29, 2015) (fig. 3). Annual precipitation on the 

valley floor is highly variable. The record of cumulative 

departure of precipitation at Borrego Valley for years 

1945–2010 shows that major and minor wet and dry periods 

are typical of the long-term climate of Borrego Valley (fig. 3).

Precipitation and air temperature data for the Borrego 

Desert region are available as monthly maps at a 2.5-mi 

(4-kilometer [km]) spatial resolution from the empirically 

based Parameter-Elevation Regressions on Independent Slopes 

Model (PRISM) precipitation and air temperature database 

(Daly and others, 2004). PRISM uses measured precipitation 

data and spatially distributes precipitation by using regressions 

to account for orographic effects. The PRISM maps were 

spatially downscaled to an 886-ft (270-meter [m]) resolution 

to estimate the monthly and annual precipitation distributions 

in the study area. This technique combines a spatial Gradient 

and Inverse Distance Squared weighting to monthly point data 

by using multiple regressions calculated for every grid cell 

for every month (Nalder and Wein, 1998; Flint and others, 

2011). Results of the PRISM simulations provide an improved 

estimation of precipitation and air temperature at fine scales 

owing to the incorporation of deterministic adiabatic lapse 

rates associated with elevation and storm tracking associated 

with latitude and longitude (Flint and others, 2011; Flint and 

others, 2012). An estimated average annual precipitation 

map for the 1945–2010 period (fig. 4A) shows that there is a 

correlation of higher elevations with higher precipitation and 

that most of the precipitation occurs in the mountain-front 

regions.

Potential Evapotranspiration

Potential evapotranspiration (PET) was estimated on 

a monthly basis from solar radiation that was modeled by 

using topographic shading and a correction for cloudiness 

(Flint and Flint, 2007a). The PET is partitioned on the basis 

of vegetation cover to represent bare-soil evaporation and 

evapotranspiration (ET) owing to vegetation. The calculation 

of PET uses the Priestley-Taylor ET equation with modeled 

net radiation and PRISM air temperature (Priestley and Taylor, 

1972).

For the Borrego Valley, the average PET was more than 

50 in./yr, higher than that in the surrounding hills (Daly and 

others, 2004). The spatial variability in average annual PET 

for 1945–2000 for the area, which is shown in figure 4B, 

illustrates the orographic effects, similar to the precipitation, 

and local evidence of the role of north- and south-facing hill 

slopes on PET in several locations. It is also clear that average 

annual PET far exceeds average annual precipitation, thus, 

emphasizing that runoff and associated recharge occur only 

occasionally in the desert during extreme events (Flint and 

others, 2012).
10 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 3. Graphs showing A, average annual precipitation and B, cumulative departure of precipitation for Borrego Valley, California.

–25

–20

–15

–10

–5

0

5

10

Cumulative departure of precipitation,

in inches

0

1

2

3

4

5

6

7

8

9

10

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Precipitation, in inches

Year

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Average precipitation (1945–2009)

A

B

sac13-0509_Figure 03 Ave precip & departure

Climatic patterns

Wet Dry Average

EXPLANATION

Climatic patterns

Wet Dry Average

EXPLANATION
Hydrologic System 11

sac13-0509_Figure 04a_PPT updated

116°20’

116°30’

116°40’

33°

30’

33°

20’

33°

10’

33°

EXPLANATION

Estimated average annual precipitation, in inches (millimeters)

 (Flint and others, 2013)

2.15 to 7.87 (54.5 to 200) 13.79 to 15.75 (351 to 400)

7.88 to 9.84 (201 to 250) 15.76 to 17.72 (401 to 450)

9.85 to 11.81 (251 to 300) 17.73 to 19.69 (451 to 500)

11.82 to 13.78 (301 to 350) 19.70 to 21.65 (501 to 550)

Boundary of basins draining to Borrego Valley

Rivers, streams, and creeks

Boundary of Borrego Valley groundwater basin

Borrego Palm

Creek

Coyote Creek

San Felipe C reek

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

A

Figure 4. Estimated average annual for Borrego Valley, California, 1945–2010, of A, precipitation and B, potential evapotranspiration.
12 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

sac13-0509_Figure 04b_PET updated

116°20’

116°30’

116°40’

33°

30’

33°

20’

33°

10’

33°

Borrego Palm

Creek

Coyote Creek

San Felipe C reek

EXPLANATION

Boundary of basins draining to Borrego Valley

Rivers, streams, and creeks

Boundary of Borrego Valley groundwater basin

24.88 to 47.24 (632 to 1,200)

47.25 to 49.21 (1,201 to 1,250)

49.22 to 51.18 (1,251 to 1,300)

51.19 to 53.15 (1,301 to 1,350)

53.16 to 55.12 (1,351 to 1,400)

55.13 to 57.09 (1,401 to 1,450)

57.10 to 59.06 (1,451 to 1,500)

59.07 to 61.02 (1,501 to 1,550)

Estimated average annual potential evapotranspiration, in inches (millimeters)

 (Flint and others, 2013)

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

B

Figure 4. —Continued
Hydrologic System 13

Climatic Trends

Long-term climate trends in the Borrego Valley 

region, including upstream portions of the watershed 

draining into Borrego Valley and potentially affecting the 

valley’s hydrologic system, indicate a slight increase in the 

PRISM-estimated annual mean maximum and minimum 

air temperatures of 0.5 degrees Celsius (°C) from 1899 to 

2008 and an associated increase in estimated mean PET of 

approximately 0.7 in. (fig. 5A, C; table 1). Mean precipitation 

was lower by about 0.4 in. during the 20th century, and the 

variability increased from a standard deviation of 3.3 in. 

during 1941–70 to 6.0 in. during 1971–2008.

Land Use

An integral part of the hydrologic system is the use and 

movement of water across the landscape. This includes the 

evolution of the development of land use in Borrego Valley 

from when cattlemen began homesteading the Borrego Valley 

in about 1875 to the modern (2000s) agricultural, recreational, 

and residential/municipal uses. During this evolution, most of 

the land in Borrego Valley was, and continues to be, privately 

owned (Moyle, 1982). Surrounding Borrego Valley are the 

public lands of the Anza-Borrego Desert State Park; the 

Cleveland National Forest lies to the southwest (fig. 1).

In order to represent land use in the Borrego Valley, 11 

land-use types have been defined: 6 crops (citrus, irrigated 

grapes, non-irrigated grapes, potatoes, dates/palms/nurseries, 

and row and other crops); fallowed/livestock; golf courses; 

residential/urban development; native vegetation, which 

includes desert annuals and cacti; and phreatophytes (mesquite 

and tamarisk) (fig. 6). In this report, ‘native vegetation’ does 

not include ‘invasive plant species’ that might be present. 

The development of maps of land use, including crops, is 

problematic because complex land-use patterns are subject 

to rapid change as a result of dynamic agricultural processes. 

Despite the uncertainty and complexity, land-use maps were 

developed for 11 different periods (fig. 6). These maps are 

based on interpreted high-altitude aerial photography that was 

supplemented with published land-use maps (Moyle, 1982; 

California Department of Water Resources, 1998; Mitten and 

others, 1988; Henderson, 2001).

Most of the valley floor is native vegetation, which is 

predominantly desert-type vegetation. The other type of native 

vegetation in the central part of the valley, in particular around 

and north of the Borrego Sink, is phreatophytic vegetation 

that directly takes up groundwater for transpiration (fig. 6). 

The predominant type of phreatophyte in the Borrego Valley 

is mesquite. Although the area of the phreatophytes mapped 

on figure 6 does not change much spatially, the vigor of these 

plants has declined (Mitten and others, 1988). By the mid1990s, most of the mesquite had died because of the decline 

in groundwater levels (Borrego Water District, 2000; Netto, 

2001). 

One of the primary land-use types that evolved in the 

Borrego Valley is agriculture. Agricultural development 

began in earnest around 1945 and continues today (2010). 

The dominant crops grown in the valley throughout history 

are table grapes, citrus, and other miscellaneous crops, 

including row crops, dates/palms/nurseries, alfalfa, palms and 

ornamentals, and potatoes. The pattern of agricultural use has 

shifted several times during 1945–2009 (fig. 6). For example, 

citrus orchards were present during 1953–2010. Table grapes 

were present and actively irrigated in Borrego Valley during 

1953–66. However, table grape irrigation ceased in 1966, 

following a labor dispute (Moyle, 1982), and the fields were 

left fallow to as late as 1980. The primary recreational land 

use in Borrego Valley is for golf courses, including the De 

Anza Country Club, the Borrego Springs Park and Community 

Service District (BSPCSD) golf courses, the Road Runner 

Country Club, and the Rams Hill Development. Growing 

components of land use in the Borrego Valley are residential 

and municipal uses (fig. 6).

Table 1. Mean and standard deviation of estimated annual air temperature, precipitation, and potential evapotranspiration for three 

periods during 1899–2008 obtained by using the Parameter-Elevation Regressions on Independent Slopes Model database for the 

regional Borrego Valley drainage basin, California.

[°C, degrees Celsius; in., inches]

Time 

period

Maximum air temperature 

(°C)

Minimum air temperature 

(°C)

Potential evapotranspiration 

(in.)

Precipitation 

(in.)

Mean Standard 

deviation Mean Standard deviation Mean Standard deviation Mean Standard deviation

1899–1940 24.8 0.8 8.6 0.7 55.1 1.1 13.0 3.8 

1941–1970 25.1 0.6 7.9 0.6 54.6 1.0 10.4 3.3 

1971–2008 25.3 0.8 9.0 0.6 55.8 1.2 12.6 6.0 
14 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 5. Trends in climate for the Borrego Valley, California, 1899–2008, including A, estimated minimum and maximum annual mean 

air temperatures; B, annual precipitation; and C, estimated potential evapotranspiration, Borrego Valley. sac13-0509_Figure 05abc climate trends

0

5

10

15

20

25

30

Estimated annual mean air 

temperature, in degrees Celsius

Annual precipitation,

in inches

Estimated potential evapotranspiration,

in inches

Year

A

B

C

18991905191019151920192519301935194019451950195519601965197019751980198519901995200020052010

0

5

10

15

20

25

30

50

51

52

53

54

55

56

57

59

58

60

18991905191019151920192519301935194019451950195519601965197019751980198519901995200020052010

18991905191019151920192519301935194019451950195519601965197019751980198519901995200020052010

y = –0.0134x + 12.949 

y = 0.0124x + 54.48 

y = 0.0096x + 24.54

y = 0.0085x + 8.0539

Maximum

Minimum
Hydrologic System 15

Figure 6. Land use in the Borrego Valley, California, during A, pre-development; B, 1953; C, 1954; D, 1959; E, 1968; F, 1979; G, 1980; H, 

1992; I, 1995; J, 2000; and K, 2009. Number(s) in parentheses indicates land-use class used in the model.

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 06a LU-Predevelopment

A

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Land use, pre-development

Phreatophytes (4) 

Native vegetation (15) 

Fault

EXPLANATION

78 Road

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

Dry lake

Land-use percentage

90.9

9.1
16 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06b_LU-1953

B

EXPLANATION

Land use, 1953

Phreatophytes (4) 

Citrus (11) 

Row and other crops (7)

Grapes (8) 

Residential/urban (2) 

Golf courses (1, 3) 

Native

vegetation (15) 

Stream channel

Boundary of Borrego Valley Fault

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Land use is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

Dry lake

Land-use percentage

84.6

3.0

2.7

8.8

0.6 0.2

Figure 6. —Continued
Hydrologic System 17

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06c_LU-1954

C

EXPLANATION

Land use, 1954

Phreatophytes (4) 

Row and other crops (7)

Residential/urban (2) 

Golf courses (1, 3) 

Dates, palms, nursery (10, 12, 13) 

Grapes (8) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

84.2

2.9

0.2

3.1 6.6

3

Figure 6. —Continued
18 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 6. —Continued

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06d_LU-1959

D

EXPLANATION

Citrus (11) 

Grapes (8) 

Native 

vegetation (15) 

Land use, 1959

Phreatophytes (4) 

Row and other crops (7)

Residential/urban (2) 

Golf courses (1, 3) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

82.9

3.1

3.0

6.6

4.0

0.4

De Anza 

Country Club
Hydrologic System 19

Figure 6. —Continued

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure -06e_LU-1968

E

EXPLANATION

0

0 5 10

5 10 MILES

KILOMETERS

Land use, 1968

Phreatophytes (4) 

Row and other crops (7)

Residential/urban (2) 

Golf courses (1, 3) 

Citrus (11) 

Non-irrigated grapes (9) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

82.7

2.1

0.2

2.5 6.6

5.1

0.7

De Anza 

Country Club

Borrego Springs 

Resort and 

Country Club

Road Runner Golf 

and Country Club
20 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 6. —Continued

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06f-LU-1979

F

EXPLANATION

Land use, 1979

Phreatophytes (4) 

Row and other crops (7)

Residential/urban (2) 

Golf courses (1, 3) 

Citrus (11) 

Dates, palms, nursery (10, 12, 13) 

Non-irrigated grapes (9) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

0

0 5 10

5 10 MILES

KILOMETERS

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

83.4

0.9

6.5

6.5 1.5

1.0

0.1

De Anza 0.1

Country Club

Borrego Springs 

Resort and 

Country Club

Road Runner Golf 

and Country Club
Hydrologic System 21

Figure 6. —Continued

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06g_LU-1980

G

EXPLANATION

Land use, 1980

Phreatophytes (4) 

Row and other crops (7)

Residential/urban (2) 

Golf courses (1, 3) 

Citrus (11) 

Dates, palms, nursery (10, 12, 13) 

Non-irrigated grapes (9) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

82.4

1.1

6.6

6.1 1.2

1.0

1.0

0.5

De Anza 

Country Club

Road Runner Golf 

and Country Club

Borrego Springs 

Resort and 

Country Club
22 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06h_LU-1992

H

EXPLANATION

Land use, 1992

Citrus (11) 

Phreatophytes (4) 

Dates, palms, nursery (10, 12, 13) 

Row and other crops (7)

Residential/urban (2) 

Golf courses (1, 3) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

81.3

1.6

8.2

6.0 0.7

1.6

0.5 De Anza 

Country Club

Road Runner Golf 

and Country Club

Rams Hill

 Golf Course

Borrego Springs 

Resort and 

Country Club

Figure 6. —Continued
Hydrologic System 23

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06i_LU-1995

I

EXPLANATION

Land use, 1995

Phreatophytes (4) 

Row and other crops (7)

Residential/urban (2) 

Golf courses (1, 3) Citrus (11) 

Dates, palms, nursery (10, 12, 13) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

78.0

1.6

8.2

5.7 0.1

2.0

0.7

3.6 De Anza 

Country Club

Road Runner Golf 

and Country Club

Rams Hill

 Golf Course

Borrego Springs 

Resort and 

Country Club

Figure 6. —Continued
24 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 6. —Continued

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06j_LU-2000

J

EXPLANATION

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

Land-use percentage

Land-use, 2000

Phreatophytes (4) 

Row and other crops (7) 

Residential/urban (2) 

Golf courses (1, 3) Citrus (11) 

Dates, palms, nursery (10, 12, 13) 

Potatoes (14) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

77.0

3.1

10.1

5.6

3.2 0.4

0.6

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

De Anza 

Country Club

Borrego Springs 

Resort and 

Country Club

Road Runner Golf 

and Country Club

Rams Hill

 Golf Course
Hydrologic System 25

Figure 6. —Continued

Clark Lake

Borrego Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Landuse is based on Moyle, 1982; 

California Department of Water Resources, 1998; 

Mitten and others, 1988; Henderson, 2001

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 06k_LU-2009

K

Rams Hill

 Golf Course

De Anza 

Country Club

Borrego Springs 

Resort and 

Country Club

Road Runner Golf 

and Country Club

Springs

Golf Club

EXPLANATION

Land use, 2009

Citrus (11) 

Phreatophytes (4) 

Dates, palms, nursery (10, 12, 13) 

Potatoes (14) 

Fallow/livestock (5, 6) 

Residential/urban (2) 

Golf courses (1, 3) 

Native vegetation (15) 

Stream channel

Boundary of Borrego Valley

 groundwater basin

0

0 5 10

5 10 MILES

KILOMETERS

78 Road

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Land-use percentage

72.5

11.1

5.6

2.1

3.6

0.9

1.2

3.0
26 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Hydrogeology

The Borrego Valley groundwater basin occupies an 

embayment of the western Imperial Valley in the Peninsular 

Ranges. The basin is between the Santa Rosa Mountains to the 

east and the San Ysidro Mountains to the west (fig. 7) (Dorsey, 

2002). Geologic units in the basin include crystalline rocks, 

consolidated sedimentary rocks, and poorly to moderately 

consolidated alluvium.

Geologic Structures

The geology of the mountains that surround the study 

area is dominated by right lateral faults of the San Jacinto 

fault zone that trend predominantly northwest to southeast 

(fig. 7A). The northwest trending Coyote Creek and 

Superstition Mountain faults (fig. 1), which have been active 

for about 600 thousand years (Dorsey, 2002), are part of the 

San Jacinto fault zone. The Coyote Creek fault, which is a 

right-lateral strike-slip fault, defines the eastern boundary of 

the valley and is a barrier to groundwater flow along most of 

its northwest–southeast extent. Groundwater-level contours 

indicate that the flow of groundwater is parallel to the fault in 

most places and that there are abrupt changes in the direction 

of flow and large differences in groundwater levels across 

the fault (Moyle, 1982). Faults can act as barriers to flow as 

a result of the deformation (compression) of water-bearing 

deposits immediately adjacent to the faults, the cementation 

of the fault zone by mineral deposits, or the juxtaposition of 

non-water-bearing deposits and water-bearing deposits (Riley 

and Worts, 2001). Another way that faults can act as barriers to 

groundwater flow is by the accumulation of low-permeability 

sediments that infill cracks on the land surface following 

earthquakes (Moyle, 1982). This phenomenon was observed 

following the April 9, 1968, magnitude 6.4 Borrego Mountain 

Earthquake on the Coyote Creek fault, where cracks as large 

as 2 ft wide were infilled by silt-laden water from flash floods 

along the 17-mi-long fracture in the 2 years following the 

earthquake (Moyle, 1982).

Folding has occurred within the Borrego Valley since the 

initiation of movement along the Coyote Creek fault and has 

formed the Desert Lodge Anticline (Dibblee, 1984), which 

is just south of the Borrego Sink near the middle of the basin 

(fig 7A, 7B). This feature effectively splits the basin into two 

distinct components. Additional plunging anticlines exist 

between the Desert Lodge Anticline and San Felipe Creek to 

the south (Dibblee, 1984). These structures effectively bisect 

the basin and offset sediments north of San Felipe Creek from 

those to the south.

Configuration of Basin

The alluvium-filled valley is underlain by crystalline 

bedrock. Moyle (1982) described the configuration of this 

bedrock on the basis of a gravity survey of Borrego Valley. 

By using the gravity survey information, new gravity data, 

and newly estimated density contrasts, Henderson (2001) 

completed an updated gravity model to define the depth 

from land surface to basement rock (fig. 8). This new gravity 

model shows an asymmetrical basin with the greatest depth 

to basement rock of approximately 3,800 feet in the eastnortheast part of the basin.

Geologic Units

The oldest geologic unit in the Borrego Valley is the 

basement complex, composed of Cretaceous granitic and 

metasedimentary rocks and pre-Cretaceous metamorphic 

rocks (Sharp, 1982; Moyle, 1982; Dorsey, 2002). These rocks 

crop out in the San Ysidro Mountains, Coyote Mountain, and 

Borrego Mountain (figs. 1, 7). The rocks of the basement 

complex have low primary porosity, some secondary porosity 

(for example, fractures), and are relatively impervious to 

groundwater flow. Basin fill consists of (1) the marine rocks 

of the possibly Miocene to possibly Pleistocene age Imperial 

Formation; (2) younger continental deposits of Pliocene and 

Pleistocene age, including the Palm Spring Formation, the 

Borrego Formation, and the Ocotillo Conglomerate (Moyle, 

1982); and (3) Quaternary alluvium.

The Imperial Formation contains the only known marine 

deposits in the region (Loetlz and others, 1975). Although 

previous researchers have subdivided the deposits overlying 

the Imperial Formation, in general they are not well-defined 

time-stratigraphic units. On the basis of fossils, these rocks 

are probably late Miocene to early Pliocene age and are 

composed of gray to yellow claystone and buff sandstone with 

oyster shells, other mollusks, and corals (Moyle, 1968). The 

overlying Palm Spring Formation consists of many thousands 

of feet of fluvial and deltaic sand, silt, and clay deposited 

by the ancestral Colorado River (Loetlz and others, 1975). 

The Imperial Formation and the Palm Spring Formation are 

weakly to moderately consolidated and are locally tilted and 

folded (Loetlz and others, 1975). The Palm Spring Formation 

grades from coarser deposits along the western mountains 

toward the center of the Imperial Valley into the lacustrine silt 

and clay of the Borrego Formation (Loetlz and others, 1975). 

In the vicinity of the study area, the Palm Spring Formation is 

composed of arkosic sandstone and red clay (Moyle, 1982).

The Borrego Formation is a thick sequence of mostly 

fine-grained lacustrine deposits typically exposed northwest of 

Borrego Mountain (Loetlz and others, 1975). In the study area, 
Hydrogeology 27

Vallecito Mountains

Clark

Lake

Borrego

Sink

Benson

Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Geology by Moyle, 1982; plate 1

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 07a_Geology

Coyote Creek fault

Coyote Mountain

Borrego Mountain

San Ysidro Mountains

Santa Rosa Mountains

A

EXPLANATION

Desert Lodge anticline

Playa deposits Basement rock

Sand deposits Continental deposits 

Younger alluvium Older alluvium

Younger fan deposits Older fan deposits

Generalized geology

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

Coyote Creek

San Felipe Creek

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Figure 7. Maps showing Borrego Valley, California, showing A, geology; B, hydrogeology; and C, generalized hydrogeologic cross 

sections A-A’ and B-B’. (Lines of section are shown in figure 7B.)
28 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Vallecito Mountains

Clark

Lake

Borrego

Sink

Benson

Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

S-22

S-22

S-2

78

A

A’

B

B’

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 07b_hydro geo

Coyote Creek fault

Coyote Mountain

Borrego Mountain

San Ysidro Mountains

Santa Rosa Mountains

B

EXPLANATION

Desert Lodge anticline

Basement rock

Fan deposits and

 lower aquifer

Upper aquifer

Hydrogeology

Stream channel

Boundary of Borrego Valley

 groundwater basin Line of sections;

B

B’

Cross section shown 

in figure 7C 78 Road

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Geology by Moyle, 1982; plate 1

Coyote Creek

San Felipe Creek

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake

Figure 7. —Continued
Hydrogeology 29

Figure 7. —Continued

Southwest Northeast

–2,000

–1,000

Sea

level

1,000

B B'

Upper aquifer

Water

table

Middle aquifer

Lower aquifer

Basement

Approximate location of A–A'

Northwest Southeast

–2,000

–1,000

Sea

level

1,000

A A’

FEET

VERTICAL EXAGGERATION ×1.5

sac13-0509_Figure 07cd_xsecs AA’ and BB’

C

BORREGO VALLEY

Borrego

Upper aquifer

Borrego

Sink

Water

table

Middle aquifer

Lower aquifer

Basement Basement

From Moyle, 1982 

Approximate location of B–B'
30 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 8. Gridded depth from land surface to basement rock in the Borrego Valley, California (modified from Henderson, 2001).

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 08 depth-to-basement

EXPLANATION

0

0 5 10

5 10 MILES

KILOMETERS

Depth to basement rock, in feet below land surface

Less than 250

251 to 500

501 to 750

751 to 1,000

1,001 to 1,250

1,251 to 1,500

1,501 to 1,750

1,751 to 2,000

2,001 to 2,250

2,251 to 2,500

2,501 to 2,750

2,751 to 3,000

3,001 to 3,250

3,251 to 3,500

3,501 to 3,750

3,751 to 3,800

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Fault

Boundary of Anza-Borrego

 Desert State Park

Dry lake
Hydrogeology 31

it is composed of light-gray lacustrine claystone, siltstone, and 

minor amounts of sandstone (Moyle, 1982). In some parts of 

the study area, the Borrego Formation is overlain with local 

unconformity by the Ocotillo conglomerate (Dibblee, 1954). 

In the study area, the Ocotillo conglomerate consists of gray 

conglomerate and fanglomerate (Moyle, 1982).

Alluvium deposited since the Pleistocene age through 

to present day is present at land surface over most of the 

Borrego Valley floor. Additional Quaternary deposits consist 

of lacustrine silt and clay deposits of Pleistocene to Holocene 

age that are present at or near the surface of the Borrego Sink, 

as well as in boreholes in the central part of the Borrego Valley 

(Netto, 2001). As stated previously, despite the designation of 

named units described earlier, the rock units of the Borrego 

Valley are crudely defined, mostly unnamed, time-stratigraphic 

units. Loetlz and others (1975) noted that units mapped as 

older alluvium consist largely of coarse-grained alluvial-fan 

deposits, including the Ocotillo conglomerate and possibly 

deposits correlative with the upper part of the Palm Spring 

Formation.

The terms older and younger alluvium (Qya) are used 

frequently in previous studies of the hydrogeology of the 

Borrego Valley (Moyle, 1982; Netto, 2001; Dorsey, 2002; 

Mitten and others, 1988). However, these terms are informal, 

are often used in a relative sense, and can mean different 

things to different authors. In previous hydrogeologic 

studies of the Borrego Valley, authors have made conflicting 

characterizations of the younger alluvium. Moyle (1982) did 

not consider Qya to be an important water bearing unit and 

represented it only as a surface veneer that transmits water 

from the intermittent streams to the groundwater system, 

whereas Netto (2001) defined Qya as a hydrostratigraphic unit 

as much as 1,200 ft thick. In contrast, the description of the 

Qcs unit of Dorsey (2002) compares favorably to the middle 

aquifer described by Moyle as the Palm Spring Formation and 

the upper QTc unit of Mitten and others (1988).

The texture model developed for this study (described 

in the “Texture Model” section of this report) was evaluated 

in the context of regional geomorphology and depositional 

environment. The spatial structure of the texture model can be 

attributed to the alignment of the river channels, particularly 

Coyote Creek (fig. 7A), which is controlled by the strike of the 

regional tectonics. The distribution and proportion of coarsegrained deposits in the upper aquifer in the Borrego Valley 

reflect a number of factors, including the short distances 

between the base level of the depositional environments and 

the source outcrops of the crystalline rocks of the San Ysidro 

Mountains, as well as the high-energy nature of Coyote Creek 

that transports sediments during winter storms and monsoonal 

rains in the summer. The finer grained texture of the middle 

aquifer reflects a lower energy sediment source to the upper 

Borrego Valley from the west and east prior to the initiation 

of slip along the Coyote Creek fault. At that time, sediments 

deposited within the study area were relatively finer grained 

than those deposited after faulting on the Coyote Creek fault 

opened the northern end of the Borrego Valley.

Aquifers

The hydrogeologic framework of Borrego Valley was 

developed through a re-evaluation and synthesis of the 

geology from previous studies that resulted in a simple 

characterization of the aquifers in the aquifer system that 

groups the water-bearing geologic units into the three 

aquifers originally defined by Moyle (1982) and recognized 

by CA-DWR (California Department of Water Resources, 

1984a). This synthesis was done by using drillers’ and 

geophysical logs from wells throughout the Borrego Valley 

(fig. 9). Although the discretization of the upper, middle, and 

lower aquifers is often difficult to discern in drillers’ logs, the 

available geophysical logs can be used to identify the contacts 

of the three aquifers. Collectively, these aquifers are variable 

in extent and range from a few feet thick up to thousands of 

feet thick (figs. 7, 10).

The upper aquifer is the regional water-table aquifer 

and consists of the saturated part of the alluvium (Quaternary 

gravels [Qg] of Dorsey, 2002). Historically, it has been 

the principal source of groundwater in Borrego Valley and 

yields as much as 2,000 gallons per minute (gal/min) to 

individual wells (Mitten and others, 1988). The upper aquifer 

is composed of Holocene to Pleistocene age alluvial, fan, 

playa, and eolian deposits. These deposits are composed of 

unconsolidated sand, gravel, silt, and clay (Mitten and others, 

1988). The upper aquifer ranges in thickness from 0 to 643 ft 

(table 2) and is thickest at the north end of the valley where 

Coyote Creek enters the basin. It thins to the southeast and 

is only about 50 ft thick near the Borrego Sink (Mitten and 

others, 1988) (fig. 10A).

The middle aquifer is composed of the upper part of 

Pleistocene age continental deposits. Moyle (1982) correlated 

the middle aquifer with the upper Palm Spring Formation/

upper QTc. The middle aquifer yields moderate quantities 

of water to wells, but is considered a non-viable source of 

water south of San Felipe Creek because of its diminished 

thickness (Mitten and others, 1988). Descriptions on well 

logs penetrating these deposits indicate that the deposits 

range in size from gravel to silt with moderate amounts of 

consolidation and cementation and that the predominant 

grain sizes range from medium sand to clay (Moyle, 1982). 

The middle aquifer is as much as 908 ft thick (table 2) in 

the northern part of the valley, but it thins substantially in a 

southeasterly direction (Mitten and others, 1988) (fig. 10B).

The lower aquifer includes the combined deposits of 

the lower Palm Spring and Imperial Formations (Moyle, 

1982; Henderson, 2001). The lower aquifer yields only small 

amounts of water to wells (Moyle, 1982); it is composed 

primarily of partly consolidated siltstone, sandstone, and 

conglomerate in the lower part of the continental deposits 

(Mitten and others, 1988). The separation of the middle and 

lower aquifers is based on drillers’ log descriptions of “hard, 

dry, red clays” that extend over the southern half of Borrego 

Valley at increasing depth to the north. Drillers’ logs indicate 

sediments above the red clays are easy to drill, whereas those 

below the red clay are hard to drill (Moyle, 1982). On the 

basis of the most recent interpretations of gravity data, this 

aquifer is as thick as 3,831 ft (table 2) and is thickest in the 

eastern part of the valley (figs. 9, 10B, 10C).
32 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 9. Location of wells with driller’s and (or) geophysical logs used to develop the hydrogeologic framework model for the Borrego 

Valley, California.

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

S-22

S-22

S-2

78

10255825

10255730

10255820

10255810

10S/6E-35Q1

10255805

10255800

10255700

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 09 Wells & gages

EXPLANATION

0

0 5 10

5 10 MILES

KILOMETERS

With driller’s log

Monitoring, with flow log 

 shown in figure 24

10255730 Well—

USGS streamgage

 and identifier

USGS streamgage with 

hydrograph shown in 

figure 15

Municipal supply

With geophysical logs Stream channel

Boundary of Borrego Valley Fault

 groundwater basin

Boundary of Anza-Borrego

 Desert State Park

78 Road

10255700

10S/6E-35Q1 Identifier

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Dry lake
Hydrogeology 33

Figure 10. Extent and approximate thickness of aquifers in Borrego Valley, California, A, upper; B, middle; and C, lower. 

Clark Lake

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 10a_Thickness_upper

A

EXPLANATION

201 to 300

50 to 100 301 to 400 501 to 600

101 to 200 401 to 500 601 to 643 Boundary of layer 1

Approximate thickness of upper aquifer (layer 1), in feet

(Thickness of aquifers are not shown where less than 50 feet)

Stream channel

Boundary of Borrego Valley Fault

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

78 Road

Dry lake

0

0 5 10

5 10 MILES

KILOMETERS
34 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 10b_thickness_middle

B

EXPLANATION

0

0 5 10

5 10 MILES

KILOMETERS

50 to 100

101 to 200

201 to 300

301 to 400

401 to 500

501 to 600

601 to 700

701 to 800

901 to 1,000

801 to 900

Approximate thickness of middle aquifer (layer 2), in feet

(Thickness of aquifers are not shown where less than 50 feet)

Boundary of layer 2

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake

Figure 10. —Continued
Hydrogeology 35

Clark Lake

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 10c_Thickness_lower

C

EXPLANATION

Constant-head boundary

 cells in layer 3

Approximate thickness of lower aquifer (layer 3), in feet

(Thickness of aquifers are not shown where less than 50 feet)

201 to 300 701 to 800 2,001 to 2,500

101 to 200 601 to 700 1,501 to 2,000

50 to 100 501 to 600 1,001 to 1,500

401 to 500 901 to 1,000 3,001 to 4,000

301 to 400 801 to 900 2,501 to 3,000

Boundary of layer 3 Stream channel

Fault

Boundary of Borrego Valley

 groundwater basin

Boundary of Anza-Borrego

 Desert State Park

0

0 5 10

5 10 MILES

KILOMETERS

78 Road Dry lake

Figure 10. —Continued
36 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Three-Dimensional Hydrogeologic Framework 

Model

This section describes a digital 3-D hydrogeologic 

framework model of the basin aquifer system developed as 

part of the study. The framework model uses information from 

a variety of sources, including existing drillers’ and electrical 

geophysical logs from water wells, cross sections, and 

geologic maps, to delineate the volumes of the aquifer system 

bounded by faults and relevant depositional or formational 

boundaries. The framework model is the digital representation 

of the interpreted geometry and thickness of subsurface 

hydrogeologic units and the geometry of folds and faults that 

bound the basin or lie within it. Specifically, the model was 

constructed to represent the subsurface geometry of the upper, 

middle, and lower aquifers and a bedrock unit. This digital 

model provides the fundamental hydrogeologic framework for 

the integrated hydrologic model of the study area.

The digital framework model can be visualized and 

explored by slicing the model volume at any chosen location. 

Two sections were cut from the framework model along the 

same two section lines as published by Moyle (1982). One 

section is aligned roughly northwest–southeast (A-A′, fig. 7C) 

and the other roughly southwest–northeast across the major 

structural grain of the basin (B-B′, fig. 7C). Together with the 

map (fig. 7B), the sections show the extent and thickness of 

the aquifers. The sections show the thickness of upper aquifer 

in the axis of the valley, underlain by the middle and lower 

aquifers. The lower aquifer dominates the southern part of the 

valley and is exposed at the surface in the western part of the 

valley where it is locally overlain by thin deposits of alluvium 

in the tributary channels. 

Selection and Compilation of Existing Well Data

Drillers’ logs were available for 230 wells in and around 

the basin (fig. 9). Well depths average 467 ft and extend to a 

maximum depth of 1,238 ft below land surface (bls). Except 

for 13 wells in the northern end of the study area that had 

geophysical logs, the drillers’ logs were the only lithologic 

data available for the study area. Data density throughout 

the study area varies (fig. 9). Population and agriculture are 

concentrated in the northern half of the basin; thus, most 

well data are from this area. The Desert Lodge Anticline, just 

south of the Borrego Sink near the middle of the study area, 

effectively subdivides the basin into two distinct regions. 

The few additional wells south of this structure constrain the 

hydrogeologic framework model in this region.

The quality of location information (geographic 

coordinates) of each well varied because of the differing use of 

Global Position System (GPS); sketched maps; or township, 

range, section, quarter-quarter-section information to identify 

well locations on the well completion reports. GPS locations 

were recently confirmed in the field by using differential 

GPS surveys. Wells that had detailed sketched maps were 

assigned a latitude and longitude by comparing sketched 

maps with layers of aerial photos in a geographic information 

system (GIS). Latitude and longitude were established for 

wells with township, range, section, and quarter-quarter 

section information given on the drillers’ logs by calculating 

the coordinates of the center of the most detailed part of the 

township/range information.

Adjustment of Aquifer Surfaces

Three-dimensional surfaces of the upper elevation of 

each aquifer (aquifer tops) were established on the basis of 

cross sections, outcrops, and information from drillers’ and 

geophysical logs. The drillers’ logs were coded by color, and 

textural information was coded by a fill symbol in the digital 

display of the hydrogeologic framework model such that the 

contacts between different deposits could be easily compared 

visually with the cross sections cut across the aquifer surfaces. 

Where geophysical well logs were available, these were 

included. Drillers’ logs were plotted as stick logs, and cross 

sections were cut between boreholes in two-dimensional (2-D) 

space against the backdrop of the aquifer classification.

The borehole data were displayed in 3-D space with color 

coding similar to that of the texture descriptions along with the 

surfaces representing the tops of the aquifers. By displaying 

the data in three dimensions, it was possible to evaluate many 

wells at once qualitatively and to rotate the view to better 

visualize each dimension.

By using these 2-D and 3-D renditions of aquifersystem texture, previous interpretations of aquifer-system 

geometry were adjusted. These adjustments were based 

primarily on evaluations of geophysical logs. Although 

there are small differences in the location and extent of the 

aquifers documented by Moyle (1982) and Mitten and others 

(1988), the aquifers have the same general geometry. Where 

data from drillers’ logs or geophysical logs indicated that 

Table 2. Description of aquifers, Borrego Valley, California.

Area 

(acres) 

Thickness

(feet)

Coarse-grained materials

(in percent)

Minimum Mean Maximum Minimum Mean Maximum

Upper aquifer 47,015 0 258 643 0 58 100 

Middle aquifer 45,730 0 267 908 0 45 100 

Lower aquifer 68,800 0 1,015 3,831 0 44 100 

Basin 73,737 107 1,270 4,545 0 48 100 
Hydrogeology 37

contact elevations differed from the previous interpretations, 

the aquifer tops were modified to represent the well data 

better. For example, the top of the middle aquifer is shown 

to dip from south to north to a lower elevation where the 

unit contacts the basement complex at the northern end of 

a northwest–southeast cross section (A-A’) in Mitten and 

others (1988). Drillers’ and geophysical logs were interpreted 

to show a roughly flat contact between the upper and middle 

aquifers. After adjusting all the contacts where the previous 

surface did not match the well data, the surface of each aquifer 

top was regenerated. As described previously, the largest 

change was the contact between the upper and middle aquifer 

in the northern part of the basin. In this region, the upper 

aquifer is a maximum of 550 ft thick, and the deposits overlap 

the middle aquifer, in which the contact is now roughly flat. 

The newly defined upper aquifer is several hundred feet 

thinner than previously described; cross sections showing the 

differences in aquifer extents among previous studies (Moyle, 

1982) and interpretations from geophysical logs are shown in 

figure 11.

Texture Model

An analysis of variability of lithology and grain size was 

done for the basin-fill units that compose the three principal 

aquifers. Variability in the basin-filling units is ultimately a 

function of the sedimentary facies, environment of deposition, 

and depositional history of the basin. Textural data, such 

as grain size, sorting, and bedding characteristics, form the 

geologic basis for estimating the hydraulic properties within 

the integrated numerical hydrologic model.

In this study, a texture model was created utilizing a 2-D 

geostatistical analysis of each aquifer, based on the percentage 

of coarse-grained deposits in the borehole interval. The texture 

model was developed by compiling and analyzing about 

230 drillers’ logs that describe the lithology to 1,238 ft bls. 

Textural information in drillers’ logs typically is ambiguous 

and inconsistent because expertise, experience, and vocabulary 

of the describers vary greatly (Faunt and others, 2009b; 

Laudon and Belitz, 1991; Belitz and others, 1993; Burow and 

others, 2004).

Figure 11. Generalized cross sections of Borrego Valley, California: A, northwest to southeast, modified from Moyle (1982), and B, 

southwest to northeast, drawn by using data from geophysical logs collected in 2012.

2,000

1,000

Sea

level

1,000

FEET

Upper aquifer

Middle aquifer

Lower aquifer

Basement

Upper aquifer

Middle aquifer

Lower aquifer

Basement

Water

table

Borrego

Sink

BORREGO VALLEY

Borrego

From Moyle, 1982 

2,000

1,000

Sea

level

1,000

FEET

Water

table

Borrego

Sink

BORREGO VALLEY

Borrego

Southwest Northeast

A Northwest Southeast

B
38 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Classification of Texture from Drillers’ Logs and 

Regularization of Well Data

Each lithologic log was divided into discrete binary 

texture classifications of either coarse-grained or fine-grained 

intervals on the basis of the description in the log (table 3). 

Where available, this characterization was adjusted on 

the basis of geophysical logs. In this study, the definitions 

of coarse-grained and fine-grained sediment differ from 

definitions used in previous studies that utilized similar 

methods (Page, 1986; Laudon and Belitz, 1991; Belitz and 

others, 1993; Burrow and others, 2004; Faunt and others 

2009b). Because the basin fill is generally composed of highenergy stream deposits in close proximity to the sources of 

sediment, and the size of the Borrego basin is relatively small 

compared to basins evaluated in other studies, classifications 

used in other basins (Faunt and others, 2009b) were adjusted 

to prevent skewing of the Borrego texture model toward 

the coarse-grained texture class. The nomenclature used in 

drillers’ logs is similar to that in the method described by Folk 

(1954) and Lane (1947), where modifiers are used to quantify 

percentages of grain sizes.

Percent coarse was initially calculated on an interval 

length of 20 ft (the most common length of drill stem and, 

thus, a commonly used interval in the description of drill 

cuttings). This calculation was used to evaluate the data for 

trends, but it was not used in the final geostatistical model. 

Through analysis, it was determined that cuttings described 

as primarily coarse grained, but with fine-grained modifiers, 

needed to be re-classified as fine grained in the Borrego 

texture model. Failing to include this analysis resulted in 

a global mean percentage of more than 90 percent coarsegrained textures in the study area, misrepresenting the actual 

textural variability and negating the utility of the texture 

model as an input to the groundwater-flow model.

In this study, coarse-grained sediment was defined as 

sediment with a primary texture of sand, gravel, pebbles, 

boulders, cobbles, or conglomerate in the drillers’ log 

(table 3). Fine-grained sediment was defined as a sediment 

with a texture consisting principally of clay, lime, loam, 

mud, or silt, but including coarse-grained sediments with a 

fine-grained texture modifier of silty, clayey, or muddy, (for 

example, clayey gravel or silty sand). Sediments with primary 

fine-grained texture descriptions with modifiers indicative 

of a coarse-grained component (sandy, cobbley, gravelly, or 

pebbley) were classified according to their primary texture (for 

example, sandy silt would be classified as fine grained).

Geostatistical Model of Coarse-Grained Texture

Geostatistics is a set of applications and statistical 

techniques used to analyze spatial and (or) temporal 

correlations of variables distributed in space and (or) time 

(Isaaks and Srivastava, 1989; Faunt and others, 2009b). An 

advantage of using geostatistical models over simple spatialinterpolation methods, such as inverse-distance weighted 

interpolation, is that the geostatistical model provides the best 

linear unbiased estimate and the set of weights that minimize 

estimation error (Journel and Huijbregts, 1978; Faunt and 

others, 2009b).

Texture Dominant 

texture Abbreviation Binary classification

Boulders Boulders G Coarse

Cobbles Cobbles G Coarse

Conglomerate Conglomerate G Coarse

Gravel Gravel G Coarse

Quicksand Sand S Coarse

Sand Sand S Coarse

Sandstone Sand S Coarse

Cobbley boulders Boulders G Coarse

Cobbley gravel Gravel G Coarse

Cobbley sand Sand G Coarse

Gravelly boulders Boulders G Coarse

Gravelly cobbles Cobbles G Coarse

Gravelly sand Sand gS Coarse

Pebbley gravel Gravel G Coarse

Pebbley sand Sand gS Coarse

Sandy boulders Boulders sG Coarse

Sandy cobbles Cobbles sG Coarse

Sandy conglomerate Conglomerate sG Coarse

Sandy gravel Gravel sG Coarse

Sandy pebbles Pebbles sG Coarse

Silty gravel Gravel zG Coarse

Silty sand Gravel zS Coarse

Clay Clay C Fine

Texture Dominant 

texture Abbreviation Binary classification

Shale Silt Z Fine

Silt Silt Z Fine

Clayey boulders Clay cG Fine

Clayey cobbles Clay cG Fine

Clayey gravel Clay cG Fine

Clayey sand Clay cS Fine

Clayey sandstone Clay cS Fine

Clayey shale Clay cZ Fine

Clayey silt Clay cZ Fine

Cobbley clay Clay gC Fine

Cobbley shale Silt gZ Fine

Gravelly clay Clay gC Fine

Gravelly silt Silt gZ Fine

Muddy clay Clay zC Fine

Muddy quicksand Silt zS Fine

Muddy sand Silt zS Fine

Pebbley clay Clay gC Fine

Pebbley shale Silt gZ Fine

Sandy clay Clay sC Fine

Sandy shale Silt sZ Fine

Sandy silt Silt sZ Fine

Silty clay Clay zC Fine

Sticky clay Clay C Fine

Table 3. Textures used to describe lithology recorded on drillers’ logs, Borrego Valley, California.
Hydrogeology 39

As mentioned previously, the lithologic descriptions 

on the logs were simplified into a binary classification of 

either coarse grained or fine grained (table 3). For use in 

the geostatistical analysis, the percentage of coarse-grained 

texture was calculated over 1-foot depth increments for each 

borehole; from these increments, the total percent coarsegrained sediment across the entire thickness of each aquifer 

was calculated from all 230 wells (figs. 9, 10). Percent 

coarse-grained sediment was calculated as the total thickness 

of coarse-grained intervals divided by the total thickness of 

the aquifer times 100. Various geostatistical algorithms were 

evaluated for their utility in producing a realistic pattern 

representing the distribution of sediment textures within the 

three aquifers. A 2-D geostatistical model, both incorporating 

kriging and cokriging methods, was used to interpolate the 

percentage of coarse-grained deposits of the nearest wells onto 

a 2,000-ft grid across each aquifer for the entire study area.

It was determined that cokriging between the percent 

coarse data from the drillers’ logs and the land surface for the 

upper aquifer, and the top surface of the middle aquifer for the 

middle aquifer, provided the best results. This relation seems 

plausible because the present-day land surface approximates 

the depositional horizon for most of the upper and middle 

aquifers and confers a natural orientation of anisotropy to the 

spatial correlation structure of the model. Ordinary kriging of 

the percent-coarse data from the drillers’ logs was used for the 

lower aquifer. A second-order trend removal was applied to the 

percent-coarse field of each aquifer’s data set. The details of 

the semivariogram (input for the geostatistical model) for each 

layer are presented in table 4.

A series of plan-view estimates of the texture distribution 

for each aquifer is shown in figure 12. The global mean 

percentage of coarse-grained texture is 48.5 percent, with the 

upper aquifer being significantly more coarse-grained than the 

middle aquifer or the lower aquifer (table 3). The 2-D kriged 

and cokriged estimates of percentage of coarse-grained texture 

highlight textural differences within and between the aquifers. 

Examination of the texture model showed that, as expected, 

the spatial distribution and characteristics of sediment in the 

three aquifers are related to the Pliocene and Pleistocene 

tectonic evolution of the basin and the infilling by the local 

drainage systems. Textural variations in the aquifers appear 

to be primarily climate driven and reflect regional rainfall 

variations that control stream incision and aggradation.

The spatial patterns of the percentage of coarse-grained 

texture for each aquifer incorporate the spatial structure of 

the textural data. The patterns show significant heterogeneity 

in the texture of the sediments that reflects the depositional 

environment and the geomorphic evolution of the region. The 

texture model of the upper aquifer has the highest percentage 

of coarse deposits (fig. 12A); it is coarsest in the vicinity of 

the mountains. The coarse-grained nature of the upper aquifer 

reflects a number of factors, including the short distances 

between the sediment sources in the surrounding uplands and 

the sites of sediment deposition and the high-energy nature 

of Coyote Creek and tributary creeks that transport sediments 

during winter storms and summer monsoonal rains. The spatial 

structure of the kriged texture model for the upper aquifer can 

be attributed to the alignment of the active drainages, whereas 

the texture models of the older aquifers are less correlated with 

modern topography. 

The texture model for the middle aquifer differs in 

spatial structure from the upper aquifer by having overall finer 

grained sediment and being less related to the modern active 

drainages, particularly Coyote Creek (fig. 12B). The middle 

aquifer is coarse grained in the west-central and northwestern 

parts of the valley, but it transitions to fine-grained sediment 

in the areas near and south of the Borrego Sink. The lower 

aquifer has coarse-grained sediment in the west-central part 

of the valley and transitions to fine-grained sediment near 

the Borrego Sink (fig. 12C). However, the lower aquifer also 

has fine-grained sediment in the eastern and northern parts 

of the valley. The lower aquifer does not extend as far north 

as the upper and middle aquifers, but extends farther to the 

south. To the south, sediments of the lower aquifer appear to 

coarsen gradually; however, the lower aquifer is much more 

consolidated and lithified than the upper and middle aquifers.

Table 4. Input for geostatistical model, Borrego Valley, California.

Aquifer Number of 

samples Input datasets Method Number of lags Lag size(meters) Model type Range(meters) Anisotropy Sill(meters)

Upper 183 Layer 1 percent coarse Cokriging 12 650 Exponential 600 No 770

Digital Elevation Model (DEM)

Middle 164 Layer 2 percent coarse Cokriging 12 1,300 Exponential 500 No 810

Layer 2 aquifer top

Lower 74 Layer 3 percent coarse Kriging 12 1,390 Exponential 1,200 No 905
40 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 12a_Percent coarse_upper

A

EXPLANATION

0

0 5 10

5 10 MILES

KILOMETERS

0 to 10

11 to 20

21 to 30

31 to 40

41 to 50

51 to 60

61 to 70

71 to 80

81 to 90

91 to 100

Percentage of coarse-grained material

Boundary of layer 1

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake

Figure 12. Percentage of coarse-grained deposits in aquifers of Borrego Valley, California: A, upper; B, middle; and C, lower.
Hydrogeology 41

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 12b_Percent coarse_middle

B

EXPLANATION

0 to 10

11 to 20

21 to 30

31 to 40

41 to 50

51 to 60

61 to 70

71 to 80

81 to 90

91 to 100

Percentage of coarse-grained material

Boundary of layer 2

Stream channel

Boundary of Borrego Valley

 groundwater basin

0

0 5 10

5 10 MILES

KILOMETERS

Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake

Figure 12. —Continued
42 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 12c_Percent coarse_lower

C

0

0 5 10

5 10 MILES

KILOMETERS

EXPLANATION

0 to 10

11 to 20

21 to 30

31 to 40

41 to 50

51 to 60

61 to 70

71 to 80

81 to 90

91 to 100

Percentage of coarse-grained material

Boundary of layer 3

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake

Figure 12. —Continued
Hydrogeology 43

Groundwater-Flow and Groundwater-Level 

Change

Prior to development in the valley, the direction of 

groundwater flow was predominantly from the northwest to 

the southeast (fig. 13A). Moyle (1982) reports that in 1945, 

groundwater elevations ranged from more than 600 ft above 

mean sea level (asl) near Coyote Creek in the northwestern 

part of Borrego Valley to about 460 ft asl in the southeastern 

part. The lowest groundwater-level elevations occurred east 

of the Borrego Sink (fig. 13A), an area of natural drainage in 

the middle of the valley that is dry most of the time. Before 

development of the valley, shallow groundwater and the 

occasional surface water that accumulated in the Borrego 

Sink after storms supported 450 acres of honey mesquite and 

other native phreatophytes (Moyle, 1982). Deep groundwater 

flowed to the east and southeast along the Coyote Creek fault 

toward Ocotillo Wells (fig. 13A). Although 1945 is considered 

approximately pre-development, in 1945, a number of wells 

are shown, and the water-level contours appear to show some 

effect of pumping from well 11S/7E-20P1. 

Large-scale agriculture predated the end of World War II, 

and by 1952–53, the increase in the number of wells and 

amount of groundwater pumped since predevelopment had 

caused groundwater-level declines in the northern part of 

the basin. Since the early 1950s, groundwater pumpage has 

exceeded recharge, and the direction of flow was altered in 

all areas of the valley by 2010 (fig. 13B). During the 11 year 

period 1952–62, groundwater levels declined at a rate of 

about 3.4 feet per year (ft/yr). Groundwater-level declines 

during 1945–2010 were as much as 2.1 ft/yr (119 ft for the 

period) in wells 10S/6E-21A1 and -21A2 in the northern 

part of the basin, where groundwater is intensively pumped 

for irrigation agriculture (figs. 13B, 14A). A second, smaller 

area of groundwater-level depression is in the west-central 

part of the basin, which is associated with pumping for 

municipal supply (figs. 13B, 14B). Although the magnitude 

of the change is smaller, the middle portion of the basin has 

a similar groundwater-level trend as the northern part of the 

valley (figs. 13B, 14C). In the southeastern part of the valley, 

where less groundwater has been pumped, groundwater-level 

declines did not exceed 6 ft during 1945–2008 (figs. 13B, 

14D). Although climate variability has a small effect on 

groundwater levels (see groundwater-level declines in the 

1950s), the effects of anthropogenic water use far outweigh 

the variability in climate. 

Groundwater Recharge

Groundwater recharge to the Borrego Valley comes from 

natural and anthropogenic sources. The primary source of 

natural recharge to the basin is infiltration from the ephemeral 

streams and washes emanating along the western and northern 

boundaries of the valley. The source of water to these streams 

is precipitation and runoff from the San Ysidro and Santa Rosa 

Mountains (fig. 1). Other potential sources of natural recharge 

include direct infiltration of precipitation, lateral groundwater 

underflow from adjacent bedrock areas and groundwater 

basins, and groundwater flow across the Coyote Creek fault, 

all of which probably are small or negligible in quantity 

(Moyle, 1982; Mitten and others, 1988).

The Borrego Valley is classified as an arid environment, 

and the average annual precipitation is much less than the 

PET. Because the average precipitation rate is 5.83 in/yr. 

(Western Region Climate Center, http://www.wrcc.dri.edu/

cgi-bin/cliMAIN.pl?ca0983 accessed September 29, 2015), 

the PET rate is 71.6 in/yr (California Irrigation Management 

System, 2012), and soil moisture requirements by plants near 

the root zone are high, recharge from direct infiltration of 

precipitation is considered negligible. However, recharge in an 

arid basin is not based solely on average annual conditions. In 

certain areas of a basin (such as the higher elevations on the 

western slopes of Borrego Valley), precipitation can exceed 

PET, and soil storage, runoff, and (or) recharge can occur.

Sources of Recharge

The major sources of natural groundwater recharge 

originate from the adjacent mountains and take the form of 

infiltration of surface water that enters the basin as runoff 

and groundwater underflow. There are three main sources 

of surface water that enter the basin from ephemeral washes 

along the western and southwestern margins of the valley. 

The major source of streamflow to the valley is Coyote Creek; 

smaller flows are contributed by Borrego Palm Creek and 

San Felipe Creek (figs. 1, 15). Most of the flow from these 

washes and underflow is runoff from winter storms in the 

San Ysidro Mountains to the west and from occasional local 

summer thunderstorms. Historical discharge data are available 

for 1950–83 for Coyote Creek, 1950–2004 for Borrego Palm 

Creek, and 1958–83 for San Felipe Creek (fig. 15).

The extreme variability of rainfall (fig. 3), and associated 

streamflow, contributes to a wide range of values for the 

portion of runoff estimated to result in groundwater recharge. 

In addition, a relatively small, but more stable, portion of 

recharge comes from underflow from the upstream portions 

of the watershed. The vast majority of the underflow is 

shallow and follows the channel network within the alluvial 

fill of the valleys because bedrock permeability in the 

surrounding drainage areas is so low. Previous investigators 

have used various approaches to estimate recharge to the 

groundwater. Moyle (1982) gathered estimates of natural 

recharge from previous studies that ranged from averages of 

3,300 to 11,000 acre-ft/yr. Mitten and others (1988) estimated 

approximately 4,800 acre-ft/yr of natural recharge. Henderson 

(2001) estimated that annual recharge from infiltrating surface 

water and underflow into the valley during 1945–2000 ranged 

from 760 to 51,000 acre-ft and averaged 6,170 acre-ft/yr. 

Estimates of annual recharge from the same sources identified 

by Netto (2001) ranged from 633 to 45,935 acre-ft with an 

average of 5,670 acre-ft/yr.
44 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 13. Water-level elevations and direction of groundwater flow in Borrego Valley, California, for A, 1945, approximately 

predevelopment, and B, 2010. (2010 data are modified from http://www.dpla.water.ca.gov/sd/groundwater/basin_assessment/basin_

asssment.html).

Coyote Creek fault

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

10S/5E-36A1

10S/6E-9L1

10S/6E-21A1,A2 10S/6E-18J1

Ocotillo

wells

500

520

490

480

525

530

470

535

460

510

550

575

600

450

700

625

850

520

530

116°20’ 116°10’

33°

20’

33°

10’

sac13-0509_Figure 13a 1945 WL contours

A

EXPLANATION

0

0 5 10

5 10 MILES

KILOMETERS

Well with hydrograph shown

 in figure 14 and identifier

Well with water-level data

11S/7E-20P1

Line of equal water level, 1945.

 Interval varies (modified

 from Moyle, 1982).

General direction of

 groundwater flow

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

600

78 Road

11S/6E-11D2

11S/7E-20P1

11S/7E-32Q1

Dry lake
Hydrogeology 45

Figure 13. —Continued

Coyote Creek fault

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

S-22

S-22

S-2

78

l

l

420

410

480

430

440

400

500

460

470

450

390

380

340

360 420

400

420 410

440

460

380

430

390

11S/7E-20P1

11S/7E-32Q1

10S/5E-36A1

10S/6E-9L1

10S/6E-21A1,A2

11S/6E-11D2

10S/6E-18J1

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 13b 2010 WL Contours

B

EXPLANATION

Well with hydrograph shown

 in figure 14 and identifier

Well with water-level data

11S/7E-20P1

Line of equal water level, 2010.

 Interval varies; hatchures

 indicate depression

General direction of

 groundwater flow

Stream channel

Boundary of Borrego Valley

 groundwater basin

600

78 Road

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake
46 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 14. Water-level elevations in selected wells in parts of the Borrego Valley, California, 1950–2010: A, northern; B, west–northern; 

C, middle; and D, southern.

119 ft decline

375

400

425

450

475

500

525

375

400

425

450

475

500

525

375

400

425

450

475

500

525

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

A

B

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Water-level elevation, in feet above NAVD 88

C

425

450

475

500

525

550

575

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

D

sac13-0509_Figure 14 hydros

10S/6E-9L1 (721)

10S/6E-18J1 (690.46)

10S/6E-21A1, -21A2 (639.52, 636.73)

EXPLANATION

Value in parentheses is landsurface elevation [NAVD 88])

10S/5E-36A1 (717.70)

EXPLANATION

Value in parentheses is landsurface elevation [NAVD 88])

11S/6E-11D2 (493.03)

EXPLANATION

Value in parentheses is landsurface elevation [NAVD 88])

11S/7E-32Q1 (694.90)

11S/7E-20P1 (570.47)

EXPLANATION

Value in parentheses is landsurface elevation [NAVD 88])
Hydrogeology 47

Figure 15. Discharge of creeks in Borrego Valley, California, 1950–2004: A, Coyote Creek; B, Borrego Palm Creek; and C, San Felipe 

Creek.

sac13-0509_Figure 15abc_daily discharge

0

200

400

600

800

Daily discharge, in cubic feet per second

1952 1958 1964 1970 1976 1982 1988 1994 2000 

10255800 Coyote Creek near

 Borrego Springs, Calif.

Period of record

0

100

200

300

Daily discharge, in cubic feet per second

1952 1958 1964 1970 1976 1982 1988 1994 2000 

10255810 Borrego Palm Creek near

 Borrego Springs, Calif.

10255700 San Felipe Creek near

 Julian, Calif.

Period of record

0

100

200

400

300

500

Daily discharge, in cubic feet per second

1952 1958 1964 1970 1976 1982 1988 1994 2000 

Period of record

A

B

C

Estimated

Daily mean

discharge

Daily mean

discharge
48 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

New anthropogenic sources of recharge accompanied 

development in the basin, including irrigation-return flow from 

agricultural fields and municipal lawns and the infiltration 

of treated wastewater and untreated wastewater from septic 

systems. From the late 1940s onward, these sources of 

anthropogenic recharge have significantly increased the 

total groundwater recharge in the valley, at times becoming 

many times larger in magnitude than natural recharge. On the 

basis of a chloride mass-balance technique, Netto (2001) 

estimated irrigation return flow to the groundwater system at 

a citrus grove and a golf course fairway to be 22 percent and 

14 percent, respectively. Any water from these anthropogenic 

sources that reaches the water table by infiltrating the vadose 

zone is part of the overall water budget. This water is a 

component of recharge in the integrated hydrologic model 

(described in the “Integrated Hydrologic Model” section of 

this report).

Transient Estimates of Natural Recharge from 

the Basin Characterization Model

As mentioned previously, estimates of the quantity, 

distribution, and source of natural groundwater recharge vary 

widely. Therefore, the regional-scale Basin Characterization 

Model (BCM; Flint and others, 2004) was used to better 

quantify components of the natural groundwater recharge 

(underflow and streamflow into the Borrego Valley). The 

BCM uses a deterministic water-balance approach to estimate 

recharge and runoff in a basin. The model uses the distribution 

of precipitation, snow accumulation and melt, PET, soil-water 

storage, and bedrock permeability to estimate a monthly 

water balance for the groundwater system. Results from the 

BCM are useful for providing bounds associated with waterbalance results of more detailed models, evaluating long-term 

climate conditions, illustrating the mechanisms responsible for 

recharge in a basin, and comparing the locations and volumes 

of recharge and runoff in different basins on a regional scale 

(Flint and Martin, 2012).

The BCM is grid based at a spatial resolution of 

886 ft (270 m) and calculates monthly recharge and runoff. 

The BCM incorporates spatially distributed parameters 

(monthly precipitation, monthly minimum and maximum air 

temperature, monthly PET, soil-water storage capacity, and 

saturated vertical hydraulic conductivity (K) of bedrock and 

alluvium) to determine where excess water is available in 

a basin and whether the excess water is stored in the soil or 

infiltrates downward into underlying bedrock. Excess water is 

partitioned by the BCM as either potential in-place recharge 

or potential runoff, depending on the saturated K of bedrock 

and alluvium. Potential in-place recharge is the maximum 

volume of water for a given month that can recharge directly 

into bedrock or deep alluvium (greater than 20 ft). Potential 

runoff is the maximum volume of water for a given month 

that runs off the mountain front or becomes streamflow. The 

total of runoff and underflow from upstream components of 

the watershed is the summation of in-place recharge and a 

percentage of runoff that is determined through calibration to 

measured streamflow (Flint and Flint, 2007a). The downscaled 

PRISM precipitation and air-temperature maps were used 

with the monthly PET results (fig. 4), available spatial maps 

of elevation, bedrock permeability estimated from the geology 

(fig. 7), and soil-water storage from the State Soil Geographic 

Data Base (STATSGO; National Resources Conservation 

Service, 2006) to calculate snow accumulation and melt snow 

and to calculate changes in soil moisture, runoff, and recharge.

The BCM calculates potential in-place recharge 

and potential runoff and generates distributions of both 

components. In this study, the BCM provided estimates of 

the underflow from the adjacent mountains and basins and 

potential runoff in stream channels into the basin. Moreover, 

the BCM can be used to compare the potential for recharge 

under the current climate (2010) and that for past wetter 

and drier climates (Flint and Flint, 2007a). The BCM model 

domain includes the watersheds that surround and drain into 

the Borrego Valley (fig. 16).

PET and snow modules of the BCM were calibrated 

regionally throughout the southwestern United States 

to measured PET data and the Moderate-Resolution 

Imaging Spectroradiometer (MODIS) snow-cover data 

(U.S. Geological Survey, 2007; Flint and Flint, 2007b). 

For this study, the model was also calibrated to measured 

unimpaired streamflow data. The determination of whether 

excess water becomes recharge or runoff is governed in 

part by the underlying bedrock characteristics that govern 

permeability (fig. 16). The greater the bedrock permeability, 

the greater the recharge and the less the runoff generated for 

a given grid cell. In small, gaged watersheds that generate 

unimpaired flows, the bedrock permeability can be adjusted 

to calculate a total watershed discharge that matches the 

measured watershed discharge. Following calibration to 

bedrock permeability, recharge and runoff can be accumulated 

for all grid cells upstream from streamgages to account for 

stream channel gains and losses to calculate basin discharge 

and optimize the fit between total measured volume and 

simulated volume for the period of record for each gage. The 

simple exponential equations described in detail in Flint and 

others (2013) were used to calculate surface-water flow 

recession, seepage, and base flow that can extend throughout 

the dry season; constrain estimates of flow and recharge when 

measured data are not available; and provide estimates of 

flows and recharge in ungaged basins. In the Borrego Valley 

region, there are three streamgages that represent unimpaired 

flows, which were used for model calibration (figs. 15, 16). 

However, two of the streamgages (10255800 and 10255700) 

are downstream from large alluvial valleys in which an 

unknown amount of runoff that reaches the streams infiltrates 

the streambeds and potentially recharges the local water table. 
Hydrogeology 49

Figure 16. Drainage basin boundaries and geology used in the Basin Characterization Model to estimate climate-driven natural 

recharge in the Borrego Valley, California.

sac13-0509_figure 16_BCM_updated

116°20’

116°30’

116°40’

33°

30’

33°

20’

33°

10’

33°

EXPLANATION

10255810

10255800

10255700

Alluvium

Conglomerate

Granite

Metamorphics

Sandstone Metasediments

Surface-water entry point

Sedimentary rocks Other rocks Boundary of basins draining to Borrego Valley

Rivers, streams, and creeks

Small subbasins in Borrego Valley

Boundary of Borrego Valley groundwater basin

U.S. Geological Survey Streamgage with 

discharge shown in figure 15 and identifier

10255810

Borrego Palm

Creek

Coyote Creek

San Felipe C reek

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection
50 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

In arid environments, varying amounts of the runoff calculated 

from excess water are lost to the unsaturated zone within the 

stream channels; for these two watersheds, it was estimated 

that about 90 percent of runoff generated by the BCM 

infiltrated through the stream channels. The streamgage that 

provided the most reliable streamflow record for analysis was 

the Borrego Palm Creek streamgage (10255810; fig. 15).

 An example of the fit of the BCM simulated annual 

streamflow to measured annual streamflow is illustrated in 

figure 17 for the Borrego Palm Creek streamgage, where the 

coefficient of determination (R2) was 0.56 (R2 for the Coyote 

Creek streamgage was 0.8). The coefficient of determination 

is a statistical measure of how well the regression line 

approximates the real data points. Low flows are uncertain 

in the calibration, because the creek goes dry almost yearly 

at BCM estimates of 10–3 cubic feet per second. Given 

the uncertainties in the various components of the input 

parameters, climate, geology, and soils data, the simulated 

volumes provided a reasonable match to the measured data for 

this streamgage. Although the drainage area for the Borrego 

Palm Creek streamgage is slightly larger than the areas of the 

other small adjacent basins and has the potential for additional 

PET from surface-water flows, the calibration of streamflow 

to that at the Borrego Palm Creek streamgage was assumed 

to represent all of the small watersheds on the western side of 

Borrego Basin. The estimated recharge and runoff for these 

small watersheds were multiplied by a factor of 0.75 to 1 to 

reduce the total recharge and runoff as a function of watershed 

size to account for additional loss to PET; the largest 

watersheds were assumed to lose the most to PET.

Figure 17. Measured annual streamflow at Borrego Palm Creek (10255810) streamgage and simulated annual streamflow from the 

Basin Characterization Model for the Borrego Palm Creek drainage watershed, Borrego Valley, California.

0.001 

0.01 

0.1 

1 

10 

100 

Streamflow, in cubic feet per second 

Measured at Borrego 

 Palm Creek (10255810)

EXPLANATION

Simulated by Basin

 Characterization Model 

Year

1953 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Hydrogeology 51

The regional BCM initial estimates of recharge and 

runoff are generally adjusted to find the best fit calibration 

for particular study areas, because of variations throughout 

the large BCM study area (Flint and others, 2004; Rewis 

and others, 2006; Flint and Martin, 2012). By using the 

best-fit calibration for the Borrego Valley, BCM estimated 

that, in total, 25 percent of the runoff and 10 percent of 

the recharge calculated by BCM became recharge to the 

watershed, with the assumption that almost all streamflow 

was lost to evapotranspiration in the channels. Analyses for 

San Felipe Creek resulted in an estimate of 9 percent of runoff 

and 3 percent of recharge that became recharge to Borrego 

Basin. Analyses for Coyote Creek resulted in an estimate of 

22 percent of runoff and 1 percent of recharge that became 

recharge to Borrego Basin. These estimates were used to 

scale the BCM recharge and runoff for all ungaged streams 

(black dots in fig. 18). The spatially distributed average annual 

potential runoff and average annual potential recharge during 

1971–2000 are shown in figure 18.

Potential runoff and potential recharge were simulated for 

the watersheds draining into the Borrego Valley on a monthly 

basis for years 1940–2007 as spatially distributed among the 

watersheds draining into Borrego Basin. For these contributing 

watersheds, the BCM-simulated total annual potential recharge 

for years 1940–2007 ranged from 0 to about 5,000 acre-ft, and 

the total potential runoff ranged from 0 to about 45,000 acre-ft 

(fig. 19A). 

The values of total annual potential runoff (fig. 19A) 

and total annual potential recharge (fig. 19B) estimated by 

using BCM have been shown to replicate climatic variability 

patterns well. However, the potential recharge estimated by 

using BCM is not necessarily equivalent to actual recharge 

because water that infiltrates past the root zone does not 

always reach the water table (Flint and Martin, 2012; Rewis 

and others, 2006). The potential for differences between 

estimated net infiltration and actual groundwater recharge 

tends to increase with increases in the unsaturated-zone 

thickness, travel time of infiltration through the unsaturated 

zone, climate variability, and geologic heterogeneity in the 

unsaturated zone. In mountainous areas, where the unsaturated 

zone is likely to be more geologically heterogeneous, the 

potential for localized perching and lateral groundwater flow 

in the shallow subsurface increases. Lateral groundwater flow 

in the shallow subsurface (seepage) can divert a portion of net 

infiltration downstream to springs or to subsurface locations 

within the zone of evapotranspiration. This is especially 

likely to be true for steep mountain watersheds underlain by 

low-permeability bedrock, such as the higher elevation areas 

of the watersheds draining to Borrego Valley. The net effect 

of seepage on a basin-wide scale is a decrease in recharge 

balanced by an increase in ET and streamflow. Flint and 

Martin (2012) showed this effect in the mountainous terrain 

a couple hundred miles northwest of Borrego around Big 

Bear by comparing results from an infiltration model that was 

modified to simulate lateral flow in the shallow groundwater 

system to those from BCM. In the Big Bear study, an 

estimated approximately 35 percent of the potential recharge 

resulted in groundwater recharge. Likewise, Rewis and 

others (2006) found that mountain-front recharge estimated 

by using an infiltration model with no lateral seepage needed 

to be reduced to about 43 percent of the upstream recharge. 

Although the percentage of recharge that becomes underflow 

to Borrego Valley is unknown, the percentage of potential 

recharge from contributing watersheds is probably even lower 

than percentages estimated for these studies because of the 

steep mountain drainage basins underlain by low-permeability 

rock in an arid environment (Flint and Martin, 2012; Rewis 

and others, 2006). Thus, as a starting point for this study, it 

was assumed that 10 percent of the in-place recharge becomes 

underflow to the Borrego Valley and that all the runoff flows 

into the Borrego Valley.

In summary, natural recharge to the Borrego Valley has 

two components: runoff and underflow from the upstream 

components of the Borrego Valley watershed (fig. 18). Note 

that there is frequently minimal or no streamflow in the 

Borrego Valley (figs. 15, 17), and significant streamflow (more 

than 10,000 acre-ft/yr or 13.8 cubic feet per second) occurred 

in only 7 years during 1945–2007, or about 11 percent of 

the time. The overall estimate of gaged and ungaged inflow 

ranges from less than 10 to approximately 44,000 acre-ft/yr, 

averaging approximately 3,600 acre-ft/yr. Because of the 

young age of many groundwater samples from wells along the 

mountain fronts, the recharge from the upstream components 

of the watershed is not thought to be negligible. On average, 

total underflow (from adjacent basins and through channel 

alluvium) is estimated to be approximately 10 percent 

of the in-place recharge, or on average approximately 

900 acre-ft/yr. Hence, the resulting estimated potential average 

annual recharge to the Borrego Valley was approximately 

4,500 acre-ft/yr and ranged from about 10 to 45,000 acre-ft/yr 

(fig. 19C).

Groundwater Discharge

Before the Borrego Valley was developed by settlers and 

farmers, groundwater discharge consisted of transpiration by 

mesquite trees and other native vegetation, discharge from 

Borrego Spring (Mendenhall, 1909), and lateral groundwater 

underflow that left the basin across the southeastern boundary 

of the valley. Before the valley was developed, long-term 

natural recharge and discharge were in dynamic equilibrium 

and, on average, were roughly equal, with short-term 

variations largely attributed to climatic variations. Over the 

long term, essentially no net change in groundwater storage 

occurred. Once the valley was settled, substantial changes 

to the amount, distribution, and type of discharge from the 

valley began. The valley has gradually been transformed to 

include farms, residential homes, and golf resorts (California 

Department of Water Resources, 1984b).
52 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

sac13-0509_figure 18a_runoff_updated

116°20’

116°30’

116°40’

33°

30’

33°

20’

33°

10’

33°

EXPLANATION

Average annual potential runoff, in inches (millimeters)

0.80 to 1.18 (21 to 30) 3.16 to 3.54 (81 to 90) 7.88 to 9.84 (201 to 250)

1.19 to 1.57 (31 to 40)

0 to 0.004 (0 to 0.1) 1.58 to 1.97 (41 to 50) 3.55 to 3.94 (91 to 100)

0.005 to 0.2 (0.2 to 5) 1.98 to 2.36 (51 to 60) 3.95 to 4.72 (101 to 120)

0.3 to 0.4 (6 to 10) 2.37 to 2.76 (61 to 70) 4.73 to 5.91 (121 to 150)

0.5 to 0.79 (11 to 20) 2.77 to 3.15 (71 to 80) 5.92 to 7.87 (151 to 200)

Surface-water entry point

Boundary of basins draining to Borrego Valley

Rivers, streams, and creeks

Small subbasins in Borrego Valley

Boundary of Borrego Valley groundwater basin

Borrego Palm

Creek

Coyote Creek

San Felipe C reek

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

A

Figure 18. Spatially distributed values for the Borrego Valley, California, 1971–2000, of potential A, runoff and B, recharge.
Hydrogeology 53

sac13-0509_figure 18b_recharge_updated

116°20’

116°30’

116°40’

33°

30’

33°

20’

33°

10’

33°

EXPLANATION

Average annual potential recharge, in inches (millimeters)

0.80 to 1.18 (21 to 30) 3.16 to 3.54 (81 to 90) 7.88 to 9.84 (201 to 250)

1.19 to 1.57 (31 to 40)

0 to 0.004 (0 to 0.1) 1.58 to 1.97 (41 to 50) 3.55 to 3.94 (91 to 100)

0.005 to 0.2 (0.2 to 5) 1.98 to 2.36 (51 to 60) 3.95 to 4.72 (101 to 120)

0.3 to 0.4 (6 to 10) 2.37 to 2.76 (61 to 70) 4.73 to 5.91 (121 to 150)

0.5 to 0.79 (11 to 20) 2.77 to 3.15 (71 to 80) 5.92 to 7.87 (151 to 200)

Surface-water entry point

Boundary of basins draining to Borrego Valley

Rivers, streams, and creeks

Small subbasins in Borrego Valley

Boundary of Borrego Valley groundwater basin

Borrego Palm

Creek

Coyote Creek

San Felipe C reek

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

B

Figure 18. —Continued
54 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 19. Simulated values for the Borrego Valley, California, 1940–2007, of total annual A, potential runoff into the valley; B, potential 

recharge in upstream portions of the watershed (potential underflow); and C, recharge (underflow and runoff) into the valley.

0

1

2

3

4

5

6

Total annual potential in-place 

underflow (recharge),

in thousands of acre-feet 

Water year 

0

10

15

5

20

25

30

35

45

40

1941 1943 1945 1947 1949 1951 1953 1955 1956 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 

1941 1943 1945 1947 1949 1951 1953 1955 1956 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 

1941 1943 1945 1947 1949 1951 1953 1955 1956 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 

Total annual recharge,

in thousands of acre-feet 

0

10

15

5

20

25

30

35

45

40

Total annual potential runoff,

in thousands of acre-feet 

sac13-0509_Figure 19abc_potential recharge

Coyote Creek

Palm Canyon

San Felipe Creek

Henderson

All others 

EXPLANATION

Coyote Creek

Palm Canyon

San Felipe Creek

Henderson

All others 

EXPLANATION

Coyote Creek

Palm Canyon

San Felipe Creek

Henderson

All others 

Coyote Creek

Palm Canyon

San Felipe Creek

Henderson

All others 

Underflow (recharge) Runoff

EXPLANATION

A

B

C
Hydrogeology 55

Evapotranspiration

Prior to development, mesquite trees, salt grass, willow, 

and rushes were reported to be abundant in the valley 

(Mendenhall, 1909). Phreatophytes occurred primarily along 

the northern part of the valley and in the area of the Borrego 

Sink (fig. 6A) where groundwater was discharged through ET. 

The Borrego Sink, a topographic low where the water table 

was within 10 ft of land surface, once sustained a thriving 

stand of mesquite and other phreatophytes. Mitten and others 

(1988) estimated that prior to 1946, about 4,300 acre-ft of 

water was discharged from phreatophytes annually by ET. 

Moyle (1982) estimated that 1,220 acre-ft was used by the 

marshland that surrounded the Borrego Sink in 1980—almost 

half of the water entering the valley that year from streams on 

the west side. Henderson (2001) reports simulated ET rates 

that ranged from 3,900 acre-ft/yr in 1946 to 132 acre-ft/yr in 

2000.

Underflow Out of the Basin

Groundwater exits the valley as lateral subsurface flow 

at the southeastern end of the valley and flows toward the 

community of Ocotillo Wells (fig. 1). Direct measurements of 

underflow are not possible, but several previous investigators 

have reported estimates from model results. Mitten and others 

(1988) estimated that about 900 acre-ft/yr of water exited the 

valley as underflow. Henderson (2001) reports much higher 

simulated underflow rates, ranging from 6,050 acre-ft/yr 

in 1946 to 2,780 acre-ft/yr in 2000, with an average of 

3,780 acre-ft/yr for the period 1945–2000. These higher rates 

can be attributed to the high, simulated rates of mountainfront recharge, which ranged from 0 to 19,860 acre-ft/yr 

and averaged 1,780 acre-ft/yr for the period 1945–2000 

(Henderson, 2001).

Groundwater Pumping

During the period of 1920–45, few wells were drilled, 

and few groundwater data were collected in the Borrego 

Valley (Moyle, 1982). Brown (1923) reported that some 

homesteads had several wells in 1917–18, some under artesian 

conditions and reportedly flowing. Although some irrigated 

date groves were established by 1926–27 (Moyle, 1982), most 

of the groundwater use was for domestic purposes until about 

the mid-1940s and was probably less than about 300 acre-ft/yr 

(Mitten and others, 1988). After the conclusion of World War 

II, the population of the Borrego Valley grew, and new wells 

were drilled for farming; irrigation for agriculture became 

the main use of groundwater. On the basis of estimates of 

ET, groundwater pumping replaced ET as the primary form 

of discharge from the aquifer system around the late 1940s 

or early 1950s (fig. 20). Although agricultural irrigation 

has remained the single most intensive use of groundwater, 

groundwater is also used by parks, residential homes, and 

golf resorts in the valley (California Department of Water 

Resources, 1984b). Annual net groundwater pumpage was 

estimated to be about 12,000 acre-ft in 1990 and nearly 

18,000 acre-ft in 2000 (Borrego Water District, 2000). In 

recent years, about 70 percent of the groundwater used 

each year has been for agriculture, about 20 percent for golf 

courses and other recreational uses, and about 10 percent for 

municipal and domestic use (residential, commercial, and the 

Anza-Borrego Desert State Park) (figs. 1, 20). Groundwaterflow model simulations by Henderson (2001) estimated 

that, in year 2000, 62 percent of the groundwater use in 

Borrego Valley was for agricultural irrigation, 22 percent for 

recreational purposes (golf course irrigation), and 16 percent 

for municipal supply to housing developments.

Figure 20. Annual and cumulative total pumpage, Borrego Valley, California, 1945–2000. 

0

100

200

300

400

500

600

700

0

5

10

15

20

25

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 

Cumulative total pumpage,

in thousands of acre-feet

Annual total pumpage,

in thousands of acre-feet

Year 

EXPLANATION

Recreational

Agricultural

Water use

Municipal

1966: Grape irrigation ends

1978: Citrus expands
56 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Agricultural Water Use

Historically, irrigation for agriculture has been the main 

anthropogenic use of groundwater. As mentioned previously, 

agriculture expanded rapidly after about 1945, and since 

the late 1940s to early 1950s, the estimated net pumpage, or 

consumptive use, of groundwater has exceeded estimated 

natural recharge. Recently, net pumpage was estimated to 

be approximately four to five times the natural recharge rate 

(Borrego Valley Water District, 2000; Netto, 2001). The 

estimates of net pumpage are based on the consumptive use 

estimated for each land-use type. The land-use types are based 

on maps, aerial photographs, and other land-use information 

available for various periods and compiled by Moyle (1982) 

and Netto (2001). The consumptive use associated with each 

land-use type is simulated in this study on the basis of the 

percentage of land cover, reference evapotranspiration, and 

crop coefficients (KC). The methods used to estimate net 

pumpage, and the magnitudes of these estimates, are described 

in more detail in the “Integrated Hydrologic Model” section of 

this report.

Recreational Water Use

Recreational water use has become the second-largest 

use of groundwater in the Borrego Valley (Borrego Water 

District, 2000; Netto, 2001). The primary use of groundwater 

for recreational activities is irrigation of several golf courses 

in the valley, including the De Anza Country Club, the 

Borrego Springs Resort and Country Club operated by the 

BSPCSD, the Road Runner Golf and Country Club, and the 

Rams Hill Development (fig. 6K). Netto (2001) describes 

the history of these courses, which is summarized here. The 

De Anza Country Club began development in 1953 and 

reached completion in 1979. During 1963–98, the BSPCSD 

operated a 9-hole course and expanded it to an 18-hole 

course in 1998. The Road Runner Country Club golf course 

began development in 1967 and completed it in about 1979. 

Construction of the Rams Hill golf course was started in 1982 

and was completed in 1983. Historically, the BWD provided 

groundwater to the Rams Hill Development; details of the 

pumping for this development, including the golf course, are 

given in section “Municipal Water Use.” As for agricultural 

water use, the consumptive use of water by golf courses is 

simulated in this study on the basis of the percentage of land 

cover, reference evapotranspiration, and KC; the methodology 

used to derive the estimates for recreational water use and the 

magnitudes of these estimates are described in more detail in 

the “Integrated Hydrologic Model” section of this report.

Municipal Water Use

Compared to crop irrigation and recreational use, 

municipal water use is a relatively small component of 

groundwater pumping in the valley; however, municipal 

water use is well documented. Historical records of municipal 

pumping have been compiled or estimated for the BWD, 

which includes the Borrego Springs Water Company, 

the BSPCSD, and Borrego Air Ranch (BAR; table 5). 

Total annual municipal pumpage estimates range from 

about 110 acre-ft in 1945 (Netto, 2001) to a high of about 

4,680 acre-ft in 2007 (J. Rolwing, Borrego Water District, 

written commun., 2011) (fig. 21). During 1945–2010, the 

cumulative amount of groundwater pumped was about 

153,000 acre-ft.

The BWD is the longest serving water purveyor in the 

valley. Several water districts have served the valley since 

1945, but they have been annexed or acquired by BWD and 

grouped into Improvement Districts (ID). Pumpage quantities 

for BWD were reported by well starting in 1983 for the wells 

in ID4 and in 1984 for wells in ID1 and ID3. ID3 represents 

the Rancho Borrego Mutual Water Company and the Golden 

Sand Mutual Water Company, which joined BWD in 1990. 

BWD purchased Borrego Spring Water Company (BSWC) 

in 1997, which is represented by ID4. In 2008, BWD began 

serving the BSPCSD, which is represented by ID5.

The BWD reported annual pumpage by well during 

1984–2010 for ID1 wells, 1983–2010 for ID4 wells, and 

2005–10 for the ID5 well (table 5). To estimate pumpage for 

periods when records were not available from BWD or not 

estimated by Netto (2001), the drillers’ logs were examined 

to determine when each well was drilled. Annual pumpage at 

each well was estimated by using the earliest known reported 

quantity and linearly decreasing that value to zero for the year 

before each well was drilled. No data were available for the 

ID4 wells for 1995 from BWD; annual pumpage was assigned 

the average values for 1994 and 1996. Monthly pumpage data 

by well were reported by BWD starting in 1987 for ID1 wells, 

1997 for ID4 wells, and 1990 for ID3 wells. When monthly 

pumpage estimates were not available, the annual pumpage 

data were apportioned on the basis of the average percentage 

of reported annual pumpage by month for all wells provided 

by BWD for 1997–2010 (fig. 22).

The BAR supplies water to a small community of homes 

in the southeastern part of the valley. Although no production 

records were available, estimates of annual groundwater 

pumpage were about 10 acre-ft for 1945–2000 (Netto, 2001) 

and 2001–2010 (J. Rolwing, Borrego Water District, written 

commun., 2011) (table 5).

Water from BWD’s ID1 wells has been used to irrigate 

the Rams Hill golf course (fig. 6K) and master-planned 

community since it was constructed in 1983. During 

1983–2010, the amount of water used annually for recreational 

and municipal purposes in the development ranged between 

about 630 acre-ft in 2010 and about 1,750 in 2007. During 

this period, the annual water demand from the Rams Hill 

development constituted 18–28 percent of the total BWD 

pumpage (fig. 23). Annual Rams Hill water use from all 

six ID1 wells for 1983–86 was reported by Netto (2001), and 

monthly water use for 1987–2010 was reported by Borrego 

Water District (J. Rolwing, Borrego Water District, written 

commun., 2011). To distribute the amount of water pumped 

from ID1 wells that was served to Rams Hill on a monthly 

basis for the BVHM (see “Integrated Hydrologic Model”

section), the average monthly percentage from 1987–2010 

was calculated. This average value was used for the monthly 

values for the years when monthly data were not available.
Hydrogeology 57

Table 5. Estimated and reported total municipal pumpage, Borrego Valley, California, 1945–2010.—Continued[Values reported in acre-feet. Abbreviations: BAR, Borrego Air Ranch, BSPCSD, Borrego Springs Park and Community Services District; BWD, Borrego Water District; ID, Improvement District; —, no data. Blue indicates yearly data reported by BWD; Green indicates yearly data reported by Netto (2001); Orange indicates monthly data reported by BWD.

Year

BWD ID1 wells ID3 

wells

Borrego Springs Water Company ID4 wells BSPCSD ID5 BAR

ID1-1 ID1-2 ID1-8 ID1-10 ID1-12 ID1-16 ID4-21 ID4-31 ID4-41 ID4-5 ID4-10 ID4-11 ID4-181 Wilcox

1945 — — — — — — 7 27 13 29 — — — 31 — — — 101946 — — — — — — 10 38 19 40 — — — 43 — — — 101947 — — — — — — 13 49 24 52 — — — 56 — — — 101948 — — — — — — 16 60 29 64 — — — 68 — — — 101949 — — — — — — 20 71 35 75 — — — 80 — — — 101950 — — — — — — 23 82 40 87 — — — 93 — — — 101951 — — — — — — 26 92 45 98 — — — 105 — — — 101952 — — — — — — 29 103 51 110 — — — 118 — — — 101953 — — — — — — 33 114 56 122 — — — 130 — — — 101954 — — — — — — 36 125 61 133 — — — 142 — — — 101955 — — — — — — 39 136 66 145 — — — 155 — — — 101956 — — — — — — 42 147 72 156 — — — 167 — — — 101957 — — — — — — 46 158 77 168 — — — 179 — — — 101958 — — — — — — 49 169 82 180 — — — 192 — — — 101959 — — — — — — 52 179 88 191 — — — 204 — — — 101960 — — — — — — 55 190 93 203 — — — 217 — — — 101961 — — — — — — 59 201 98 214 — — — 229 — — — 101962 — — — — — — 62 212 104 226 — — — 241 — Start up — 101963 — — — — — — 65 223 109 237 — — — 254 — 100 — 101964 — — — — — — 68 234 114 249 — — — 266 — 100 — 101965 — — — — — — 72 245 120 261 — — — 279 — 100 — 101966 — — — — — — 75 256 125 272 — — — 291 — 100 — 101967 — — — — — — 78 267 130 284 — — — 303 — 100 — 101968 — — — — — — 81 277 136 295 — — — 316 — 100 — 101969 — — — — — — 85 288 141 307 — — — 328 — 100 — 101970 — — — — — — 88 299 146 319 — — — 340 — 100 — 101971 Start up — — 91 310 152 330 — — — 353 — 100 — 101972 2.96 3.72 34.56 53.48 — — 94 321 157 342 — — — 365 — 100 — 101973 5.88 7.41 69.18 106.94 — — 98 332 162 353 — — — 379 — 100 — 101974 8.80 11.10 103.80 160.40 — — 101 313 168 365 — — — 390 — 100 — 101975 11.72 14.79 138.42 213.86 — — 104 354 173 377 — — — 402 — 100 — 101976 14.64 18.48 173.04 267.32 — — 107 364 178 388 — — — 415 — 100 — 101977 17.56 22.17 207.66 320.78 — — 111 375 184 400 — — — 427 — 100 — 101978 20.48 25.86 242.28 374.24 — — 114 386 189 411 — — — 440 — 100 — 101979 23.40 29.55 276.90 427.70 — — 117 397 194 423 — — — 425 — 100 — 10

Table 5. Estimated and reported total municipal pumpage, Borrego Valley, California, 1945–2010.
58 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley Table 5. Estimated and reported total municipal pumpage, Borrego Valley, California, 1945–2010.—Continued [Values reported in acre-feet. Abbreviations: BAR, Borrego Air Ranch, BSPCSD, Borrego Springs Park and Community Services District; BWD, Borrego Water District; ID, Improvement District; —, no data. Blue indicates yearly data reported by BWD; Green indicates yearly data reported by Netto (2001); Orange indicates monthly data reported by BWD. Year BWD ID1 wells ID3 wells Borrego Springs Water Company ID4 wells BSPCSD ID5 BAR ID1-1 ID1-2 ID1-8 ID1-10 ID1-12 ID1-16 ID4-21 ID4-31 ID4-41 ID4-5 ID4-10 ID4-11 ID4-181 Wilcox 1980 26.32 33.24 311.52 481.16

—

— 120 408 200 435 

—

—

— 464

— 100

— 10

1981 29.24 36.93 346.14 534.62

—

— 124 419 205 446 

—

—

— 477

— 100

— 10

1982 32.16 40.62 380.76 588.08

—

— 127 430 210 458 

—

—

— 489

— 100

— 10

1983 35.08 44.31 415.38 641.54

—

— 130 440.58 215.51 469.19

—

—

— 501.44

— 100

— 10

1984 38.00 48.00 450.00 695.00 Start up

— 130 466.52 325.10 481.06

—

—

— 363.42

— 100

— 10

1985 1.40 35.34 407.09 600.78 316.90

— 141 466.52 325.10 504.14

—

—

— 363.42

— 100

— 10

1986 0.01 0.01 387.88 578.75 354.13

— 153 466.52 325.10 504.14

—

—

— 363.42

— 100

— 10

1987 99.76 75.32 337.92 656.55 317.55

— 144 327.98 324.57 515.79 Start up

—

— 544.84

— 100

— 10

1988 168.99 87.95 374.53 784.42 755.60

— 144 294.21 658.04 541.55 43.27

—

— 288.86

— 100

— 10

1989 223.01 104.68 333.45 329.38 764.16

— 144 238.17 548.87 495.01 255.75 Start up

— 371.23

— 100

— 10

1990 85.51 61.66 305.88 451.11 942.39 Start up 118 221.80 822.87 546.65 172.65 19.61

— 126.26

— 100

— 10

1991 63.94 64.43 373.30 544.02 961.04 6.96 100 253.81 504.50 503.78 15.08 143.56

— 407.51

— 100

— 10

1992 231.50 3.60 153.11 25.35 570.06 974.05 100 333.73 474.91 466.17 54.98 202.45

— 684.75

— 100

— 10

1993 125.84 0.00 45.57 81.27 556.20 1145.20 102 267.47 411.12 446.92 32.09 219.43

— 676.61

— 100

— 10

1994 168.59 0.00 39.10 24.85 523.29 971.61 101 366.73 384.81 491.23 3.90 213.47

— 616.78

— 100

— 10

1995 3.77 18.58 34.46 55.88 557.06 1,072.59 126 279.73 521.19 561.38 0.00 188.90 Start up 374.86

— 100

— 10

1996 24.67 15.15 2.80 102.67 702.20 1,091.95 182 192.72 657.57 631.52 stopped 164.33 392.89 132.93

— 100

— 10

1997 36.87 26.76 73.85 130.11 636.15 1,056.08 138 197.20 623.35 657.71

— 211.33 286.80 114.28

— 100.00

— 10

1998 21.39 20.56 41.28 88.03 722.68 1,054.58 118 174.01 730.37 417.04

— 221.24 319.49 115.60

— 100.00

— 10

1999 30.67 33.70 55.37 137.44 732.43 924.68 125 164.66 667.54 406.04

— 182.38 274.68 249.88

— 100.00

— 10

2000 60.85 30.95 12.00 168.68 710.59 1,012.72 115 209.84 660.74 454.55

— 168.73 389.76 153.18 Start up 100.00

— 10

2001 22.15 23.39 64.74 368.90 607.90 610.07 126 244.22 639.00 392.71

— 179.81 344.98 87.29 0.92 100.00

— 10

2002 24.16 26.39 8.71 421.40 829.43 734.95 138 251.84 631.46 184.74

— 182.86 702.14 91.56

— 100.00

— 10

2003 52.35 51.35 0.72 288.93 864.50 650.24 129 241.95 526.03 275.23

— 182.15 703.93 89.01

— 100.00

— 10

2004 15.31 6.31 6.17 217.31 741.49 834.59 113 209.45 311.84 385.10

— 140.92 737.68 74.52 11.45 100.00 Start up 10

2005 15.00 65.41 77.81 322.74 478.72 415.85 93 196.58 581.41 229.27

— 134.31 534.31 78.89 5.69 100.00 311.60 10

2006 18.54 27.45 215.91 186.52 662.09 625.88 95 163.14 604.05 489.59

— 134.26 385.54 86.71 10.71 100.00 242.89 10

2007 114.20 96.30 251.36 328.09 843.61 737.18 104 135.31 557.29 592.58

— 118.02 393.84 71.48 17.56 100.00 209.11 10

2008 91.51 77.56 111.30 359.25 589.80 654.98 154 123.38 283.93 606.18

— 105.00 553.54 71.36 47.80 100.00 10.98 10

2009 38.66 20.99 24.11 325.83 834.83 583.25 150 122.07 Failed 571.25

— 104.43 786.25 97.42 71.94 100.00 225.53 10

2010 11.08 13.49 44.47 195.73 385.88 439.76 173 26.84

— 584.76

— 111.68 734.74 58.29 3.48 100.00 173.6 10

1Pumpage was reported before drill date of current well; before this date, pumpage occurred in a well in the same general vicinity.
Hydrogeology 59

Figure 21. Annual and cumulative municipal pumpage, Borrego Valley, California, 1945–2010.

Figure 22. Average percentage of reported annual pumpage, by month, for Borrego Water District wells, Borrego Valley, California, 

1997–2010. sac13-0509_Figure 21 BW-Q

0

20

40

60

80

100

120

140

160

180

0

1945 1950 1955 1960 1965 1970 1975 1980

Year

1985 1990 1995 2000 2005 2010

1

2

3

4

5

Cumulative pumpage,

in thousands of acre-feet 

Municipal pumpage, in thousands of acre-feet 

Borrego Water District ID1 Wells

Borrego Water District ID4 Wells

Other wells

Cumulative 

See table 5 for list of Borrego 

Water District wells

EXPLANATION

0

2

4

6

8

10

12

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Average percentage of reported annual pumpage, by month

Month
60 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Groundwater-Quality Sampling and 

Wellbore Flow

Hydrologic and chemical data, including stable and 

radioactive isotopes, were collected by using various 

methods during the study. Water samples from production 

and agricultural wells were used to determine the chemical 

and isotopic composition of groundwater with depth and 

spatially throughout the basin. In addition, wellbore-flow and 

depth-dependent groundwater-quality data were collected 

concurrently in selected irrigation wells to determine the 

variations in flow and water quality with depth, as described 

by Izbicki and others (1999).

Wells sampled by the USGS for water-quality analyses 

were pumped continuously to purge at least three casing 

volumes of water (Wilde and others, 1999). Sampling points 

were as close to the wellhead as possible and were upstream 

from water-storage tanks and wellhead treatment systems. 

Samples were collected by using an apparatus consisting of 

Teflon® tubing and stainless steel fittings. Field measurements 

of dissolved oxygen, temperature, pH, and specific 

conductance were made at each of the sampling sites prior 

to sampling (U.S. Geological Survey, variously dated). Field 

measurements were recorded at 5-minute (min) intervals for 

at least 30 min; when these values remained stable for 20 min, 

samples were collected. 

Detailed sampling protocols are described in Koterba 

and others (1995) and the USGS National Field Manual 

(U.S. Geological Survey, variously dated); only brief 

descriptions are given here. Before sample collection, all 

polyethylene sample bottles were rinsed three times with 

groundwater. Samples for analysis of stable isotopes of 

hydrogen and oxygen were collected unfiltered and stored at 

ambient temperature in 60 milliliter (mL) glass containers 

sealed with conical caps. All other samples were filtered by 

using a 0.45-micrometer (μm) pore-size capsule filter that was 

rinsed with 2 liters (L) of deionized water and then rinsed with 

1 L of groundwater prior to sample collection. Samples for 

analysis of trace elements, major cations, and gross alpha and 

beta radiation were preserved to pH less than 2 with certified, 

traceable 7.5-Normal (N) nitric acid. Samples for major anion, 

total dissolved solid (residue on evaporation), and nutrient 

analyses were not preserved with acid; nutrient samples were 

stored in a brown polyethylene bottle. Samples for analysis of 

stable isotopes of carbon and dissolved inorganic carbon and 

carbon-14 (14C) abundance were bottom-filled into 500-mL 

glass bottles that first were overfilled with three bottle volumes 

of groundwater. These samples had no headspace and were 

sealed with conical caps to avoid atmospheric contamination. 

All filtered samples were stored and shipped cold after 

collection.

Samples collected by the USGS for inorganic 

constituents (trace elements, major and minor ions, and 

nutrients) were analyzed at the USGS National Water Quality 

Laboratory in Denver, Colorado. Stable isotopes of hydrogen 

and oxygen (deuterium and oxygen-18) were analyzed at 

the USGS Reston Stable Isotope Laboratory in Virginia, 

stable isotopes of carbon and 14C were analyzed at the Woods 

Hole Oceanographic Institution, National Ocean Sciences 

Accelerator Mass Spectrometry Facility, in Woods Hole, 

Massachusetts, and gross alpha and gross beta radioactivity 

were analyzed by Eberline Services in Richmond, California. 

Samples for tritium (3H) were analyzed at the USGS Stable 

Isotope and Tritium Laboratory, Menlo Park, California.

Figure 23. Pumpage by Rams Hill Development and total pumpage by Borrego Water District, Borrego Valley, California, 1983–2010.

sac13-0509_Figure 23 BWD_RamsHill

0

1

2

3

4

5

6

7

Pumpage, in thousands of acre-feet

1983 1985 1990 1995

Year

2000 2005 2010

Total Borrego Water District use

Total Rams Hill use

EXPLANATION
Groundwater-Quality Sampling and Wellbore Flow 61

Wellbore Flow and Depth-Dependent WaterQuality Sampling

Groundwater flow in the wellbore (wellbore flow) and 

depth-dependent water-quality data were collected from well 

10S/6E-35Q1 (MW-4 of BWD) in August 2011 to determine 

the distribution of flow and water-quality constituents to the 

well (figs. 9, 24). The combination of wellbore-flow and 

depth-dependent water-quality data is especially effective 

in assessing changes in aquifer properties and water quality 

with depth (Izbicki and others, 1999). The wellbore flow log 

was collected by using an electromagnetic (EM) flowmeter 

(U.S. Geological Survey, 2013). The EM flowmeter measures 

velocities according to Faraday’s Law, where the voltage 

generated by the movement of charged ions in water flowing 

through an induced magnetic field is proportional to the 

velocity of water flowing through the field. The tool has a 

range of 0.3–260 feet per minute and is suitable both for the 

low velocities in un-pumped wells and for the high velocities 

in pumped wells (Newhouse and others, 2005). Depthdependent water-quality data were collected while the well 

was being pumped by using a small-diameter sampling hose, 

as described by Izbicki and others (1999). To collect a waterquality sample, the hose is pressurized to greater than the 

hydrostatic pressure of water at the sample depth and lowered 

into the well. When the sample depth is reached, the hose is 

vented at the surface and water from the well enters the hose 

at the sample depth. The hose is retrieved, and the sample is 

expelled from the hose by using nitrogen gas. The process is 

repeated at several depths to construct a water-quality profile 

within the well.

Well 10S/6E-35Q1 has a single screened interval from 

85 to 390 ft bls and a static groundwater level of 95 ft bls. The 

pump intake was at 131 ft bls; water in the well was flowing 

downward above the pump and upward below it (fig. 24A). 

It was not possible to measure flow in the well above the 

pump intake by using an EM flowmeter, and the percentage of 

total flow above the pump intake was estimated on the basis 

of flowmeter-calibration data. The wellbore-flow logs were 

collected from 151 ft bls to the bottom of the well while it 

was being pumped at approximately 160 (gal/min); drawdown 

was approximately 15 ft. On the basis of the wellbore flow 

log (fig. 24A), about 51 percent of the water entered the 

well above 151 ft bls, 48 percent from 151 to 255 ft bls, and 

only about 1 percent from below 255 ft bls. The low volume 

produced from the bottom part of the well could be due to the 

lower transmissivity of the middle aquifer at this location and 

depth or possibly could be a result of encrustation of the well 

screen in this interval.

Water-quality samples were collected at four specific 

depths in the well while the well was being pumped; a bulk 

sample also was collected at the pump discharge point at land 

surface, which represents the composite water quality from 

all of the zones (fig. 24B). The sample from 350 ft bls was 

collected first and represents water in the aquifer below that 

depth; the next sample, from 275 ft bls, is a mixture of water 

representing the first sample and water that entered the well 

between the two sample depths. The sample collected at 125 ft 

bls, above the pump intake, is a composite of groundwater that 

has entered the well above that depth.

The quality of groundwater entering the well between 

selected depths can be estimated by coupling the velocity-log 

data and depth-dependent water-quality data (Izbicki, 2004). 

By measuring the concentrations of a constituent at two 

sequential depths (C1 and C2) in the well, the concentration of 

the water entering the well in the zone between the two sample 

depths (Ca) can be calculated by using measurements of the 

wellbore flow (estimated from the flow log and the diameter 

of the well) at the same depths as the samples (Q1 and Q2) in a 

mass-balance equation: 

[(C1Q1-C2Q2)/Qa] = Ca (1)

where

Qa = (Q1 – Q2).

Sources of Water-Quality Data

Several sources of water-quality data were used for 

this study. Data from the USGS, the California Department 

of Public Health (CA-DPH), BWD, and the California 

Department of Water Resources were combined into one 

database and can be accessed at http://ca.water.usgs.gov/

projects.borrego/borrego-water-quality.html. These data 

include trace elements, major ions, radionuclides, volatile 

organic compounds (VOCs), and pesticides. Chemical 

analyses completed by the USGS, which include the 

radioactive and stable isotope data, are also stored in the 

USGS National Water Information System (NWIS) database 

at http://waterdata.usgs.gov/ca/nwis/. Data retrieved from 

the CA-DPH database also can be accessed at http://www.

cdph.ca.gov/certlic/drinkingwater/Pages/EDTlibrary.aspx and 

represent more than 9,200 water-quality records from 30 wells 

sampled between 1985 and 2010.
62 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 24. Well construction for well 10S/6E-35Q1, Borrego Valley, California: A, wellbore-flow log and B, concentrations of selected 

dissolved constituents by depth, under pumping conditions. sac13-0509_Figure 24ab MW-4 QW

0 500 1,000

Total

dissolved

solids, 

in milligrams

per liter

Nitrate

as nitrogen, 

in milligrams

per liter

0 0.5 1 0 1 2 0 100 200 300

Sulfate, 

in milligrams

per liter

Arsenic, 

in micrograms

per liter

Manganese, 

in micrograms

per liter

0 300 500

Depth, in feet below land surface (BLS) 

0 50 100 150 200

Flow rate, in gallons per minute 

1% total flow 

100% total flow

49%

total

flow

Well MW-4

A (10S/6E-35Q1) B

0

50

100

150

200

250

300

350

400

0

50

100

150

200

250

300

350

400

Upper

aquifer

Middle

aquifer

Perforated

interval

85–390 feet

Flow rate

160 gal/min

Pump

intake

Manganese 

 values at 

 275 feet 

 BLS are 

 measured, not 

 calculated.

EXPLANATION

Boundary between upper and middle aquifer

Pump intake (131 feet)
Groundwater Quality and Age 63

Groundwater Quality and Age

The quality of groundwater in the Borrego Valley is a 

concern because of the reliance on groundwater in the basin 

for agricultural, recreational, and public supply. Historical 

data reported from the early 1950s (Burnham, 1954) show 

that most of the wells contained total dissolved solids (TDS) 

at concentrations that exceeded 500 mg/L and were as high as 

1,533 mg/L. California’s water-quality maximum secondary 

recommended contaminant level (CA-SMCL) for TDS is 

500 mg/L, and the upper contaminant level is 1,000 mg/L 

(California Department of Public Health, 2013). Nitrate 

concentrations also are of concern because of the potential 

for contamination of groundwater from septic effluent, 

sewage disposal, irrigation-return water, and decomposition 

of native vegetation. The California water-quality maximum 

contaminant level (CA-MCL) for nitrate as nitrogen (NO3-N) 

is 10 mg/L (California Department of Public Health, 2013); 

Moyle (1982) reported historical NO3-N concentrations that 

ranged from 0 to 68 mg/L.

To estimate the age (time since recharge) of groundwater 

in the basin, samples for analysis of radioactive isotopes 

of hydrogen (tritium, 3H) and carbon (carbon-14, 14C) were 

collected from selected wells throughout the basin. The age 

of water from wells can be used to determine the timing and 

sources of recharge and the movement of groundwater.

Changes in Groundwater Quality Compared to 

Changes in Groundwater Levels

As discussed previously in the “Groundwater-Flow 

and Groundwater-Level Change” section, groundwater 

levels have changed significantly in several areas in the 

Borrego Valley since the mid-1940s (figs. 13, 14). Most 

areas have had groundwater-level declines because the 

amount of groundwater pumped has exceeded recharge. To 

determine whether these declines have resulted in a change 

in water quality, groundwater-level and water-quality data 

were evaluated for several production wells throughout the 

valley. As groundwater levels decline, the contributions of 

groundwater flow from various parts of the aquifer system 

to the wells can change. Lowering the water table in shallow 

aquifers can draw chemical constituents from anthropogenic 

and natural sources present near or at the water table into a 

well. Declining groundwater levels also cause a decrease in 

the saturated thickness of shallow aquifers, which could result 

in a larger proportion of the groundwater pumped from a well 

coming from aquifers that are deeper and could have poorer 

water quality. Groundwater from deeper alluvial aquifers 

typically is older, is in contact with aquifer materials longer, 

and can contain more dissolved solids and less dissolved 

oxygen, resulting in the potential for the degradation of water 

quality.

Selected water-quality constituents from CA-DPH 

were used to investigate potential changes in water quality 

with declining groundwater levels over time. To reduce the 

number of records evaluated, constituents were reviewed, 

and samples were selected on the basis of two criteria: (1) the 

sample contained a constituent with at least one valid reported 

concentration within the period of record that was greater 

than one-half the associated regulatory threshold and (2) the 

constituent was measured a minimum of four times during the 

period of record and had a minimum of 25-percent increase in 

concentration between the initial and final measurements. 

TDS and sulfate were the only two constituents in 

all the samples that met the criteria for evaluation. TDS 

concentrations collected from three wells in the Borrego 

Valley—0S/6E-18R1 (ID4-3), 11S/6E-7K3 (ID4-2), and 

11S/6E-23J1 (ID1-8; fig. 25)—show trends related to water 

level changes. Wells 10S/6E-18R1 and 11S/6E-7K3 are 

perforated both in the shallow and middle aquifer, with 

about 75 percent of the screened interval in the middle 

aquifer; well 11S/6E-23J1 is perforated in all three aquifers, 

with about 7 percent of the screened interval in the shallow 

aquifer, 17 percent in the middle aquifer, and 76 percent in 

the lower aquifer. Historical TDS concentrations in samples 

collected from these wells range from 328 to 910 mg/L and 

do not exceed the upper CA-SMCL of 1,000 mg/L. Wells 

10S/6E-18R1 and 11S/6E-7K3 had declining groundwater 

levels coupled with increases in TDS concentrations. Well 

10S/6E-18R1 had a groundwater-level decline of 75 ft 

during 1987–2008 and TDS concentrations that ranged 

from 572 mg/L in 1985 to 910 mg/L in 2002 (fig. 25A). 

Groundwater levels in well 11S/6E-7K3 declined 50 ft, 

and TDS concentrations increased from 342 to 510 mg/L 

during 1985–2010 (fig. 25B). In contrast, groundwater levels 

increased in 11S/6E-23J1 by 36 ft, and TDS concentrations 

increased from 328 to 460 mg/L over an approximately 

20-year period (fig. 25C). The reason for the increase in TDS 

is not certain and would take further investigation to determine 

it; however, it could be a result of irrigation return flow. The 

increase in groundwater levels in this part of the valley could 

be a result of a decrease in pumping and (or) a change in the 

distribution of wells pumping at the nearby Rams Hill golf 

course (fig. 6K).

No water samples had sulfate concentrations that 

exceeded the CA-SMCL of 500 mg/L. Wells 10S/6E-18R1 

and 11S/6E-23J1 had increases in sulfate concentrations over 

time; sulfate concentrations for well 10S/6E-18R1 increased 

during 1985–2007 from 248 to 400 mg/L (fig. 25A), and 

sulfate concentrations for well 11S/6E-23J1 increased during 

1991–2010 from 47 to 110 mg/L (fig. 25C). The reason for 

this increase is not certain and would take further investigation 

to determine it; however, it could be a result of irrigation 

return flow or changes in water quality with depth and the 

larger fraction of water now coming from the lower part of the 

system. 
64 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 25. Concentrations of total dissolved solids and sulfate, and groundwater levels for wells A, 10S/6E-18R1, B, 11S/6E-7K3, and 

C, 11S/6E-23J1 in the Borrego Valley, California, 1980–2010.

Year

Well ID4-3, 

 (10S/6E-18R1)

Well ID4-2,

 (11S/6E-7K3)

Well ID1-8,

 (11S/6E-23J1)

Total dissolved solids (TDS) and sulfate (SO4) concentration, in milligrams per liter

360

380

400

420

440

460

480

360

380

400

420

440

460

480

360

380

400

420

440

460

480

200

300

400

500

600

700

800

900

0

100

200

300

400

500

600

700

0

100

200

300

400

500

600

700

Water-level elevation, in feet above N

A

VD 88

SO4

SO4

SO4

TDS

Water

level

Water

level

Water

level

TDS

TDS

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2006 2010

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2006 2010

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2006 2010

sac13-0509_Figure 25 QW-WL

A

B

C
Groundwater Quality and Age 65

Distribution and Variation of Groundwater 

Quality

Water-quality data gathered from individual wells for 

this study were evaluated to determine whether any chemical 

constituents sampled exceeded either the California maximum 

contaminant level (CA-MCL) or California secondary 

maximum contaminant level (CA-SMCL). These data can be 

reviewed in the database created for this study (http://ca.water.

usgs.gov/projects.borrego/borrego-water-quality.html). With 

the exception of three fluoride concentrations from three wells 

that ranged from 2.69 to 4.87 mg/L (the CA-MCL is 2 mg/L), 

only nitrate and TDS concentrations frequently exceeded the 

CA-MCL and CA-SMCL, respectively. Moyle (1982) reports 

that historical data from the agricultural area in the northern 

part of the basin showed that some shallow wells contained 

groundwater that exceeded the CA-MCL of 10 mg/L for 

NO3-N and the CA-SMCL of 1,500 mg/L for TDS. In an effort 

to improve the quality of the water from their wells, some 

landowners either abandoned or destroyed wells perforated in 

the upper aquifer and installed deeper wells perforated only in 

the middle and lower aquifers. As a result, recent water-quality 

data for the shallow aquifer are sparse, and the evaluation of 

water quality in the shallow aquifer for this study was based 

predominantly on historical data reported by Moyle (1982) 

and Burnham (1954) and recorded in well files in the USGS 

project office in San Diego, California.

Distribution of Nitrates and Total Dissolved 

Solids

Water-quality samples from wells distributed throughout 

the valley show that NO3-N concentrations ranged from less 

than 1 mg/L to almost 67 mg/L. NO3-N concentrations were 

highest in the shallow aquifer and exceeded the CA-MCL 

of 10 mg/L in some samples from the shallow and middle 

aquifers in the northwestern part of the basin (fig. 26). NO3-N 

concentrations in samples from the lower aquifer did not 

exceed 6.7 mg/L.

Water-quality data show that TDS concentrations ranged 

from less than 500 mg/L to as high as 2,330 mg/L. Similar to 

the nitrate concentrations, the maximum TDS concentrations 

were in samples from the shallow aquifer and generally were 

highest in the northwestern part of the basin (fig. 27). TDS 

concentrations in samples from the middle aquifer were 

as high as 1,350 mg/L. With the exception of one sample, 

TDS concentrations in the lower aquifer did not exceed 

the CA-SMCL of 1,000 mg/L, and most samples had TDS 

concentrations lower than those in samples from the upper and 

middle aquifers.

Three wells in the northern part of the basin were 

sampled in 2011 in an effort to obtain recent water-quality data 

from adjacent wells to relate nitrate and TDS concentrations 

to depth. As was documented by Moyle (1982), most, if not 

all, shallow wells in this part of the basin have been either 

destroyed or abandoned because of poor water quality. 

Consequently, it was possible to sample wells perforated only 

in the middle and lower aquifers. Wells 10S/6E-15D3 and 

10S/6E-15D4 are perforated exclusively in the middle aquifer, 

and well 10S/6E-21A2 has 90 percent of its perforations 

in the lower aquifer. The top of the perforations of well 

10S/6E-15D4 are 180 ft above the top of the perforations 

in well 10S/6E-15D3. Therefore, of these three wells, 

10S/6E-15D4 is considered the shallowest site, 10S/6E-15D3 

is considered the mid-depth site, and 10S/6E-21A2 is 

considered the deepest site.

Samples collected from 10S/6E-15D4 (shallowest site) 

had the poorest groundwater quality of the three wells (http://

ca.water.usgs.gov/projects.borrego/borrego-water-quality.

html). The NO3-N concentration was 37.3 mg/L, which was 

at least 40 times more than those for the two deeper wells. 

Nitrate concentrations in samples from wells 10S/6E-15D3 

(mid-depth site) and 10S/6E-21A2 (deepest site) were 0.92 

mg/L and 0.02 mg/L, respectively. The two deeper wells 

did not have any constituents’ concentrations greater than 

the regulatory thresholds. However, samples collected 

from well 10S/6E-15D3 had molybdenum and fluoride 

concentrations of 38.9 μg/L and 1.69 mg/L, respectively, 

which were near the U.S. Environmental Protection Agency 

lifetime health advisory level of 40 micrograms per liter 

(μg/L) for molybdenum and the CA-MCL of 2 mg/L for 

fluoride. Samples from well 10S/6E-21A2 had molybdenum 

and fluoride concentrations of 33.1 μg/L and 0.75 mg/L, 

respectively. The TDS concentration in well 10S/6E-15D4 

was 1,350 mg/L, which was twice the concentration in well 

10S/6E-15D3 and almost three times the concentration in well 

10S/6E-21A2. Possible causes of increasing TDS in wells 

include irrigation with saline water, the downward movement 

of salts that have accumulated in the unsaturated zone, and 

the dissolution of evaporative minerals (Suarez, 1989). In arid 

areas, salts build up in the unsaturated zone over thousands of 

years and can be mobilized by irrigation water, subsequently 

degrading groundwater quality (Scanlon and others, 2007).

Variations in Water Quality with Depth

Groundwater flow and depth-dependent water-quality 

data were collected from well 10S/6E-35Q1 to determine 

whether there were variations in the distribution of flow 

or chemical constituents with depth (fig. 24). The sample 

of water collected at the surface, or the bulk sample, is a 

composite of the water quality from all producing zones in the 
66 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 26. Distribution of nitrate as nitrogen concentrations in the upper, middle, and lower aquifers, Borrego Valley, California, for the 

most recent sample.

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 26 NO3 by layer-upper, middle & lower

EXPLANATION

Well and nitrate, as nitrogen, concentration, in milligrams per liter

Less than 1

1 to 3

20.3 to 66.8

Upper aquifer Middle aquifer

Less than 1

1 to 3

28 to 37.3

Lower aquifer

Less than 1

1 to 3

6.7

Stream channel

Boundary of Borrego Valley

 groundwater basin

Wells with identifier have water-level and water-quality data shown in figure 25.

78 Road

Well ID4-3,

 (10S/6E-18R1)

Well ID

 (10S/6E-15D4)

Well ID

 (10S/6E-15D3)

Well ID

 (10S/6E-21A2)

Well ID4-2,

 (11S/6E-7K3)

Well ID1-8,

 (11S/6E-23J1)

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake
Groundwater Quality and Age 67

Figure 27. Distribution of total dissolved solids concentrations in the upper, middle, and lower aquifers, Borrego Valley, California.

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 27 TDS by layer

Stream channel

Boundary of Borrego Valley

 groundwater basin

Well and total dissolved solids concentration,

 in milligrams per liter

Less than 500

501 to 1,000

1,500 to 2,330

Upper aquifer Lower aquifer

Less than 1

501 to 1,000

1,570

Middle aquifer

Less than 500

501 to 1,000

1,001 to 1,350

78 Road

EXPLANATION

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake

Well ID

 (10S/6E-15D4)

Well ID

 (10S/6E-15D3)

Well ID

 (10S/6E-21A2)
68 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

well; no water-quality constituent concentrations in the bulk 

sample were greater than the CA-MCL or CA-SMCL (http://

ca.water.usgs.gov/projects.borrego/borrego-water-quality.

html). The differences in water quality in the discrete zones 

sampled were minor and showed little variation with depth 

(fig. 24B). With the exception of the manganese concentration 

in the sample collected at 350 ft bls, no constituents from 

the depth-dependent sampling exceeded either the CA-MCL 

or CA-SMCL; the manganese concentration from 350 ft bls 

was 239 μg /L, exceeding the CA-SMCL of 50 μg/L. The 

calculated value of the manganese concentration at 275 ft bls 

(20.6 μg/L) likely was affected by the relatively high volume 

of water entering the well in the interval above 350 ft bls 

compared to that entering from below that level (fig. 24). As 

discussed in the “Wellbore Flow and Depth-Dependent WaterQuality Sampling” section, only 1 percent of the flow entered 

the well below 275 ft bls; however, the contrast in flow in the 

interval between 275 and 350 ft bls and that below 350 ft bls 

was enough to reduce the concentration by about an order of 

magnitude. The smaller volume produced at the bottom of the 

well could be due to the lesser transmissivity of the middle 

aquifer at this location and depth or possibly could be a result 

of encrustation of the screened interval.

Groundwater Age

Groundwater samples from selected wells were analyzed 

for tritium and 14C for the purpose of determining the age of 

groundwater in the Borrego Valley. Tritium data are available 

for 9 wells, and 14C data are available for 11 wells. Eight wells 

both have tritium and 14C data (fig. 28). Tritium 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 

and nitrogen (Craig and Lal, 1961), by aboveground nuclear 

explosions, and by the operation of nuclear reactors. Tritium 

enters the hydrologic cycle following oxidation to tritiated 

water. Tritium values in precipitation under natural conditions 

would be about 3–15 tritium units (TU) (Craig and Lal, 1961; 

Clark and Fritz, 1997). One tritium unit is equivalent to one 

tritium atom in 1018 atoms of hydrogen (Taylor and Roether, 

1982). Aboveground nuclear explosions resulted in a large 

increase in tritium values in precipitation, beginning about 

1952 and peaking in 1963, of more than 1,000 TU in the 

northern hemisphere (Michel, 1989). Because tritium is part of 

the water molecule, its transport through the groundwater-flow 

system is considered conservative (unaffected by geochemical 

reactions other than radioactive decay); therefore, tritium is 

an excellent tracer of the movement of groundwater recharged 

less than 50 years before present (2010).

Carbon-14 is produced by the interactions between 

cosmic rays and nitrogen gas in the earth’s atmosphere and 

has a half-life of about 5,730 years (Mook, 1980). The 14C age 

is calculated on the basis of the decrease in 14C activity as 

a result of radioactive decay since groundwater recharge, 

relative to an assumed initial 14C concentration (Clark and 

Fritz, 1997). An average initial 14C activity of 100 percent 

modern carbon was assumed for this study, with estimated 

errors of calculated groundwater ages as much as about 

20 percent. Calculated 14C ages in this study are referred to as 

“uncorrected” because they have not been adjusted to account 

for any exchanges with sedimentary sources of carbon (Fontes 

and Garnier, 1979).

Tritium values and uncorrected carbon-14 ages from 

groundwater samples indicate that in most areas of the 

Borrego Valley, the groundwater was recharged thousands 

of years before present (ybp). Of the nine wells sampled for 

tritium, only well 9S/6E-31E3 near Coyote Creek and well 

10S/5E-25R1 near Borrego Palm Creek had measureable 

tritium values (greater than 0.7 TU; fig. 28), indicating that 

a portion of water from these wells was recharged to the 

groundwater system since 1952. The remaining seven tritium 

samples had values less than 0.7 TU. Water with tritium 

values less than 0.7 TU are considered “tritium dead” and 

indicate that the groundwater from these wells recharged the 

groundwater system more than about 50 years prior to sample 

collection (or prior to 1960). Uncorrected 14C ages ranged 

from less than 70 ybp in the two wells near the recharge areas 

of Coyote and Borrego Palm Creeks to almost 26,000 ybp 

in wells at the southeast end of the valley, with a median 

14C age of 7,000 ybp (fig. 28). Groundwater ages increased for 

samples from wells that were at increasingly greater distances 

from the recharge areas. Groundwater also was older in deeper 

wells, which is consistent with longer flow paths and the lower 

hydraulic conductivities of in the deeper parts of the aquifer 

system. Samples from three adjacent wells in the northern part 

of the basin showed that groundwater was progressively older 

with depth. Ages were about 1,600 ybp in a well screened 

between 460 and 760 ft bls (10S/6E-15D4), about 3,400 ybp 

in a well screened between 640 and 800 ft bls (10S/6E-15D3), 

and about 16,000 ybp in a well screened between 800 and 

890 ft bls (10S/6E-21A2).
Groundwater Quality and Age 69

Figure 28. Distribution of tritium values and uncorrected carbon-14 ages for wells in the Borrego Valley, California. (TU, tritium unit; 

<, less than; >, greater than; na, not applicable)

Clark Lake

Borrego

Sink

Benson 

Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

9S/6E-31E3

10S/5E-25R1

1,600

1,900

<70

<70

3,400

16,000

11,000

14,000

26,000

10,000

7,000

na

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 28 GW age

EXPLANATION

7,000

Less than 0.7 TU, tritium dead

Greater than 0.7 TU, tritium live

No sample for tritium

Uncorrected carbon-14

 ages, in years before 

 present

Tritium activity

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

na No sample for carbon-14

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake
70 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Land Subsidence

Groundwater-level declines accompanying the long-term 

extraction of groundwater cause an increase in the effective 

(intergranular) stress on the aquifer-system skeleton (matrix) 

that varies with respect to the distribution of hydraulic 

head changes in the aquifers and aquitards (interbeds and 

confining units) constituting the aquifer system. Depending 

upon the magnitude of the increased effective stress with 

respect to the stress history of the aquifer system, either 

relatively small-magnitude, reversible (elastic) or relatively 

larger magnitude, irreversible (inelastic) aquifer-system 

compaction and accompanying land subsidence can result. 

Aquifer-system compaction and land subsidence occur in 

many alluvial groundwater basins in California and elsewhere 

where significant quantities of water have been removed from 

storage in the aquifer system (Galloway and others, 1999). 

Two methods were employed as part of this study to assess 

land subsidence in Borrego Valley. In the first method, Global 

Positioning System (GPS) surveys were done, and continuous 

GPS (CGPS) data were gathered and analyzed. The GPS 

surveys were done in December 2008 and March 2009 to 

derive accurate land-surface elevations, which were compared 

to historical spirit-leveling data from the 1960s and 1970s. In 

the second method, interferometric synthetic aperture radar 

(InSAR) satellite remote sensing techniques were used to 

assess short-term (2003–07) land subsidence at a high spatial 

resolution over a larger area. InSAR provides spatially detailed 

ground-deformation maps (interferograms) that can elucidate 

spatially detailed patterns of vertical deformation for specific 

time spans (approximately months to several years or more). 

InSAR data complement the CGPS data, which measure 

deformation at high temporal frequencies over longer periods 

at a series of points.

Global Positioning System 

The GPS is a U.S. Department of Defense satellitebased navigation system designed to provide continuous 

worldwide positioning and navigation capability. GNSS stands 

for Global Navigation Satellite System and is the standard 

generic term for satellite navigation systems that provide 

autonomous geo-spatial positioning with global coverage. 

This term includes GPS and is specific to the United States’ 

GNSS system. For this study, GPS measurements were made 

on geodetic monuments and reference marks (referenced to 

measuring points) on groundwater wells to determine their 

3-D positions and to establish a geodetic or land-subsidence 

monitoring network (fig. 29). The modern 3-D positions of the 

geodetic monuments were used to determine, combined with 

results from historical spirit-leveling surveys, relative changes 

in land-surface elevations since the 1960s and 1970s. The GPS 

measurements also were used to establish baseline values for 

comparisons with results of future surveys. The 3-D positions 

of groundwater wells were determined for this study primarily 

to improve the crude elevations derived from topographic 

maps on which many of the groundwater levels, and thereby 

much of the groundwater hydraulic gradient information, are 

based. Historical well elevations were corrected by more than 

5 ft at 10 wells and by almost 30 ft at 1 well. The corrected 

elevations give a better estimate of groundwater levels and 

groundwater-flow directions in the valley.

The land-subsidence monitoring network consists of 

25 geodetic monuments used as GPS stations spaced about 

1 mile or less from each other (fig. 29). Most geodetic 

monuments are flat metal disks anchored in the ground or 

to a structure and can be used in making repeated surveying 

measurements of horizontal and (or) vertical positions. 

Historical data for monuments in the Borrego Valley were 

compiled from the National Oceanic and Atmospheric 

Administration National Geodetic Survey (formerly the 

U.S. Coast and Geodetic Survey) and reviewed to determine 

the location, timing, and quality of the vertical-control data, 

and suitability for GPS measurements.

Ellipsoid Heights and Elevations

GPS measurements at the 25 geodetic monuments in the 

land-subsidence monitoring network and at 79 groundwater 

wells were made during December 1–5, 2008, and March 

9–13, 2009 (table 6). Measurements were made by using 

one dual-frequency, full-wavelength P-code GPS receiver 

(Trimble 4400); one data collector (Trimble TSC1); and 

compact antennas (with ground plane; Trimble 33429-00) 

to determine horizontal coordinates and ellipsoid heights. 

The ellipsoid height is the vertical coordinate relative to a 

geodetically defined reference ellipse—the reference ellipsoid 

that closely approximates the Earth’s shape in the study area is 

the North American Datum of 1983 (NAD 83). The ellipsoid 

heights were converted to North American Vertical Datum 

of 1988 (NAVD 88) elevations by using GEOID03 (http://

www.ngs.noaa.gov/GEOID/). This enabled the computation 

and comparison of elevation changes from historical spiritleveling elevation measurements. The differences in the 

elevations were used to determine the location and magnitude 

of vertical land-surface changes between the previous surveys 

and the results from this study. Trimble Geomatics Office 

(version 1.63) software was used for GPS computations by 

making baselines and least-squares adjustments.

The GPS surveying combined static and real time 

kinematic (RTK) methods to obtain coordinates of the 

26 monuments and the 79 wells. The RTK method is a 

differential measurement of one location (rover station) to 

another location (base station) where static, or longer term, 

data are concurrently collected. Accuracies of the rover 

station coordinates derived by differential measurements 

are partially dependent on accurate coordinates of the base 

stations. To achieve accurate coordinates of the base stations, 

static GPS data were collected for one location (BASE, no 

monument) in the 2008 survey and two locations (CL-5 and 

BOR33, monuments) in the 2009 survey (fig. 29). GPS 
Land Subsidence 71

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 29 Geo monuments

EXPLANATION

P486

CL 5

CL 1

CL 33

CL 32

CL 30

Z 1326

X 1326

W 1326

V 1326

U 1326

T 1326 S 1326

R 1326

Q 1326

P 1326

N 1326

BOR 34

BOR 33

BOR 12

BOR 11

BOR 10

16 JRH

15 JRH

14 JRH

11 AAR

Borrego

airport

BASE

Geodetic monuments

16 JRH GPS station and identifier

BOR 33 GPS base station and identifier

P486 Continuous GPS station and identifier

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake

Figure 29. Location of geodetic monuments used as Global Positioning System stations, Borrego Valley, California.
72 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

data were collected at BASE for a total of nearly 34 hours 

on four different days, at CL-5 for nearly 20 hours on 

three different days, and at BOR33 for about 17.5 hours on 

two different days. As static GPS data were being collected 

at BASE, CL-5, or BOR33, RTK GPS data were collected 

concurrently at the other 24 geodetic monuments in the 

land-subsidence monitoring network and the 79 groundwater 

wells. For the geodetic monuments, CL-5 was selected to 

be the primary base station because it is a first-order vertical 

monument, and three differential measurements (one per day) 

were made at each geodetic monument rover station relative 

to CL-5. Two measurements were made at each geodetic 

monument rover station relative to BOR33, and only eight of 

the geodetic monuments were measured relative to BASE; 

these measurements were used for quality control. For the 

groundwater wells, data relative to each of the base stations 

are presented because measurements relative to more than one 

base station were generally not made.

To obtain accurate coordinates for the base stations 

(BASE, CL5, and BOR33), the GPS data were post-processed 

by using baseline and least-squares adjustment computations 

with three nearby CGPS stations and by using precise satellite 

orbital data and accurate coordinates of the CGPS stations 

produced by the International GPS Service and Scripps Orbit 

and Permanent Array Center (SOPAC), respectively. The GPS 

measurement frequency of the CGPS stations is 30 seconds, 

and the measurements were archived by SOPAC. Data from an 

additional CGPS station (P486) at the Borrego Airport (fig. 29) 

were used as quality control by computing coordinates for 

P486 as part of the GPS data post-processing and comparing 

them with the coordinates computed by SOPAC.

To determine the horizontal coordinates and ellipsoid 

heights of the rover stations, the coordinates of the three base 

stations were held fixed at the positions determined during 

the first phase, and the horizontal coordinates and ellipsoid 

heights for the rover stations were determined relative to each 

of the three base stations. GEOID03 was then applied to the 

ellipsoid heights to generate NAVD 88 elevations (table 6). 

The 2009 ellipsoid heights and elevations of the geodetic 

monuments in table 6 are relative to that of base station CL-5, 

except for the elevation of BOR33; the more accurate results 

from the static measurements at BOR33 are presented. The 

Table 6. Location of geodetic monuments in the land-subsidence monitoring network with ellipsoid heights for 2009, and elevations for 

1969, 1978, 1995, and 2009, Borrego Valley, California.

[Latitude, longitude, northing, easting, and ellipsoid height are referenced to the North American Datum of 1983. Elevations are referenced to the North 

American Vertical Datum of 1988. The 2009 elevations were derived from the NGS GEOID03. DMS, degree minute second; ft, foot; m, meter; NGS, National 

Geodetic Survey; —, no data]

Monument 

name

Latitude

(DMS)

Longitude

(DMS)

2009

Ellipsoid 

height 

(m)

2009 

Elevation 

(m) 

1969 

(NGS) 

elevation 

(ft)

1978 

(NGS) 

elevation 

(ft)

1995 

(NGS) 

elevation 

(ft)

2009 

(NGS) 

elevation 

(ft) 

1978–2009 

elevation 

change 

(ft) 

11AAR 33° 17ʹ 44ʺ 116° 17ʹ 55ʺ 155.255 188.091 616.570 616.973 616.967 617.094 0.120

14JRH 33° 15ʹ 25ʺ 116° 20ʹ 54ʺ 131.500 164.257 538.975 539.227 539.047 538.898 –0.329

15JRH 33° 13ʹ 15ʺ 116° 20ʹ 07ʺ 121.103 153.922 504.598 504.995 504.995 504.992 –0.003

16JRH 33° 11ʹ 30ʺ 116° 19ʹ 49ʺ 205.070 237.859 779.811 780.237 — 780.376 0.139

BOR10 33° 13ʹ 15ʺ 116° 20ʹ 43ʺ 129.458 162.226 531.819 532.233 532.275 532.237 0.004

BOR11 33° 13ʹ 43ʺ 116° 20ʹ 53ʺ 129.686 162.447 532.567 532.981 533.020 532.962 –0.019

BOR12 33° 14ʹ 15ʺ 116° 20ʹ 53ʺ 126.223 158.987 521.314 521.724 521.731 521.610 –0.114

BOR331 33° 14ʹ 33ʺ 116° 20ʹ 54ʺ 125.352 158.115 518.509 518.922 518.932 518.748 –0.174

BOR34 33° 10ʹ 12ʺ 116° 20ʹ 07ʺ 337.686 370.387 1,214.524 1,214.941 — 1,215.176 0.235

CL1 33° 15ʹ 25ʺ 116° 20ʹ 21ʺ 128.415 161.215 528.853 — — 528.919 —

CL30 33° 18ʹ 14ʺ 116° 15ʹ 10ʺ 182.371 215.302 705.851 706.261 — 706.370 0.109

CL32 33° 17ʹ 44ʺ 116° 16ʹ 46ʺ 172.232 205.122 672.547 672.951 — 672.970 0.020

CL33 33° 16ʹ 46ʺ 116° 17ʹ 55ʺ 138.991 171.887 563.830 564.231 — 563.931 –0.300

CL51 33° 16ʹ 06ʺ 116° 17ʹ 55ʺ 133.423 166.352 545.973 — — 545.773 —

N1326 33° 18ʹ 11ʺ 116° 13ʹ 23ʺ 226.412 259.385 — 850.919 — 850.999 0.080

P1326 33° 18ʹ 12ʺ 116° 14ʹ 26ʺ 194.339 227.293 — 745.615 — 745.710 0.095

Q1326 33° 18ʹ 06ʺ 116° 15ʹ 56ʺ 175.643 208.553 — 684.096 — 684.228 0.132

R1326 33° 15ʹ 54ʺ 116° 17ʹ 55ʺ 132.680 165.617 — 543.450 543.463 543.361 –0.089

S1326 33° 15ʹ 27ʺ 116° 18ʹ 25ʺ 125.023 157.948 — 518.351 518.328 518.201 –0.151

T1326 33° 15ʹ 27ʺ 116° 19ʹ 30ʺ 124.557 157.417 — 516.416 516.383 516.460 0.044

U1326 33° 15ʹ 25ʺ 116° 20ʹ 12ʺ 127.345 160.156 — 525.553 525.461 525.446 –0.107

V1326 33° 12ʹ 32ʺ 116° 19ʹ 47ʺ 131.595 164.424 — 539.578 — 539.447 –0.131

W1326 33° 12ʹ 09ʺ 116° 19ʹ 47ʺ 152.250 185.068 — 607.157 — 607.176 0.019

X1326 33° 10ʹ 45ʺ 116° 19ʹ 58ʺ 277.263 310.007 — 1,016.893 — 1,017.079 0.186

Z1326 33° 09ʹ 20ʺ 116° 20ʹ 41ʺ 447.907 480.519 — 1,576.216 — 1,576.501 0.285

1

Base station.
Land Subsidence 73

accuracy of the geodetic-monument 2009 ellipsoid heights is 

plus or minus 0.5 ft at the 95-percent confidence level, which 

was determined by the spread of values determined from 

multiple GPS observations. Additionally, the error associated 

with elevations determined by GPS surveying could be as 

much as plus or minus 0.07 ft at the 95-percent confidence 

level because of inaccuracies in the GEOID03 model (http://

www.ngs.noaa.gov/GEOID/GEOID03/tech.shtml, accessed 

December 4, 2009). The 2009 elevations of the groundwater 

wells are relative to BASE, CL-5, or BOR33 and are accurate 

to plus or minus 1.0 ft at the 95-percent confidence level. 

The groundwater-well elevations are less accurate than the 

geodetic monuments accuracy because repeat measurements 

were not made at the wells.

Land Subsidence at Geodetic Monuments

The land-surface elevations determined from the GPS 

surveys at the geodetic monuments were compared to the 

elevations derived from historical leveling surveys (1978–

2009) to determine the magnitude of vertical land-surface 

changes (table 6). Generally, the comparison of monument 

elevations measured during surveys made at different times 

by different agencies with various standards of accuracy 

and networks of various geographic scales incorporates 

uncertainties of at least plus or minus 0.2 ft (Ikehara and 

others, 1997). As a result of various errors and because 

elevations in historical surveys were determined by using 

some elevation values that were originally referenced to the 

National Geodetic Vertical Datum of 1929, the uncertainty of 

land-surface elevation change determinations involving 2009 

elevations is at least plus or minus 0.54 ft. This error was 

computed by applying the root sum of squares (RSS) method 

to the errors associated with the GPS survey (0.5 ft), the error 

of the geoid (0.07 ft), and the assumed errors associated with 

of the historical data (0.2 ft).

GPS Derived Elevations

Elevations derived from GPS measurements made at 

the 25 geodetic monuments in 2009 were compared with 

all available historical elevations, with emphasis placed 

on the historical elevations derived during a 1978 survey 

(table 6). The elevations derived from the 1978 survey were 

emphasized for elevation-change determinations because all 

but 2 of the 25 monuments (CL-1 and CL-5) were included 

in the 1978 survey, and more importantly, a comparison of 

elevations derived in 1969 and in 1978 revealed a 0.397- to 

0.427-ft elevation rise for 11 of the 12 monuments that were 

included in both historical surveys (data for the remaining 

monument indicates an elevation rise of 0.253 ft). The narrow 

range of values of elevation rise among the 11 monuments 

combined with the relative stability of the monuments shown 

by subsequent surveys cast doubt on the accuracies of the 

1969 elevations. Comparison of the 1978 elevations with the 

2009 elevations indicates that the elevations of 13 monuments 

increased 0.004–0.285 ft, and elevations of 10 monuments 

decreased 0.003–0.329 ft, during the 32-year period. For the 

two monuments that were not included in the 1978 survey, 

the comparison of the elevations derived from the 1969 

and the 2009 surveys indicates that the elevation of one 

monument increased by 0.066 ft, and the elevation of the other 

monument decreased by 0.200 ft. All of the elevation changes 

are within the expected error of 0.54 ft; therefore, there 

have not been significant land-surface elevation changes at 

23 monuments during the 32-year period from 1978 to 2009 or 

at 2 monuments during the 41-year period from 1969 to 2009.

The 3-D position of CGPS site P486 at the Borrego 

Airport has been continuously recorded since early 2005 and 

indicates about 0.787 in. of subsidence by 2010 (fig. 30). The 

vertical measurements both indicate a seasonal and a longer 

term component during 2005–10, which is not uncommon in 

areas where groundwater levels fluctuate seasonally. The data 

from this site indicated that an average net change of about 

0.2 in. of subsidence occurs annually. An average of about 

0.4 in. or less of subsidence occurs during the summer months, 

and about 0.2 in. or less of uplift occurs in the winter months, 

on average.

Interferometric Synthetic Aperture Radar 

Interferometric synthetic aperture radar (InSAR) is 

a satellite-based remote sensing technique that can detect 

ground-surface displacement on the order of centimeters, 

which is an effective way to measure vertical changes of land 

surface over a large area between two or more specific periods. 

Detecting changes requires two radar images of the same 

area taken from approximately the same position in space, 

but at two different times. Comparing two synthetic aperture 

radar (SAR) images produces an interferogram that shows 

the differences in the reflected signals over an area of 0.621 

by 0.621 mi (100 by 100 km). InSAR techniques have been 

used to investigate deformation resulting from earthquakes 

(Massonnet and others, 1993), volcanoes (Massonnet and 

others, 1995), and land subsidence (Massonnet and others, 

1997; Fielding and others, 1998; Galloway and others, 1998; 

Amelung and others, 1999, Hoffmann and others, 2001; Sneed 

and others, 2001).

SAR imagery is produced by reflecting radar signals 

off a target area and measuring the two-way travel time 

back to the satellite. The SAR interferometry technique 

uses two SAR images of the same area acquired at different 

times and “interferes” (differences) them, resulting in maps 

called interferograms that show line-of-sight ground-surface 

displacement (range change) between the two periods. The 

generation of an interferogram produces two components: 

the amplitude and the phase. The amplitude component is 

the measure of the signal intensity returned to the satellite 

and shows buildings, roads, mountains, and other reflective 

features; the phase component is proportional to range 

distance and shows the coherent displacements imaged by 

the radar. If the ground has moved away from (subsidence) 
74 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

sac13-0509_Figure 30 Vertical position of CGPS P486

–1.2

–1

–0.8

–0.6

–0.4

–0.2

0

0.2

0.4

0.6

7-day average adjusted to 0

Vertical position of CGPS P486, in inches

2005 2006 2007 2008 2009 2010

Year

60-day moving average

Figure 30. Vertical position of Continuous Global Positioning System site P486, 2005–10, in the Borrego Valley, California.

or toward (uplift) the satellite between the times of the two 

acquisitions, a slightly different part of the wavelength is 

reflected back to the satellite, resulting in a measurable phase 

shift that is proportional to range change. The map of phase 

shifts, or interferogram, is depicted with a repeating color 

scale that shows relative range change between the first and 

the second acquisitions.

InSAR signal quality is partly dependent on satellite 

position (average distance between satellite and study area), 

topography, atmospheric effects, ground cover, land-use 

practices, and temporal separation of the interferogram. 

Strict orbital control is required to control the look angle and 

position of the satellite precisely. Successful application of 

the InSAR technique is contingent on looking at the same 

point on the ground from the same position in space, such 

that the horizontal distance between each satellite pass, 

or perpendicular baseline, is minimized. Differences in 

perpendicular baselines greater than about 650 ft generally 

produce excessive topographic effects (parallax) that can mask 

real signal. Phase shifts can be caused by variable atmospheric 

mass associated with different elevations. A digital elevation 

model (DEM) is used in the interferogram generation process 

to reduce the effects caused by elevation differences (and also 

to georeference the image). Phase shifts also can be caused 

by laterally variable atmospheric conditions, such as clouds 

or fog, because the non-uniform distribution of water vapor 

differentially slows the radar signal over an image (Zebker 

and others, 1997). Atmospheric artifacts can be identified by 

using several independent interferograms, which are defined as 

interferograms that do not share a common SAR image. When 

apparent ground motion is detected only in one interferogram, 

or a set of interferograms sharing a common SAR image, then 

the apparent motion likely is due to atmospheric phase delay 

and can be discounted.

The type and density of ground cover also can 

significantly affect interferogram quality. Densely forested 

areas are prone to poor signal quality because the C-band 

wavelength (2.2 in.) cannot effectively penetrate thick 

vegetation and is either absorbed or reflected back to the 

satellite from varying depths within the canopy, resulting in 

incoherent signal. Sparsely vegetated areas and populated 

areas, however, generally have high signal quality because 

bare ground, roads, and buildings have high reflectivity and 

are relatively uniform during at least some range of InSAR 

time scales. Certain land-use practices, such as farming, 

also cause incoherent signal return. The tilling and plowing 

of farm fields cause large and non-uniform ground-surface 

change that cannot be resolved with InSAR. Signal quality 

also is adversely affected by larger temporal separations, 

because there is more opportunity for non-uniform change 
Land Subsidence 75

to occur both in municipal and non-municipal areas. For the 

Borrego Valley, which is sparsely vegetated and fairly flat, 

many of these error sources were minimized by producing 

interferograms with perpendicular baselines less than 200 m 

and by examining several independent interferograms.

For this study, SAR data from the European Space 

Agency’s Earth Remote Sensing 1 and 2 (ERS-1 and ERS-2) 

and ENVISAT satellites were used to map and measure range 

(phase) changes. The three satellites are side looking, orbit 

the Earth at an elevation of approximately 800 km, and have 

35-day repeat cycles. Thirty-four interferograms with temporal 

baselines ranging from 35 days to 47 months were developed 

for this study by using twenty-four SAR scenes acquired by 

the European ENVISAT satellite, and eight interferograms 

with temporal baselines ranging from about 5 to 18 months 

were developed for this study by using ten SAR scenes 

acquired by the ERS-1 and ERS-2 satellites (table 7). Annual 

and seasonal time spans were preferred for interferogram 

production. Annual or multiannual periods are more likely to 

capture inelastic (permanent) compaction than are seasonal 

time spans, assuming groundwater levels are similar or lower 

at the time of the second SAR acquisition than at the first 

SAR acquisition. Seasonal periods are more likely to capture 

elastic (recoverable) deformation than are annual periods, 

assuming groundwater levels are dissimilar at the times of the 

two SAR acquisitions. Several interferograms with annual and 

multiannual periods were produced; however, the preferred 

spring to fall or fall to spring periods could not be produced 

because of incompatible SAR scenes or because data were 

not available. Interferograms with time spans that combined 

annual and seasonal components (for example, 1.5-year time 

span) were produced (table 7).

 The interferograms of the Borrego area were interpreted 

and contoured to show small land-surface-elevation changes 

(fig. 31). Some interferograms show no subsidence, and 

some interferograms show small amounts of subsidence. 

Nearly all of the agricultural areas in interferograms are 

uninterpretable. Because the amounts of subsidence are small 

in individual interferograms, three coherent interferograms 

(November 30, 2003–September 5, 2004, September 5, 2004–

October 15, 2006, October 15, 2006–September 30, 2007) 

spanning almost 4 years were stacked, or summed, to create a 

longer period interferogram from which subsidence contours 

were interpreted (fig. 31). The interferograms indicate that the 

average maximum annual subsidence rate during this period 

was about 0.2 in/yr. These rates are consistent with data from 

CGPS site P486 (fig. 30) and subsidence rates reported by 

Van Zandt (2004). Seasonal deformation rates could not be 

resolved with InSAR data because the rates are low (less 

than the resolution of the interferograms—about 5 mm—for 

any given period) and compatible SAR scenes with desirable 

acquisition dates were not available. However, as discussed 

in the “Land Subsidence at Geodetic Monuments” section, 

continuous data from CGPS P486 indicate that subsidence 

occurs mostly during the summer months, and about half 

of that subsidence is recovered during the winter months 

(fig. 30).

Table 7. Interferograms processed from the European Space 

Agency’s satellites for Borrego Valley, California.

[mm/dd/yyyy, month, day, year]

Satellites

First 

acquisition

(mm/dd/yyyy)

Second 

acquisition

(mm/dd/yyyy)

Time 

span 

(days)

ERS-1 and ERS-2 12/01/1995 10/11/1996 315

ERS-1 and ERS-2 10/11/1996 02/13/1998 490

ERS-1 and ERS-2 04/04/1997 03/20/1998 350

ERS-1 and ERS-2 09/26/1997 03/20/1998 175

ERS-1 and ERS-2 02/13/1998 06/18/1999 490

ERS-1 and ERS-2 03/20/1998 01/29/1999 315

ERS-1 and ERS-2 03/20/1998 09/15/2000 910

ERS-1 and ERS-2 06/18/1999 11/05/1999 140

ENVISAT 10/26/2003 12/24/2006 1,155

ENVISAT 10/26/2003 07/22/2007 1,365

ENVISAT 10/26/2003 08/26/2007 1,400

ENVISAT 10/26/2003 09/30/2007 1,435

ENVISAT 11/30/2003 09/05/2004 280

ENVISAT 11/30/2003 01/23/2005 420

ENVISAT 11/30/2003 10/15/2006 1,050

ENVISAT 05/23/2004 09/25/2005 490

ENVISAT 09/05/2004 01/23/2005 140

ENVISAT 09/05/2004 10/15/2006 770

ENVISAT 11/14/2004 12/19/2004 35

ENVISAT 11/14/2004 12/04/2005 385

ENVISAT 11/14/2004 03/19/2006 490

ENVISAT 11/14/2004 11/19/2006 735

ENVISAT 12/19/2004 12/04/2005 350

ENVISAT 12/19/2004 03/19/2006 455

ENVISAT 12/19/2004 11/19/2006 700

ENVISAT 02/27/2005 06/12/2005 105

ENVISAT 02/27/2005 02/12/2006 350

ENVISAT 02/27/2005 03/19/2006 385

ENVISAT 04/03/2005 12/24/2006 630

ENVISAT 04/03/2005 07/22/2007 840

ENVISAT 04/03/2005 08/26/2007 875

ENVISAT 04/03/2005 09/30/2007 910

ENVISAT 06/12/2005 12/24/2006 560

ENVISAT 06/12/2005 08/26/2007 805

ENVISAT 12/04/2005 03/19/2006 105

ENVISAT 12/04/2005 11/19/2006 350

ENVISAT 02/12/2006 03/19/2006 35

ENVISAT 03/19/2006 11/19/2006 245

ENVISAT 10/15/2006 09/30/2007 350

ENVISAT 12/24/2006 07/22/2007 210

ENVISAT 07/22/2007 09/30/2007 70

ENVISAT 08/26/2007 09/30/2007 35
76 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 31. Line and area of equal subsidence as interpreted from Interferometric Synthetic Aperture Radar satellite imagery of the 

Borrego Valley, California, November 2003–September 2007. 

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

Uninterpretable area

0 5 10 15

0

5

10

15

10

15

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 31 InSAR subsidence

Three interferogram stack from

Nov 30, 2003 to Sept 30, 2007

EXPLANATION

Line and area of equal subsidence—

 Interval is 5 millimeters, or mm (0.20 in.), dashed where 

 approximate. (10 mm is 0.39 in.; 15 mm is 0.59 in.)

Uninterpretable area

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

10

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake
Groundwater-Flow Models 77

Groundwater-Flow Models

Two models were developed to simulate hydrologic 

processes in the Borrego Valley. AnalyzeHole (Halford, 2009) 

was used to simulate two-dimensional (2-D) groundwater flow 

and estimate aquifer properties at a pumped well on a small 

scale. MODFLOW (Harbaugh, 2005) was used to simulate the 

integrated regional hydrologic system. The integrated regional 

model was used to simulate the effects of increased water 

demand from 1945 to 2010 and to evaluate several future 

water-use scenarios.

Wellbore-Groundwater-Flow Model

Groundwater flow was simulated around well 

10S/6E-35Q1 to evaluate the hydraulic properties of 

the aquifer system contributing water to the well. Well 

10S/6E-35Q1 and the adjacent upper and middle aquifers were 

simulated by using AnalyzeHOLE, a wellbore analysis tool 

for simulating steady-state flow in a well and the surrounding 

aquifer system (Halford, 2009). AnalyzeHOLE simulates 

wellbore flow by using an axisymmetric, radial geometry 

in a 2-D MODFLOW model (Harbaugh and others, 2000). 

Well 10S/6E-35Q1 was selected because it was possible to 

collect groundwater-flow and water-quality data with depth 

concurrently, and the screened interval was continuous 

through the upper and middle aquifers.

The radial wellbore-groundwater-flow model was 

295 ft thick and was divided uniformly into 300 rows (the 

equivalent of layers in a 3-D model), each with a thickness 

of 0.98 ft. The model thickness represents the aquifer system 

between the static groundwater level in 2011 (95 ft bls) and 

the bottom of the well (390 ft bls). The radial distance from 

the well was divided into 57 radial columns and represents 

a total distance of 200,000 ft. The radial distance was large 

so that the flow near the well would not be affected by the 

boundary conditions. The well was simulated as a high K 

zone of 1.6 × 109 feet per day (ft/d) in the first column of 

the model. The screened casing of the well and the gravel 

pack were simulated in the second and third columns of the 

model with hydraulic conductivities of 200 ft/d. No-flow 

boundary conditions were used at the top, bottom, and edges; 

therefore, simulated pumped water from the well was derived 

exclusively from groundwater storage. Regional groundwater 

flow and regional pumping effects were not simulated, and 

pumping stresses from the simulated well were assumed to 

dominate groundwater flow within the system.

The aquifers were assumed to be radially symmetric, 

flat lying, and laterally extensive through the model domain. 

Hydraulic properties representing the aquifer materials 

initially were assigned to each layer from literature values 

(Freeze and Cherry, 1979) based on the lithologic and 

geophysical logs from the well and were adjusted during 

model calibration. 

The model was calibrated by adjusting, within reasonable 

ranges, K values associated with the lithology assigned to 

individual model layers until there was a reasonable match 

between the simulated and measured distribution of flow 

within the well and between the simulated and measured 

drawdown (fig. 32). Estimates for aquifer transmissivity 

were based on a relation between specific capacity (pumping 

volume per unit of drawdown) and transmissivity (Thomasson 

and others, 1960). The hydraulic conductivities of the 

lithologic units (table 8) were adjusted so that the simulated 

transmissivity values of the model domain were near the 

estimated transmissivity values and that a reasonable 

match between the simulated and measured drawdown in 

the well was achieved. Estimates of transmissivity from 

specific capacity data provided by BWD (Borrego Water 

District, written commun., 2007) ranged from about 8,300 to 

8,600 gallons per day per foot.

The initial hydraulic conductivities assigned to the 

model layers below 255 ft bls allowed too much flow into 

the well. The wellbore log (fig. 24A) showed that only about 

1 percent of flow to the well was from below 255 ft bls. The 

wellbore-flow data were matched during model calibration by 

decreasing the K of the deeper units to values similar to those 

of clayey sand (table 8).

The calibrated hydraulic conductivities in the model 

range from 0.5 ft/d for the lower part of the model to 350 ft/d 

in the highest producing zone (table 8). The highest producing 

zone is a thin layer of gravelly sand present at about 204 ft bls, 

as shown by the flow log (fig. 32), which is too thin to be 

noted in the geophysical or driller’s logs, but contributes 

copious amounts of water to the well. The calibrated hydraulic 

conductivities of the other water-bearing units ranged from 

7 to 45 ft/d (table 8).

Integrated Hydrologic Model

The BVHM is an integrated hydrologic model that 

simulates the use, movement, and storage of water throughout 

the groundwater basin, over time. The BVHM was developed 

by using the finite-difference groundwater modeling software 

One Water Hydrologic Model (OWHM; Hanson and others, 

2014a). OWHM is a numerical code that incorporates a 

dynamically integrated water supply-and-demand accounting 

in agricultural areas and areas of native vegetation and 

has made possible more detailed and realistic simulations 

of hydrologic systems. OWHM uses MODFLOW-2005 

(MF2K5; Harbaugh, 2005) and incorporates an updated 

version of the Farm Process (FMP; Schmid and others, 2006a, 

2006b; Schmid and Hanson, 2009; Hanson and others, 2014a). 

In addition, in OWHM a new numerical solution procedure 

has been employed, known as the Newton Solver (Niswonger 

and others, 2011). This solver’s enhanced capability improves 

the overall numerical stability of MF2K5 with particular 

improvements in model cells that transition between wet and 

dry conditions.
78 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley Depth, in feet below land surface

0.1 1 10 100 1,000 0

0

100

200

300

400

25 50 75 100

Hydraulic conductivity, in feet per day Discharge, in gallons per minute

125 150 175

Pump

intake

Direction of flow

Direction

of flow

Silty sand 1

Silty sand 2

Sand 1

Gravelly sand

Sand 1

Sand 2

Clayey sand

Pump

intake

Silty sand 1

Silty sand 2

Sand 1

Gravelly sand

Sand 1

Sand 2

Clayey sand

Hydraulic

conductivity Measured

flow

Simulated

flow

Aquifer system Aquifer system

sac13-0509_Figure 32 MV-4 logs simulated

0 10 20 30 40 50 60

Long-normal resistivity, in ohms per meter 

Upper

Aquifer

Middle

Aquifer

Upper

Aquifer

Middle

Aquifer

Figure 32. Simulated lithology, calibrated hydraulic conductivity distribution, and simulated and measured flow logs from well 

10S/6E-35Q1, Borrego Valley, California.

Table 8. Details of radial groundwater-flow model construction for well 10S/6E-35Q1, Borrego Valley, California.

[ft, feet; ft/d, feet per day; gal/min, gallons per minute; s, seconds; ×, multiplied by]

Data Value

Spatial discretization

Grid dimensions 295 ft thick by

200,000 ft wide

Number of layers 1

Number of rows 300

Thickness of rows 0.98 ft

Number of columns 57

Size of columns Variable

Column 1 (well) 0.5 ft

Column 2 (screen) 0.017 ft

Column 3 (gravel pack) 0.17 ft

Columns 4–181 Multiplier 1.258

Side boundary condition No flow

Bottom boundary condition No flow

Upper boundary condition Water table

(Initial water level 95 ft below land surface)

Hydraulic properties

Porosity 0.001

Specific storage 2.5 × 10–6 (1/ft)

Anisotropy 0.1

Data Value

Hydraulic properties—Continued

Hydraulic conductivity (calibrated)

Clayey sand 0.5 ft/d

Silty sand1 7 ft/d

Silty sand2 10 ft/d

Sand1 20 ft/d

Sands above pump 37 ft/d

Sand2 45 ft/d

Gravelly sand 350 ft/d

Gravel pack 200 ft/d

Well screen 200 ft/d

Well 1.6 × 109 ft/d

Temporal discretization

Stress periods 1

Length of stress period 2 days

Time steps 54

Time step multiplier 1.2

Initial time step 1.8 s

Pumping rate 160 gal/min
Groundwater-Flow Models 79

Spatial Discretization

The total active modeled area is 73,876 acres on a 

finite-difference grid consisting of 30 rows, 75 columns 

(2,250 cells), and 3 layers (fig. 33). About 36 percent of the 

cells (803 cells) are an active part of the hydrologic model. 

The model grid has a uniform horizontal discretization of 

about 92 acres per square cell (2,000 ft by 2,000 ft) and is 

oriented subparallel to the tectonic structure and Coyote Creek 

22 degrees west of true north (fig. 33). The grid orientation 

and cell size were chosen to be parallel to the general direction 

of groundwater flow and the same as previous models to 

facilitate the upgrade from existing models. The coordinates 

for the total model grid are summarized in table 11.

The model layering is a series of three layers that are 

aligned with the aquifers discussed previously. The top of the 

hydrologic model is represented by the elevation of the land 

surface and is a composite of model layers 1 and 3 (fig. 33). 

The upper aquifer model layer (layer 1) ranges in thickness 

from an assumed minimum of 50 ft to an estimated maximum 

of about 643 ft. The second model layer is coincident with 

the middle aquifer and ranges in thickness from an assumed 

minimum of 50 ft to an estimated maximum of about 908 ft. 

The third model layer is coincident with the extent of the 

lower aquifer and ranges in thickness from an assumed 

minimum of 50 ft to an estimated maximum of about 3,831 ft.

Temporal Discretization

In order to represent the growing season adequately, 

and the dynamics of changing precipitation, streamflow, 

and PET that collectively drive the major water supply and 

demand components, the annual hydrologic cycle of the 

BVHM was discretized into monthly stress periods and two 

equal time steps per stress period. Periods of user-specified 

model inflows, outflows, and boundary heads are referred 

to as stress periods. Variations in stresses are simulated by 

changing these model inputs from one stress period to the 

next. The inputs, which include groundwater pumping, 

precipitation, PET, stream inflows, water applied to irrigate 

crops, and underflow beneath the major streams, are assumed 

to be constant within each stress period. Stress periods are 

further divided into two time steps per stress period (month), 

which are units of time for which groundwater levels and 

flows are numerically simulated for all model cells. The total 

simulation period was 60.25 years (or 975 monthly stress 

periods) from October 1929 through December 2010. The first 

192 stress periods (years 1930–45) are considered a model 

spin-up period, and the model calibration as well as the target 

simulation period used for analysis was October 1945 through 

December 2010.

The BVHM was developed on the basis of historical 

conditions for the analysis of the use and movement of 

water throughout the valley to provide a basis for addressing 

groundwater availability and sustainability analyses. The 

BVHM was constructed in three major phases. The first 

phase was the conversion of the existing flow models into an 

updated OWHM model. In the second phase, new and existing 

data were collected, compiled, and combined. In this step, 

the hydrogeologic framework model was developed from 

the previous studies and new data. This framework model 

includes stratigraphic units and the distribution of hydraulic 

properties. In the final step, the inflows and outflows of the 

updated and revised conceptual model were combined with 

the hydrogeologic framework model into the BVHM. The 

components (processes and packages) of OWHM used for the 

BVHM are summarized in table 9.

The BVHM was adjusted during implementation of these 

model development phases, but calibrated primarily after 

the final phase both by using trial-and-error and automated 

parameter-estimation methods. The automated nonlinear 

regression-based parameter-estimation software, referred to 

as PEST (Doherty, 2010a, b, c; Doherty and Hunt, 2010), was 

used to help with the calculation of sensitivities and parameter 

estimation. The model was calibrated to groundwater levels 

and groundwater level differences (drawdown). 

The Borrego Valley was split into three major waterbalance regions on the basis of water-use types (agricultural 

usage; recreational usage; and other usage, including 

undeveloped native and historical phreatophytic, residential, 

and municipal areas) for water accounting purposes. These 

three regions comprise 52 water-balance subregions (WBSs) 

that roughly coincide with the current major parcels in the 

valley (table 10). For ease of description, even though land 

use varied over time within these subregions, the subregion 

boundaries were kept constant over time.

The BVHM model components can be grouped in 

terms of the discretization and boundaries, initial conditions, 

aquifer characteristics, simulation of recharge, waterbalance subregions, land use, simulation of discharge, and 

groundwater inflows and outflow. The next few sections 

summarize the model components within these groups.

Discretization and Boundaries

The BVHM includes the major alluvial deposits of the 

entire Borrego Valley, bounded on the northeast and east 

by the Coyote Creek fault, on the south by the Vallecito 

Mountains, and on the west and northwest by the San Ysidro 

Mountains (fig. 33). The southeastern boundary coincides with 

the surface-water divide, which is southwest of Ocotillo Wells 

and represented by constant-head boundary cells in the BVHM 

(fig. 33). The finite-difference model grid used to represent the 

land surface and the subsurface alluvial deposits consists of a 

series of orthogonal square model cells (fig. 33).
80 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Table 9. Summary of One Water Hydrologic Model packages and processes used in the integrated hydrologic model of Borrego Valley, 

California.

[FMP, Farm Process; GWF, groundwater flow process; GLO, Global file; OBS, observation process; OWHM, One Water Hydrologic Model]

Computer program 

(packages, processes, 

parameter estimation)

Function Reference

Processes and solver

Groundwater flow (GWF) 

processes of MODFLOW-2005

Setup and solve equations simulating a basic groundwater 

flow model.

McDonald and Harbaugh (1988), 

Harbaugh and others (2000), Hill and 

others (2000), Harbaugh (2005)

Newton-Raphson solver package 

(NWT)

Solves groundwater flow equations; requires convergence 

of heads and (or) flow rates.

Niswonger and others (2011)

Farm process (FMP3) Setup and solve equations simulating use and movement 

of water on the landscape as irrigated agriculture, 

municipal landscape, and natural vegetation.

Schmid and others (2006a, b), Schmid 

and Hanson (2009), Hanson and others 

(2014a)

Files

Name file (Name) Controls the capabilities of OWHM utilized during a 

simulation. Lists most of the files used by the GLO, 

OBS, and FMP processes.

Harbaugh (2005)

Output control option (OC) Used in conjunction with flags in other packages to output 

head, drawdown, and budget information for specified 

time periods into separate files.

Harbaugh (2005)

List file Output file for allocation information, values used by 

the GWF process, and calculated results such as head, 

drawdown, and the water budget. 

Harbaugh (2005)

Discretization 

Basic package (BAS6) Defines the initial conditions and some of the boundary 

conditions of the model.

Harbaugh (2005)

Discretization package (DIS) Space and time information. Harbaugh (2005)

Multiplier package (MULT) Defines multiplier arrays for calculation of model-layer 

characteristics from parameter values.

Harbaugh (2005), Schmid and 

Hanson (2009)

Zones (ZONE) Defines arrays of different zones. Parameters may be 

composed of one or many zones.

Harbaugh (2005)

Aquifer parameters

Upstream-weighted property 

flow package (UPW)

Calculates the hydraulic conductance between cell centers. Niswonger and others (2011)

Hydrologic flow barriers (HFB6) Simulates a groundwater barrier by defining a hydraulic 

conductance between two adjacent cells in the same layer.

Hsieh and Freckelton (1993)

Boundary conditions

Constant head boundaries (CHB) Head-dependent boundary condition used along the edge 

of the model to allow groundwater to flow into or out 

of the model under a regional gradient.

McDonald and Harbaugh (1988), 

Harbaugh (2005)

Recharge and discharge

Multi-node wells (MNW2) Simulates pumping from wells with screens that span 

multiple layers.

Konikow and others (2009)

Streamflow routing (SFR2) Simulates the routed streamflow, infiltration, exfiltration, 

runoff, and return flows from FMP.

Niswonger and Prudic (2005)

Flow head boundary (FHB1) Simulates head- and flow-dependent boundaries along 

the edge of the model to allow groundwater flow into 

the model under a regional gradient.

Leake and others (1997)

Unsaturated zone flow package 

(UZF)

Simulates the infiltration and exfiltration of water 

below the root zone through the unsaturated zone 

in combination with FMP.

Niswonger and others (2006)

Output, observations, and sensitivity

Head observation (HOB) Defines the head observation and weight by layer(s), row, 

column, and time and generates simulated values for 

comparison with observed values.

Hill and others (2000), Harbaugh (2005)

Hydmod (HYD) Compiles simulated values for specified locations at 

each time step for groundwater levels and streamflow 

attributes.

Hanson and Leake (1998)

Parameter file (PVAL) Specifies parameter values used in other packages. Harbaugh (2005)
Groundwater-Flow Models 81

Vallecito Mountains

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 33_Upper active layer

EXPLANATION

Ocotillo

wells

Upper-most active layer Boundary of

Constant-head boundary 

 cells in layer 3

Upper aquifer (layer 1)

Lower aquifer (layer 3)

Upper aquifer (layer 1)

Middle aquifer (layer 2)

Lower aquifer (layer 3)

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake

San Ysidro Mountains

Coyote Creek fault

Figure 33. Model grid with active model cells for the upper and lower aquifers in the Borrego Valley Hydrologic Model, Borrego Valley, 

California.
82 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Initial Conditions

For transient models, initial conditions define the system 

state at the beginning of the simulation. There is a long 

history of groundwater development and irrigation in the 

study area that predate the beginning of the simulation period; 

therefore, the initial conditions might not be representative 

of steady-state, pre-development conditions. Rather, the 

initial conditions represent a dynamic steady state that 

fluctuates with the climate variability. Prior to development, 

which started in the early 1900s, the hydrologic system was 

responding to changes driven by the natural cycles of climate 

variability. Shallow groundwater levels could have been 

present in many parts of the basin, as evidenced by riparian 

areas along some reaches of the creeks and by mesquite and 

other phreatophytes at and around the Borrego Sink (fig. 6). 

As a result of development combined with the wetter than 

normal climatic conditions for 1939–45, the hydrologic 

conditions during 1945 are likely not representative of 

near-steady-state, pre-development hydrologic conditions. 

Initial conditions consist of the distribution of groundwater 

levels at every active cell within each of the three aquifers, 

which were estimated for pre-development and applied to 

October 1929. The 1945 contour map from Moyle (1982) 

(fig. 13A) and groundwater-level data for years that appear 

not to be influenced by pumping were used to create the predevelopment groundwater-level map.

The simulation started with October 1929, and model 

calibration began with October 1945 (the period referred to 

previously as the model spin-up period). Following the initial, 

preliminary model calibration, initial heads were further 

refined by periodically using the simulated heads from the end 

of the spin-up period (September 1945) of simulation during 

calibration.

When the simulation was started, the simulated heads 

and flows changed in response to the initially specified 

and ongoing inflows and outflows. Climatic variability and 

pumping stresses on the system change rapidly, and the 

inconsistencies between the initial conditions and the early 

simulated values generally are not problematic because the 

next stress regime soon dominates the solution (Hill and 

Tiedeman, 2007). As a result, the comparison of observed and 

simulated values becomes meaningful after a relatively short 

simulated period. This study and previous studies (Belitz and 

Phillips, 1995; Faunt and others, 2009a) show that the time 

frame for the stabilization from misspecification of initial 

conditions is typically less than several months to years of 

the simulation. Because of the incorporation of the thick 

unsaturated zone in the model, a comparatively long spin-up 

period of 16 years was used to eliminate significant effects 

caused by uncertainty in the initial conditions.

Aquifer Type

In this study, the uppermost active model layers were 

simulated as unconfined, and those below them as convertible 

from confined to unconfined, to allow simulation of the 

drainage and evolution of unsaturated conditions in the thick 

unsaturated zone in the valley. As the water table declined 

below the bottom of an unconfined layer, that layer went 

dry, and the underlying layer was converted from confined 

to unconfined. For unconfined model layers, the saturated 

thickness and associated transmissivity changed with 

declining or rising groundwater levels; saturated thickness and 

transmissivity were constant in confined layers. The regions 

of large groundwater-level declines and the related thick 

unsaturated zone in the central part of the valley are illustrated 

by the groundwater-level map from 2010 (fig. 13B). Declines 

in groundwater-levels ranged from 0 to more than 100 ft 

during 1945–2010 (figs. 2, 14).

Table 10. Summary of groupings of water-balance subregions into regions for the Borrego Valley Hydrologic Model, Borrego Valley, 

California.

Water-balance subregion number Water-balance region Water-balance region description

1–27, 29, 30, 32–35, 37–43, 46, 47, 49–51 Agricultural usage. Various parcels used for irrigated agriculture.

28, 31, 36, 44, 45, 48 Recreational usage. Golf courses.

52 Other usage (undeveloped, municipal, 

residential, and phreatophytic areas).

Natural vegetation, municipal areas, residential areas, 

and non-developed previous phreatophytic areas.

Table 11. Coordinates of the Borrego Valley Hydrologic Model for Borrego Valley, California.

[Model grid is rotated 22 degrees west; coordinates below are calculated at the cell center of the model grid using the North American Datum of 1983 in the 

Universal Transverse Mercator (UTM) Projection of North America, Zone 11; each model cell is 2,000 feet by 2,000 feet. DMS, degree, minute, second]

Corner 

of 

model grid

Model 

coordinates X 

(column)

Model 

coordinates Y 

(row)

Latitude 

(DMS)

Longitude 

(DMS)

UTM coordinates X 

(easting) 

(meters)

UTM coordinates Y 

(northing) 

(meters)

Northeast 1 1 33° 25ʹ 16ʺ 116° 23ʹ 35ʺ 556,440 3,698,150

Northwest 1 30 33° 18ʹ 50ʺ 116° 32ʹ 00ʺ 543,438 3,686,173

Southeast 75 1 33° 07ʹ 12ʺ 116° 04ʹ 03ʺ 587,003 3,664,971

Southwest 75 30 33° 00ʹ 46ʺ 116° 12ʹ 28ʺ 574,001 3,652,994
Groundwater-Flow Models 83

Aquifer Characteristics

The Borrego Valley has three aquifers that can be 

characterized by variations in hydraulic properties based on 

the textural distribution of coarse- and fine-grained sediments 

and zones that represent sediments derived from particular 

depositional environments (figs. 12, 34). The hydraulic 

properties represent the ability of the aquifer to transmit 

water and to store or release water; hydraulic properties 

differ according to grain size and the degree of sorting of the 

sediments, which are functions of lithology and depositional 

environment. Thus, considerable spatial variation exists in 

the hydraulic properties of the heterogeneous aquifer system. 

The hydraulic water-transmitting properties of the aquifer 

sediments are represented by their horizontal (Kh) and vertical 

(Kv) hydraulic conductivities. The hydraulic storage properties 

both include the properties of the aquifers and any finegrained interbeds or confining units. Storage properties of the 

unconfined model layers are represented by the specific yield 

and specific storage in the confined model layers (see “Storage 

Properties” section). The relation between hydrogeologic 

units, lithology, sediment texture, and hydraulic properties has 

been described in many previous studies (Laudon and Belitz, 

1991; Phillips and Belitz, 1991; Leighton and others, 1994; 

Fio and Leighton, 1995; Belitz and Phillips, 1995; Burow and 

others, 2004, and Phillips and others, 2007; Faunt and others, 

2009a, 2009b; Sweetkind and others, 2013).

Textural Analysis

Heterogeneity, lateral and vertical variation in sediment 

texture and related hydrogeologic properties, affects the 

direction and rate of groundwater flow, as well as the 

magnitude and distribution of aquifer-system storativity. 

The textural distribution was used to define the Kh and Kv

and storage property distributions for the hydrologic model. 

As in many of the previous studies identified, this textural 

distribution was based on drillers’ and geophysical logs. The 

primary variable selected for the textural analysis was the 

percentage of coarse-grained sediments, with the complement 

being the percentage of fine-grained sediments.

As described earlier, the distribution of sediment texture 

in the three aquifers of the Borrego Valley aquifer system 

was based on a reanalysis of the hydrogeology. In the model, 

within each aquifer, the fraction of coarse- and fine-grained 

sediments within the thickness of each aquifer model layer 

was estimated on a cell-by-cell basis (fig. 12). Texture was 

estimated at the cell centers of the model grid for each of the 

aquifers.

Calculation of Hydraulic Properties

Textural-based estimates of hydraulic properties were 

made separately for each of the three aquifers. The hydraulic 

properties of an aquifer are its transmission and storage 

properties. The transmission properties of the Borrego Valley 

aquifer are represented by the K in this study. Equivalent Kh

and Kvs are assumed to be correlated to sediment texture (the 

fraction of coarse-grained and fine-grained sediment). The K 

was calculated by using the estimated binary sediment texture 

for each model cell and Kh and Kv estimates for each textural 

end member (coarse and fine). 

 Separate methods were used to calculate estimates 

for horizontal and vertical hydraulic conductivities. The Kh

(Kh,i) was calculated as the weighted arithmetic mean of the 

hydraulic conductivities of the coarse-grained (Kc ) and finegrained (Kf

 ) lithologic end members and the distribution of 

sediment texture for each model cell i:

 Kh,i = + 

  K Fc c, , i f K Ff i (2)

where 

Fc,i is the fraction of coarse-grained sediment in a 

cell, estimated from sediment texture data 

as described in the previous section, and 

Ff,i is the fraction of fine-grained sediment in a 

cell (1 – Fc,i).

Because Kf is much smaller than Kc, the arithmetic mean 

heavily weights the coarse-grained end member for Kh.

Faunt and others (2009a) identify the power mean as 

useful for defining Kv values. In addition, their work also 

includes a review of the literature that describes the use of the 

power mean for estimating K. A power mean is given by the 

following equation:

M x

n

x p

k

p

k

n p

( ) = 



 



 =

∑

1

1

1

(3)

where 

p is the averaging power-mean exponent, 

n is the number of elements being averaged, and 

Xk is the kth element in the list.

The Kv between model layers ( Kv k, + 1

2 ) was calculated as 

the pth weighted power mean of the hydraulic conductivities 

of the coarse- and fine-grained lithologic end members (Faunt 

and others, 2009a):

K F K F K

v k c k c

p

f k f

p p

, , + + , + = + 

  12

1

2

1

2

1

(4)

where 

Fc k, + 1

2 is the fraction of coarse-grained sediment 

between layer midpoints, and

F

f k, + 1

2 is the fraction of fine-grained sediment 

between layer midpoints.
84 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 34_Hk zones

EXPLANATION

Alluvium

Sand deposits

Fan deposits

Sink or playa deposits Constant-head boundary 

 cells in layer 3

Geologic type Boundary of

Stream channel

Boundary of Borrego Valley

 groundwater basin Upper aquifer (layer 1)

Middle aquifer (layer 2)

Lower aquifer (layer 3)

Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake

Figure 34. Model grid with geologic types of depositional zones used for parameter zones of hydraulic properties for the Borrego 

Valley Hydrologic Model, Borrego Valley, California.
Groundwater-Flow Models 85

The harmonic mean is a weighted power mean with 

the exponent p = −1.0 in equation 4 and results in increased 

vertical anisotropy. The geometric mean is a weighted power 

mean with p = 0.0 in equation 4 and results in decreased 

vertical anisotropy. Phillips and Belitz (1991) determined that 

Kv could be calculated by using either weighted harmonic 

or weighted geometric means. Belitz and others (1993) 

represented the Kv with the weighted harmonic mean. The 

Kv can be represented as power means in which p varies 

between −1.0 (the harmonic mean) and 0.0 (the geometric 

mean) (Faunt and others, 2009b). The relation between K and 

percentage coarse-grained deposits, based on K end members 

and exponent of the power mean, is nonlinear. The harmonic 

and geometric means more heavily weight the fine-grained end 

members; as a result, the Kv values are much lower than the 

horizontal. Dimitrakopoulos and Desbarats (1993) determined 

that the value of p depends to some extent on the size and 

thickness of the grid blocks used to discretize the model 

domain; smaller grid cells resulted in lesser values of p. The 

exponent p was set for each model layer and adjusted during 

model calibration. 

Hydraulic properties can be approximated from the 

results of various aquifer tests done in supply wells. Netto 

(2001) documented four such aquifer tests performed to 

measure transmissivity in the Borrego Valley. At each well a 

constant discharge rate test was performed, with groundwater 

levels measured during the drawdown and recovery phases. 

On the basis of these tests, Netto (2001) reports K values 

ranging from 2 ft/d in clays interbedded with sands to 

336 ft/d in a coarse sand unit (table 12). Interbedded clays 

and gravels were estimated to have a K of 17 ft/d; the older 

more cemented and lithified interbedded clays and gravels of 

the Palm Spring Formation (lower aquifer) were estimated 

to have a K of 10 ft/d. Henderson (2001) used these data to 

calibrate a groundwater-flow model. His model-estimated 

Kh values, which ranged from 0.1 (clay) to 178 ft/d (gravel), 

were a function of texture (table 12). Henderson estimated 

a horizontal to vertical K ratio (Kh/Kv) of 10 in the units 

equivalent to the upper and middle aquifers and 1 in the lower 

aquifer. Results from the models of Henderson (2001) and 

Mitten and others (1988) indicated that groundwater flow 

in Borrego Valley is relatively insensitive to variations in 

estimated Kh and Kv.

Hydraulic Conductivity of Lithologic End Members

The lithologic end-member hydraulic conductivities used 

in this study represent the coarse- (Kc) and fine-grained (Kf) 

sediments. Parameter estimation was used, in combination 

with the sediment texture model and known stratigraphic 

units, to estimate the end-member K values that minimized 

the error between simulated and measured values. These end 

members were used to estimate the horizontal and Kv for 

each cell in the model. The components of the aquifer system 

in Borrego Valley have somewhat different depositional 

environments and textural compositions that affect the endmember K values. Thus, the end members of each aquifer 

were estimated separately. In addition, each of the aquifers 

were further subdivided into as many as four parameter zones 

to facilitate model calibration and represent areas that consist 

of different depositional environments better (fig. 34). For 

the upper aquifer, all four zones were utilized, and two zones 

each were utilized for the middle and lower aquifers. Because 

of the different geologic properties by aquifer, different end 

members were estimated for the sand deposits and alluvium. 

For all aquifers, a single horizontal and Kv was estimated 

for the fan deposits. In the area of the Borrego Sink, a single 

horizontal and Kv was estimated for all three aquifers for this 

relatively uniform fine-grained deposit. For the middle and 

lower aquifers (layers 2 and 3), except for the Borrego Sink 

zone in the middle of the basin (fig. 33), the distributions of 

horizontal and Kv varied with the distribution of sediment 

texture throughout the extent of each aquifer (fig. 12). 

The Upstream-Weighted Property Flow Package (UPW) 

was used to simulate the hydraulic properties. The final 

parameters from model calibration representing hydraulic 

properties and related scale factors are discussed in the section 

“Model Calibration.” Hydraulic conductivities generally 

decrease with depth and with increasing distances from the 

original source of the sediments in adjacent mountain ranges 

and river channels, which is consistent with the fining-down 

and fining-toward-the-basin-center sequences observed in 

the aquifer sediments and texture model. However, there are 

several areas where smaller values of K have been estimated at 

depth owing to fine-grained textures and secondary alteration, 

such as cementation. Coarser grained sediments were assumed 

to be present near stream channels in the alluvium in the upper 

reaches of all three aquifers.

Table 12. Summary of hydraulic properties from previous studies 

and the radial groundwater-flow model, Borrego Valley, California.

[See table 8 for description of the radial groundwater flow model. ft, foot; ft/d, 

feet per day; —, no data]

Aquifer Source

Upper Middle Lower

Horizontal hydraulic conductivity (ft/d)

50 4–5 1–4 Moyle (1982)

43–81 5.8 1.4 Mitten and others (1988) 

2–336 2–336 2–10 Netto (2001)

0.1–178 0.1–178 1 Henderson (2001)

7–350 0.5 — Table 8 (this report)

Vertical hydraulic conductivity (ft/d)

0.43–0.81 0.058 0.014 Mitten and others (1988) 

0.1–18 0.01–18 1 Henderson (2001)

0.7–35 0.05 — Table 8 (this report)

Specific storage (1/ft)

— 0.000001 0.000001 Mitten and others (1988) 

— 0.000002 0.000002 Henderson (2001)

— 0.0000025 — Table 8 (this report)

Specific yield (in percent)

20 10 5 Moyle (1982)

14 7 3 Mitten and others (1988) 

2–28 2–28 — Netto (2001)

2–20 2–20 7.5 Henderson (2001)

15 15 — Table 8 (this report)
86 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Storage Properties

The hydraulic properties used to simulate the changes 

in storage of water within the saturated parts of the aquifer 

system consist of two components: specific yield and specific 

storage. Specific yield and specific storage represent and 

govern the reversible uptake and release of water to and 

from storage. Specific yield represents unconfined storage, or 

gravity-driven drainage (de-saturation) or filling (re-saturation) 

of sediments with water with changes in water-table elevation. 

It is the volumetric fraction of the bulk aquifer volume, 

expressed as a ratio or percentage, that a given aquifer yields 

when water is allowed to drain out of it under the force of 

gravity. Furthermore, specific yield is a function of sediment 

porosity (void space) and moisture-retention characteristics, so 

it cannot exceed sediment porosity. A schematic representation 

of specific yield is shown in figure 35. The storage coefficient 

(or storativity) represents the component of confined storage 

and equals the specific storage times the aquifer thickness. 

Given the coarse-grained nature of most of the three aquifers 

in the Borrego Valley, confined storage was simulated by using 

a single specific storage value for each aquifer. On the basis of 

the aquifer tests mentioned in the “Calculation of Hydraulic 

Properties” section, Netto (2001) estimated the storage 

coefficient to be 6.45 × 10–4. Specific yield typically is orders 

of magnitude larger than specific storage and is volumetrically 

the dominant storage parameter for the valley.

The UPW was used to define storage properties in each 

of the aquifers represented in the model. This includes the 

storage coefficient and specific yield for each aquifer. All 

aquifers (model layers) in the simulations were convertible 

from confined to unconfined, and a specific yield was specified 

for each layer. A single specific yield value was estimated 

for each zone representing the percentage of coarse-grained 

deposits in each layer, and the same zones were used in all 

three layers (fig. 36). Specific yield ranged from 30 percent for 

the coarse-grained parts of the upper aquifer to 0.5 percent for 

the fine-grained parts of the lower aquifer.

Unsaturated Hydraulic Properties

In addition to the saturated hydraulic properties, the 

model also required specification of parameters used to 

simulate vertical flow through the unsaturated zone. In this 

model, this was done in the Unsaturated Zone Flow (UZF) 

package of MODFLOW (Niswonger and others, 2006). UZF 

is a one-dimensional simplification of flow in the unsaturated 

zone. For this model, the unsaturated Kv was estimated to 

be one uniform property. This is an oversimplification, but 

adequately represents a delay in recharge to the saturated zone. 

The initial water content of unsaturated deposits was assumed 

to be 0.10; this self-adjusted during the spin-up period. 

A Brooks-Corey Epsilon value of 3.5 was used to define the 

relation of water content to Kv (Niswonger and others, 2006; 

Brooks and Corey, 1964).

Figure 35. Schematic diagram showing magnitudes (%, percent) of specific yield for the upper, middle, and lower aquifers, Borrego 

Valley, California.

1 FOOT

1 FOOT

1 FOOT

Static

water

level

Reduced

water

level

Water drained

by gravity

from

1 cubic foot

of sand

with different

porosities

1

SQUARE

FOOT

14% 7% 3%

Upper

aquifer

Middle

aquifer

Lower

aquifer
Groundwater-Flow Models 87

Figure 36. Model grid with texture zones used to define the distribution of specific yield for the Borrego Valley Hydrologic Model, 

Borrego Valley, California.

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 36 Sy zones

EXPLANATION

Texture zone for specific yield

9

10

1

2

3

4

5

6

7

8

11

12

13

14

Model-grid boundary

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake
88 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Recharge

Recharge to the Borrego Valley comes from natural 

and anthropogenic sources. As mentioned previously, the 

primary source of natural recharge to the basin is infiltration 

from the ephemeral streams and washes entering the Borrego 

Valley from the adjacent mountains. Surface-water runoff 

was simulated by using the Streamflow Routing Package 

(SFR; Niswonger and Prudic, 2005; Prudic and others, 

2004); the head-dependent boundary condition allows for 

streamflow routing, the capture and conveyance of overland 

runoff, groundwater discharge (gaining stream reaches), and 

streamflow infiltration into the aquifer (losing stream reaches). 

The SFR package was applied by using a streamflow routing 

network composed of 84 stream segments representing Coyote 

Creek, San Felipe Creek, Borrego Palm Creek, and other 

smaller tributaries. This network was used to simulate the 

inflows of 24 canyon stream channels from the surrounding 

mountains, streamflow infiltration, and occasional outflows 

along the Borrego Valley streamflow network (fig. 37). 

Runoff simulated by FMP within the active model domain 

was redirected to the SFR streamflow networks and provided 

a small component of groundwater recharge and streamflow 

during the wettest months. The stage-discharge relations 

were assumed to be constant for each group of stream cells 

(reaches) used to discretize the stream segments for the model 

cells representing the SFR stream network (fig. 37). The 

streambed elevations, based on the DEM, for the beginning 

and end of each segment were specified, along with the 

streambed thickness and Kv of reaches within each segment. 

For simplicity, a wide rectangular channel for the stream 

geometry and a constant estimation of Manning’s “n” was 

used during the simulation.

Inflows from the runoff in stream channels entering the 

24 canyons were specified as input to the SFR (fig. 37). The 

BCM provided deterministically simulated monthly runoff in 

stream channels to the basin (figs. 16, 19). The BCM domain 

includes the watersheds that surround and drain into the 

Borrego Valley (fig. 16).

For application to the BVHM, the monthly runoff 

volumes from the BCM for the 24 surface-water entry points 

were used as inflow rates for each BVHM monthly stress 

period and provided the intermittent runoff inflows along the 

outer boundary of the active BVHM model area. Although 

streamflows typically are not constant over monthly periods, 

the monthly volume of inflow estimated by using the BCM 

model was preserved. Despite this simplification, flow from 

runoff varied widely on a monthly basis, based on the BCM. 

Runoff inflows to the SFR network were simulated by SFR 

as rapidly infiltrating the unsaturated zone and ultimately 

recharging the groundwater system by assuming a high 

streambed Kv and a delay for vertical flow through the 

unsaturated zone below the streambed. The unsaturated zone 

delay is specified in SFR in a manner similar to that of the 

UZF package described in section “Unsaturated Hydraulic 

Properties.”

The total estimated average runoff and recharge to the 

basins surrounding Borrego Valley is 4,700 acre-ft/yr, of 

which about 3,650 acre-ft/yr (78 percent) was estimated to 

represent runoff into the valley. Because these estimates 

were based on a model, factors were developed to allow for 

scaling both of runoff and underflow to allow for adjustments 

during model calibration to best match measured groundwater 

levels and groundwater-level changes in the valley. A total of 

12 scaling factors, consisting of 2 scaling factors (for runoff 

and underflow) for 6 creeks or washes (San Felipe Wash, 

Coyote Creek, Henderson Creek, Borrego Palm Creek, and 

2 groupings of other intermittent washes) were estimated 

separately (fig. 37). Mountain block recharge was assumed 

to be a component of the underflow from the upstream 

components of the watershed.

In addition to these natural sources of recharge, irrigation 

return flow from agricultural fields and municipal lawns and 

infiltration of treated and untreated wastewater also contribute 

to recharge. These sources of anthropogenic recharge have 

substantially increased the total recharge into the valley. The 

irrigation return flow from agricultural fields was simulated by 

BVHM as part of the FMP. As mentioned previously, BVHM 

uses UZF to estimate the delay associated with flow moving 

through the unsaturated zone (Niswonger and others, 2006). 

Depending on the unsaturated-zone thickness, permeability, 

and residual moisture content, it can take years to decades for 

irrigation return flow to pass through the unsaturated zone. 

In addition, not all water that passes through the root zone 

percolates to the water table within the simulation period 

because some water is held in storage in the unsaturated 

zone. Therefore, a portion of the water that is applied to 

previously unirrigated land or seeps from septic tanks might 

not arrive at the underlying water table for decades, depending 

on the application rate, the depth to water, the properties 

of the unsaturated zone, and the initial water content of the 

unsaturated zone (Izbicki and others, 2002).

Most of the homes in the area utilize septic-tank 

treatment and disposal systems. The BWD (J. Rolwing, 

Borrego Water District, written commun., 2011) estimated that 

about 80 percent of the domestic water deliveries are to homes 

with septic-tank systems. Potential recharge from this water 

use is difficult to quantify, but is believed to be small. Mills 

(2009) estimated an average indoor usage of 100 gallons per 

day per home and a 50 percent loss rate owing to evaporation 

and transpiration. On the basis of this estimate, the infiltration 

from septic tanks is simulated at an application rate of 

0.056 acre-foot per year per home at land surface into the 

unsaturated zone by using UZF. The infiltration from irrigation 

of municipal lawns and treated and untreated wastewater was 

assumed to be negligible (Henderson, 2001).
Groundwater-Flow Models 89

Figure 37. Model-grid stream cells used to simulate streamflow in the Borrego Valley Hydrologic Model, Borrego Valley, California. 

(K, hydraulic conductivity; Kv, vertical hydraulic conductivity; COYOTE, Coyote Creek; Palm, Borrego Palm Creek; SANFELIPE, San Felipe 

Creek; TRIB, unidentified tributary streams, TRIBU, upper portions of unidentified tributary streams; YAQUI, Yaqui Creek).

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 37 Stream Grid

EXPLANATION

Coyote Creek

Borrego Palm

Creek

San Felipe Creek

78 Road

Model-grid stream cell

K_COYOTE

K_PALM

K_SANFELIPE

K_TRIB

K_TRIBU

K_YAQUI

Surface-water entry point

Model-grid boundary

Stream channel

Boundary of Borrego Valley

 groundwater basin

Boundary of Anza-Borrego

 Desert State Park

Fault Dry lake
90 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Water-Balance Subregions 

The evolution of the landscape is a combination of 

changes in land use and related land ownership in Borrego 

Valley over the 66 years of the historical simulation, 

1945–2010. Parcels defined for 2010 were used to define land 

ownership and divide the valley into WBSs (fig. 38). The 

footprint of these WBSs was held constant throughout the 

simulation; however, the land-use types change over time in 

each WBS and reflect the evolution of land use. The landuse type determines the water demand for native vegetation 

or irrigated crops, which is used to calculate the required 

groundwater pumping for irrigation.

Landscape Water Use

The FMP provides a coupled simulation of the 

groundwater and surface-water components of the hydrologic 

cycle for irrigated and non-irrigated areas. A dynamic 

allocation of groundwater recharge and groundwater 

pumping was simulated on the basis of residual crop-water 

demand after surface-water deliveries (in Borrego Valley 

surface water is not used for irrigation) and root uptake 

from shallow groundwater. For a given stress period, the 

estimation of irrigation groundwater pumping in FMP was 

dependent on satisfying demands for ET from precipitation 

and variable irrigation efficiencies that govern the availability 

of excess water available for deep percolation. For a 

complete description of these components, please see the 

FMP manual (Schmid and others, 2006a). The FMP not only 

estimates supply, demand, movement, and consumption of 

irrigation water, but also estimates these components for 

natural vegetation. To summarize, the use and movement of 

water on the landscape is fully coupled with streamflow and 

groundwater flow and is dependent on atmospheric (and soil) 

conditions through precipitation and reference ET. 

The FMP simulates the demand components representing 

the crop irrigation requirement (CIR) that are subject to 

crop and farm-specific irrigation efficiencies and the supply 

components representing precipitation, direct uptake from 

groundwater, uptake of soil moisture, and irrigation from 

groundwater pumping. The FMP also simulates additional 

head-dependent inflows and outflows from the landscape, 

such as surface runoff from precipitation and irrigation to 

the streamflow network and groundwater recharge as deep 

percolation of water in excess of actual evapotranspiration 

(ETact) and runoff (Schmid and others, 2006a, b; Schmid and 

Hanson, 2009). Changes in soil moisture were not simulated 

by the FMP and were assumed to be negligible at the monthly 

time scale.

Inflows and outflows throughout the WBSs on the 

landscape were simulated by FMP. Water mass within each 

WBS was calculated and balanced for each simulation time 

step (Schmid and others, 2006a, b; Schmid and Hanson, 

2009). The FMP dynamically integrates irrigation water 

demand from ET with water supply and irrigation efficiencies. 

In order to do this, the FMP first calculates crop water demand 

as the transpiration or consumption of water by plants and the 

related evaporation on the basis of cell-by-cell estimations 

for each WBS. The FMP then determines a residual plant 

water demand that cannot be satisfied by precipitation and 

by root uptake from groundwater, if available from shallow 

groundwater near the root zone. This residual water demand 

is the vegetation’s irrigation requirement for the cells with 

irrigated crops (that is, exclusive of any natural vegetation), 

which is called CIR and is calculated on a cell-by-cell basis.

The CIR is then adjusted (increased) by accounting 

for evaporative losses from irrigation and other inefficiency 

losses to yield a final total farm delivery requirement (TFDR). 

For Borrego Valley, where groundwater is the sole source 

of irrigation water, FMP attempts to satisfy the TFDR by 

using groundwater pumpage. This demand is not met when 

the demand exceeds the capacity of the wells for a specific 

WBS, either because groundwater levels dropped below the 

maximum screen-interval depth or the pumping rate of a given 

well is exceeded. The amount of excess water from irrigation 

(irrigation efficiencies) and precipitation that is not effectively 

used for crop growth then becomes either overland runoff to 

nearby streams or groundwater recharge as deep percolation 

below the root zone, on the basis of parameters specified in the 

FMP. In the BVHM, all the excess water ultimately becomes 

groundwater recharge. Thus, the FMP dynamically links the 

demand, supply, and related change in head. All of the supply 

and demand components are then tabulated into WBS budgets, 

which complement the groundwater flow, and streamflow 

budgets, which collectively represent the hydrologic cycle 

within Borrego Valley.

In order to estimate the inflows and outflows, the FMP 

dynamically simulates the supply and demand components 

for a WBS by integrating the following computational 

components specific to Borrego Valley’s hydrologic setting:

1. TFDR, which is largely dependent on the CIR, but also 

depends on efficiency, climate variability (PET and 

precipitation), and variable aquifer head.

2. Groundwater pumping, which is equivalent to the TFDR 

in the BVHM.

3. Net recharge (deep percolation) to groundwater, which is 

taken to be the sum of excess irrigation and precipitation 

minus ET from groundwater.

OWHM maintains a mass balance for each WBS budget, 

for the streamflow network, and for the groundwater-flow 

system. Flows between these budgets are accommodated by 

head-dependent inflows and outflows, such as ETact, runoff 

and infiltration, or ET from groundwater. For the BVHM, 

the processes of evaporation, transpiration, runoff, deep 

percolation to groundwater, and groundwater pumping were 

simulated. The simulated groundwater pumpage reflects 

climatic differences and differences in agricultural practices 

(including irrigation method) among defined WBSs. The 

BVHM model provides a detailed transient analysis of 

changes in groundwater storage in relation to climatic 

variability, urbanization, land use, and changes in irrigated 

agriculture.
Groundwater-Flow Models 91

Figure 38. Model grid with water balance subregions used to account for water usage in the Borrego Valley, California.

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 38 water balance regions

EXPLANATION

Water balance subregions—Colored cells 

represent different subregions

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake
92 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Delivery Requirement

The TFDR is determined as the CIR of all WBS cells for 

irrigated crops and is increased sufficiently to compensate for 

irrigation efficiencies and ET from groundwater. The amount 

of ET from groundwater is a function of soil type, water-table 

elevation, land-use type, root depths, and the anoxia and 

wilting points assigned to each land-use type. The TFDR data 

requirements for computing consumptive use on a cell-by-cell 

basis include soil-type, land-use (distribution of crop types 

and natural vegetation, including phreatophytes), and climate 

data. The consumptive use of each land-use type is simulated 

in this study on the basis of the percentage of land cover, root 

depths, KCs, irrigation efficiencies, and runoff (tables 13–16). 

Although the exact value of these variables is uncertain, the 

basis of these estimates comes from various agricultural 

water-use studies (Allen and others, 1998; Snyder and others, 

1987a, b). Multipliers were used to adjust these values during 

calibration. 

Soils

The BVHM soils were simplified into two categories—

silty clay and sandy loam—from the Soil Survey Geographic 

Database (SSURGO; Natural Resources Conservation 

Service, 2006; fig. 39). The capillary fringe was estimated 

for each soil type and ranges from 1 to 10 ft thick. These soil 

attributes were used for the entire simulation period, and the 

cell-by-cell distribution was independent of the crop type 

and WBS. The FMP associates the distributed soil types with 

the specified capillary fringes and internal coefficients that 

allow individual analytical solutions for the calculation of ET 

(Schmid and others, 2006a).

Land Use

The FMP can be used to estimate components of 

consumptive use for a wide variety of land uses, including 

vegetation in irrigated or non-irrigated agriculture, fallow 

fields, phreatophytes and other natural vegetation, golf 

courses, and residential/municipal landscape settings. The 

FMP also can be used to simulate an assortment of irrigation 

methods that span the spectrum from flood irrigation to 

sprinklers to drip irrigation.

For the Borrego Valley, the land-use attributes are defined 

on a cell-by-cell basis and include residential/municipal, 

recreational, agricultural, and native uses. The land use that 

covered the largest fraction of each cell (about 92 acres) 

was assigned to represent land use in that cell. Land use can 

change gradually or rapidly in response to changes in climate, 

urbanization, zoning, or farming practices. This required 

making decisions as to how and when to make land-use 

changes to the modeled domain between years with known 

land-use distribution. For the BVHM, the land-use patterns 

were linearly changed from one land-use time frame to the 

next. Before development, about 10 percent of land use 

consisted of phreatophytes, and 90 percent was other types of 

native vegetation and bare ground. In 2009, 78 percent was 

natural vegetation (6 percent phreatophytes and 72 percent 

other native types), 11 percent residential/municipal, 8 percent 

developed agricultural land, and about 3 percent recreational 

uses (golf courses) (table 17). Eleven land-use categories, 

referred to as “virtual crops,” were defined on the basis of the 

land-use maps and fifteen crop-type indexes (fig. 6); these 

virtual crops were represented by an index number in the FMP 

(tables 15–17). For the entire simulation period, these virtual 

crops were used to drive the use and movement of water for 

each WBS.

Discharge

Before the Borrego Valley was developed, groundwater 

discharge consisted of ET and lateral groundwater underflow. 

As the valley was developed, discharge gradually transformed 

to include groundwater pumpage for agricultural, recreational, 

and residential/municipal uses.

Natural Discharge

Natural discharge occurs both as ET and a small amount 

of underflow through the southeastern end of the valley. 

Phreatophytes are present primarily along the northern part of 

the valley and in the area of the Borrego Sink (fig. 6), where 

groundwater is discharged through ET. As mentioned in the 

“Evapotranspiration” section, prior to 1946, almost all the 

water entering the valley was discharged by ET in and around 

the Borrego Sink, and by 1980, only about 30 percent of this 

amount was discharging naturally from the marshland that 

surrounded the Borrego Sink owing to declining groundwater 

levels (Moyle, 1982). Henderson (2001) reported simulated 

ET rates that ranged from 3,900 acre-ft/yr in 1946 to 

132 acre-ft/yr in 2000. In this study, the direct evaporation 

from the water table and ET from phreatophytes are simulated 

by the FMP. In the FMP, the magnitude of this natural 

discharge is dependent on the thickness of the capillary fringe, 

the rooting depth, crop-coefficients for phreatophytes, and the 

depth to groundwater (Schmid and others, 2006a). Underflow 

through the southeastern end of the valley is discussed in the 

“Specified (Constant) Head Boundary” section.

Groundwater Pumpage

Groundwater pumpage is a major part of the hydrologic 

budget of Borrego Valley and is grouped into the three 

categories for this study—(1) agricultural; (2) recreational; 

and (3) municipal, which includes municipal and domestic/

rural residential supply. Agricultural and recreational 

pumpages include pumpage from all wells used to supply 

water for irrigation of crops and golf courses, respectively, 

and are estimated by using the FMP. Municipal pumpages 

for municipal and domestic/rural residential water supply are 

specified by using reported values. Most farm and municipal 

wells are simulated as multi-aquifer wells with the MultiNode Well Package (MNW2; Konikow and others, 2009) that 

pump from as many as three aquifers (model layers). Farm 
Groundwater-Flow Models 93

Table 13. Summary of Borrego Valley land-use types in the Borrego Valley Hydrologic Model, Borrego Valley, California.

[NA, not available]

Land-use 

type

(index number)

Irrigated Root depth, 

in feet

Root uptake pressure heads, in feet

Fraction of surface-water 

runoff from precipitation (left) 

and irrigation (right)

Anoxia Lower optimal 

range

Upper optimal 

range Wilting (Dimensionless)

Golf courses (1, 3) Yes 3.28 –0.13 –0.28 –11.40 –80.00 0.06 0.14

Residential/urban (2) No 0.82 –0.13 –0.28 –11.40 –80.00 0.06 na

Phreatophytes (4) No 15.27 0.50 0.13 –8.25 –115.00 0.05 na

Fallow/livestock (5, 6) No 0.82 –0.08 –0.20 –8.25 –115.00 0.40 na

Row and other crops (7) Yes 1.64 –0.15 –0.30 –5.45 –80.00 0.25 0.06

Grapes (8) Yes 6.56 –0.15 –0.30 –7.25 –80.00 0.06 0.05

Non-irrigated grapes (9) No 6.56 –0.15 –0.30 –7.25 –80.00 0.06 na

Citrus (11) Yes 4.00 –0.15 –0.30 –6.00 –80.00 0.06 0.25

Dates, palms, nursery (10, 12, 13) Yes 4.92 –0.15 –0.30 –6.00 –80.00 0.09 0.13

Potatoes (14) Yes 3.28 –0.15 –0.30 –52.10 –80.00 0.28 0.06

Native (15) No 14.47 –0.08 –0.20 –8.25 –115.00 0.40 na

Table 14. Monthly crop coefficients for each land-use type simulated in the Borrego Valley Hydrologic Model, Borrego Valley, 

California.

Land-use type 

(Farm process crop 

index number)

Irrigated1 Jan Feb March April May June July August Sept Oct Nov Dec

Golf courses (1, 3) Yes 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Residential/urban (2) No 1.00 1.00 1.00 1.00 0.50 0.50 0.50 0.50 0.50 0.75 0.80 0.85

Phreatophytes (4) No 1.00 1.00 1.00 0.48 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.50

Fallow/livestock (5, 6) No 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Row and other crops (7) Yes 0.90 0.90 0.90 0.90 0.90 0.75 0.70 0.90 0.70 0.90 0.90 0.90

Grapes (8) Yes 0.35 0.35 0.75 0.80 0.81 0.81 0.81 0.81 0.75 0.50 0.50 0.35

Non-irrigated grapes (9) No 0.30 0.30 0.75 0.80 0.81 0.81 0.81 0.81 0.75 0.50 0.35 0.33

Citrus (11) Yes 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65

Dates, palms, nursery (10, 12, 13) Yes 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96

Potatoes (14) Yes 0.70 0.90 1.15 1.15 0.90 0.75 0.10 0.10 0.10 0.10 0.10 0.51

Native (15) No 0.80 0.80 0.80 0.40 0.10 0.10 0.10 0.10 0.10 0.10 0.40 0.50

1

Crop coefficients are adjusted by season with multipliers (see “Model Calibration” section of this report).
94 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley Table 15. Summary of fractions of transpiration and evaporation, by month, for Borrego Valley land-use type, or virtual crops, Borrego Valley, California.

[Abbreviations: FMP, MODFLOW Farm Process]

Land-use type

(FMP crop index number) January February March April May June July August September October November December

Fraction of transpiration

Golf courses (1, 3) 0.50 0.68 0.68 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.68 0.68Residential/urban (2) 0.20 0.68 0.68 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.68 0.68Phreatophytes (4) 0.13 0.58 0.58 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.58 0.58Fallow/livestock (5, 6) 0.68 0.68 0.68 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.68 0.68Row and other crops (7) 0.10 0.75 0.75 0.75 0.71 0.71 0.71 0.71 0.71 0.71 0.75 0.75Grapes (8) 0.80 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85Non-irrigated grapes (9) 0.20 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75Citrus (11) 0.90 0.78 0.78 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.78 0.78Dates, palms, nursery (10, 12, 13) 0.80 0.75 0.64 0.64 0.64 0.64 0.64 0.64 0.64 0.64 0.75 0.75Potatoes (14) 0.80 0.67 0.67 0.67 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75Native (15) 0.15 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86

Fraction of evaporation from irrigation

Golf courses (1, 3) 0.50 0.32 0.32 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.32 0.32Residential/urban (2) 0.80 0.32 0.32 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.32 0.32Phreatophytes (4) 0.87 0.42 0.42 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.42 0.42Fallow/livestock (5, 6) 0.32 0.32 0.32 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.32 0.32Row and other crops (7) 0.90 0.25 0.25 0.25 0.29 0.29 0.29 0.29 0.29 0.29 0.25 0.25Grapes (8) 0.20 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15Non-irrigated grapes (9) 0.80 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25Citrus (11) 0.10 0.22 0.22 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.22 0.22Dates, palms, nursery (10, 12, 13) 0.20 0.25 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.25 0.25Potatoes (14) 0.20 0.33 0.33 0.33 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25Native (15) 0.85 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14

Fraction of evaporation from precipitation

Golf courses (1, 3) 0.35 0.22 0.22 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.22 0.22Residential/urban (2) 0.56 0.22 0.22 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.22 0.22Phreatophytes (4) 0.61 0.29 0.29 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.29 0.29Fallow/livestock (5, 6) 0.22 0.22 0.22 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.22 0.22Row and other crops (7) 0.63 0.18 0.18 0.18 0.20 0.20 0.20 0.20 0.20 0.20 0.18 0.18Grapes (8) 0.14 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11Non-irrigated grapes (9) 0.56 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18Citrus (11) 0.07 0.15 0.15 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.15 0.15Dates, palms, nursery (10, 12, 13) 0.14 0.18 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.18 0.18Potatoes (14) 0.14 0.23 0.23 0.23 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18Native (15) 0.60 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
Groundwater-Flow Models 95

Table 16. Monthly efficiency for each land-use type, or virtual crop, simulated in the Borrego Valley Hydrologic Model, Borrego Valley, 

California.

[FMP, MODFLOW Farm Process; na, not available]

Land-use type

(FMP crop index number)1,2 Base 1940s 1950s 1960s 1970s 1980s 1990s 2000s 2010s

Golf courses (1, 3) 0.75 0.75 0.83 0.86 0.86 0.86 0.88 0.90 0.94

Residential/urban (2) 0.75 0.75 0.83 0.86 0.86 0.86 0.88 0.90 0.94

Phreatophytes (4) na na na na na na na na na

Fallow/livestock (5, 6) na na na na na na na na na

Row and other crops (7) 0.74 0.75 0.83 0.86 0.86 0.86 0.88 0.90 0.93

Grapes (8) 0.77 0.75 0.83 0.86 0.86 0.86 0.88 0.90 0.96

Non-irrigated grapes (9) na na na na na na na na 0.94

Citrus (11) 0.78 0.75 0.83 0.86 0.86 0.86 0.88 0.90 0.98

Dates, palms, nursery (10, 12, 13) 0.79 0.75 0.83 0.86 0.86 0.86 0.88 0.90 0.99

Potatoes (14) 0.79 0.75 0.83 0.86 0.86 0.86 0.88 0.90 0.99

Native (15) na na na na na na na na na

1

Efficiencies were adjusted by decades with multipliers (see “Model Calibration” section of this report). 

2

Efficiencies were specified but were not used for nonirrigated land use.

Table 17. Percentage of each land-use type, or virtual crop, used in simulations for 11 periods of mapped land use, Borrego Valley, 

California.

[FMP, MODFLOW Farm Process]

Land-use type 

(FMP crop index number)

Land use percentage

Predevelopment 1953 1954 1959 1968 1979 1980 1992 1995 2000 2009

Golf courses (1, 3) 0.0 0.2 0.2 0.4 0.7 0.9 1.1 1.6 1.6 3.1 3.0

Residential/urban (2) 0.0 0.6 3.1 4.0 5.1 6.5 6.6 8.2 8.2 10.1 11.1

Phreatophytes (4) 9.1 8.8 6.6 6.6 6.6 6.5 6.1 6.0 5.7 5.6 5.6

Fallow/livestock (5, 6) 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 2.1

Row and other crops (7) 0.0 2.7 3.0 3.0 2.5 1.5 1.2 0.7 2.0 0.0 0.0

Grapes (8) 0.0 3.0 2.9 3.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Non-irrigated grapes (9) 0.0 0.0 0.0 0.0 2.1 0.1 1.0 0.0 0.0 0.0 0.0

Citrus (11) 0.0 0.0 0.0 0.0 0.2 1.0 1.0 1.6 3.6 3.2 3.6

Dates, palms, nursery (10, 12, 13) 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.5 0.7 0.4 0.9

Potatoes (14) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 1.2

Native (15) 90.9 84.6 84.2 82.9 82.7 83.4 82.4 81.3 78.0 77.0 72.5
96 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 39 soils

EXPLANATION

Silty clay

Soil type

Sandy loam

Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake

Figure 39. Model grid with two types of soil categories used in the Borrego Valley Hydrologic Model, Borrego Valley, California.
Groundwater-Flow Models 97

wells were simulated as a combination of single-aquifer farm 

wells (Schmid and others, 2006a) and multi-aquifer wells 

that collectively supply groundwater to each WBS (fig. 38), 

as needed, for irrigation during each monthly stress period. 

Pumpage from single-aquifer farm wells was simulated in the 

manner of the WEL package (Harbaugh and others, 2000), 

while the multi-aquifer wells were simulated with MNW2. 

Total pumpage for each WBS (virtual farm) was distributed 

among each of the farm wells within the WBS on the basis 

of the average pumping rate up to the pumping capacity of 

each well (which did not come into effect in this simulation) 

(Schmid and others, 2006a).

Agricultural Pumpage 

Discharge from agricultural wells generally is not 

metered in the Borrego Valley. Therefore, agricultural water 

use is simulated by an indirect estimation of pumpage. 

The two most common methods of indirectly estimating 

agricultural pumpage utilize power consumption and 

consumptive use of water. The use of electric power records 

is considered unreliable for estimating pumpage because 

many wells are powered by either electric or diesel power 

sources, and there is the inherent complexity of additional 

uses for electricity on a farm-by-farm basis. Consumptive-use 

estimates, such as satellite remote-sensing derived estimates, 

are also considered unreliable because they do not account for 

the combined consumption of precipitation and water applied 

for irrigation and do not capture the variability in consumption 

with changing climate (Hanson and others, 2014; Schmid 

and others, 2006a). The estimation of agricultural pumpage 

by using the FMP provides physically based, dynamic, and 

linked pumpage estimates as an alternative to indirect methods 

(Schmid and others, 2006a).

Pumpage for agricultural supply is estimated as a 

combination of CIR and efficiencies required to satisfy the 

TFDR for all wells that deliver water to a particular WBS. 

Efficiencies include those from irrigation-water conveyance 

(canals or irrigation pipes) and potential losses from runoff 

and deep percolation below the root zone during irrigation. 

The groundwater pumpage required to satisfy TFDR can be 

estimated by taking into account any potential surface-water 

supply, the efficiency of irrigation (fractions of losses to 

runoff and deep percolation), effective precipitation, ET from 

groundwater, and fractions of transpiration and evaporation 

within each model cell. Because all irrigation is supplied by 

groundwater pumpage in Borrego Valley, no surface-water 

supplies were simulated. Unmetered pumpage is estimated by 

the FMP on the basis the TFDR. As many as 71 farm wells 

were used to simulate pumpage for irrigation; the number of 

wells, and the pumpage, vary over time. There is no known 

reported pumpage for Borrego Valley that can be used as 

additional calibration data for agricultural pumpage.

For each well, the aquifer for which pumpage was 

simulated was based on the available construction information. 

Specifically, the drillers’ logs were analyzed, and the top 

and bottom intervals for each well were assigned. The FMP 

allocated pumpage to wells within a WBS by the average 

fraction of total required pumpage, up to the capacity specified 

on the drillers’ logs for each well. Figure 40 shows the 

distribution of simulated annual pumpage by aquifer.

Recreational Pumpage

Recreational water use, which is predominantly irrigation 

of golf courses, is also simulated by the FMP. As mentioned 

previously, the BWD supplies groundwater to the Rams Hill 

golf course and development. Therefore, this pumpage was 

subtracted from the total reported municipal pumpage values 

used in the model. The reported amount of water used by 

Rams Hill was used as a calibration target to constrain the 

FMP-derived pumpage estimate for this water use.

Municipal Pumpage

Pumpage for municipal and domestic/rural residential 

water supply was based on selected reported monthly and 

annual pumpage on a well-by-well basis for the ID and 

BSPCSD wells (see “Municipal Water Use” section). The 

actual locations of municipal-supply wells were used in the 

model, and the locations of domestic wells were either actual 

or virtual in selected land parcels that were represented by 

WBSs (figs. 9, 38). MNW2 was used to simulate municipal 

groundwater pumpage. The open-screened interval or total 

depth was used to identify the model layers from which 

pumping occurred.

Groundwater Inflows and Outflows

Boundary conditions were applied at some model cells 

to simulate the inflows and outflows from the active model 

region as groundwater underflow and intermittent stream 

inflows (fig. 37). Three general types of boundary conditions 

were used in the model: specified flow, specified head, 

and head-dependent flow boundaries. The head-dependent 

flow boundaries were only used for stream infiltration and 

underflow in stream channels and are not discussed in this 

section. Inflows and outflows simulated as flows across the 

hydrologic boundaries include recharge and discharge for the 

groundwater system, as well as interdependent flows, such 

as ET and irrigation, between groundwater, streams, and the 

landscape.

Specified (No Flow) Flow Boundaries

No-flow boundaries were used for the bottom of the 

model and most lateral boundaries. The lower boundary was 

defined by the estimate of the depth of the basement rock. 

Lateral no-flow boundaries represented the contact between 

the low-permeability crystalline rocks that bound the foothills 

and the unconsolidated alluvial sediments of Borrego Valley 

(figs. 7, 34). Lateral boundaries on the east were coincident 

with the northwest-southeast trending Coyote Creek fault.
98 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

sac13-0509_fig40_updated

Figure 40 pumpage by layer

A

B

0

2

4

6

8

10

12

18

16

14

20

1945 

1950 

1955 

1960 

1965 

1970 

1975 

1980 

1985 

1990 

1995 

2000 

2005

2010

Groundwater pumpage by aquifer, in thousands of acre-feet

Year 

1945 

1950 

1955 

1960 1965 

1970 

1975 

1980 

1985 

1990 

1995 

2000 

2005

2010

Year 

0

2

4

6

8

10

12

18

16

14

20

Groundwater pumpage by water use, in thousands of acre-feet

EXPLANATION

Upper aquifer (layer 1)

Middle aquifer (layer 2)

Lower aquifer (layer 3)

EXPLANATION

Recreational

Agricultural

Water use Climatic patterns

Wet

Dry

Average

Climatic patterns

Wet

Dry

Average

Municipal

1966: Grape irrigation ends

1978: Citrus expands

1966: Grape irrigation ends

1978: Citrus expands

Figure 40. Simulated annual groundwater pumpage and climatic patterns from the Borrego Valley Hydrologic Model, Borrego Valley, 

California, 1945–2010, by A, aquifer and B, water use.
Groundwater-Flow Models 99

Specified Flow Boundaries

Groundwater underflow was simulated by using the 

Flow Head Boundary (FHB1) Package (Leake and Lilly, 

1997); this head-dependent boundary condition allowed for 

underflow from the upstream portions of the watershed. The 

FHB1 package was applied by using 24 areas of alluvium 

along stream segments representing Coyote Creek, San Felipe 

Creek, Borrego Palm Creek, and other smaller tributaries. 

The underflow for each of the 24 canyons was spread among 

the cells at the edge of the model domain in these canyons. 

As in the SFR, the BCM provides deterministically simulated 

monthly estimates of the underflow from the adjacent 

mountains and basins (figs. 16, 19). The rate of underflow 

was simplified as an average for the simulation period to 

allow for the time integration of variable travel times of 

saturated and unsaturated groundwater-flow processes in the 

upstream portions of the watershed. As mentioned previously, 

scaling factors for six creeks or washes (San Felipe Wash, 

Coyote Creek, Henderson Creek, Borrego Palm Creek, and 

two groupings of other intermittent washes) were estimated 

separately (fig. 37). Mountain block recharge was assumed 

to be a component of the underflow from the upstream 

components of the watershed.

Specified (Constant) Head Boundary

The farthest downstream region of the Borrego Valley 

is a lateral hydrologic boundary of the groundwater-flow 

system that was simulated as a head-dependent flow boundary 

(fig. 34). This region represents a hydraulic connection with 

the valley to the southeast. It was simulated by using the 

Constant Head Boundary Package (CHD; Harbaugh, 2005). 

Constant-head cells were placed near lower Borrego Valley in 

the vicinity of Ocotillo Wells (fig. 33) because groundwater 

levels in that area (approximately mean sea level) indicate a 

steady-state condition (Mitten and others, 1988). Constanthead boundaries were specified in three cells in layer 3 with 

time-constant boundary heads. On the basis of data from 

Ocotillo Wells and previous simulations, this area of Borrego 

Valley does not appear to be affected by groundwater-level 

fluctuations or variations in groundwater discharge and 

recharge in the upper part of the aquifer system and can be 

treated as a constant-head boundary (Mitten and others, 1988).

Model Calibration

Calibration of the BVHM was accomplished by using 

a combination of trial-and-error and automated processes to 

determine the set of parameter values that minimized misfits 

(residuals) between observed (calibration-targets: heads and 

flows) and simulated values. The hydrologic framework 

and consumptive-use properties were modified as part of 

this process. The BVHM requires specification of about 

100 parameters that vary spatially and temporally, making 

it a challenge to optimize parameter estimation. As a result, 

parameterization was introduced to allow a limited number of 

parameter values to define the model inputs that still vary in 

space and time.

Parameter Data

Hydraulic properties were estimated by using the GaussMarquardt-Levenberg method, a widely used algorithm 

employed in PEST (Doherty, 2010a). This method modifies 

the initial values of parameters supplied by the modeler and 

subsequently adjusts these values such that the weighted sum 

of the squared differences between the observations and their 

corresponding model-simulated values is minimized. Each 

model cell is populated with values for each physical property 

(for example, K or specific yield). For most hydrologic 

models, this can result in an extremely large number of 

parameters, which can be reduced by using parameterization 

techniques such as zonation (for example, Doherty, 2003; 

Doherty and others, 2010a). In the BVHM presented in 

this report, there are many sources of nonlinearity, and the 

parameter-estimation problem has a relatively large number 

of parameters (highly parameterized) because many physical 

processes are being simulated.

Following Hill and Tiedeman (2007), the term 

“parameter” is used to define model inputs. Because the 

BVHM includes many complex hydrologic processes 

requiring that parameters be distributed in space and time, 

the potential number of model parameters that could be 

estimated is large. Therefore, model parameterization and the 

approach to parameter estimation were designed to estimate a 

limited number of parameter values that sufficiently define the 

simulated processes. The parameter values were adjusted by a 

combination of informed estimates (for example, professional 

judgement, expected values gleaned from the literature) 

and a systematic application of the parameter estimation 

method in order to narrow the range of possible solutions to 

produce simulated values that best matched the measured 

observations. Many of the parameters were specified apriori, 

and 137 parameters (table 18) were estimated during the 

automated calibration process. Fewer parameters often were 

estimated simultaneously for single, preliminary calibration 

runs. Extensive parameterization was used to characterize the 

following properties:

• Hydraulic conductivities, such as aquifer Kh and Kv in 

model layers 1–3, including an estimation of the power 

mean (described later), Kv of the streambed, the height 

of the capillary fringe, and the saturated Kv used in 

the computation of unsaturated Kvs of the unsaturated 

zone.

• Storage properties, such as specific yield of model 

layers 1–3 (where appropriate), specific storage of 

model layers 1–3, and saturated water content and 

initial water content of the unsaturated zone for the 

streambeds and, generally, for the rest of the valley.

• Scalar multipliers for runoff and underflow from the 

upstream portions of the watershed. 

• Parameters within the landscape, such as scalar 

multipliers over time for irrigation efficiencies, stress 

factors for crop coefficients, and fractions of runoff 

both from precipitation and irrigation.
100 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Table 18. Parameter values estimated for the Borrego Valley Hydrologic Model, Borrego Valley, California.—Continued

[ft/d, feet per day; na, not available]

Parameter 

identifier Parameter description Value Unit

Composite 

scaled 

sensitivity

Farm Process properties

SEPTIC_RC Rate of septic return flow. 1.69E–06 ft/d 1.33E–14

SILTYCLAY Capillary fringe of silty clay. 1.64E+01 ft 3.87E–02

SAND Capillary fringe of sand. 1.64E+01 ft 1.52E–01

RO_P_GOLF Fraction of runoff of precipitation from recreational areas. 5.56E–02 na 1.47E–03

RO_P_PHR Fraction of runoff of precipitation from phreatophytic areas. 6.99E–02 na 1.34E–03

RO_P_NTV Fraction of runoff of precipitation from native vegetation. 4.00E–01 na 1.40E–01

RO_P_ROW Fraction of runoff of precipitation from row crops. 2.61E–01 na 4.68E–03

RO_P_GRP Fraction of runoff of precipitation from vineyards. 5.31E–02 na 6.02E–03

RO_P_PALM Fraction of runoff of precipitation from palm nurseries. 8.75E–02 na 9.35E–05

RO_P_CIT Fraction of runoff of precipitation from citrus. 6.22E–02 na 1.40E–03

RO_P_POTO Fraction of runoff of precipitation from potatoes. 3.20E–01 na 6.67E–04

RO_I_GOLF Fraction of runoff of irrigation from recreational areas. 1.28E–01 na 2.81E–03

RO_I_ROW Fraction of runoff of irrigation from row crops. 5.89E–02 na 2.50E–03

RO_I_GRP Fraction of runoff of irrigation from vineyards. 5.00E–02 na 7.40E–03

RO_I_PALM Fraction of runoff of irrigation from palm nurseries. 1.34E–01 na 1.53E–03

RO_I_CIT Fraction of runoff of irrigation from citrus. 2.50E–01 na 1.02E–02

RO_I_POTO Fraction of runoff of irrigation from potatoes. 5.96E–02 na 4.18E–05

ROOT_NTV Rooting depth of natural vegetation. 9.61E+00 ft 4.34E–03

ROOT_PHR Rooting depth of phreatophytic vegetation. 2.28E+01 ft 1.26E–02

Streamflow properties

SFR_RO Scalar multiplier on runoff from northern small basins. 8.00E–01 Scalar 6.05E–02

SFR2_RO Scalar multiplier on runoff from southern small basins. 8.00E–01 Scalar 8.63E–02

SF_RO Scalar multiplier on runoff from San Felipe basin. 9.23E–01 Scalar 4.74E–02

PALM_RO Scalar multiplier on runoff from Palm Canyon basin. 9.00E–01 Scalar 7.13E–02

HEN_RO Scalar multiplier on runoff from Henderson Canyon basin. 8.00E–01 Scalar 4.48E–02

COY_RO Scalar multiplier on runoff from Coyote Creek basin. 1.01E+00 Scalar 3.23E–01

Underflow properties

fhb_various Scalar multiplier on underflow from adjacent basin. 3.50E+00 Scalar 1.00E–06

fhb4 Scalar multiplier on underflow from adjacent basin. 8.88E+00 Scalar 3.94E–02

fhb5 Scalar multiplier on underflow from adjacent basin. 8.70E–01 Scalar 4.08E–02

fhb6 Scalar multiplier on underflow from adjacent basin. 1.26E+00 Scalar 4.17E–02

fhb7 Scalar multiplier on underflow from adjacent basin. 1.00E–01 Scalar 4.80E–02

fhb8 Scalar multiplier on underflow from adjacent basin. 1.22E–01 Scalar 4.08E–02

fhb9 Scalar multiplier on underflow from adjacent basin. 1.24E–01 Scalar 4.08E–02

fhb10 Scalar multiplier on underflow from adjacent basin. 3.32E–01 Scalar 4.17E–02

fhb11 Scalar multiplier on underflow from adjacent basin. 2.86E–01 Scalar 4.17E–02

fhb12 Scalar multiplier on underflow from adjacent basin. 1.20E–01 Scalar 2.77E–04

fhb13 Scalar multiplier on underflow from adjacent basin. 8.17E–01 Scalar 4.17E–02

fhb14 Scalar multiplier on underflow from adjacent basin. 1.73E+00 Scalar 4.18E–02

fhb15 Scalar multiplier on underflow from adjacent basin. 1.01E+00 Scalar 4.08E–02

fhb17 Scalar multiplier on underflow from adjacent basin. 5.15E–01 Scalar 2.24E–02

fhb18 Scalar multiplier on underflow from adjacent basin. 4.17E+00 Scalar 4.58E–02

fhb19 Scalar multiplier on underflow from adjacent basin. 2.39E–01 Scalar 4.13E–02

fhb20 Scalar multiplier on underflow from adjacent basin. 1.48E–01 Scalar 4.12E–02

fhb21 Scalar multiplier on underflow from adjacent basin. 1.50E–01 Scalar 4.12E–02

fhb22 Scalar multiplier on underflow from adjacent basin. 1.00E–01 Scalar 5.41E–02

fhb23 Scalar multiplier on underflow from adjacent basin. 1.78E+00 Scalar 3.97E–02

fhb24 Scalar multiplier on underflow from adjacent basin. 1.27E–01 Scalar 4.13E–02

fhb26 Scalar multiplier on underflow from adjacent basin. 9.75E+00 Scalar 7.81E–02

fhb27 Scalar multiplier on underflow from adjacent basin. 9.75E+00 Scalar 7.74E–02

fhb28 Scalar multiplier on underflow from adjacent basin. 1.06E–01 Scalar 4.17E–02

Table 18. Parameter values estimated for the Borrego Valley Hydrologic Model, Borrego Valley, California.
Groundwater-Flow Models 101

Table 18. Parameter values estimated for the Borrego Valley Hydrologic Model, Borrego Valley, California.—Continued

[ft/d, feet per day; na, not available]

Parameter 

identifier Parameter description Value Unit

Composite 

scaled 

sensitivity

Underflow properties—Continued

fhb30 Scalar multiplier on underflow from adjacent basin. 9.75E+00 Scalar 3.40E–02

fhb32 Scalar multiplier on underflow from adjacent basin. 9.75E+00 Scalar 1.18E–01

fhb33 Scalar multiplier on underflow from adjacent basin. 9.75E+00 Scalar 6.59E–02

fhb34 Scalar multiplier on underflow from adjacent basin. 9.75E+00 Scalar 6.59E–02

Stream channel properties

Kv_TRIB Vertical hydraulic conductivity of small tributary streambeds. 3.28E+00 ft/d 3.26E–02

Kv_TRIBU Vertical hydraulic conductivity of upper tributary streambeds. 1.29E+01 ft/d 4.50E–02

Kv_COYOTE Vertical hydraulic conductivity of Coyote Canyon streambed. 6.56E+01 ft/d 8.94E–02

Kv_PALM Vertical hydraulic conductivity of Palm Canyon streambed. 1.64E+01 ft/d 4.58E–02

Kv_YAQUI Vertical hydraulic conductivity of Yaqui Canyon streambed. 2.93E+00 ft/d 1.11E–02

Kv_SANFELIPE Vertical hydraulic conductivity of San Felipe streambed. 6.56E–01 ft/d 1.74E–02

strthick Thickness of streambed. 4.92E+01 ft 7.82E–03

Unsaturated zone properties

UZF_VK Hydraulic conductivity of unsaturated zone. 2.30E–02 ft/d 7.56E–02

thts Maximum fraction of saturation of unsaturated zone in stream channels. 5.66E–02 ft 1.97E–03

thti Initial fraction of saturation of unsaturated zone in stream channels. 2.45E–02 ft 1.41E–03

thts_ALL Maximum fraction of saturation of unsaturated zone. 4.63E–01 ft 2.48E–01

thti_ALL Initial fraction of saturation of unsaturated zone. 8.81E–03 ft 8.85E–03

Storage properties

ss_l1 Specific storage upper aquifer (layer 1). 5.08E–07 na 4.19E–03

ss_l2 Specific storage upper aquifer (layer 1). 1.59E–06 na 2.63E–03

ss_l3 Specific storage upper aquifer (layer 1). 8.53E–07 na 4.77E–03

SY_L1_Z1 Specific yield upper aquifer (layer 1) zone 1. 1.55E–01 na 1.65E–03

SY_L1_Z2 Specific yield upper aquifer (layer 1) zone 2. 1.34E–01 na 1.66E–02

SY_L1_Z3 Specific yield upper aquifer (layer 1) zone 3. 5.00E–02 na 1.67E–02

SY_L1_Z4 Specific yield upper aquifer (layer 1) zone 4. 3.00E–01 na 3.45E–01

SY_L1_Z5 Specific yield upper aquifer (layer 1) zone 5. 1.51E–01 na 1.61E–01

SY_L1_Z6 Specific yield upper aquifer (layer 1) zone 6. 1.06E–01 na 1.06E–01

SY_L1_Z7 Specific yield upper aquifer (layer 1) zone 7. 1.52E–01 na 2.49E–01

SY_L1_Z8 Specific yield upper aquifer (layer 1) zone 8. 5.00E–02 na 3.49E–02

SY_L1_Z9 Specific yield upper aquifer (layer 1) zone 9. 7.12E–02 na 5.10E–02

SY_L1_Z10 Specific yield upper aquifer (layer 1) zone 10. 8.91E–02 na 8.15E–02

SY_L1_Z11 Specific yield upper aquifer (layer 1) zone 11. 1.40E–01 na 0.00E+00

SY_L1_Z12 Specific yield upper aquifer (layer 1) zone 12. 1.40E–01 na 0.00E+00

SY_L1_Z13 Specific yield upper aquifer (layer 1) zone 13. 1.40E–01 na 0.00E+00

SY_L1_Z14 Specific yield upper aquifer (layer 1) zone 14. 1.50E–01 na 0.00E+00

SY_L2_Z1 Specific yield middle aquifer (layer 2) zone 1. 7.44E–02 na 0.00E+00

SY_L2_Z2 Specific yield middle aquifer (layer 2) zone 2. 6.65E–02 na 0.00E+00

SY_L2_Z3 Specific yield middle aquifer (layer 2) zone 3. 3.00E–01 na 7.73E–15

SY_L2_Z4 Specific yield middle aquifer (layer 2) zone 4. 3.17E–02 na 4.58E–04

SY_L2_Z5 Specific yield middle aquifer (layer 2) zone 5. 3.00E–01 na 1.39E–02

SY_L2_Z6 Specific yield middle aquifer (layer 2) zone 6. 2.90E–02 na 1.83E–03

SY_L2_Z7 Specific yield middle aquifer (layer 2) zone 7. 2.90E–02 na 5.37E–03

SY_L2_Z8 Specific yield middle aquifer (layer 2) zone 8. 2.00E–01 na 1.34E–02

SY_L2_Z9 Specific yield middle aquifer (layer 2) zone 9. 2.90E–02 na 8.54E–03

SY_L2_Z10 Specific yield middle aquifer (layer 2) zone 10. 4.11E–02 na 2.19E–02

SY_L2_Z11 Specific yield middle aquifer (layer 2) zone 11. 7.00E–02 na 0.00E+00

SY_L2_Z12 Specific yield middle aquifer (layer 2) zone 12. 7.00E–02 na 0.00E+00

SY_L2_Z13 Specific yield middle aquifer (layer 2) zone 13. 7.00E–02 na 0.00E+00

SY_L2_Z14 Specific yield middle aquifer (layer 2) zone 14. 7.00E–02 na 0.00E+00
102 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Table 18. Parameter values estimated for the Borrego Valley Hydrologic Model, Borrego Valley, California.—Continued

[ft/d, feet per day; na, not available]

Parameter 

identifier Parameter description Value Unit

Composite 

scaled 

sensitivity

Storage properties—Continued

SY_L3_Z1 Specific yield lower aquifer (layer 3) zone 1. 3.00E–02 na 9.35E–15

SY_L3_Z2 Specific yield lower aquifer (layer 3) zone 2. 3.00E–02 na 1.40E–14

SY_L3_Z3 Specific yield lower aquifer (layer 3) zone 3. 3.00E–02 na 1.40E–14

SY_L3_Z4 Specific yield lower aquifer (layer 3) zone 4. 2.67E–02 na 0.00E+00

SY_L3_Z5 Specific yield lower aquifer (layer 3) zone 5. 2.67E–02 na 0.00E+00

SY_L3_Z6 Specific yield lower aquifer (layer 3) zone 6. 3.81E–02 na 5.74E–05

SY_L3_Z7 Specific yield lower aquifer (layer 3) zone 7. 7.65E–02 na 9.96E–04

SY_L3_Z8 Specific yield lower aquifer (layer 3) zone 8. 3.14E–02 na 4.41E–03

SY_L3_Z9 Specific yield lower aquifer (layer 3) zone 9. 3.98E–02 na 1.30E–03

SY_L3_Z10 Specific yield lower aquifer (layer 3) zone 10. 8.45E–02 na 1.60E–02

SY_L3_Z11 Specific yield lower aquifer (layer 3) zone 11. 6.18E–02 na 8.25E–04

SY_L3_Z12 Specific yield lower aquifer (layer 3) zone 12. 2.90E–02 na 3.14E–02

SY_L3_Z13 Specific yield lower aquifer (layer 3) zone 13. 2.90E–02 na 5.42E–02

SY_L3_Z14 Specific yield lower aquifer (layer 3) zone 14. 5.14E–02 na 2.38E–02

Hydraulic properties

kc1 Hydraulic conductivity of coarse-grained fraction of upper aquifer (layer 1). 9.84E+01 ft/d 1.42E–01

kf1 Hydraulic conductivity of fine-grained fraction of upper aquifer (layer 1). 7.08E+00 ft/d 1.11E–02

kc2 Hydraulic conductivity of coarse-grained fraction of middle aquifer (layer 2). 6.56E+00 ft/d 5.56E–02

kf2 Hydraulic conductivity of fine-grained fraction of middle aquifer (layer 2). 1.97E–01 ft/d 4.39E–03

kc3 Hydraulic conductivity of coarse-grained fraction of lower aquifer (layer 3). 1.05E+00 ft/d 1.98E–01

kf3 Hydraulic conductivity of fine-grained fraction of lower aquifer (layer 3). 6.32E–03 ft/d 2.74E–02

kc1s Hydraulic conductivity of coarse-grained fraction of upper aquifer (layer 1)—sand rich area. 2.16E+02 ft/d 8.76E–02

kf1s Hydraulic conductivity of fine-grained fraction of upper aquifer (layer 1)—sand rich area. 7.60E+01 ft/d 4.81E–02

HK_SINK Horizontal hydraulic conductivity of sink. 5.37E+00 ft/d 3.04E–03

VK_SINK Vertical hydraulic conductivity of sink. 2.05E+00 ft/d 8.72E–04

HK_OLDA Horizontal hydraulic conductivity of older alluvium. 2.95E–01 ft/d 6.45E–03

VK_OLDA Vertical hydraulic conductivity of older alluvium. 2.95E–01 ft/d 3.24E–03

Power mean coefficient

powerl1 Coefficient of power mean, upper aquifer (layer 1). –1.21E–01 na 5.04E–04

powerl2 Coefficient of power mean, middle aquifer (layer 2). 3.47E–01 na 1.28E–03

powerl3 Coefficient of power mean, lower aquifer (layer 3). –1.67E–01 na 2.51E–02

Farm Process scalars

sfac_d1 Multiplier for efficiencies for 1940s. 1.00E+00 Factor 9.47E–01

sfac_d2 Multiplier for efficiencies for 1950s. 1.10E+00 Factor 6.52E–01

sfac_d3 Multiplier for efficiencies for 1960s. 1.13E+00 Factor 2.22E–01

sfac_d4 Multiplier for efficiencies for 1970s. 1.15E+00 Factor 9.21E–01

sfac_d5 Multiplier for efficiencies for 1980s. 1.15E+00 Factor 3.99E–01

sfac_d6 Multiplier for efficiencies for 1990s. 1.17E+00 Factor 3.64E–01

sfac_d7 Multiplier for efficiencies for 2000s. 1.20E+00 Factor 3.32E–01

sfac_d8 Multiplier for efficiencies for 2010s. 1.25E+00 Factor 9.84E–03

sfac_fal Multiplier for fall crop coefficients. 8.50E–01 Factor 4.91E–01

sfac_win Multiplier for winter crop coefficients. 1.07E+00 Factor 5.26E–01

sfac_spr Multiplier for spring crop coefficients. 9.00E–01 Factor 6.02E–01

sfac_sum Multiplier for summer crop coefficients. 9.00E–01 Factor 6.26E–01
Groundwater-Flow Models 103

Initial input parameters were specified within ranges of 

reasonable values. A combination of PEST (Doherty, 2010a, 

b, c; Doherty and Hunt, 2010) and manual adjustments were 

used in the parameter estimation and sensitivity analyses to 

derive the estimated parameter values that resulted in best fits 

of simulated head values to the calibration targets and that 

reasonably reflected the hydrologic conditions measured in 

the aquifer system, including measured groundwater-level 

elevations and associated long-term trends as well as estimated 

discharge. Although a number of parameters were estimated 

during calibration (table 18 and list in previous paragraph), a 

summary of some of the more critical parameters follows.

As described in the “Calculation of Hydraulic Properties” 

section of this report, Kh and Kv were estimated for every 

cell in the BVHM on the basis of sediment texture and endmember K values (Kc

 and Kf) by using the averaging method 

(weighted arithmetic average for Kh; power mean for Kv). 

These end-member K values and the p value used in the power 

mean were adjusted by using PEST to minimize model error. 

During initial calibration efforts, the same end-member Ks and 

p were used for the entire valley. As calibration progressed, 

it became evident that more hydraulic parameters were 

necessary to represent the system with sufficient accuracy. 

Values of Kc and Kf were then defined separately for each of 

the aquifers, accounting for differences in their depositional 

environments. Separate p values were also used for each of 

the aquifers. The estimated end-member K for each aquifer 

was somewhat different; the Kv also differed according to 

the power mean and the end-member K (table 18). Several 

specific-yield values defined by zones generally representing 

groupings of similar percentage of coarse-grained deposits 

(fig. 36) were adjusted during calibration.

The streambed Kv parameters also were estimated. 

Six streambed Kv zones were identified (fig. 37), and low 

streambed Kvs were used as initial estimates, then adjusted 

during calibration (table 18). In addition, scalar multipliers 

were estimated to adjust the total amount of flow into the 

system from the BCM-simulated runoff and underflow from 

the adjacent basins. HYDMOD, a program for extracting 

time-series data from MODFLOW (Hanson and Leake, 1998), 

was used to examine surface-water flow at downstream points 

around the Borrego Sink and flow exiting the model along San 

Felipe Creek. Although the magnitude of these surface-water 

flows is unknown, during extremely wet periods, stormwater 

has often reached the Borrego Sink and flowed out of the 

basin along San Felipe Creek. Streambed properties were 

constrained so that the simulated flows occurred periodically 

during wet events and not continually throughout the 

simulation.

Observation Data

The ability of the transient hydrologic flow model to 

simulate data accurately that agree with observation data was 

evaluated. Observations are groundwater-level measurements 

obtained in the field and estimated or measured pumpage. 

Calibration adjustments were related to the combined fitting 

of the groundwater levels, groundwater-level differences 

over time, and pumpage observations (fig. 41). A comparison 

of the simulated values to observed values for pumpage 

was done to assess the capacity of the model to simulate 

the effects of changing stresses on the hydrologic system 

reasonably over time. The comparison provided a measure 

of model performance over various historical time intervals. 

The resulting error distributions constrained the parameter set, 

and the comparison between observed and simulated values 

provided a basis for sensitivity analysis of selected parameters. 

Details on this comparison are summarized in the section 

“Calibration Procedure.” Furthermore, an overall estimate 

of model fit was made by using the groundwater-level data. 

Because of the large number of variables adjusted as part of 

model calibration, as well as assumptions and simplifications 

made in the development of the BVHM, there could be 

significant uncertainties and limitations in the application 

of the model. These uncertainties and model limitations 

are discussed later in this report in the section “Model 

Uncertainty, Limitations, and Improvements.”

The largest suite of observation values used for 

calibration consisted of groundwater levels and changes in 

groundwater levels over time. BWD maintains a database 

of key wells in the Borrego Valley in which groundwater 

levels are regularly measured as part of the monitoring 

network. The USGS NWIS database also has historical 

groundwater-level measurements. In addition, CA-DWR 

routinely measures groundwater levels in the Borrego Valley. 

These data were combined to form a database of available 

groundwater levels throughout the Borrego Valley from 

1945 to 2010; 2,224 groundwater-level measurements in 

73 single- and multiple-aquifer wells were used for model 

calibration (fig. 41). For calibration, measured water levels 

were compared with the simulated water levels from the 

model row and column in which the well was located and the 

model layer in which the well was screened (well interval 

open to the aquifer system). Water levels for a well that was 

screened through multiple model layers were compared to the 

corresponding average (thickness-weighted) of the simulated 

water levels in all of these layers. Measured water levels were 

used in two ways—as direct observations of groundwater level 

and as observations of drawdown. For the drawdown data, it 

was assumed that the first groundwater-level measurement in 

a well is the reference value; that is, subsequent drawdown is 

calculated as deviations from the first value. Drawdown data 

were used in this study to emphasize in the calibration the 

importance of relative changes in water levels over time. Such 

processing of raw observation data, in this case drawdowns, 

can potentially add information that increases the signal-tonoise ratio of the observation dataset (Westenbroek and others, 

2012). Hydrographs for selected observation wells are shown 

in figure 42.
104 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

11S/6E-16A2

11S/6E-16N1

11S/6E-22D1 11S/6E-23J1 11S/6E-25A1

11S/6E-25C1

3

4

5

6

7

8

1

2

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 41 hobs sites

Rams Hill

EXPLANATION

Observation well used

 for hydrogeologic model

11S/6E-25C1 Rams Hill supply well and number

Observation well with hydrograph

 shown in figure 42; number is label 

 on hydrograph

3 Stream channel

Boundary of Borrego Valley

 groundwater basin

78 Road

Boundary of Anza-Borrego

 Desert State Park

Fault

Dry lake

Figure 41. Location of observation wells used in the calibration of the Borrego Valley Hydrologic Model, Borrego Valley, California.
Groundwater-Flow Models 105

sac13-0509_fig42_updated

1945 1950 1960 1970 1980 1990 2000 2010

Year

1945 1950 1960 1970 1980 1990 2000 2010

Year

1945 1950 1960 1970 1980 1990 2000 2010 1945 1950 1960 1970 1980 1990 2000 2010

1945 1950 1960 1970 1980 1990 2000 2010 1945 1950 1960 1970 1980 1990 2000 2010

1945 1950 1960 1970 1980 1990 2000 2010 1945 1950 1960 1970 1980 1990 2000 2010

1

3

5

7

2

4

6

8

Water-level elevation, in feet above NAVD 88Water-level elevation, in feet above NAVD 88

See figure 41 for location of wells

400

420

440

460

480

380

500

520

400

420

440

460

480

380

500

520

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EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

EXPLANATION

Observed

Layer 1

Layer 2

Layer 3

Climatic patterns

Wet Dry Average

010S006E10L001S

010S006E21A001S and -002S

010S006E33Q001S

011S007E32Q001S

010S006E09L001S

010S006E18R001S

010S006E29N001S

011S006E22A001S

Figure 42. Hydrographs showing simulated and measured water-level elevations for the Borrego Valley Hydrologic Model, Borrego 

Valley, California, 1945–2010.
106 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Regularization

Many techniques in parameter-estimation theory provide 

systematic methods for dealing with under-determined 

parameter-estimation problems. These techniques are 

generally referred to as regularization techniques (Hunt and 

others, 2007). Tikhonov regularization (Tikhonov and Arsenin, 

1977; Doherty, 2003) is provided in the PEST software and 

was used in this study.

Tikhonov regularization is a form of Bayesian estimation 

in which a composite objective function is minimized (Yeh, 

1986). This composite objective function consists of the 

standard measurement objective function and a “penalty 

term” that penalizes the composite objective function when 

parameters deviate from their preferred or expected value, 

which is based on prior information. The combined objective 

function is represented by the following equation:

Φ Φ = +  r m Φ (4)

where 

Φ is the composite or overall objective function, 

Φr is the penalty function for parameter 

deviations from expected values, 

Φm is the least-squares objective function, and 

 μ is the trade-off or regularization weighting 

factor. 

Tikhonov regularization determines the optimal 

regularization weighting factor, μ, given a modeler-specified 

level of calibration, that is, a desired value for Φm, denoted 

as Φm

1 . Therefore, the inverse problem is considered to have 

converged when Φ Φ m m ≤ 1 ; however, in practice it is often 

the case that Φ Φ m m ≈ 1 . In the PEST software used here, the 

Tikhonov regularization algorithm determines the optimal μ

such that Φr is minimized (Doherty, 2003).

Prior estimates of model parameters for input into the 

BVHM were assigned, for the most part, by using values 

reported in Mitten and others (1988) and Henderson (2001). 

Additional parameters resulting from modifications to their 

models were assigned values on the basis of professional 

judgment and knowledge of the area. Initial estimates for 

recharge were obtained from the results of the BCM presented 

in this report and the results of Mitten and others (1988) and 

Henderson (2001). Other model parameters, for which no 

prior information was available, were assigned values similar 

to those of nearby parameters of the same types for which 

some information was available. This approach for assigning 

parameter values tends toward the use of a simpler model 

parameterization by interjecting a precondition for local 

homogeneity.

The preliminary regularized PEST results indicate that 

some of the values used for prior information appeared to 

be incorrect. For example, the assumption of homogeneity 

was slightly erroneous, or the assigned values were slightly 

erroneous. Therefore, the prior or initial values of these 

parameters were modified to reflect the trends toward 

higher or lower values of the erroneous parameter values, 

as indicated by the iterative estimation process. This 

iterative process of minimizing the composite objective 

function (equation 4) with a value set for Φm

1 in PEST, then 

changing the target parameter values slightly and using 

PEST again to minimize the composite objective function, 

was repeated until a subjective desired level of calibration 

was achieved, while maintaining parameter values that were 

consistent with the hydrogeologic conceptualization of the 

region. For each attempt at calibrating the model, Tikhonov 

regularization was employed with an appropriate value set for 

Φm

1 . A regularization group was defined in PEST for each 

parameter group. The final calibrated parameter values are 

listed in table 18.

Tikhonov regularization provides the means to estimate 

all model parameters regardless of parameter sensitivity to 

the observations used for calibration. Therefore, in this report, 

all 115 parameters in the BVHM were estimated by using 

PEST with Tikhonov regularization, in which a prior or target 

value is assigned to each parameter. The calibrated parameter 

values and their composite sensitivities, as computed by PEST 

(Doherty, 2010a, chap. 5, p. 5–16), are listed in table 18. Note 

that these composite sensitivities were computed by using 

only the relations between the parameters and the model 

outputs that correspond to the observation data; that is, the 

regularization component was not included in this calculation.

The overall model fit for water-level comparisons is 

generally good when the simulated head values are compared 

against the measured groundwater levels. About 90 percent 

of the residuals were between −20 and +20 ft, and more than 

50 percent were between −5 and 5 ft (fig. 43). Simulated 

groundwater levels closely matched measured groundwater 

levels and showed essentially no bias, as indicated by an 

average residual of 0.1 ft and the relatively small magnitude 

of most of the residuals. Overall, the residuals tended to 

underestimate groundwater levels slightly (positive residuals). 

The residuals ranged from −100 to 53 ft (observed minus 

simulated; figs. 43, 44), and the standard deviation and root 

mean square error (RMSE) were both approximately 11 ft. 

However, there are some extremely negative residuals, which 

are considered an artifact of some of the groundwater levels 

representing pumping conditions in the well rather than a static 

condition. The total change in measured groundwater levels in 

wells ranged from −140 ft to slight rises in local areas. In the 

plot of simulated in relation to measured groundwater levels 

in figure 44, the outliers show an overestimation of measured 

groundwater levels at the lowest water-level elevations.

Hydrographs comparing simulated and measured 

groundwater levels for selected wells illustrate the match of 

groundwater levels throughout system (fig. 42). The minimum 

period over which model simulations can accurately reproduce 

fluctuations in the groundwater-flow system (the response 

time of the model) varies with the depth to groundwater, 

hydrologic setting, hydraulic properties, climate, and land 

use. The amplitude of monthly fluctuations in simulated heads 

is generally greater than that of fluctuations in measured 
Groundwater-Flow Models 107

heads owing to the varying actual pumping rates that are 

approximated by constant rates during monthly stress periods, 

the transition over time between unconfined and confined 

conditions, and the varying thickness of the unsaturated zone.

The hydrographs indicate that model results generally 

provide a good fit (fig. 42). The fluctuations in groundwater 

levels reflect the effects of climate, streamflow infiltration, 

and interannual changes in land use. The goal of the 

model calibration was to try to match individual groups 

of hydrographs, minimize bias, and minimize the sum of 

squared weighted residuals for all simulated heads and head 

differences. The use of WBSs that represent multiple farms, 

estimated pumpage, spatially and temporally coarsely defined 

(multi-year) land-use and crop distributions, and simplifying 

assumptions made for spatially distributing pumpage could 

limit the ability of the model to accurately simulate the 

water budget and associated groundwater levels. Much of 

the error, and the primary source of the average error, can be 

associated with the lack of spatial and temporal detail in landuse estimates, which drive ET consumption and demand for 

irrigation pumping.

The simulated groundwater levels for 1945–2010 are 

in general agreement with the measured groundwater levels 

and changes in groundwater levels. The model closely 

matched measured groundwater levels during 2000–10, but 

overestimated or underestimated groundwater levels during 

earlier periods. The results indicate that the model reasonably 

represents seasonal changes as well as major features in the 

climate record, such as the dry period from 1946 to 1970; 

periods of higher than normal precipitation from 1976 to 

1985; and individual extreme (dry or wet) years, such as 

1956, 1982, and 2004 (fig. 3). The most evident feature in the 

groundwater-level data and the model is the effect of pumping, 

particularly for agricultural use, which is exemplified by 

low groundwater levels in the north-central part of the basin 

(figs. 41, 42). The results indicate that the model simulates 

groundwater-level changes that reasonably reflect the 66-year 

history of groundwater-level changes observed in the study 

area (fig. 44).

Pumpage Observations

Pumpage for agricultural and recreational use is an 

uncertain component of groundwater discharge that was 

estimated by using the FMP during model calibration. The 

exact temporal and spatial uncertainty associated with this 

pumpage during 1945–2010 cannot be quantified and has 

the potential for bias. Pumpage for municipal and industrial 

uses is metered and is considered to be a known stress. For 

recreational pumpage, BWD has an estimated total for the 

Figure 43. Histogram of distribution of groundwater-level residual (observed minus simulated) values for the Borrego Valley Hydrologic 

Model, Borrego Valley, California.

Figure 44. Simulated water level-elevations in relation 

to measured water-level elevations for the Borrego Valley 

Hydrologic Model, California, Borrego Valley, California.

sac13-0509_fig43

fig. 43 residual Histogram

0

100

200

300

400

500

600

700

Less than –25

–24 to –15

–14 to –10

–9 to –5

–4 to 0

1 to 5

Residuals, in feet

6 to 10

11 to 15

16 to 20

21 to 25

Greater than 26

Frequency

350

370

390

410

430

450

470

490

510

530

550

350 400 450 500 550

Simulated water-level elevation,

in feet above NAVD 88

Measured water-level elevation, in feet above NAVD 88

1 to 1 correlation line
108 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Rams Hill golf course. Initially, this usage was extracted as 

a subset of the municipal pumpage estimates and used as 

pumpage observations for the 336 months during 1983–2010. 

Although estimates were generally in agreement, the 

Rams Hill usage was for more than just the golf course, 

and estimates were therefore regarded as highly uncertain. 

Ultimately, the estimates were not used during the calibration 

process. However, for model application, the BWD estimate of 

pumpage for the Rams Hill golf course was used.

Groundwater-Level Maps

As part of calibration, the spatially distributed BVHMsimulated groundwater levels for 1945 (Moyle, 1982) and 

2010 were qualitatively evaluated by reviewing the simulated 

values with groundwater-level maps for those years (fig. 45). 

The simulated groundwater levels are in general agreement 

with the hand-contoured groundwater-level maps for 

1945–2010. The groundwater-level maps were useful during 

the model calibration for providing additional information on 

the effects of flow boundaries and the adjustments to selected 

hydraulic properties, such as vertical hydraulic conductivities. 

The sequence of contoured measured groundwater levels and 

the simulated thematic maps indicate that groundwater levels 

in the agricultural center of the Borrego Valley continue to 

decline. By 2010, groundwater-level declines of more than 

120 ft persisted in the northern part of the basin, and more 

than 75 ft persisted in the municipal pumping center on the 

western side of the basin, a pattern partially replicated by the 

BVHM simulation (figs. 44, 45). Although groundwater-level 

declines exceeded 75 ft in the municipal pumping center, these 

declines might not reflect ambient (static) conditions and are 

thought to represent groundwater levels that have not fully 

recovered from pumping before measurement. As a result, the 

simulated map in this area is somewhat subdued compared to 

the hand-contoured map.

Calibration Procedure

Calibration consisted of a systematic application of 

the parameter estimation method to the range of possible 

solutions. Because some model parameters demonstrated 

significant correlations, parameter estimation software, 

including PEST (Doherty, 2010a, b, c; Doherty and Hunt, 

2010), could not be used directly for all sensitivity analyses 

and parameter estimation. A combination of PEST and manual 

adjustments were used in the parameter estimation and 

sensitivity analyses.

A total of 137 parameters were defined in the BVHM 

(table 18) and used during the automated calibration process. 

These parameters were farm-process properties and scalars, 

streamflow and stream-channel properties, underflow scalars, 

unsaturated-zone properties, and hydraulic parameters of the 

aquifers (storage and hydraulic properties and power mean 

coefficients; table 18). Hydraulic properties were initially 

assigned values on the basis of published values and earlier 

modeling studies, then were adjusted during model calibration. 

Parameters were adjusted within ranges of reasonable values 

to best-fit historical hydrologic conditions measured in the 

aquifer, stream network, and landscape.

Calibration started with the landscape processes and was 

followed by adjustment of hydraulic properties, unsaturatedzone properties, and streambed properties. Because many of 

these properties are head-dependent or correlated through their 

exchange of water, they were adjusted recursively through 

automated and trial-and-error analysis. The calibration process 

also required modifications to the parameter framework. For 

example, calibration required additional partitions of hydraulic 

property zones for the aquifers.

Calibration of transient-state conditions was dependent 

on the components of the use and movement of water 

across the landscape and their interplay with the streamflow 

network and groundwater-flow system. Calibration started 

with adjustments of parameters in the landscape, such as 

fractions of transpiration, irrigation efficiencies, stress factors 

for crop coefficients, and fractions of runoff. The calibration 

of the groundwater-flow system involved adjustment of Kv

for the streambed parameters that control recharge driven by 

streamflow infiltration from the streamflow network, runoff 

parameters that control areal recharge driven by the landscape 

process, and parameters that control discharge driven by 

pumping and evapotranspiration, which, in turn, are driven by 

the landscape process. Many of the water-budget components 

are specified values of inflows. Inflows were specified model 

inputs, and some were adjusted by scaling factors during 

calibration. Some inflows were well understood and were 

not adjusted during calibration. These include municipal 

pumpage, monthly precipitation and ETo, and many WBS 

and crop properties. The remaining water-budget components 

that were calculated by the model include ETact, groundwater 

pumpage for agricultural and recreational uses, runoff from 

irrigation and from precipitation, farm net recharge, infiltration 

through the unsaturated zone, wellbore flow through longscreened wells, and change in storage. The implementation 

of the MNW2 package maintained the net pumpage values, 

but redistributed groundwater extraction and flow vertically 

between layers by simulating intra-well groundwater flow. 

This groundwater flow in the wells occurs not only during 

periods of pumping and in undestroyed and unused wells, but 

also in wells that are used only periodically for water supply 

or irrigation.

Farm Process Parameters

FMP parameters that were adjusted during calibration 

include selected crop properties, such as scale factors for 

seasonal crop coefficients, fractions of total precipitation, 

runoff from excess precipitation, irrigation for selected 

crops and natural vegetation, and seasonal scale factors 

for irrigation efficiencies. The scale factors (scalars) for 

seasonal crop coefficients are analogous to stress factors that 

amplify or reduce the crop coefficients, which were estimated 

under unstressed conditions. Because crop coefficients 

were estimated under unstressed conditions, and the actual 

timing of crop planting was unknown, the crop coefficients 
Groundwater-Flow Models 109

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

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S-2

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KILOMETERS

sac13-0509_Figure 45a 1945 WL contours

A

EXPLANATION

General direction of

 groundwater flow

Model-grid boundary

Stream channel

Boundary of Borrego Valley

 groundwater basin

Fault

Boundary of Anza-Borrego

 Desert State Park

78 Road

Water-level contour, 

 measured 2010.

 Interval varies; 

 hatchures indicate 

 depression

490

Dry lake

Simulated water level, in feet, 1945

36.09 to 50

51 to 100

101 to 150

151 to 200

201 to 250

251 to 300

301 to 350

351 to 400

401 to 450

451 to 500

501 to 550

551 to 600

601 to 650

651 to 700

701 to 1,200

Figure 45. Simulated water levels for the Borrego Valley Hydrologic Model, Borrego Valley, California, A, in 1945; B, 2010; and C, 

simulated drawdown from 1945 to 2010.
110 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

l

l

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116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 45b 2010 WL Contours

B

EXPLANATION

Simulated water level, in feet, 2010

36.09 to 50

51 to 100

101 to 150

151 to 200

201 to 250

251 to 300

301 to 350

351 to 400

401 to 450

451 to 500

501 to 550

551 to 600

601 to 650

651 to 700

701 to 1,200 General direction of

 groundwater flow

Model-grid boundary

Stream channel

Boundary of Borrego Valley

 groundwater basin

Fault

Boundary of Anza-Borrego

 Desert State Park

78 Road

Water-level contour, 

 measured 2010.

 Interval varies; 

 hatchures indicate 

 depression

490

Dry lake

Figure 45. —Continued
Groundwater-Flow Models 111

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

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R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 45c Sim drawdown 2010

EXPLANATION

C

Simulated drawdown, in feet, 1945–2010

41 to 50

0 to 10 51 to 60 91 to 100

11 to 20 61 to 70 101 to 110

21 to 30 71 to 80 111 to 120

31 to 40 81 to 90 121 to 130 Model-grid boundary

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake

Figure 45. —Continued
112 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

required changes by a factor of 0.85 to 1.08 (table 18) to 

improve calibration results. Irrigation efficiencies (which 

include typical irrigation efficiencies and adjustments for 

errors in crop type, percentage of land cover, and crop 

distribution) were adjusted by decade to allow for changes 

over time, particularly for changes in irrigation practices. 

For simplification, it was assumed that all crops and farms 

changed their efficiency practices by the same factor. Irrigation 

efficiencies (table 16) were scaled by a factor of 1.00 to 1.25 

that increases with time. Runoff from selected crops and 

native vegetation was added to the water available for deep 

percolation or for overland runoff to the streamflow network 

that seeps into the unsaturated zone. The fractions of losses 

to runoff were adjusted for native vegetation, phreatophytes, 

recreational areas, and various crops. Similarly, fractions of 

runoff from precipitation were increased to control the deep 

percolation and additional runoff from the native vegetation, 

which is the largest component of the land use in Borrego 

Valley.

Irrigation efficiencies were estimated for all golf 

courses to be 86 percent, or 14 percent irrigation return 

flow to the groundwater system, on the basis of results of 

the chloride mass-balance analysis, assuming irrigation 

by using a wide-area, broadcast type of sprinkler (Netto, 

2001). The BVHM simulated a range of 75–94 percent 

(averaging 85 percent) efficiency for golf courses. Assuming 

citrus groves are irrigated by using a “micro sprinkler” type 

irrigation, Netto (2001) estimated an irrigation efficiency of 

78 percent. The BVHM estimates a range of 75–98 percent 

(averaging 84 percent) efficiency for citrus groves. Netto 

(2001) estimated all other irrigation efficiency at 86 percent. 

In general, the BVHM estimated an average efficiency that 

increased over time from 75 percent to slightly greater than 

90 percent, averaging about 85 percent. Note that these 

efficiency estimates most likely include adjustments to help 

simulate changes in crop coverage and type, as well as other 

factors affecting crop demand not specified in detail in the 

simulation.

Hydraulic Parameters

The model was used to estimate the values of 15 

hydraulic properties, 35 storage properties, and 5 unsaturatedzone properties during model calibration. The values of 

Kc and Kf for each model layer were adjusted to produce 

simulated heads representing the long-term trends in the 

aquifers that best matched the measured heads and estimated 

streamflow losses. Because of the differences in depositional 

environments in the various zones within each layer, the 

hydraulic properties were adjusted for Kh, Kv, and storage 

properties (fig. 34; table 18). Other properties were estimated, 

including porosities; specific yields; specific storage for 

coarse- and fine-grained end members; and the exponent of the 

power mean, which is used to estimate the Kv. 

The calibration of hydraulic properties required the 

adjustment and rescaling of these intrinsic properties on the 

basis of groundwater-level hydrographs (fig. 42). The most 

sensitive parameters were Kvs that, in part, controlled the 

seasonal amplitudes and differences in groundwater levels 

between aquifer layers. Scaled reductions of hydraulic 

conductivities for selected confined zones, and scaled 

increases of Kv and storage properties for unconfined zones, 

were required. Horizontal hydraulic conductivities required 

more zonation in the upper and middle aquifers than in the 

lower aquifer (table 18). Because the model was relatively 

sensitive to values of specific yield, these were also split into 

additional zones and estimated in the automated parameter 

estimation.

Streamflow Properties

As mentioned previously, a wide rectangular channel for 

the stream geometry and a constant estimation of Manning’s 

“n” were used for the simulations. Values for these parameters 

are not well known in this area and were not sensitive 

during calibration. The model also required calibration of 

the streambed Kv parameters. Groups of stream segments 

that stream channels were observed in the field to share 

characteristics, such as bed material and morphology, and 

which had similar size drainage basins, were represented by 

six parameters of streambed Kv and a parameter representing 

the thickness of the streambed sediments (fig. 37; table 18). 

The groupings and calibrated Kv values ranged from 0.65 ft/d 

along the armored channel of San Felipe Creek to about 

65 ft/d along the gravelly channels (table 18).

Sensitivity Analysis

The BVHM was sensitive to changes in various climate 

and landscape properties, hydraulic and storage properties of 

the aquifers, and streambed vertical hydraulic conductivity. 

The model periodically had numerical convergence problems 

when some of the model parameters were perturbed too far. 

In some cases, this required a change to the model structure 

or parameter distribution. As a result, the use of systematic 

parameter-estimation techniques to estimate values of selected 

model parameters and related sensitivities that are based 

on perturbation approaches was augmented on the basis of 

guidance from sensitivity and trial-and-error analysis.

The sensitivity process in PEST identifies the sensitivity 

of computed values at the locations of measurements to 

changes in model parameters. PEST was used to identify 

which parameters to include in the Parameter Estimation 

Process (Hill and others, 2000) and to adjust the parameter 

values during calibration. Results of the sensitivity process 

indicate that the model was most sensitive to about 15 

parameters related to scale factors of the crop coefficients, 

climate-based scale factors for runoff from precipitation and 

irrigation, and irrigation efficiency. The sensitivity of the 

parameters is shown in table 18 and figure 46. The model 

was most sensitive to the scaling of crop coefficients, which 

controls groundwater demand. The next most important 

parameters were the factors adjusting the amount of BCMsimulated streamflow and underflow into the basin and, 

therefore, the amount of recharge to the basin. The other 

more sensitive parameters are mostly storage properties and 

irrigation efficiencies.
Groundwater-Flow Models 113

Model Uncertainty, Limitations, and 

Improvements 

The model documented in this report can be used to 

evaluate water-management scenarios throughout Borrego 

Valley. However, in order to use the BVHM effectively, the 

uncertainty associated with its simulated hydrologic conditions 

needs to be understood. The BVHM is a simplification of the 

Borrego Valley hydrologic system and, as such, has some 

inherent limitations. The accuracy of model results is related 

strongly to the quality and spatial distribution of input data 

and of measurements of components of the groundwater 

the system (such as groundwater levels, streamflow, and 

pumping) used to constrain model calibration. The inflows and 

outflows in the model were a combination of measured values, 

adjustments to represent conceptualizations of the system, 

BCM-simulated inflows, and other landscape and hydrologic 

values specified for simulating hydrologic processes through 

the use of the model codes OWHM and the associated 

numerical processes (for example, FMP) and packages (for 

example, MNW2). Differences between simulated and actual 

hydrologic conditions arise from a number of sources and 

are collectively known as model error that results in potential 

model uncertainty.

Although the model was designed with the capability 

of being accurate everywhere, the conceptual and 

numerical model still retains simplifications that could 

restrict appropriate use of the current model to regional 

and subregional spatial scales and within seasonal to interannual temporal scales. Potential future refinements and 

enhancements could improve the level of accuracy and 

the spatial and temporal resolution. Proper design and 

calibration of flow models, along with better estimates of 

inflows, outflows, and changing spatially and temporally 

variable conditions, such as land use, can minimize some 

of the inherent limitations. Limitations of the modeling 

software, assumptions made during model development, 

and results of model calibration and sensitivity analysis are 

factors that could constrain the appropriate use of this model. 

However, the current model can be used to identify areas 

where improvements are needed in the simulation of specific 

processes or in the data used to simulate existing features that 

would likely improve model accuracy and relevance.

Model discretization in space and time can be a potential 

source of error and uncertainty. Models represent a hydrologic 

system as a series of discrete spatial units through which 

intrinsic properties and flows are assumed to be uniform. 

The use of a discretized model to represent a hydrologic 

system introduces limitations from features that occur at 

scales smaller than the discretization. Transient models are 

further discretized into a series of discrete units of time, 

during which specified hydrologic inflows and outflows are 

held constant. The use of monthly stress periods and two time 

steps per stress period in the BVHM inherently assumes that 

temporal variations of inflows, outflows, and groundwater 

levels could be approximated as piecewise linear changes 

over the specified time intervals. Changes at shorter time 

scales were not simulated and were not discernible in the 

model results, which could contribute to some additional 

temporal uncertainty. The temporal scale used in the BVHM 

was expressly designed to separate the supply and demand 

components of water use for agriculture.

Differences between simulated and measured hydrologic 

variables also can arise from the numerical solution, which 

attempts to provide a cell-by-cell mass balance of inflows 

and outflows by using an iterative approximation of the 

governing flow equations. Mass-balance errors are minimized 

by ensuring the model solution reaches a reasonable state of 

mass balance within each 2-week time step, as specified by 

a user-defined tolerance. The 2-week time steps were used in 

order to remain consistent with the assumptions of the current 

version of the FMP. The cumulative mass balance of the model 

was within 0.5 percent of the total flow over the 43 years of 

simulation.

Figure 46. Most sensitive parameters for the Borrego Valley Hydrologic Model, Borrego Valley, California.

sac13-0509_fig46

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

sfac_d1

sfac_d2

sfac_sum

sfac_spr

sfac_win

sfac_fal

sfac_d5

sfac_d6

SY_L1_Z4

sfac_d7

COY_RO

SY_L1_Z7

thts_ALL

sfac_d3

kc3

SY_L1_Z5

SAND

kc1

RO_P_NTV

fhb32

SY_L1_Z6

k_coyote

kc1s

SFR2_RO

SY_L1_Z10

fhb26

fhb27

UZF_VK

PALM_RO

Parameter identifier

Composite scaled sensitivity, unitless 

sfac_d4

EXPLANATION

See table 18 for names and explanations of all parameter identifiers.

Storage (specific yield) property

Parameter groups

Unsaturated-zone property

Fraction of runoff of precipitation 

from native vegetation

Farm process efficiency scalar

Horizontal hydraulic conductivity

Scalar multiplier on underflow from 

adjacent basin

Streamflow runoff scalar

Farm process seasonal scalar

Capillary fringe of sand

Vertical hydraulic conductivity of 

streambed
114 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

An additional component of model error arises from 

how well model-input values represent the actual hydrologic 

system. The accuracy of the calibrated model is contingent 

on the accuracy of the specified inflows and of the specified 

comparison flows. Model calibration provides a means to use 

comparisons to constrain the differences between real-world 

and simulated mass flows indirectly. Thus, the degree to which 

a simulated condition provides a reasonable representation 

of the hydrologic system can be evaluated by comparing 

simulated hydrologic conditions with those observed in the 

field, which in turn, provides a mass-constrained calibration. 

The performance and accuracy of BVHM are constrained 

primarily by groundwater levels; streamflows and diversions; 

pumping; and, to a lesser degree, streamflow losses and 

head differences. The model is used to develop a conceptual 

understanding of the flow system by quantifying the regional 

inflows and outflows and their relative proportions. Because 

the Borrego Valley flow system is inherently complex, 

as for all models, simplifying assumptions were made in 

developing and applying the numerical code OWHM. The 

model solves for average conditions within each 92-acre cell 

for each 2-week time step with the parameters interpolated 

or extrapolated from measurements or estimated during 

calibration. Thus, results from the model are most appropriate 

for subregional spatial scales and annual to inter-annual 

temporal scales for comparative analysis and generalized 

estimates of flows.

Several elements of the revised model remain uncertain 

and would require additional investigation to improve the 

accuracy of the simulation of groundwater and surface-water 

flow, the regional groundwater-storage changes, and the use 

and movement of water across the landscape. For example, 

some of the inputs to the FMP that are necessary to calculate 

water use remain uncertain, and model features, such as 

agricultural and recreational pumpages, could be sensitive to 

a few of these parameters, such as crop coefficients, irrigation 

efficiencies, multiple cropping, or monthly land use. More 

accurate estimates of agricultural and recreational pumpages 

would improve the model results. In addition, monthly 

or seasonal information on the distribution and change in 

land-use patterns would greatly increase the accuracy of the 

simulation. Many of the stresses that are driven by these 

land uses varied throughout the simulation period at higher 

frequencies than the lower frequency (average of about 

0.2 yr−1) estimates of land use. This is evident by the improved 

simulation since 1992, when land-use estimates used in the 

model were more frequent. These variations also are driven by 

climatic conditions and growing periods. Hence, the changes 

appear seasonally and by climatic-driven events that can be 

yearly or multi-year in length. Because the land use was based 

on a generalized classification for the early years and selected 

crop categories, some of the agricultural composite crop 

classes were replaced with the composite crop of identical 

extent from the most recent land-use map. For example, where 

only cropland was specified, the interpreted composite crops 

were embedded. This assumes the farmer would be growing 

the same type of crop in a given area over the time frame of 

the hydrologic simulation when that land-use map was used. 

In some cases, such as orchards, this is generally a good 

assumption; in other cases, the crops could have changed 

several times during the years represented by the land-use 

map. This is an aspect that could use much improvement in 

future refinements of the model. Simulating future conditions 

is inexact because estimates of most input data are uncertain 

for future years and could be better delineated in terms of 

their relation to climate changes. Natural vegetation, including 

phreatophytes, represents between 75 and 100 percent of the 

land use and, as such, is critical for estimating the runoff and 

recharge in Borrego Valley.

Some inflows, such as ungaged streamflow remain 

relatively uncertain, and the model could potentially benefit 

from improved estimates if additional capture of local runoff 

is a primary component of any revised BMP. Although 

estimation of the ungaged streamflow or linkage to a model 

that specifically routes streamflow and simulates recharge 

from the tributary drainage areas was outside the scope of this 

study, this could improve modeling of the streamflow network 

and the simulation of streamflow infiltration in the mountainfront regions.

Model uncertainty could be reduced by using additional 

field estimates, such as horizontal and vertical hydraulic 

conductivities and storage properties. Additional estimates of 

Kh could be obtained by using slug tests at observation-well 

sites or specific-capacity approximations from single-aquifer 

supply wells to constrain the model properties further. 

In general, the textural database is more certain for the 

upper aquifer and the upper part of the middle aquifer and less 

certain for parts of the lower middle and lower aquifers. The 

difference between simulated and measured heads generally 

increases with depth below the land surface. This could reflect 

the decreasing accuracy of textural data, which were used to 

estimate the hydraulic conductivities, with increasing depth 

below the land surface. The information for the lower aquifer 

could be especially uncertain, because few wells currently 

produce from this unit in the Borrego Valley.

Several of the processes in the model potentially 

could allow for refined simulation of selected flow features. 

Improved modeling of multi-aquifer wells to account for 

better estimates of actual pumping capacities of all wells could 

increase the accuracy of simulated pumpage. Similarly, the 

simulation of runoff within the FMP could be enhanced to 

better simulate the intensity of wet-year winter precipitation 

events, which would facilitate better estimates of runoff. 

Antecedent soil moisture is not accounted for by FMP. This 

could cause underestimation of runoff during the wet years 

along with overestimation of the unsatisfied crop demand used 

to drive the simulated pumping.

A hydrologic flow model is a valuable tool for testing 

the conceptualization of the hydrologic system and for 

predicting the response of the system to changes in aquifer 

stresses. However, a model is only an approximation of 

the actual aquifer system and, therefore, does not exactly 

represent the actual system. The model relies on estimates 

of aquifer properties and stresses, which have some degree 

of uncertainty. Although some of this uncertainty has been 

evaluated in this work, the model still lacks the small-scale 

spatial and temporal variability present in the actual system.
Hydrologic Flow Analysis 115

Water levels and drawdowns calculated by the model are 

average values for the area represented by each model cell. 

Simulated groundwater levels can vary considerably from 

measured groundwater levels because of well location, depth, 

and construction. For example, wells could be screened over 

a depth represented by more than one model layer, whereas 

corresponding measured groundwater levels could represent 

an unknown composite of the hydraulic heads across this 

screened interval. However, the size of the model cells and 

the length of the stress periods of the model used in this work 

are appropriate for the resolution of available data and for 

simulations on a regional scale. Because model uncertainty 

increases greatly with the decreasing size of the area of 

interest, the model generally is not meant to be used to address 

detailed, local-scale problems. 

Natural and agricultural recharge are difficult to measure; 

therefore, the recharge rates and temporal distribution of 

recharge were based on the model calibration results. The 

calibration process resulted in a higher rate of natural recharge 

than had been estimated in previous studies. Additional 

hydrogeologic data are needed to confirm that the natural 

recharge rates used in the model are accurate. 

Owing to uncertainty in some parameters used in the 

model (especially in the estimated natural recharge), as well as 

uncertainty in some components of the model structure (such 

as the agricultural component of pumping), model results from 

predictive simulations are to be used with caution. The model, 

like most models, is not ideally suited for predicting absolute 

groundwater levels. The most appropriate application of the 

model is comparing the relative effects of different watermanagement scenarios on the aquifer system.

In summary, some potential components that could 

improve the accuracy and reduce uncertainty of the simulation 

could include, but are not limited to the following:

1. Improved temporal estimates of land use.

2. Improved estimation and application of crop and 

irrigation properties. 

3. Improved mapping of density, temporal distribution, 

and areal extent of natural vegetation, particularly 

phreatophytes.

4. Improved estimates of ungaged stream inflows through 

linkage to a daily or sub-daily precipitation-runoff model 

that simulates routed streamflow.

5. Improved estimates of hydraulic properties through field 

tests. 

6. Improved texture estimates at depth.

7. Improved simulation of multi-aquifer wells to account 

for well pumping capacities. 

8. Improved simulation of wet-year winter runoff within 

FMP.

9. Inclusion of antecedent soil moisture in FMP.

Despite all of these potential limitations, the BVHM 

represents the most realistic, accurate, and reliable means, at 

present, for understanding many aspects of the hydrologic 

system of the Borrego Valley that are needed for planning 

and evaluating scenarios for managing water resources. 

Although all models have limitations, the options for testing 

alternative scenarios would be much more limited without the 

hydrologic information provided by the BVHM. When used 

correctly, BVHM can contribute to a better understanding of 

the hydrologic system. As more data become available, and as 

more modeling capabilities are developed, the current BVHM 

provides a foundation for updating and refining the model 

to improve its use as a tool to inform management of water 

resources in the Borrego Valley.

Hydrologic Flow Analysis 

The components of water budget over time with and 

without anthropogenic effects are shown in figures 47 and 48. 

The graphs in figures 47A and 48A use the climatic patterns 

of the 66-year simulation period and the assumed landuse pattern from pre-development (all natural vegetation) 

continued over time. These graphs show the variability in the 

water budget that results from climatic variability alone. The 

natural recharge is seepage from the land surface, recharge 

from streams, and underflow from the upstream portions of 

the watershed. Most of the recharge is streamflow, which 

varies from less than 500 acre-ft/yr in dry years to more 

around 20,000 acre-ft/yr in the wettest years. Although the 

pattern of precipitation and recharge from streamflows and 

underflows from the adjacent basins is similar, the relative 

magnitudes of the peaks vary (figs. 47–49). This is due to local 

variability in precipitation patterns, but annual precipitation is 

not a direct indicator of runoff and recharge for the monthly 

simulations. For example, a single wet month during an 

average year could be driving the flow result. A comparison 

of figures 47B and 48B indicates the anthropogenic effects 

(municipal, recreational, and agricultural) on the water budget. 

Recharge from the streamflow loss and adjacent basins is the 

major inflow to the system. Recharge through the unsaturated 

zone from irrigation return flow is relatively small. Note that 

because of the depth to water and thickness of the unsaturated 

zone, the travel time through the unsaturated zone in much 

of the area ranges from tens to hundreds of years and might 

not reach the regional saturated groundwater-flow system 

in these short-term simulations. These simulated estimates 

are corroborated with the 14C and tritium ages discussed 

previously in the report. Although groundwater-storage 

changes reflect the climatic variability, there is an obvious 

effect from pumping in the groundwater basin (figs. 47–48).
116 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

sac13-0509_fig47ab

Fig. 47b. Major components of groundwater budget

Fig. 47a Major components of groundwater budget

–25

–20

–15

–10

–5

0

5

10

15

20

25

1945 1955 1965 1975 1985 1995 2005

Years

B

1950 1960 1970 1980 1990 2000

–25

–20

–15

–10

–5

0

5

10

15

20

25

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2011

A

Basic groundwater budget without anthropogenic effects,

in thousands of acre-feet

2011

Constant head

Stream leakage

Seepage through unsaturated zone

Groundwater pumpage

Underflow

EXPLANATION

Climatic patterns

Wet

Dry

Average

Storage

Constant head

Recharge from streams and watershed

Seepage through unsaturated zone

Evapotranspiration from groundwater

Evapotranspiration from groundwater

EXPLANATION

Climatic patterns

Wet

Dry

Average Underflow

Storage

Basic groundwater budget with anthropogenic effects,

in thousands of acre-feet

Figure 47. Simulated components of the basic groundwater budget by using climatic patterns A, with no anthropogenic effects and 

B, with anthropogenic effects for the Borrego Valley Hydrologic Model, Borrego Valley, California, 1945–2010. In a groundwater model, 

storage relates to flow into and out of the groundwater system. Storage becomes more negative as water moves from the groundwater 

flow system into groundwater storage.
Hydrologic Flow Analysis 117

Nsac13-0509_fig48ab

et Gro

u

ndWater H

y

drolo

gic

B

u

d

g

et =

>

Fig. 4

8

b

N

et Gro

u

ndWater B

u

d

g

et =

>

Fig. 4

8a 

, with anthropogenic effects Net groundwater budget 

in thousands of acre-feet 

Groundwater inflow Groundwater outflow

–40–30–20–1

0

0

10203040

, with no anthropogenic effects Net groundwater budget 

in thousands of acre-feet

A

1946

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

–40–30–20–1

0

0

10203040

1946

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Groundwater inflow Groundwater outflow

Water year

B Storage Groundwater pumpage

Seepage through 

unsaturated zone

Underflow

Stream leakage Evapotranspiration from

groundwater

Constant head

EXPLANATION

Climatic patterns

WetDryAverage

EXPLANATION Storage

Seepage through unsaturated zoneUnderflowStream leakageEvapotranspiration from groundwaterConstant head

Climatic patterns

WetDryAverage

Figure 48. Simulated components of the net groundwater budget from the Borrego Valley Hydrologic Model, Borrego Valley, California, 1945–2010, by using climatic patterns A, with no anthropogenic effects and 

B, with anthropogenic effects.
118 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Figure 49. Precipitation, streamflow, and underflow from adjacent watersheds and basins for the Borrego Valley, California, 1945–2010.

sac13-0509_fig49

Fig. 49 Precipitation in the basin and inflow from adjacent watersheds 

0

10

Years

20

30

40

50

60

Streamflow and underflow from adjacent basins or 

subbasins, in thousands of acre-feet 

0

1

2

3

4

5

6

7

8

9

10

Precipitation, in inches

Precipitation

Streamflow from adjacent basins or subbasins

Underflow from adjacent basins or subbasins

EXPLANATION

Climatic patterns

Wet Dry Average

1946

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

The simulated landscape budget is shown in figure 50.

As in the previous figures, the graph in figure 50A uses the 

climatic pattern for the 66-year simulation period and the 

estimated land-use pattern from pre-development through 

2010. The magnitude of precipitation that is directly 

evaporated as a result of the arid conditions in the valley is 

shown in figures 50A and B. As a result, little unsaturatedzone recharge occurs from direct precipitation. Figures 47A, 

48A, and 50A and table 19 show that despite the variability 

in climate, ET from groundwater was relatively constant 

before development at about 7,100 acre-ft/yr. The ET from 

groundwater both enters and leaves the landscape system and 

directly links the landscape process with the groundwater-flow 

process (fig. 50). Figures 47B and 50B show that pumpage 

totals around 19,000 acre-ft/yr in recent years (2005–10). In 

the budgets, groundwater pumpage is balanced by reductions 

of ET from groundwater and groundwater storage (figs. 47B, 

48B, 50B).

The simulated monthly groundwater budget components 

during 2010 are shown in figure 51. As expected, the pumpage 

(and groundwater-storage loss) increases during the warmer 

summer months and decreases during the winter and early 

spring. January and October show some small decreases 

relative to the adjacent months, most likely reflecting local 

variability. Recharge from streamflow from the upstream 

portion of the watershed does not vary dramatically because 

2010 was a comparatively dry year; because underflow was 

specified in the model by use of a head-dependent flow 

boundary, and heads near this boundary are largely unaffected, 

underflow is approximately equal to the predevelopment rate. 

Recharge through the unsaturated zone increases slightly 

during the winter and early spring. The ET from groundwater 

appears to increase slightly in the spring, when groundwater 

levels are higher from stormflow, and phreatophytic vegetation 

would be most active. The small amount of discharge out the 

southern end through the constant-head boundary is largely 

unaffected, and flow out of the system is approximately equal 

to the predevelopment rate.

 The simulated annual changes in storage and the 

cumulative change (loss) in storage, given the climatic 

variability from 1945 through 2010, are shown in figure 52.

Because the period from 1945 through 1970 was generally 

dry, there is a cumulative loss in storage from natural sources. 

This loss is recovered during the relatively wetter period from 

1971 to 2010 (fig. 52). The magnitude of these changes is 

small, however, compared to the magnitude of storage change 

resulting from groundwater development (fig. 52). Because 

discharge (groundwater pumping) has exceeded recharge for 

most of the 66-year simulation period, and no other sources of 

inflow to the groundwater system are available for capture, a 

significant amount of water has been removed from storage. 

For the simulation period, approximately 440,000 acre-ft of 

water were removed from storage in the groundwater basin, 

and nearly 400,000 acre-ft can be attributed to pumpage.

Table 19. Simplified groundwater budget for pre-development 

and 2010, Borrego Valley Hydrologic Model, Borrego Valley, 

California.

[na, not applicable]

Groundwater budget 

(acre-feet per year)

Pre-development 

(average)1 2010

IN

Unsaturated-zone recharge 1,719 1,225 

Streamflow and underflow from adjacent 

watersheds

 5,395 1,587 

 Storage loss 478 15,568 

OUT

Flow out southeastern end (constant head) 518 517 

Evapotranspiration of groundwater 7,074 453 

Pumpage na 17,410 

1

Average based on climatic conditions without development for 1945 

through 2010.
Hydrologic Flow Analysis 119 Figure 50. Simulated landscape budget with climatic patterns from the Borrego Valley Hydrologic Model, Borrego Valley, California, 1945–2010, 

A, with no anthropogenic effects 

and 

B, with anthropogenic effects.

T

sac13-0509_fig50a

o

t

al L

a

n

d

s

c

a

p

e

B

u

d

g

e

t

t

h

r

o

u

g

h

tim

e

f

o

r

B

o

r

r

e

g

o

Valle

y, C

alif

o

r

nia

=

>

Fig. 5

0

a

–70

–80

–60–50–40–30–20–1

0

0

1020304050607080

Water year

Simulated landscape budget with no anthropogenic effects, in thousands of acre-feet

Flow into landscape system Flow out of landscape system 

Evapotranspiration from groundwaterRunoff

EXPLANATION

Deep percolationPrecipitationEvapotranspiration from precipitation

Climatic patterns

WetDryAverage

1946

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

A
120 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley Figure 50. —Continued

sac13-0509_fig50b

–80–70–60–50–40–30–20–1

0

0

1020304050607080

Water year

Simulated landscape budget with anthropogenic effects, in thousands of acre-feet

Flow into landscape system Flow out of landscape system 

Evapotranspiration from groundwaterRunoff

EXPLANATION

Deep percolationPumpagePrecipitationEvapotranspiration from irrigationEvapotranspiration from precipitation

Climatic patterns

WetDryAverage

1946

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

B
Hydrologic Flow Analysis 121

Figure 51. Simulated monthly net groundwater budget, Borrego Valley Hydrologic Model, Borrego Valley, California, 2010.

Figure 52. Simulated annual and cumulative changes in groundwater storage by using climatic patterns with no anthropogenic effects 

and with anthropogenic effects, Borrego Valley Hydrologic Model, Borrego Valley, California, 1945–2010.

sac13-0509_fig51

Water Year 2010 Net Groundwater budget fig. 51

–25

Month

–20

–15

–10

–5

0

5

10

15

20

25

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept

Underflow from adjacent basins

Evapotranspiration of groundwater

Groundwater pumpage

Recharge through unsaturated zone

Surface water

from watersheds

Constant head

Storage

EXPLANATION

Simulated monthly net groundwater budget,

in hundreds of acre-feet

–500

–400

–300

–200

–100

0

Water year

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Simulated groundwater-storage changes, in thousands of acre-feet

Storage change

Cumulative storage change

Storage change (no anthropogenic effects)

Cumulative storage (no anthropogenic effects)

EXPLANATION

Climatic patterns

Wet Dry Average
122 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Future Groundwater-Management 

Scenarios

The simulation results in the previous section, along with 

the measured changes in groundwater levels, indicate that 

groundwater usage currently exceeds the amount of water 

recharging the Borrego Valley. As a result, groundwater is 

being removed from storage, and water availability is likely 

to be a limiting factor in meeting future water demands. In 

order to understand the effects of this reduction in storage 

better, water managers are considering different groundwater 

management scenarios to manage their available water 

resources. For this analysis, six water-use scenarios were 

considered during a 50-year period (2011–60):

1. No change in the magnitude and distribution of 

pumping, or the 2010 status quo.

2. Low growth over 50 years (agricultural and recreational 

pumpages are decreased linearly over time to 5 times 

the 2010 rates for 2060, while municipal pumpage is 

increased linearly over time to 76 times the 2010 rates 

for 2060).

3. Medium growth over 50 years (agricultural pumpage is 

decreased linearly over time to 25 times the 2010 rates 

for 2060, and recreational pumpage is decreased linearly 

over time by 50 times the 2010 rates for 2060, while 

municipal pumpage is increased linearly over time to 

33 times the 2010 rates for 2060).

4. High growth over 50 years (agricultural pumpage is 

decreased linearly over time to zero for 2060, and 

recreational pumpage is decreased linearly over time 

by 5 times the 2010 rates for 2060, while municipal 

pumpage is increased linearly over time to 79 times the 

2010 rates for 2060). 

5. Water-usage reduction to avoid future groundwaterstorage depletion over 50 years (agricultural and 

recreational pumpage is decreased linearly over time 

to 32 times the 2010 rates for 2060, and municipal 

pumpage is decreased linearly over time by 52 times the 

2010 rates for 2060).

6. Management scenario water-usage reduction over 

20 years (agricultural pumpage is reduced linearly over 

time to 40 times the 2010 rates for 2030, and recreational 

and municipal pumpages are each reduced linearly over 

time to 50 times the 2010 rates for 2030; then, usage 

is held constant at the 2030 rate for the next 30 years, 

2031–60).

The calibrated BVHM was used to simulate the 

hydrologic effects of the six groundwater management 

scenarios with monthly stress periods. The projected pumpage 

rates for the six management scenarios are summarized in 

table 20. In order to include climate variability in all six of 

these scenarios, it was assumed that the climatic inputs of the 

last 50 years repeated in reverse from the last calibration year 

(2010). Note that this results in a relatively dry period near the 

end of the simulation. For the first five scenarios, the changes 

in groundwater pumpage are spread throughout the basin for 

each of the water-use types (agricultural, recreational, and 

municipal) evenly over the 50-year scenario simulation. In 

Scenario 6, the changes occur in the first 20 years, then the 

land use and municipal pumpage are held constant for the 

remaining 30 years. Slight variations occur in all scenarios 

for the agricultural and recreational pumpages owing to 

climatic factors. For the municipal and Rams Hill recreational 

pumpage, the pumpage change was accomplished by using 

a multiplier to change the total pumpage. For agricultural 

and the remaining recreational pumpage, the reduction was 

accomplished by randomly removing crops, as needed, for 

each of the scenario simulations. The cumulative change in 

groundwater storage for the six water-management scenarios 

is shown in figure 53. The water tables simulated for each 

scenario in 2060 are shown along a longitudinal cross section 

of the basin in figure 54.

Table 20. Groundwater budgets for six management scenarios from the Borrego Valley Hydrologic Model, Borrego Valley, California, 

2011–60.

[Pumping rates are in acre-feet per year.]

Scenario

2010 Pumping rates Percent of 2010 pumping rates 2060 Pumping rates

Agricultural Recreational Municipal Total Agricultural Recreational Municipal Agricultural Recreational Municipal Total

1 13,162 4,113 1,006 18,281 100 100 100 13,162 4,113 1,006 18,281 

2 13,162 4,113 1,006 18,281 50 50 176 6,581 2,056 1,771 10,408 

3 13,162 4,113 1,006 18,281 25 50 233 3,291 2,056 2,344 7,691 

4 13,162 4,113 1,006 18,281 0 50 379 0 2,056 3,813 5,869 

5 13,162 4,113 1,006 18,281 32 32 52 4,212 1,316 523 6,051 

61 13,162 4,113 1,006 18,281 40 50 50 5,265 2,056 503 7,824 

1

Scenario 6 represents the scalar change occurring over 20 years between 2010 and 2030.
Future Groundwater-Management Scenarios 123

Figure 53. Cumulative change in groundwater storage for six water-management scenarios simulated by using the Borrego Valley 

Hydrologic Model, Borrego Valley, California, 1945–2060. 

sac13-0509_fig53

Cumulative Storage change for water managementscenarios

–1,100

–1,000

–800

–600

Simulated cumulative change in groundwater storage, in thouands of acre-feet

–400

–200

0

100

–100

–300

–500

–700

–900

1945 1955 1965 1975 1985 1995 2005

Water year

2015 2025 2035 2045 2055 2060

Scenario1

Scenario2

Scenario3

Scenario4

Scenario5

Scenario6

EXPLANATION

Climatic patterns

Wet

Dry

Average

–1,000

Sea

level

1,000

A A’ FEET

Vertical exaggeration ×10

EXPLANATION

Simulated - 1945 

Simulated - 2010 

Scenario 1 - 2060 

Scenario 2 - 2060 

Scenario 3 - 2060 

Scenario 4 - 2060

Scenario 5 - 2060

Scenario 6 - 2060

Water table Water table

BORREGO VALLEY

Borrego

Upper aquifer Borrego

Sink

Middle aquifer

Lower aquifer

Basement

Basement

Northwest Southeast

Figure 54. Cross section showing the simulated groundwater level tables for 1945 and 2010 and for six management scenarios 

projected for 2060, Borrego Valley Hydrologic Model, Borrego Valley, California (Line of section is shown in fig. 7b).
124 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Scenario 1: Status Quo

Scenario 1 consists of simulating the same stresses 

as the last calibration year (2010) for the 50-year horizon 

with no other management strategy. Although the simulated 

future pumpage is decreased for some years owing to climate 

variations, the future pumpage is still greater than simulated 

recharge for all years; consequently, the model simulation 

resulted in continued drawdowns and a large loss in storage 

(figs. 53, 55). Simulated drawdowns for 2011 through 2060 

exceed 125 ft in the northern agriculturally dominated part 

of the valley, and groundwater levels decline to the middle 

aquifer in most of the basin as the upper aquifer drains.

Scenarios 2–4: Low, Medium, and High 

Municipal Growth Over 50 Years

Scenarios 2, 3, and 4, associated with low, medium, 

and high growth, respectively, involve reducing agricultural 

and recreational pumpages in various combinations, while 

linearly increasing municipal pumpage at different rates 

over 50 years. The low-growth scenario consists of an 

increase in municipal pumpage linearly to a maximum of 

1.76 times 2010 rates until 2060 (table 20). The mediumgrowth scenario linearly increases municipal pumpage to 

2.33 times the 2010 rates until 2060, and the high-growth 

scenario linearly increases municipal pumpage to 3.79 times 

the 2010 rates until 2060. Like the status quo Scenario 1, 

simulated future pumpage exceeds recharge rates in Scenarios 

2 and 3. Therefore, each of these two scenarios increases the 

loss in storage and drawdowns. Simulated maximum waterlevel declines occurred in the southwestern part of the basin, 

particularly near the BWD supply wells. Because agricultural 

and recreational pumping continue in Scenarios 2 and 3, 

drawdown and storage losses continue in the areas where this 

pumping occurs. With the eventual removal of agricultural 

pumpage, Scenario 4 reaches a pumping rate that is less than 

the rate of recharge to the groundwater system at the end of 

the 50-year scenario.

Scenario 5: Water-Usage Reduction to Avoid 

Future Groundwater Storage Depletion Over 

50 Years

In terms of water-resources management, ‘sustainable’ 

is a subjective term whose definition typically depends on 

various socio-economic, cultural, aesthetic factors in addition 

to physical-process factors, and thus, there are many ways to 

define sustainability. In Scenario 5, sustainability is defined 

as groundwater recharge, on average, equaling groundwater 

discharge (over the long run there is no change in groundwater 

storage). In order to reach sustainability as defined, combined 

groundwater losses from ET, underflow, and pumping cannot 

exceed inflows from recharge and underflow. To accomplish 

this balance, agricultural and recreational pumpages were 

both reduced to 32 percent of current rates, and municipal 

pumpage was reduced to 52 percent of current rates (2010). 

These changes were applied linearly over 50 years. Simulated 

maximum water-level declines occur in the northern and 

western parts of the basin, where pumping is centered. 

Because agricultural, recreational, and municipal pumping 

continue, drawdown and storage losses continue in the areas 

where this pumping occurs. As total outflows (pumpage, ET, 

and underflow) approach the rate of total inflows (recharge 

and underflow), the cumulative change in storage approaches 

a constant value (slope approaches zero; fig. 53). Simulated 

drawdowns from 2011 through 2060 are approximately 60 ft 

in the northern, agriculturally dominated, part of the valley. 

Because pumpage rates are lower than for Scenario 1, the 

levels do not decline into the middle aquifer in most of the 

basin. The results for this scenario indicate that there would 

be small storage gains and losses that fluctuate with climatic 

variability. However, in the long run, the groundwater levels 

are relatively stable, although still much lower than predevelopment conditions.

Scenario 6: Management Scenario for Rapid 

Changes Over 20 Years

In order to simulate what is thought to be realistic, but 

more rapid, changes during the next 20 years (2011–30), 

municipal and recreational pumpages both were reduced to 

50 percent of current rates (2010), and agricultural pumpage 

was reduced to 40 percent of current rates by 2030 (table 20; 

fig. 53). These changes were applied linearly over 20 years. 

The 2030 pumpage rates were then held constant for the next 

30 years to 2060. Simulated drawdowns from 2011 through 

2060 were greater than 25 ft throughout much of the northern 

part of the basin. Because agricultural, recreational, and 

municipal pumping continue at rates greater than recharge, 

drawdown and storage losses continue in the areas where this 

pumping occurs (fig. 56). Because pumpage is lower than in 

Scenario 1, the levels do not decline to the middle aquifer 

in most of the basin. Although in the long run, groundwater 

levels would continue to decline, they would decline at slower 

rates than those simulated in Scenario 1, and storage gains and 

losses would fluctuate with climatic variability.
Future Groundwater-Management Scenarios 125

Figure 55. Simulated drawdown projected for scenario 1, or the status quo scenario, 2060 minus 2010, Borrego Valley Hydrologic 

model, Borrego Valley, California.

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 55 drawdown_scen1

EXPLANATION

Simulated drawdown, in feet, for scenario 1 (2060 - 2010)—

 Negative number indicates rise

26 to 50

–13 to 0

1 to 25

101 to 125

51 to 75

76 to 100

126 to 150

151 to 175

Model-grid boundary

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake
126 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Clark Lake

Borrego

Sink

Benson Lake

T

9

S

T

10

S

T

11

S

T

12

S

T

13

S

R5E R6E R7E R8E R9E

Base from U.S. Geological Survey 

National Elevation Dataset, 2006

Albers Equal Area Conic Projection

S-22

S-22

S-2

78

116°20’ 116°10’

33°

20’

33°

10’

0

0 5 10

5 10 MILES

KILOMETERS

sac13-0509_Figure 56 Sim drawdown_Scen6

EXPLANATION

Simulated drawdown, in feet, for scenario 6 (2060 - 2010)—

 Negative number indicates rise

–13 to 0

1 to 25

26 to 50

51 to 75

Model-grid boundary

Stream channel

Boundary of Borrego Valley

 groundwater basin Boundary of Anza-Borrego

 Desert State Park

Fault

78 Road

Dry lake

Figure 56. Simulated drawdown projected for Scenario 6, 2060 minus 2010, Borrego Valley Hydrologic Model, Borrego Valley, 

California.
Summary and Conclusions 127

Summary and Conclusions

The Borrego Valley is a small (110 squaremiles) valley 

in the northeastern part of San Diego County, California, 

about 60 miles northeast of San Diego. During historical 

times, groundwater provided the water supply for the valley. 

Since development began in the valley, groundwater has been 

used for agricultural, recreational, and municipal purposes. 

Groundwater-levels have declined more than 100 feet in some 

parts of the groundwater basin in response to anthropogenic 

activities, resulting in an increase in pumping lifts; reduced 

well efficiency; dry wells, changes in water quality; and loss 

of natural groundwater discharge, principally through reduced 

evapotranspiration from groundwater. Future growth and a 

lack of access to imported water could continue to increase 

these effects. In order to understand the groundwater-flow 

system better and to provide a tool to aid in groundwater 

management, a study was done by the U.S. Geological Survey, 

in cooperation with the Borrego Water District, to develop a 

numerical model of groundwater flow in the Borrego Valley 

groundwater basin that uses old and new hydrogeologic 

information.

Evaluation of the hydrogeologic information indicates 

that the groundwater-flow system consists of three aquifers: 

the upper, middle, and lower. The three aquifers, which were 

identified on the basis of the hydrologic properties, age, 

and depth of the unconsolidated deposits, consist of gravel, 

sand, silt, and clay alluvial deposits and clay and silty clay 

lacustrine deposits. Prior to groundwater development in 

the valley, recharge occurred primarily in response to the 

infiltration of runoff and underflow from the surrounding 

mountains. Groundwater flowed from the recharge areas to 

discharge areas around the Borrego Sink, where it discharged 

from the aquifer system as evapotranspiration. Partial 

barriers to horizontal groundwater flow, such as faults, 

have been identified on the eastern edge of the groundwater 

basin. Groundwater-level declines owing to groundwater 

development have eliminated the natural sources of discharge, 

and pumping for agricultural, recreational, and municipal 

uses has become the primary source of discharge from 

the groundwater system. Infiltration of return flows from 

agricultural irrigation has become an additional source of 

recharge to the aquifer system.

Historical and current water-quality and hydrologic 

data were compiled and collected for wells throughout the 

study area. Water samples from wells were used to determine 

the chemical and isotopic composition of groundwater with 

depth throughout the basin. Total dissolved solids (TDS) 

concentrations ranged from less than 500 milligrams per liter 

(mg/L) to 2,330 mg/L. California’s water-quality maximum 

upper contaminant level (CA-SMCL) for TDS is 1,000 mg/L. 

The TDS concentrations were generally highest in the shallow 

aquifer and in the northern part of the Borrego Valley. Nitrate 

as nitrogen concentrations (NO3-N) ranged from less than 

1 mg/L to 67 mg/L. Like TDS, concentrations of NO3-N were 

highest in the shallow aquifer and in the northern part of the 

valley. Samples collected from wells both tapping the shallow 

and middle aquifers had concentrations that exceeded the 

CA-MCL of 10 mg/L. Samples collected from three wells 

in the agricultural area of the northern valley indicate that 

shallow groundwater has higher concentrations of TDS and 

NO3-N than groundwater from the middle and lower aquifers. 

The concentration of NO3-N in the sample from the shallowest 

well was 40 times greater than in the samples collected from 

the two deeper wells; TDS concentrations in samples from 

shallow wells were at least twice as much as concentrations in 

samples from the deeper wells. The source of relatively high 

concentrations of NO3-N to wells could be the application 

of nitrate fertilizers, downward movement of salts that have 

accumulated in the unsaturated zone, and (or) the effluent 

from septic-tank systems. The TDS in wells could be due to 

irrigation using saline water, downward movement of salts that 

have accumulated in the unsaturated zone, and (or) dissolution 

of evaporative minerals.

Declining groundwater levels can induce a change in 

water quality because there is the potential to change the 

distribution of flow from the underlying aquifers to wells. 

TDS and sulfate were the only two constituents that showed 

an increase in concentration with a simultaneous decrease in 

groundwater level. Well 10S/6E-18R1 had a groundwater-level 

decline of 75 feet (ft) and TDS concentrations that increased 

from 572 to 910 mg/L. Sulfate (CA-SMCL is 500 mg/L) 

concentrations in this well increased from 248 to 400 mg/L. 

Groundwater levels in well 11S/6E-7K3 declined 50 ft, and 

TDS concentrations increased from 342 to 510 mg/L. In 

contrast, groundwater levels increased in well 11S/6E-23J1 by 

36 ft, TDS concentrations increased from 328 to 460 mg/L, 

and sulfate concentrations increased from 47 to 110 mg/L.

Tritium values and uncorrected carbon-14 ages (14C) 

from groundwater samples indicate that in most areas of the 

Borrego Valley groundwater was recharged thousands of years 

before present (ybp). Two of the nine wells sampled for tritium 

(9S/6E-31E3 near Coyote Creek; 10S/5E-25R1 near Borrego 

Palm Creek) had measurable tritium values, indicating that a 

portion of water being tapped by these wells was recharged 

since 1952. Uncorrected 14C ages ranged from less than 70 ybp 

in the two wells near the recharge areas of Coyote and Borrego 

Palm Creeks to almost 26,000 ybp in wells at the southeastern 

end of the valley. Groundwater ages increased in samples from 

wells at increasingly greater distances from the recharge areas. 

Groundwater age also appeared to be older in deeper wells, 

likely because of the longer flow paths and lower hydraulic 

conductivities of deeper sediments.

The long-term extraction of groundwater causes an 

increase in the effective stress of the aquifer-system materials; 

this increased stress can result in irreversible compaction 

of the aquifer system. This compaction results in land 

subsidence in many desert areas where long-term pumping 

has exceeded recharge. Three methods were employed as 

part of this study to assess the land subsidence in Borrego 

Valley─Global Positioning System (GPS) surveys, continuous 

GPS (CGPS) data, and interferometric synthetic aperture 
128 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

radar (InSAR) remote sensing techniques. InSAR results, 

derived from synthetic aperture radar data, provide spatially 

detailed magnitudes and distributions of vertical deformation 

of the ground surface for a specific periods. The InSAR data 

complement the CGPS data, which measure deformation over 

longer periods at greater measurement frequency at a series 

of points. The GPS surveys, CGPS data, and InSAR analyses 

show little land subsidence has occurred in the Borrego Valley. 

Specifically, the GPS surveys of the elevation changes are 

within the expected uncertainty of 0.54 ft; therefore, there 

has not been significant land-surface elevation change during 

the 41-year period from 1969 to 2009. InSAR analyses done 

during 2003–07 confirm these results for a broad spatial area 

in the valley and show that annual subsidence rates are about 

0.15 inches per year, with a maximum of less than 0.6 inches 

over the 5-year period.

An updated version of the Borrego Valley groundwaterflow model, the Borrego Valley Hydrologic Model (BVHM), 

has been developed on the basis of previously published 

conceptual and numerical models. Physical processes have 

been added to the model simulation to obtain an improved 

representation of the landscape and aquifer system. Additional 

processes consist of (1) explicit simulation of the supply and 

demand components of the landscape, including agricultural, 

recreational, and municipal water use; (2) simulations of 

stream leakage and return-flow delays associated with 

movement through the unsaturated zone; (3) deterministic 

simulations of inflows from tributary drainage areas and basins 

adjacent to Borrego Valley; and (4) groundwater pumping 

distributions across multiple model layers.

The BVHM is an integrated hydrologic model that can 

simulate the use and movement of water throughout the 

basin. The BVHM was developed with the finite-difference 

groundwater modeling software One Water Hydrologic Model 

(OWHM). OWHM is a numerical code that incorporates a 

dynamically integrated water supply-and-demand accounting 

in agricultural areas and areas of native vegetation and has 

made possible more detailed and realistic simulations of 

hydrologic systems. OWHM is based on MODFLOW-2005 

and incorporates an updated version of FMP as well as 

updated MODFLOW packages. These packages are all 

documented by Hanson and others (2014a).

The BVHM was calibrated by using a parameterestimation software suite known as PEST. On the basis 

of preliminary calibration results and analysis of geologic 

and geophysical data, modifications were made to the 

conceptualized geologic framework. The contact between 

the upper and middle aquifers was raised in the northern part 

of the basin to match recent geophysical data. In addition, 

the depth to basement rock was increased, particularly near 

the Coyote Creek fault, on the basis of new drillers’ logs and 

gravity measurements.

The model was calibrated by using the Tikhonov 

regularization functionality in PEST. This allows for formal 

inclusion of prior information in the parameter-estimation 

process. All model parameters were regularized such that 

they tend toward target or preferred parameter values; the 

regularization targets were based on conceptual geologic 

knowledge and previous simulation results.

The BVHM was developed on the basis of historical 

conditions for analysis of the use and movement of water 

throughout the valley and to provide a basis for groundwater 

availability and sustainability analyses. The BVHM finitedifference grid consists of 30 rows, 75 columns (2,250 cells), 

and 3 layers. Spatially, about 36 percent of the cells (803 cells) 

are an active part of the hydrologic model. The model has a 

uniform horizontal discretization of 92 acres per cell (2,000 ft 

by 2,000 ft) and is oriented subparallel to the tectonic structure 

and Coyote Creek, 22 degrees west of true north. Vertically, 

the model has three layers representing the upper, middle, 

and lower aquifers. The Coyote Creek fault on the eastern 

edge of the valley is thought to act as a horizontal-flow 

barrier and forms the eastern boundary of the model. The 

surrounding mountains form most of the remainder of the 

model boundaries. A small amount of flux out of the southern 

extent of the basin is simulated. The model was calibrated to 

simulate (pre-development) conditions, represented by 1945 

groundwater levels and transient-state conditions during 1945–

2010, by using groundwater-level data. Initial estimates of 

the aquifer-system properties and stresses were obtained from 

previously published numerical models of the Borrego Valley 

groundwater basin; estimates also were obtained from recently 

collected hydrologic, land subsidence, and hydrochemical 

data and from results of a Basin Characterization Model 

(BCM) of recharge and surface-water flow from the upstream 

portions of the watershed. Some of these initial estimates were 

modified during model calibration. Groundwater pumpage 

for agriculture was estimated on the basis of irrigated 

crop acreage and crop consumptive-use data. Pumpage 

for municipal supply, which is metered, was compiled 

and entered into a database used for this study. Estimated 

combined annual agricultural, recreational, and municipal 

pumpage peaked at around 19,600 acre-feet (acre-ft) during 

2005–10. Recharge from irrigation return flows was estimated 

to be about 20–30 percent of agricultural and recreational 

pumpages; delays associated with return flow moving through 

the unsaturated zone were also simulated. The annual quantity 

of natural recharge initially was based on estimates from 

previous studies and on the BCM, but was allowed to vary 

during calibration. During model calibration, pre-development 

natural recharge was simulated at an increased rate from 

the previous estimates, generally at or slightly less than 

5,000 acre-feet per year (acre-ft/yr) to 7,100 acre-ft/yr. This 

estimate, however, takes into consideration a proportional 

increase in estimated groundwater evapotranspiration.

The main source of recharge to the system is underflow 

from the upstream portions of the watershed and runoff 

from creeks and streams draining the upstream portions of 

the watershed that, with the exception of runoff generated 

in response to exceptionally large and infrequent storms, 

quickly seeps into the permeable streambeds and infiltrates 

through the unsaturated zone. Over the 66-year study period, 
Summary and Conclusions 129

on average, the natural recharge that reaches to the saturated 

groundwater system is approximately 5,700 acre-ft/yr, but 

natural recharge fluctuates in the arid climate from less than 

1,000 to more than 25,000 acre-ft/yr. Of this 5,700 acre-ft/yr, 

about 1,700 acre-ft/yr seeped into the ground during wetter 

years to undergo rapid evapotranspiration. Another 

approximately 1,400 acre-ft/yr on average comes as underflow 

from upstream portions of the watershed. Because agricultural, 

recreational, and municipal land uses have developed, 

recharge also occurred from excess irrigation and septic-tank 

effluent. Recharge from irrigation return flows was estimated 

to be about 20–30 percent of agricultural and recreational 

pumpages. Although a small amount of recharge from septic 

tanks occurs, it is negligible relative to natural recharge and 

return flow from agricultural and recreational pumpages. 

The BVHM uses a one-dimensional unsaturated-zone model 

to estimate the delay associated with return flow moving 

through the unsaturated zone. Depending on the thickness, 

permeability, and residual moisture content in the relatively 

thick unsaturated zone, it would take tens to hundreds of 

years for return flow to pass through the unsaturated zone. 

In addition, not all water that passes through the root zone 

reaches the water table because some water contributes to 

storage in the unsaturated zone as the depth to the water 

table increases. Therefore, water that is applied to previously 

unirrigated land might not reach the underlying water table for 

decades. 

Groundwater discharge occurs from three primary 

sources—(1) evapotranspiration in areas where the water table 

is shallow and direct uptake from plants (mostly in and around 

the Borrego Sink) can occur; (2) a small amount of seepage 

from the southern end of the basin; and (3) groundwater 

pumpage for agricultural, recreational, and municipal uses. 

Natural discharge from evapotranspiration ranged from 

approximately 7,100 acre-ft/yr prior to development to 

virtually zero during the mid-1990s to 2010, because the 

groundwater levels in the basin dropped below the reach of 

the mesquite in the basin. Seepage out the southern end of 

the basin is small and relatively stable over time, at about 

500 acre-ft/yr. Groundwater pumpage for agriculture and 

recreation was estimated on the basis of irrigated acreage and 

consumptive-use data. Pumpage for municipal supply was 

compiled from water-use records. Simulated combined annual 

agricultural, recreational, and municipal pumpage peaked at 

around 19,600 acre-ft during 2005–10.

Results of the calibrated model simulations indicate 

that simulated groundwater pumpage exceeded recharge in 

most years, resulting in an estimated cumulative depletion in 

groundwater storage of about 440,000 acre-ft. Groundwater 

pumping resulted in simulated groundwater levels declining 

by more than 150 ft relative to 1945 conditions in pumping 

areas. The decline in groundwater levels is the result of this 

depletion of groundwater storage. In turn, the simulated 

decline in groundwater levels has resulted in the decrease 

in natural discharge from the basin. Because the aquifer 

system consists of few fine-grained sediments, few areas 

are susceptible to compaction, and little land subsidence or 

compaction of fine-grained deposits has occurred.

The calibrated BVHM was used to simulate the response 

of the aquifer to six future 50-year (2011 to 2060) pumping 

scenarios: (1) no change in the agricultural, recreational, and 

municipal pumpage rates, or status quo; (2–4) various levels 

of reductions in agricultural and recreational pumpage rates 

coupled with small to large increases in municipal pumpage 

rates; (5) reduction in all groundwater usage to avoid future 

groundwater-storage depletion over 50 years; and (6) a less 

severe, but more rapid, reduction in all groundwater usage 

over 20 years, followed by 30 years at a constant, much lower 

usage rate. 

Results from Scenario 1 indicate that the total drawdown 

observed since pre-development would continue, with values 

exceeding 125 ft in the northern agriculturally dominated part 

of the valley and groundwater-level declines into the middle 

aquifer in most of the basin. Because of the lower hydraulic 

conductivity and storage properties of the middle aquifer 

relative to the upper aquifer, continued pumping at these 

rates would result in more rapid water-level declines in the 

future and possibly a reduction in water quality. Scenarios 2–4 

evaluated various combinations of increases and reductions 

in agricultural, recreational, and municipal pumpages. The 

pumpage rate in 2 of 3 of these scenarios, although less than 

in Scenario 1 (status quo), still exceeds the average annual 

recharge rate. As a result, groundwater levels still decline, 

and there is a continued cumulative loss in storage. Basically, 

groundwater levels would continue to drop if more water 

is being extracted from the groundwater basin than is being 

recharged on a long-term basis. As more groundwater levels 

drop from the relatively storage rich and permeable upper 

aquifer to the middle and lower aquifers, the rate at which 

groundwater levels would drop and storage depletion would 

occur would accelerate. Furthermore, if the water quality is 

less desirable deeper in the system, as existing information 

indicates, then the water quality of pumped water would 

deteriorate as well.

In Scenario 5, water usage is reduced in all three 

categories (agricultural, recreational, and municipal) to reach 

a sustainable level over a 50-year span. The sustainable level 

equates to total discharge equaling the long-term average 

recharge to the basin. In order to avoid future groundwaterstorage depletion, agricultural and recreational pumpages 

were reduced to 32 percent of current rates, and municipal 

pumpage was reduced to 52 percent of current rates. These 

changes were applied linearly over 50 years. Simulated 

maximum groundwater-level declines occurred in the northern 

and western parts of the basin where pumping is centered. 

Because agricultural and recreational pumping continues, 

drawdown and storage losses continue in the areas where this 

pumping occurs. As the rate of discharge reaches the rate of 

recharge, there is no net change in storage, and the cumulative 

loss in storage does not change significantly. Although in the 

long run, groundwater levels would not rise significantly, 

and the change in storage would be negligible, in Scenario 5, 

groundwater levels and storage changes would fluctuate, as 

they have historically, with climatic variability. For example, 
130 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

during relatively wet years, more water could go into storage 

than is extracted; however, during moderate and relatively dry 

years, more water could be extracted than goes into storage.

In Scenario 6, in order to simulate what is thought to 

be realistic, but more rapid, changes in the next 20 years 

(2011–30), municipal and recreational pumpages were reduced 

to 50 percent of current rates (2010), and agricultural pumpage 

was reduced to 40 percent of the current rate. These changes 

were applied linearly over 20 years and continued for the 

next 30 years, until 2060. Simulated drawdowns from 2011 

through 2060 were greater than 25 ft throughout much of the 

northern part of the basin. Because agricultural, recreational, 

and municipal pumping continue at rates greater than the 

rate of recharge, drawdown and storage losses continue in 

the areas where this pumping occurs. Although in the long 

run, groundwater levels would continue to decline, they 

would not decline as fast as they would during the status quo 

(Scenario 1) simulation, and storage gains and losses would 

fluctuate with climatic variability.

In order to maintain the usefulness of a numerical 

hydrologic flow model, such as the BVHM, periodic updates 

are required. As changing conditions of the actual hydrologic 

system continue to respond to the stresses, and as new 

information on the surface-water and groundwater systems 

becomes available, the BVHM could be updated to improve 

the model and its utility as a water-management tool for the 

Borrego Valley.

References Cited

Allen, R.G., Pereira, L.S., Raes, D., and Smith, M., 1998, 

Crop evapotranspiration—Guidelines for computing crop 

water requirements: Food and Agriculture Organization of 

the United Nations, Irrigation and Drainage Paper 56, 300 

p., accessed April 16, 2009, at http://www.fao.org/docrep/

X0490E/X0490E00.htm. Errata sheet, accessed April 16, 

2009, at http://www.kimberly.uidaho.edu/water/fao56/

index.html.

Amelung, Falk, Galloway, D.L., Bell, J.W., Zebker, H.A., and 

Laczniak, R.J., 1999, Sensing the ups and downs of Las 

Vegas: InSAR reveals structural control of land subsidence 

and aquifer-system deformation: Geology, v. 27, no. 6, p. 

483–486.

Belitz, Kenneth, and Phillips, S.P., 1995, Alternative to 

agricultural drains in California’s San Joaquin Valley: 

Results of a regional-scale hydrogeologic approach: Water 

Resources Research, v. 31, no. 8, p. 1845–1862.

Belitz, Kenneth, Phillips, S.P., and Gronberg, J.M., 1993, 

Numerical simulation of groundwater flow in the central 

part of the Western San Joaquin Valley, California: U.S. 

Geological Survey Water-Supply Paper 2396, 69 p.

Borrego Water District, 2000, Groundwater management study 

technical committee workbook: Borrego Water District, 

Borrego Springs, Calif., 100 p.

Brooks, R.H., and Corey, A.T., 1964, Hydraulic properties 

of porous media: Hydrology Papers, Colorado State 

University, 24 p.

Brown, J.S., 1923, The Salton Sea region, California, A 

geographic, geologic, and hydrologic reconnaissance, with 

a guide to desert watering places: U.S. Geological Survey 

Water-Supply Paper 497, 292 p.

Burnham, W.L., 1954, Data on water wells in Borrego, 

Ocotillo, San Felipe, and Vallecito Valley areas, Eastern San 

Diego County, California: U.S. Geological Survey OpenFile Report, 60 p.

Burow, K.R., Shelton, J.L., Hevesi, J.A., and Weissmann, 

G.S., 2004, Hydrogeologic characterization of the Modesto 

area, San Joaquin Valley, California: U.S. Geologic Survey 

Scientific Investigations Report 2004–5232, 54 p.

California Department of Public Health, 2013, Chemicals 

and contaminants in drinking water, accessed November 4, 

2014, at http://www.waterboards.ca.gov/drinking_water/

certlic/drinkingwater/Chemicalcontaminants.shtml.

California Department of Water Resources, 1998, San Diego 

County Land use survey, accessed January 31, 2014, at 

http://www.water.ca.gov/landwateruse/lusrvymain.cfm.

California Department of Water Resources, 2003, California’s 

groundwater, update 2003: California Department of Water 

Resources Bulletin 118, 246 p.

California Department of Water Resources, 1981, A 

preliminary evaluation of recharging the Borrego Valley 

ground water basin with local runoff: California Department 

of Water Resources Technical Information Record 1335–

4301–A, by H. Iwanaga.

California Department of Water Resources, 1983a, A 

preliminary evaluation of cost of imported water supplies 

to the Borrego Valley: California Department of Water 

Resources Technical Information Record 1335–12–C–3, by 

K.K. Hatai.

California Department of Water Resources, 1983b, A 

preliminary evaluation of historical and projected water 

demand for Borrego Valley: California Department of Water 

Resources Technical Information Record 1335–12–C–1, by 

K.K. Hatai.

California Department of Water Resources, 1983c, Preliminary 

evaluation of annual recharge to the Borrego Valley ground 

water basin: California Department of Water Resources 

Technical Information Record 1335–11–B–1, by K.K. 

Hatai.
References Cited 131

California Department of Water Resources, 1984a, Borrego 

Valley water management plan: California Department of 

Water Resources 45 p.

California Department of Water Resources, 1984b, Evaluation 

of future operations of the Borrego Valley ground water 

basin: California Department of Water Resources Technical 

Information Record 1335–11–B–2, by P. Louie and H. 

Iwanaga.

California Department of Water Resources, 2015, California 

Sustainable Groundwater Management Act, accessed July 3, 

2015, http://www.water.ca.gov/cagroundwater/moresgma.

cfm.

California Irrigation Management System, 2012, California 

ETo zones map: California Department of Water Resources, 

accessed January 15, 2014, http://wwwcimis.water.ca.gov/

App_Themes/images/etozonemap.jpg.

Clark, I.D., and Fritz, P., 1997, Environmental isotopes in 

hydrogeology: New York, Lewis Publishers, 328 p.

Craig, H., and Lal, D., 1961, The production rate of natural 

tritium: Tellus, v. 13, no. 1, p. 85–105.

Daly, C., Gibson, W.P., Doggett, M., Smith, J., and Taylor, 

G., 2004, Up-to-date monthly climate maps for the 

conterminous United States, in Proceedings of the 14th 

American Meteorological Society Conference on applied 

climatology, January 13–16, 2004: Seattle, Wash., American 

Meteorological Society, Paper P5.1, CD-ROM.

Dibblee, T.W., Jr., 1954, Geology of the Imperial Valley 

region, in Jahns, R.H., ed., Geology of southern California: 

California Division of Mines Bulletin 170, chap. 2, p. 

21–28.

Dibblee, T.W., Jr., 1984, Stratigraphy and tectonics of the 

San Felipe Hills, Borrego Badlands, Superstition Hills, 

and vicinity, in Rigsby, C.A., ed., The Imperial Basin—

Tectonics, Sediments, and Thermal Aspects: Pacific Section 

SEPM, p. 31–34.

Dimitrakopoulos, Roussos, and Desbarats, A.J., 1993, 

Geostatistical modeling of grid block permeabilities for 3D 

reservoir simulators: Reservoir Engineering, v. 8, p. 13–18. 

Doherty, J.E., 2003, Groundwater model calibration using 

pilot points and regularization: Ground Water, v. 41, no. 2, 

p. 170–177, doi:10.1111/j.17456584.2003.tb02580.x.

Doherty, J., 2010a, PEST, Model-independent parameter 

estimation—User manual (5th ed., with slight additions): 

Brisbane, Australia, Watermark Numerical Computing. 

Doherty, J., 2010b, Addendum to the PEST manual: Brisbane, 

Australia, Watermark Numerical Computing.

Doherty, J., 2010c, BeoPEST for Windows: Brisbane, 

Australia, Watermark Numerical Computing.

Doherty, J.E., and Hunt, R.J., 2010, Approaches to highly 

parameterized inversion—A guide to using PEST for 

groundwater-model calibration: U.S. Geological Survey 

Scientific Investigations Report 2010–5169, 59 p.

Dorsey, R.J., 2002, Stratigraphic record of Pleistocene 

initiation and slip on the Coyote Creek fault, Lower Coyote 

Creek, southern California, in Barth A., ed., Contributions 

to crustal evolution of the Southwestern United States: 

Boulder, Colorado, Geological Society of America Special 

Paper 365, p. 251–269.

Faunt, C.C., Hanson R.T., Schmid, Wolfgang, Belitz, K., 

and Predmore, S., 2009a, Documentation of the Groundwater Flow Model: U.S. Geological Survey Professional 

Paper 1766, Chapter C of Ground-Water Availability of 

California’s Central Valley, Claudia Faunt, ed., p. 121–212

Faunt, C.C., Belitz, K., and Hanson, R.T., 2009b, Texture 

Model of the valley fill deposits of the Central Valley, 

California: Hydrogeology Journal, DOI 10.1007/s10040–

009–0539–7, 25 p.

Fielding, E.J., Blom, R.G., and Goldstein, R.M., 1998, Rapid 

subsidence over oil fields measured by SAR interferometry: 

Geophysical Research Letters, v. 27, p. 3215–3218.

Fio, J.L., and Leighton, D.A., 1995, Hydrogeologic 

framework, historical development of the ground-water 

system, and general hydrologic and water-quality conditions 

in 1990, South San Francisco Bay and Peninsula area, 

California: U.S. Geological Survey Open-File Report 

94–357, 46 p.

Flint, A.L, and Flint, L.E., 2007a, Application of the Basin 

Characterization Model to estimate in-place recharge 

and runoff potential in the Basin and Range carbonaterock aquifer system, White Pine County, Nevada, and 

adjacent areas in Nevada and Utah: U.S. Geological Survey 

Scientific Investigations Report 2007–5099, 19 p., http://

pubs.water.usgs.gov/sir20075099.

Flint, L.E., and Flint, A.L., 2007b, Regional analysis of 

groundwater recharge, in Stonestrom, D.A., Constantz, Jim, 

Ferre, T.P.A., and Leake, S.A., eds., Groundwater recharge 

in the arid and semiarid southwestern United States: U.S. 

Geological Survey Professional Paper 1703–B, p. 29–60, 

http://pubs.usgs.gov/pp/pp1703/b/.

Flint, L.E., and Martin, Peter, eds., with contributions by 

Brandt, Justin, Christensen, A.H., Flint, A.L., Flint, L.E., 

Hevesi, J.A., Jachens, Robert, Kulongoski, J.T., Martin, 

Peter, and Sneed, Michelle, 2012, Geohydrology of Big 

Bear Valley, California: Phase 1—Geologic framework, 

recharge, and preliminary assessment of the source and 

age of groundwater: U.S. Geological Survey Scientific 

Investigations Report 2012–5100, 112 p.
132 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Flint, A.L., Flint, L.E., Hevesi, J.A., and Blainey, J.M., 

2004, Fundamental concepts of recharge in the Desert 

Southwest, a regional modeling perspective, in Hogan, 

J.F., Phillips, F.M., and Scanlon, B.R., eds., Groundwater 

recharge in a desert environment, the Southwestern United 

States: American Geophysical Union, Water Science and 

Applications Series, v. 9, p. 159–184.

Flint, A.L., Flint, L.E., and Masbruch, M.D., 2011, Input, 

calibration, uncertainty, and limitations of the Basin 

Characterization Model, Appendix 3 of Heilweil, V.M., and 

Brooks, L.E., eds., Conceptual Model of the great Basin 

Carbonate and Alluvial Aquifer System: U.S. Geological 

Survey Scientific Investigations Report 2010–5193, p. 

149–163.

Flint, L.E., Flint, A.L., Stolp, B.J., and Danskin, W.R., 2012, 

A basin-scale approach for assessing water resources in a 

semiarid environment: San Diego region, California and 

Mexico: Hydrology and Earth System Sciences, v. 16, p. 

1–17.

Flint, L.E., Flint, A.L., Thorne, J.H., and Boynton, R., 2013, 

Fine-scale hydrological modeling for climate change 

applications; using watershed calibrations to assess 

model performance for landscape projections: Ecological 

Processes, v. 2, no. 25, 21 p.

Folk, R.L., 1954, The distinction between grain size and 

mineral composition in sedimentary rock nomenclature: 

Journal of Geology, v. 62, no. 4, p. 344–359.

Fontes, J.C., and Garnier, J.M., 1979, Determination of the 

initial 14C activity of the total dissolved carbon: A review of 

the existing models and a new approach: Water Resources 

Research, v. 15, no. 2, p. 399–413.

Freeze, A.R., and Cherry, J.A., 1979, Groundwater: 

Englewood Cliffs, N.J., Prentice-Hall, Inc., 604 p.

Galloway, D.L., Hudnut, K.W., Ingebritsen, S.E., Phillips, 

S.P., Peltzer,G., Rogez, F., and Rosen, P.A., 1998, Detection 

of aquifer system compaction and land subsidence using 

interferometric synthetic aperture radar, Antelope Valley, 

Mojave Desert, California: Water Resources Research, v. 

34, no. 10, p. 2573–2585.

Galloway, D.L., Jones, D.R., and Ingebritsen, S.E., eds., 1999, 

Land subsidence in the United States: U.S. Geological 

Survey Circular 1182, 177 p., http://pubs.usgs.gov/circ/

circ1182/, accessed Sept. 21, 2015.

Halford, K.J., 2009, AnalyzeHOLE—An integrated wellbore 

flow analysis tool. U.S. Geological Survey Techniques and 

Methods 4-F2, 46 p.

Hanson, R.T., and Leake, S.A., 1998, Documentation for 

HYDMOD, A program for time-series data from the U.S. 

Geological Survey’s modular three-dimensional finitedifference ground-water flow model: U.S. Geological 

Survey Water-Resources Investigations Report 98–564, 

57 p.

Hanson, R.T., Boyce, S.E., Schmid, Wolfgang, Hughes, 

J.D., Mehl, S.M., Leake, S.A., Maddock, Thomas, III, and 

Niswonger, R.G., 2014a, One-Water Hydrologic Flow 

Model (MODFLOW-OWHM): U.S. Geological Survey 

Techniques and Methods 6–A51, 120 p., http://dx.doi.

org/10.3133/tm6A51.

Hanson, R.T., Flint, L.E., Faunt, C.C., Gibbs, D.R., and 

Schmid, W., 2014b, Hydrologic models and analysis of 

water availability in Cuyama Valley, California: U.S. 

Geological Survey Scientific Investigations Report 2014–

5150, 150 p., http://dx.doi.org/10.3133/sir20145150.

Harbaugh, A.W., 2005, MODFLOW-2005, The U.S. 

Geological Survey modular groundwater model—The 

groundwater flow process: U.S. Geological Survey 

Techniques and Methods, book 6, chap. A16, [variously 

paged].

Harbaugh, A.W., Banta, E.R., Hill, M.C., and McDonald, 

M.G., 2000, MODFLOW-2000, The U.S. Geological 

Survey modular groundwater model—User guide to 

modularization concepts and the groundwater flow process: 

U.S. Geological Survey Open-File Report 00–92, 121 p.

Henderson, T.W., 2001, Hydrogeology and numerical 

modeling of the Borrego Valley aquifer system: Master’s 

Thesis, San Diego State University, 260 p.

Hill, M.C., and Tiedeman, C.R., 2007, Effective groundwater 

model calibration: with analysis of data, sensitivities, 

predictions, and uncertainty: New York, Wiley and Sons, 

464 p.

Hill, M.C., Banta, E.R, Harbaugh, A.W., and Anderman, 

E.R., 2000, MODFLOW-2000, The U.S Geological 

Survey modular ground-water model—User guide to the 

observation, sensitivity, and parameter-estimation processes 

and three post-processing programs: U.S. Geological 

Survey Open-File Report 00–184, 209 p.

Hoffmann, J., Zebker, H.A., Galloway, D.L., and Amelung, 

F., 2001, Seasonal subsidence and rebound in Las Vegas 

Valley, Nevada, observed by synthetic aperture radar 

interferometry: Water Resources Research, v. 37, no. 6, p. 

1551–1566.

Hogan, C.M., 2009, California Fan Palm: Washingtonia 

filifera, in Stromberg, Nicklas, ed., GlobalTwitcher.com. 
References Cited 133

Hsieh, P.A., and Freckleton, J.R, 1993, Documentation of a 

computer program to simulate horizontal-flow barriers using 

the U.S. Geological Survey’s modular three-dimensional 

finite-difference ground-water from model: U.S. Geological 

Survey Open-File report 92–477, 32 p.

Hunt, C.B., 1967, Physiography of the United States: San 

Francisco and London, W.H. Freeman and Co., 480 p.

Hunt, R.J., Doherty, J., and Tonkin, M.J., 2007, Are models 

too simple? Arguments for increased parameterization: 

Issue Paper in Ground Water, v. 45, no. 3, p. 254–261. 

doi:10.1111/j.1745-6584.2007.00316.x.

Ikehara, M.E., Predmore, S.K., Swope, D.J., 1997, Geodetic 

network to evaluate historical elevation changes and 

to monitor land subsidence in lower Coachella Valley, 

California, 1996: U.S. Geological Survey Water-Resources 

Investigations Report, 97–4237, scale 1:63,000, 1 sheet.

Isaaks, E. H., and Srivastava, R. M., 1989, An introduction to 

applied geostatistics: New York, Oxford University Press, 

561 p.

Izbicki, J.A., 2004, A small-diameter sample pump for 

collection of depth-dependent samples from production 

wells under pumping conditions: U.S. Geological Survey 

Fact Sheet 2004–3096, 2 p. 

Izbicki, J.A., Christensen, A.H., and Hanson, R.T., 1999, U.S. 

Geological Survey combined well-bore flow and depthdependent water sampler: U.S. Geological Survey Fact 

Sheet, FS–196–99.

Izbicki, J. A., Radyk, John, and Michel, R. L., 2002, 

Movement of water through a thick unsaturated zone 

underlying Oro Grande and Sheep Creek Washes in the 

western Mojave Desert, USA: Hydrogeology Journal, v. 10, 

no. 3, p. 409–427.

Journel, A.G., and Huijbregts, C.J., 1978, Mining geostatistics: 

New York, Academic Press, 600 p.

Konikow, L.F., Hornberger, G.Z., Halford, K.J., and Hanson, 

R.T., 2009, Revised multi-node well (MNW2) package for 

MODFLOW ground-water flow model: U.S. Geological 

Survey Techniques and Methods 6–A30, 67 p.

Koterba, M.T., Wilde, F.D., and Lapham, W.W., 1995, 

Groundwater data-collection protocols and procedures 

for the National Water-Quality Assessment Program—

Collection and documentation of water-quality samples 

and related data: U.S. Geological Survey Open-File Report 

95–399, 113 p.

Lane, E. W., 1947, Report of the subcommittee on sediment 

terminology: American Geophysical Union Transactions, v. 

28, no. 6, p. 936–938.

Laudon, J., and Belitz, Kenneth, 1991, Texture and 

depositional history of late Pleistocene-Holocene alluvium 

in the central part of the western San Joaquin Valley, 

California: Bulletin of the Association of Engineering 

Geologists, v. 28, no. 1, p. 73–88.

Leake, S.A., and Lilly, M.R., 1997, Documentation of a 

computer program (FHB1) for assignment of transient 

specified-flow and specified-head boundaries in applications 

of the modular finite-difference ground-water flow model 

(MODFLOW): U.S. Geological Survey Open-File Report 

97–571, 50 p.

Leighton, D.A., Fio, J.L., and Metzger, L.F., 1994, Database of 

well and areal data, South San Francisco Bay and Peninsula 

area, California: U.S. Geological Survey Water-Resources 

Investigations Report 94–4151, 47 p.

Loetlz, O.J., Irelan, Burge, Robison, J. H., and Olmsted, F. 

H., 1975, Hydrogeologic Reconnaissance of the Imperial 

Valley, California: U.S. Geological Survey Professional 

Paper 486–K, 54 p.

Lucas, L.L., and Unterweger, M.P., 2000, Comprehensive 

review and critical evaluation of the half-life of tritium: 

Journal of Research of the National Institute of Standards 

and Technology, v. 105, no. 4, p. 541–549.

Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, 

G., Feigl, K., and Rabaute, T., 1993, The displacement field 

of the Landers earthquake mapped by radar interferometry: 

Nature, v. 364, p. 138–142.

Massonnet, D., Briole, P., and Arnaud, A., 1995, Deflation of 

Mount Etna monitored by spaceborne radar interferometry: 

Nature, v. 375, p. 567–570.

Massonnet, D., Holzer, T., and Vadon, H., 1997, Land 

subsidence caused by the East Mesa geothermal 

field, California, observed using SAR interferometry: 

Geophysical Research Letters, v. 24, p. 901–904.

McDonald, M.G., and Harbaugh, A.W., 1988, A modular 

three-dimensional finite-difference groundwater flow 

model: U.S. Geological Survey Techniques of WaterResources Investigations, book 6, chap. A1, 596 p.

Mendenhall. W.C., 1909, Some desert watering places in the 

southeastern California and southwestern Nevada: U.S. 

Geological Survey Water-Supply Paper 224, 98 p.

Michel, R.L., 1989, Tritium deposition in the continental 

United States: U.S. Geological Survey Water-Resources 

Investigations Report 89–4072, 46 p.

Mills, W.R., 2009, Borrego Water District integrated water 

Resources management plan final report: 128 p., accessed 

August 31, 20152 at http://www.borregowd.org/uploads/

IWRMP_Final_3.2009.pdf.
134 Hydrogeology, Hydrologic Effects of Development, and Simulation of Groundwater Flow in the Borrego Valley

Mitten, H.T., Lines, G.C., Berenbrock, Charles., and Durbin, 

T.J., 1988, Water resources of Borrego Valley and vicinity, 

California, San Diego County, California; Phase 2, 

Development of a groundwater flow model: U.S. Geological 

Survey Water-Resources Investigation Report 87–4199, 

27 p.

Mook, W.G., 1980, The dissolution-exchange model for dating 

of groundwater with 14C., in Fritz, P., and Fontes, J.C., 

eds., Handbook of environmental isotopes geochemistry: 

Amsterdam, Elsevier, v. 1, p. 50–74.

Moyle, W.R., Jr., 1968, Water wells and springs in the 

Borrego, Carrizo, and San Felipe Valley areas, San Diego 

and Imperial Counties, California: California Department of 

Water Resources, Bulletin 91–15, 16 p. 

Moyle, W. R., 1982, Water resources of Borrego Valley and 

vicinity, California; Phase 1, Definition of geologic and 

hydrologic characteristics of basin: U.S. Geological Survey 

Open-File Report 82–855, 39 p.

Nalder, I. A., Wein, R.W., 1998, Spatial interpolation of 

climatic normals: test of a new method in the Canadian 

boreal forest: Agricultural and Forest Meteorology, v. 92, 

p. 211–225.

Natural Resources Conservation Service, 2006, U.S. General 

Soil Map (STATSGO2), U.S. Department of Agriculture, 

accessed January 14, 2015, at https://catalog.data.gov/

dataset/u-s-general-soil-map-statsgo2-for-the-united-statesof-america.

Netto, S.P., 2001, Water Resources of Borrego Valley San 

Diego County, California: Master’s Thesis, San Diego State 

University, 143 p. 

Newhouse, M.W., Izbicki, J.A., and Smith, G.A., 2005, 

Comparison of velocity-log data collected using impeller 

and electromagnetic flowmeters: Groundwater, v. 43, no. 3, 

p. 434–438.

Niswonger, R.G., and Prudic, D.E., 2005, Documentation 

of the Streamflow-Routing (SFR2) Package to include 

unsaturated flow beneath streams—A modification to SFR1: 

U.S. Geological Survey Techniques and Methods 6–A13, 

50 p.

Niswonger, R.G., Prudic, D.E., and Regan, R.S., 2006, 

Documentation of the Unsaturated-Zone Flow (UZF1) 

Package for modeling unsaturated flow between the land 

surface and the water table with MODFLOW-2005: U.S. 

Geological Survey Techniques and Methods 6–A19, 62 p.

Niswonger, R.G., Panday, Sorab, and Ibaraki, Motomu, 

2011, MODFLOW-NWT, A Newton formulation for 

MODFLOW-2005: U.S. Geological Survey Techniques and 

Methods 6–A37, 44 p. 

Page, R.W., 1986, Geology of the fresh groundwater basin 

of the Central Valley, California, with texture maps and 

sections: U.S. Geological Survey Professional Paper 

1401–C, 54 p.

Phillips, S.P., and Belitz, Kenneth, 1991, Calibration of a 

texture-based model of a ground water flow system, western 

San Joaquin Valley, California: Ground Water, v. 29, no. 5, 

p. 702–715.

Phillips, S.P, Green, C.T., Burow, K.R., Shelton, J.L., and 

Rewis, D.L., 2007, Simulation of multiscale groundwater flow in part of the northeastern San Joaquin Valley, 

California: U.S. Geological Survey Scientific Investigations 

Report 2007–5009, 43 p.

Priestley C.H.B., and Taylor, R.J., 1972, On the assessment 

of surface heat flux and evaporation using large-scale 

parameters: Monthly Weather Review, v. 100, no. 2, 

p. 81–92.

Prudic, D.E., Konikow, L.F., and Banta, E.R.,2004, A new 

streamflow-routing (SFR1) Package to simulate streamaquifer interaction with MODFLOW-2000: U.S. Geological 

Survey Open-File Report 2004–1042, 95 p.

Rewis, D.L., Christensen, A.H., Matti, J., Hevesi, J.A., 

Nishikawa, T., and Martin, P., 2006, Geology, groundwater 

hydrology, geochemistry, and groundwater simulation of 

the Beaumont and Banning storage units, San Gorgonio 

Pass Area, Riverside County, California: U. S. Geological 

Survey Scientific Investigations Report 2006–5026, 191 p.

Riley, F.S., and Worts, G.F., Jr., 2001, Geologic reconnaissance 

and test-well drilling program, Marine Corps Training 

Center, Twentynine Palms, California: U.S. Geological 

Survey Open-File Report 98–166, 64 p.

Scanlon, B.R., Jolly, Ian, Sophocleous, Marios, and Zhang, 

Lu, 2007, Global impacts of conversions from natural to 

agricultural ecosystems on water resources: Quantity versus 

quality: Water Resources Research, v. 43, W03437.

Schmid, Wolfgang, and Hanson R.T., 2009, The Farm Process 

Version 2 (FMP2) for MODFLOW-2005—Modifications 

and Upgrades to FMP1: U.S. Geological Survey Techniques 

in Water Resources Investigations, book 6, chap. A32, 

102 p. 

Schmid, Wolfgang, Hanson, R.T., Maddock, III, T.M., and 

Leake, S.A., 2006a, User’s guide for the Farm process 

(FMP) for the U.S. Geological Survey’s modular threedimensional finite-difference ground-water flow model, 

MODFLOW-2000: U.S. Geological Survey Techniques and 

Methods 6–A17, 127 p.
References Cited 135

Schmid, Wolfgang, Hanson, R.T., and Maddock III, Thomas, 

2006b, Overview and advances of the Farm Process for 

MODFLOW-2000: Managing groundwater systems: 

Conference Proceedings, International Groundwater 

Modeling Center, Golden, Colo., May 21–24, 2006, 

p. 23–27.

Sharp, R.V., 1982, Tectonic setting of the Imperial Valley 

region, in The Imperial Valley, California, Earthquake of 

October 15, 1979: U. S. Geological Survey Professional 

Paper 1254, p. 5–14.

Sneed, Michelle, Ikehara, M.E., Galloway, D.L., and 

Amelung, Falk, 2001, Detection and measurement of 

land subsidence using global positioning system and 

interferometric synthetic aperture radar, Coachella Valley, 

California, 1996–98: U.S. Geological Survey WaterResources Investigations Report 01–4193, 26 p.

Snyder, R.L., Lamina, B.J., Shaw, D.A., and Pruitt, W.O., 

1987a, Using reference evapotranspiration (ETo) and crop 

coefficients to estimate crop evapotranspiration (Etch) for 

agronomic crops, grasses, and vegetable crops: Berkeley, 

Calif.: Cooperative Extension of the University of 

California Division of Agriculture and Natural Resources, 

Leaflet 21427, 12 p.

Snyder, R.L., Lamina, B.J., Shaw, D.A., and Pruitt, W.O., 

1987b, Using reference evapotranspiration (ETo) and crop 

coefficients to estimate crop evapotranspiration (Etch) for 

trees and vines: Berkeley, Calif., Cooperative Extension of 

the University of California, Division of Agriculture and 

Natural Resources, Leaflet 21428, 8 p. 

Sweetkind, D.S., Faunt, C.C., and Hanson, R.T., 2013, 

Construction of 3-D geologic framework and textural 

models for Cuyama Valley groundwater basin, California: 

U.S. Geological Survey Scientific Investigations Report 

2013–5127, 46 p.

Suarez, D.L., 1989, Impact of agricultural practices on 

groundwater salinity: Agricultural Ecosystems and 

Environment, 26, p. 215–227.

Taylor, C.B., and Roether, W., 1982, A uniform scale for 

reporting low-level tritium measurements in water: 

International Journal of Applied Radioactive Isotopes, v. 33, 

p. 377–382.

Thomasson, H.G. Jr., Olmsted, F.H., and LeRoux, E.F., 

1960, Geology, water resources, and usable groundwater 

storage capacity of part of Solano County, California: U.S. 

Geological Survey Water-Supply Paper 1464, 693 p.

Tikhonov, A.N., and Arsenin, V. Y., 1977, Solution of Ill-Posed 

Problems: New York, Wiley, 258 p.

U.S. Bureau of Reclamation, 1968, Inland basins projects, 

Borrego Valley, California: U.S. Bureau of Reclamation, 

interim report, 62 p.

U.S. Bureau of Reclamation, 1972, Inland basins projects, 

California and Nevada: U.S. Bureau of Reclamation 

Reconnaissance Investigations, chap. 7, p. 15–22.

U.S. Geological Survey, [variously dated], National field 

manual for the collection of water-quality data: U.S. 

Geological Survey Techniques of Water-Resources 

Investigations, book 9, chaps. A1–A9, available online at 

http://pubs.water.usgs.gov/twri9A.

U.S. Geological Survey, 2007, MODIS data products: U.S. 

Geological Survey data available on the web, accessed May 

16, 2007, at http://modis.gsfc.nasa.gov/data/dataprod/.

U.S. Geological Survey, 2013, Vertical flowmeter logging: 

U.S. Geological Survey Ground Water Information: Branch 

of Geophysics, accessed October 29, 2013, at http://water.

usgs.gov/ogw/bgas/flowmeter/.

Van Zandt, A. J., 2004, Surface deformation in the Western 

Salton Trough as observed by InSAR: San Diego State 

University, accessed January 16, 2015, at http://scec.usc.

edu/internships/sure/sites/scec.usc.edu.internships.sure/files/

projects/2004VanZandt.pdf.

Waring, G.A., 1915, Springs of California: U.S. Geological 

Survey Water-Supply Paper 338, 410 p.

Westenbroek, S.M., Doherty, J.E., Walker, J.F., Kelson, V.A., 

Hunt, R.J., and Cera, T.B., 2012, Approaches in highly 

parameterized inversion: TSPROC, A General TimeSeries Processor to Assist in Model Calibration and Result 

Summarization: U.S. Geological Survey Techniques and 

Methods Report, book 7, Section C, chap. 7, 73 p.

Wilde, F.D., Radtke, D.B., Gibs, J., and Iwatsubo, R.T., 1999, 

Collection of water samples: U.S. Geological Survey 

Techniques of Water-Resources Investigations, book 9, 

chap. A4, accessed June 19, 2012, at http://pubs.water.usgs.

gov/twri9A4/.

Yeh, W.-G., 1986, Review of parameter identification 

procedures in groundwater hydrology: the inverse problem: 

Water Resources Research, v. 22, no. 2, p. 95–108.

Zebker, H.A., Rosen, P.A., and Hensley, S., 1997, Atmospheric 

effects in interferometric synthetic aperture radar surface 

deformation and topographic maps: Journal of Geophysical 

Research-Solid Earth, v. 102, no. B4, p. 7547–7563.
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Faunt and others—Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the Borrego Valley, San Diego, California—Scientific Investigations Report 2015–5150

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155150