GROUNDWATER RESOURCES PROGRAM

Professional Paper 1766

U.S. Department of the Interior

U.S. Geological Survey

Groundwater Availability of the

Central Valley Aquifer, California

Sacramento Valley

San Joaquin Valley
 Generalized block diagrams showing post-development hydrologeology of the Sacramento and San Joaquin Valleys, California.
Groundwater Availability of the 

Central Valley Aquifer, California

Edited by Claudia C. Faunt

Chapter A

Introduction, Overview of Hydrogeology, and Textural Model of 

California’s Central Valley

By Claudia C. Faunt, Randall T. Hanson, and Kenneth Belitz

Chapter B

Groundwater Availability in California’s Central Valley

By Claudia C. Faunt, Kenneth Belitz, and Randall T. Hanson

Chapter C

Numerical Model of the Hydrologic Landscape and Groundwater 

Flow in California’s Central Valley

By Claudia C. Faunt, Randall T. Hanson, Kenneth Belitz, Wolfgang Schmid, 

Steven P. Predmore, Diane L. Rewis, and Kelly McPherson

Appendix 1 

Supplemental Information—Modifications to 

Modflow-2000 Packages and Processes

By Wolfgang Schmid and R.T. Hanson

Groundwater Resources Program

U.S. Department of the Interior

U.S. Geological Survey

Professional Paper 1766
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Faunt, C.C., ed., 2009, Groundwater Availability of the Central Valley Aquifer, California: U.S. Geological Survey 

Professional Paper 1766, 225 p. 

ISBN 978-1-4113-2515-9
iii

Foreword

An adequate supply of groundwater is essential for the Nation’s health and economic well 

being. Increased use of groundwater resources and the effects of drought have led to concerns 

about the future availability of groundwater to meet domestic, agricultural, industrial, and environmental needs. The resulting effects of competition for groundwater from human and environmental uses need to be better understood to respond to the following basic questions that are 

being asked about the Nation’s ability to meet current and future demands for groundwater. Do 

we have enough groundwater to meet the needs of the Nation? Where are these groundwater 

resources? Is groundwater available where it is needed? To help answer these questions, the 

U.S. Geological Survey’s (USGS) Groundwater Resources Program is conducting large-scale 

multidisciplinary regional studies of groundwater availability, such as this study of the Central 

Valley Aquifer System, California.

Regional groundwater availability studies quantify current groundwater resources, evaluate how 

those resources have changed through time, and provide tools that decision makers can use 

to forecast system responses to future development and climate variability and change. These 

quantitative studies are, by design, large in scope, can include multiple aquifers, and address 

critical groundwater issues. The USGS has previously identified the Nation’s principal aquifers 

and they will be used as a framework to classify and study regional groundwater systems.

The groundwater availability studies being conducted for each regional groundwater flow system emphasize the use of long-term groundwater monitoring data, in conjunction with groundwater models, to improve understanding of the flow systems and assess the status and trends 

in groundwater resources in the context of a changing water budget for the aquifer system. The 

results of these individual groundwater availability studies will be used collectively as building 

blocks towards a national assessment of groundwater availability. In addition, these studies will 

provide the foundational information and modeling tools needed to help State and local resource 

managers make water availability decisions based on the latest comprehensive quantitative 

assessment given their regional water-management constraints and goals.

Matthew C. Larsen, Associate Director for Water

U.S. Geological Survey
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Contents

Chapter A

Introduction, Overview of Hydrogeology, and Textural Model of California’s Central Valley

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

Geographic Information System (GIS) ..............................................................................................1

Texture Modeling ..................................................................................................................................2

Hydrologic System Modeling..............................................................................................................2

Introduction.....................................................................................................................................................3

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

Methods of Analyses............................................................................................................................6

Data Compilation..........................................................................................................................6

Numerical Model .........................................................................................................................6

Previous Investigations........................................................................................................................7

Recent Regional Groundwater Models....................................................................................7

Central Valley Regional Aquifer-System Analysis (CV-RASA) ....................................7

California Central Valley Groundwater-Surface-Water Simulation Model 

(C2VSIM) ..............................................................................................................10

Study Area.....................................................................................................................................................10

Climate .................................................................................................................................................10

Sacramento Valley .............................................................................................................................10

Delta and Eastside Streams..............................................................................................................15

San Joaquin Basin of the San Joaquin Valley ...............................................................................15

Tulare Basin of the San Joaquin Valley ..........................................................................................17

Water-Balance Subregions...............................................................................................................18

Geologic History and Setting .....................................................................................................................18

Hydrogeology................................................................................................................................................20

Aquifer Characteristics......................................................................................................................20

Textural Analysis.................................................................................................................................23

Selection and Compilation of Existing Well Data .................................................................25

Classification of Texture from Drillers’ Logs and Regularization of Well Data................26

Geostatistical Modeling Approach.........................................................................................26

Regions and Domains ......................................................................................................26

Geostatistical Model of Coarse-Grained Texture........................................................27

3-D Model of Percentage of Coarse-Grained Texture................................................29

Results of Texture Model..........................................................................................................30

Sacramento Valley............................................................................................................30

San Joaquin Valley ...........................................................................................................39

Hydrologic System..............................................................................................................................40

Climate ........................................................................................................................................41

Surface Water............................................................................................................................46
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Groundwater ..............................................................................................................................47

Predevelopment Recharge, Discharge, Water Levels, and Flows ...........................48

Groundwater/Surface-Water Interaction.....................................................................48

Aquifer-System Storage ..................................................................................................48

Water Budget ....................................................................................................................53

Acknowledgments.......................................................................................................................................53

References Cited..........................................................................................................................................54

Chapter B 

Groundwater Availability in California’s Central Valley

Introduction ..................................................................................................................................................59

Development and the Hydrologic System ...............................................................................................59

Surface-Water and Groundwater Development History .............................................................59

Land Use...............................................................................................................................................60

Agricultural Land Use ...............................................................................................................61

Urban Land Use..........................................................................................................................61

Development and Changes to the Hydrologic Budget ..........................................................................62

Hydrologic Budget Components ......................................................................................................62

Recharge and Discharge..........................................................................................................66

Aquifer-System Storage ...........................................................................................................67

Intra-Annual Variations in Typical, Dry, and Wet Years.......................................................70

Temporal Variation in the Hydrologic Budget ...............................................................................72

Spatial Variation in the Hydrologic Budget ....................................................................................79

Water Levels and Groundwater Flow .............................................................................................79

Land Subsidence.................................................................................................................................98

Surface Water and the Environment.............................................................................................101

Global Climate Change and Variability ...................................................................................................102

Past Climates.....................................................................................................................................102

Future Climate Projections..............................................................................................................102

Groundwater Sustainability and Management ....................................................................................103

Groundwater Sustainability ............................................................................................................103

Groundwater Management ............................................................................................................104

Conjunctive Use .......................................................................................................................106

Water Banking..........................................................................................................................107

Other Management Strategies..............................................................................................108

Monitoring the Hydrologic System .........................................................................................................109

Groundwater......................................................................................................................................110

Surface Water...................................................................................................................................111

Subsidence ........................................................................................................................................112

Water Quality.....................................................................................................................................112

Land Use and Climate.......................................................................................................................114

Summary......................................................................................................................................................114

References Cited........................................................................................................................................116
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Chapter C

Numerical Model of the Hydrologic Landscape and Groundwater Flow in 

California’s Central Valley

Introduction ................................................................................................................................................121

Model Development ..................................................................................................................................121

Discretization ....................................................................................................................................123

Spatial Discretization and Layering......................................................................................123

Temporal Discretization .........................................................................................................126

Boundary Conditions .......................................................................................................................126

Specified-Flow Boundaries....................................................................................................128

Pumpage ...................................................................................................................................128

Agricultural Pumpage ....................................................................................................128

Urban Pumpage ..............................................................................................................130

Recharge from and Discharge to the Delta.........................................................................131

Recharge from and Discharge to Canals and Streams.....................................................132

Water-Table Simulation ...................................................................................................................132

Farm Process (FMP).........................................................................................................................132

Delivery Requirement..............................................................................................................134

Soils ..............................................................................................................................135

Land Use...........................................................................................................................135

Virtual Crop Maps ..................................................................................................139

Crop-Type Data.......................................................................................................143

Climate Data ....................................................................................................................151

Reference Evapotranspiration (ETo)...................................................................151

Precipitation ...........................................................................................................152

Surface-Water Supply ............................................................................................................152

Groundwater Supply ...............................................................................................................152

Net Recharge ...........................................................................................................................154

Hydraulic Properties.........................................................................................................................154

Hydraulic Conductivity............................................................................................................156

Storage Properties ..................................................................................................................158

Hydrogeologic Units ...............................................................................................................160

Hydrogeologic Structures .....................................................................................................160

Initial Conditions................................................................................................................................160

Model Calibration and Sensitivity ...........................................................................................................161

Observations Used in Model Calibration ......................................................................................161

Water-Level Altitudes, Water-Level Altitude Changes, and 

Water-Level Altitude Maps.......................................................................................163

Water-Table and Potentiometric-Surface Maps .......................................................167

Sacramento Valley.................................................................................................167

Delta .....................................................................................................................167

San Joaquin Valley ...............................................................................................170

Tulare Basin............................................................................................................170

Streamflow Observations.......................................................................................................170

Boundary Flow Observations.................................................................................................173
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Subsidence Observations ......................................................................................................173

Pumpage Observations...........................................................................................................179

Water-Use Observations ........................................................................................................179

Water-Delivery Observations.................................................................................................181

Model Parameters ...........................................................................................................................181

Sensitivity Analysis ..........................................................................................................................191

Simulation Results and Budget ...............................................................................................................191

Recharge and Discharge.................................................................................................................192

Aquifer-System Storage ..................................................................................................................197

Model Uncertainty and Limitations ........................................................................................................203

Future Work ................................................................................................................................................205

References Cited........................................................................................................................................207

Appendix 1

Supplemental Information—Modifications to Modflow-2000 Packages and Processes

Introduction.................................................................................................................................................213

Layer-Property Flow Package (LPF) .......................................................................................................213

Multiplier Package (MULT).......................................................................................................................213

Time-Series Package (HYDMOD)............................................................................................................213

Streamflow Routing Package (SFR1)......................................................................................................214

The Farm Process (FMP1) ........................................................................................................................214

Concepts and Input Instructions for New FMP1 Features ........................................................214

Data for each Simulation........................................................................................................215

Data for each Stress Period...................................................................................................216

Root Uptake Under Variably Saturated Conditions (PSI specified in Item 14)........................217

Current Concept of Root Uptake from Unsaturated Conditions.......................................217

Expanded Concept of Root Uptake from Variably Saturated Conditions........................217

Input Instructions.....................................................................................................................221

Matrix of On-Farm Efficiencies (OFE specified in Items 7 or 24)...............................................221

Input Requirements .................................................................................................................221

Data Output...............................................................................................................................221

Non-Irrigation Crops (NONIRR Specified in Items 15 or 27)......................................................222

Consumptive Use Options (ICUFL Specified in Item 2; ETR Specified as New Item) ............222

Semi-Routed Delivery (ISRDFL in Item 2; REACH in Items 20 or 34).........................................222

Semi-Routed Surface-Water Runoff-Return Flow (ISRRFL in Item 2 and ROW COLUMN 

SEGMENT REACH Specified as New Item) ....................................................................223

Farm-Related Data List for Semi-Routed Runoff-Return Flow Locations 

(New Item Added After Items 20 or 34): ........................................................223

Additional Auxiliary Variable (AUX NOCIRNOQ Specified in Item 2) .......................................224

Farm Budget Output Options (IFBPFL Specified in Item 2) ........................................................224

References Cited........................................................................................................................................225
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Figures

Chapter A

Figure A1. Map of Central Valley major geomorphic provinces, alluvial fans of the 

 San Joaquin Basin, and extent and thickness of Corcoran Clay ....................................5

Figure A2. Diagram showing the relation and flow of information used in analyzing the 

 Central Valley Hydrogeologic system .................................................................................7

Figure A3. Diagram showing the diversity of data types and categories included in the 

 centralized geospatial database ..........................................................................................8

Figure A4. Diagram showing an example of the detail for compilation, integration, and 

 analysis for one data type (water-level nformation) .........................................................9

Figure A5. A, Map of surface-water inflows and average annual precipitation for 

 September 1961 through September 2003 throughout the Central Valley, 

 California. B, Map showing average annual reference evapotranspiration (ETo) 

 for September 1961 through September 2003 throughout the Central Valley, 

 California ................................................................................................................................12

Figure A6. Graph of average monthly precipitation for Redding, Davis, and 

 Bakersfield, California ..........................................................................................................14

Figure A7. Map of general features of the surface-water system in the Central 

 Valley, California....................................................................................................................16

Figure A8. Map of distribution of water-balance subregions (WBSs) used for 

 surface-water delivery and estimation of groundwater pumpage...............................19

Figure A9. Generalized cross-sections showing pre- and post-development of the 

A, Sacramento Valley. B, Central part of the San Joaquin Valley, 

 California.................................................................................................................................21

Figure A10. A, Map of Central Valley showing groundwater basins and subbasins, 

 groupings of basins and subbasins into spatial provinces and domains for 

 textural analysis. B, Map showing distribution of wells used for mapping texture. 

C, Graph showing count of wells for each depth increment by domains 

 through 1,200 feet.................................................................................................................24

Figure A11. Generalized hydrogeologic section (A–A’) indicating the vertical discretization 

 of the numerical model of the groundwater-flow system in the Central Valley, 

 California ................................................................................................................................29

Figure A12. Maps showing kriged distribution of coarse-grained deposits for layers 1, 3,

 Corcoran Clay, 6, and 9 of the groundwater-flow model. A, Layer 1. B, Layer 3. 

C, Corcoran Clay. D, Layer 6. E, Layer 9.............................................................................31

Figure A13. Block diagram of kriged texture within groundwater-flow model ..............................36

Figure A14. Map showing distribution of coarse-grained deposits for the upper 50 feet 

 for part of the Central Valley...............................................................................................37

Figure A15. Graph of cumulative distributions of kriged sediment textures for model 

 layers in the A, Sacramento Valley. B, San Joaquin Valley and Tulare Basin ...........38

Figure A16. A, Bar chart of total inflow from 44 gaged streams flowing into the 

 Central Valley, California, water years 1962–2003. B, Graph of average annual 

 precipitation in the Central Valley, California, water years 1962–2003. 

C, Pie chart of total surface-water flow into the Central Valley, California, 

 water years 1962–2003 .......................................................................................................42
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Figure A17. Graph of cumulative departure from average annual precipitation at 

 Redding, Davis, Fresno, and Bakersfield, California ......................................................44

Figure A18. Graph of cumulative departure of monthly precipitation (Parameter-

 elevation Regressions on Independent Slopes Model (PRISM) data from Davis, 

 California), cumulative departure of the Pacific Decadal Oscillation (PDO)

 index, and cumulative departure of the monthly reference evapotranspiration 

 (ETo) values (gridded values of California Irrigation Management Information 

 System’s (CIMIS) stations from Redding, Davis, and Bakersfield, 1960–2004) .........45

Figure A19. Graph of cumulative departure of streamflow diversions from the 

 Bear River by South Sutter Water District, California; cumulative annual 

 temperature from California Irrigation Management Information System’s 

 (CIMIS) stations at Davis and the Pacific Decadal Oscillation (PDO) index, 

 1960–2004 ..............................................................................................................................47

Figure A20. Map of pre-development groundwater map....................................................................49

Figure A21. Maps of distribution of A, Pre-1900 land-use patterns. B, Land-use 

 patterns in 2000 for the Central Valley, California ...........................................................50

Figure A22. Map of distribution of selected streams and canals, and average 

 estimated gains and losses for selected segments .......................................................52

Figure A23. Diagrams of pre-development water budget and post-development 

 water budget.........................................................................................................................53

Chapter B

Figure B1. Diagram showing average water budget for water years 1962–2003.........................63

Figure B2. Pie charts and histograms showing simulated landscape budget for the 

 Central Valley for typical (1975), dry (1990), and wet (1998) years ...............................64

Figure B3. Pie charts and histograms showing simulated groundwater budget for the 

 Central Valley for typical (1975), dry (1990), and wet (1998) years ...............................65

Figure B4. Maps of A, Estimated change in hydraulic head in upper part of the 

 aquifer system from 1860 to 1961. B, Simulated change in hydraulic head in 

 lower part of the aquifer system from spring 1962 to spring 2003 ...............................68

Figure B5. Graphs showing monthly groundwater budget for the Central Valley for a 

 dry year (1990), typical year (1975), and wet year (1998)...............................................71

Figure B6. Graphs of annual A, Delivery requirement, landscape recharge, 

 surface-water deliveries, and agricultural pumpage. B, Groundwater 

 withdrawals for agricultural and urban use for the entire Central Valley 

 between 1962 and 2003 .......................................................................................................73

Figure B7. Stacked bar chart showing simulated groundwater budget changes between 

 water years 1962 and 2003 for the Central Valley, California........................................75

Figure B8. Stacked bar chart showing simulated annual changes in aquifer-system 

 storage between water years 1962 and 2003 for the Central Valley, California .......76

Figure B9. Graph showing simulated cumulative annual changes in aquifer-system storage 

 between water years 1962 and 2003 for the Central Valley, California ......................77

Figure B10. Pie charts and histograms of average annual groundwater budget for the 

A, Sacramento Valley. B, Delta and Eastside Streams. C, San Joaquin Valley. 

D, Tulare Basin .....................................................................................................................80

Figure B11. Stacked bar chart of simulated flow through multi-zone wells ...................................84
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Figure B12. Map of altitude of the A, Water table in the unconfined part of the aquifer

 system. B, Potentiometric surface of the confined part of the aquifer system for 

1961. C, Unconfined part of the aquifer system. D, Potentiometric surface of 

 the confined part of the aquifer system for 1976. E, Unconfined part of 

 the aquifer system. F, Potentiometric surface of the confined part of the 

 aquifer system, for 2000 .....................................................................................................86

Figure B13. Map showing hydrographs for representative wells in the Central Valley, 

 California................................................................................................................................92

Figure B14. Map of estimated depth to water table in spring 2000...................................................96

Figure B15. A, Map of areal extent of land subsidence in the Central Valley and 

 locations of extensometers. B, Graph of compaction data from selected 

 extensometers and total simulated pumpage in the Central Valley ............................99

Figure B16. Graph of measured compaction in relation to head decline in the 

 San Joaquin Valley.............................................................................................................101

Figure B17. Graph showing streamflow gains and losses in the Central Valley 

 between 1962 and 2003 .....................................................................................................105

Figure B18. Diagram of relationship of water-level changes and critical heads to 

 subsidence and inelastic compaction ...........................................................................109

Chapter C

Figure C1. Map of Central Valley Hydrologic Model grid: A, Extent of San Joaquin 

 Formation, Corcoran Member of the Tulare Formation, crystalline bedrock, 

 and horizontal flow barriers. B, Upper-most active layer............................................124

Figure C2. Map of distribution of general-head boundary cells and major streams 

 and canals with streamflow-routing cells (including location of inflows and 

 diversions)...........................................................................................................................127

Figure C3. Map of distribution of urban and agricultural wells simulated in the 

 Central Valley Hydrologic Model ....................................................................................129

Figure C4. Graph of urban pumpage from U.S. Geological Survey and California 

 Department of Water Resources data............................................................................131

Figure C5. Flow chart of water inflows to and outflows from a “farm” as simulated 

 by the Farm Process (FMP) .............................................................................................133

Figure C6. Map of agricultural soils for the Central Valley, California, derived from 

 STATSGO data ....................................................................................................................136

Figure C7. Graph showing cumulative departure of precipitation, Pacific Decadal 

 Oscillation (PDO) Index, climate windows, and time frame land-use maps 

 were applied .......................................................................................................................137

Figure C8. Map showing virtual crops for 1960 (modified with 2000 data), including 

 pie chart of percentage of different virtual crops ........................................................140

Figure C9. Map showing virtual crops for 1973, including pie chart of percentage of 

different virtual crops ........................................................................................................141

Figure C10. Map showing virtual crops for 1992, including pie chart of percentage of 

 different virtual crops.......................................................................................................142

Figure C11. Map showing virtual crops for 1998, including pie chart of percentage of 

 different virtual crops.......................................................................................................144
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Figure C12. Map showing virtual crops for 2000, including a pie chart of percentage of 

 different virtual crops.......................................................................................................145

Figure C13. Graphs showing monthly crop coefficients for virtual crops in the 

 Central Valley, California..................................................................................................147

Figure C14. Graph showing relation between hydraulic conductivity and percentage 

 coarse-grained deposits based on hydraulic conductivity end members and 

 exponent of the power mean ..........................................................................................155

Figure C15. Map of distribution of calibration data (groundwater levels, gains and 

losses of streamflow, and subsidence observations)................................................162

Figure C16. Map of distribution of wells with water-level-altitude data for the 

 simulation period 1961–2003, and location of wells selected for model 

 calibration ..........................................................................................................................164

Figure C17. Graph of plots of simulated water-level altitude values compared with the 

 measured water-level altitudes for the A, Entire modeled area (and with inset 

 with histogram of residuals). B, Sacramento Valley. C, Delta and Eastside. 

 D, San Joaquin Basin. E, Tulare Basin ..........................................................................165

Figure C18. Maps of the simulated A, Water-table altitude in spring 1976. 

 B, Potentiometric-surface altitude in spring 1976 for the calibrated transient 

 groundwater-flow model of the Central Valley ............................................................168

Figure C19. Map showing distribution of stream gain/loss segments used for model 

 calibration A, Measured gaining and losing reaches for selected stream 

 reaches for 1961–1977. B, Simulated gaining and losing reaches for 

 selected stream reaches for 1961–1977 for the Central Valley, California..............171

Figure C20. Map of distribution of historical subsidence, estimated from 1961 to 

 1977 extensometer data, Central Valley, California.....................................................174

Figure C21. A, Map of distribution of total simulated subsidence for water years 

 1962 through 2003, and locations of subsidence measurements. B, Graph 

 of aggregate compaction measured at, and simulated subsidence for, 

 selected extensometer locations, Central Valley, California.....................................175

Figure C22. Graph of agricultural pumpage from 1961–77 estimated from power 

 records compared to Central Valley Hydrologic Model simulated agricultural 

 pumpage for the Central Valley, California ...................................................................180

Figure C23. Graph showing A, Annual water deliveries. B, Monthly water deliveries 

 from the mid 1970s to mid 1980s for water-balance subregion 14............................182

Figure C24. Graph showing single and double cropped crop coefficient values for 

 truck crops .........................................................................................................................183

Figure C25. Graph showing original and adjusted crop coefficient values for truck and 

 cotton crops.......................................................................................................................184

Figure C26. Map showing distribution of cells used for streams, colored by streambed 

 hydraulic conductivity values for cells estimated during calibration......................190

Figure C27. Graph showing relative composite sensitivity of computed water-level 

 altitudes, flows, and subsidence information at calibration points to changes 

 in parameters.....................................................................................................................192
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Figure C28. Pie chart and histograms showing average annual components of farm 

 budget for water years 1962–2003 .................................................................................193

Figure C29. Stacked bar chart showing farm budget changes through time for the 

 Central Valley, California..................................................................................................194

Figure C30. Graphs showing farm budget changes through time for water-balance 

 subregion 13, western San Joaquin Valley, Central Valley, California. 

 A, Water years 1962–2003. B, Water years 1977–1985 ...............................................195

Figure C31. Stacked bar chart showing groundwater budget changes through time 

 for the Central Valley. A, All values. B, Net values......................................................197

Appendix

Figure 1-1. Diagram of evaluation of active and inactive parts of a variably 

 saturated root zone in FMP .............................................................................................218

Figure 1-2. Diagram of conceptualization to the change of transpiration uptake 

 from a saturated root zone with varying water level ..................................................220
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Tables

Chapter A

Table A1. Water-balance subregions within the Central Valley, California ...................................11

Table A2. Distribution of statistical properties for the percentage of coarse-grained 

 deposits for the Central Valley, Calfornia, by domain, including variogram and 

 variogram models....................................................................................................................28

Table A3. Central Valley, California, groundwater flow model layer thicknesses and depths ....29

Chapter B

Table B1. Summary of the simulated landscape budget for average (water years 

 1962-2003), typical (1975), dry (1990), and wet (1998) years for the Central Valley, 

 California ..................................................................................................................................66

Table B2. Summary of the simulated groundwater budget for average (water years 

 1962-2003), typical (1975), dry (1990), and wet (1998) years for the Central Valley, 

 California ..................................................................................................................................66

Table B3. Selected average annual hydrologic budget components for water years 

 1962–2003 for each of the 21 water balance areas in the Central Valley, 

 California ..................................................................................................................................78

Chapter C

Table C1. MODFLOW-2000 packages and processes used with the hydrologic flow 

 model of the Central Valley, California...............................................................................122

Table C2. Coordinates of the Central Valley Hydrologic Model grid..............................................126

Table C3. Land-use periods with acreage in square miles and percentage of 

 different virtual crop categories .........................................................................................138

Table C4. Summary of Central Valley, California, virtual crop categories and properties .........146

Table C5. Summary of fractions of transpiration and evaporation by month for 

 Central Valley, California, virtual crops .............................................................................150

Table C6. Average area-weighted composite efficiency for each water-balance 

 subregion of the Central Valley, California, through the simulation period .................151

Table C7. Average reference evapotranspiration (ETo) by month for the Central Valley, 

 California, for 1961–2003 based on temperature data using the Hargreaves–

 Samani equation....................................................................................................................153

Table C8. Measured and simulated hydraulic properties................................................................157

Table C9. Estimated and Central Valley Hydrologic Model-simulated average and unit 

 evapotranspiration of applied water (ETaw) for the Central Valley, California ..........180

Table C10. Parameter values estimated for the Central Valley Hydrologic Model........................185

Table C11. Simulated farm budget for the Central Valley, California, in acre-feet per year ........199

Table C12. Simulated groundwater budget for the Central Valley, California, 

 in acre-feet per year ............................................................................................................200
Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

mile, (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2) 

acre 0.004047 square kilometer (km2)

square foot (ft2) 929.0 square centimeter (cm2)

square foot (ft2) 0.09290 square meter (m2)

square inch (in2) 6.452 square centimeter (cm2)

section (640 acres or 1 square mile) 259.0 square hectometer (hm2) 

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

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

acre-foot (acre-ft) 0.001233 cubic hectometer (hm3) 

Flow rate

acre-foot per day (acre-ft/d) 0.01427 cubic meter per second (m3/s)

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

acre-foot per year (acre-ft/yr) 0.001233 cubic hectometer per year (hm3/yr)

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 follows:

°F=(1.8×°C)+32

Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 

(NGVD 29). 

Altitude, as used in this report, refers to distance above or below the NGVD 29.

NGVD 29 can be converted to the North American Vertical Datum of 1988 (NAVD 88) by using the 

National Geodetic Survey conversion utility available at URL http://www.ngs.noaa.gov/TOOLS/

Conversion Factors

Inch/Pound to SI

xv
xvi

Well-Numbering System

	 Wells are identified and numbered according to their location in the rectangular system for the subdivision of public lands. Identification consists of the township number, north or south; the range number, east or

west; and the section number. Each section is divided into sixteen 40-acre tracts lettered consecutively (except I

and O), beginning with "A" in the northeast corner of the section and progressing in a sinusoidal manner to "R"

in the southeast corner. Within the 40-acre tract, wells are sequentially numbered in the order they are inventoried. The final letter refers to the base line and meridian. In California, there are three base lines and meridians; Humboldt (H), Mount Diablo (M), and San Bernardino (S). All wells in the study area are referenced to

the San Bernardino base line and meridian (S) Well numbers consist of 15 characters and follow the format

011N001E24Q008S. In this report, well numbers are abbreviated and written 11N/1E-24Q8. Wells in the same

township and range are referred to only by their section designation, 24Q8. The following diagram shows how the

number for well 11N/1E-24Q8 is derived.

D C B A

E F G H

M L K J

N P Q R

6 5 4 3 2

7 8 9 10 11

18 17 16 15 14 13

19 20 21 22 23

30 29 28 26 25

31 32 33 34 35 36

27

R2E R3E

T11N

T12N

T13N

R1E

RANGE

TOWNSHIP

R2W R1W

T10N

T9N

R1E

Well-numbering diagram (Note: maps in this report use abbreviated well numbers such as "24Q8")

SECTION 24

T11N

11N/1E-24Q8

1

12

Mt. Diablo Meridian

24
Executive Summary

California’s Central Valley covers about 20,000 square

miles and is one of the most productive agricultural regions

in the world. More than 250 different crops are grown in the

Central Valley with an estimated value of $17 billion per year.

This irrigated agriculture relies heavily on surface-water diversions and groundwater pumpage. Approximately one-sixth of

the Nation’s irrigated land is in the Central Valley, and about

one-fifth of the Nation’s groundwater demand is supplied from

its aquifers. 

The Central Valley also is rapidly becoming an important

area for California’s expanding urban population. Since 1980,

the population of the Central Valley has nearly doubled from

2 million to 3.8 million people. The Census Bureau projects

that the Central Valley’s population will increase to 6 million

people by 2020. This surge in population has increased the

competition for water resources within the Central Valley and

statewide, which likely will be exacerbated by anticipated

reductions in deliveries of Colorado River water to southern

California. In response to this competition for water, a number

of water-related issues have gained prominence: conservation

of agricultural land, conjunctive use, artificial recharge, hydrologic implications of land-use change, and effects of climate

variability.

To provide information to stakeholders addressing these

issues, the USGS Groundwater Resources Program made a

detailed assessment of groundwater availability of the Central

Valley aquifer system, that includes: (1) the present status of

groundwater resources; (2) how these resources have changed

over time; and (3) tools to assess system responses to stresses

from future human uses and climate variability and change.

This effort builds on previous investigations, such as the

USGS Central Valley Regional Aquifer System and Analysis

(CV-RASA) project and several other groundwater studies in

the Valley completed by Federal, State and local agencies at

differing scales. The principal product of this new assessment

is a tool referred to as the Central Valley Hydrologic Model

(CVHM) that accounts for integrated, variable water supply

and demand, and simulates surface-water and groundwaterflow across the entire Central Valley system.

The development of the CVHM comprised four major

elements: (1) a comprehensive Geographic Information

System (GIS) to compile, analyze and visualize data; (2) a

texture model to characterize the aquifer system;(3) estimates

of water-budget components by numerically modeling the

hydrologic system with the Farm Process (FMP); and (4)

simulations to assess and quantify hydrologic conditions.

Geographic Information System (GIS)

The GIS for the CVHM is used to store, analyze, link,

and visualize both the spatial and temporal model input

and output data. Because the three-dimensional (3-D)

groundwater-flow model of the heterogeneous Central Valley

aquifer system includes complex surface-water management

processes, the GIS is extremely useful for recognizing and

understanding spatial relations within and between data sets.

Because data transformation (including mathematical functions or logical operations), reformatting, and integration

are accomplished relatively easily using GISs, the CVHM

GIS was extremely valuable to the hydrologic modeling. In

particular, the CVHM GIS was used to assist in the conversion

of remotely sensed land-use information and topography from

digital elevation models into input to the groundwater model.

The link between the groundwater model and the GIS, however, was accomplished with the aid of computer programs for

translating input and output data. Information from multiple,

often disparate, datasets were combined, processed, and (or)

resampled to produce spatial and temporal data sets needed for 

the groundwater model input and (or) observations. In

addition, the groundwater model results can be readily

visualized spatially and temporally using the CVHM GIS and

accompanying translation programs.

Chapter A. Introduction, Overview of Hydrogeology, and Textural 

Model of California’s Central Valley

By Claudia C. Faunt, Randall T. Hanson, and Kenneth Belitz
2 Groundwater Availability of the Central Valley Aquifer, California

Texture Modeling

The Central Valley is a large structural trough filled with

sediments of Jurassic to Holocene age, as much as 3 miles

deep in the San Joaquin Valley, which comprises the southern

two-thirds of the Central Valley, and as much as 6 miles deep

in the Sacramento Valley, comprising the northern one-third.

Most of the freshwater, however, is contained in the upper part

of the sediments consisting of post-Eocene continental rocks

and deposits (Williamson and others, 1989), with thicknesses

ranging from 1,000 to 3,000 feet. Aquifer-system sediments

comprise heterogeneous mixtures of unconsolidated to semiconsolidated gravel, sand, silt, and clay.

In order to better characterize the aquifer-system deposits, lithologic data from approximately 8,500 drillers’ logs of

boreholes ranging in depth from 12 to 3,000 ft below land

surface were compiled and analyzed to develop a 3-D texture

model. The lithologic descriptions on the logs were simplified into a binary classification of coarse- or fine-grained.

The percentage of coarse-grained sediment, or texture, then

was computed from this classification for each 50-foot depth

interval of the drillers’ logs. A 3-D texture model was developed for the basin-fill deposits of the valley by interpolating

the percentage of coarse-grained deposits onto a 1-mile spatial

grid at 50-foot depth intervals from land surface to 2,800 feet

below land surface.

The resulting 3-D texture model shows substantial

heterogeneity and systematic variation in the texture of the

sediments. These results correlate well with depositional

source areas, independently mapped geomorphic provinces,

and factors affecting the development of alluvial fans. In

general, the Sacramento Valley predominantly is fine-grained

and reflects the more fine-grained volcanic-derived sediments.

However, some relatively coarse-grained deposits do occur

along the river channels and the alluvial fans emanating from

the Cascade Range and the northern Sierra Nevada.

In the San Joaquin Valley, especially on the eastern side,

the areas of coarse-grained texture are more widespread than

the areas of fine-grained texture and occur along the major rivers. In the southern part of the San Joaquin Valley, the alluvial

fans derived from the glaciated parts of the Sierra Nevada

are much coarser grained than the alluvial fans to the north.

In contrast to the eastern San Joaquin Valley, the western San

Joaquin Valley generally is finer-grained and is underlain by

the Corcoran Clay Member of the Tulare Formation (hereafter

referred to as the Corcoran Clay). These finer textures reflect

the source material: shales and marine deposits of the Coast

Range. These rocks generally yield finer-grained sediments

than the granitic parent rocks that make up the alluvial fans

on the eastern side of the valley. In addition, this finer-grained

texture may be related to the fact that the western side of the

valley has lower elevation drainage basins and is drained internally with no outlet for exporting the finer-grained materials.

This area of predominately fine-grained texture is associated

with the largest amount of subsidence attributed to groundwater withdrawals recorded in the valley.

Hydrologic System Modeling

The complex hydrologic system of the Central Valley

is simulated using a number of advanced components of the

USGS’s numerical modeling code MODFLOW-2000 (MF2K).

The Farm Process (FMP) for MF2K is used to simulate the

groundwater and surface-water components of the hydrologic

cycle and to assess and quantify the hydrologic conditions.

The FMP dynamically allocates groundwater recharge and

groundwater pumpage on the basis of crop water demand,

surface-water deliveries, and depth to the water table. This

approach is particularly useful in the Central Valley where

private groundwater pumping for irrigation is not metered.

The FMP simulates un-metered historical pumpage and

the delivery of surface water for 21 water-balance regions

within the Central Valley for water years 1962–2003. The farm

delivery requirement (irrigation requirement) is calculated

from consumptive use, effective precipitation, groundwater

uptake by plants, and on-farm efficiency. The FMP links with

a number of existing MF2K Packages. The Streamflow Routing Package (SFR1) is linked to facilitate the simulated conveyance of surface-water deliveries. If surface-water deliveries

do not meet the farm delivery requirement, the FMP invokes

simulated groundwater pumping to meet the demand. Based

on this demand, the FMP uses specified irrigation efficiencies to calculate irrigation return flow. Although the FMP can

account for various economic and other management criteria,

these criteria were not simulated in this model.

Utilizing MODFLOW and the FMP, the CVHM simulates groundwater and surface-water flow, irrigated agriculture, land subsidence, and other key processes in the Central

Valley on a monthly basis. This model was developed at scales

relevant to water management decisions for the entire Central

Valley aquifer system, which was discretized horizontally into

20,000 model cells of 1-mi2 areal extent, and vertically into

10 layers ranging in thickness from 50 to 1,800 ft. The texture

model was used to estimate hydraulic conductivity for every

cell in the model. Land subsidence, an important consequence

of intense groundwater pumpage in susceptible aquifer systems, especially in the San Joaquin Valley, is simulated using

the SUB Package. Intra-borehole flow, an important mechanism for vertical flow within and between hydrogeologic units

in parts of the valley, is simulated across the Corcoran Clay

using the MNW Package.
Introduction 3

The hydrology of the present-day Central Valley and

the CVHM model are driven by surface-water deliveries and

associated groundwater pumpage, which in turn reflect spatial

and temporal variability in climate, water availability, land

use, and the water-delivery system. In general, the Sacramento

Valley receives more precipitation than the drier, more heavily pumped San Joaquin Valley. The surface-water delivery

system developed for the valley redistributes a significant

part of this water from north to south. The simulated monthly

water budgets indicate that precipitation and surface-water

deliveries supply most of the water consumed in the initial

part of the growing season, whereas increased groundwater

pumpage augments these supplies later in the season. Generally, the model shows that during wet years water is taken into

groundwater storage in the aquifer system, and during dry

years water is released from groundwater storage. Even during

dry years, however, the model shows that some recharge to

the groundwater system occurs during winter or early spring

precipitation. 

During recent decades, changes in the surface-water

delivery system have had profound effects on the hydrologic system. Because of the abundance of surface water

and smaller amounts of pumpage, the Sacramento Valley

and Sacramento–San Joaquin Delta generally have experienced relatively little groundwater storage depletion. The San

Joaquin Valley has experienced large changes in groundwater

storage. In the early 1960s, groundwater pumpage exceeded

surface-water deliveries in the San Joaquin Valley, causing

water levels to decline to historic lows on the west side of the

San Joaquin Valley, which resulted in large amounts of subsidence. In the late 1960s, the surface-water delivery system

began to route water from the wetter Sacramento Valley to the

drier, more heavily pumped San Joaquin Valley. The surfacewater delivery system was fully functional by the early 1970s,

resulting in water-level recovery in the northern and western

parts of the San Joaquin Valley. Overall, the Tulare Basin part

of the San Joaquin Valley still is showing dramatic declines in

groundwater levels and accompanying increased depletion of

groundwater storage.

Climate variability has had profound effects on the Central Valley hydrologic system. For example, the droughts of

1976–77 and 1987–92 led to reduced surface-water deliveries

and increased groundwater pumpage, thereby reversing the

overall trend of groundwater-level recovery and re-initiating

land subsidence in the San Joaquin Valley. Since the mid1990s, although annual surface-water deliveries generally

have exceeded groundwater pumpage, water still is being

removed from storage in most years in the Tulare Basin. Other

than the large loss in storage in the Tulare Basin, on average

there has been little overall change in storage throughout the

rest of the Central Valley.

The CVHM is designed to be coupled with forecasts from

Global Climate Models (GCMs) and to allow for efficient

updates using remotely sensed data. Implementation of the

FMP using GIS tools facilitates the use of remotely sensed

evapotranspiration data. The tools allow for the spatial and

temporal input data for the model to be updated more efficiently. This capability, in turn, facilitates using the model

with climate data derived from GCMs. The input data for

the crop-based water budget are consistent with output data

from the GCMs. This facilitates using CVHM to forecast

the potential supply of surface-water deliveries, demand for

groundwater pumpage, and changes in groundwater storage in

the Central Valley.

In the future, with the aid of GIS tools, the CVHM

also could be used as a platform to connect the simulation 

of hydrologic processes with water allocation/optimization

models (for example, CALSIM). The CVHM could be used to

evaluate sub-regional issues such as proposed exportation of

water from the Sacramento Valley to Southern California, or

the restoration of salmon habitat in the San Joaquin River. The

relatively detailed database on texture properties coupled with

water-level altitudes may make CVHM particularly useful for

assessing artificial recharge sites. These types of sub-regional

issues could be evaluated using sub-regional models dynamically linked to the regional CVHM through the embeddedmodel technology of the local grid refinement (LGR)

capability within MODFLOW.

Introduction

For more than 50 years, California’s Central Valley has

been one of the most productive agricultural regions of the

world, which is due in large part to an ample supply of irrigation water. Using fewer than 1 percent of U.S. farmland, the

Central Valley supplies 8 percent of U.S. agricultural output

(by value) (Great Valley Center, 1999) and produces one quarter of the Nation’s food (Great Valley Center, 1998), including

40 percent of the Nation’s fruits, nuts, and other table foods

(Bertoldi, 1989). In 2002, 250 different crops were grown,

with an estimated value of $17 billion per year (Great Valley

Center, 2005). The predominate crop types are cereal grains,

hay, cotton, fresh and processing tomatoes, vegetables, citrus,

tree fruits, nuts, table grapes, and wine grapes.

Paradoxically, most of the area is arid to semiarid and

naturally is water-deficient (Bertoldi, 1989). Agriculture has

been sustained by the development of an extensive system of

reservoirs and canals and also by the availability of groundwater. Approximately 75 percent of the irrigated land in

California and 17 percent of the Nation’s irrigated land is in

the Central Valley (Bureau of Reclamation, 1994). In addition, about 20 percent of the Nation’s groundwater demand is

supplied from pumping Central Valley aquifers, making it the

second-most-pumped aquifer system in the U.S. (Bureau of

Reclamation, 1994; Planert and Williams, 1995; Alley, 2006).

As impressive as these numbers are from an agricultural

water-use perspective, the Central Valley is rapidly becoming

an important area for California’s expanding urban population.

Between 1970 and 2000, the population in the Central Valley doubled, reaching 6.5 million people in 2005 (California

Department of Finance, 2007) and future growth is projected

to continue.
4 Groundwater Availability of the Central Valley Aquifer, California

Because the Central Valley contains so many communities, industries, and ecosystems that depend directly or indirectly on groundwater, and because competition for available

water is intensifying, there is a need to quantify the region’s

water resources and the trends affecting them so that potential

future water-use conflicts can be reduced or avoided. Although

the Central Valley lies entirely within the State of California (fig. A1), its long history of groundwater development to

support agriculture, and the complexity and immensity of the

local, State and National economic factors related to the availability of the Valley’s groundwater, underscore the National

importance of this vital resource. In response, the U.S.

Geological Survey (USGS) is assessing the availability of the

Central Valley’s water resources, particularly its groundwater.

The availability and sustainability of groundwater as a

source of supply is a function of many factors—both natural and human—that control its use. Natural factors include 

the quantity and quality of water, climate, and environment.

Human factors include the laws, regulations, and economics

(U.S. Geological Survey, 2002). Water problems in California

can be categorized under three broad headings: (1) problems of natural distribution (both spatial and temporal); (2)

technical-hydrologic problems; and (3) political, legal, and

social problems (California Department of Water Resources,

2005). Although the third category is referred to at times, this

report focuses on the first two categories. The focus of this

study is on improving the fundamental knowledge of groundwater availability in the Central Valley, including water fluxes

(groundwater levels and flows and surface-water inflows,

diversions, and deliveries), storage, and water use by

agriculture and other human activities.

Purpose and Scope

The purpose of this report is to describe groundwater

availability in the Central Valley. The descriptions are derived

largely from the study results, including modeling; however,

they also utilize the extensive literature on California’s Central

Valley. The report comprises three chapters and an appendix.

Chapter A (this chapter) summarizes the study’s purpose and

scope and provides an overview of the hydrogeology of the

study area. The hydrogeologic description includes the geologic framework and regional groundwater-flow. Chapter B

describes an analysis and assessment of groundwater availability in the Central Valley—the principal focus of this

report. Included in Chapter B are descriptions of the effects of 

development on the flow system, groundwater sustainability

and management, and monitoring of the groundwater system.

Chapter C documents the development of a three-dimensional

(3-D), finite-difference numerical model of the Central Valley regional groundwater-flow system. This model is used to

evaluate the groundwater availability described in Chapter B. 

Relative to the previously developed Central Valley Regional

Aquifer System Analysis (CV-RASA) model (Williamson and

others, 1989), the current model was extended to incorporate

a slightly larger geographic area (fig. A1), has a finer spatial

and temporal discretization, uses a more-detailed depiction

of subsurface geology, and simulates monthly water budgets

for April 1961 through September 2003. Finally, an appendix

documents modifications to MODFLOW-2000 (MF2K) that

were required to align the functionality of MF2K with the

landscape, hydrologic and geologic architecture of the Central

Valley.

In support of the assessment of groundwater availability

in the Central Valley, the study has three objectives:

1. Develop a better understanding of the 3-D internal

architecture of the freshwater-bearing deposits of the

Central Valley;

2. Utilize enhanced water-budget analysis techniques

to estimate water-budget components (recharge,

discharge, storage) for the groundwater flow system

in areas dominated by irrigated agriculture; and

3. Quantify the Central Valley’s groundwater-flow

system to enable the forecasting of system response

to stresses from human and environmental stresses at

scales relevant to water-management decisions.

The first objective is achieved through the development

of a texture model. This texture model is documented in the

“Aquifer Characteristics” section of Chapter A of this report.

The second objective is achieved through the development of

the “Farm Process” (FMP) as an additional simulation component within MF2K. A U.S. Geological Survey Techniques and

Methods report documents the Farm Process (Schmid and others, 2006). The application of the FMP within the context of

simulating the irrigated agriculture and as much as possible of

the hydrologic cycle in the Central Valley also is documented

in Chapter C. The final objective is accomplished using a

quantitative numerical modeling tool, referred to in this report

as the Central Valley Hydrologic Model (CVHM). CVHM

consists of a linked landscape-process and groundwater-flow

model that is described in Chapter C. Chapter B describes the 

application of this model for an analysis of groundwater availability in the Central Valley.
Introduction 5

S

acramento River

San

Joaquin

River

Sutter

Buttes

Tulare

Basin

San Joaquin Basin

Kings

River

Carquinez

Straits

San Francisco

Bay

Tulare

Lake bed

Population centers

S

i

e

r

r

a

N e

v a d a

S

a

c

r

a

m

e

n

t

o

V a

l

l

e

y

S

a

n

J o a q u i n

V a l l e y

C

o

a

s

t

R

a

n

g e

s

Cascade Range

Klamath Mts

124° 123° 122° 121° 120° 119°

40°

39°

38°

37°

36°

35°

0

0 100 200 Kilometers

100 200 Miles

Tehachapi Mts

Pa c i f i c

O c e a n

Red Bluff

Redding

Davis Sacramento

Stockton

Modesto

Davis

Fresno

Visalia 

Bakersfield

NEVADA

CALIFORNIA

EXPLANATION

Line of section (figure A11)

Extent of San Joaquin Basin

 fluvial fans

Selected faults

Major streams and selected

 canals

Historical lakes

Geomorphic province—

Basin

Delta

Dissected uplands

Sacramento River flood plains

Sutter Buttes

Younger fan deposits

Extent of Corcoran Clay

Shaded relief derived from U.S. Geological Survey National Elevation Dataset, 2006. 

Albers Equal Area Conic Projection

Kettleman

A A' Hills

A

A'

Stockton

Fault

Pacific Ocean

San

Francisco

Central

Valley

C

A

L

I

F

O

R

N

I

A

Figure A1. Central Valley major geomorphic provinces (modified from Davis and others, 1959; Olmstead and Davis, 1961; and 

Jennings,1977), alluvial fans of the San Joaquin Basin (Weissmann and others, 2005), and extent and thickness of Corcoran Clay 

(modified from Page,1986 and Burow and others, 2004).
6 Groundwater Availability of the Central Valley Aquifer, California

Methods of Analyses

Assessment of groundwater resources is an evolving process. The technology, available data, groundwater usage, spatial distribution of demand, and issues of concern all change

over time (Reilly, 2005). An improved understanding of the

groundwater-flow system can be developed as more data are

collected and analytical tools become available. These analytical tools include improved computer simulation techniques,

as well as improved data-integration and data-management

practices. In order to understand the status of the Central Valley groundwater system, basic information on the geologic

framework, boundary conditions, hydraulic head (water level)

distribution, water quality, and the transmission and storage

properties of the aquifer system must be known or estimated.

Human activities, such as irrigation amounts and water withdrawals, also must be accounted for in the calculation of water

availability (Reilly, 2005).

The evaluation of Central Valley water resources included

the collection, integration and management of new and existing data, and the development and calibration of the CVHM.

The CVHM is used to help quantify the groundwater availability. The results, conclusions, and limitations discussed in this 

report are based on analyses of the data and the CVHM.

Data Compilation

Six major classes of data were collected or compiled as

part of this investigation: (1) borehole lithologic data regarding sediment characteristics; (2) hydrologic data consisting

of precipitation records, historical water levels in wells, and

streamflows; (3) compaction data related to subsidence; (4)

water-use data from previous studies; (5) spatial land-use

data, including crop type; and (6) surface-water deliveries and

diversions. In addition, information from other modeling studies of the Central Valley was reviewed, analyzed, and compiled. In particular, the previous CV-RASA model (Williamson and others, 1989) and the current Central Valley modeling

effort by the CA-DWR (C. Brush, California Department of

Water Resources, written commun., February 21, 2007) were

used. These data were incorporated into the CVHM. Details

regarding the data are described in Chapter C of this report. 

An overview of the data types and data-integration and

data-management techniques follows.

During the past decade, Geographic Information Systems

(GIS) have advanced considerably as tools for storing, analyzing, manipulating, displaying, and modeling surface-water and

groundwater data. GIS tools also are useful for linking spatial

and temporal data to model input and output. When developing a 3-D numerical hydrologic model of a heterogeneous

aquifer system having complex surface-water management

processes, such as the Central Valley, compilation of a complex array of different categories and types of data is required

(figs. A2 and A3). Developing even one of these data types can

be considered a major task on its own. For example, the steps

for compiling, analyzing, and building the water-level data

necessary for input data sets and observations is summarized

in figure A4. A more detailed description of the development,

compilation, and analysis of information for the hydraulic 

properties database is described in the “Textural Analysis” 

section of this chapter.

The geospatial database and GIS techniques were

extremely valuable to the CVHM, by facilitating the transformation (including mathematical functions or logical operations), reformatting, and integration of data used in the CVHM

(figs. A2 and A3). The link between the CVHM and the GIS

also required development of computer programs for translating input and output data. With the GIS and translation programs, information from the disparate datasets was combined,

processed, and (or) re-sampled to produce spatial and temporal 

data sets needed for the CVHM input or observations. Utilizing GIS, the CVHM results were visualized spatially and

temporally along with the observation data (fig. A2).

Numerical Model

Development of the CVHM resulted in a comprehensive

synthesis of the hydrologic data and the capability to analyze

the response of the hydrologic system to changes in stress. The

CVHM provides a quantitative framework that can be used as

a tool to help manage water resources.

Given the large increase in available data and major

improvements in simulation tools, the CV-RASA model (Williamson and others, 1989) was updated using the U.S. Geological Survey’s modular modeling software, MF2K (Harbaugh

and others, 2000; Hill and others, 2000). The incorporation

of the Farm Process (FMP) (Schmid and others, 2006) was

integral to modeling the water budget components. Likewise,

incorporation of new modules, such as the multi-node well

(MNW) (Halford and Hanson, 2002), subsidence (SUB)

(Hoffmann and others, 2003), and streamflow routing (SFR1)

(Prudic and others, 2004) packages, aided in more realistic

simulation of the system. The development and calibration of

the CVHM is documented in detail in Chapter C of this report. 

The CVHM includes simulation of groundwater-flow

in a sand-silt-clay aquifer system. The system has been 

subject to groundwater withdrawals, land subsidence, and

recharge by both natural processes and excess irrigation water.

CVHM incorporates a dynamically integrated water supplyand-demand accounting system at monthly time scales for

both agricultural areas and areas of native vegetation. The

CVHM provides for a more accurate simulation of irrigated

agriculture, surface-water, and groundwater-flow across the

entire Central Valley system than the previous CV-RASA

model. Analyses of the transient effects of variability in

surface-water deliveries and associated groundwater pumpage

are presented for three specific climatic conditions: drought,

wet, and typical year conditions.
Introduction 7

Texture

Model

DATA

V I S U A L I Z A T I O N

Flow Model

CVHM

Groundwater

Levels

and Flows

Hydrogeologic

Properties

Centralized

Geospatial

Database

Figure A2. The relation and flow of information used in analyzing the Central Valley Hydrogeologic system. Both interpretive and 

modeling data flow in and out of the centralized geospatial database. The texture model, data in the geospatial database, and modeling 

results are visualized throughout the data gathering, analysis, and modeling stages of the project. 

Previous Investigations

Because of the long history of groundwater development

and its impacts in the Central Valley, there are many hydrologic investigations of the Central Valley aquifer system. The

CA-DWR, the USGS, and various local and Federal agencies

have all completed numerous studies. Many of these studies

are summarized by Bertoldi and others (1991). The earliest systematic studies were done by California’s first State

Engineer, William Hall, and his staff (Hall, 1886; Hall, 1889;

and Mendenhall and others, 1916). Bertoldi (1979) compiled a

bibliography of nearly 600 reports on groundwater in the Central Valley. Since then, a number of site-specific and regional

groundwater models have been completed by various Federal,

State, and local agencies as well as private consultants. Two

of these studies are regional modeling efforts and are summarized below.

Recent Regional Groundwater Models

Central Valley Regional Aquifer-System Analysis 

(CV-RASA)

The USGS initiated the Regional Aquifer-System Analysis (RASA) program in 1978 in response to Federal and State

needs for information to improve management of the Nation’s

groundwater resources. The objective of the RASA program

was to define the regional geohydrology and establish a framework of information that could be used for regional assessment of groundwater resources. Twenty-five regional aquifer

systems were studied under the RASA program, including the

Central Valley (Sun and Johnston, 1994).

The CV-RASA project provided a wealth of information on the Central Valley (Williamson, 1982; Diamond and

Williamson, 1983; Hull, 1984; Mullen and Nady, 1985; Page,

1986; Williamson and others, 1989; Bertoldi and others 1991;

among many others), including a regional groundwater-flow

model. The groundwater-flow model simulated conditions

from 1961 to 1977, a period of large and variable stresses on

the groundwater-flow system, but at a relatively coarse spatial

scale. The CV-RASA model grid cells were 36 mi2, and the 

freshwater-bearing deposits were represented by four model

layers. The resulting model represented flow conditions for

large regions, but generally was inadequate at scales less than

about 500 mi2. Because water-management decisions typically

are made at the scale of individual water districts, which

often are smaller than 500 mi2, the CV-RASA model cannot

be used appropriately for providing information relevant to

management decisions at those scales.
8 Groundwater Availability of the Central Valley Aquifer, California

Centralized

Geospatial

Database

Original Data Types

Soils Topography Remote Sensing

Hydrology Chemistry Anthropogenic Controls

Geology Vegetation & Land Use

Climate Geophysics

Soil Surveys (STATSGO)

Soil Properties Data

Digital Elevation Models

Topographic Maps

Satellite Imagery

MoDIS

Geologic Maps

Geologic Cross Sections

Drillers Logs

Land Use/Land Cover Maps (DWR)

Agricultural Data

Crop Data

Precipitation Data

Temperature Data

Humidity

Evapotranspiration

Water Well Information

Aquifer Tests

Aquifer Properites

Aquifer Chemistry

Spring Chemistry

SU Chemistry

Surface Water Delivery

Surface Water Diversions

Environmental Requirements

Surface Water Data Redox Chemistry

Gravity

Seismic/Earthquake

Magnetics/Resistivity

Figure A3. The diversity of data types and categories included in the centralized geospatial database. Data types in bold were used 

specifically in this study.

The CV-RASA model initially utilized a water budget

that was based on net recharge to the flow system. During

model calibration, the net recharge fluxes were changed substantially. Because the simulated water budget was significantly different from the estimated budget, it was unclear

whether uncertainty in the budgets (simulated and estimated)

was due to errors in the budget components or to simulation errors. As a consequence, use of the CV-RASA model to

simulate variations in different budget components was not

very useful, and it was clear that refined budget estimates were

needed. Subsequent to the CV-RASA model, Gronberg and

Belitz (1992), Belitz and others (1993), and Brush and others

(2004) each developed an alternative approach to estimating

the water budget based on crop water use, irrigation efficiency,

and surface-water deliveries and applied the approach to parts

of the San Joaquin Valley.
Introduction 9

Figure A4. An example of the detail for compilation, integration, and analysis for one data type (water-level nformation).

Hydrology

Water Well Information

Hydrographs Calibration

Observations

Aquifer Tests

Aquifer Properites

Surface Water Data

DWR USGS

Combined

Water-Level

Database

Data

Analysis

CVHM

Subset

21,417 wells

873,073 water levels

206 wells

19,725 water levels

Overlapping records

Missing information

Location discrepancies

Construction

Water levels

Water-level gradients

Change in water levels

Measurement discrepancies

Temporal distribution

Specific time periods

Climate control

Water balance areas

Basin to foothills

Continuous record

Spatial distribution

Water Levels

Locations

Construction

Water Levels

Locations

Construction
10 Groundwater Availability of the Central Valley Aquifer, California

California Central Valley Groundwater-Surface-Water 

Simulation Model (C2VSIM) 

CA-DWR currently (2008) is using the 3-D finite element code Integrated Water Flow Model (California Department of Water Resources, 2007b) to develop an integrated

groundwater-surface-water model for the Central Valley;

this model is referred to as C2VSIM. C2VSIM simulates the

development of the groundwater-flow system and groundwater-surface water interactions on a monthly basis from October

1921 to September 2003 (C. Brush, California Department of

Water Resources, written commun., February 21, 2007). The

groundwater-flow system is represented with three layers, each

having 1,393 elements ranging in size from about 2 to 65 mi2. 

The model of the groundwater-flow system is coupled with

one-dimensional land-surface, streamflow, lake, and unsaturated-zone processes. Land-surface processes are simulated

using 21 subregions corresponding to CA-DWR water-supply

planning areas. The surface-water network is simulated using

449 stream nodes representing 75 stream reaches, with 80

diversion locations providing 108 deliveries. The compilation

of the monthly water delivery and diversion information for

these 21 subregions is a substantial contribution toward understanding the hydrology of the Central Valley. The calibrated

C2VSIM model will be used to simulate the groundwater-flow

system and calculate stream accretions and depletions for use 

in CALSIM-III (California Department of Water Resources,

2007a). CALSIM-III is a reservoir-river basin simulation

model used for planning and management of the State Water

Project and Central Valley Project, which are large surfacewater storage and distribution networks in California’s Central

Valley (C. Brush, U.S. Geological Survey, written commun.,

2006).

Study Area

The Central Valley, also known as the Great Valley of

California, covers about 20,000 mi2 and is one of the more 

notable structural depressions in the world. It occupies a central position in California—bounded by the Cascade Range to

the north, the Sierra Nevada to the east, the Tehachapi Mountains to the south, and the Coast Ranges and San Francisco

Bay to the west, the valley is a vast agricultural region drained

by the Sacramento and San Joaquin Rivers (fig. A1). The valley averages about 50 miles (mi) in width and extends about

400 mi northwest from the Tehachapi Mountains to near Redding (fig. A1). Generally, the land surface has very low relief.

Its configuration is the result of millions of years of alluvial

and fluvial deposition of sediments derived from the bordering

mountain ranges. Most of the valley lies close to sea level, but

is higher along the valley margins. Most of the valley boundary along the eastern edge is about 500 feet (ft) above sea level

and most of the western boundary ranges from 50 to 350 ft

above sea level. Near the apexes of some alluvial fans in the

south along the margin of the valley, the maximum elevation

is about 1,700 ft. 

For convenience of discussion, the valley can be divided

into two large parts: the northern one-third is known as the

Sacramento Valley and the southern two-thirds is known as the

San Joaquin Valley (fig. A1). The San Joaquin Valley can be

split further into the San Joaquin Basin and the Tulare Basin

(fig. A1 and table A1). In this report, the term San Joaquin

Valley will be used to represent the southern two-thirds of the

Central Valley, as a whole. Where more detail is warranted, the

northern part of the San Joaquin Valley, the San Joaquin Basin,

will be distinguished from southern part, the Tulare Basin.

The San Joaquin and Sacramento Valleys meet in the Delta

area where the combined discharge of the Sacramento and San

Joaquin Rivers flows through the Central Valley’s one natural

outlet, the Carquinez Strait, on its way to San Francisco Bay

and the Pacific Ocean (fig. A1). Just east of the Delta, several

streams issue from the Sierra Nevada into the valley and flow

to the Delta in an area referred to as the Eastside Streams.

Climate 

Climate in the Central Valley is arid-to-semiarid hot,

Mediterranean. Precipitation during an average year ranges

from 13 to 26 inches in the Sacramento Valley (46 inches

in the extreme northern part of the valley) and from 5 to

18 inches in the San Joaquin Valley (fig. A5A). Dramatic

deviations from average climatic conditions are manifested

as droughts or floods. Most of the Central Valley is prone to

flooding. About 85 percent of the precipitation falls during

November through April, half of it during December through

February in average years (fig. A6). The valley is hot and dry

during the summer, and cool and damp in the winter, when the

area frequently is covered by a ground fog known regionally

as “tule fog.” Reference evapotranspiration (ETo) is relatively

high, and ranges from 45 inches in the Sacramento Valley to

56 inches in the San Joaquin Valley (fig. A5B). In general,

most of the valley is in a state of perennial water deficiency;

ETo exceeds precipitation by as much as 3 ft. Overall, precipitation exceeds ETo during the winters and ETo exceeds

precipitation during the summers.

Sacramento Valley 

Geographically, the Sacramento Valley is bounded on the

east by the Sierra Nevada and on the west by the Coast Range

and Klamath Mountains. The only significant topographic

feature here, or on the Central Valley floor at large, is Sutter Buttes, a volcanic remnant in the south-central part of the

Sacramento Valley (fig. A1). The Sacramento River, which is

the longest river system in the State of California, flows from

the Cascade Range in the north to the San Francisco Bay/Sacramento–San Joaquin River Delta; major tributaries are the Pit

(north of the study area), Feather, Yuba, Bear, and American

Rivers (fig. 5A). 
Study Area 11

Regions Site 

identifier

General 

description

DWR DSA 

number

Total 

area 

(mi2)

Routed 

surfacewater 

deliveries

Non-routed 

surfacewater 

deliveries

Sacramento Valley 1 Sacramento River above Red Bluff (Redding Basin) DSA 58 611 2 None

2 Red Bluff to Chico Landing (Red Bluff, Corning,

Bend, Antelope, Dye Creek, Los Molinos, and Vina

Basins)

DSA 10 1,163 3 None

3 Colusa Trough (Most of Colusa Basin and Capay

Valley Basin)

DSA 12 1,112 4 None

4 Chico Landing to Knights Landing proximal to the

Sacramento River

DSA 15 560 1 None

5 Eastern Sacramento Valley foothills near Sutter Buttes

(North and South Yuba, East Butte and eastern

parts of West Butte and Sutter Basins)

DSA 69 957 2 6

6 Cache-Putah area (Western Solano and most of Delta

and Yolo Basins)

DSA 65 1,044 4 None

7 East of Feather and South of Yuba Rivers (North

American Basin)

DSA 70 534 4 4

Eastside Streams 8 Valley floor east of the Delta (Cosumnes and parts of

South American and Eastern San Joaquin Basins)

DSA 59 1,362 6 None

Delta 9 Delta (parts of Solano, Eastern San Joaquin, South

American, and most of Tracy Basins)

DSA 55 1,026 1 None

San Joaquin Basin 10 Delta-Mendota Basin DSA 49A 1,083 1 7

11 Modesto and southern Eastern San Joaquin Basin DSA 49B 664 6 None

12 Turlock Basin DSA 49C 540 5 None

13 Merced, Chowchilla, and Madera Basins DSA 49D 1,648 6 2

Tulare Basin 14 Westside and Northern Pleasant Valley Basins DSA 60A 1,071 None 3

15 Tulare Lake and Western Kings Basin DSA 60B 1,423 4 5

16 Northern Kings Basin DSA 60C 478 2 1

17 Southern Kings Basin DSA 60D 569 2 1

18 Kaweah and Tule Basins DSA 60E 1,358 4 4

19 Western Kern County and Southern Pleasant Valley

Basin

DSA 60F 1,365 2 3

20 Northeastern Kern County Basin DSA 60G 705 2 3

21 Southeastern Kern County Basin (Arvin-Maricopa

area)

DSA 60H 1,105 3 2

TOTAL — — — 20,378 64 41

Table A1. Water-balance subregions within the Central Valley, California. 

[General description based on depletion study area (DSA) names (where available) or subareas from Williamson and others (1989; fig. A27). DSA 49 is subdivided into four subregions A–D, and DSA 60 is subdivided into eight subregions A–H. Routed surface water deliveries are conveyed along streams or canals

to a water-balance subregion. Non-routed surface water deliveries, or water transfers, are surface-water deliveries to a water-balance subregion not connected

to a stream or major canal. This conveyance typically occurs through small canals or diversion ditches. DWR, California Department of Water Resources; mi2, 

square mile]

The city of Sacramento and the surrounding communities form the major population center of the region. With the

exception of Redding, cities and towns north of Sacramento

are located in mainly agricultural areas. The 1995 population

of the Sacramento Valley was 2.4 million (California

Department of Water Resources, 2003).

The Sacramento Valley has mild winters and

hot, dry summers. The natural levees that border the

Sacramento–Feather River system create backwater basins of

heavy clay soils that sustain rice farms and duck clubs. Truck,

field, orchard, and rice crops are grown on approximately

2.1 million acres; rice represents about 23 percent of the total

acreage (California Department of Water Resources, 2003).
12 Groundwater Availability of the Central Valley Aquifer, California

EXPLANATION

Precipitation station

 and identifier

Selected streams

 and rivers

0 50 100 Miles

0 50 100 Kilometers

Red Bluff

Davis

Davis

Bakersfield

Average annual inflow,

 in thousands of acre-feet per year—

Less than 100

100.1 to 500

500.1 to 1,000

1,000.1 to 2,000

2,000.1 to 3,000

3,000.1 to 4,000

4,000.1 to 5,000

Greater than 5,000

Average annual

 precipitation,

 in inches per year—

6 to 8

9 to 10

11 to 12

13 to 14

15 to 16

17 to 18

19 to 20

21 to 22

23 to 24

25 to 26

27 to 28

29 to 30

31 to 32

33 to 34

35 to 36

37 to 38

39 to 40

41 to 42

43 to 44

45 to 46

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Los Gatos

Creek

Fresno

Slough

Kaweah

River

Kern

River

White

River

Tule

River

Kings

River

Mokelumne

River

Cosumnes

River

American

River

Feather

River

Yuba

River

Bear

River

Calaveras

River

Stanislaus

River

Tuolumne

River

Merced

River

San Joaquin

River

San Joaquin River

Sacramento River

A

Figure A5. A, Surface-water inflows and average annual precipitation for September 1961 through September 2003 throughout the Central Valley, 

California. B, Average annual reference evapotranspiration (ETo) for September 1961 through September 2003 throughout the Central Valley, California. 

ETo data were calculated from PRISM temperature data (Climate Source, 2006). The surface-water inflows are from U.S. Geological Survey files and 

California Department of Water Resources (C. Brush, written commun., February 21, 2007). Precipitation data are from Parameter-Elevation Regressions 

on Independent Slopes Model (PRISM) data (Climate Source, 2006). 
Study Area 13

EXPLANATION

Precipitation station

 and identifier

Selected streams

 and rivers

0 50 100 Miles

0 50 100 Kilometers

Red Bluff

Davis

Davis

Bakersfield

Average annual reference evapotranspiration,

 in inches per year—

45.5 to 46

46.1 to 47

47.1 to 48

48.1 to 49

49.1 to 50

50.1 to 51

51.1 to 52

52.1 to 53

53.1 to 54

54.1 to 55

55.1 to 56

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

B

Figure A5. Continued. 
14 Groundwater Availability of the Central Valley Aquifer, California

Depending on location, agriculture in the Sacramento

Valley relies on a variable combination of surface water and

groundwater. Groundwater accounts for less than 30 percent

of the annual supply used for agricultural and urban purposes

in this area. The Sacramento Valley, generally rich in surface

water, provides water for much of California’s urban and

agricultural needs (California Department of Water Resources,

2003). With growing demand for high-quality water throughout the state, water transfers from the Sacramento Valley to

other parts of the state are being evaluated more carefully.

Several areas have passed ordinances that regulate or impede

these transfers. CA-DWR studies indicate that additional

ecosystem protection and restoration efforts are needed 

to improve habitat for threatened and endangered species

(California Department of Water Resources, 2003). Because

these environmental efforts require additional water, they may

ultimately affect the availability of water resources for other

purposes.

MONTH

AVERAGE PRECIPITATION, IN INCHES

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Bakersfield

Davis

Red Bluff

Monthly average precipitation (1961 to 2003) at—

EXPLANATION

Figure A6. Average monthly precipitation for Redding, Davis, and Bakersfield, California (Climate Source, 2006).
Study Area 15

Delta and Eastside Streams

The Sacramento and San Joaquin Rivers flow through

the northern and southern parts of the valley, respectively, and

join to form the San Francisco Bay/Sacramento–San Joaquin

River Delta (referred to in this report as the Delta) (fig. A1). 

The Delta also receives freshwater inflow from the Cosumnes,

Mokelumne, and Calaveras Rivers and other small streams on

the eastside. The shared Delta is a large expanse of interconnected canals, streams, sloughs, marshes, wetlands, and peat

islands just south of the State capital. The Delta is a delta

in name only. In reality, it is an estuary and the reverse of a

classic delta, in that multiple rivers are coming together as

opposed to one river that no longer is confined to its channel

and expands in width. The Delta is a major source of water

for domestic, industrial, and agricultural uses as well as an

important habitat to 750 animal and plant species. The Delta

also supports species listed as threatened or endangered: Delta

smelt, Chinook salmon, and steelhead.

The Delta covers an area of about 1,000 mi2 of estuary. 

The Delta has 1,100 mi of rivers and 1,600 mi of levees. The

levees protect farmlands as much as 22 ft below sea level.

Approximately 70 percent of surface-water flow to the Delta

comes from the Sacramento River and the remainder comes

from the San Joaquin River. Water from the Sacramento River

is much fresher than water from the San Joaquin River. As the

freshwater moves westward through the Delta to the San Francisco Bay, it is underlain by an increasingly thick wedge of

tidal salt water from the Pacific Ocean. Since the construction

of major dams on rivers in the Delta drainage basin, water in

the Delta has remained generally suitable for agricultural and

urban uses, however, salt levels increase during dry periods.

The Delta is the heart of a massive, engineered northto-south water-delivery system (fig. A7). At the southern end

of the Delta, water is regulated and pumped into canals to be

transported southward. State and Federal contracts provide for

export of up to 7.5 million acre-ft per year, although historically they have not reached this amount. About 83 percent of

water exported from the Delta is used for agriculture and the

remainder is used for urban and environmental purposes in

Central and Southern California (California Department of

Water Resources, 1998). Because of environmental concerns,

particularly the decline of the Delta smelt, the exports sometimes are curtailed. This focus on ecosystem restoration often 

leads to conflicts with agricultural and urban interests and

regulatory droughts.

In the late 1800s, large-scale agricultural development

in the Delta required levee-building to prevent historically

frequent flooding on the low-lying Delta islands. An extensive

network of drainage ditches prevents islands from flooding

internally. The accumulated drainage is pumped through or

over the levees into stream channels. Subsidence of the Delta

islands, principally caused by decomposition of organic carbon in the peat soils, threatens the stability of island levees. As

subsidence progresses, the levee system will become increasingly vulnerable to catastrophic failure during floods and

earthquakes (Ingebritsen, and others, 2000; Lund and others,

2007). In addition, historical records show that sea level in San

Francisco Bay has risen between 4 and 8 inches over the past

100 years (Ryan and others, 1999), possibly related to global

warming. Whatever the reasons, water levels have risen, environmental issues have increased, and many levees have begun

to fail—some have failed. As a result, the Delta has become

an area of national concern resulting in the development of a

major Federal-State-stakeholder effort known as the “CALFED Bay-Delta program.” CALFED is a collaboration among

25 State and Federal agencies that came together with a mission: to improve water supplies in California and the ecological health of the Delta. In addition to the CALFED program,

Lund and others (2007) issued a vision statement for the Delta

whereby they suggest that alternatives, including a by-pass

canal, may need to be considered to address the water-resource

demands, water quality, and environmental issues of the Delta.

San Joaquin Basin of the San Joaquin Valley

Geographically, the San Joaquin Basin is bounded on

the east by the Sierra Nevada and on the west by the Coast

Ranges (fig. A1). The Delta borders its northern extent and the

internally drained Tulare Basin borders its southern extent.

Significant geographic features include the San Joaquin River

and the southern part of the Delta. The San Joaquin River runs

southwestward as it drains part of the Sierra Nevada, and turns

northwestward as it courses along the axis of the northern San

Joaquin Valley. The major tributaries, including the Stanislaus,

Tuolumne, and Merced Rivers, have drainage basins in the

Sierra Nevada to the east (fig. A5A). 

The San Joaquin Basin contains a half-dozen cities with

populations exceeding 50,000 in 2000 (California Department of Finance, 2007), including Stockton, Turlock, Merced,

and Modesto. A large part of the population of the basin is

involved in all facets of agricultural production. Gradually, the

population is shifting towards supporting the large urban areas

and industry. Most of the population is centered in the northern part of the basin near Stockton. The total population of the

San Joaquin Basin in 2000 was approximately 2 million (Great

Valley Center, 2005).

The San Joaquin Basin has mild winters. Although

the summers are particularly hot and dry, the area has been 

extensively developed for agriculture. In 2000, approximately

two-thirds of the area was used for agriculture. The southwestern half of the San Joaquin Basin has long been known for its

cotton fields, but recent drops in cotton prices have caused a

rapid shift to other crops, particularly almond orchards. On the 

eastern side of the San Joaquin Basin, alluvial fans are dominated by deciduous fruit and nut orchards. The remainder of 

the irrigated area is covered by pasture, truck, and field crops.
16 Groundwater Availability of the Central Valley Aquifer, California

Figure A7. General features of the surface-water system in the Central Valley, California.

Shasta Lake

Corning

Canal

Tehama-Colusa

Canal

Glenn-Colusa

Canal

Lake Oroville

Folsom Lake

Folsom South Canal

Pardee Lake

Hetch Hetchy

Aqueduct 

Mokelumne

Aqueduct 

Friant-Kern

Canal

Cross Valley

Canal

Kern

Water Bank

Camanche Reservoir

California

Aqueduct

EXPLANATION

State Water Project

Historic lakes

Reservoir

Federal Water Project

Local Canal/

Aqueduct Projects

Precipitation station

 and identifier

Selected streams

 and rivers

County boundary

0 50 100 Miles

0 50 100 Kilometers

Red Bluff

Davis

Davis

Bakersfield

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Tulare

Co

Kings

Co

Madera

Co

Mariposa

Co Merced

Co

Stanislaus

Co

San Joaquin

Co

Tuolumne

Co

Fresno

Co

Calaveras

Co

Amador Co Sacramento

Co

Solano

Co

Napa

Co

Yolo

Co

El Dorado Co

Placer

Co

Sutter

Co

Nevada

Co

Yuba

Co

Colusa

Co

Glenn

Co

Butte

Co

Tehama

Co

Shasta

Co

Kern

Co
Study Area 17

Although surface water is used when it is available, the

region relies heavily on groundwater. Groundwater accounts

for about 30 percent of the annual supply of both types of 

water used for agricultural and urban purposes (California

Department of Water Resources, 2003; Chapter C). During

periods of drought, the groundwater usage increases. Essentially, all natural flows in area streams are diverted for agricultural and municipal use (Moore and others, 1990). Only about

8 percent of the historic San Joaquin Valley wetland acreage

remains today (Moore and others, 1990). Plans to restore the

habitat and fish populations on the San Joaquin River through

higher releases of water from Friant Dam have spurred growing concerns over long-term availability of the Sierra water

supplies. As a result, new surface storage reservoirs in the

upper San Joaquin Basin are being considered (California

Department of Water Resources, 2003).

Tulare Basin of the San Joaquin Valley

Geographically, the southern part of the San Joaquin

Valley, the Tulare Basin, is bounded by the Sierra Nevada on

the east, the Tehachapi Mountains on the south, and by the

Coast Ranges on the west (fig. A1). The northern extent is less

well-defined, but generally corresponds to the Kings River.

Significant geographic features include the Tulare Lake Basin

and the Kettleman Hills. The Tulare Basin was separated from

the southern end of the San Joaquin Basin by the merging of

alluvial fans from the Kings River to the east and Los Gatos

Creek to the west. The Kings, Kaweah, Kern, and the Tule

Rivers drain into the Tulare Lake Basin; historically, the bulk

of the flow is from the Kings River. At the present time, only a

minor part of the flow from the Kings River enters the Tulare

Lake Basin (and most of this is diverted before reaching

Tulare Lake). The Kings River bifurcates in the Kings River

Fan to form two rivers referred to as the North Fork and South

Fork, but currently much of the flow is diverted or lost to

seepage before it reaches this point. The North Fork flows into

Fresno Slough and ultimately to the San Joaquin River. The

South Fork flows towards Tulare Lake.

The Kings, Kaweah, Kern, and the Tule Rivers issue

from steeply plunging canyons in the Sierra Nevada onto

broad, extensive alluvial fans. Over many thousands of years,

the natural flow of these rivers formed networks of streams

and washes on the slopes of the alluvial fans and terminated in

topographically closed sinks, such as Tulare Lake, Kern Lake,

and Buena Vista Lake. Historically, Tulare Lake was the largest freshwater lake west of the Mississippi River (Moore and

others, 1990) and the second largest (by surface area) freshwater lake in the United States (Moore and others, 1990). The

lake surface fluctuated annually with the variation in rainfall,

runoff, and snowmelt. Further south, the Kern River terminated in two smaller lakes that today, like the former Tulare

Lake, have dried and the waters that fed them have long since

diverted to irrigation.

The Tulare Basin, wider than the San Joaquin Basin and

Sacramento Valley, contains the population centers of Fresno,

Bakersfield, and Visalia. About 4 percent of the basin area is

urban. Although most of the basin’s population is focused on

agricultural activities, Bakersfield is well known for its oil

fields. The total population of the Tulare Basin in 2000 was

approximately 4 million (Great Valley Center, 2005).

The Tulare Basin also has mild winters and hot dry

summers. When first viewed by Don Pedro Fages in 1772,

he described the southern-most part of the Tulare Basin as a

barren desert waste with scattered saltbush (Atriplex) (Parsons, 1987). Beyond the desert area were tule marshes, fed

by streams carrying Sierra snowmelt, that became the home

for migrating waterfowl for several months each winter.

Despite these transient marsh areas, the area is dry and the

valley summer heat is intense. The present-day Tulare Basin

has been developed extensively for agriculture and petroleum

extraction. Agricultural fields, vineyards, and orange groves

are interspersed with oil fields (Parsons, 1987). Grains, cotton,

and corn are the main agricultural crops in the Tulare Basin.

Historically, groundwater has been important to both

urban and agricultural users (California Department of Water

Resources, 2003). Until recently, Fresno and Visalia were

entirely dependent on groundwater for their supply, and

Fresno was the second largest city in the U.S. reliant solely

on groundwater (California Department of Water Resources,

2003). These cities are slowly adding surface water to their

supplies. Water used for agriculture in the Tulare Basin constituted 69 percent of the total water use in 1998 and 86 percent

of the total in 2001 (Great Valley Center, 2005).

Surface water is preferred over groundwater because of

relative costs. Uncertainty and limitations of surface-water

deliveries from the Delta are of growing concern. Groundwater often is used to replace much of the shortfall in surfacewater supplies. Because groundwater is a finite resource, alternate sources of water either are being considered or starting to

be used. For example, some of the more permeable deposits

recently have been used for groundwater recharge programs.

Water districts have recharged several million acre-ft of water

for future use and transfer through water banking programs

(California Department of Water Resources, 2003). The

groundwater recharge programs store excess water during wet

periods for extraction during dry periods (California Department of Water Resources, 2005).

Each of the river systems in the region (Kings, Kaweah,

Tule, and Kern) has unique environmental water needs

because of the arid nature of the region and extensive modifications for agriculture. There has been significant activity on

the Kings and Kern Rivers to restore flows for habitat as well

as for recreation. 
18 Groundwater Availability of the Central Valley Aquifer, California

Water-Balance Subregions

For the purpose of this work, the Central Valley was

divided into 21 previously identified areas, termed “water-balance subregions” (WBSs) in this study (fig. A8 and table A1). 

Many of the WBSs initially were identified by CA-DWR and

Bureau of Reclamation (BOR) as numbered “Depletion Study

Areas” (California Department of Water Resources, 1977).

Since their initial identification, many of the depletion study

areas have been subdivided further into the WBSs (C. Brush,

California Department of Water Resources, written commun.,

February 21, 2007; California Department of Water Resources,

2003). The WBSs are used as accounting units for surfacewater delivery and for estimation of groundwater pumpage.

The boundaries generally represent hydrographic rather than

political subdivisions, particularly in the San Joaquin and

Tulare Basins. Where possible, the description in table A1

identifies the hydrographic basin that generally coincides with

the WBS; otherwise, a general description of the part of the

valley is given. In the Sacramento Valley and the Delta, the

boundaries usually do not coincide with hydrographic basin

boundaries. The specifics of the water-budget delivery and

diversion system are discussed in Chapter C. For simplicity

of reporting purposes, these 21 WBSs are grouped into the

five regional areas: the Sacramento Valley, Delta, Eastside

Streams, San Joaquin Basin, and Tulare Basin (table A1).

Geologic History and Setting

The Central Valley, as its name implies, is virtually one

large sediment-filled valley between the Coast Ranges and the

Sierra Nevada (Farrar and Bertoldi, 1988). The Sierra Nevada,

which forms the eastern side of the valley, is the eroded edge

of a huge tilted block of crystalline rock. The valley fill overlies a westward-sloping surface of basement rocks that are the

subsurface continuation of the Sierra Nevada. Emplacement

of the Sierra Nevada batholith ended around 85 million years

ago (Ma) in the Mesozoic Era. The Sierra Nevada topography

is a consequence of two periods of uplift (Wakabayashi and

Sawyer, 2001). The most recent and most significant uplift of

the range began about 5 Ma in the Miocene Epoch; however,

significant relief predates this uplift and was a result of uplift

that occurred at least 50 million years earlier in the Eocene

Epoch in a different tectonic setting. Throughout the orogenesis of the Sierra Nevada, the crestal elevations of the southern

part of the range greatly exceeded those of the northern part of

the range. The northeast corner of the valley is at the southern

end of the Cascade Range and contains volcanic rocks. Geologically, this area of the valley is relatively young; most of

the volcanic activity was during late Tertiary to Holocene time

(last 10 million years). The only prominent non-sedimentary 

rock feature in the entire Central Valley is the Sutter Buttes, a

Pliocene and Pleistocene volcanic plug that rises abruptly to

an altitude of 2,000 ft above the flat valley floor (Farrar and

Bertoldi, 1988).

A huge volume of sediments of deep marine, shallow

marine, deltaic, and continental origin fill the Central Valley

(Farrar and Bertoldi, 1988). The valley fill deposits range in

thickness from zero on the eastern edge of the valley to more

than 50,000 ft on the western edge (Wentworth and others,

1995). During and since marine deposition, sediments derived

from erosion of igneous and metamorphic rocks and consolidated marine sediments in the surrounding mountains have

been transported into the valley by streams. These continental

sediments at the southern end of the valley have an average

thickness of about 2,400 ft (Planert and Williams, 1995).

The continental sediments consist mostly of basin-fill or lake

deposits of sand and gravel interbedded and mixed with clay

and silt. Depending on location, deposits of fine-grained

materials—mostly clay and silt—compose as much as 

50 percent of the thickness of the valley-fill sediments (Planert

and Williams, 1995). 

Alluvial fans, some of which are more than 1,000 ft

thick, have formed on all sides of the Central Valley. The finegrained detritus carried by streams is moved farther toward the

valley axis, leaving the coarse-grained materials closer to the

valley margins. The coarse-grained sediments in the fans typically are associated with stream channels. On the eastern side

of the valley, these stream channels are large, laterally migrating distributary channels. Over time, shifting stream channels

create coalescing fans, forming broad sheets of inter-fingering,

wedge-shaped lenses of gravel, sand, and finer detritus. The

texture of these fan deposits is controlled by many factors,

including the bedrock source materials, drainage basin area,

elevation, and tectonic basin subsidence rate.

Structurally, other than the Sutter Buttes, the Central

Valley is rather nondescript. The valley is transected by two

cross-valley faults, the Stockton Fault and White Wolf Fault

(Hackel, 1966). The Stockton Fault and associated Stockton

arch together form a geologic divide between the Sacramento

and the San Joaquin Valleys (fig. A1). During an examination

of water levels throughout the valley, several smaller structures were inferred as possibly affecting groundwater-flow.

Davis and others (1959) and Olmstead and Davis (1961)

described geomorphic provinces of the San Joaquin and Sacramento Valleys, respectively. These geomorphic maps were

combined with Jennings’ (1977) map to develop a map of geomorphic provinces for the entire Central Valley (fig. A1). The 

map shows the extent of the fans in the valley as well as the

dissected uplands and basins. Because the location and type

of these provinces generally has been stable and continuous

throughout the time of sediment deposition, the characteristics

of these provinces relate to the character of the deposits.
Geologic History and Setting 19

1

2

3

4

5

6

7

8

10

11

12

12

13

14

15

16

17

18

19 20

21

9

EXPLANATION

California Department of Water Resources

 water-balance subregion and identifier. Water- 

 balance subregion identifier with red highlight

 indicates subregion receiving deliveries not

 shown by arrows

Major streams and canals

Diversion locations

Direction of water movement

 from diversion location, and

 water-balance subregion affected.

 Length of arrow does not relate

 to volume of diversion

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Figure A8. Distribution of water-balance subregions (WBSs) used for surface-water delivery and estimation of groundwater 

pumpage. These areas are based on areas defined by the California Department of Water Resources and are used as “virtual farms” 

for accounting by the Farm Process in the simulation of the hydrologic system of the Central Valley with MODFLOW-2000. WBSs 5, 7, 10, 

and 13-21 receive surface-water that is not shown in the stream network (non-routed deliveries).
20 Groundwater Availability of the Central Valley Aquifer, California

Weissmann and others (2005) recently examined factors

controlling sequence development of alluvial fans in the San

Joaquin Basin (fig. A1). They determined that the character 

of the fans is dependent on the fan’s position in the basin and

its drainage-basin characteristics. In particular, four factors

appear to control development of these fans: tectonic basin

subsidence rates, ratio of degree of change in sediment supply

to change in discharge, local base-level changes, and basin

width. These characteristics ultimately control the grain size

and sorting of the deposits.

The Kings River fan has relatively thick deposits with

vertical stacking owing to the wide valley width, connection

to glaciated parts of the Sierra Nevada, and high tectonic basin

subsidence rates. Conversely, the Chowchilla River fan was

not connected to a glaciated region and, as a result, had very

little change in sediment supply to discharge ratios. This, in

turn, resulted in thinner depositional sequences and no incised 

valleys. These characteristics ultimately control the grain size

and sorting of the deposits. The “Textural Analysis” section 

describes the relation of the geomorphology to the textural

model in more detail.

Hydrogeology

Detailed descriptions of the physical hydrogeology of

the Central Valley are contained in Page (1986), Farrar and

Bertoldi (1988), and Williamson and others (1989). A brief

overview, based on these earlier works, is presented here. The

overview contains a description of the hydrogeologic units

and aquifer characteristics as well as the hydrologic system

(including climate variability, surface water, and groundwater). A large part of the “Aquifer Characteristics” section is

devoted to a detailed examination of the distribution of coarseand fine-grained sediments in the valley.

Aquifer Characteristics

The sediments of the Central Valley compose an aquifer

system comprising confining units and unconfined, semiconfined, and confined aquifers. This aquifer system generally consists of alluvial deposits shed from the surrounding

Sierra Nevada and Coast Ranges (fig. A9). The chief source of 

groundwater in the Central Valley is located within the upper

1,000 ft of deposits (Page, 1986). Fresh groundwater (dissolved solids less than 2,000 mg/L) occurs at depths of more

than 3,000 ft in the alluvial deposits that fill structural troughs

along the west side of the Sacramento Valley and in the

southern part of the San Joaquin Valley. Below the freshwater

zone is saline water; primarily connate water contained in the

thick, marine sedimentary rocks (fig. A9) (Planert and Williams, 1995). In places, saline water is found at shallow depths

in continental deposits. Such occurrences of saline water can

result from upward migration of connate water, evaporative

concentration, or estuarine water trapped during sedimentation

(Farrar and Bertoldi, 1988).

Traditionally, an assessment of the geologic framework of a groundwater basin focuses on a description of the

hydrogeologic or stratigraphic units that compose the aquifer

system. Physiography, weathering characteristics, and soils

have been used on a limited basis to map formations in the

Central Valley; however, defining specific stratigraphic units

in the subsurface has been difficult because differences in

lithology are not apparent (Bertoldi and others, 1991). As a

result, most groundwater studies of the Central Valley define

hydrogeologic units—aquifers and confining units, rather than

stratigraphic units. Early studies simply conceived of just

one unconfined aquifer in the Sacramento Valley that was not

correlated with any particular stratigraphic unit (Bryan, 1923;

Bloyd, 1978). Early studies of the San Joaquin Valley conceived of two aquifers separated by a regional confining unit.

This confining unit is a stratigraphic unit, the Corcoran Clay

Member of the Tulare Formation (referred to in this report as

the Corcoran Clay. Recently, Williamson and others (1989)

described the Central Valley as one continuous heterogeneous

aquifer system. In general, this study follows the same conceptual framework as Williamson and others (1989).

Although a number of stratigraphic units have been identified (Tuscan, Tehama, Tulare, and San Joaquin formations),

their spatial character and extent is poorly known. Dudley and

others (2006) defined the extent of the Tuscan and Tehama formations in a small area near Sutter Buttes and identified them

as units that appear to be locally important for groundwater

resources. Unfortunately, Dudley’s work is not complete.

Unlike the Corcoran Clay in the south, the drillers’ logs examined in the current CVHM study and prior studies, and electric

logs analyzed by Page (1986), reveal no extensive, continuous

fine-grained deposits in the Sacramento Valley. These studies

reveal fine-grained sediments likely associated with the relatively low energy drainage basins and nearby volcanic activity

interbedded with coarse-grained alluvial sediments (fig. A9A). 

In this study, no regional stratigraphic units are defined in the

Sacramento Valley.

In the San Joaquin Valley, the upper semi-confined

aquifer comprises three hydrogeologic units that grade into

each other: Coast Ranges alluvium, Sierran alluvial deposits,

and flood-basin deposits (Laudon and Belitz, 1991; Belitz and

others, 1993) (fig. A9B). The Coast Ranges alluvium, derived

from the Coast Ranges to the west, varies from sands and

gravels in creek-channel deposits near the heads of fans to silts

and clays in interfan and distal fan areas (Laudon and Belitz,

1991), and is 800 ft thick along the Coast Ranges and pinches

out near the valley trough (Miller and others, 1971). The Sierran alluvium, generally coarser-grained than the Coast Ranges

alluvium, is derived from the Sierra Nevada to the east. It

consists of well-sorted medium- to-coarse-grained fluvial

deposits, ranging from 400 to 500 ft thick in the valley trough,

and thinning eastward and westward (Miller and others, 1971).

The Coast Ranges alluvium and Sierran alluvium interfinger

near the surface at the valley trough, and the contact extends
Hydrogeology 21

westward with increasing depth (fig. A9B). Flood-basin deposits, as much as 35 ft thick, derived from both the Coast Ranges

and the Sierra Nevada, lie along and beneath the valley trough

(Laudon and Belitz, 1991).

Numerous lenses of fine-grained sediments are distributed throughout the San Joaquin Valley and generally

constitute more than 50 percent of the total thickness of the 

valley fill. Generally, these lenses are discontinuous and not

vertically extensive or laterally continuous. However, during the Pleistocene, as much as 6,600 mi2 of the San Joaquin

Valley was inundated by lakes that accumulated up to 150 ft

of diatomaceous clay, often referred to as the E-clay or the

Corcoran Clay (Page and Bertoldi, 1983; Farrar and Bertoldi,

1988). This clay is a low-permeability, areally extensive,

lacustrine deposit (Johnson and others, 1968) as much as 200 

ft thick (Davis and others, 1959; Page, 1986) (fig. A1). This 

continuous clay divides the groundwater-flow system of the

western San Joaquin Valley into an upper semi-confined zone

and a lower confined zone (Williamson and others, 1989;

Belitz and Heimes, 1990; Burow and others, 2004).

Figure A9. Pre- and post-development of the A, Sacramento Valley. B, Central part of the San Joaquin Valley, California. (Modified 

from Belitz and Heimes, 1990; and Galloway and others, 1999).

A

Coast Ranges

Sierra Nevada

River

Slough

Fresh-water

recharge

Water

movement

Upper part

of the aquifer

system

Bedrock

Marine

deposits

Lower part

of the aquifer

system

Clayey

lenses

Potentiometric surface for the

lower part of the aquifer system

Water

table

Pumping wells

Fault

Old saline water
22 Groundwater Availability of the Central Valley Aquifer, California

The extent and distribution of the Corcoran Clay was

defined for use in this study because the unit is one of the few

regionally mapable deposits in the valley (fig. A1). The 3-D

thickness and extent of the Corcoran Clay (Page, 1986; Burow

and others, 2004) was used to define the domain of the clay

(fig. A1). It generally is very fine grained; however, isolated,

coarser zones are apparent from drillers’ logs, particularly

where the clay is less than 20 ft thick, as identified by Page

(1986). In addition to its confining properties, laboratory tests

indicate that the clay is highly susceptible to compaction.

Since development, thousands of large-diameter irrigation

wells perforated in the aquifers above and below the Corcoran

Clay have increased the hydraulic connection between these

aquifers and substantially has increased equivalent vertical

hydraulic conductivity of the aquifer system (Williamson and

others, 1989; Bertoldi and others, 1991; and Gronberg and

Belitz, 1992).

In parts of the southern Coast Ranges and where it dips

into the valley, the San Joaquin Formation is shallow enough

to be part of the freshwater-bearing deposits. Allegra HosfordScheirer (U.S. Geological Survey, written commun., 2004)

developed a 3-D integrated stratigraphic framework model for

the San Joaquin Valley. Included in her model is the extent and

thickness of the San Joaquin Formation and older units. Where

the San Joaquin Formation does occur within the CVHM, the

deposits are identified as such.

B

Coast Ranges

Sierra Nevada

River

Slough Fresh-water

recharge

Water

movement

Upper part

of the aquifer

system

(Sierran

deposits)

Bedrock

Marine

deposits

Lower part

of the aquifer

system

Clayey

lenses

Corcoran Clay

(confining layer)

Old saline water

Potentiometric surface of the

lower part of the aquifer system

Water

table

Subsidence area

Pumping wells

Figure A9. Continued. 
Hydrogeology 23

Locally, additional hydrogeologic units have been

mapped in parts of the Central Valley (Burow and others,

2004; Phillips and others, 2007). Other than the units identified above, however, the 3-D extent of regionally extensive

units generally is unavailable. Fortunately, for understanding

regional and local flow patterns and to quantify groundwater

in storage, the physical properties of the aquifer materials and

the distribution of these properties are more important than are 

the delineation of formation boundaries (Bertoldi and others,

1991). 

In general, the valley deposits compose an aquifer system

characterized by large variations in properties. The watertransmitting properties of the aquifer sediments, as represented

by hydraulic conductivity (K) and vertical anisotropy, are

functions of lithology and differ according to grain size and

the degree of sorting of the sediments. There is considerable

variation in the hydraulic properties of the deposits from place

to place. The relation between hydrogeologic units, lithology,

and aquifer characteristics (hydraulic conductivity and vertical

anisotropy) has been elucidated in many previous studies in

the Central Valley (Davis and others, 1959 and 1964; Page,

1986; Williamson and others, 1989; Bertoldi and others,

1991; Laudon and Belitz, 1991; Phillips and Belitz, 1991;

Belitz and Phillips, 1995; Burow and others, 2004; C. Brush,

USGS, written commun., 2006; and Phillips and others, 2007).

Because there is limited stratigraphic control and measurements of hydraulic properties of the aquifer system are scarce, 

material properties in many studies of the Central Valley were

estimated on the basis of the distribution of sediment texture

derived from drillers’ logs, geologic logs, and geophysical

logs (Page, 1983, 1986; Laudon and Belitz, 1991; Burow and

others, 2004). Texture is defined as the percentage of coarsegrained sediment present within a specified subsurface depth

interval (Laudon and Belitz, 1991).

For the purpose of evaluating the distribution of texture,

the Central Valley was divided into nine regions on the basis

of groups of groundwater basins and subbasins (fig. A10). 

These regions will be discussed in more detail in the “Results 

of Texture Model” section.

Textural Analysis

Lateral and vertical variations in sediment texture affect

the direction and rate of groundwater-flow as well as the magnitude and distribution of aquifer-system compaction, manifested as land subsidence. Therefore, the textural distribution

was used to define the vertical and lateral hydraulic conductivity and storage property distributions for the CVHM.

As in previous studies, this study relies on lithologic data

from drillers’ logs, which are frequently assumed to be poor

sources of lithologic information. However, a number of previous studies in the Central Valley have shown that logs, particularly drillers’ logs, if carefully selected, are useful sources

of lithologic information. Page (1986) utilized 685 geophysical logs to investigate the texture of deposits above the base of

freshwater in the entire 20,000 mi2 area of the Central Valley.

Later investigations, particularly those by Laudon and Belitz

(1991), show that drillers’ logs can provide valid texture information if the logs are classified and perforated on the basis of

the degree of detail in the log. In addition to regional studies,

different depth intervals at subregional scales ranging from

500 to 1,000 mi2 in the west-central San Joaquin Valley have

been studied (Prokopovich, 1987; Belitz and Heimes, 1990;

Laudon and Belitz, 1991; Belitz and others, 1993; C. Brush,

USGS, written commun., 2006). Brush and others (C. Brush,

USGS, written commun., 2006) developed texture maps for

a 1,000-mi2 area in the upper part of the western San Joaquin

Valley. Burow and others (2004) developed a 3-D kriged

estimate of the percentages of coarse-grained texture based on

more than 3,500 drillers’ logs in a 900-mi2 subregion in the

Modesto area in the eastern San Joaquin Valley.

Recently, other geostatistical approaches have been

applied to relatively small areas within the Central Valley.

Phillips and others (2007) used applied transition-probability

geostatistical approaches (TProGS) to derive the spatial distribution of sedimentary hydrofacies in a nearly 6.5-mi2

 study 

area of the eastern San Joaquin Valley near the Merced River

(fig. A5A). Burow and others (2004) developed a hydrofacies model in a 19-mi2 study area near Modesto. Weissmann

and others (2002) constructed a sequence stratigraphic model

of the 64-mi2 stream-dominated Kings River alluvial fan by

combining multiple adjacent individual realizations. Application of the TProGS approach to an area as large as the Central

Valley is difficult because the sequence-stratigraphic boundaries largely are undefined and because there are a large number

of depositional settings.

Based on a methodology developed in earlier work by

Page (1983, 1986), Laudon and Belitz (1991), Phillips and

Belitz (1991), and Burow and others (2004), the primary

variable selected for the textural analysis in this study was the

percentage of coarse-grained texture, as compiled from drillers’ logs of wells and boreholes drilled in the Central Valley

(fig. A10B). 
24 Groundwater Availability of the Central Valley Aquifer, California

EXPLANATION

S

a

n

J o

a

q u i

n

V a l l e y

S

a

c

r

a

m

e

n

t

o

V a

l

l

e

y

0 50

0 50 100 Kilometers

100 Miles

10

11

12

20

21

22

23

24

30

Redding

Western Sacramento

Eastern Sacramento

Tracy/Delta-Mendota

Westside

Northern San Joaquin

Chowchilla-Madera

Kings

Tulare/Kern

Well used for

 texture analysis

Number Name

A B

S

acra

mento River

San

Joaquin

River

Kings

River

30

11

22

12

20

24

21

23

10

EXPLANATION

10

11

12

20

21

22

23

24

30

Redding

Western Sacramento

Eastern Sacramento

Tracy/Delta-Mendota

Westside

Northern San Joaquin

Chowchilla-Madera

Kings

Tulare/Kern

Stream or river

Number Name

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

0 50 100 Miles

0 50 100 Kilometers

Orestimba

Creek

Bear

Creek

Merced River

Textural analysis spatial

provinces and domains

Textural analysis spatial

provinces and domains

Figure A10. A, Central Valley showing groundwater basins and subbasins, groupings of basins and subbasins into spatial provinces 

and domains for textural analysis. B, Distribution of wells used for mapping texture. C, Count of wells for each depth increment by 

domains through 1,200 feet. Because less than 1 percent of the logs extend past 1,200 feet, increments below 1,200 feet were not shown. 

Detailed description of the spatial provinces and domains are in table A2.
Hydrogeology 25

Selection and Compilation of Existing Well Data

A database was constructed to organize information

on well construction and subsurface lithology in the study

area using the database design of Burow and others (2004).

Although more than 150,000 optically scanned drillers’ logs

were obtained from the California Department of Water

Resources, this study did not attempt to utilize all of these

logs. As noted by Laudon and Belitz (1991), Belitz and others

(1993), and Burow and others (2004), textural information in

drillers’ logs commonly is ambiguous and inconsistent because

expertise, experience, and vocabulary of the describers vary

greatly. For this study an algorithm was devised to select a

subset of good-quality logs that were spatially distributed

throughout the valley. Three criteria were considered: specificity of location, degree of detail of geologic description, and

density of selected wells. Logs that lacked location information and had poor lithologic descriptions or were illegible were

not selected. Lithologic descriptors were subjectively evaluated on the basis of the amount of detail in the descriptions 

and the depth of the log. Logs with abundant details were subjectively selected. There was no attempt to condition the data

or analyses to the higher quality holes. The density of selected

well logs used in the texture analysis was based on the quality

of drillers’ logs. If two “higher-quality” logs were available for

a quarter township, the search was satisfied and the next quarter township was evaluated. If not, then up to four lesser-quality logs were selected and then the next quarter township was

examined. Preliminary analysis of the drillers’ logs indicated

that this process resulted in logs yielding sufficient detail to

map the texture at 50-ft-depth intervals on a 1-mile grid. The

average thickness of the intervals in the selected drillers’ logs

was 15.5 feet, and 95 percent of the intervals were less than

50 feet thick. On average, 20 intervals per log were defined

and more than 80 percent of the selected logs had 10 or more

different lithologic characteristics.

Latitude-longitude locations were derived from the township, range, section, and quarter-quarter section given on the

drillers’ logs. The location was calculated to the center of the

most detailed part of the township/range information. If more

than one point was available for a given location, the subsequent points were located randomly within the most detailed

township/range designation.

DEPTH TO MIDPOINT OF INCREMENT, IN FEET BELOW LAND SURFACE

NUMBER OF WELLS USED FOR TEXTURE ANALYSIS

10

11

12

20

21

22

23

24

30

Redding

Western Sacramento

Eastern Sacramento

Tracy/Delta-Mendota

Westside

Northern San Joaquin

Chowchilla-Madera

Kings

Tulare/Kern

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 6,000 6,500 7,000 7,500

EXPLANATION

Number Name

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

850

900

950

1,000

1,050

1,100

1,150

1,200

C

Number of wells,

 colored by basin

Figure A10. Continued.
26 Groundwater Availability of the Central Valley Aquifer, California

Burow and others (2004) developed a database from

about 3,500 drillers’ logs in the 900-mi2 Modesto area. These

logs are densely spaced and represent about one-third of the

total wells drilled in the Modesto area. Therefore, this area

was not resampled and their more detailed database was used

where it existed. As a result, the data density for this region,

which covers only about 5 percent of the Central Valley,

is much higher than the rest of the valley. In this study, the

sediment descriptions and depth intervals were entered into

the database exactly as they appeared on the drillers’ log. The

database of existing wells constructed for this 20,000-mi2 

study area contains information from 8,497 drillers’ logs. This

represents about 5 percent of the total number of wells drilled

in the Central Valley. Although the database does not include

all well records, the data provides a representative sample of

the existing wells. Well depths range from 13 to 3,114 ft below

land surface, with a median depth of 321 ft.

Classification of Texture from Drillers’ Logs and 

Regularization of Well Data

Each lithologic log interval was classified using a discrete

binary texture classification of either “coarse grained” or

“fine grained” on the basis of the description in the log. In

this study, coarse-grained sediment is defined as consisting

of sand, gravel, pebbles, boulders, cobbles, or conglomerate.

Fine-grained sediment is defined as consisting principally of

clay, lime, loam, mud, or silt. These definitions of “coarse

grained” and “fine grained” are similar to those originally

defined by Page (1986) and later used by Williamson and

others (1989), Phillips and Belitz (1991), Laudon and Belitz

(1991), Belitz and others (1993), Burow and others (2004),

and Brush and others (C. Brush, USGS, written commun.,

2006). For use in statistical and geostatistical analysis, the percentage of coarse-grained texture was calculated over 50-foot

depth intervals in each of the 8,497 logs in the database. This

regularized data set consists of 46,878 data values of percentage of coarse-grained texture, referred to in this report as

“texture values”. General statistical analyses were computed

to examine spatial changes in percentage of coarse-grained

deposits (count, mean, and standard deviation), both laterally

and with depth. The global mean percentage of coarse-grained

texture is 36 percent, with a standard deviation of 32 percent.

The graph in figure A10C shows that the majority of the

texture values were for depths less than 200 ft. For many of

the well logs, texture values are discontinuous from the ground

surface to the bottom of the borehole. Thus, none of the depth 

intervals include texture values for all 8,497 logs. For depth

intervals shallower than 500 ft, there are more than 1,300

texture values available within each of the nine study domains.

For depths greater than 1,100 ft, less than 100 texture values

exist for a given depth interval. Only 129 logs had texture

values for depth intervals below 1,000 ft, and only 16 drillers’

logs had data for intervals at depths greater than 1,800 ft.

Analysis of the sample variance for each depth interval indicated that the variability of the average percentage of coarsegrained texture increased with increasing depth for depths

greater than 300 ft. The primary reason for the increased

variability is most likely the decrease in the number of drillers’

logs available for wells of increasing depth (fig. A10C). For

depth intervals with less than approximately 1,000 texture values (depth intervals greater than 550 ft), the number of drillers’

logs likely is insufficient to represent the average percentage

of coarse-grained texture at a given depth.

Geostatistical Modeling Approach

The geostatistical methods employed in this study are

similar to those used by Burow and others (2004). 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). An advantage of using geostatistical models instead of

simple spatial interpolation methods, such as inverse-distance

weighted interpolation, is that the geostatistical model provides the best linear unbiased estimate and provides a set of

weights that minimize estimation error (Journel and Huijbregts, 1978). In addition, the model is fitted to the observed

spatial correlation structure, whereas simple interpolation

methods are based on an assumed spatial correlation structure. 

Furthermore, anisotropy in the spatial correlation structure can

be modeled by combining several different models aligned

along the principal axis of anisotropy to form a nested set of

models. 

Regions and Domains

Because of the large size of the Central Valley and multiple depositional environments, the study area was divided

into the nine regions (fig. A10). The mean versus the standard

deviation of the texture data was evaluated for “stationarity”

(Journel and Huijbregts, 1978) and to identify and remove

any proportional effect. Two criteria were used to identify and

remove any proportional effect: groundwater subbasins, and

position relative to the Corcoran Clay (above, below, and outside). First, the study area was separated into its two dominant

valleys, Sacramento and San Joaquin. The Sacramento Valley

was divided into the major groundwater basins (California

Department of Water Resources, 2003): the Redding Area

Basin on the north, and the Sacramento Valley Basin to the

south (fig. A10A). Although the Sacramento Valley Basin has

been subdivided into 18 subbasins (California Department of

Water Resources 2003), this amount of detail was not warranted; therefore, the subbasins within this basin were lumped

into two domains, one east and one west of the Sacramento

River.
Hydrogeology 27

The San Joaquin Valley was separated into three major

parts: the eastern and the western parts of the San Joaquin

Valley and the southern, more internally drained, part. On the

western side, the Tracy and Delta–Mendota subbasins were

grouped into one region, as were the Westside and Pleasant

Valley sub-basins (fig. A10A). Likewise, the eastern side of the

valley was divided into three regions that were groupings of

groundwater subbasins (fig. A10A). The southern part, referred 

to here as the Tulare/Kern region in figure A10A, includes the 

four southern groundwater subbasins of the San Joaquin

Valley Basin (California Department of Water Resources,

2003). 

The San Joaquin Valley was divided laterally and vertically (table A2). Laterally, the domains consist of groupings

of similar groundwater subbasins described above (fig. A10A). 

However, because the hydrogeology of the San Joaquin Valley

is dominated by the Corcoran Clay, where this clay exists,

the regions were subdivided above and outside the extent the

Corcoran Clay (table A2). This resulted in two more domains:

the extent and thickness of the Corcoran Clay, and the area

below the clay. Thus, to assure stationarity within a domain,

17 domains were identified (9 spatial provinces, and divisions

of the provinces into the 6 spatially based domains above the

Corcoran Clay, a domain within the Corcoran Clay, a domain

below the Corcoran Clay, and a domain where the Corcoran

Clay is absent) (figs. A10A, A10B, and table A2). 

Geostatistical Model of Coarse-Grained Texture

Because present-day land surface represents a depositional horizon, the spatial correlation model was developed

using depth below land surface as the z axis. Three-dimensional variograms for each of the 17 domains (table A2) were

developed. The variogram models for each domain were

defined using nested structures; each model included a nugget,

an exponential variogram model, and often a nested Gaussian

variogram model. Horizontal anisotropy was oriented with

the trend of the valley axis within each domain. Therefore,

the principal axes of horizontal anisotropy of the domains are

nearly north-south in the Sacramento Valley and nearly northwest-southeast in the San Joaquin Valley. Reflecting the geometry and depositional environment of the Central Valley, the

variograms typically have a horizontal range in the hundreds

of miles along the axis of the valley and tens of miles perpendicular to the valley axis, and a much smaller vertical range of

165 to 820 ft (table A2). Although nugget values range from 0

to 50 percent of the sill, the nugget typically is about one-third

of the sill. The largest variance is in the northern and southern

domains (Redding and Tulare/Kern outside the extent of the

Corcoran Clay), where streams enter the valley from three

directions.

Texture was estimated at the cell-centers of a 3-D grid.

The grid is oriented with the long axis roughly parallel to

the Central Valley axis and has a uniform cell spacing of 1

mi in the x and y axis directions. The resulting grid consists

of 98 cells in the x-axis direction and 441 cells in the y-axis

direction. The vertical discretization is defined by the established 50-foot depth intervals, starting with the midpoint of

the first interval at 25 ft below land surface and extending 46

cells in the vertical direction to 2,300 ft below land surface.

Because areas outside the basin boundary (fig. A1) were not

estimated, the discretization defined a total of 20,533 grid cells

in the lateral direction and a total of 944,518 grid cells for the

entire 3-D grid.

Using the 3-D variogram models described above and in

table A2, textural values for 50-foot depth intervals (the center

of cell along the z axis) were estimated using 3-D kriging for

each domain. The 17 domain models then were merged to

form one model at 50-foot depth intervals for the entire Central Valley. Because data points for the entire model area were

available for estimation within each domain, the transition

from one domain to the next usually is smooth. The estimate

at each grid cell-center was constrained by the number of data

points, rather than by the spatial dimensions of the search 

neighborhood. Therefore, for locations of the estimation grid

having densely spaced and relatively deep boreholes with continuous drillers’ logs, the effective search neighborhood was

relatively small. For locations of the estimation grid which

contain sparsely spaced drillers’ logs, the effective search

neighborhood was expanded vertically and laterally until the

threshold number of texture values was reached. The estimation neighborhood contained at least two texture values for

each kriged estimate. Estimates in the corners, lower layers,

and along the boundaries of the grid are extrapolated rather

than interpolated values. As is indicated by the nugget and

range of the variograms, the assumption that, in heterogeneous

alluvial sediments, texture at a point is related to texture at surrounding points several miles away may not always be valid.

Therefore, in areas where texture data are sparse, the texture

maps should be regarded as showing only general trends and

averages. Conversely, in areas where texture data are variable

and closely spaced, the 3-D kriging may produce smoothed

estimates. These results occur because the kriging algorithm

used is a function of both distance and direction. 

At the scale of the Central Valley, kriging was done at

points instead of volumes. The point estimates were used to

map the texture within the Corcoran Clay. Where the Corcoran

Clay is thin and, therefore, underrepresented by the 50-foot

depth intervals, the texture distribution within the Corcoran

Clay showed some gridding artifacts that parallel the depth

and thickness of the Corcoran Clay. These artifacts could be

the result of imperfect mapping of the depth to the Corcoran

Clay, the regularized 50-foot incremented data banding in and

out of the Corcoran Clay, and (or) misidentification and (or)

generalization of the extent and thickness of the Corcoran

Clay on Page’s (1986) map. To better represent the textural

distribution within the Corcoran Clay, the 3-D boundary

defined by Page (1986), and later modified by Burow and others (2004), was used to segregate points thought to represent

the clay. These points were used to develop a two-dimensional

kriged map of the percentage of coarse texture in the Corcoran

Clay.
28 Groundwater Availability of the Central Valley Aquifer, California Table A2. Distribution of statistical properties for the percentage of coarse-grained deposits for the Central Valley, Calfornia, by domain, including variogram and variogram models. [All values in feet unless noted otherwise. Spatial province numbers are used to help correlate with figure A2. Variogram model parameters: Slicing height, 50 feet (15.23 m); Lag, 2,000 feet (600 meters); toler- ance (percent of lag), 0.5; number of lags, 50] Valley Spatial province Spatial province number2 Domain Area (square miles) Variogram Variogram models3 Num- ber of samples Mean Variance Anisotropy/ rotation (degrees) Nugget Model 1 - Exponential Model 2 - Gaussian Sill Directional scales Sill Directional scales

1 2 3 1 2 3

Sacramento Redding 10 Redding 597 1,010 39 1,348 0 1,300 2,400 5,000 15,000 500 800 98,000 328,000 700

Western 

SacramentoValley

11 Western Sacramento 3,524 6,423 25 820 10 1,100 1,200 16,000 21,000 400 300 131,000 656,000 2,300

Eastern

SacramentoValley

12 Eastern Sacramento 2,588 4,632 32 1,069 15 1,300 1,600 20,000 28,000 400 500 164,000 492,000 500

San Joaquin Tracy/Delta–

Mendota

20 Tracy/Delta–Mendota

above Corcoran Clay

1,198 1,466 45 1,029 20 1,000 2,300 16,000 59,000 800

Tracy/Delta–Mendota

Corcoran Clay absent

508 433 29 820 20 0 2,700 16,000 8,000 200

Westside 21 Westside above Corcoran Clay 844 1,343 30 982 47 1,000 2,200 20,000 30,000 500

Westside Corcoran Clay

absent

384 481 33 1,012 47 1,300 2,000 17,000 39,000 500

Northern San

Joaquin 

22 Northern San Joaquin

above Corcoran Clay

865 4,470 51 876 31 1,600 1,200 459,000 33,000 600

Northern San Joaquin

Corcoran Clay absent

2,373 8,931 34 936 31 300 2,100 3,000 4,000 200 600 66,000 164,000 300

Chowchilla–

Madera

23 Chowchilla–Madera

above Corcoran Clay

375 330 37 691 35 0 2,200 30,000 20,000 200

Chowchilla-Madera

Corcoran Clay absent

488 1,119 36 878 35 1,100 1,500 21,000 21,000 600

Kings 24 Kings - above Corcoran

Clay

498 1,157 46 577 30 1,000 900 26,000 39,000 200

Kings - Corcoran Clay

absent

1,027 1,560 47 837 30 1,000 1,600 39,000 30,000 500

Tulare/Kern 30 Tulare/Kern - above

Corcoran Clay

2,788 2,562 43 1,042 28 500 2,100 11,000 8,000 300 900 164,000 295,000 2,000

Tulare/Kern - Corcoran

Clay absent

2,500 2,775 41 1,314 28 1,200 2,000 16,000 25,000 700

Corcoran Clay N/A Inside Extent of Corcoran Clay 6,568 374 26 874 33 0 2,900 20,000 20,000 0 9,800 197,000 656,00 197,000

Below Corcoran Corcoran Clay 6,568 4,685 35 1,138 33 900 1,400 6,000 4,000 400 1,400 180,000 558,000 400
Hydrogeology 29

3-D Model of Percentage of Coarse-Grained Texture

The texture model was developed in two stages. In the

first stage, the texture was estimated for each of the 50-ft

thick layers. In the second stage, the resulting estimates

were aggregated vertically into 10 model layers, resulting in

approximately 205,330 nodes. Although the lateral spacing,

1 mi2, stayed the same, the vertical spacing was increased with

depth. In general, the model layers range from 50- to 400-feet

thick; the thickness of each layer is 50 ft more than the layer

above (table A3). 

Where the Corcoran Clay exists, the layers above the clay

were modified so that the clay was explicitly represented by

layers 4 and 5 (fig. A11). In order to complete this representation, the relative thicknesses of layers 1–3 above the clay were

modified. The vertical thickness above the clay was divided

evenly between the three layers below a specified maximum

thickness. The specified maximum thicknesses of layers 1

Layer Thickness 

(feet)

Depth to 

base outside 

Corcoran Clay 

(feet)

Texture 

figure

1 50 50 A9(a)

2 100 150 —

3 150 300 A9(b)

4 Variable 301 A9(c)

5 Variable 302 A9(c)

6 198 500 A9(d)

7 250 750 —

8 300 1,050 —

9 350 1,400 A9(e)

10 400 1,800 —

Table A3. Central Valley, California, groundwater flow model 

layer thicknesses and depths.

[Layers 4 and 5 represent Corcoran Clay where it exists; elsewhere a 1 foot

thick phantom layer; they are kept only to keep track of layer numbers]

Figure A11. Generalized hydrogeologic section (A–A’) indicating the vertical discretization of the numerical model of the groundwaterflow system in the Central Valley, California. Line of section shown on figure A1 (altitudes are along row 355; layer numbers indicate 

model layer).

Cross section along row 355

Land surface

Layer 10

Layer 9

Layer 8

Layer 7

Layer 6

Layer 3

Layer 2 Layer 1

Corcoran Clay

Not in model

A A'

0 30,000 60,000 90,000 120,000 150,000 180,000 210,000 240,000 270,000 300,000

DISTANCE ALONG SECTION, IN FEET

ALTITUDE, IN FEET

-2,000

-2,500

-1,000

-1,500

-500

NGVD 29

1,000

500
30 Groundwater Availability of the Central Valley Aquifer, California

and 2 were 50 and 100 ft, respectively. Any residual thickness was added to layer 3, as necessary, to reach the depth of

the top of the Corcoran Clay. In addition, in layer 3, an area

about 3-mi wide around the area of the Corcoran Clay was

modified, where necessary, to allow a smooth transition from

the non-uniform varying depth and thickness of the Corcoran

Clay to the regular depth intervals of the model layers. The

texture value for each layer was determined by averaging the

percent-coarse value for the appropriate model layer. Layer 1

of the texture model is identical to that of the texture lattice.

Likewise, layers 4 and 5 within the Corcoran Clay correspond

to the 2D kriged values. Outside of the area of the Corcoran

Clay, layers 4 and 5 do not exist and are considered phantom

layers that are kept only to keep track of layer numbering (see

Chapter C).

Results of Texture Model

The relatively low mean percentage of coarse-grained

deposits indicates a prevalence of fine-grained texture

throughout the region (table A2). This tendency was identified previously by Page (1986), Belitz and others (1993), and

Burow and others (2004). The spatial patterns of the percentage of coarse-grained texture are shown in five representative

layers in the texture model (fig. A12) and oblique views of the

model (fig. A13). For shallow depths (0–150 ft for the upper

two model layers), the large number of drillers’ logs reduces

the variance of the estimate. Layers 1 and 3 (figs. A12A and 

A12B) are quite similar in detail; layer 9 is smoothed, relative

to the others, where there is a lack of drillers’ logs (figs. A12D

and A12E). Layer 1 represents the uppermost 50 ft, and

because of the data density, is the most detailed layer in the 

model (fig. A12A). Layer 3, which still shows significant

detail, represents the interval between 150 and 300 ft below

the land surface (figs. A11 and A12B). Layers 4 and 5 represent the Corcoran Clay and generally are very fine-grained

(fig. A12C). Layer 6 represents the 200-foot interval directly

beneath the Corcoran Clay where it is present, or the 300- to

500-foot interval below the land surface (fig. A12D). Finally,

layer nine represents the sparsely penetrated 350-foot interval

between 1,050 and 1,400 ft below the land surface (and deeper

below the Corcoran Clay) (fig. A12E and table A2). 

The 3-D kriged estimates of percentage coarse-grained

texture show significant heterogeneity in the texture of the

sediments (figs. A14 and A15). Zones of very coarse-grained

texture (greater than 90 percent coarse-grained) are locally

significant; however, the results indicate a predominance of

intermediate values of 30–70 percent coarse-grained texture

(figs. A12 and A13). This distribution of texture is described

by the 17 domains defined in table A2. It also can be related to 

the geomorphic provinces and alluvial fan morphology of the

Sacramento River (fig. A14). In the following paragraphs, the

general textural modeling results are described for each of the

domains.

Sacramento Valley

As mentioned previously, the Sacramento Valley was

divided into domains equivalent to the three parts that were

based on groundwater basins or subbasins: the Redding Basin,

and the two groups of subbasins east and west of the Sacramento River. The Redding domain is the coarsest (mean

percentage of coarse-grained deposits of 39 percent, from the

interval data) of the three domains. The western part of the

Redding domain becomes coarser with depth. Most of the

area in the eastern and western Sacramento domains, including the Delta, is predominantly fine-grained (figs. A12 and 

A13); the eastern Sacramento Valley domain is coarser (mean

of 32 percent) than the western domain (mean of 25 percent).

The fine-grained nature of the Sacramento Valley reflects

a number of factors, including more fine-grained volcanicderived source-area sediments and the lack of glacially derived

deposits. Except for the drainage basins draining Lake Tahoe,

the northern Sierra Nevada drainage basins have a much lower

average elevation and a less-coarse depositional character than

the glaciated drainage basins to the south. This resulted in a

higher percentage of fine-grained texture. In addition, the lack

of extensive tectonic basin subsidence in the Sacramento Valley may have resulted in most of the sediments being removed

from the individual drainage basins. However, some coarsegrained isolated deposits are in the shallow part of the Sacramento Valley (layer 1; figs. A1 and A12A) along the channel of

the Sacramento River and the distal parts of the fans emanating from the Cascade Range, the northern Sierra Nevada, and

the American River drainage basin. The coarsest deposits

correlate with the Sacramento River channel and flood-plain

before it widens into more of a basin-type province (fig. A14). 

Hence, the southernmost Sacramento Valley is similar to the

northern San Joaquin Valley, and the character of the alluvial

fans in this part of the valley is similar to that of the Tuolumne

or Stanislaus River fans (fig. A12). Although somewhat less

variable with depth, both the eastern and western Sacramento

domains remain relatively fine grained with depth with some

coarser areas along the western edge of the Sierra. These areas

most likely represent older alluvial fans of the Sierra (figs. A12 

and A13). 

The overall change in sediment texture between the

Sacramento and San Joaquin Valleys can be seen in the

cumulative distribution curves plotted in figure A15. Overall, the Sacramento Valley texture is more fine-grained. The

fine-grained consistent texture of the Corcoran Clay is visible

in figure A15B. In addition, the changes in sediment texture,

with depth, are evident. As would be expected, there is more

variability in the shallow sediments. In the Sacramento Valley,

at the fine-grained end, the change is systematic from layer 1

down to 10. Toward the coarser end of the distribution curves,

the change is not so systematic. In the San Joaquin Valley

curves, there is more contrast between the depth layers than in

the Sacramento Valley curves.
Hydrogeology 31

Well locations

A

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

EXPLANATION

Percentage of coarse-grained

 material

0 to 10

>10 to 20

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90 to 100

Central Valley boundary

Wells having logged data

7,263 wells have logged data

Layer 1

> is greater than.

10

11

12

20

21

22

23

24

30

Redding

Western

Sacramento

Eastern

Sacramento

Tracy/DeltaMendota

Westside

Northern

San Joaquin

ChowchillaMadera

Kings

Tulare/Kern

Enlarged area

shown

on figure 14

Enlarged area

shown

on figure 14

Layer 1

Land surface

Not in model

Figure A12. Kriged distribution of coarse-grained deposits for layers 1, 3, Corcoran Clay, 6, and 9 of the groundwater-flow model. Inset 

shows distribution of wells used in that depth interval. 
32 Groundwater Availability of the Central Valley Aquifer, California

Well locations

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

5,952 wells have logged data

13,235 depth increments intercepted

Wells having logged data

EXPLANATION

Percentage of coarse-grained material

0 to 10

>10 to 20

> is greater than.

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90 to 100

Central Valley boundary

Layer 3

10

11 12

20

21

22

23

24

30

Redding

Western

Sacramento

Eastern

Sacramento

Tracy/DeltaMendota

Westside

Northern

San Joaquin

ChowchillaMadera

Kings

Tulare/Kern

B

Layer 3

Land surface

Not in model

Figure A12. Continued.
Hydrogeology 33

Figure A12. Continued.

Well locations

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Wells having logged data 

1,306 wells have logged data

1,534 depth increments intercepted

EXPLANATION

Percentage of coarse-grained material

0 to 10

>10 to 20

> is greater than.

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90 to 100

Central Valley boundary

Layer Corcoran Clay

10

11 12

20

21

22

23

24

30

Redding

Western

Sacramento

Eastern

Sacramento

Tracy/DeltaMendota

Westside

Northern

San Joaquin

ChowchillaMadera

Kings

Tulare/Kern

C

Corcoran Clay Land surface

Not in model
34 Groundwater Availability of the Central Valley Aquifer, California

Figure A12. Continued.

D

Well locations

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Wells having logged data

2,007 wells have logged data

8,312 depth increments intercepted

EXPLANATION

Percentage of coarse-grained material

0 to 10

>10 to 20

> is greater than.

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90 to 100

Central Valley boundary

Layer 6 10

11 12

20

21

22

23

24

30

Redding

Western

Sacramento

Eastern

Sacramento

Tracy/DeltaMendota

Westside

Northern

San Joaquin

ChowchillaMadera

Kings

Tulare/Kern

Layer 6

Land surface

Not in model
Hydrogeology 35

Figure A12. Continued.

Well locations

E

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

79 wells have logged data

309 depth increments intercepted

Wells having logged data

EXPLANATION

Percentage of coarse-grained material

0 to 10

>10 to 20

> is greater than.

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90 to 100

Central Valley boundary

Layer 9 10

11 12

20

21

22

23

24

30

Redding

Western

Sacramento Eastern

Sacramento

Tracy/DeltaMendota

Westside

Northern

San Joaquin

ChowchillaMadera

Kings

Tulare/Kern

Layer 9

Land surface

Not in model
36 Groundwater Availability of the Central Valley Aquifer, California

Percent coarse-grained material—

0 to 10

>10 to 20

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90 to 100

Inactive in model

Approximate extent

of Tulare Lake bed

EXPLANATION

Box encompassing

extent of model

Corcoran

Clay

Line of cross section

shown on figure 11

(Row 355)

> is greater than.

Figure A13. Kriged texture within groundwater-flow model. 
Hydrogeology 37

Sacramento River

flood plains Sacra

mento

River

Mokelumne

River

Kaweah

River

Kings

River

Fresno River

Chowchilla River

Bear Creek

Orestimba

Creek

Merced River

Tuolumne River

Stanislaus River

Calaveras

River

San

Jo

a

quin

River

EXPLANATION

San Joaquin Basin

fluvial-fan boundaries

Major stream

or canal

Geomorphic province—

Basin

Delta

Dissected uplands

Sacramento River

flood plains

Fan deposits

Percent coarse-grained

 material—

0 to 10

>10 to 20

>20 to 30

>30 to 40

>40 to 50

>50 to 60

>60 to 70

>70 to 80

>80 to 90

>90 to 100

0 50 Miles

0 50 Kilometers

> is greater than.

Figure A14. Distribution of coarse-grained deposits for the upper 50 feet for part of the Central Valley. The map is overlain with the 

major geomorphic provinces of the Central Valley and the fluvial fans of the San Joaquin Basin (modified from Davis and others, 1959; 

Olmstead and Davis, 1961; Jennings, 1977; and Weissmann and others, 2005).
38 Groundwater Availability of the Central Valley Aquifer, California

0.0 0.2 0.4 0.6 0.8 1.0

COARSE-GRAINED MATERIAL, IN PERCENT 

Layer 1

Layer 2

Layer 3

Layer 6

Layer 7

Layer 8

Layer 9

Corcoran Clay Layer 10

20

0

40

60

80

100

EXPLANATION

San Joaquin Valley

A

Sacramento Valley

0.0 0.2 0.4 0.6 0.8 1.0

CUMULATIVE PROBABILITY

COARSE-GRAINED MATERIAL, IN PERCENT 

20

0

40

60

80

100

B

Figure A15. Cumulative distributions of kriged sediment textures for model layers in the A, Sacramento Valley. B, San Joaquin Valley 

and Tulare Basin.
Hydrogeology 39

San Joaquin Valley

Texture in the San Joaquin Valley is punctuated by the

distribution of the Corcoran Clay domain. In contrast to the

texture of the overlying and underlying domains (table A2), 

the Corcoran Clay domain has zones of very fine-grained

texture throughout and it is the finest-grained domain (mean

of 26 percent) in the San Joaquin Valley (fig. A12C). Despite

the overall fine-grained nature of this deposit, the results of

this study indicate some coarse-grained areas within areas

previously defined as part of the Corcoran Clay (Page, 1986;

Burow and others, 2004). These coarser areas generally

occur where the Clay is thin, and partly may be an artifact of

regularizing texture over the 50-foot depth intervals. Many of

these thin coarser areas are found along the edges of the clay

and are not laterally extensive. A more extensive coarser area

is found along the northern part of the clay along the Merced

River, Bear Creek, and Orestimba Creek. Adjacent to the

border between the Kings and the Westside domains, the clay

is particularly thin and consistently coarser grained across the

clay, grading from east to west. South of the Tulare Lake bed

(fig. A12), the clay is thin and, except for a few beds, is not

very fine-grained in this model (figs. A12C and A13). In this 

area, it is possible that the wells penetrate the deeper Tertiary

sediments. Before the Corcoran Clay was deposited, this area

probably was alluvial/deltaic, as the southern seaway was

open as recently as the late Pliocene, when the Coast Ranges

were uplifted. As a result, the sediments are interbedded. The

Pleistocene sediments may be thick here because tectonic

basin subsidence, combined with the narrowness of the valley,

may have forced the Kern River to deposit its sediment near

the mountain front.

Below the Corcoran Clay, the mean texture is larger

than that in the domain of the Corcoran Clay (table A2 and 

fig. A12D). Except for the Westside domain, the domains

below the Corcoran Clay are finer grained than the domains

above. Although the area below the Tulare Lake bed usually

is thought of as a clay plug, the nine wells identified in this

study that extend below the base of the Corcoran Clay near

the Tulare lake bed record alternating series of sands and clays

(coarse and fine-grained sequences). As a result, the texture

model of the lower layers (figs. A12E and A13) shows a relatively coarse-grained area in the southern part of the Central

Valley.

The San Joaquin Valley, above and beyond the extent

of the Corcoran Clay, is segregated laterally into three major

parts: the eastern and the western parts of the San Joaquin

Valley, and the southern more internally drained part of the

valley. The deposits reflect the difference in source materials

and surface-water influx of the Coast Ranges and the Sierra

Nevada. Areas of coarse-grained texture are more widespread

than the areas of fine-grained texture. The coarse-grained areas

are prevalent in many of the fans in the Sierra foothills, and

below the San Joaquin River channel along the axis of the

valley (figs. A12 and A13). Generally, the fine-grained texture

zones are in the proximal interchannel and distal floodplain

areas. Along the valley axis, the coarsest deposits lie west of

the present-day channel of the San Joaquin River, indicating

the channel may have shifted eastward.

The domains of the eastern San Joaquin Valley (Northern

San Joaquin, Chowchilla–Madera, and Kings) show a different

geometry and distribution of deposits. These domains generally agree with the areas mapped by Weismann and others

(2005). These domains suggest a complex spatial structure

that can be attributed to the effects of the east-west alignment

of the tributary rivers and the fans (fig. A14) along the Sierra

Nevada foothills, combined with the asymmetry of the northsouth aligned San Joaquin River dominating the central part of

the area. At least in the shallow parts of the system, the alignment of the San Joaquin River and its tributaries’ channels and

the physiography of the valley played an important role in the

depositional history (Burow and others, 2004).

The deeper sediments may not reflect the orientation of

the streams, especially in the northern San Joaquin Valley

where the Tertiary sediments are much shallower. Although

the depth intervals are consistent depths from the land surface,

they represent potentially different depositional environments

from north to south because of the differences in tectonic basin 

subsidence rates. For example, the coarse-grained sediments

at 500 ft below land surface in the Kern Basin probably are

Pleistocene sediments, whereas the sediments at 500 ft below

land surface in the Modesto/Merced area probably are Tertiary

in age and are more fine grained because of the difference in

environment during deposition. The southern seaway also may

have changed the course of the rivers, and some of them may

have been oriented more north-south. During Pliocene and

older ages, it is thought that the San Joaquin Valley drained to

the south, thus, possibly affecting the orientation of streams

and the location of fluvial/deltaic deposits in the south

(Bartow, 1991).

In the northern part of the Northern San Joaquin domain,

dissected uplands along the Sierra Nevada generally are

fine-grained. The Calaveras River and Mokelumne River

fans formed in a narrow part of the basin near the valley

outlet (fig. A14). The Calaveras fan (figs. A12 and A14) is not 

connected to glaciated parts of the Sierra Nevada, serving to

limit the supply of readily available coarse-grained sediments

(Weissmann and others, 2005). These fans exemplify these

characteristics with their fine-grained texture. Conversely, as

the Stanislaus and Merced Rivers leave the finer-grained, dissected uplands, the texture becomes coarser toward the valley

axis at their confluences with the San Joaquin River, owing

to the coarser sediments deposited by the San Joaquin River.

Although these rivers connect to glaciated parts of the Sierra

Nevada (like the very coarse-grained Kings River fan to the

south), their relatively smaller drainage basins are at lower elevations and their outlets have a lower subsidence rate (Weissmann and others, 2005). Similarly, the Tuolumne River drainage basin is about the same size and elevation as that of the

Kings River; however, its outlet has a lower subsidence rate.

As expected, the Stanislaus, Merced, and Tuolumne River fans

are moderately coarse-grained, but not as coarse-grained as the
40 Groundwater Availability of the Central Valley Aquifer, California

Kings River fan. In addition, areas south of each of these three

present-day channels are coarser grained, possibly indicating

that the rivers have migrated north. In the southern-most part

of the Northern San Joaquin domain (the Merced Basin south

of Bear Creek), the basin geomorphic province reaches farther

east, and this correlates with the extent of the finer-grained

deposits mapped using the texture model (fig. A14).

The Chowchilla–Madera domain, dominated by the

Chowchilla River fan and Fresno River fan (figs. A12 and 

A14), generally is fine-grained. These fans have drainage

basins that tap only non-glaciated parts of the Sierra Nevada

(Weissmann and others, 2005). These fans did not experience

large changes in the supply of sediment to the drainage basin,

relative to the amount of water discharging from the basin

(Weissmann and others, 2005); therefore, they did not develop

the deep incised valleys, and they appear to have relatively

moderate grain sizes throughout their extent. As expected,

visually there is a correlation between the extent of the basin

geomorphic province in this domain and the finer-grained

texture (fig. A14). 

The Kings domain, dominated by the San Joaquin River

fan and Kings River fan, are much coarser grained than the

alluvial fans to the north (figs. A12A and A14). Both fans

are connected to glaciated parts of the Sierra Nevada. These

glaciated sections provide an abundance of coarse-grained

sediments to the basin. The San Joaquin River fan is similar in

character to the Tuolumne River fan. It was developed in an

area having relatively low subsidence rates, has a deep modern

incised valley, and connects to the axial San Joaquin River

(Weissmann and others, 2005). As a result, it is relatively

coarse-grained, particularly near the river channel and its apex.

It is finer grained in the basin geomorphic province outside

the active channel to the southwest. The Kings River fan is an

example of fan development in a part of the valley that is wide

and has high subsidence rates (Weissmann and others, 2005).

As a result, this fan has relatively thick deposits with vertical

stacking and is one of the coarsest-grained areas in the Central

Valley, particularly near its apex.

The texture data in layers 2 and 3 show coarse-grained

deposits, some greater than 70 percent coarse, north of the

present day Kings River and south of the current San Joaquin

River (figs. A12B, A14, and A15), indicating that these rivers

may have changed their course, migrating to the south and

north, respectively. In contrast to the Kings River fan, the

Kaweah River fan, which drains into the subsided area of the

Tulare Lake Basin, generally is fine grained (fig. A5A); both

fans have some coarse-grained deposits near their respective

apex. This finer-grained nature may be related to the fact that

the Kaweah River fan has a significantly smaller drainage

basin with a lower contributing basin elevation. In addition,

although its drainage basin was glaciated, it never had a large

trunk glacier like the Kern, Kings, San Joaquin, Merced, and

Tuolumne drainage basins (Matthes, 1960); rather, it had a

series of separate, small glacial systems in tributary streams.

The Tulare/Kern domains are surrounded on three sides

by mountains, and are drained internally. Most of the southern

parts of the Tulare/Kern domains show a predominance of

coarse-grained areas in the upper seven layers of the model

(figs. A12, A13, and A15). The Tulare Lake bed in the northwestern part of the domain above and within the Corcoran

Clay is predominantly fine-grained (figs. A12A–C and A13). 

The domains in the western San Joaquin Valley (Tracy/

Delta-Mendota and Westside regions) are relatively finegrained (fig. A12). This is especially true in comparison to 

the eastern San Joaquin Valley and the Tulare/Kern domains.

These finer textures reflect the source material: shales and

marine deposits in the Coast Ranges. These rocks usually yield

finer-grained sediments than the granitic rocks that are the

sediment source for the fans on the eastern side of the Valley.

The Westside domains, both above and beyond the extent of

the Corcoran Clay, and the northern part of the Tulare/Kern

domains are especially fine-grained. Except for the Corcoran

Clay and the area around the Delta, the Westside domains

are among the finest-grained areas in the Central Valley. This

finer-grained nature may be attributed to flashy, debris flow

fan type deposits from small drainage basins characteristic of

this part of the Central Valley and (or) the fact that the area

is internally drained with no outlet for exporting the finergrained deposits (fig. A14). It is interesting to note that this

area has more tile drains than any other area in the Central

Valley. In addition, this area has the largest amount of

pumping-induced subsidence recorded in the valley.

Despite this predominance of fine-grained deposits,

coarse textures are found in the Westside and Tracy/Delta–

Mendota spatial provinces. The western edge of these regions

are coarser grained along the alluvial fans of the Coast

Range, and coarse deposits are evident below a depth of 50 ft

(fig. A12B) beneath land surface along the San Joaquin River.

These coarser deposits correspond to the area identified by

Laudon and Belitz (1991) as the Sierran Sands. Above the

Corcoran Clay, the Tracy/Delta–Mendota domain generally

is fine grained near the Delta and gets coarser to the southeast, particularly along the San Joaquin River. Similar trends

continue farther below the land surface until an abrupt break at

the Corcoran Clay.

Hydrologic System

The hydrologic system of the Central Valley comprises

three principal components that govern the storage of water

and affect its availability: the snowpack of the Sierra Nevada;

an extensive system of rivers, streams, dams, lakes, and other

storage and conveyance systems for surface water; and finally,

the aquifer system that stores and conveys groundwater. Water

is stored as snow generally accumulates in the Sierra Nevada

during the late fall and winter and releases as snow melts during late winter, spring, and early summer. About 78 percent of

the total unimpaired streamflow occurs from January through

June (Williamson and others, 1989). During the past 100

years, much of this surface-water flow has been controlled by

dams at the base of reservoirs (fig. A12). These dams capture 
Hydrogeology 41

and store the surface water entering the Central Valley for use

during the dry season.

Below these dams, a complex network of streams and

canals distributes the water throughout the valley. Within the

Central Valley, there are approximately 160 streams or rivers

totaling 1,512 mi in length and 6,291 constructed agricultural

channels with a total length of 19,812 mi (California Regional

Water Quality Control Board, 1992). This network of streams

and canals delivers surface water to Federal, State, local, and

private water users. The water storage and delivery infrastructure —dams and canals—have been designed largely on

the basis of the historical snowpack. A brief discussion of the

surface-water system is presented in this section. Chapter B

discusses this complex surface-water network in more detail.

Water supplied from aquifers is a less formal and less 

recognized contribution to California’s water supply (California Department of Water Resources, 1998). Although some

groundwater is used even when surface water is available,

because groundwater generally is more expensive than surface

water, it usually is used when surface water is not available.

An overview of the groundwater system is presented in this

section. The spatial and temporal patterns of groundwater

development are discussed in more detail in Chapter B.

Climate variability affects the volume, and spatial and

temporal distribution, of surface water as well as the potential

recharge to, storage within, and increased discharge from the

groundwater system. Climate variability also may affect the

timing of snow melt, runoff, and other components of the Central Valley hydrologic system, including those associated with

possibly prolonged growing seasons.

Climate 

Climate affects each of the three principal components

that govern the storage of water and affect its availability.

Climate encompasses the temperatures, humidity, rainfall, and

numerous other meteorological factors in a given region over

long periods of time. The climate of a location is affected by

its latitude, terrain, persistent snow cover, as well as nearby

oceans and their currents. Hence, climate varies spatially and

temporally. In the Central Valley, climate varies spatially from

north to south as well as from east to west. Regional-scale

topography is the major influence on precipitation. The source

of precipitation to the Central Valley and adjacent mountain

ranges is moist air masses that are swept inland from the

Pacific Ocean on the prevailing westerlies as winter storm

fronts. The mountain ranges cause orographic lifting of the

moist air masses, which causes cooling, condensation, and

precipitation—predominantly on the western slopes—and

leaves a rain shadow on most eastern slopes. Accordingly, the

eastern slopes of the Coast Ranges and the valley floor are

in the rain shadow of the Coast Ranges, and heavy precipitation falls on the western slopes of the Sierra Nevada. This

precipitation, occurring both as rainfall and snowfall on the

Sierra Nevada, is the major source of water entering the valley

(fig. A16). In particular, in most years the storms leave large

accumulations of snow in the Sierra Nevada during the winter

months. 

The climate variability of moisture from the North

Pacific, where the winter frontal storms originate, is reflected

in the index known as the Pacific Decadal Oscillation (PDO)

(Mantua and others, 1997; Mantua, 2006). Subtropical

moisture from El Nino-Southern Oscillation (ENSO) events

and monsoonal moisture from the North American monsoon

system (NAMS) affect climate in the western United States

(Hanson and others, 2006), and may affect climate in the

Central Valley.

There also is a distinct variation in precipitation, volume

of snowpack, and timing of snow melt from north to south. In

terms of surface-water supply, the Central Valley is geographically dichotomous, with a surplus of water in the northern part

of the valley and a deficit in the southern part of the valley.

The northern half of the Central Valley (the Sacramento Valley) has more precipitation than the drier San Joaquin Valley

(fig. A5A). Streamflow from the major drainages entering the

valley reflect this dichotomy (figs. A5 and A16). The Sacramento River drainage basin, which encompasses and feeds the

Sacramento River, supplies approximately two-thirds of the

surface water to the Central Valley. In contrast, the larger San

Joaquin River drainage basin only contributes about one-third

of the surface water (fig. A16C). Under the current climatic 

regime, streamflow from the major drainages entering the valley largely is a result of the melting of snowpack.

Recent studies indicate that the relative amounts and

timing of precipitation and inflow from drainages entering

the Central Valley are changing (Dettinger, 2005; Dettinger

and Earman, 2007). Long-term trends in regional climate can

be identified from a graph of the cumulative departure from

the average precipitation (fig. A17). Wet periods represent 

the positively sloped limbs and the dry periods represent the

negatively sloped limbs of the cumulative departure curve,

respectively.

The cumulative departure graphs show wetter-thanmedian periods and drier-than-median periods since the 

late 1800s at several stations throughout the Central Valley.

The precipitation at the various stations over the past several decades exhibit similar patterns throughout the valley

(fig. A17). Based on these cumulative departures, wet and dry

periods were defined (fig. A17). Even though there are many

exceptions, the wet and dry periods generally are aligned

with the variations in the cycles of the PDO index (fig. A17). 

Because annual precipitation in the San Joaquin Valley is

much less than in the Sacramento Valley, the magnitude of the

departure is less in the south (figs. A16 and A17). Although

somewhat masked by human activities, variations in stream

flow at gages for water years 1962–2003 (fig. A16A) also 

demonstrate these wet and dry cycles.
42 Groundwater Availability of the Central Valley Aquifer, California

Figure A16. A, Total inflow from 44 gaged streams flowing into the Central Valley, California, water years 1962–2003. 

B, Average annual precipitation in the Central Valley, California, water years 1962–2003. C, Total surface-water flow into the Central 

Valley, California, water years 1962–2003. 

"Normal"

to

Dry

Dry Wet Dry Wet

"Normal"

to

Dry

"Variable"

to

Dry

Dry Wet Dry Wet

"Variable"

to

Wet

"Variable"

to

Wet

"Variable"

to

Dry

Median

(26,300)

75th percentile

(38,200)

WATER YEAR

EXPLANATION

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

WATER YEAR

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Kings River

Tuolumne River

Stanislaus River

Merced River

Cottonwood Creek Kern River

Other Sacramento Valley streams Other San Joaquin Valley streams

Sacramento River

Feather River

American River

Yuba River

10,000

0

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0

30

25

20

15

10

5

FLOW RATE, IN THOUSANDS OF ACRE-FEET PER YEAR

MEAN PRECIPITATION, IN INCHES PER YEAR

25th percentile

(11.0)

75th percentile

(16.8)

Median

(13.0)

A

B

25th percentile

(18,800)

Representative Water Year

Typical

1975

Dry

1990

Wet

1998

Representative Water Year

Typical

1975

Dry

1990

Wet

1998
Hydrogeology 43

Between 1875 and 2005, seven predominantly wet

periods and six predominantly dry periods were identified.

The cumulative departures from average shown in figure A17

suggest that most areas have been subject to a precipitation

deficit since the 1930s. This deficit grew during the 1950s

and 1960s, and, with the exception of Red Bluff, underwent

a gradual recovery thereafter. Most areas show trends of significant recovery in the 1990s. During the second half of the

20th century, California experienced multiyear droughts during

1959–61, 1976–77, and 1987–92 (California Department of

Water Resources, 1998). These droughts are represented by

the periods labeled “Dry” or “Variable to Dry” in figure A17. 

There are some notable spatial differences in the cumulative departure of precipitation. For example, the southern part

of the valley (Bakersfield station) shows an increase in precipitation from about 1935 to 2005, whereas the northern part

of the valley (Red Bluff station) shows a dramatic decrease

between 1945 and the early 1990s. However, other than during the 1987–92 drought, the pattern for all stations from the

early 1980s to the present is characterized by above-average

precipitation.

Kings River

Tuolumne River

Stanislaus River

Merced River

Cottonwood Creek Kern River

Other Sacramento Valley streams Other San Joaquin Valley streams

Sacramento River

Feather River

American River

Yuba River

S a c r a m e n t o V a l l e y

R i v

e

r

s

S

a

n

J

o

a q

u

i n

V a l l e y R i v e r s

EXPLANATION

24.03%

13.69%

8.95%

6.12%

6.00%

5.45%

3.54%

3.24%

2.42%

2.17%

16.54%

7.85%

C

Figure A16. Continued.
44 Groundwater Availability of the Central Valley Aquifer, California

This study focuses on water years 1962 through 2003

(figs. A16 and A18). The first part of this period, 1962–77,

is part of a cooler and drier (negative) PDO period, whereas

the period 1978–2003 is warmer and wetter. The southern

part of the valley shows smaller changes in average monthly

precipitation, and all stations show small increases in summer

precipitation that, nevertheless, are greatly exceeded by

evapotranspiration (ET) (figs. A5 and A6).

TIME, IN YEARS

CUMULATIVE DEPARTURE OF PRECIPITATION, IN INCHES PER YEAR

PACIFIC DECADAL OSCILLATION (PDO) INDEX, UNITLESS

Fresno

Davis

Bakersfield

Pacific Decadal Oscillation (PDO) Index-24 month moving average

1875 1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

-80

-70

-60

-50

-40

-30

-20

-10

0

40

10

20

30

50

Red Bluff

Annual cumulative departure of precipition—

EXPLANATION

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Wet Dry Wet Dry Wet Dry Dry Wet Dry Wet

Figure A17. Cumulative departure from average annual precipitation at Redding, Davis, Fresno, and Bakersfield, California. For 

reference, a 24-month moving average of the Pacific Decadal Oscillation Index is also plotted.
Hydrogeology 45

ET, like precipitation and runoff, is affected by climate

variability (fig. A18) and is directly dependent on a combination of temperature, wind, relative humidity, and net radiation.

During periods of high cloudiness, ET is reduced. The PDO

and the position and strength of the California Pressure Anomaly and the North Pacific High (and related Hadley cell) affect

ET (Hidalgo and others, 2005). The cumulative departure

curves for ET show less variation than precipitation (fig. A18). 

For example, the interannual and interdecadal cycles of ET

generally are in opposite phase with the precipitation and PDO

cycles. However, three Central Valley stations show less similarity in interannual cycles of ET since the end of the previous

drought in 1992 (fig. A18) and are intermittently in phase with

PDO cycles, which may suggest that other climate forcings,

such as climate change as well as other climate cycles (for

example, ENSO and NAMS), may be contributing to these

TIME, IN YEARS

CUMULATIVE DEPARTURE OF PRECIPITATION AND

REFERENCE EVAPOTRANSPIRATION, IN INCHES PER YEAR

PACIFIC DECADAL OSCILLATION (PDO) INDEX, UNITLESS

-50

-40

-30

-20

-10

10

0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Davis

Davis

Bakersfield

Pacific Decadal Oscillation (PDO) average annual value

Red Bluff

Annual cumulative departure of 

 reference evapotranspiration (ETo)—

Annual cumulative departure of precipitation

 from PRISM data

EXPLANATION

"Variable"

to

Dry

"Variable"

to

Wet

"Variable"

to

Dry Dry Wet Dry Wet

Figure A18. Cumulative departure of monthly precipitation (Parameter-elevation Regressions on Independent Slopes Model (PRISM) 

data from Davis, California), cumulative departure of the Pacific Decadal Oscillation (PDO) index, and cumulative departure of the 

monthly reference evapotranspiration (ETo) values (gridded values of California Irrigation Management Information System’s (CIMIS) 

stations from Redding, Davis, and Bakersfield, 1960–2004). 
46 Groundwater Availability of the Central Valley Aquifer, California

relations. Although generally showing similar trends, ET also

has different magnitudes and patterns over the different areas

of the Central Valley.

Surface Water

Streamflow is a critically important factor in the water

supply of the Central Valley. The streamflow and reservoir

storage almost entirely are dependent on precipitation and

snowmelt in the Sierra Nevada and part of the Klamath Mountains in the north. Few perennial streams of any significant

size enter the valley from the west. Snowpack and resulting

surface-water flow in the northern part of the Central Valley

is much greater than that in the southern part of the valley

(fig. A16). Almost three-quarters of the inflow to the Central

Valley is from the Sacramento River drainage basin, and more

than three-quarters of this flow is from four rivers: Sacramento, Feather, American, and Yuba Rivers (fig. A16C). North 

of the Central Valley, the Sacramento River has its headwaters

in the Trinity Mountains west of Mount Shasta, and a large

quantity of flow is contributed to the Sacramento River from

the Pit River. Several other large streams with steep gradients

flow westward from the Sierra Nevada and join these rivers

close to the axis of the Central Valley. Flows in the Sierran

streams are seasonally variable, with high flows coinciding

with the snowmelt that comes in the late spring. Many of

these streams have been dammed, and outflows are stored for

release as irrigation water, power generation, and urban supply

along the Central Valley’s vast natural and man-made surfacewater system. Flows generally are intermittent in the smaller

streams that drain eastward from the Coast Ranges.

Rivers feeding the San Joaquin River drainage basin are

much smaller (fig. A16C). The Kings, Tuolumne, Stanislaus,

Merced, Kern, and Mokelumne Rivers make up three quarters of the volume of water entering this drainage basin. The

San Joaquin River has its headwaters in the Sierra Nevada.

Prior to development of agriculture, flow in the San Joaquin

River was sustained entirely from runoff from the western

slope of the Sierra Nevada. Flow in the river is sustained by

a combination of Sierra runoff, releases from upstream dams,

and agricultural wastewater derived from drained fields that

use irrigation water imported from outside the San Joaquin

drainage area. Flows generally are intermittent in the streams

that drain eastward from the Coast Ranges and typically do

not reach the San Joaquin River. In the southern part of the

valley, the Kings, Kaweah, Tule, and Kern Rivers flow into the

internally drained Tulare Basin. Before development, flow was

seasonally variable and spring runoff often would accumulate

in Tulare Lake. Presently, the flows from these rivers are controlled by dams and have been redirected to various parts of

the Central Valley and elsewhere in the State of California.

Streamflow in the Central Valley is highly variable. The

median flow for water years 1962–2003 was 28.9 million

acre-ft/yr (fig. A16A). The highest flow, 78 million acre-ft, was

in1983. The highest combined flows coincided with the one of

the highest average annual precipitation rates for the Central

Valley (fig. A16B). Conversely, 1977 was a dramatically lowflow year with about one-third the median flow, 10 million

acre-ft. The low flow in 1977 followed two consecutive years

of drought. The total annual flow was within 10 percent of the

median annual flow of these streams only for 8 years during

1961–2003 (fig. A16A). 

The variation in precipitation from north to south produces differences in the timing of runoff in the two valleys.

Peak runoff in the Sacramento Valley generally lags peak

precipitation in the surrounding mountains by 1–2 months,

whereas peak runoff in the San Joaquin Valley generally

lags peak precipitation by 5 months (Williamson and others,

1989; Bertoldi and others, 1991). As a consequence of the

geographic, seasonal, and climatic variations, the Sacramento

Valley frequently floods, whereas rivers in the San Joaquin

Valley alternately flood or have abnormally low flows during

droughts (Bureau of Reclamation, 1994).

Streamflow in the Central Valley is influenced by variability in climate (Meko and Woodhouse, 2005) (fig. A16A). 

The cumulative departure of streamflow diversions from the

Bear River shows consistency with wet/dry cycles (fig. A19). 

For example, there are larger streamflow diversions during wet

years. It also shows a general alignment with the PDO climate

index (Mantua and Steven, 2002) cycles. Interestingly, from

1968 to 1986 there is an increase in diversions and the PDO

index. Between 1968 and 1975 there is a steady increase in

diversions and the PDO index. During the drought in 1976–77,

however, there is a sudden decline in diversions that is not

reflected in the PDO index. After this short drought, the pattern of steady increase in diversions continues until the next

drought begins in 1987. As with the previous drought, diversions from Bear River generally decrease during the 1987–92

drought. The first few years of this drought also are accompanied by a decrease in the PDO index. During the period

1960–92, changes in diversions generally lagged behind the

PDO index by about 5 years (correlation of 0.45). Throughout

the 1987–92 drought, there appears to be a systematic and sustained decrease in diversions that persisted for a couple years

into the following wet period.

In contrast, since the 1987–92 drought, the demand

for streamflow diversion appears to be inversely correlated

with the cycles in the PDO index. After 1999, the streamflow diversion appears to become more inversely correlated

with the PDO index and annual temperature differences. One

interpretation of these data is that demand for surface water is

constrained partly by climate as availability of surface water,

but also is controlled by other factors. These factors may 

include agriculture growth, markets, changing crop types, and

inter-annual variation in ET.
Hydrogeology 47

The timing and magnitude of precipitation and streamflow directly affects the availability and use of surface water

for agriculture and the environment. Heavy winter and spring

runoff do not correspond to the period of peak agricultural

and urban water demand, which occurs in the summer. To

ameliorate timing and distribution of surface-water supplies,

California has developed and maintained a complex surfacewater storage and distribution network (fig. A7). In addition, 

this distribution network is used for flood control, recreation,

and regulating environmental flows.

Groundwater 

Groundwater often is a more dependable source of water

than surface water because of the high temporal and spatial

variability associated with precipitation, runoff, and surfacewater availability in the Central Valley. The principal sources

of water to the present-day groundwater system are recharge

from precipitation, leakage from streams and surface-water

bodies, and return flow from irrigated agriculture (which currently is the vastly dominant source of recharge in the southern

half of the valley, as discussed in this section).

Figure A19. Cumulative departure of streamflow diversions from the Bear River by South Sutter Water District, California; cumulative 

annual temperature from California Irrigation Management Information System’s (CIMIS) stations at Davis and the Pacific Decadal 

Oscillation (PDO) index, 1960–2004. 

"Variable"

to

Dry

"Variable"

to

Wet

"Variable"

to

Dry

Dry Wet Dry

Wet

TIME, IN YEARS

CUMULATIVE DEPARTURE OF STREAMFLOW DIVERSION, IN ACRE-FEET PER YEAR

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

0

-100

100

-200

200

300

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

CUMULATIVE ANNUAL TEMPERATURE DIFFERENCE, IN DEGREES FAHRENHEIT

PDO-INDEX, UNITLESS

Periods of Pacific Decadal Oscillation [PDO]

 (Mantua, 2006)

Cool (Negative) Cool

(Negative)

Cool

(Negative)

Warm (Positive)

Bear River Diversion cumulative departure average

Pacific Decadal Oscillation (PDO) average annual value

Cumulative annual temperature difference, 1964-2003

EXPLANATION
48 Groundwater Availability of the Central Valley Aquifer, California

Predevelopment Recharge, Discharge, 

Water Levels, and Flows

Prior to development, groundwater in both the confined

and unconfined aquifers of the aquifer system generally moved

from recharge areas in the upland areas surrounding the Central Valley toward discharge areas in the lowlands along the

valleys axes and the Delta (Davis and others, 1959; Olmstead

and Davis, 1961) (figs. A1 and A20). Groundwater-flowed

largely toward the Sacramento or San Joaquin Rivers or Tulare

Lake. Areal recharge from precipitation provided about

75 percent of recharge, and seepage from stream channels

provided the remaining recharge (Williamson and others,

1989). The eastern-valley streams carrying runoff from the

Sierra Nevada and the Klamath Mountains provided the bulk

of the recharge derived from streams (Bertoldi and others,

1991). Most of this occurred as mountain-front recharge in

the coarse-grained upper alluvial fans where streams enter the

basin. 

Groundwater discharge occurred primarily through ET

from marshes along the trough of the valley and through

upward leakage to streams flowing toward the Delta (Williamson and others, 1989). The marshy discharge areas comprised

wetland and riparian vegetation and intermittent lakes and

sloughs. These areas generally corresponded with the areas of

flowing, artesian wells mapped prior to 1900 along the valley

trough (Hall, 1889; Mendenhall and others, 1916). These flowing wells and marshy discharge areas indicate that groundwater-flow was upward in the central part of the valley.

In the Tulare Basin, groundwater was discharged directly

to Tulare Lake and through ET to the riparian areas surrounding the lake (fig. A21). Groundwater also discharged through

ET prior to natural vegetation, including a mix of valley and

foothill hardwoods in the Sacramento Valley, grasslands

throughout the lower parts of the valley floor, and alkali desert

scrub and chaparral in the San Joaquin and Tulare Basins

(California State University, Chico, 2003) (fig. A21). Other 

than by ET, the Delta is the only outlet for natural discharge

of surface or groundwater (Bertoldi and others, 1991, p. 17)

(fig. A21). Direct groundwater outflow to the Delta is thought

to have been negligible (Galloway and Riley, 1999).

Groundwater/Surface-Water Interaction

Under natural conditions, water flowed between the

groundwater-flow system and the stream network within the

Central Valley. Despite the fact that current surface-water

flows are partly controlled by releases from dams, the surfacewater system in the Central Valley still exchanges water with

the groundwater system through gaining and losing sections

of the streambed. Mullen and Nady (1985) quantified streamflow gains and losses of stream reaches throughout the Central

Valley. The average results of their analyses are plotted in

figure A22. As the streams flow into the valleys, much of the

water percolates into the ground. Diverting streamflows to irrigation and lowering groundwater levels by pumping reduces

the low summer flows of the streams. In the Sacramento Valley, streams issuing from the Coast Range generally are losing

(Mullen and Nady, 1985). Conversely, the Feather River issuing from the Sierra Nevada generally is gaining, especially in

the lower reaches. In the upper reaches, the streambed changes

from gaining to losing based on climatic conditions. Further to

the south, the American, Mokelumne, and lower reaches of the

Calaveras Rivers all appear to be losing streams. The upper

reaches of the streams appear to fluctuate between gaining

and losing streams depending on the amount of flow coming

out of the Sierra Nevada that year. In the northern parts of the

San Joaquin Valley, Mullen and Nady (1985) show that the

Stanislaus, Tuolumne, and Merced Rivers generally are gaining streams. More recent work by Phillips and others (2007)

shows that these streams are losing in their upper and middle

reaches and are becoming gaining streams in their lower

reaches about two-thirds of the way toward the valley trough.

The San Joaquin River north of Deadman’s Creek along the

valley trough generally is an area of groundwater discharge

(gaining stream) and the river receives water from the aquifer

system over most of its length. In the lower reaches such as

this, streamflow entering the channel as groundwater discharge

(base flow) generally is the primary component of streamflow;

however, streamflow may be augmented by surface-water

runoff during heavy precipitation events and irrigation return

flows from agriculture. Conversely, Mullen and Nady (1985)

found that streams issuing from the southern part of the Sierra

Nevada and the Coast Ranges into the Tulare Basin generally

are losing streams, particularly in the coarse-grained sediments

adjacent to the mountain fronts (fig. A22). 

Aquifer-System Storage

The quantity of groundwater in storage has been estimated by several investigators (Davis and others, 1959; Olmsted and Davis, 1961; Williamson and others, 1989). Williamson and others (1989) estimated the amount of water in storage

from a study of several thousand well logs in which values of

specific yield were assigned to depth intervals according to

texture, as mapped by Page (1986). Approximately 800 million acre-ft of freshwater is reported to be stored in the upper

1,000 ft of sediments in the Central Valley (Bertoldi and others, 1991, p. 27). As has been pointed out by many investigators and most recently by Alley (2006, 2007), numbers such as

these, although calculable, are impractical and likely misleading. Much of the water in storage cannot be extracted without

serious consequences. Therefore, this type of calculation of 

volume of water in storage was not updated in this study.

However, to address groundwater sustainability, the general

magnitude of this number is discussed later in this report.
Hydrogeology 49

40

240

80

320

120

200

280

0

360

160

400

500

600

700

700

160

320

160

360

40

400

500

40

240

400

240

280

240

200

280

200

360

200

160

160

160

400

120

360

400

200

320

360

320

360

240

360

400

500

EXPLANATION

Predevelopment water levels

Water-level altitude,

 in feet above NGVD 29

0

1 to 50

51 to 100

101 to 200

201 to 400

401 to 800

Central Valley boundary

Historical lakes

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Figure A20. Pre-development groundwater map (modified from Williamson and others, 1989).
50 Groundwater Availability of the Central Valley Aquifer, California

Figure A21. Distribution of A, Pre-1900 land-use patterns (modified from California State University, Chico, 2003), B, land-use patterns 

in 2000 (California Department of Water Resources, 2000) for the Central Valley, California. 

EXPLANATION

Major streams

Vegetation types—

Alkali desert scrub

Aquatic

Grassland

Other floodplain habitat

Riparian

Valley/Foothill hardwood

Wetland

Central Valley boundary

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

A
Hydrogeology 51

Alfalfa

Almond and pistachio

Corn

Cotton

Cucurbits

Dry beans

Fallow and idle

Grain

Native classes

Not surveyed

Onions and garlic

Other decidious

Other field

Other truck

Pasture

Potatoes

Rice

Safflower

Subtropical

Sugar beets

Tomatoes

Urban

Vineyard

Water

EXPLANATION

Major streams

Land use or vegetation types—

Central Valley boundary

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

B

Figure A21. Continued. 
52 Groundwater Availability of the Central Valley Aquifer, California

Figure A22. Distribution of selected streams and canals, and average estimated gains and losses for selected segments (Mullen and 

Nady, 1985).

Stony Creek

Cache Creek

Putah Creek

Kern River

Kaweah

River

Kings River

Fresno Slough

San Joaquin

Fresno

San Joaquin

Merced

Tuolumne

Deadman's Creek

Calaveras

Stanislaus

Mokelumne

Sacramento Feather

American

EXPLANATION

Central Valley boundary

Selected streams and canals

0 50 100 Miles

0 50 100 Kilometers

-54.97 to -44.42

-44.39 to -14.81

-14.78 to -1.48

-1.45 to 0

0.03 to 1.48

1.51 to 14.81

14.84 to 44.42

44.45 to 113.07

Out of groundwater

system

Into groundwater

system

GAIN LOSS

STREAMFLOW

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

1

2

3

4

5

6

7 7

8

10

11

12

13

14

15

16

17

18

19 20

21

9

California Department of Water Resources

 water-balance subregion and identifier.

Groundwater/surface-water interaction,

 in acre-feet per year
Acknowledgments 53

Water Budget

Water development for irrigation has had a pronounced

effect on the hydrologic budget of the Central Valley. The

development of surface-water and groundwater resources in

support of agriculture has fundamentally altered the recharge

and discharge components of the Valley’s water budget.

Chapter B discusses the post-development hydrologic budget.

Williamson and others (1989) developed a pre-development

groundwater budget for the Central Valley that inherently

accounts for the relation between the surface-water and

groundwater systems. This budget is summarized here and in

figure A23. During predevelopment conditions, the recharge

and discharge of groundwater was about 1.5 million acre-ft

per year (fig. A23). As described earlier, groundwater was

recharged by infiltration from precipitation and surface-water

leakage. Precipitation averaged about 12.4 million acre-ft per

year. On average, an estimated 10.9 million acre-ft of this

water directly evapotranspired before reaching the groundwater system (Williamson and others, 1989). This left an average 1.5 million acre-ft of precipitation to annually recharge

the groundwater system. An average 31.7 million acre-ft of

surface-water flowed annually into the Central Valley. Streamflow losses that are infiltrated from the surface-water system

accounted for an annual average 0.5 million acre-ft of groundwater recharge.

Groundwater was discharged (withdrawn) to streams,

springs, or seeps, evaporated to the atmosphere, and transpired by plants (fig. A23). An average 1.7 million acre-ft of

groundwater was evapotranspired each year. Annually, an

average 0.3 million acre-ft was lost to gaining reaches of

streams. Prior to development, the groundwater system generally was in equilibrium; except for fluctuations caused by climatic changes, discharge was approximately equal to recharge,

and the volume of water in storage remained

relatively constant.

Acknowledgments

A project of this magnitude could not be completed

without the help of many individuals and organizations.

First, the authors acknowledge the U.S. Geological Survey’s

Groundwater Resources Program for their support of largescale multidisciplinary studies of groundwater availability

across the United States, including this study. The work would

not have been possible without the data, technical input, and

collaboration provided by the California Department of Water

Resources (CA-DWR). In particular, Tariq Kadir, Charlie

Brush, Emin C. Dogrul, Michael Moncrief, and Eric Senter

provided invaluable assistance. We are grateful to our USGS

colleagues Carol Sanchez, editor; Larry Schneider, illustrator;

Steve Predmore, GIS specialist; and Steve Phillips and Brian

Clark, technical reviewers. Finally, this work owes a debt of

gratitude to the original CV-RASA studies and the accomplishments of Sandy Williamson, Dave Prudic, and many

others.

Figure A23. Pre-development water budget and post-development water budget. Values are in millions of acre-feet per year. 

Central Valley

Aquifer 

Precipitation

(12.4)

Evapotranspiration

(12.6)

Surface-Water

 Gain (0.3)

[swout]

Surface-Water

 Loss (0.5)

[swin]

(1.5)

(10.9)

(1.7)

Central Valley

Surface Processes
54 Groundwater Availability of the Central Valley Aquifer, California

References Cited

Alley, W.M., 2006, Tracking U.S. ground-water reserves for

the future: Environment, v. 48, no. 3, p. 10–25.

Alley, W.M., 2007, Another water budget myth: The significance of recoverable ground water in storage: Ground

Water, v. 45, no. 3, p. 251.

Belitz, Kenneth, and Heimes, F.J., 1990, Character and evolution of the ground-water flow system in the central part of

the western San Joaquin Valley, California: U.S. Geological

Survey Water-Supply Paper 2348, 28 p.

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 ground-water flow in the central

part of the Western San Joaquin Valley, California: U.S.

Geological Survey Water-Supply Paper 2396, 69 p.

Bertoldi, G.L., 1979, A plan to study the aquifer system of the

Central Valley of California: U.S. Geological Survey OpenFile Report 79–1480, 48 p.

Bertoldi, G.L., 1989, Ground-water resources of the Central

Valley of California: U.S. Geological Survey Open-File

Report 89–251, 2 p.

Bertoldi, G.L., Johnston, R.H., and Evenson, K.D., 1991,

Ground water in the Central Valley, California—A summary

report: U.S. Geological Survey Professional Paper 1401-A,

44 p.

Bloyd, R.M. Jr., 1978, Ground-water conditions in the Sacramento Valley, California 1912, 1961, and 1971: Report to

the U.S. Geological Survey.

Brush, C.F., Belitz, Kenneth, and Phillips, S.P., 2004, Estimation of a water budget for 1972–2000 for the Grasslands

Area, Central Part of the Western San Joaquin Valley,

California: U.S. Geological Survey Scientific-Investigations

Report 2004–5180, 51 p.

Brush, C.F., Dogrul, E.C., Kadir, T.N., Moncrief, M.R., Shultz,

S., Tonkin, M., and Wendell, D., 2006, Modeling the evolution of a Regional Aquifer System with the California Central Valley ground-water-surface water simulation model

(C2VSIM): American Geophysical Union, Fall Meeting

2006, abstract #H11A–1226.

Bureau of Reclamation, 1994, The Central Valley Project overview: Bureau of Reclamation History Program Research on

Historic Reclamation Projects, accessed April 16, 2009, at

http://www.usbr.gov/dataweb/html/cvpintro.html

Bryan, Kirk, 1923, Geology and ground-water resources of

the Sacramento Valley, California: U.S. Geological Survey

Water-Supply Paper 495, 285 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. Geological Survey Scientific–Investigations Report 2004–5232, 54 p.

California Department of Finance, 2007, E-4 population

estimates for cities, counties and the State, 2001–2007, with

2000 Benchmark, Sacramento, California, accessed April

16, 2009, at http://www.dof.ca.gov/HTML/DEMOGRAP/

ReportsPapers/Estimates/E4/E4-01-06/HistE-4.asp

California Department of Water Resources, 1977, Input data

for 1980 and 2000 level Central Valley depletion studies,

Memorandum dated August 22, 1977, 135 p.

California Department of Water Resources, 1998, California

water plan update: Bulletin 160–98, 3 v., accessed April 16,

2009, at http://rubicon.water.ca.gov/pdfs/b160cont.html

California Department of Water Resources, 2000, Explanations of land use attributes used in database files associated

with shape files: Land and Water Use Section, 11 p.

California Department of Water Resources, 2003, California’s

ground water, update 2003: California Department of Water

Resources Bulletin 118, 246 p.

California Department of Water Resources, 2005, California

water plan update 2005, Volume 1 – Strategic Plan: California Department of Water Resources Bulletin 160–05, CD.

California Department of Water Resources, 2007a, CALSIMIII, accessed April 16, 2009, at http://bayDeltaoffice.water.

ca.gov/modeling/hydrology/CalSimIII/index.cfm

California Department of Water Resources, 2007b, IWFM:

Integrated water flow model, accessed April 16, 2009, at

http://bayDeltaoffice.water.ca.gov/modeling/hydrology/

IWFM/index.cfm

California Regional Water Quality Control Board, Central

Valley Region, 1992, Staff Report: Considerations of Water

Body Designations to Comply with the Provisions of the

Water Quality Control Plan for ISWP.

California State University, Chico, 2003, The Central Valley

historic mapping project: California State University, Chico,

Department of Geography and Planning and Geographic

Information Center, 25 p.

Carle, S.F. and Fogg, G.E., 1996, Transition probability–

based indicator geostatistics: Mathematical Geology, v. 28,

p. 453–476.
References Cited 55

Carle, S.F., Labolle, E.M., Weissmann, G.S., Vanbrocklin, D.,

and Fogg, G.E., 1998, Conditional simulation of hydrofacies architecture: a transition probability/Markov approach,

in Fraser, G.S., and Davis, J.M., Hydrogeologic models of

sedimentary aquifers: SEPM, Concepts in Hydrogeology

and Environmental Geology, no. 1, p. 147–170.

Climate Source, 2006, Precipitation data from PRISM data,

accessed April 16, 2009, at http://www.climatesource.com/

Davis, G.H., Green, J.H., Olmsted, F.H., and Brown, D.W.,

1959, Ground-water conditions and storage capacity in the

San Joaquin Valley, California: U.S. Geological Survey

Water-Supply Paper 1469, 287 p.

Davis, G.H., Lofgren, B.E., and Mack, Seymour, 1964, Use

of ground-water reservoirs for storage of surface water in

the San Joaquin Valley, California: U.S. Geological Survey

Water-Supply Paper 1618, 125 p.

Dettinger, M.D., 2005, From climate-change spaghetti to

climate-change distributions for the 21st Century California: San Francisco Estuary & Watershed Science, v. 3,

issue 1, article 4, 14 p., accessed April 16, 2009, at http://

repositories.cdlib.org/jmie/sfews/vol3/iss1/art4

Dettinger, M.D. and Earman, S., 2007, Western ground water

and climate change—Pivotal to supply sustainability or vulnerable in its own right?: Ground Water News and Views,

v. 4, no. 1, p. 4–5.

Diamond, Jonathan, and Williamson, A.K., 1983, A summary

of ground-water pumpage in the Central Valley, California,

1961–77: U.S. Geological Survey Water-Resources Investigations Report 83–4037, 70 p.

Dudley, Toccoy, Spangler, Debbie, Fulton, Alan, Staton, Kelly,

Lawrence, Seth, Ehorn, Bill, and Ward, Michael, 2006,

Seeking an understanding of the ground-water aquifer

systems in the Northern Sacramento Valley: An update,

accessed April 16, 2009, at http://ucce.ucdavis.edu/files/

filelibrary/2280/37336.pdf

Farrar, C.D. and Bertoldi, G.L., 1988, Region 4, Central

Valley and Pacific Coast Ranges, in Back, William, Rosenshein, J.S., and Seaber, P.R., eds., Hydrogeology: Boulder,

Colorado, Geological Society of America, Geology of North

America, v. O-2, p. 59–67.

Fogg, G.E., Carle, S., and Green, C.T., 2001, A connected

network paradigm for the alluvial aquifer system, in Zhang,

Dongxiao, and Winter, C.L., eds., Theory, modeling and

field investigation in hydrogeology: GSA Special Publication, A Special Volume in Honor of Shlomo P. Neuman’s

60th Birthday, 252 p.

Galloway, D.L., Jones, D.R., and Ingebritsen, S.E., 1999, Land

subsidence in the United States: U.S. Geological Survey

Circular 1182, 175 p.

Galloway, D.L., and Riley, F.S., 1999, San Joaquin Valley,

California—Largest human alteration of the Earth’s surface:

in Galloway, D.L., Jones, D.R., and Ingebritsen, S.E., eds.,

Land Subsidence in the United States: U.S. Geological

Survey Circular 1182, p. 23-34, accessed February 2, 2008,

at http://pubs.usgs.gov/circ/circ1182/

Great Valley Center, 1998, Agricultural land conservation in

the Great Central Valley, 32 p., accessed April 15, 2009, at 

http://www.greatvalley.org/publications/agpubs/ag_land_

conservation.pdf

Great Valley Center, 1999, State of the great Central Valley: Assessing the region via indicators—The economy:

State of the Great Central Valley Indicators Series, 56 p.,

accessed April 15, 2009, at http://www.greatvalley.org/

pub_documents/2005_1_7_16_55_28_Indicators%20-%20

Economy%201.pdf

Great Valley Center, 2005, State of the great Central Valley: Assessing the region via indicators—The economy:

State of the Great Central Valley Indicators Series, 49 p.,

accessed April 15, 2009, at http://www.greatvalley.org/

pub_documents/2005_1_18_13_59_43_indicator_econ05_

report.pdf

Gronberg, J.A., and Belitz, K.R., 1992, Estimation of a water

budget for the central part of the western San Joaquin Valley, California: U.S. Geological Survey Water-Resources

Investigations Report 91–4192, 22 p.

Hackel, Otto, 1966, Summary of the geology of the Great

Valley, p. 217–238, in Bailey, E.H. ed., Geology of Northern California: California Division of Mines and Geology

Bulletin 190, 508 p.

Halford, K.J. and Hanson, R.T., 2002, User guide for the

drawdown-limited, Multi-node well (MNW) package for

the U.S. Geological Survey’s modular three-dimensional

finite-difference ground-water flow model, versions MODFLOW-96 and MODFLOW-2000: U.S. Geological Survey

Open-File Report 02–293, 33 p., accessed April 16, 2009, at 

http://pubs.usgs.gov/of/2002/ofr02293/text.html

Hall, W.H., 1886, Physical data and statistics of California:

State Engineering Department of California, 396 p.

Hall, W.H., 1889, Irrigation in California: National Geographic, v. 2, no. 4, p. 281.

Hanson, R.T., Dettinger, M.D., and Newhouse, M.W., 2006,

Relations between climate variability and hydrologic time

series from four alluvial basins across the southwestern

United States: Hydrogeology Journal, v. 14, no. 7,

p. 1122–1146.
56 Groundwater Availability of the Central Valley Aquifer, California

Harbaugh, A.W., Banta, E.R., Hill, M.C., and McDonald,

M.G., 2000, MODFLOW-2000: U.S. Geological Survey

modular ground-water model—User guide to modularization concepts and the ground-water flow process: U.S.

Geological Survey Open-File Report 00–92, 121 p.

Hidalgo, H.G., Cayan, D.R., Dettinger, M.D., 2005, Sources

of variability of evapotranspiration in California: Journal of

Hydrometeorology, v. 6, p. 3–19.

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, Jörn, Leake, S.A., Galloway, D.L., and Wilson,

A.M., 2003, MODFLOW–2000 ground-water model—user

guide to the subsidence and aquifer-system compaction

(SUB) package: U.S. Geological Survey Open-File Report

03–233, 46 p.

Hull, L.C., 1984, Geochemistry of ground water in the Sacramento Valley, California: U.S. Geological Survey Professional Paper 1401-B, 36 p.

Ingebritsen, S.E., Ikehara, M.E., Galloway, D.L., and Jones,

D.R., 2000, Delta subsidence in California: U.S. Geological

Survey Fact Sheet 005–00, 4 p., accessed April 16, 2009, at

http://ca.water.usgs.gov/archive/reports/fs00500/fs00500.

pdf

Isaaks, E.H., and Srivastava, R.M., 1989, An introduction to

applied geostatistics: New York, Oxford University Press,

561 p. 

Jennings, C.W., 1977, Geologic map of California: California Division of Mines and Geology, Sacramento, scale

1:750,000, 1 sheet.

Johnson, A.I., Moston, R.P., and Morris, D.A., 1968, Physical

and hydrologic properties of water-bearing deposits in subsiding areas in California: U.S. Geological Survey Professional Paper 497-A, 71 p.

Journel, A.G. and Huijbregts, C.J., 1978, Mining geostatistics:

New York, Academic Press, 600 p.

Laudon, Julie, 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.

Lund, Jay, Hanak, Ellen, Fleenor, William, Howitt, Richard,

Mount, Jeffrey, and Moyle, Peter, 2007, Envisioning futures

for the Sacramento–San Joaquin Delta: San Francisco, California, Public Policy Institute of California, 284 p.

Mantua, N.J., 2006, The Pacific decadal oscillation (PDO)

index: data obtained from the University of Washington’s Joint Institute for the Study of the Atmosphere and

Oceans web-page, accessed April 16, 2009, at http://jisao.

washington.edu/pdo/PDO.latest

Mantua, N.J., Hare, S.R., Zhang, Y., Wallace, J.M., and Francis, R.C., 1997, A Pacific interdecadal climate oscillation

with impacts on salmon production: Bulletin of the American Meteorological Society, v. 78, p. 1069–1079.

Mantua, N.J. and Steven, H., 2002, The Pacific decadal oscillation: Journal of Oceanography, v. 58, no. 1, p. 35–44.

Matthes, F.E., 1960, Reconnaissance of the geomorphology and glacial geology of the San Joaquin Basin, Sierra

Nevada, California: U.S. Geological Survey Professional

Paper 329, 62 p.

Meko, D.M., and Woodhouse, C.A., 2005, Tree-ring footprint of joint hydrologic drought in Sacramento and Upper

Colorado River basins, western USA: Journal of Hydrology,

v. 308, no. 1–4, pp. 196–213.

Mendenhall, W.C., Dole, R.B., and Stabler, Herman, 1916,

Ground water in San Joaquin Valley, California: U.S. Geological Survey Water-Supply Paper 398, 310 p.

Miller, R.E., Green, J.H., and Davis, G.H., 1971, Geology of

the compacting deposits in the Los Banos–Kettleman City

subsidence area, California: U.S. Geological Survey Professional Paper 497-E, 45 p.

Moore, S.B., Winckel, J., Detwiler, S.J., Klasing, S.A., and

Gaul, P.A., 1990, Fish and wildlife resources and agricultural drainage in the San Joaquin Valley, California: Sacramento, California, San Joaquin Valley Drainage Program,

974 p.

Mullen, J.R., and Nady, Paul, 1985, Water budgets for major

streams in the Central Valley, California, 1961–77: U.S.

Geological Survey Open-File Report 85–401, 87 p.

Olmstead, F.H., and Davis, G.H., 1961, Geologic features and

ground-water storage capacity of the Sacramento Valley,

California: U.S. Geological Survey Water-Supply Paper

1497, 287 p.

Page, R.W., 1983, Geology of the Tulare Formation and other

continental deposits, Kettleman City area, San Joaquin

Valley, California, with a section on ground-water management considerations and use of texture maps: U.S. Geological Survey Water-Resources Investigations Report 83-4000,

24 p.

Page, R.W., 1986, Geology of the fresh ground-water basin of

the Central Valley, California, with texture maps and sections: U.S. Geological Survey Professional Paper 1401-C,

54 p.
References Cited 57

Page, R.W., and Bertoldi, G.L., 1983, A Pleistocene diatomaceous clay and a pumiceous ash: California Geology, v. 36,

no. 1, p. 14–20.

Parsons, J.J., 1987, A geographer looks at the San Joaquin Valley, 1987, Carl Sauer memorial lecture, accessed April 16,

2009, at http://geography.berkeley.edu/ProjectsResources/

Publications/Parsons_SauerLect.html

Phillips, S.P. and Belitz, Kenneth, 1991, Calibration of a textured-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 ground-water

flow in part of the northeastern San Joaquin Valley, California: U.S. Geological Survey Scientific-Investigations Report

2007–5009, 43 p.

Planert, Michael, and Williams, J.S., 1995, Ground water atlas

of the United States: Segment 1, California, Nevada: U.S.

Geological Survey Hydrologic Atlas 730-B, 1 atlas, 28 p.

Prokopovich, N.P., 1987, Textural composition of near-surface

alluvium in west-central San Joaquin California: Bulletin

of the Association of Engineering Geologists, v. 24, no. 1,

p. 59–81.

Prudic, D.E., Konikow, L.F., and Banta, E.A., 2004, A new

streamflow-routing (SFR1) package to simulate streamaquifer interaction with MODFLOW-2000: U.S. Geological

Survey Open-File Report 04–1042, 95 p.

Reilly, T.E., 2005, Assessing the nation’s ground-water

resources: U.S. Geological Survey 125th Anniversary Articles, accessed April 16, 2009, at http://www.usgs.gov/125/

articles/groundwater.html

Ritzi, R.W., Jr., 2000, Behavior of indicator variograms and

transition probabilities in relation to the variance in lengths

of hydrofacies: Water Resources Research, v. 36,

p. 3375–3381.

Ryan, Holly, Gibbons, Helen, Hendley II, J.W., and Stauffer,

P.H., 1999, El Niño sea-level rise wreaks havoc in California’s San Francisco Bay Region: U.S. Geological Survey

Fact Sheet 175–99, 4 p., accessed April 16, 2009, at

http://pubs.usgs.gov/fs/1999/fs175-99/fs175-99.pdf

Schmid, W., Hanson, R.T., Maddock III, T.M., and Leake,

S.A., 2006, User’s guide for the Farm Process (FMP1)

for the U.S. Geological Survey’s modular three-dimensional finite-difference ground-water flow model, MODFLOW-2000: U.S. Geological Survey Techniques and

Methods 6-A17, 127 p.

Sun, R.J., and Johnston, R.H., 1994, Regional AquiferSystems Analysis program of the U.S. Geological Survey,

1978-92: U.S. Geological Survey Circular 1099, 126 p.

U.S. Geological Survey, 2002, Concepts for national assessment of water availability and use: U.S. Geological Survey

Circular 1223, 34 p.

Introduction 

This chapter focuses on the availability of groundwater in the Central Valley. Therefore, the chapter includes a

description of the effects of development and climate on the

hydrologic system, a discussion of groundwater sustainability

and management, and provides suggestions for monitoring the

hydrologic system. A transient hydrologic model, referred to

here as the Central Valley Hydrologic Model (CVHM), was

developed as part of this study (Chapter C, this report). The 

CVHM incorporates monthly time-varying hydrologic-budget

components for 21 water-balance subregions (WBSs) within

the Central Valley for water years 1962 to 2003. The CVHM

was used to evaluate the effects of climate, water-delivery

systems, and land-use practices on the groundwater system.

The descriptions of these effects are derived from this model

as well as from the data and information gathered, compiled,

and analyzed as part of this study. The chapter also incorporates information from the extensive literature on California’s

Central Valley.

Development and the 

Hydrologic System

The spatial and temporal differences between the natural

distribution of water in the Central Valley, and the agricultural

and urban demands for that water, have led to large-scale

engineering of the hydrologic system. Simply stated, the

mountains adjacent to the Central Valley have an average

annual surplus of water, whereas the valley proper has an

average annual water deficit (precipitation minus ET as much

as –40 in.). Agricultural development and human population

are concentrated in the precipitation-deficient valley. If applied

irrigation-water requirement is added to the annual natural

deficit, the average annual water deficiency is greater than 70

in. in places (fig. A5). Despite the magnitude of these numbers,

they do not account for the seasonal and geographic water

distribution issues. For example, the largest demand for water

is in the drier spring-summer growing season of the drier and

heavily agriculturally developed Tulare Basin.

As early as the 1870s, various plans were proposed to

transfer “excess” water from the Sacramento River to the

drier areas in the San Joaquin Valley (California Department

of Water Resources, 1994). Dams were proposed to store

water for release in the drier spring-summer growing season.

Initially, these plans focused on surface water, but the plans

began to include groundwater as well as time progressed. To

meet the substantial water needs of California’s growing agriculture and urban population, a massive surface-water diversion and delivery system was constructed by Federal, State,

and local water purveyors (fig. A7). Because the surface-water

diversion and delivery system cannot always meet all of the

water demand, groundwater increasingly has been relied on to

help rectify the imbalance.

Surface-Water and Groundwater 

Development History 

The use of surface and groundwater has had a long

history in the Central Valley. Development of surface water,

primarily for irrigation, began in the 1700s (Bertoldi and

others, 1991). By the late 1800s, gold rush settlers had begun

ranching and dryland farming. Early farming was concentrated

close to the Delta, where the water table was high year round

and surface water was readily available. Since the 1860s,

farmers around the Delta began to build levees and pump

water to reclaim the land for agriculture.

As the gold mining began to diminish, agriculture grew

in the Delta and Central Valley, as did the need for a reliable

water supply. By the mid 1800s, it was clear that a major

redistribution of the state’s water would have to occur if farming were to continue in the San Joaquin Valley. Starting in the

mid 1800s with the Miller and Lux agricultural enterprise,

cooperatives and development companies formed, and by

1900 an extensive system of canals had been built to supply

surface water to the southern San Joaquin Valley (Igler, 2001).

Around 1880, during the same time that surface-water systems

were being engineered, groundwater development began in

the Central Valley (Bertoldi and others, 1991). Groundwater

initially was developed in regions where shallow groundwater was plentiful. In particular, in the San Joaquin Valley,

groundwater extraction enabled farms and cities to flourish.

Chapter B. Groundwater Availability in 

California’s Central Valley 

By Claudia C. Faunt, Kenneth Belitz, and Randall T. Hanson
60 Groundwater Availability of the Central Valley Aquifer, California

This especially was true where flowing wells were commonplace near the central part of the valley near the historic lake

basins (fig. A21A). After 1900, the yields of flowing wells had

diminished as water levels declined, and it became necessary

to install pumps in wells to sustain production rates. By 1913,

the estimated annual groundwater extraction in the Central

Valley was 360,000 acre-ft (Bertoldi and others, 1991, p. 22).

Around 1930, the development of an improved deep-well turbine pump and rural electrification enabled additional groundwater development for irrigation (Galloway and Riley, 1999).

For years, San Joaquin Valley farmers pumped the valley’s

groundwater, causing large and widespread declines in the

water table and increased pumping costs. Many wells eventually went dry and thousands of acres of farmland were forced

out of production. In addition, the constant threat of flooding

was becoming an issue as was the encroachment of saltwater

into Delta channels during the dry spring-summer growing

season.

The Federal government has long played a major role

in development of the West’s surface-water resources. As

early as 1875, the U.S. Army Corps of Engineers began work

on the Sacramento and Feather Rivers to improve navigation. In 1919, shortly after retiring from the USGS, Lt. Col.

R.B. Marshall proposed a comprehensive, statewide plan for

conveyance and storage of California’s Central Valley water

supplies, which was published in 1920 by the California State

Irrigation Association (Marshall, 1920). This plan served as

the framework for the State Water Plan (SWP), which later

formed the basis for the Federal Central Valley Project (CVP).

The cornerstone of the CVP, officially formed in 1935, is the

plan to use water from the San Joaquin and Sacramento Rivers

to irrigate 12 million acres in the San Joaquin Valley. The CVP

is a complex operation of interrelated projects built to control

floodwaters, irrigate the semi-arid acreage of California’s Central Valley, and where possible provide low cost power. As part

of the CVP, in 1939, the entities that held Miller and Lux’s (as

well as others) historical water rights traded the rights to take

water from the San Joaquin River for rights to Sacramento

River water stored in Shasta Lake.

In the early 1900s, State water planners recognized that

the Delta would be integral in supplying supplemental water

to support the growing population of southern California and

prevent increased groundwater overdraft in the Central Valley.

Additionally, the need for flood control on the Feather River

was recognized, as was the San Joaquin Valley’s need for an

outlet for saline irrigation drainage water. After years of debate

and study, what was to become the State Water Project (SWP)

officially was begun in the early 1950s. As a result of these

two massive water projects, two main canals reroute water that

would have gone to the San Joaquin River to be available for

people holding pre-1914 water rights. Since the mid 1940s, the

CVP has used the Madera and Friant–Kern Canals to divert

San Joaquin River water southward to Kern County (fig. A7). 

In exchange, farmers on the west side of the San Joaquin

Valley that had rights to the San Joaquin River began receiving water by way of the Delta–Mendota Canal (SWP) in 1951

based on Kern County’s rights to water from Lake Shasta as

part of the CVP (fig. A7). 

Hence, the Central Valley relies on a combination of

local and imported surface water and local groundwater. The

CVP, SWP, and local agencies provide a network of diversions and deliveries throughout the Central Valley. The DWR

C2VSIM model simulates this complex network as a series

of 41 inflows, 66 diversions, and 21 subregions (C. Brush,

California Department of Water Resources, written commun.,

February 21, 2007; fig. A7). Generally, when available, most

farmers irrigate with surface water. When surface water is not

available (such as later in the spring–summer growing season

or during droughts) or not economical, farmers typically use

groundwater.

This critical resource has not been redistributed without

conflict. In the 1970s, growth in environmental awareness

ultimately led to the inclusion of environmental factors into

the water distribution process (California Department of Water

Resources, 2005). These conflicts culminated in numerous

environmental regulations and constraints embodied in the

1992 CVP Improvement Act (CVPIA). The CVPIA brought

fundamental change to CVP operations and water allocation

schemes. The results of this legislation have begun to be

realized (California Department of Water Resources, 1994).

Presently, environmental flows to protect critical components

of the ecosystem, including the Delta smelt, are beginning to

be enforced. These environmental flows may substantially

reduce the amount of surface water available for farming and

(or) urban uses and, thereby, increase reliance on groundwater.

The potential increased reliance on groundwater is likely to

lower groundwater levels, thereby increasing pumping costs,

inducing further subsidence, and decreasing surface-water

base flows.

Land Use

California’s Central Valley is one of the most modified

rural environments in the world (California State University,

Chico, 2003). Abundant water (through irrigation) combined

with the long growing season results in an exceptionally

productive agricultural economy. Consequently, California’s

agricultural land-use patterns are dynamic. Market demand

and resource limitations, particularly water, cause large shifts

of land in and out of agricultural uses. Urbanization and development also have resulted in changes in land-use patterns.

Because agricultural and urban land-use changes have had a

dramatic effect on groundwater availability, an overview of

agricultural and urban land use is given below.
Development and the Hydrologic System 61

Agricultural Land Use

Agriculture in the valley has developed through three

overlapping stages: cattle ranching in the early days, followed by “dry-land” farming of small grains, and finally,

specialized and intensified irrigated farming. Cattle ranching

ended as a consequence of the disastrous drought of 1863–64,

which resulted in the loss of almost all the cattle in California

(Bureau of Reclamation, 1994). Combined with the demise

of cattle ranching, population growth, the low cost of land,

and the post 1869 development of the railroads shifted the

agricultural focus toward grain production. Dry-land farming of wheat and barley expanded until the latter part of the

19th century and then declined as other grain-growing regions

developed and irrigated farming proliferated (Bureau of

Reclamation, 1994).

Mechanization greatly changed agriculture in the Central

Valley. As transportation methods evolved, particularly the

railroad, Central Valley agriculture expanded in the early

1900s. The invention of the deep-well turbine pump around

1930 allowed water to be pumped from greater depths.

Mechanical harvesting of many crops was in place along

with refrigerated railroad transportation to supplement or

replace transportation by truck in the 1950s. This new form

of transportation facilitated wider distribution of orchard (for

example, citrus, avocados, and nuts) and truck (for example,

tomatoes, melons, squash and lettuce) crops. The tomato 

harvester, and most recently the mechanical harvesting of

grapes, also increased production (Parsons, 1987).

Water, soils, microclimate, pests, economic and historical 

factors, and the choices of individual farmers are all involved

in deciding which crops to plant. Some crops, like almonds

and alfalfa, are found almost everywhere in the Central Valley (fig. A21B). Other crops are confined sharply to restricted

areas. For example, most of the orange growers are in a narrow thermal belt near the mountains on the east side of the San

Joaquin Valley (California Department of Water Resources,

2000). Cotton generally is confined to the San Joaquin Valley,

with most cotton grown west of the valley axis. Rice generally

is confined to the fine-grained soils of the Sacramento Valley. Throughout the Central Valley, soils are deep and fertile

and the growing season is long, allowing much of the valley

to have crops grown and harvested two or three times in a

year on a single field. Changing market conditions may lead

to rapid shifts of land-use. Periodically, new crops are introduced, and new strains of crops always are being developed.

For example, in the 1980s, safflower was added and, more

recently, pistachios. More than half of all the grapes grown in

the US now are produced in the San Joaquin Valley (Parsons,

1987; Great Valley Center, 2005). Nut crop plantings soared in

the last few decades; almond acreage far exceeds that of any

other tree crop, and in the late 1980s was nearly half of that

of cotton. Dairies, moving from other parts of California, are

beginning to locate to Tulare and Kings County. Despite the

predominance of agriculture, the valley’s economy is not fully

dependent on agriculture. Kern County is one of the nation’s

largest oil-producing counties, and urban areas are expanding

rapidly throughout the valley.

Agriculture still is the dominant land use in the Central

Valley. The three major regions of the Central Valley (the

Sacramento Valley, the San Joaquin Basin, and the Tulare

Basin) had 1.9 million, 2 million, and 3 million irrigated crop

acres, respectively, in 2000, representing 60 percent of the valley floor by area (California Department of Water Resources,

2000). In terms of production dollar value, the Sacramento

Valley accounted for 12 percent of the agricultural production,

while the San Joaquin Valley (San Joaquin and Tulare Basins

combined; fig. A1) accounted for 88 percent. Between 1997

and 2002, growth of agricultural production increased by 4

percent in the valley as a whole. This represented a 6-percent

increase in the San Joaquin Valley and a slight decrease in

agricultural production in the Sacramento Valley (Great Valley

Center, 2005).

Urban Land Use

The Central Valley is an important area for California’s

expanding urban population. Between 1970 and 2000, the

population doubled in the Central Valley and reached

6.5 million in 2005. Future growth is expected to continue,

according to Census Bureau projections, suggesting that the

Central Valley’s population will increase to 10 million by

2030. The vast majority of the population is in the major

urban areas of Sacramento, Fresno, Bakersfield, Stockton,

and Modesto (fig. A1). Fresno and Sacramento are two of the

fastest growing cities in California (California Department of

Finance, 2007).

These large increases in population have resulted in a

shift in land-use in the Central Valley (California Department

of Conservation, 2007). Between 1961 and 2000, urban areas

have increased from 3 to 7 percent of the total area (California

Department of Water Resources, 2000; California State University, Chico, 2003; table C3). During this same time frame,

native vegetation decreased by at least 4 percent while croplands remained relatively constant. However, between 1990

and 2002, about 4 percent of the Central Valley’s irrigated

farmland was converted to other uses, primarily for housing

and other urban uses (Great Valley Center, 2005). Hence, some

of the land has had multiple changes in use. Between 1962

and 2003, pumpage of groundwater for municipal and industrial uses (urban) in the Central Valley increased more than

threefold from about 0.6 to nearly 2 million acre-ft/yr, despite 

increased use of surface water and implementation of various

urban conservation measures (California Department of Water

Resources, 2005; C. Brush, California Department of Water

Resources, written commun., February 21, 2007).
62 Groundwater Availability of the Central Valley Aquifer, California

Population growth alone does not determine changes

in land use. Where and how the growth is accommodated

can make a difference. Development of the most productive

farmland, rather than less productive land, places a premium

on how efficiently the population is accommodated. Despite

the urbanization of nearly 100,000 acres during the 1990s,

the market value of Central Valley farm products increased

(American Farmland Trust, 2006). Farmers have increased

the value of output by shifting from “extensive” crops such

as barley, oats, and sugar beets to higher-value fruits, nuts,

vegetables, and ornamental horticultural crops. More than onethird of the increase in market value between 1992 and 2002 is

attributed to a 56-percent increase in dairy production (American Farmland Trust, 2006).

Development and Changes to the 

Hydrologic Budget

As would be expected, the long history of agricultural

and urban development has greatly altered the groundwater

budget in the Central Valley (figs. A23 and B1). Chapter A

describes the groundwater budget prior to development. Presently, the agricultural and urban water demands are met by a

combination of groundwater withdrawals and large imports of

surface water. For nearly every city in the San Joaquin Valley,

groundwater is the principal supply for municipal and industrial water demand. Because of the large effect that irrigated

agriculture has on the system, the overall water budget for the

system is separated into two linked parts for the purposes of

analysis: a landscape budget, encompassing the surface processes (including most of the components of the agricultural

part of the system), and a groundwater budget, encompassing fluxes into, through, and out of the aquifer system. Large

volumes of water are moved into and out of these systems

through groundwater pumpage and a surface-water delivery

system.

This report primarily focuses on the groundwater part

of the budget and its linkages to the landscape budget. The

landscape budget includes inputs from precipitation, surfacewater deliveries, and groundwater pumpage, and outputs of

evapotranspiration (ET), runoff, and groundwater recharge.

For accounting purposes, ET from groundwater is included

in the landscape budget. In terms of the groundwater budget,

the withdrawals from pumpage are balanced by a combination

of increased recharge, decreased non-pumping discharge, and

removal of water from storage. The aquifer receives recharge

from precipitation, streamflow losses, and excess irrigation

water. The excess irrigation water originates from a combination of surface-water deliveries and groundwater pumpage.

Other than groundwater withdrawals from pumping wells,

groundwater leaves the system predominantly through wells,

ET, flux to streams and, to a small degree, through discharge

to the Delta. When total recharge exceeds total discharge,

water is added to storage; when the reverse is true, water is

removed from storage, which can trigger aquifer-system compaction and associated land subsidence.

Recharge from a combination of precipitation and

excess applied irrigation water and discharge by groundwater pumpage are the dominant stresses on the groundwater

system (fig. B1). These stresses have had substantial effects on

groundwater levels. These water-level changes (predominantly

declines, but rises in some areas) have altered groundwater

flow rates and directions, changed flows that are exchanged

with streams and the adjacent aquifers and confining units in

the aquifer system, altered groundwater quality, and resulted

in a change in groundwater storage. More details on these

stresses and elements of the groundwater budget are discussed

in the next few sections. The discussion draws heavily on the

results of updated numerical model (Central Valley Hydrologic Model [CVHM]) simulations conducted as a part of this

study (Chapter C). Where necessary, the results of simulations 

by Williamson and others (1989) and summaries by Bertoldi

and others (1991) are used to augment the discussion.

Hydrologic Budget Components

The inputs and outputs of the water budgets for the landscape and the groundwater systems are presented in figures B2 

and B3 and tabulated in tables B1 and B2, respectively.

Because these systems are linked, many of the water-budget

components pertain to both systems (fig. B1). The landscape 

and groundwater budgets vary significantly from year to year

(tables B1 and B2 and figs. B2 and B3). During wet years,

relatively inexpensive surface water typically is used for

irrigation, and during dry periods, many farms predominantly

use groundwater. Because of great variability in the hydrologic conditions, a “typical” water year seldom is seen. In this

report, a “typical” year is defined as one with annual precipitation and inflow that are near the long-term median values. In

order to portray the system through its typical patterns and

extremes, 3 recent years were selected: (1) 1975 was selected

as a “typical” year because inflow is near the median value,

even though precipitation is slightly lower than the median;

(2) 1990 was selected as a representative dry year because of

a prolonged series of low inflows and small precipitation rates

during the drought 1987–92; (3) 1998 was selected as a typical

wet year because it had relatively high inflows and extremely

high precipitation. These 3 years will be used for the basis of

discussion in many of the following sections. Where applicable, average hydrologic values for water years 1962–2003 also

are used. Although an average year is rare and, as pointed out

earlier, wet and dry extremes are more common, averages are

used to the overall cumulative affect over the 42-year period.

Water years that begin in October of the preceding calendar

year are used because they represent the time period over

which a complete annual hydrologic cycle normally occurs.
Development and Changes to the Hydrologic Budget 63

Change in Storage

(including Subsidence)

(1.4)

Central Valley

Aquifer 

Central Valley

Surface Processes

Precipitation

(15.8)

Groundwater Recharge from

Irrigation and Precipitation

(7.8)

—Indicates loss of storage in aquifer system

Evapotranspiration

(25.6)

Surface-Water

 Gain from

Groundwater

System (2.2)

Surface-Water

 Deliveries (10.2)

Groundwater

 Flow to Delta (0.1)

Surface-Water

 Loss from

Groundwater

System (2.5)

Runoff

(1.1)

Surface-Water

System

Groundwater

Pumpage

(9.3)

Agricultural

(8.6)

Municipal

(1.1)

Flow through

Boreholes

(0.4)

INFLOW

(26.3)

OUTFLOW

to DELTA

(27.2)

Figure B1. Average water budget for water years 1962–2003. This budget includes the landscape and groundwater components and 

their linkages. Values in millions of acre-ft/yr. A diagram showing the pre-development water budget is shown in figure A23.
64 Groundwater Availability of the Central Valley Aquifer, California

39%

30%

21%

10%

Simulated landscape water budget

EXPLANATION

INPUT OUTPUT

Typical Year 1975

Dry Year 1990

Wet Year 1998

14.53

11.28

7.93

5 3.93

0

10

15

20

25

30

MILLIONS OF ACRE-FEET

5

0

10

15

20

25

30 30

MILLIONS OF ACRE-FEET

5

0

10

15

20

25

30

MILLIONS OF ACRE-FEET

5

0

10

15

20

25

MILLIONS OF ACRE-FEET

5

0

10

15

20

25

30

MILLIONS OF ACRE-FEET

5

0

10

15

20

25

30

MILLIONS OF ACRE-FEET

30%

25%

35%

10%

3% 68%

29%

Evapotranspiration Runoff

Deep percolation

78%

1%

21%

55%

5%

40%

64% 19%

10%

7%

30.18

8.95

4.54

3.19

25.52

1.02

10.99

25.99

0.49

6.92

25.85

2.14

18.65

8.36

11.69

3.33

10.12

Precipitation

Surface-water

 deliveries

Groundwater

 pumpage

Evapotranspiration

 from ground water

Figure B2. Simulated landscape budget for the Central Valley for typical (1975), dry (1990), and wet (1998) years.
Development and Changes to the Hydrologic Budget 65

Simulated groundwater budget

EXPLANATION

INPUT OUTPUT

Typical Year 1975

Dry Year 1990

Wet Year 1998

32%

6% 0%

9%

51%

2%

Specific yield and

 compressibility of water

Elastic and inelastic

 matrix storage

Surface-water loss to

 groundwater system

Recharge from

 landscape system

Intra-borehole flow

1%

0%

0%

43%

3%

15%

38%

Specific yield and

 compressibility of water

Elastic and inelastic

 matrix storage

Groundwater lost

 to streams

Evapotranspiration from

 groundwater

Pumpage

33%

7% 0% 13% 10%

37%

32%

10%

6%

0%

9%

43%

38%

4%

2% 18%

11%

27%

4%

25%

5%

10%

55%

6.50

0.03

1.13 1.90

10.19

0.44

4

0

8

12

16

20

MILLIONS OF ACRE-FEET

7.67

0.11

1.66 2.36

3.05

8.72

4

0

8

12

16

20

MILLIONS OF ACRE-FEET

2.78

0.06 0.58 1.11

6.29

0.39

4

0

8

12

16

20

MILLIONS OF ACRE-FEET

9.43

0.03

2.99

1.64 2.00

12.60

4

0

8

12

16

20

MILLIONS OF ACRE-FEET

20.86

0.01

1.47

7.46

18.14

0.31

4

0

8

12

16

20

MILLIONS OF ACRE-FEET

8.83

0.45 1.00

4.12

2.65

6.18

4

0

8

12

16

20

MILLIONS OF ACRE-FEET

Figure B3. Simulated groundwater budget for the Central Valley for typical (1975), dry (1990), and wet (1998) years.
66 Groundwater Availability of the Central Valley Aquifer, California

Table B1. Summary of the simulated landscape budget for average (water years 1962-2003), typical (1975), dry (1990), and wet (1998) 

years for the Central Valley, California. 

[All values in millions of acre-feet per year. Totals may not agree because of rounding. Deep percolation is defined as excess water (irrigation and precipitation)

beyond the active root zone (Schmid and others, 2006). Groundwater recharge: deep percolation – evapotranspiration from groundwater]

Input to landscape system Output from landscape system

Groundwater 

recharge Precipitation

Surface 

surface-water 

deliveries

Groundwater 

pumpage

Evapotranspiration from 

groundwater

Evapotranspiration Runoff Deep percolation

Average 15.8 10.2 8.6 3.7 25.6 1.1 11.4 7.7

Typical 14.5 11.3 7.9 3.9 25.5 1 11 7.0

Dry 10.1 8.4 11.7 3.3 26 0.5 6.9 3.6

Wet 30.2 8.9 4.5 3.2 25.8 2.1 18.6 15.4

Table B2. Summary of the simulated groundwater budget for average (water years 1962-2003), typical (1975), dry (1990), and wet (1998) 

years for the Central Valley, California. 

[All values in millions of acre-feet per year. Totals may not agree because of rounding. Pumpage includes urban and agricultural pumpage (groundwater pumpage into landscape system). Net recharge from landscape system matches net groundwater recharge from the landscape budget in table B1]

Input to the groundwater system

Storage from 

specific yield and 

compressibility of 

water

Elastic and inelastic storageStream leakage to 

groundwater 

Recharge from 

landscape process

Intra-borehole 

flow

General head 

boundaries

Average 8.1 1 2.6 10.7 0.4 0.0

Typical 6.5 1.1 1.9 10.2 0.4 0.0

Dry 2.8 0.6 1.1 6.3 0.4 0.1

Wet 20.9 1.5 7.5 18.1 0.3 0.0

Output from the groundwater system

Storage 

(specific yield and 

compressibility 

of water)

Storage 

(elastic and 

inelastic 

compaction) 

Groundwater 

inflow to 

streams

Discharge 

from landscape 

process

Pumpage General head 

boundaries

Average 8.5 2 2.2 3. 9.7 0.1

Typical 7.7 1.7 2.4 3.1 8.7 0.1

Dry 8.8 3 1.6 2.6 12.6 0

Wet 18.9 1 4.1 2.6 6.2 0.5

Net in relation to groundwater availability

Storage from 

specific yield and 

compressibility of 

water

Elastic and inelastic 

storage

Stream 

interaction

Recharge 

from landscape 

process

Pumpage General head 

boundaries

Average –0.3 –0.1 0.3 7.8 –9.3 –0.1

Typical –1.2 –0.5 –0.5 7.1 –8.3 –0.1

Dry –6.6 –2.4 –0.5 3.7 –12.2 0

Wet 12 0.5 3.3 15.5 –5.9 –0.4

Recharge and Discharge

Delivery of surface water for irrigation, combined with

pumping for irrigation and public supply, has greatly altered

the amount and distribution of recharge to, and discharge

from, the groundwater system. Recharge rates from precipitation have not changed significantly from predevelopment

times (Williamson and others, 1989).

Prior to development, natural recharge to, and discharge

from, the system was in a dynamic steady state, with an

estimated 2 million acre-ft/yr moving through the system

(fig. A20). Soon after irrigated agriculture began in the late

1800s, water pumped for irrigation exceeded the amount of

natural recharge. Recharge to the groundwater system comes

from two main sources: percolation of water past the root

zone, and stream losses. Prior to development, the source of

percolating water was infiltration of precipitation.
Development and Changes to the Hydrologic Budget 67

During the period between 1962 and 2003, an average

of 18.9 million acre-ft of water was required annually for

irrigation, supplied in approximately equal proportions from

groundwater and surface water (table B1 and fig. B2). Part

of this irrigation water was consumed by crops. However,

it is important to recognize that not all irrigation water is

consumed: some runs off and some returns to the groundwater system by deep percolation, canal leakage, and other

mechanisms. 

The primary source of recharge has become deep percolation of irrigation water past crop roots, sometimes referred

to as recharge from excess applied irrigation water. Of the

average 13.3 million acre-ft of groundwater recharged annually from 1962 to 2003, less than 1 percent was from infiltration from the Delta, 19 percent was from streams by way of

stream-flow leakage, and 79 percent was from the landscape

processes, which include recharge from excess applied irrigation water and from precipitation (table B2 and fig. B1). Average annual groundwater recharge varies between typical, dry,

and wet years; 12.1, 7.5, and 25.6 million acre-ft, respectively.

During dry years, recharge is reduced to a little more than onehalf the average recharge; recharge during wet years is almost

double the average. In typical and dry years, the contribution

from stream-flow leakage was about 15 percent. However,

during wet years, the streams generally flow at higher rates

for longer periods of time and the simulated contribution from

stream-flow leakage increases to 24 percent.

Groundwater pumpage and ET from crops has replaced

natural ET as the primary mechanism of discharge from the

groundwater system (fig. B1). Groundwater flow out the Delta

relatively is negligible (fig. B3). ET varies annually, but the

total ET does not vary much from the average annual rate of

25.6 million acre-ft/yr. Roughly 15 percent of the ET is met

directly from the uptake of shallow groundwater.

Groundwater pumpage is by far the largest discharge

from the groundwater system. Pumpage is physically possible to measure; yet in the Central Valley it is one of the most

uncertain components of the entire water budget. As a result,

agricultural pumpage often is estimated from the consumptive

use of water. Consumptive use of water in this context refers

to all evaporation and transpiration by a particular crop. If this

quantity is known, groundwater pumpage may be estimated

by taking into account surface-water supply, effective precipitation, and irrigation efficiency. In this study, the numerical

model CVHM was used to estimate groundwater pumpage.

The CVHM employs the newly developed Farm Process for

MODFLOW, which uses this method of estimating agricultural pumpage. Details of the model are found in Chapter C

and Appendix 1.

For the 1962–2003 timeframe, the CVHM simulation

indicates that average withdrawals from irrigation wells were

about 8.6 million acre-ft/yr. Surface-water deliveries averaged

10.2 million acre-ft/yr (table B1 and fig. B1). Hence, between

1962 and 2003, withdrawal from wells provided about

46 percent of the 18.8 million acre-ft of irrigation water

required annually. As with recharge, annual surface-water

deliveries and groundwater pumpage also vary with climatic

variations. Typical, dry, and wet year surface-water deliveries

were 11.3, 8.4, and 8.9, while agricultural groundwater pumpage was 7.9, 11.7, and 4.5 million acre-ft. Thus, even during

wet years, about one-third of irrigation water is derived from

groundwater pumpage. As expected, during drier years, this

proportion increases. 

Aquifer-System Storage

In the Central Valley, groundwater pumpage is the most

significant human activity that affects the amount of groundwater in storage and the rate of discharge from the aquifer

system (table B2 and figs. B1 and B2). A high concentration

of broadly distributed wells and the multiple broadly distributed cones of depression have produced water-level declines

across large areas (fig. B4). The principal areas of historical 

water-level changes prior to water year 1962 are shown in

figure B4A. Figure B4B shows areas where additional changes

in water levels have occurred between 1962 and 2003. In contrast to as much as 400 ft of drawdown since predevelopment,

some areas of the San Joaquin Valley have recovered more

than 300 ft since 1961, while others have had little recovery.

Despite this recovery, the result of the changes in water levels

since pre-development has been the extraction of millions of

acre-ft of water from aquifer-system storage.

Storage of water within the aquifer system can be quantified in terms of the specific yield for unconfined groundwater

flow, and the storage coefficient for confined flow, respectively. Specific yield represents gravity-driven dewatering of

shallow, unconfined sediments at a declining water table, but

also accommodates a rising water table. The specific yield is

dimensionless and represents the volume of water released

from or taken into storage per unit head change per unit area

of the water table. Specific yield is a function of porosity and

specific retention of the sediments in the zone of water-table

fluctuation. Where the aquifer system is confined, storage

change is governed by the storage coefficient, which is the

product of the thickness of the confined-flow system and its

specific storage. The specific storage is the sum of two component specific storages—the fluid (water) specific storage and

the matrix (skeletal) specific storage, which are governed by

the compressibilities of the water and skeleton, respectively

(Jacob, 1940). Specific storage has units of 1 over length and

represents the volume of water released from or taken into

storage in a confined flow system per unit change in head

per unit volume of the confined flow system. Therefore, the

storage coefficient of a confined flow system is dimensionless

and, similar to specific yield, represents the volume of water

released from or taken into storage per unit head change.
68 Groundwater Availability of the Central Valley Aquifer, California

Estimated change in hydraulic head,

 predevelopment to 1961, in feet—

Rise of 0 to 40

Decline from 0 to 40

Decline from 40 to 80

Decline from 80 to 120

Decline from 120 to 200

Decline from 200 to 300

Decline from 300 to 400

Decline of more than 400

EXPLANATION 

0 50 100 Miles

0 50 100 Kilometers

Central Valley boundary

A

Water-balance subregion

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

S

a

c

r

a

m

e

n

t

o

V a

l

l

e

y

S

a

n

J o

a

q u i n

V a l l e y

Tulare

Basin

Delta

Figure B4. A, Estimated change in hydraulic head in upper part of the aquifer system from 1860 to 1961 (modified from Williamson and 

others, 1989; Bertoldi and others, 1991). B, Simulated change in hydraulic head in lower part of the aquifer system from spring 1962 to 

spring 2003.
Development and Changes to the Hydrologic Budget 69

20_37212

19_38742

19_37289

18_33084

16_30057

15_34255

15_29737

14_30806

14_28350

13_25144

12_22972

11_21412

10_25517

09_19824

08_14958

07_13870 06b12975

06_14809

05_10854

04_07807

03b06329

02_05172

01_01645

Tul a re

B a si n

Delt a

Simulated change in hydraulic head, 

 1961 to 2003, in feet—

EXPLANATION 

0 50 100 Miles

0 50 100 Kilometers

Central Valley boundary

Water-balance subregion

Hydrograph well and identifier

 See figure 13 for hydrographs

B

11_21412

Rise of 0 to 40

Decline from 0 to 40

Decline from 40 to 80

Decline from 80 to 120

Decline from 120 to 200

Decline from 200 to 300

Decline from 300 to 400

Rise of 80 to 120

Rise of 200 to 300

Rise of 300 to 400

Rise of greater than 400

Rise of 120 to 200

Rise of 40 to 80

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Figure B4. Continued.
70 Groundwater Availability of the Central Valley Aquifer, California

Two skeletal compressibilities and, thereby, two skeletal

specific storages and two storage coefficients can be further

defined, one each for the elastic range of stress and one each

for the virgin or inelastic range of stress. The elastic and

inelastic stress ranges are defined by the previous maximum

stresses imposed on the aquifer system (Terzaghi and Peck,

1948; Riley, 1969). The previous maximum stress can be

expressed as a critical head—the previous minimum head, so

that head changes in the stress range above the critical head

(elastic stress range) result in elastic deformation (reversible

compaction and expansion) of the aquifer system, and head

declines in the stress range below the critical head (inelastic

stress range) result in inelastic compaction (largely irreversible) of the aquifer system. A head decline below the previous critical head establishes a new critical head so that any

subsequent head increase results in elastic expansion of the

aquifer system. 

This process governs the compaction of the aquifer

system and the land subsidence that can accompany groundwater level declines in the Central Valley. Deformation of

the aquifer system is proportional to the change in storage in

the aquifer system. For confined flow systems in the elastic

range of stress, the change in storage principally is governed

by the elastic skeletal specific storage and the thickness of the

aquifer system; and, in the inelastic range of stress, the change

in storage principally is governed by the inelastic skeletal

specific storage and the thickness of compressible fine-grained

deposits in the aquifer system. In the Central Valley, the

inelastic skeletal specific storage typically is 30 to several

hundred times larger than the elastic skeletal specific storage

(Ireland and others, 1984). Thus, depending on the thickness

of fine-grained compressible deposits in the aquifer system,

large storage changes and, thereby, significant amounts of

permanent compaction and land subsidence, can accompany 

groundwater level declines below the critical head threshold.

Hence, water released by the inelastic compaction of finegrained deposits in the Central Valley aquifer system is a

major source of water (table B2). Furthermore, the groundwater withdrawals have resulted in the permanent loss of storage

capacity by the inelastic compaction of fine-grained sediments.

This is discussed in more detail in the section of this report 

entitled “Land Subsidence.”

Between 1962 and 2003, an average 9.1 million acre-ft

of water went into storage annually, with an average removal

from storage of about 10.5 million acre-ft/yr (table B2 and 

figs. B3 and B4). This average annual net loss in storage

represents about 11 percent of net annual pumpage (table B2). 

In typical years, the average annual net loss in storage is about

1.4 million acre-ft. In dry years, about 9.0 million acre-ft of 

water are removed from storage, and during wet years more

than 12.5 million acre-ft are returned to storage (table B2). 

Even though volumetrically, wet years exceed dry years in

terms of changes in storage, overall water is being removed

from storage.

Intra-Annual Variations in Typical, Dry, and Wet 

Years

Water use in the Central Valley varies seasonally and the

sources of irrigation water vary greatly from season-to-season.

Although some pumping occurs in all months, the vast majority of groundwater withdrawals occur during the spring-summer growing season (between March and September) (fig. B5), 

whether the climatic condition is dry, typical, or wet. In a

typical year, almost 90 percent of groundwater is withdrawn in

the spring-summer growing season and about 10 percent in the

fall-winter dormant season (between October and February)

(fig. B5). 

Water typically is taken into storage during the wet winter

months (December through March) and released from storage

during the drier growing season (May through September)

(fig. B5A). The timing and volume of these storage changes

reflect the climatic regime (wet or dry year), groundwater

pumpage, and the availability of precipitation and surface

water (fig. B5). The spring-summer growing seasons relies on

irrigation from groundwater pumpage and surface-water deliveries. The groundwater pumpage removes large amounts of

water from storage in the aquifer system. This period of pumpage occurs when natural recharge rates are smallest, making

the effects of pumpage largest during the spring-summer

growing season. Although generally not as large of a volume

as the pumpage, excess applied irrigation water recharges the

shallow part of the aquifer during the growing season.

The relatively wet dormant period is a time of water-level

recovery. During typical or wet years, the December through

March period receives significant groundwater recharge

(fig. B5). From year to year, whether the climate is dry, typical,

or wet, significant groundwater recharge, mostly from precipitation, occurs in January and February. The vast majority of

this recharge occurs in the Sacramento Valley. Even so, for the

valley as a whole, there is little water removed from storage

during the dormant period.

Water pumped from the aquifer system may not be 

quickly replenished. In some areas, particularly the wetter

Sacramento Valley, groundwater that is pumped can be replenished annually during the non-irrigation season by recharge

from precipitation and streams. In other areas, replenishment 

only occurs in years of abundant precipitation. Although urban

land use may consume about an equivalent rate of water as

agricultural land use, the timing of these withdrawals may

be different. Hence, with land use shifting from agricultural

to urban, the seasonal fluctuation of recharge and discharge

may change. For example, in 1998, the relatively increased

irrigation efficiency and large amount of evapotranspiration

from groundwater overwhelms recharge in the spring-summer

growing season (fig. B5). 
Development and Changes to the Hydrologic Budget 71

Figure B5. Monthly groundwater budget for the Central Valley for a dry year (1990), typical year (1975), and wet year (1998). Values are 

relative to water availability. Hence, an increase in storage is shown as a positive value.

Typical Year - 1975

Dry Year - 1990

Wet Year - 1998 (Note scale change)

A

B

C

Net groundwater budget

Pumpage

Groundwater recharge from

 landscape

Stream interaction

Elastic and inelastic

 matrix storage

Specific yield and

 compressibility of water

EXPLANATION

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

1

0

-1

-2

-3

-4

-5

2

3

4

5

NET MONTHLY CHANGE IN GROUNDWATER BUDGET, IN MILLIONS OF ACRE-FEET PER YEAR

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

MONTH

1

0

0

-1

-2

-3

-4

-5

-5

2

3

4

5

5

10

15

25

20
72 Groundwater Availability of the Central Valley Aquifer, California

Temporal Variation in the Hydrologic Budget 

Hydrologic input and output to both the landscape and

groundwater budgets, which vary through time, are represented in the 1962–2003 CVHM simulation. These variations

predominantly are a result of the combined influences of

climate variability, surface-water delivery systems, land-use

changes, and farming practices. Climate variability can be

seen in a graph of cumulative departure from average precipitation (fig. A17). In general, inflows of surface water follow

the same climatic pattern as the precipitation (fig. A16). Since

the second half of the 20th century, California experienced

multiyear droughts in 1959–61, 1976–77, and 1987–92

(California Department of Water Resources, 1998). Based on

climate variability, time periods can be classified as wetter

periods, drier periods, and variations in between (figs. A16 and 

A17). Figure B6A shows the classification of climate variability for water years 1962 through 2003 and the main components of the landscape system and their changes through time:

delivery requirement (DR), landscape recharge, surface-water

deliveries, and agricultural pumpage.

The DR fluctuates with changes in climate, land use, and

farming practices. Figure B6A shows how the DR fluctuates

with the climate. Warmer periods cause an increase in ET

and, as a result, an increase in the demand for irrigation water.

The 1976–77 and 1987–92 droughts show an increasing DR

through each drought (fig. B6A). This partly is a result of an 

increase in potential ET, and partly a result of lowered water

levels during each drought. When water levels drop too far,

the plants that used to get all or part of their water from the

water table must be irrigated. Overall, the wet period from

1978 through 1985 showed a gradual decrease in DR. In

1983, an extremely wet year, the DR decreased dramatically.

The cooler wetter year caused many fields to flood. Where

vegetation was growing, much of the demand was met by

precipitation because the potential ET was lower. From 1993

through 1998, like the previous wet period, the DR decreased

(fig. B6A). In this later wet period, the decrease was more

dramatic. The decrease coincides with and likely is partially

a result of changes in land use that, in many cases, included

cultivation of more water-efficient crops and improved irrigation efficiencies.

Landscape recharge includes recharge from excess

irrigation water as well as precipitation. Because of the large

component of recharge from precipitation in the Sacramento

Valley, landscape recharge shows the most direct correlation

to the climate classifications (fig. B6A). During the droughts

of 1976–77 and 1987–1992, the landscape recharge decreased.

Superimposed on the climate effects is an inverse relation

between landscape recharge and DR. The landscape recharge

fluctuates inversely with the DR during the 1960s through

the mid-1970s. The wet periods, 1978–1985 and 1993–1998,

show larger magnitude landscape recharge fluctuations.

Although not as dramatic as the decrease in DR because of the

superimposed climatic effects, the agricultural pumpage shows

a general decline in the 1990s. This decline most likely is a

result of increased irrigation efficiencies.

The surface-water deliveries curve on figure B6A reflects

the history of the surface-water delivery system, climate variability, and the DR. Abundant winter and spring precipitation

are stored in reservoirs and released, as needed, to help control

flooding and provide irrigation water. California’s two largest

water projects (CVP and SWP) form a complicated surfacewater delivery system. This delivery system uses a series of

reservoirs, streams, and canals to store and divert surface

water throughout the valley. In particular, the system transfers the abundant water in the Sacramento and San Joaquin

River systems south and southwest to drier parts of the valley

(fig. A7). During the 1960s (figs. B6 and B7), surface-water

deliveries remained relatively constant. In the late 1960s,

additional parts of the delivery system were completed and

there was an increase in surface-water deliveries to the heavily irrigated Tulare Basin. The 1976–77 drought resulted in

record low storage in surface reservoirs and a rapid decrease

in surface-water deliveries (fig. B6A). Partly in response to

the 1976–77 drought, there was an expansion of the delivery

system and an increase in the importation of surface water on

the west side of the San Joaquin Valley. The 1987–92 drought

resulted in a decrease in surface-water deliveries to the lowest prolonged rate since the delivery system was put in place

(table B1 and fig. B6A). During and after this period, fiscal

and environmental concerns slowed the development of new

reservoirs. Nevertheless, except for the effects of climatic

variability, on average the surface-water deliveries remained

remarkably stable between 1962 and 2003.

On average, groundwater pumpage has decreased

between 1962 and 2003. In general, groundwater pumpage

is correlated directly to the DR because groundwater is used

when surface water is not available. During the 1960s (figs. B6

and B7), surface-water deliveries were relatively constant and

groundwater pumpage fluctuated with the DR.

Climatic variations and the resulting surface-water

supply directly affect the demand and the amount of groundwater required to meet agricultural and urban water demands

(fig. B6A). During wet periods and the first part of a drought,

surface-water reservoirs can be used to supply water for irrigation. In the later part of a drought, water in storage in surface

reservoirs is depleted and farmers turn to pumping groundwater. Therefore, dry periods generally lead to increased

pumping from wells. This is particularly apparent in severe

short droughts or later periods of prolonged droughts. The

groundwater pumpage in 1977 and 1990–1992 exemplify this

increase in groundwater pumpage (fig. B6A). 
Development and Changes to the Hydrologic Budget 73

Figure B6B shows the relation of urban, agricultural,

and total simulated pumpage during 1962–2003. In the last

40 years, millions of acre-ft/yr of water has been redistributed

from agricultural production to urban and environmental uses

(California Department of Water Resources, 1998; 2005).

Between 1962 and 2003, withdrawals for urban uses ranged

from 0.6 to 2.0 million acre-ft/yr. This pumpage for urban uses

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Dry Wet

Time frame land-use maps were applied, from Chapter C

Figure C8

4/61-3/68

Figure C9

4/68-3/78

Figure C10

4/78-3/93

Figure C11

4/93-3/99

Figure C12

4/99-9/03

EXPLANATION

Delivery requirement

Landscape recharge

Agricultural pumping

Surface-water deliveries

A

WATER YEAR

MILLIONS OF ACRE-FEET PER YEAR

Typical

5

0

10

15

20

25

30

Representative water year Dry Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Figure B6. A, Annual delivery requirement, landscape recharge, surface-water deliveries, and agricultural pumpage. B, Annual 

groundwater withdrawals for agricultural and urban use for the entire Central Valley between 1962 and 2003. Other than urban 

pumpage, all values are simulated by the Central Valley Hydrologic Model (CVHM) (Chapter C). Urban pumpage compiled by California 

Department of Water Resources (C. Brush, California Department of Water Resources, written commun., February 21, 2007). 

represents a steady increase from less than 5 percent in 1962 to 

about 30 percent in the late 1990s to early 2000s of the groundwater pumped in the Central Valley (fig. B6B). On average,

12 percent (1.2 million acre-ft/yr) of the withdrawals were for

urban uses (tables B1 and B2 and fig. B6B). 
74 Groundwater Availability of the Central Valley Aquifer, California

Changes in land use, agricultural practices, surface-water

deliveries, and urban pumpage have affected the groundwater

system. Figure B7 shows the simulated net annual changes to

the groundwater system between 1962 and 2003. Figure B8

shows just the storage components of the simulated groundwater budget. The yellow bars on the charts show net changes

in storage resulting from compressibility of water in confined

parts of the aquifer system and specific yield in unconfined

parts of the aquifer system (for example, the water table). The

green bars show changes in storage resulting from both elastic

and inelastic compaction. Figure B9 shows the cumulative

change in storage from 1962 through 2003 for the Central

Valley as a whole and for the individual major basins.

During1962–78, water predominantly was removed from

storage (figs. B8 and B9). During the relatively dry period

1962–68, before much of the surface-water delivery system

was available to the Tulare Basin, groundwater was pumped at

a high rate from wells in the Tulare Basin. In the late

1960s, increased importation of surface water to the heavily

irrigated Tulare Basin combined with the somewhat wetterthan-average climate caused groundwater pumpage to decline,

water levels to recover, and many wells to be unused

(figs. B6A and B7). Despite the increased importation of

surface water, during 1969–75, water still was released from

storage in the Tulare Basin. However, water was taken into

storage in the Sacramento Valley and the Central Valley as a

whole, partially as a result of the relatively wetter climate during this period (figs. B8 and B9). 

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Dry

Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

WATER YEAR

SIMULATED PUMPAGE, IN MILLIONS OF ACRE-FEET PER YEAR

Total

Simulated pumpage—

Urban

Agricultural

5

0

10

15

20

EXPLANATION

B

Figure B6. Continued.
Development and Changes to the Hydrologic Budget 75

The Central Valley’s most severe recorded drought

occurred during 1976–77. Two consecutive years of minimal precipitation (fig. A8) (fourth driest and the driest year 

in recorded history) resulted in record low storage in surface

reservoirs and a rapid decrease in surface-water deliveries.

This, in turn, caused an increase in groundwater pumpage

and, as a consequence, extremely low groundwater levels.

Socioeconomic and environmental impacts during these

extreme drought conditions were severe (California Department of Water Resources, 2005). The simulated landscape

and groundwater budgets reflect these drought conditions

(table B3). During the 2-year drought, simulated recharge to

the groundwater system reached a low of nearly 3.4 million

acre-ft/yr, less than half of the average, and the simulated

agricultural pumpage reached a high of 15.6 million acre-ft/yr

(or about 1.7 times the average) (fig. B6A). Simulated surfacewater deliveries (fig. B6A) reached an all-time low of 7.0

million acre-ft/yr; in turn, the percentage of irrigation water

from groundwater peaked at 69 percent. This intense drought

resulted in a large removal of water from storage. Through

"Variable"

to

Dry

Period of growth of water supplies Period of abundance Period of reduced supplies Period of

drought

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Dry Wet

Available water resources

EXPLANATION

WATER YEAR

ANNUAL CHANGE IN GROUNDWATER AVAILABILITY, IN MILLIONS OF ACRE-FEET PER YEAR

Typical

-30

-20

-10

0

10

30

20

Representative water year

Dry

Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Specific yield and

 compressibility of water

Groundwater flow

 to Delta

Elastic and inelastic

 matrix storage

Stream interaction

Landscape recharge

Pumpage

Net groundwater budget

Figure B7. Simulated groundwater budget changes between water years 1962 and 2003 for the Central Valley, California.
76 Groundwater Availability of the Central Valley Aquifer, California

1977 the simulated cumulative loss in storage since 1962 was

33 million acre-ft with a total of 19.9 million acre-ft lost during these 2 extremely dry years, 1976–77 (fig. B9).

Between the droughts of 1976–77 and 1987–92, the

climate was wetter and cropping patterns changed. During this

period, the average irrigation efficiency increased from about

60 to 70 percent (California Department of Water Resources,

1994; 2005). This increase in irrigation efficiency is attributed

largely to growers using more efficient drip and sprinkler

irrigation as opposed to less efficient irrigation methods such

as flood irrigation. Other improvements that contributed to

the increased efficiencies include laser-leveling of furrowirrigated fields, and shortening of furrows (particularly on

large corporate farms). As an outcome, the DR decreased

resulting in a reduction in applied water. Both surface-water

deliveries and groundwater pumpage decreased (fig. B6A). 

Because groundwater pumpage decreased by a larger volume,

the CVHM simulates the percentage of irrigation water met by

groundwater pumpage reached a minimum of about 30 percent

during the wet period from 1982 to 1983. With the reduced

pumping rates, the simulation shows that local groundwater levels partially recovered, and depletion of groundwater

storage virtually stopped. In years with more available water,

significant volumes of water were taken into storage (figs. B8

and B9). The CVHM simulates the amount of water entering storage during these 2 wetter years exceeded the amount

removed by 14.8 million acre-ft.

Figure B8. Simulated annual changes in aquifer-system storage between water years 1962 and 2003 for the Central Valley, California. 

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Dry Wet

EXPLANATION

WATER YEAR

NET ANNUAL CHANGE IN STORAGE, IN MILLIONS OF ACRE-FEET PER YEAR

Typical

-10

-15

-5

0

5

10

15

Representative water year Dry Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Elastic and inelastic

 matrix storage

Specific yield and

 compressibility of water
Development and Changes to the Hydrologic Budget 77

The 1987–92 drought was associated with increases in

ET (fig. A18) and DR and a decrease in surface-water deliveries to the lowest prolonged rate in the study period (table B1

and fig. B6). As a result, the CVHM shows that groundwater

pumpage increased dramatically and exceeded surface-water

deliveries (fig. B6A). Ultimately, pumpage increased to rates

close to the 1970s levels. Although not as extreme as the

1970s, the CVHM shows that these high pumping rates continued for an extended period of time. During this prolonged

drought, aquifer storage decreased at a dramatic rate, water

levels declined, and subsidence briefly increased (table B3 and 

figs. B3, B8, and B9). The CVHM simulates the cumulative

change in storage reaching a maximum loss of 47.5 million

acre-ft in the mid-1990s (fig. B9). 

Between 1993 and 1998, with the return of a relatively

wet climate, the CVHM simulates that surface-water deliveries increased, groundwater pumpage decreased, and except

for 1994 surface-water deliveries, exceeded groundwater

pumpage (fig. B6). Similar to the period between the previous droughts, groundwater levels partially recovered and

approximately 24.3 million acre-ft of water returned to

storage (fig. B9). During 1999–2003, with stable surfacewater deliveries, more efficient irrigation systems, changes to

lower-water-use crops, and overall relatively moderate-to-wet

climate, the total average agricultural pumpage decreased to

about 5.7 million acre-ft/yr (fig. B6A). Despite the relatively

wet climate during this period, the decrease in excess irrigation water resulted in one of the lowest landscape recharge

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry

Dry

Wet Wet

EXPLANATION

WATER YEAR

CUMULATIVE CHANGE IN GROUNDWATER STORAGE, IN MILLIONS OF ACRE-FEET

Typical

0

-20

-60

-40

-80

20

Representative water year Dry Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Central Valley Overall

By region

Sacramento

Delta and Eastside Streams

San Joaquin Basin

Tulare Basin

Figure B9. Simulated cumulative annual changes in aquifer-system storage between water years 1962 and 2003 for the Central Valley, 

California. 
78 Groundwater Availability of the Central Valley Aquifer, California

rates in the simulation period of 3.4 million acre-ft/year in 

2001 (figs. A9 and B6A). It is interesting to note that despite

increased irrigation efficiency, surface-water deliveries

remained relatively constant. Even though agricultural pumpage decreased (fig. B6A), about 19.1 million acre-ft of water

was released from storage between 1999 and 2003 (figs. B8

and B9). The loss of storage can be explained by a decrease

in excess irrigation water (2001 was the lowest recharge

rate in the simulation period) resulting from increased urban

pumpage, more efficient farming practices, and (or) decreased

recharge from below-median precipitation.

In the Central Valley as a whole, the simulated magnitudes of the stream gains and losses generally are small

and are similar from year to year. Principally during wet or

variable-to-wet periods, streams lose water to the groundwater

system. During dry and the 1998–2003 variable-to-dry

periods, streams generally gain water from the groundwater

system (table B2 and fig. B7). 

Likewise, in the Central Valley as a whole, during 1962–

2003 there was a simulated net release of water from aquifersystem storage (fig. B8) and accompanying groundwater level

declines (fig. B4). Simulated withdrawals from storage occur

during most years. During years when more precipitation and

imported surface water is available for irrigation, agricultural

pumpage decreases and groundwater recharge is taken into

storage (fig. B7). The difference between simulated annual

groundwater recharge and discharge for the period 1962–2003

indicates a net loss of 57.7 million acre-ft from aquifer-system 

storage (fig. B9, table B3). 

Table B3. Selected average annual hydrologic budget components for water years 1962–2003 for each of the 21 water balance areas 

in the Central Valley, California. 

[Values in acre-feet; totals may not sum because of rounding]

Water-balance subregion 

Area 

(square 

miles)

Net storage 

from specific yield and 

compressibility of 

water1

Net elastic 

and 

inelastic storage1

Net 

stream 

leakage2

Net 

pumpage

Net 

recharge 

from landscape3

Precipitation

EvapotranspirationSurfacewater 

deliveries

1 611 36,000 13,000 –144,000 45,000 453,000 1,063,000 547,000 46,000

2 1,163 –17,000 23,000 –294,000 557,000 768,000 1,496,000 1,269,000 129,000

3 1,112 –39,000 3,000 –212,000 49,000 508,000 1,125,000 1,300,000 717,000

4 560 –34,000 0 –494,000 6,000 –19,000 562,000 635,000 78,000

5 957 –34,000 –1,000 –200,000 65,000 466,000 1,200,000 1,101,000 439,000

6 1,044 –47,000 10,000 34,000 506,000 522,000 1,137,000 1,315,000 329,000

7 534 2,000 4,000 –38,000 186,000 222,000 590,000 512,000 172,000

Sacramento Valley 5,981 –99,000 52,000 1,348,000 1,414,000 2,920,000 7,173,000 6,6799,000 1,910,000

Eastside Streams (8) 1,362 –26,000 7,000 95,000 850,000 721,000 1,365,000 1,444,000 205,000

Delta (9) 1,026 –218,000 3,000 705,000 467,000 –200,000 975,000 1,603,000 64,000

10 1,083 –36,000 29,000 64,000 60,000 89,000 588,000 1,465,000 983,000

11 664 –21,000 0 –98,000 85,000 251,000 509,000 901,000 643,000

12 540 –56,000 1,000 39,000 45,000 131,000 384,000 702,000 440,000

13 1,648 43,000 67,000 163,000 754,000 474,000 1,092,000 2,233,000 936,000

San Joaquin Basin 3,935 –70,000 97,000 168,000 944,000 945,000 2,573,000 5,301,000 3,002,000

14 1,071 179,000 165,000 6,000 934,000 418,000 432,000 1,631,000 716,000

15 1,423 26,000 146,000 239,000 1,603,000 708,000 607,000 2,225,000 757,000

16 478 89,000 35,000 33,000 202,000 212,000 299,000 518,000 358,000

17 569 54,000 28,000 170,000 445,000 348,000 358,000 852,000 442,000

18 1,358 158,000 198,000 104,000 1,135,000 710,000 715,000 2,237,000 821,000

19 1,365 85,000 133,000 0 754,000 334,000 494,000 1,275,000 367,000

20 705 74,000 92,000 19,000 252,000 240,000 295,000 892,000 610,000

21 1,105 83,000 81,000 130,000 324,000 272,000 414,000 1,333,000 1,096,000

Tulare Basin 8,074 748,000 878,000 701,000 5,649,000 3,188,000 3,614,000 10,963,000 5,167,000

Total 20,378 300,000 1,000,000 300,000 9,300,000 7,600,000 15,700,000 25,900,000 10,300,000

 1Positive values indicate water levels are rising and water is being taken into storage, and negative values indicate water levels are falling and water is being

released from storage.

2

Positive values indicate water is leaving the surface-water system and recharging the groundwater system, and negative values indicate water is entering the

surface-water system and discharging from the groundwater system.

3

Positive values indicate water is leaving the landscape system and is recharging the groundwater system, and negative values indicate water is leaving the

landscape system, predominantly through evapotranspiration, and discharging from the groundwater system.
Development and Changes to the Hydrologic Budget 79

Spatial Variation in the Hydrologic Budget

The hydrologic budget for the Central Valley varies spatially (figs. B9 and B10, and table B3). Precipitation is much

larger in magnitude and is a larger percentage of recharge in

the Sacramento Valley than in the southern areas of California

(table B3). Likewise, streamflow interaction is a much larger

percentage of the budget in the Sacramento Valley than the

San Joaquin Valley (fig. B10). More agriculture and irrigation

occur in the warmer and drier San Joaquin Valley. Although

some surface-water deliveries reallocate water within the

Sacramento Valley, the deliveries predominantly are to the San

Joaquin Valley, especially to the Tulare Basin. In addition to

surface-water deliveries, the irrigation requirements necessitate substantial groundwater pumpage. On average, 68 percent

of pumpage in the Central Valley occurs in the San Joaquin

Valley (11 percent from the San Joaquin Basin and 57 percent

from the Tulare Basin). Groundwater recharge in the Sacramento Valley chiefly is from natural recharge (precipitation),

whereas recharge in the San Joaquin Valley principally derives

from excess applied irrigation water. However, on average,

groundwater recharge in the Sacramento Valley volumetrically

approximates recharge in the San Joaquin Valley.

The change in the amount of water in storage varies

spatially because of the spatial variation in hydrologic stresses

(table B3 and figs. B9 and B10). Somewhat surprisingly, the

simulated and measured water levels indicate that water has

been added to aquifer-system storage in and around the Delta

and Eastside Streams (fig. B10B). Volumetrically and historically, there has been very little overall change in storage in

the Sacramento Valley (fig. B10A) and San Joaquin Basin

(fig. B10C) (table B3 and fig. B9). Conversely, despite the

surface-water deliveries, a substantial amount of water has

been removed from aquifer-system storage in the Tulare Basin

(table B2 and figs. B9 and B10D). Changes in the amount

of storage in each of the regions reflect the climate. During

wetter periods there are increases in storage, and during drier

periods there are decreases in storage. The magnitude of these

changes is most evident in the Tulare Basin and most subdued

around the Delta (figs. B9 and B10). In the mid-1970s, there 

was a discernible reduction in the amount of pumpage on the

west side of the San Joaquin Valley (Tulare Basin) because

of increased availability of imported surface water for irrigation in that area. A general increase in storage occurs in the

San Joaquin Basin, and relatively no change in storage in the

Tulare Basin, from the mid 1970s until the 1987–92 drought

when deliveries were curtailed (fig. B9). In addition, generally

rising water levels also are evident in figure B4B. 

Water Levels and Groundwater Flow 

Groundwater levels and associated groundwater flows

have responded to changes in the groundwater budget. Prior

to development, the Central Valley aquifer system was driven

by natural conditions in which natural discharge was in a

long-term dynamic equilibrium with natural recharge, and

longer-term changes in groundwater storage were negligible

(Planert and Williams, 1995). Groundwater flowed from areas

of higher altitude along mountain fronts to areas of discharge

along rivers and marshes near the valley trough. Principally,

this discharge occurred to the Sacramento and San Joaquin

Rivers and Tulare Lake. Recharge predominantly was from

rain and snowmelt in the mountains that became stream leakage at the valley margins in the northern and eastern parts

of the valley. Precipitation falling on the valley floor was

not consumed fully by ET, and some excess water infiltrated

beyond the root zone, recharged the water-table, and subsequently flowed toward the rivers and surrounding marshes. At

the valley margins the hydraulic gradient was downward—

hydraulic head in the shallow part of the aquifer system was

greater than the head in the deeper parts of the system; thus,

groundwater moved downward. Conversely, the hydraulic

gradient was upward in discharge areas near the valley trough,

where water typically moved upward to discharge in rivers

and marshes. Groundwater that was not evaporated or transpired by plants discharged either into the Sacramento and the

San Joaquin Rivers that drained to the Delta or into the closed

Tulare Basin from which it was consumed by ET (Planert and

Williams, 1995). Most of the water in the San Joaquin Valley

moved laterally, but a small amount leaked upward through

the intervening confining unit (Planert and Williams, 1995).

Upward vertical flow to discharge areas from the deep confined part of the aquifer system was impeded partially by confining clay beds, particularly the Corcoran Clay Member of the

Tulare Formation (hereafter referred to as the Corcoran Clay).

Because of the higher head in the confined part of the system

during the early years of groundwater development, flowing

wells were drilled into the deep aquifer in low-lying areas near

rivers and marshes. Large-scale groundwater development for

both agricultural and urban uses has modified the groundwater

levels and flow patterns, relative to predevelopment conditions (fig. B4). Groundwater flow has become more rapid and

complex. Groundwater pumpage and application of excess

irrigation water has resulted in steeper hydraulic gradients as

well as shortened flow paths between sources and sinks.
80 Groundwater Availability of the Central Valley Aquifer, California

1

2

3

4

5

6

7

-0.29

0

-0.03 -0.18

-1.66

2.88

-1.37

Sacramento

-2

-3

-4

-5

0

-1

1

2

3

4

MILLIONS OF ACRE-FEET

3% 5%

0%

0%

26%

45%

21%

A

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

MILLIONS OF ACRE-FEET

1

2

3

4

5

6

7

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

EXPLANATION 

0 50 100 Miles

0 50 100 Kilometers

Central Valley boundary

Water-balance subregion

 boundary and identifier

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

1

Specific yield and

 compressibility of water

Flow of groundwater

 to Delta

Elastic and inelastic

 matrix storage

Stream interaction

Landscape recharge

Pumpage

Flow between regions or

 water-balance subregion

Net annual groundwater budget,

 water years 1962-2003

Figure B10. Average annual groundwater budget for the A, Sacramento Valley. B, Delta and Eastside Streams. C, San Joaquin Valley. 

D, Tulare Basin. Schematic bar charts of average annual groundwater budget for the 21 water-balance subregions (WBSs) in the 

Central Valley also are shown. 
Development and Changes to the Hydrologic Budget 81

-2

-3

-4

-5

0

-1

1

2

3

4

MILLIONS OF ACRE-FEET

0.32

-0.09

0.05

0.00

0.65 0.28

-1.12

Delta and

Eastside Streams

2%

13%

3%

0%

26%

11%

45%

B

0.5

1.0

-1.0

-0.5

0.0 0.5

1.0

-1.0

-0.5

0.0

9

8 8

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

9

EXPLANATION 

0 50 100 Miles

0 50 100 Kilometers

Central Valley boundary

Water-balance subregion

 boundary and identifier

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

8

Specific yield and

 compressibility of water

Flow of groundwater

 to Delta

Elastic and inelastic

 matrix storage

Stream interaction

Landscape recharge

Pumpage

Flow between regions or

 water-balance subregion

Net annual groundwater budget,

water years 1962-2003

Figure B10. Continued. 
82 Groundwater Availability of the Central Valley Aquifer, California

Figure B10. Continued. 

-2

-3

-4

-5

0

-1

1

2

3

4

MILLIONS OF ACRE-FEET

-0.21 -0.04

0

-0.02

0.04

0.71

-0.60

San Joaquin

-2

-3

-4

-5

0

-1

1

2

3

4

MILLIONS OF ACRE-FEET

3%

13%

0%

1%

2%

44%

37%

C

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

MILLIONS OF ACRE-FEET

11

11

10

10

12

12

13

13

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

EXPLANATION 

0 50 100 Miles

0 50 100 Kilometers

Central Valley boundary

Water-balance subregion

 boundary and identifier

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

11

Specific yield and

 compressibility of water

Flow of groundwater

 to Delta

Elastic and inelastic

 matrix storage

Stream interaction

Landscape recharge

Pumpage

Flow between regions or

 water-balance subregion

Net annual groundwater budget,

water years 1962-2003
Development and Changes to the Hydrologic Budget 83

Figure B10. Continued. 

-2

-3

-4

-5

-6

0

-1

1

2

3

4

MILLIONS OF ACRE-FEET

-0.88

0

-0.49

0.50

3.26

-5.20

Tulare

8%

0% 1%

5%

5%

50% 31%

0.06

D

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-0.5

0.0

0.5

1.0

-1.0

-1.5

-0.5

0.0

MILLIONS OF ACRE-FEET

15

15

14

14

19

19

16

16

17

17

21

21

18

18

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET MILLIONS OF ACRE-FEET

MILLIONS OF ACRE-FEET

0.5

1.0

-1.0

-0.5

0.0

20

20

MILLIONS OF ACRE-FEET

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

EXPLANATION 

Central Valley boundary

Water-balance subregion

 boundary and identifier

15

Specific yield and

 compressibility of water

Flow of groundwater

 to Delta

Elastic and inelastic

 matrix storage

Stream interaction

Landscape recharge

Pumpage

Flow between regions or

 water-balance subregion

Net annual groundwater budget,

water years 1962-2003
84 Groundwater Availability of the Central Valley Aquifer, California

Well depths in the Central Valley are determined by the

depth of permeable aquifer material and by the quality of the 

groundwater. In general, wells typically are less than 500 ft

deep in the Sacramento Valley but are as deep as 3,500 ft in

the San Joaquin Valley (Planert and Williams, 1995). The

greater depth of well construction is necessitated by the low

permeability of the unconfined part of the aquifer system in

the western and southwestern San Joaquin Valley and the presence of highly mineralized water and water high in selenium

in the upper parts of the aquifer system in the western San Joaquin Valley (Planert and Williams, 1995). The construction of

about 100,000 irrigation wells, many of which have long intervals of perforated casing, has provided hydraulic connections

between permeable zones within the aquifer system. Where

these wells are open to the entire aquifer system, they allow

flow through the boreholes (intra-borehole flow) between

the shallow unconfined to semi-confined parts and the deep

confined parts of the aquifer system. The resulting hydraulic

connection, provided by these multi-zone wells, substantially

increases the equivalent vertical hydraulic conductivity of the

aquifer system, particularly the of the Corcoran Clay confining

unit (fig. B11) (Page and Balding, 1973; Londquist, 1981; Williamson and others, 1989; Bertoldi and others, 1991; Gronberg

and Belitz, 1992). The dramatic lowering of hydraulic heads

in the confined parts of the aquifer system has resulted in a

large, net downward movement of water through boreholes.

This vertical flow through boreholes occurs in both pumped

and non-pumped wells, and increases during the growing

season and droughts when the hydraulic head gradient across

the Corcoran Clay increases. The amount of water that flows

downward through one large-diameter well is equivalent to the

estimated natural leakage through the Corcoran Clay over an

area of approximately 7 mi2 (Williamson and others, 1989). If 

the multi-zone wells were absent, then the heads likely would

adjust to accommodate the decreased vertical fluxes.

Figure B11. Simulated flow through multi-zone wells. 

"Variable"

to Dry "Variable"

to Dry

"Variable"

to Wet Dry Wet Dry Wet

WATER YEAR

NET ANNUAL PUMPAGE AND FLOW THROUGH MULTI-NODE WELLS,

IN MILLIONS OF ACRE-FEET PER YEAR

Typical

0

-1.0

-2.0

-5.0

-4.0

-3.0

1.0

Representative water year Dry Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Intra-borehole flow

Pumpage

EXPLANATION
Development and Changes to the Hydrologic Budget 85

Between 1860 and the early 1960s, intensive groundwater development significantly lowered water levels and altered

groundwater flow patterns. By the 1960s, excess irrigation

water (predominantly from imported surface water) had

become the dominant source of recharge. The rapid agricultural development and the associated increases in groundwater

pumpage resulted in strong vertical head gradients, substantial

vertical flow to wells pumping from the deeper part of the

system, and significant head declines throughout the valley

(fig. B4A). These areas of head declines often were widespread, attributable to the distributed pumpage for irrigated

agriculture. The increased surficial recharge and groundwater

withdrawal have increased vertical flow in the system. For

example, water on a lateral flow path may be removed repeatedly by pumping and reapplied at the surface. By and large,

the system still behaves this way. Water generally moves from

irrigated areas toward pumping-induced broad head depressions in agricultural areas or toward cones of depression at

urban pumping centers (Bertoldi and others, 1991). There still

is a significant lateral and vertical component of flow toward

the major rivers and streams where groundwater discharges to

the surface-water system either along rivers and streams or in

the Delta.

Three years; 1961, 1976, and 2000; were selected to

show the configuration of the water table and potentiometric

surface during the 1962–2003 period of study (fig. B12). The 

1961 and 1976 maps were developed from measured data for

the CV-RASA studies (Williamson and others, 1989) and the

2000 maps were developed from simulations developed for

this study (Chapter C). The year 1961 represents the beginning of the study period, 1976 represents water levels for the

early part of the study after the delivery of significant quantities of imported surface water had begun and prior to the

intense 1976–77 drought, and 2000 represents more recent

conditions. When compared, these potentiometric-surface 

maps show the cumulative effects of various combinations of

pumpage, surface-water deliveries, and recharge from excess

applied irrigation water from 1961 through 2003. Hydrographs

from wells throughout the region show the changes through

time (fig. B13). 

Changes to the system are both temporal and spatial in

nature. Temporal variability of measured water levels generally is dominated on shorter time scales by the irrigation season and longer time scales by the natural climate. Simulations

indicate that seasonal fluctuations reach several hundred feet

in the deeper wells penetrating the confined part of the aquifer

system on the west side of the San Joaquin Valley. Conversely,

these fluctuations generally are less than 5 ft at the water table

(fig. B13). 

Adjacent to rivers, variability is dominated by river stage,

and a combination of these factors influence water levels

within about 1 mi of the rivers. Longer-term temporal variability predominately reflects the climate variability. The heads

in the confined part of the aquifer system have varied significantly from year to year in some areas, declining in years

of greater-than-average groundwater pumpage and recovering in years of reduced pumpage (fig. B13). Data from wells

(fig. B13, wells 02_05172, 06b12975, 14_30806, 15_29737,

and 19_37289) show that vertical hydraulic gradients from the

water table to the deeper production zones associated with the

major alluvial fans are strongly downward and larger during

the spring-summer growing season than during the fall-winter

dormant period. As one moves from the edges of the valley

toward the valley trough, the gradients reverse. Under the

valley trough, the gradient is in an upward direction (fig. B13, 

wells 04_07807, 15_34255). Over the Central Valley as a

whole, the water levels in the water table wells (in general,

this is layers 1 through 3 on fig. B13) remain fairly constant 

during the 1962–2003 period, with some fluctuations in intensively irrigated or pumped areas (figs. B12A, B12C, and B13). 

By 1961, pumpage lowered water levels in the Sacramento Valley by 30–80 ft in the areas between major tributaries flowing from the Sierra Nevada (fig. B4A). In addition, 

water-level changes between 1860 and 1961 show a pumping

center just north of the Delta. Similar water-level declines

occur between the tributaries on the northeastern side of the

San Joaquin Valley (fig. B4A). In general, water levels have

declined slightly in the Sacramento Valley between 1962 and

2003 (figs. B4B and B13). Water levels have declined the most

in the Sacramento and Stockton urban areas (figs. B4B, B12B, 

and B12F). Water levels have risen slightly in the area around

the Delta. Much of this water-level rise is remediated by

pumping local groundwater into a series of drainage canals.

In 2000, groundwater in the Central Valley generally was

within 50 ft of the land surface in the northern, central, and

western areas of the valley floor (fig. B14). In the San Joaquin

Valley, the area where water was within 50 ft of land surface

generally coincided roughly with the area of predevelopment

flowing wells. The groundwater table generally is deeper along

the southern and eastern margins of the valleys where generally coarse-grained alluvial fans allow the rapid infiltration of

surface water and of any available precipitation (fig. B14). The 

western margin of the San Joaquin Valley has similar deeper

water tables not shown at the scale of fig. B14.
86 Groundwater Availability of the Central Valley Aquifer, California

80

40

200

240

0

280

120

160

400

320

360

500

600

700

120

400

320

160

320

120

360

80

0

240

360

360

200

40

400

280

120

500

80

280

160

500

200

320

120

120

400

240

240

240

360

280

40

120

200

200

160

320

280

400

120

120

320

EXPLANATION

Spring 1961

Water-level altitude

 (from Williamson and others, 1989),

 in feet above NGVD 29—

Major streams and canals

Direction of flow

0

1 to 50

51 to 100

101 to 200

201 to 400

401 to 800

0 50 100 Miles

0 50 100 Kilometers

A

Central Valley boundary

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Figure B12. Altitude of the A, Water table in the unconfined part of the aquifer system (Williamson and others, 1989). B, Potentiometric surface of the confined 

part of the aquifer system for 1961 (Williamson and others, 1989). C, Unconfined part of the aquifer system (Williamson and others, 1989). D, Potentiometric 

surface of the confined part of the aquifer system for 1976 (Williamson and others, 1989. E, Unconfined part of the aquifer system (simulated, Chapter C). 

F, Potentiometric surface of the confined part of the aquifer system, for 2000 (simulated, Chapter C). 
Development and Changes to the Hydrologic Budget 87

Figure B12. Continued.

80

0

40

120

160

200

240

-40 -80

280

400

320

-120

-160

500

360

600

80

280

400

240 280

160

120

280

280 280

320

320

200

160

-40

40

120

-80

0

40

160

280

200

80

500

40

240

80

0

80

160

40

80

240

40

120

80

240

400

120

160

280

0

280

-160

EXPLANATION

Spring 1961

-200 to -100

-99 to 0

1 to 100

101 to 250

251 to 500

501 to 750

Potentiometric surface of the lower part of the

 aquifer system (from Williamson and others, 1989),

 in feet above NGVD 29—

0 50 100 Miles

0 50 100 Kilometers

Major streams and canals

B

Central Valley boundary

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Direction of flow
88 Groundwater Availability of the Central Valley Aquifer, California

Figure B12. Continued.

0

240

80

40

200

280

160

120

320

500

400

600

700

400

120

280

320

200

400

0

240

200

280 240

240

120

320

160

160

80

120

320

40

320

40

120

120

320

80

400

EXPLANATION

Water-level altitude

 (from Williamson and others, 1989),

 in feet above NGVD 29—

0

1 to 50

51 to 100

101 to 200

201 to 400

401 to 800

0 50 100 Miles

0 50 100 Kilometers

Major streams and canals

C

Central Valley boundary

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Direction of flow

Spring 1976
Development and Changes to the Hydrologic Budget 89

Figure B12. Continued.

80

0

120

40

160

200

240

-40

400

320

280

-80

500

360

160

80

80

0

320

280

80

-40

-40

160 400

160

280

40

80

240

-80

0

120

40

40

240

0

160

0

0

200

320

200

200

320

120

80

40

80

80

200

120

40

200

40

160

-40

0

400

280

EXPLANATION

Spring 1976

-200 to -100

-99 to 0

1 to 100

101 to 250

251 to 500

Potentiometric surface of the lower part

 of the aquifer system (from Williamson and others, 1989),

 in feet above NGVD 29—

Major streams and canals

0 50 100 Miles

0 50 100 Kilometers

D

Central Valley boundary

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Direction of flow
90 Groundwater Availability of the Central Valley Aquifer, California

Figure B12. Continued.

440

480

320

360

280

400

240

200

160

160

200

280

240

240

200

200

200

240

280

120

120

120

160

120

160

120

120

160

80

80

80

80

80

80

40

40

40

EXPLANATION

Simulated water-level altitude,

 in feet above NGVD 29—

40

80

120

160

200

240

280

320

360

400

440

480

Central Valley boundary

Major streams and canals

0 50 100 Miles

0 50 100 Kilometers

E

Spring 2000

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Direction of flow
Development and Changes to the Hydrologic Budget 91

Figure B12. Continued.

0

160

160

200

200

240

280

40

280

320

360

400

120

440 440

40

80

80

240

40

240

280

280

120

280

80

80

80

160

240

0

160

120

120

120

120

40

200

80

240

400

440

480 160

EXPLANATION

Spring 2000

Central Valley boundary

Major streams and canals

0 50 100 Miles

0 50 100 Kilometers

F

Simulated potentiometric surface of the lower part

 of the aquifer system, in feet above NGVD 29—

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Direction of flow

0

40

80

120

160

200

240

280

320

360

400

440

480
92 Groundwater Availability of the Central Valley Aquifer, California

Layer 2

Layer 2

Layer 3

Layer 2

Layer 3

Layer 6

Layer 7

Layer 1

Layer 2

Layer 1

Layer 2

Layer 6

1960 1970 1980 1990 2000

WATER-LEVEL ALTITUDE, IN FEET ABOVE NGVD 29

WATER YEAR

EXPLANATION

WATER YEAR

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Well 01_01645

Note scale change

Well 02_05172

Well 06_14809

Well 03b06329

Well 04_07807

Well 05_10854

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 7

Layer 6

Layer 8

Layer 9

Layer 10

Measured head of well 

Land-surface elevation at well Simulated water-level elevation from model for—

200

250

300

350

400

450

500

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Figure B13. Hydrographs for representative wells in the Central Valley, California (locations shown in figure B4B).
Development and Changes to the Hydrologic Budget 93

EXPLANATION

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 7

Layer 6

Layer 8

Layer 9

Layer 10

Measured head of well 

Land-surface elevation at well Simulated water-level elevation from model for—

WATER-LEVEL ALTITUDE, IN FEET ABOVE NGVD 29

WATER YEAR WATER YEAR

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Well 06b12975

Well 10_25517 Well 11_21412

Well 09_19824

Well 07_13870

Well 08_14958

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Layer 2

Layer 1

Layer 2

Layer 2

Layer 3

Layer 6

Layer 1

Layer 2

Layer 3

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 5

Layer 7

Figure B13. Continued.
94 Groundwater Availability of the Central Valley Aquifer, California

EXPLANATION

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 7

Layer 6

Layer 8

Layer 9

Layer 10

Measured head of well 

Land-surface elevation at well Simulated water-level elevation from model for—

WATER-LEVEL ALTITUDE, IN FEET ABOVE NGVD 29

WATER YEAR WATER YEAR

NGVD

29

1960 1970 1980 1990 2000 1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Well 12_22972

Well 15_29737

Well 14_28350

Land-surface elevation 348 feet

Note scale change

Well 13_25144

Well 14_30806

Land-surface elevation 266 feet

Note scale change

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Layer 1

Layer 2

Well 15_34255

Layer 2

Layer 3

Layer 6

Layer 7

Layer 6

Layer 7

Layer 3

Layer 4

Layer 5

Layer 4

Layer 5

Layer 6

Layer 8

Layer 9

Layer 10

Layer 8

Layer 9

Layer 10

Layer 6

Layer 7

Layer 7

200

100

-100

-200

-300

-400

-500

-600

NGVD

29

200

100

-100

-200

-300

-400

-500

-600

Figure B13. Continued.
Development and Changes to the Hydrologic Budget 95

EXPLANATION

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 7

Layer 6

Layer 8

Layer 9

Layer 10

Measured head of well 

Land-surface elevation at well Simulated water-level elevation from model for—

WATER-LEVEL ALTITUDE, IN FEET ABOVE NGVD 29

WATER YEAR WATER YEAR

1960 1970 1980 1990 2000 1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

250

300

350

200

150

100

50

1960 1970 1980 1990 2000 1960 1970 1980 1990 2000

-50

250

200

150

NGVD

29

100

50

1960 1970 1980 1990 2000

Well 19_37289

Land-surface elevation 279 feet

Note scale change

Well 20_37212

Land-surface elevation 590 feet

Note scale change

Well 18_33084

Well 18c33884

Note scale change

Well 19_38742

Land-surface elevation 269 feet

Well 16_30057

Land-surface elevation 213 feet

-50

250

200

150

NGVD

29

NGVD 29

100

50

1960 1970 1980 1990 2000

Layer 3

Layer 4

Layer 5

Layer 6

Layer 2

Layer 3

Layer 2

Layer 3

Layer 6

Layer 7

Layer 6

Layer 7

Layer 3

Layer 4

Layer 5

Layer 5

Layer 8

Layer 8

Layer 6

Layer 6 Layer 7

200

100

-100

-200

-300

-400

-500

-600

250

300

350

200

150

100

50

NGVD 29

NGVD 29

Figure B13. Continued.
96 Groundwater Availability of the Central Valley Aquifer, California

50

50

100

50

50

50

50

50

50

100

50

50

100

100

100

100

100

150

150

100

100

100

150

150

200

100

EXPLANATION

Central Valley boundary

Line of equal depth-to-water,

 Spring 2000, in feet.

 Contour interval is 50 feet

Area with depth-to-water

 less than 50 feet

Depth-to-water, by well,

 in feet below land surface

0 to 50

50 to 100

100 to 150

150 to 200

200 to 500

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Figure B14. Estimated depth to water table in spring 2000.
Development and Changes to the Hydrologic Budget 97

The most obvious effects of development were in the

western San Joaquin Valley. Although predevelopment flow

generally was toward the San Joaquin River, large withdrawals from deep wells in the western and southern parts of the

aquifer system reversed the direction of lateral flow in the

confined part of the system until the water moved toward the

withdrawal centers (Planert and Williams, 1995) (figs. B4 and 

B12). Water levels in the confined part of the aquifer system

declined as much as 400 ft by 1961 (Bull and Miller, 1975)

(fig. B4A), and water levels reached record lows in the earlyto-mid 1960s (Thomas and Phoenix, 1976). This decline was

caused by heavy pumping from the confined part of the aquifer

system below the Corcoran Clay combined with the resistance

to vertical flow provided by the Corcoran Clay and other

fine-grained lenses in the aquifer system (Bertoldi and others,

1991). Heads at depth originally were above land surface,

and by 1961 were below sea level. This head decline also is

notable because water levels declined more than 100 ft over an

extensive area, indicating considerable depletion from storage.

A significant part of this loss of water from storage is irreversible and came from the inelastic compaction of fine-grained

deposits (Poland and others, 1975).

In the San Joaquin Valley, recharge from excess irrigation water has greatly exceeded the estimated predevelopment recharge rate. The combination of the magnitude of the

withdrawals from the confined parts of the aquifer system

and increased recharge to the water table reversed the vertical

hydraulic gradient over much of the San Joaquin Valley. As

a result, much of the water in the upper parts of the aquifer

system that flowed laterally toward the San Joaquin River

under predevelopment conditions began to leak downward

through the confining beds into the confined part of the aquifer

system. Groundwater pumpage reached a sustained maximum

and confined water levels generally reached a low during the

1960s (figs. B12 and B13). 

Importation of surface water beginning in the late 1960s

and early 1970s has, to some extent, decreased the magnitude

of these anthropogenic changes by reducing the reliance on

groundwater in the southwestern San Joaquin Valley (Belitz

and Heimes, 1990) (figs. B4, B6A, and B12). The combined 

effect of increased availability of imported surface water and

decreased groundwater pumpage was a large-scale, rapid

recovery of the water table and heads in the confined part of

the aquifer system (figs. B12 and B13). In some parts of the 

western San Joaquin Valley, groundwater levels in the confined part of the aquifer system have recovered to pre-1960

levels. This is evident in the differences in the water table

and potentiometric-surface maps between 1961 and 1976

(figs. B12B and B12D).

Figure B11 shows an overall trend of increased pumpage

from wells penetrating both above and below the Corcoran

Clay. However, there is a decreasing trend in intra-borehole

flow. Because many of these wells are in urban areas where

pumpage continued to increase, pumpage did not actually

decrease when surface-water deliveries became available

around 1972; it leveled off. Davis and others (1964) estimated

that about 100,000 acre-ft/yr flowed through wells (intraborehole flow) from the upper part of the aquifer system to the

lower, confined part of the aquifer system in the western part

of the San Joaquin Valley in the early 1960s. During the peak

of the withdrawal season, net downward flow may be, on average, as much as 0.3 cubic foot per second per well (Planert and

Williams, 1995). Simulations indicate that between 1962 and

2003, an average of 400,000 acre-ft/yr of water moved into

the confined part of the aquifer system through intra-borehole

flow (table B2 and fig. B11). Williamson and others (1989) 

suggest that volumetrically the majority of intra-borehole flow

may occur through multi-zone wells throughout the Central

Valley that are outside the spatial extent of the Corcoran Clay.

This is relative to the amount of intra-borehole flow across the

Corcoran Clay.

In 1977, a severe drought caused a decrease in surfacewater deliveries, resulting in a resumption of pumpage and a

rapid decline in water levels (fig. B13). Groundwater pumpage

reached a brief maximum and confined water levels generally

reached a low during the 1977 drought. After this drought,

irrigation efficiencies improved and surface-water deliveries

increased, resulting in the rapid recovery of water levels to

near pre-development levels (fig. B13). Water levels declined

to near their prior 1977 lows during the 1987–92 drought

(fig. B13). Following this drought, surface-water deliveries

were re-established. In addition, many new water conservation

techniques were applied. As a result, groundwater pumpage

decreased and groundwater levels rose again in much of the

San Joaquin Valley (fig. B13). However, in some isolated parts

of the San Joaquin Valley where imported surface water generally has not been available, large withdrawals have continued

and water levels have continued to decline. For example, on

the southeastern side of the San Joaquin Valley, water levels

dropped by more than 150 ft in the unconfined part of the

aquifer system between 1962 and 2003 (figs. B4B and B12, 

and B13). Except for isolated areas, the groundwater flow patterns in the aquifer system are the same from the mid-1970s to 

2003.

Increased surficial recharge from excess applied irrigation waters and decreased groundwater pumpage has caused

the water table to rise dramatically in some areas (Belitz and

others, 1993) (figs. B12 and B13). When surface water was

imported, groundwater pumpage decreased and irrigation

increased because the imported water was less expensive. This

influx of water, coupled with decreased discharge from wells,

overwhelmed the permeability of the fine-grained system.

The associated rise in hydraulic heads was large, as shown in

figure B4B. For example, in the heavily irrigated San Joaquin

Valley, fine-grained deposits limit the rate of downward flow

(fig. B14). When irrigation occurs over a period of time, the

water levels rise in the clayey deposits and eventually can

drown the roots of crops. As a result of this and other factors,

an extensive network of subsurface drains was installed on the

west side (WBS 14) to limit the rise of the shallow water table.

Likewise, in coarser grained areas, shallow wells have been

installed to pump out the excess irrigation water.
98 Groundwater Availability of the Central Valley Aquifer, California

Flow through the aquifer system has increased more

than six fold, from about 2 million acre-ft/yr prior to development (Williamson and others, 1989) to an average of about

12 million acre-ft/yr (including ET directly from groundwater)

between 1962 and 2003 (figs. A20 and B1). The increased 

flow through the aquifer system predominantly is a result of

increased pumpage and increased recharge. The increased

recharge mostly is from excess applied irrigation water resulting from imported surface water or recirculated groundwater.

Land Subsidence

In the Central Valley, the typically slow process of

draining fine-grained deposits has caused the permanent and

irreversible compression or consolidation of fine-grained

deposits. This consolidation has resulted in extensive land subsidence, particularly in the San Joaquin Valley. Galloway and

others (1999) compiled a summary on the phenomena of land 

subsidence that includes the Central Valley. By far, the largest

magnitude and areal extent of land subsidence in the Central

Valley is attributable to aquifer-system compaction caused by

groundwater pumpage (Poland and others, 1975; Ireland and

others, 1984; Farrar and Bertoldi, 1988; Bertoldi and others, 1991; Galloway and Riley, 1999) (fig. B15). However,

other processes have contributed to land subsidence locally

in the Central Valley (Poland and Everson, 1966; Poland,

1984; Galloway and Riley, 1999), including principally: (1)

oxidation and compaction of peat soils following drainage of

marshland; (2) hydrocompaction resulting from compaction of

moisture-deficient sediments following application of water;

(3) compaction of sediments in petroleum reservoir rocks

caused by withdrawal of fluids from oil fields; and (4) tectonic

subsidence (Farrar and Bertoldi, 1988; Bertoldi and others,

1991) (fig. B15). Compaction of peat soils and subsequent land

subsidence has occurred around the Sacramento–San Joaquin

Delta (Poland and Evenson, 1966) (fig. B15A). Draining the

islands of the Delta allowed the peat to oxidize, resulting in

subsidence of the land surface on the developed islands in the

central and western Delta at long-term average rates of 1–3

inches per year and resulting in large areas of many islands

becoming more than 15 ft below sea level (Rojstaczer and

others, 1991; Rojstaczer and Deverel, 1993; Deverel and

Rojstaczer, 1996; Ingebritsen and Ikehara, 1999). Hydrocompaction, also known as “near-surface subsidence” refers to

moisture-deficient and (or) poorly sorted deposits, “glued”

together by clay, that compact following the first application

of water. This type of subsidence has resulted in 5 to 10 ft (2

to 3 m) of subsidence in the dry areas along the western and

southern margins of the San Joaquin Valley (Bull, 1961; Bull,

1964; Poland and Evenson, 1966; Bull, 1972, Bull, 1973; Galloway and Riley, 1999). Compaction of sediments due to the

withdrawal of oil and gas has caused land subsidence locally;

however, the magnitude is uncertain (Fielding and others,

1998; Galloway and Riley, 1999). Subsidence of up to 1 ft

has been attributed to this process in the oil field near Bakersfield (Farrar and Bertoldi, 1988; Galloway and Riley, 1999).

Although directly related to the large volume of sediments in

the Tulare Basin, subsidence due to tectonic movement in the

post-development period has been negligible compared to the

other four processes (Williamson and others, 1989). 

One of the earliest and most obvious results of groundwater pumpage was widespread land subsidence in the San

Joaquin Valley (Poland and others, 1975; Bertoldi, 1989;

Galloway and Riley, 1999). Subsidence from groundwater

pumpage began in the mid-1920s (Bertoldi, 1989). By 1970,

significant land subsidence (more than 1foot) due to the withdrawal of groundwater had occurred in about half of the San

Joaquin Valley, or about 5,200 mi2 (Poland and others, 1975)

(fig. B15). One of the largest volumes of land subsidence in

the world caused by human activities is in this part of the

Central Valley (Poland and others, 1975; Bertoldi and others,

1991; Galloway and Riley, 1999). Prior to 1990, an estimated

one-third of the volume of water pumped from storage in the

Los Banos–Kettleman City area came from compaction of

fine-grained beds that resulted in land subsidence (Poland and

others, 1975). 

The San Joaquin Valley has three principal areas of

subsidence caused by groundwater withdrawals: (1) 1,500 mi2

in the Los Banos–Kettleman City area, (2) 800 mi2 in the 

Tulare–Wasco area, and (3) 400 mi2 in the Arvin–Maricopa

area (Poland and others, 1975; Thomas and Phoenix, 1976;

Ireland and others, 1984) (fig. B15). In the Los Banos–Kettleman City area, head declines in the confined part of the aquifer

system of as much as 500 ft due to groundwater withdrawals

caused inelastic compaction of the clayey beds and resulted 

in as much as 28 ft of recorded land subsidence (Poland and

others, 1975; Ireland and others, 1984; Galloway and Riley,

1999). This area is characterized by the highest percentage of

fine-grained material (approximately 30 percent) within the

upper 2,000 ft of the aquifer system in the San Joaquin Valley

(figs. A13 and B6). Although the largest concentration of clay

is in the Corcoran Clay, little water has been extracted from

these layers due to their low permeability, and a negligible

fraction of the total simulated aquifer-system compaction in 

the San Joaquin Valley during 1962–2003 is attributable to the

Corcoran Clay.

Small areas of the Sacramento Valley also have been

affected by subsidence (fig. B15). Recent studies have documented that as much as 4 ft of subsidence has occurred in the 

Sacramento Valley since 1954 (Blodgett and others, 1990;

Ikehara, 1994; Ikehara, 1995). In Yolo County, increased

groundwater withdrawals caused land subsidence of several

feet in the early 1990s (Ikehara, 1995). 
Development and Changes to the Hydrologic Budget 99

32S/28E-20Q1

11N/1E-24Q8

EXPLANATION

Subsidence measurement locations

 (extensometers)—Data for labeled

 sites shown on figure 15B

Historic lakes

Major land-subsidence area from Ireland, 1984.

Area of subsidence due to compaction

 of peat and based on field surveys in 1952

Area of subsidence due to compaction

 of peat, more than 10 feet as of 1981

Area of subsidence due to hydrocompaction

Area of subsidence due to water-level

 decline, more than 1 foot as of 2000

17S/15E-14Q1

0 50 100Miles

0 50 100 Kilometers

18S/16E-33A1

23S/25E-16N3

14S/12E-12H1

13S/15E-31J3

A

Central Valley boundary

Simulated subsidence 1962-2003, in feet

5

10

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

B

A

C

Figure B15. A, Areal extent of land subsidence in the Central Valley (modified from figure 9 Thomas and Phoenix, 1976; figure 11 

Bertoldi and others, 1991) and locations of extensometers. Contours show simulated subsidence for water years 1962-2003. 

B, Compaction data from selected extensometers and total simulated pumpage in the Central Valley.
100 Groundwater Availability of the Central Valley Aquifer, California

Besides loss of water from inelastic compaction, subsidence throughout the Central Valley, particularly the San

Joaquin Valley, has caused damage to structures such as

aqueducts, roads, bridges, buildings, and well casings. Other

important and expensive damages include the need for

realignment of canals as they lose their constant gradient, and

releveling of furrowed fields, many of which are laser-leveled

for maximum irrigation efficiency. In addition, in low lying

areas, subsidence has increased the potential for flooding and

seawater encroachment (Bertoldi, 1989).

The measured compaction, in relation-to-water-level

declines at a well in the southwestern San Joaquin Valley,

demonstrates correlations between climate, groundwater use,

and subsidence (fig. B16) (Galloway and others, 1999). This

correlation can be summarized as follows. The1960s were

marked by steady head decline and a high rate of compaction

(figs. B12, B13, and B16). The importation of surface water

and the associated decrease in groundwater pumpage in the

early and middle 1970s was accompanied by a steady recovery

of water levels and a reduced rate of compaction. During the

severe drought of 1976–77, diminished deliveries of imported

water prompted pumping of groundwater to meet irrigation

demands. This was marked by a sharp decline in water levels

and a short period of renewed compaction (figs. B15 and B16). 

Ireland and others (1984) report that artesian heads generally

declined 10 to 20 times faster during the drought than during

the period of long-term drawdown and inelastic compaction

that ended in the late 1960s. Thus, much of the water pumped

during the drought probably was supplied by elastic storage,

though some inelastic compaction did occur (fig. B16). Following the drought, recovery to pre-drought water levels was

rapid and compaction virtually ceased. The negative compaction (rebound) measured immediately after pumpage returned

to predrought levels indicates that part of the compaction

during the drought was elastic (fig. B16). Between the 1970s

and late-1980s, land subsidence greatly slowed or stopped in

most areas (fig. B15B). The most recent prolonged Statewide

drought lasted 6 years from 1987–92 (fig. A9). During these

years, the groundwater extractions increased dramatically,

especially in the San Joaquin Valley, which caused increased

Figure B15. Continued.

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Dry Wet

1960 1970 1980

YEAR

1990 2000

GROUND MOVEMENT, IN FEET

0.0

0.5

1.5

2.0

2.5

-0.5

-1.0

-1.5

1.0

Rebound Subsidence

SIMULATED PUMPAGE, IN MILLIONS OF ACRE-FEET PER YEAR

5

0

10

15

20

Total simulated

 pumpage for the

 Central Valley

B

11N/1E-24Q8 17S/15E-14Q1

18S/16E-33A1

32S/28E-20Q1

23S/25E-16N3

14S/12E-12H1 13S/15E-31J3

EXPLANATION
Development and Changes to the Hydrologic Budget 101

compaction and land subsidence in some areas (figs. B15B

and B16). Since the early 1990s, compaction has been slowed

greatly or stopped in most areas (fig. B15B). In some areas, 

there has been a rebound of the land surface (Al Steele,

California Department of Water Resources, written commun.,

2004) (fig. B15B). However, subsidence has resumed locally in

places. The vertical drainage of fine-grained deposits may proceed very slowly and lag far behind the changing water levels

in the aquifer system (Galloway and others, 1999). Changes

through time in the amount of subsidence or rebound are measured by compaction at extensometers at specific points in the

valley (fig. B15B). Over larger areas, satellite-borne differential Interferometric Synthetic Aperture Radar (InSAR) can be

used to map land-surface deformation (Galloway and others,

2000; Brandt and others, 2005; Galloway and Hoffmann,

2007). More details are discussed in the “Subsidence” subsection of the “Monitoring the Hydrologic System” section. 

Surface Water and the Environment

In the Central Valley, as in most places, the environment and surface- and groundwater systems are intimately

linked. Under predevelopment conditions and during the early

period of development, the Central Valley had considerable

swamps, marshes, sloughs, riparian habitat, and an extensive Delta region (fig. A21A). Many flow regimes no longer

resemble natural conditions, largely because of efforts to

manage water through diversions for agricultural and urban

demands. Groundwater pumpage also has intercepted groundwater that previously discharged to these surface-water bodies

and has induced infiltration of water from surface-water

bodies (groundwater recharge). In some areas, groundwater

pumpage has lowered the water table and surface water has

been diverted in other areas. As a consequence, the surfacewater bodies have reached or fallen below minimum stages

or streamflows needed to support fish populations, wetland

vegetation, and water conveyances (California Department

of Water Resources, 2003; California Department of Water

Resources, 2005). This has resulted in an extensive loss of

riparian vegetation and wildlife habitat (fig. A21B). Examination of figures A21A and A21B shows the replacement of

extensive marshlands with irrigated agriculture. In the Tulare

Basin, most of these marshlands were drained and now are

used for agriculture (fig. A21B). The Delta originally extended

up the Sacramento and San Joaquin Rivers. It has been

reduced substantially in area since predevelopment conditions by groundwater pumpage and surface-water diversions

(fig. A21B). 

A recent California Water Plan update (California Department of Water Resources, 2005) defines environmental water

use as: dedicated flows in State and Federal wild and scenic

rivers, Bay–Delta outflows, instream flow requirements, and

applied water delivered to managed freshwater wildlife areas.

Environmental water allocations have increased steadily since

approval of the 1957 California Water Plan. A considerable

amount of water now is dedicated to environmental water uses

(California Department of Water Resources, 2003).

Figure B16. Measured compaction in relation to head decline in the San Joaquin Valley. The effects of drought on groundwater levels 

and associated subsidence also are evident. (Modified from Galloway and others, 1999; and Swanson, 1998.)

Drought Drought

200

400

600

12

0

DEPTH-TO-WATER,

IN FEET BELOW LAND SURFACE

COMPACTION,

IN INCHES

1960 1970 1980

YEAR

1990

No data

No data

Deep well

During the droughts of 1976-77 and

1987-91, deliveries of imported water

to the west side of the San Joaquin

Valley were cut back. More groundwater

was pumped to meet the demand, resulting

in a drop in the water table and

consequent compaction. 

When water levels recover,

compaction and land subsidence can abate.

Some elastic expansion of the aquifer

system has occured but the compacted

materials can never return to their

pre-compacted thickness. 
102 Groundwater Availability of the Central Valley Aquifer, California

One of the major challenges in restoring the environment

will be providing enough surface water of adequate quality to

restore river habitat and fish populations along the San Joaquin

River while maintaining water-supply reliability for other

purposes. The river’s historic salmon populations upstream

of the Merced River were eradicated in the 1940s as a result

of water being diverted with the construction of the Friant

Dam. In 2004, a judge ruled that the U.S. Bureau of Reclamation (Reclamation) violated State Fish and Game codes

by not providing sufficient water to sustain fish populations.

To complicate matters, high salinity caused by agricultural

drainage discharge and wastewater return flows already is a

problem for fish in the lower San Joaquin River. With the mandate for the environmental flows, the long-term availability

of the Sierra Nevada water supplies for agricultural and urban

uses is a concern. The Reclamation is studying the feasibility

of a new surface storage reservoir in the Upper San Joaquin

basin to augment storage. In addition, artificial recharge and

the pumping of additional groundwater to help meet environmental and other demands also are being evaluated. In years

with below-average rainfall and surface-water inflows from

the Sierra Nevada, all environmental, urban, and agricultural

surface-water demands may not be met. As a result, meeting

the long-term water demands, while balancing protections for

water quality and environmental uses, will require groundwater resources. 

Global Climate Change and Variability

California’s water-delivery system and agriculture have

been developed and operated based on the climatic record of

the past century (California Department of Water Resources,

2005). The delivery system assumes a certain spatial distribution and amount of runoff, storage in snowpack, and timing

coinciding with the growing season. During the study period

(1962–2003), surface water generally has been available

except during extreme droughts. 

Past Climates

The effects of historical climate variability on the hydrologic system can be used to assess the system’s responses to

future climatic conditions. The response of the hydrologic

system during dry years in the historical record can be used as

an indicator of possible changes in the landscape and groundwater budgets in future droughts. Similarly, wet years in the

historical record can be used as indicators of possible changes

for future wet periods. The hydrologic period of record is

about 100 years, with mostly qualitative information extending

back another 100 years. Tree-ring indices provide a surrogate

for hydrologic conditions. These indices have been used to

reconstruct streamflow on the Sacramento River since more

than 1,000 years prior to the historic period of record (Stine,

1994; Meko and Woodhouse, 2005). For example, on the basis

of tree-ring indices, the 6-year drought of the 1930s was one

of the most severe during the last few hundred years (California Department of Water Resources, 2005; 2006). Based on

relict tree stumps, Stine (1994) showed that California experienced severe, sustained drought conditions with a duration

of greater than 2 centuries before 1112 A.D. and a duration of

greater than 140 years before 1350 A.D.

Total precipitation over the Sierra Nevada, from which

all major drainages enter the Central Valley, decreases from

north to south (fig. A5A). Contrary to the higher volumes

of total precipitation in the north as compared to the south, 

annual snow accumulations are greater in the higher elevations

of the southern Sierra Nevada (Bales and others, 2006). The

period 1948–2002 had progressively higher average winter

and spring temperatures. This warming trend is a result of a

combination of effects related to Pacific Decadal Oscillation

(PDO) inter-decadal cycles and to a springtime warming trend

that spans the PDO cycles (Stewart and others, 2005; Knowles

and others, 2006). These warmer temperatures have resulted

in snowmelt runoff 1–4 weeks earlier (Stewart and others,

2005) and a growing season that has been extended by more

than a month (California Department of Water Resources,

2006). In addition, since the 1950s, spring and early summer

runoff has declined progressively (California Department of

Water Resources, 2006). Data also show an overall decline

in the amount of water stored annually in the northern Sierra

Nevada snowpack. The same effect is noted, to a lesser degree,

in the southern Sierra Nevada snowpack.

Future Climate Projections

Results from Global Climate Models (GCMs) indicate

that California’s hydrologic conditions will continue to shift

from historical conditions (California Department of Water

Resources, 2005). Although the extent and timing of the

long-term changes remain unknown, the projections include

increased temperatures, changes in precipitation (including

reductions to the Sierra Nevada snowpack and more precipitation in the form of rain), an earlier snowmelt, possibly larger

floods, a rise in sea level, and other phenomena (Dettinger

and Cayan, 1995; California Department of Water Resources,

2005; Stewart and others, 2005).

The most common projection from GCMs is an increase

in temperature of as much as 5°C (9°F) for California in the

21st Century (Dettinger, 2005). GCMs predict less groundwater recharge along mountain fronts because of the expected

reduced Sierra Nevada snowpack (California Department

of Water Resources, 2005; Dettinger and Earman, 2007).

With increased temperatures, ET rates would be expected

to increase. Other indirect effects of a temperature increase 

include earlier budding of orchard crops, premature ripening

of crops (particularly grapes), the increased ability to grow

more than one crop in a season, and reduced milk production 

from dairy herds (California Climate Change Center, 2006).
Groundwater Sustainability and Management 103

Projected climate changes could significantly alter California’s precipitation pattern, intensity, and amount. Although

the changes in regional precipitation are difficult to estimate,

GCMs indicate modest changes in precipitation for California

in the 21st Century (Dettinger, 2005) with changes becoming

more significant with time. Simulated responses in GCMs for

river basins contributing to the Sacramento Valley (American River Basin) and San Joaquin Valley (the Merced River

Basin) suggest that seasonal runoff will occur a month earlier

with only a 2.5°C (4.5°F) increase in temperature, but that the

average annual streamflow may not change (Dettinger and others, 2004). However, less snowpack would mean less natural

springtime replenishment of water storage in the surfacewater reservoirs. More variability in rainfall, wetter at times

and drier at times, could place more stress on the reliability 

of existing flood management and water-storage systems.

Because most streamflow from the Sierra Nevada is dominated

by snowmelt and because the Central Valley’s engineered

water-delivery system partially depends on the snowpack for

storage, the timing and magnitude of snowmelt runoff may

affect the supply of water.

GCMs project sea-level rises ranging from 7 to 23 inches

above the 1980–99 average by 2100 (Intergovernmental Panel

on Climate Change, 2007). The biggest effects of sea-level

rise could be on the Delta, where sea-level rise would threaten

levee stability, disrupt the environment including tidal wetland

restoration, and increase salinity in surface water and groundwater in areas adjacent to the Delta. These effects to the Delta

would threaten freshwater exports to southern California

(24 million people).

Projected changes in other climate factors, such as solar

radiation (for example, changes in cloud cover), relative

humidity, and carbon dioxide concentrations, remain uncertain. A net reduction of net radiation and (or) an increase in

humidity could help offset some of the effects of an increase in 

temperature (Hidalgo and others, 2005). Long-term increases

in worldwide atmospheric carbon-dioxide concentrations

may reduce plant water consumption (California Department of Water Resources, 2005). GCMs indicate that for the

Sacramento Valley the increase in carbon dioxide likely is to

mitigate many other aspects of climate change and provide a

significant buffer for sustainable food production (Aerts and

Droogers, 2004).

Increased ET would result in increased water demand for

urban, agricultural, and environmental uses. The increased ET

and associated water demand likely would occur simultaneously with a change in water supply. A warmer, wetter winter

would increase the amount of runoff available for groundwater

recharge; however, this additional runoff in the winter likely

would occur when many surface-water reservoirs either are

near or at maximum capacity. The water will require other

forms of storage beyond the current surface-water reservoir capacity, such as managed aquifer recharge systems or

additional surface-water reservoirs. Additionally, reductions

in late spring and early summer runoff, and higher ET related

to warmer temperatures, would reduce the amount of water

available for recharge and surface storage during the dry

season. If the total surface-water storage and the amount of

surface water available during the dry season are reduced, the

amount and timing of groundwater pumpage ultimately may

be affected.

Groundwater Sustainability and 

Management 

Groundwater sustainability can be defined as the achievement of an acceptable tradeoff between groundwater use and

the long-term effects of that use (Alley, 2006). Sustainability

requires an iterative process of monitoring, analysis, and application of management practices. Hence, groundwater availability and, ultimately, its sustainability in the Central Valley is

an issue that is interrelated to groundwater management.

Groundwater Sustainability

The concept of sustainability is inherently subjective

and ambiguous. This is because what is or is not considered

sustainable is based, in part, on social and philosophical 

issues that can change with time (Alley and Leake, 2004).

As a result, the term is not specifically defined in this report

and factors that affect sustainability are discussed. Factors

that limit sustainability include physical, chemical, economic, 

environmental, legal, philosophical, or institutional. This study

focuses on the physical constraints that may affect groundwater sustainability. 

The term “groundwater reserves” is used to emphasize

the fact that groundwater, like other limited natural resources,

can be depleted (Alley, 2006). Despite the fact that most

groundwater resources can be replenished, this depletion is

key. Depletion of aquifer-system storage by pumpage has had

a substantial effect in the Central Valley. Water-level records

and previous studies (Williamson and others, 1989) confirm

that large amounts of water were removed from storage prior

to 1960. Between 1962 and 2003, simulations indicate that

aquifer-system storage has been depleted by 57.7 million

acre-ft, and water-level altitudes have dropped significantly

(fig. B4). The long-term decrease in aquifer-system storage

between 1962 and 2003, although very large, represents only

a small fraction of the approximately 800 million acre-ft of

freshwater stored in the upper 1,000 ft of sediments in the

Central Valley (Bertoldi and others, 1991, p. 27). As Alley

(2007) points out, this volume of groundwater in storage is

not by itself meaningful in analyses of water availability; it

is used here for context. As a practical matter, it is impossible to remove all water from storage by pumpage. Many

other factors limit the amount of water that can be recovered.

Aquifer-system permeabilities, well yields, the cost of drilling

wells, the cost of energy for lifting water, and the design of the

well and pump can limit the availability of water. Similarly,
104 Groundwater Availability of the Central Valley Aquifer, California

institutional factors, such as use restrictions, basin adjudication, and surface-water rights, in essence, limit the availability

of water.

Depletion of the water in storage can have substantial

related consequences. These consequences include changes

in surface-water quality, quantity, temperature, and in land

subsidence. In turn, these consequences factor into large

environmental issues by changing and (or) degrading habitats.

These related effects may constitute the primary constraint to 

groundwater development. For example, in the San Joaquin

Valley, land subsidence is an important constraint on how

much groundwater can be extracted in an area.

The Central Valley faces competing demands for water

resources. These demands include providing water supply for

growing urban areas, agriculture, and environmental uses.

The population of the Central Valley is predicted to increase

dramatically. Agricultural water demand is driven by climate,

agricultural land use, crop selection, and farming practices.

Because agricultural operations are businesses that seek

to make profits, the crop mix typically is driven by market

prices of agricultural commodities. The multi-faceted effects

of global climate change on agriculture are not understood

thoroughly.

The demand for water resources by people may directly

compete with environmental uses such as maintaining minimum streamflows, preventing seawater inundation of coastal

areas, and preserving habitats for fish and birds. Examination

of changes in stream inflows and outflows, with respect to

climate, may provide insight into how groundwater use may

affect surface-water systems and, ultimately, the environment

(fig. B17). In general, during wetter periods, streamflow gains

and losses increase, particularly the losses. During periods of

drought, streamflow gains and losses decrease, and the streams

dependent on groundwater for baseflow may not have enough

input from groundwater to sustain environmental flows. Both

surface-water diversions and groundwater pumpage exacerbate this problem. In addition, runoff from irrigated agriculture and feedlot operations is beginning to be monitored, as

it is a threat to water quality (California Department of Water

Resources, 2005). This threat likely is to remain a significant

and potentially expensive challenge with no simple solution.

Since the late 1970s, State and Federal water projects

have not expanded with growing urbanization, agriculture, and

environmental uses. Although irrigated agriculture continues

to use the vast majority of groundwater, urban groundwater use increased dramatically between 1980 and 2003. The

Central Valley’s population reached 6.5 million people in 2005

and is projected to increase, possibly reaching 10 million by

2030 (California Department of Finance, 2007). Despite this

projected increase in urban population, agriculture is expected

to consume more water than would be consumed by urban

users (fig. B6B). Although agricultural acreage has declined,

agricultural yields and revenue have increased in the Central

Valley. Recently, water deliveries for irrigation have been

reduced in recognition of environmental needs. These trends

are projected to continue.

During1962–2003, most water demands were met in

most water years (California Department of Water Resources,

2005). In the future, competing demands for water for urban,

environmental, and agricultural uses and the effects of global

climate change may decrease the number of years in which

water demands for agricultural uses are met. Thus far, farmers have adjusted their practices to grow more crops per

acre-foot of applied water. The increased efficiencies result

from changes in crop type, increased irrigation efficiency,

improved productivity, and other improvements. For example,

from 1980 to 2000, the annual statewide harvest increased

by 50 percent as measured in tons of crops per acre foot of 

water applied (California Department of Water Resources,

2005). As a result of these improvements, the rate of groundwater storage loss has declined since 1998. Although the rate

has decreased, based on an examination of simulated system

conditions between 1998 and 2003, groundwater continues

to be removed from storage (fig. B9). Hence, even disregarding the projected increased groundwater use accompanying

climate change, historical usage indicates that the Central

Valley groundwater system cannot meet competing demands

indefinitely.

Projections based on GCMs indicate the likelihood of

less surface water and precipitation along with increases

in temperatures. These changes will lead to larger water

demands. Assuming these projections are correct, then

declines in storage likely are to continue. In addition, environmental uses, including reducing the amount of water pumped

from the Delta to increase fish populations, may lead to even

larger water demands. Thus, as discussed in the following section, meeting competing demands likely will benefit from an

integrated water-management approach. Possible management

actions include enhancements in conjunctive use of surface

water and groundwater, artificial recharge, and the use of

recycled or reclaimed water.

Groundwater Management 

The effects of groundwater management may not be

realized for many years. Therefore, groundwater sustainability requires a long-term view toward management of water

resources. In the Central Valley, groundwater historically was

viewed as a convenient resource that allowed for settlement

nearly anywhere. Recently, the economic and environmental aspects of groundwater development have begun to be

considered (Alley, 2006). In order to be sustainable, groundwater resources must be used and managed in a manner that

can be maintained for an indefinite amount of time without

causing unacceptable economic, environmental, or social

consequences. This study has developed a tool, the CVHM

(described in Chapter C of this report), which managers could

use to help address implications of different management

options.
Groundwater Sustainability and Management 105

Figure B17. Streamflow gains and losses in the Central Valley between 1962 and 2003.

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Dry Wet

WATER YEAR

STREAMFLOW LEAKAGE, IN MILLIONS OF ACRE-FEET PER YEAR

Typical

-3.0

-4.0

-2.0

-1.0

0

1.0

2.0

3.0

4.0

9.0

8.0

6.0

7.0

5.0

Representative water year Dry Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Surface-water loss to

 groundwater system

Surface-water gain from

 groundwater system

EXPLANATION
106 Groundwater Availability of the Central Valley Aquifer, California

Over the past century, the Central Valley’s water demand

and water-management practices have changed significantly.

Recent water demand can be separated into three categories:

agricultural, urban, and environmental. Demand and management practices for all three categories have changed over time.

Initially, water demand was largely local to support relatively

small-scale irrigation agriculture near perennial streams

(Bertoldi and others, 1991). In the 1800s, hundreds of miles

of canals were constructed to transport water to where it was

needed for gold-washing operations (Planert and Williams,

1995). This was the beginning of the surface-water delivery

system. In the late 1800s, water demand mainly was for agricultural needs and was met through surface-water deliveries.

Gradually both surface- and groundwater resources were used

to meet demand. 

Although groundwater and surface water are closely

interconnected and considered a single resource by many

(Winter and others, 1998), they are treated differently by State

law. When the Water Commission Act defined the allocation

of surface water rights in 1914, it did not address allocation of

groundwater resources. Though the regulation of groundwater

has been considered, the California Legislature has repeatedly held that groundwater management should remain a local

responsibility (Sax, 2002). Legally, any landowner can pump

groundwater as long as it is put to a reasonable and beneficial use (California Department of Water Resources, 2005).

Counties and other local agencies can regulate groundwater

resources within their boundaries (California Department of

Water Resources, 2005). Some local agencies have adopted

groundwater ordinances or groundwater management programs under a variety of statutory authorities. Many notable

changes have occurred since the last extended drought of

1987–92. In some areas, water once used for irrigated agriculture is now used for urban uses, groundwater replenishment,

and environmental restoration. For example, the State of

California passed legislation in 1995 that requires higher flows

to protect the Delta, and other legislation either exists or is

proposed to protect other environmental systems.

Water can be managed in a sequence of both spatial and

temporal uses that make additional surface water available for

environmental systems. In many cases, careful water management can improve flows in the rivers. These flows can then be

diverted and used consumptively by urban and (or) agricultural water users or to improve water quality.

GCMs indicate that warmer periods with less snowpack and more extreme precipitation events are likely. These

factors could lead to increased demand for water by crops

and reduced availability of surface water. At the same time,

continued urbanization, agricultural development, and emerging water markets are expected to occur. Because of the long

time frames involved with these processes and climate-change

trends, and the physical and operational complexities of the

Central Valley water resources, one feasible approach to

evaluate the potential effects of different management

alternatives would use the CVHM and linked GCM. For the

long-term analyses, projections linked to GCMs may be useful to assess the future climatic effects on water supply and

demand (California Department of Water Resources, 2005;

Hanson and Dettinger, 2005).

Management strategies typically comprise a number of

general approaches that can be used alone or in conjunction

with each other. For example, aquifer systems can be effectively and economically used as subsurface reservoirs and conveyances to store as well as transmit groundwater. If surplus

water is stored in aquifers during wet periods or periods of low

water demand, a high percentage of the stored water typically

can be recovered to meet water demands during dry periods or

periods of high demand when surface-water supplies are less

available. The major strategies applicable to the Central Valley

are summarized in the next section.

The supply and demand of regional water resources

typically is assessed at three temporal scales or levels of

analysis. First, management analysis can occur on a daily to

monthly level to determine allocations and to distribute water

resources. Second, the analysis can occur on an inter-annual

to inter-decadal level to assess actions related to development

and water markets. Finally, analyses can occur on interdecadal to century timeframes to assess policy and capital 

improvement projects that are required for long-term adaptation to climate change, growth, technology, and environmental issues. These levels of analyses include changes in water

demands and regulations, improvements in conservation, and

upgrades to water infrastructure. These changes can alter the

current system and may alter the effects of future droughts.

The CVHM is a tool that can be used to assess the effects of

supply and demand at the second and third levels of analyses.

Conjunctive Use

Conjunctive use can be defined as the use of water from

multiple sources to meet a demand. Water managers in the

Central Valley have been applying conjunctive use for many

decades. Because precipitation and runoff are distributed nonuniformly in space and time, the availability of surface-water

supplies is variable. There is a long history of shifting between

local groundwater, local surface water, and imported water

in response to the spatial and temporal variability of surfacewater supplies. Especially during droughts, allocations from

the SWP and the CVP are susceptible to restrictions, cutbacks, and curtailments, thereby placing increased reliance on

groundwater to meet water demand and (or) motivating users

to reduce water consumption. Typically, for agricultural water

users this is achieved by pumping more groundwater,

switching to lower water-use crops and more efficient

irrigation practices, and fallowing farmland.
Groundwater Sustainability and Management 107

During the past 50 years, the growing water demands of

many Central Valley areas were met by large Federal (CVP),

State (SWP), and interregional projects that moved water great

distances across the state. Although these projects now serve

as the backbone of California’s water-supply system, they

might not have sufficient future supplies to support California’s growing population and environmental needs while, at

the same time, maintaining agricultural production, particularly in the context of predicted climate change. The strains

on the system are indicated by the continuing loss of aquifersystem storage as is shown in figure B9.

The traditional strategy for managing variations in the

hydrologic cycle has been to build surface reservoirs. Reservoirs are used for surface-water storage on the major drainages entering the Central Valley. Generally, these surface

reservoirs are used to store water for various uses: flood

control, water supply, recreation, and (or) power generation.

However, when a large snowpack melts rapidly in the Sierra

Nevada or a big storm occurs, the available surface water may

exceed the storage capacity of the local surface reservoirs.

In addition, surface reservoirs are expensive to construct,

can cause environmental damage, and allow for significant

evaporation losses during long droughts (California Department of Water Resources, 2005). Furthermore, the recent trend

has been toward dam removal. This trend cast doubt as to

whether surface reservoirs can be used to solve the expanding water-supply problems. As a result, other options, such as

aquifer-storage-and-recovery systems increasingly are being

considered. 

Regional partnerships have been formed to address many

water-management problems. Every year, hundreds of water

transfers (totaling hundreds of thousands of acre-ft) take place

between water users for a wide variety of reasons (California

Department of Water Resources, 2005). For example, the

Sacramento Valley watershed provides water for much of the

Central Valley and the rest of the State by way of the CVP and

the SWP. Conversely, the Tulare Basin now imports more surface water than any other region in the State (Umbach, 1998).

In the Tulare Basin, some agencies are trading water on a

daily basis or are making in-lieu trades of surface water for

groundwater to be used at a later date (groundwater banking).

As demand grows for high-quality water throughout California, water transfers from the Sacramento Valley are being

evaluated more closely. Some counties have passed ordinances

regulating out-of-basin water transfers. Conversely, projects

such as State Water Resources Control Board’s (SWRCB)

Phase 8 Bay-Delta Water-Quality Control Plan propose

exports of large amounts of water from the Sacramento Valley

to the Delta and from the Delta to southern California metropolitan areas (State Water Resources Control Board, 1998).

However, a recent report indicates that because of a variety

of historical changes that have occurred in the Delta and in

natural forces that will continue to operate there, fundamental

changes to the Delta are inevitable (Lund and others, 2008).

The report states that a peripheral canal is a necessary component in the long-term solution to the sustainability of the Delta.

Hence, such water transfers from the Sacramento Valley to the

south may need to occur through a peripheral canal.

Water Banking

In water banking, surplus water is “banked” in the

groundwater system for the purpose of augmenting or restoring the water supply. Water banking is popular among some

governmental and non-governmental organizations because

a water bank generally involves far less change to the natural

landscape than a surface-water reservoir, often provides wildlife habitat, and is less expensive than constructing a surfacewater reservoir (California Department of Water Resources,

2005). The primary purpose of a water bank is to recharge,

store, and recover water to improve water supply for its participants during periods of water shortage. During these water

shortages, increased groundwater pumpage can be used to

offset shortfalls in surface-water supplies. Thus, surface-water

reservoirs and water banks can be used together to effectively

coordinate the use of groundwater and surface water.

Three main options of water banking are available:

(1) “in lieu” recharge, (2) artificial recharge by infiltration

ponds and (or) well injection, and (3) pumpage designed to

induce inflow of freshwater from surface waterways. “In lieu”

recharge refers to using surface water in lieu of groundwater,

thereby allowing the groundwater system to recover. The

banked water is returned to the owner by release of entitlement and (or) pumping back to the surface-water system

during times of water shortage. The second option, artificial

recharge, includes engineered surface impoundments and

direct-well injection. Surface impoundments involve excess

surface water being placed in ponds and allowed to percolate

into the ground. These surface impoundments fill quickly

with runoff and slowly recharge the groundwater system. They

can provide significant environmental benefits, including the

enhancement of habitat for threatened and endangered species,

waterfowl, and other wildlife. Surface impoundments are common in the Central Valley; injection wells are not. The final

option, pumpage designed to induce inflow of freshwater from

surface-water bodies, is not common in the Central Valley.

Water banking primarily is done through surface-water

impoundments in the southern part of the Central Valley in

Kern County. Kern County banks water from local rivers, the

California Aqueduct, and the Friant–Kern Canal. The area

conveniently is situated, in terms of geology and proximity, to

water-supply and delivery systems. Most of the water banks

are located on alluvial fans, consisting of sandy sediments on

the valley floor in proximity to the mountains (fig. A1). These 

sandy sediments are highly permeable and, therefore, are
108 Groundwater Availability of the Central Valley Aquifer, California

well suited for surficial recharge and later recovery by

high-capacity wells.

The three major water banks (Arvin–Edison, the Kern

Water Bank, and the Semitropic Water Storage District water

bank), all located in Kern County, have a combined storage

capacity of about 3 million acre-ft (Kern Water Bank Authority, 2007; Semitropic Water Storage District, 2007). That is

more than five times the amount of water in Millerton Lake,

one of the larger reservoirs feeding the Central Valley surfacewater system. A new water bank, the Madera Ranch Project,

is being proposed. This project would divert floodwaters from

the Delta and possibly from the San Joaquin River during wet

years, spread them over thousands of acres, and create a marsh

habitat. 

The existing banking system has yet to be tested with a

severe drought. During such a drought, the water banks will

pump groundwater out of storage and provide the water to

their customers through the canals of the local, State, and

Federal water projects. It is possible that a high rate of pumpage during recovery of stored water may have adverse effects

of severely lowering hydraulic heads and possibly inducing

subsidence.

A favorable location for an artificial recharge site is

where coarse-grained deposits are present (fig. A12) and the 

water table is relatively deep (fig. B14). Hence, data gathered

in this study can be used to identify favorable locations on a

regional scale. The texture model indicates that most of the

southeastern part of the valley is a good candidate for artificial

recharge sites (fig. A10). More site-specific studies would be

required to determine which of these areas would be most

suitable. Other factors that need to be considered include local 

variations in the geology, the location of infrastructure such as

canals to transport the water to the artificial recharge site and

from the wells, and land ownership. One approach to mitigate

groundwater depletion is to locate these artificial recharge sites

in or near areas where large losses in groundwater storage

have been identified.

The potential adverse effects of artificial recharge, particularly subsidence, also must be considered. Loading the water

table increases geostatic loads on the underlying confined part

of the aquifer system. If additional pumpage does not offset

these increased loads, they may tend to exacerbate increased

compaction in these units.

Other Management Strategies

In addition to conjunctive use and water banking, other

management strategies are being considered and (or) used

in the Central Valley. Groundwater pumpage is being controlled or regulated through implementation of restrictions on

some types of water use, limits on withdrawal volumes, and

establishment of critical levels for hydraulic heads (California

Department of Water Resources, 2005). Advanced water conservation, improved water-use efficiency, and increased desalination and recycling of water sources techniques are practiced

in many of the irrigation districts. In many areas, traditional

flood-irrigation methods have been replaced by methods that

use less water, such as drip systems or micro-sprinklers. The

ability to monitor soil moisture and other crop-related data 

also are allowing for more efficient irrigation and harvesting

schedules (California Department of Water Resources, 2005).

California Department of Water Resources (1994) reported

an average improvement in irrigation efficiency of 10 percent

during the 1980s in the Central Valley. 

There also are a variety of ways to prevent or mitigate

subsidence induced by groundwater withdrawal. Though it

is not possible to reverse the effects of this type of subsidence that has already occurred, additional subsidence can be 

stopped or slowed. When water levels are maintained above

the critical heads, generally the historic low water levels

reached in a specific location, aquifer-system compaction,

and subsidence predominantly is elastic and recoverable.

Only when water levels drop below this critical head does the

aquifer system compact inelastically and the land subsidence 

becomes permanent (fig. B18). Hence, maintaining water levels above these critical heads will help prevent the occurrence

of permanent subsidence. However, because of hydrodynamic

lag, residual compaction may continue long after water-level

declines in the aquifers essentially have stabilized (Galloway

and Riley, 1999). To maintain water levels above these critical heads, options include stopping or reducing groundwater

withdrawals, carefully managing the placement and production of groundwater supply wells, and using artificial recharge

to offset withdrawals. Furthermore, it also may be possible to

identify areas that most likely are susceptible to subsidence 

and limit usage of those areas to activities that likely are to

suffer only minor effects from subsidence (Galloway and

others, 1999). 
Monitoring the Hydrologic System 109

Monitoring the Hydrologic System

Management of the Central Valley’s water-resources

benefits from long-term monitoring of the hydrologic system,

particularly the groundwater and surface-water systems at

appropriate locations. An infrequent snapshot of conditions

is important, but insufficient. It is important to incorporate

comprehensive monitoring strategies designed to monitor the

significant seasonal, annual, and multi-annual (decadal and

longer) trends of key system variables. Some variables; such

as surface-water flows, water quality, or compaction may help

quantify information that is relevant to groundwater availability that water levels cannot directly measure, such as subsidence. Despite the size and value of its groundwater resource,

California does not have an integrated, monitoring network

for all the variables necessary for evaluating this resource.

The reasons for this are many; one is that groundwater generally is locally controlled. State and Federal agencies become

involved only when groundwater is directly related to the

mission of a particular agency or if a local agency requests

assistance. Many of these system variables have been identified, collected, and analyzed as part of this study. However,

water-quality, which also is an important determinant of water

availability, is beyond the scope of this study.

One of the main challenges in monitoring the Central

Valley hydrologic system is managing the flow of information.

A large variety of agencies produce vast arrays of hydrologic

data. For example, this study found more than 873,000 water

levels from more than 21,000 wells from multiple agencies

for the 1962–2003 study period (fig. A4). Similarly, though

more than 150,000 driller’s logs were identified in the Central

Valley, only a small part (approximately 9,000) of these logs

was digitized for use in this study because of the large effort

involved. Additionally, acreage data for more than 300 crop

types from farms ranging in size from less than 1 acre to

several square miles were identified for the recent period since

the mid-1990s. On the other extreme, crop acreage data

generally are unavailable for earlier years.

h1

h1

h

h

b

b

CH1

CH1 Critical head at time, t1

t1

Total land subsidence,

Head in well at 

Head in well at 

which equals compressibility times h

Difference in head,

Water table

(h2 minus h1)

CH2 Critical head at time, t2

t2

h2

h2

=CH2

When h1 is greater than CH1, sediments are

overconsolidated with compression, and land

subsidence is predominately elastic and recoverable 

When h2 is equal to CH2, sediments are

normally consolidated with additional head

decline causing inelastic and nonrecoverable

land subsidence

Compaction of the aquifer system

is concentrated in the aquitards

Long-term decline in water level

modulated by the seasonal cycles

of groundwater pumping 

Time

Depth

to

water

Land surface

Clay and silt

(aquitards)

Land

surface

Rearranged, compacted granular

aquitard skeleton with reduced

porosity and groundwater

storage capacity

Granular aquitard

skeleton defining

fluid-filled pore spaces

storing groundwater

When long-term pumping

lowers ground water levels 

and stresses on the aquitards

beyond the preconsolidationstress thresholds, the

aquitards compact and the

land surface subsides

permanently.

t2 greater than t1

t1 t2

Figure B18. Relationship of water-level changes and critical heads to subsidence and inelastic compaction. 
110 Groundwater Availability of the Central Valley Aquifer, California

Tools are needed to manage, organize, and analyze these

data. One such tool is a numeric model of the hydrologic system. Numerical models for simulating the groundwater system

have played an increasing role in the evaluation of groundwater availability and management alternatives. The numerical

model integrates many independent hydrologic stress variables

to estimate dependent variables and, thus, is a useful tool for

helping to monitor the groundwater system as a whole. The

model is a quantitative means for evaluating the water balance

of an aquifer system as it is affected by land use, climate, and 

groundwater withdrawals, and is a means of exploring how

these changes affect different parts of the system, including

streamflow, water levels, subsidence, and other system variables. Thus, modeling can provide a basis for designing and

evaluating a monitoring network.

The CVHM can be considered a tool for identifying,

organizing, and integrating the necessary monitoring data. The

CVHM also can be used to formulate and answer important

questions. The CVHM can be used to address groundwater

depletion issues such as critically low groundwater levels and

other consequences of groundwater storage depletion including subsidence, streamflow losses, and reduced availability of

water for ET.

An inventory of existing data and data-management tools

was compiled as part of the modeling task of this study. Key

data gaps and tool needs were identified. Like many areas, the

Central Valley needs better data, better access to existing data,

and data-management tools to produce useful and integrated

information. Given the vast quantities of water levels and well

logs, among other types of data, the need for more data may

seem contradictory. However, analyses of the wells during this

study showed that the quality of the data, particularly the wellconstruction information, often was deficient. In some cases,

the lack of information made the data that were available

unusable. In addition, the spatial (laterally and vertically) and

temporal distribution of data often were inadequate.

A complete and integrated monitoring network would

include many of the inflows and outflows, as well as response

attributes that reflect the state of the system. The more

complete and integrated the monitoring network, the better

analysis tools, like the CVHM, will be and will remain useful

for helping with water-resources management.

Groundwater

Groundwater levels from wells are the key type of data in

most groundwater monitoring networks. They are the primary

sources of information about groundwater reserves, the hydrologic stresses on aquifers, and the effects of these stresses on

groundwater recharge, storage, discharge, and, ultimately, its

availability (Alley, 2006). Groundwater level is a dependent

variable in the CVHM and reveals how the system has

responded to stress. The long-term, systematic measurement

of water levels, collected over years and decades, provides

the essential data needed to evaluate temporal variability in

groundwater availability; to monitor the long-term effects of

aquifer-system development and management; to develop

groundwater models, such as CVHM; to forecast trends; and

to design, implement, and monitor the effectiveness of groundwater management and protection programs (Taylor and Alley,

2001). 

As part of the development of the CVHM, 206 wells

were identified to calibrate the model (fig. C16). These wells

have detailed construction information and were selected to

maximize the spatial and temporal distribution of data and

information used to constrain the model. These measurements include water levels measured in wells open to different

depths in order to monitor vertical gradients and to capture

the three-dimensional nature of the groundwater flow system.

Some wells were selected in close proximity to each other to

cover the period of record and to make sure long-term waterlevel trends, as well as seasonal changes, were represented.

Where necessary, additional wells were selected to make sure

key climate periods (relatively wet and dry) were included.

Thus, these wells, or a subset of these wells, could be used as

a starting point to identify wells suitable for a comprehensive

groundwater-level monitoring network. One specific observation made while compiling these hydraulic head data was the

lack of construction data, specifically screen intervals, for

many of the water-level monitoring wells in both the DWR

and USGS databases. In particular, a few hundred wells for

which there are hydraulic head data were not used for various

purposes owing to missing construction and screen-interval

data. Determining screen intervals for the observation wells

with large data records would significantly increase the data

available from monitoring the groundwater system. A cooperative effort among the stakeholders of the Central Valley

groundwater resources could possibly go a long way toward

resolving this data gap.

Currently, groundwater levels throughout the Central

Valley are measured annually through the effort of the USGS,

State (California Department of Water Resources), and local

agencies. These agencies monitor some of their own wells,

but mostly use private irrigation and (or) domestic wells to

monitor water levels. However, the lack of coordination of

this monitoring effort results in inefficiencies, and may result

in inadequacies in the future if monitoring at key sites in the

various monitoring networks is curtailed unilaterally. An

analysis of available data and current data-collection activities

is needed to determine whether current monitoring is adequate

to support regional and broader-scale decision-making for

effective water management.
Monitoring the Hydrologic System 111

The number of long-term monitoring wells in the Central Valley appears to be declining because of limitations in

funding and human resources, among other factors. DWR

(1998) summarizes several factors that have contributed to the

reduction:

1. Funding for data programs in many agencies,

which often was insufficient, has been reduced

significantly.

2. When private properties change ownership,

some new owners rescind permission for agency

personnel to enter the property and measure the 

well.

3. Because the appropriateness of many private

wells that have been monitored in the past is

being brought into question, they are being

dropped from monitoring networks. The appropriateness of using these private wells is questionable because they often are perforated over

long intervals encompassing multiple aquifers in

the subsurface and, in some cases, construction 

details for the well are unknown.

4. Some wells with long-term records actually

reach the end of their usefulness because the 

casing collapses or something falls into the well,

making the well unusable, or the groundwater

level drops below the bottom of the well.

Despite the declining number of monitoring wells in

the valley as a whole, at least 48 dedicated single or multicompletion monitoring wells have been installed since 1997

in the northern Sacramento Valley. Funding for these 48 wells

was provided by grants awarded to Tehama, Butte, Colusa,

and Glenn Counties through grant sources. At the request of

the counties and (or) local agencies, DWR provided construction oversight and data analysis for these wells. These new

monitoring wells now make it possible to measure groundwater levels and collect groundwater samples for laboratory

analysis from different parts of the aquifer system. These data 

make it possible to measure the potential for water to move in

a vertical direction between different geologic formations, and

to evaluate whether these formations can be used to represent

different aquifer systems. In particular, work needs to be done

to determine if flow in the aquifer system is governed more

by grain-size distribution or formation boundaries. Unconformities can cause permeability contrasts that tend to isolate 

the aquifers; if so, delineation of the extent of these unconformities would be an important step towards understanding

recharge and three-dimensional groundwater flow in the

Sacramento Valley aquifer system (Dudley and others, 2006).

In addition to water-level monitoring, geophysical

techniques could be used to delineate water-level changes.

Gravity methods can be used to measure gravitational changes

that result from changes in groundwater storage either locally

(microgravity) or regionally (satellite-based gravity measurements) (Yeh and others, 2006). Several satellite sensors

(installed in current and near-future satellite missions) have

demonstrated the capability for monitoring soil moisture, snow

water equivalent, heights of inland water bodies (for example

rivers, lakes, reservoirs), and changes in total water storage

(the aggregate of all of the snow, surface waters, soil moisture and groundwater). In particular, the Gravity Recovery

and Climate Experiment (GRACE) satellite mission provides

monthly estimates of column integrated land water storage by

observing variations of Earth’s gravity field. These estimates

include a contribution from all the components of land water

storage, both above and below ground (Yeh and others, 2006).

Combining these regional-scale techniques with well measurements may allow for an improved understanding of groundwater levels and storage.

InSAR can provide the areal extent of groundwater depletion where it is linked to subsidence and can detect uplift from

artificial recharge (Galloway and Hoffmann, 2007). InSAR

will be discussed in more detail in the Subsidence section of 

this report. 

Surface Water

The status of groundwater resources needs to be placed

in the context of the complete water budget. Because surface

water and groundwater are linked inseparably, monitoring of

both systems in the Central Valley is necessary.

The surface-water data needed include: inflow, diversions, deliveries, and gaged flows. These data often are

unavailable or, when available, often the data have been collected by multiple agencies at different time intervals using

different methodologies. There are 43 gaged inflows (fig. A5A) 

measured by a combination of the USGS, Reclamation, and

DWR. The vast majority of these inflows are controlled by

reservoir releases. However, the DWR has compiled the complex diversion and delivery history for 21 subregions (WBSs)

covering the Central Valley. These data include 108 diversions

from the surface-water system, compiled from a combination

of Federal, State, and local agencies. Water from these diversions is delivered to the WBSs. The CVHM directly utilizes

inflows, diversions, and streamflow-gage data. Indicators of

surface-water availability, such as snowpack, streamflow, and

surface-water storage, could be monitored to provide a more

comprehensive status of the surface-water system.
112 Groundwater Availability of the Central Valley Aquifer, California

Gaged streamflow rates are important for a number of

reasons. Gaged flow rates are useful for calibration criteria

and monitoring compliance with environmental regulations.

Disputes over whether groundwater pumpage has had or will

have an effect on a particular river or spring often is the driving force behind discussions about the sustainability of many

groundwater systems (Alley, 2006). Changes in flow rates

through time between various gages frequently can answer

these questions. Streamflow measurements from 65 gages

throughout the Central Valley are available from the

USGS database. Unfortunately, many of these gages are no

longer operated by the USGS; fortunately some are now measured by other agencies. The CVHM could be used to evaluate 

the effect of groundwater pumpage on streamflow and

groundwater levels supporting marshes and lakes.

Subsidence

One of the generally unrecognized limitations in groundwater availability is subsidence from groundwater withdrawal.

If pumpage demands are large enough, subsidence can occur

(fig. B4). In the Central Valley, land subsidence has resulted

in damage to buildings, aqueducts, well casings, bridges, and

highways and has caused flooding. These damages have cost

millions of dollars (Planert and Williams, 1995).

Several methods are available to monitor land subsidence

(Galloway and others, 1999). The most basic approaches use

repeat geodetic surveys such as conventional spirit leveling or

GPS surveys. Another approach is to use compaction recorders or vertical (borehole) extensometers (Riley, 1969; 1986).

These devices use a pipe or a cable inside a well casing. The

pipe inside the casing extends from land surface to some depth

through compressible sediments. A stable platform at land

surface holds instruments that monitor change in distance

between the top of the pipe and the platform. If the inner pipe

and casing penetrate the entire thickness of compressible

sediments, then the device measures actual land subsidence.

If both groundwater levels and compaction of sediments are

measured, then the data can be analyzed to determine elastic

and inelastic storage properties that can be used to predict

future subsidence (Riley, 1969). At least six extensometers and

colocated monitoring wells were installed by the USGS in the

San Joaquin Valley. Several of these still are being monitored,

although irregularly, by DWR. 

Recently DWR, together with 20 Federal, State, and local

agencies, has installed and surveyed a land-elevation measurement network in the Sacramento Valley (California Department of Water Resources, 2008). This network allows landsurface elevations to be measured accurately with GPS at 339

survey monuments covering 10 counties in the Sacramento

Valley. The monuments will be re-surveyed every 3 years. In

addition to this GPS network, DWR monitors 13 extensometers and adjacent groundwater levels in monitoring wells in

the Sacramento Valley. Several of the extensometers document

subsidence and one site, Zamora, shows 0.5 ft of net subsidence from 1994 to 2007.

Another subsidence monitoring method uses InSAR,

whereby individual radar images from satellites are compared

and interferograms are produced. InSAR can provide hydrogeologic information for alluvial aquifer systems susceptible

to aquifer-system compaction (Galloway and Hoffmann,

2007). InSAR makes high-density measurements over large

areas using radar signals from Earth-orbiting satellites to

measure changes in land-surface altitude at high degrees of

measurement resolution and spatial detail (Galloway and

others, 2000). Under the best conditions, land-surface elevation changes on the order of 0.2 to 0.4 inches can be determined. The InSAR information can provide the areal extent

of groundwater depletion where it is linked to subsidence, and

can detect uplift from artificial recharge. This method is the

best approach for obtaining comprehensive spatial coverage

of land subsidence over large regions like the Central Valley

(Brandt and others, 2005). The main limitation for InSAR in

the Central Valley is the loss of coherence owing to groundsurface disturbances caused by cultivation. Special techniques

are required to extract stable points for observations in agricultural areas; reflector cubes also can be deployed as permanent monuments analogous to benchmarks. Some reflectors

could be collocated with benchmarks to tie the leveling and

InSAR monitoring systems together. The advent of permanent

scatterer InSAR techniques (Feretti and others, 2001) shows

promise in overcoming this principal limitation.

Water Quality

Groundwater-quality data are necessary for the protection

of groundwater resources because deterioration of groundwater quality virtually may be irreversible, and treatment of contaminated groundwater can be expensive. Therefore, groundwater contamination from natural sources and human activities

places constraints on groundwater availability (Alley, 2003;

2006). Various water-management actions potentially have

groundwater-quality effects. Therefore, water quality needs to

be considered in conjunction with information about changes

in water levels and water in storage in evaluating the availability and sustainability of groundwater.
Monitoring the Hydrologic System 113

In general, freshwater is available throughout most

of the Central Valley. Locally, dissolved solids, selenium,

boron, nitrate, and pesticides are of concern and monitored 

(Planert and Williams, 1995). The water quality, particularly

the concentration of dissolved solids, can reflect the chemical

characteristics of the streams that recharge the aquifer and the

depth of the water. Streams from the Cascade Ranges and the

Sierra Nevada, which primarily are igneous rocks, have very

small dissolved-solids concentrations. Streams that issue from

the Coast Ranges, which predominantly are marine sedimentary rocks, have much higher dissolved-solids concentrations.

Therefore, groundwater in the Sacramento Valley and eastern

San Joaquin Valley has much smaller dissolved-solid concentrations than groundwater on the western side of the San

Joaquin Valley (Planert and Williams, 1995). Groundwater

in the agricultural areas has the tendency to become excessively saline and damaging to crops because evaporation of

sprayed irrigation water and ET of soil moisture and shallow

groundwater leaves behind dissolved salts. Shallow irrigation

wells can worsen the problem by recirculating the shallow,

saline groundwater. This is a particular problem that is being

monitored in the Delta and the San Joaquin Valley. Several

irrigation return-water drainage systems are being operated to

help reduce this problem. In addition, dissolved-solids concentration generally increases with depth in the Central Valley.

Because wells generally are deeper in the western and southern parts of the San Joaquin Valley, they are more likely to

produce water with larger dissolved-solids concentrations than

the shallower wells in the Sacramento Valley and the eastern

part of the San Joaquin Valley (Planert and Williams, 1995).

Marine rocks form the western boundary of the San Joaquin Valley and contain relatively large amounts of selenium.

This selenium is found in the soils and groundwater, and is

concentrated in the soil by ET (Planert and Williams, 1995).

Excess irrigation water applied for leaching salts from the

soil leaches selenium from the soil and transports it to shallow groundwater or to surface drains. Large concentrations

of dissolved selenium have been detected in shallow groundwater and surface drains (Planert and Williams, 1995). Boron

is found in concentrations potentially harmful to plants in the 

northern and southwestern parts of the Sacramento Valley and

in the southern part of the Tulare Basin (Planert and Williams,

1995). Large concentrations of boron also have been detected

in shallow groundwater in the western part of the San Joaquin

Valley. Excessive concentrations of nitrate, which are potentially harmful to humans and some crops, have been found in

shallow groundwater in three areas in the Sacramento Valley

and sporadically in the San Joaquin Valley (Planert and

Williams, 1995). The source of the nitrate is attributed to 

effluent from waste-treatment facilities; discharge from septic

tanks, feed lots, and dairies; or from leaching of nitrogen fertilizers. Agricultural use of pesticides is widespread; pesticides

have been found in groundwater throughout the Central Valley,

particularly in the San Joaquin Valley where they have been

found in every county.

Sustainability, with respect to groundwater quality, only

can be determined by observing groundwater quality over

time. If conditions worsen, local managers may need to take

steps to prevent further harm to groundwater quality. For these

reasons, delineation of areas of contributing recharge to existing water supplies is important, as is identification of potential

source areas for future water supplies. To create a serviceable

monitoring network, Federal, State, and local agencies use

private irrigation and (or) domestic wells along with agencyinstalled monitoring wells to monitor water quality. These

monitoring wells include multi-zone wells, which are very

important for understanding changes in water-quality with

depth.

Design of a water-quality monitoring network is beyond

the scope of this study. Several studies have included designs

for such a network for large parts of the Central Valley. The

USGS’s San Joaquin and Sacramento Valley National Water

Quality and Assessment (NAWQA) programs have established

multiple water-quality networks in the Central Valley (Domagalski, 1998; Dubrovsky and others, 1998). The USGS’s

Priority Basin Project, a component of the California Water

Board’s Groundwater Ambient Monitoring and Assessment

program (GAMA) program, is an ongoing comprehensive

assessment of statewide groundwater quality. The program

is designed to help better understand and identify risks to

groundwater resources (Belitz and others, 2003). Groundwater

is being sampled at many locations across the State, including

the Central Valley, in order to characterize its constituents and

identify trends in groundwater quality. The results of these

tests will provide information for water agencies to address a

variety of issues ranging in scale from local water supply to

statewide resource management. The GAMA program was

developed in response to the Groundwater Quality Monitoring

Act of 2001 (Sections 10780-10782.3 of the Water Code): a

public mandate to assess and monitor the quality of groundwater used as public supply for municipalities in California.

The goal of the Groundwater Quality Monitoring Act of 2001

is to improve statewide groundwater quality monitoring and

facilitate the availability of information about groundwater

quality to the public. 
114 Groundwater Availability of the Central Valley Aquifer, California

Land Use and Climate

In addition to monitoring data on natural systems, estimation of water withdrawals and consumptive use is an essential

part of computing a water budget for a developed aquifer

system. Although groundwater pumpage is physically possible

to measure, it is commonly not measured or tabulated in the 

valley because few wells are metered and because there is no

requirement to do so for private wells. As a result, monitoring

land use, particularly to the extent of monitoring native vegetation, urban areas, and irrigated agriculture (including crop

type), is critical to the estimation of groundwater use, and, in

turn, successful water-resource management. Because land use

largely drives water use and consumption, land-use data need

to be collected with adequate spatial and temporal detail that

reflect the complexity of changing land use and the dynamics of agricultural activities. Most of the historical land-use

information is based on interpreted high-altitude aerial photography. During the 1980s, Landsat’s Thematic Mapper (TM)

satellite sensor data were used to produce the North American

Land Class Data (NLCD), which included the Central Valley. DWR has been collecting and mapping detailed land-use

information by county on a rotating basis for several decades.

Since 2000, DWR has begun to release detailed digital data

sets (California Department of Water Resources, 2000). Several tools now available for monitoring land use from remotely

sensed data include LANDSAT, MODIS, and InSAR. MODIS

data are available as often as every 8 days at a spatial resolution ranging from 820 ft to 0.6 miles.

Mapping of the type and distribution of land use is integral to calculating the crop irrigation demand and, ultimately,

the water use in the Central Valley. For a given land use, the

demand can be calculated from two variables: crop coefficient

(Kc) and reference evapotranspiration (ETo) (Chapter C). A

number of studies report Kc values for most crops. The different studies report consistent values. ETo values change

with climate and can be estimated from temperature. Hence,

temperature data, currently available from DWR’s California

Irrigation Management Information System (CIMIS) stations,

are a necessary part of the monitoring network. In addition to

temperature, CIMISs 120 weather stations throughout California provide additional information useful in estimating crop

water use. Estimated parameters (such as ETo, net radiation

[Rn], dew point, temperature, etc.) and measured parameters

(such as solar radiation [Rs], air temperature [T], relative

humidity [RH], wind speed [u], etc.) are stored in the CIMIS

database for unlimited free access by registered CIMIS data

users. 

Summary

This chapter focuses on the availability of groundwater

in the Central Valley. The spatial and temporal differences

between the natural distribution of water in the Central Valley,

and the agricultural and urban demands for that water, have

led to a massive surface-water diversion and delivery system.

Because the surface-water diversion and delivery system cannot always meet all of the water demand, groundwater is relied

upon to help rectify the imbalance. 

Because of the large effect that irrigated agriculture has

on the system, the overall water budget for the hydrologic

system is separated into two linked parts for the purposes of

analysis: a landscape budget, encompassing the surface processes (including most of the components of the agricultural

part of the system), and a groundwater budget, encompassing

fluxes in, through, and out of the aquifer system.

The hydrology of the Central Valley is driven by surfacewater deliveries and associated groundwater pumpage, which

in turn reflect spatial and temporal variability in climate,

regional differences in water availability and agricultural

practices, and temporal changes in the water-delivery system.

In general, the Sacramento Valley receives more precipitation

than the drier San Joaquin Valley. Historically, water managers have been able to respond to hydrologic challenges in

the valley. Consequently, the San Joaquin Valley relies more

heavily on groundwater pumpage than the Sacramento Valley.

The surface-water delivery system developed for the valley

redistributes this water from north to south through the Delta.

The Delta is the heart of a massive north-to-south waterdelivery system whose giant engineered arterials transport

up to 7.5 million acre-feet per year southward (California

Department of Water Resources, 1993). About 83 percent of

this water is used for agriculture and the remainder for various

urban uses in central and southern California. Two-thirds of

California’s population (more than 20 million people) get at

least part of their drinking water from the Delta (Delta

Protection Commission, 1995).

Monthly water budgets, computed as a part of this study,

were used to examine the fate of water in the Central Valley.

The aquifer receives recharge from precipitation, streamflow

losses, and excess irrigation water. The excess irrigation water

originates from a combination of surface-water deliveries and

groundwater pumpage. Other than groundwater withdrawals

from wells, groundwater leaves the system predominantly

through ET, flux to streams, and, to a small degree, through

discharge to the Delta. When total recharge exceeds total

discharge, water is added to storage; when the reverse is

true, water is removed from storage, which can trigger

aquifer-system compaction and associated land subsidence. 
Summary 115

Groundwater pumpage is the most significant human

activity that affects the amount of groundwater in storage and

the rate of discharge from the aquifer system. A high concentration of broadly distributed wells and the multiple broadly

distributed cones of depression have produced water-level

declines across large areas. Pumpage is physically possible

to measure; yet in the Central Valley it is one of the least

certain components of the entire water budget. In this study,

the numerical model CVHM was used to estimate groundwater pumpage. During the 1962–2003 timeframe, the CVHM

indicates that average withdrawals from irrigation wells were

about 8.7 million acre-ft/yr. 

Hydrologic input and output to the landscape and groundwater budgets vary with time. These variations predominantly

are a result of the combined influences of climate variability,

surface-water delivery systems, land-use changes, and farming practices. The monthly budgets indicate that precipitation

and surface-water deliveries supply most of the consumption

in the initial part of the growing season, whereas increased

groundwater pumpage augments these supplies later in the

season. Although some pumpage occurs in all months, the vast

majority of groundwater withdrawals occur during the springsummer growing season. As a result, water typically is taken

into storage during the wet winter months (December through

March) and released from storage during the drier growing

season (May through September).

Groundwater levels and associated groundwater flows

have responded to changes in the groundwater budget. Prior

to development, the Central Valley aquifer system was driven

by natural conditions in which groundwater flowed from areas

of higher altitude along mountain fronts to areas of discharge

along rivers and marshes near the valley trough. Large-scale

groundwater development for both agricultural and urban uses

has modified the groundwater levels and flow patterns, relative

to predevelopment conditions. Groundwater flow has become

more rapid and complex. Groundwater pumpage and excess

irrigation water have resulted in steeper hydraulic gradients as

well as shortened flow paths between sources and sinks.

Flow through the aquifer system has increased more

than six fold, from about 2 million acre-ft/yr prior to development (Williamson and others, 1989) to an average of about

12 million acre-ft/yr (including ET directly from groundwater) between 1962 and 2003. The increased flow through the

aquifer system predominantly is a result of increased pumpage

and increased recharge. The increased recharge mostly is from

excess applied irrigation water resulting from imported surface

water or recirculated groundwater.

During recent decades, changes in the surface-water

delivery system and climate have had large effects on the

hydrologic system. In the late 1960s, the surface-water delivery system began to route water from the wetter Sacramento

Valley to the drier, more heavily pumped San Joaquin Valley.

In the San Joaquin Valley, prior to the late 1960s, groundwater

pumpage exceeded surface-water deliveries, causing water

levels to decline to historic lows on the west side of the San

Joaquin Valley, which resulted in decreases in groundwater

storage and large amounts of subsidence. The surface-water

delivery system was fully functional by the early 1970s, resulting in water-level recovery in the northern and western parts

of the San Joaquin Valley. Overall, the Tulare Basin, located

in the southern part of the San Joaquin Valley, still is showing

dramatic groundwater level declines and groundwater

storage deficits. Because of the abundance of surface water

and smaller amounts of pumpage, except locally, the

Sacramento Valley and Delta have had little cumulative

loss in groundwater storage.

The Central Valley hydrologic system responds to

changes in the climate. During wet years, relatively inexpensive surface water typically is used for irrigation. During dry

periods, many farms predominantly use groundwater. During

dry years, groundwater pumpage exceeds recharge and water

is removed from storage. For example, during the droughts of

1976–77 and 1987–92 when surface water was less available, more groundwater was pumped. As a result, water levels

dropped and subsidence was reinitiated, particularly in the

Tulare Basin of the San Joaquin Valley. During wet years

where more precipitation and imported surface water were

available for irrigation, pumpage decreased and groundwater

was taken into storage. In most years, water was removed

from storage, particularly in the Tulare Basin. In the

Central Valley as a whole, during 1962–2003 there was a

simulated net release of 57.7 million acre-ft of water from

aquifer-system storage and accompanying groundwater-level

declines. 

The change in the amount of water in storage varies

spatially because of the spatial variation in hydrologic stresses.

On average, 68 percent of pumpage in the Central Valley

occurs in the San Joaquin Valley (11 percent from the San

Joaquin Basin and 57 percent from the Tulare Basin). Volumetrically, there has been very little overall change in storage

in the Sacramento Valley and San Joaquin Basin. Conversely,

despite the surface-water deliveries, a substantial amount of

water has been removed from aquifer-system storage in the

Tulare Basin.

In the Central Valley, the typically slow process of

draining fine-grained deposits has caused the permanent and

irreversible consolidation of fine-grained subsurface deposits.

This consolidation has resulted in extensive land subsidence,

particularly in the San Joaquin Valley. Significant land subsidence (more than 1 foot) due to the withdrawal of groundwater

has occurred in about half of the San Joaquin Valley, or about

5,200 mi2. Small areas of the Sacramento Valley also have

been affected by subsidence. 
116 Groundwater Availability of the Central Valley Aquifer, California

In the Central Valley, as in most places, the environment and surface- and groundwater systems are intimately

linked. Under predevelopment conditions and during the early

period of development, the Central Valley had considerable

swamps, marshes, sloughs, riparian habitat, and an extensive

Delta region. Many flow regimes no longer resemble natural

conditions, largely because of efforts to manage water through

diversions for agricultural and urban demands. Groundwater

pumpage also has intercepted groundwater that previously

discharged to these surface-water bodies, and has induced

infiltration of water from surface-water bodies (groundwater

recharge).

California’s water-delivery system and agriculture have

been developed and operated based on the climatic record of

the past century. The surface-water delivery system assumes

a certain spatial distribution and amount of runoff, storage in

snowpack, and timing coinciding with the growing season.

During the study period (1962–2003), water generally has

been available, except during extreme droughts. 

California’s water-management systems have been

designed and operated in the context of the recent hydrologic

record. Global Climate Models (GCMs) indicate that California’s future hydrologic conditions may be different from

those reflected in the past record. Although the extent and

timing of the long-term changes remain uncertain, the projections include increased temperatures, changes in precipitation

(including reductions to the Sierra Nevada snowpack and more

precipitation in the form of rain), an earlier snowmelt, possibly

larger floods, and a rise in sea level. These long-term changes

in temperature and precipitation may lead to increased water

usage, decreased surface-water availability, and, consequently,

increased groundwater pumpage and reduced groundwater

storage. The question remains as to whether the existing management strategies can accommodate the changed conditions.

Groundwater sustainability requires an iterative process

of monitoring, analysis, and application of management practices. In the Central Valley, depletion of aquifer-system storage

by pumpage has had a substantial effect. This depletion also

has had substantial related consequences. These consequences 

include changes in surface-water quality, quantity, and temperature, and land subsidence. In turn, these consequences 

factor into large environmental issues by changing and (or)

degrading habitats. These related effects may constitute the

primary constraint to groundwater development.

The Central Valley faces competing demands for water

resources. These demands include providing water supply for

growing urban areas, agriculture, and environmental uses. The

demand for water resources by people may directly compete with environmental uses such as maintaining minimum

streamflows, preventing seawater inundation of coastal areas,

and preserving habitats for fish and birds. Sustainable development likely will benefit from an integrated water-management

approach. Possible management actions include enhancements

in conjunctive use of surface water and groundwater, artificial

recharge, and the use of recycled or reclaimed water.

In order to quantify groundwater availability and to

evaluate the sustainability of the groundwater resources,

continued and enhanced monitoring of the Central Valley’s

hydrologic system is needed. Because of the interconnection

of the hydrologic system, a monitoring network should include

groundwater levels, surface-water flows, subsidence, water

quality, land use, and climate variables (including temperature,

precipitation, and snowpack).

One of the main challenges in monitoring the Central

Valley hydrologic system is managing the flow of information.

The CVHM can be used as a tool for identifying, organizing,

and integrating the necessary monitoring data into a form

where scenarios of possible future consequences of natural

and anthropogenic stresses on the hydrologic system can be

simulated and evaluated. In particular, the CVHM can be used

to address groundwater depletion issues such as: critically low

groundwater levels and other consequences of groundwater

storage depletion including subsidence, streamflow losses, and

reduced availability of water for ET. Finally, the CVHM, used

together with the GIS, has many other possible uses.

References Cited

Aerts, J., and Droogers, Peter, eds., 2004, Climate change in

contrasting river basins: Adaptation strategies for water,

food and environment: CABI Publishing, Manchester,

England, 288 p.

Alley, W.M., 1993, General design consideration, in Alley,

W.M., ed., Regional ground-water quality: New York, Van

Nostrand Reinhold, 634 p.

Alley, W.M., 2006, Tracking U.S. ground-water reserves for

the future: Environment, v. 48, no. 3, p. 12–25.

Alley, W.M., 2007, Another water budget myth: The significance of recoverable ground water in storage: Ground

Water, v. 45, no. 3, p. 251.

Alley, W.M. and Leake, S.A., 2004, The journey from safe

yield to sustainability: Ground Water, v. 42, no. 1, p. 12–16.

American Farmland Trust, 2006, The future is now: Central Valley farmland at the tipping point, accessed April

15, 2009, at http://www.farmland.org/programs/states/

futureisnow/default.asp

Bales, R.C., Molotoch, N.P., Painter, T.H., Dettinger, M.D.,

Rice, R., Dozier, J., 2006, Mountain hydrology of the

western United States: Water Resources Research, v. 42,

W08432, doi:10.1029/2005WR004387, 13 p.
References Cited 117

Belitz, Kenneth, Dubrovsky, N.M., Burow, K.R., Jurgens, Bryant, and Johnson, Tyler, 2003, Framework for a groundwater quality monitoring and assessment program for California: U.S. Geological Survey Water-Resources Investigations

Report 03-4166, 78 p., accessed April 15, 2009, at http://

water.usgs.gov/pubs/wri/wri034166/

Belitz, Kenneth, and Heimes, F.J., 1990, Character and evolution of the ground-water flow system in the central part of

the western San Joaquin Valley, California: U.S. Geological

Survey Water-Supply Paper 2348, 28 p.

Belitz, Kenneth, Phillips, S.P., and Gronberg, J.M., 1993,

Numerical simulation of ground-water flow in the central

part of the Western San Joaquin Valley, California: U.S.

Geological Survey Water-Supply Paper 2396, 69 p.

Bertoldi, G.L., 1979, A plan to study the aquifer system of the

Central Valley of California: U.S. Geological Survey OpenFile Report 79–1480, 48 p.

Bertoldi, G.L., 1989, Ground-water resources of the Central

Valley of California: U.S. Geological Survey Open-File

Report 89–251, 2 p.

Bertoldi, G.L., Johnston, R.H., and Evenson, K.D., 1991,

Ground water in the Central Valley, California—A summary

report: U.S. Geological Survey Professional Paper 1401-A,

44 p.

Blodgett, J.C., Ikehara, M.E., and Williams, G.E., 1990, Monitoring land subsidence in Sacramento Valley, California,

using GPS: Journal of Surveying Engineering, v. 116, no. 2,

p. 112-130.

Brandt, J.T., Bawden, G.W., and Sneed, Michelle, 2005,

Evaluating Subsidence in the Central Valley, CA, using

InSAR: EOS. Transactions of the American Geophysical Union, v. 85, no. 52 , 2005 Fall Meeting Supplement,

Abstract G51C-0851.

Brandt, J.T., Bawden, G.W., and Sneed, Michelle, 1964,

Alluvial fans and near-surface subsidence, western Fresno

County, California: U.S. Geological Survey Professional

Paper 437-A, 71 p.

Brandt, J.T., Bawden, G.W., and Sneed, Michelle, 1972, Prehistoric near-surface subsidence cracks in western Fresno

County, California: U.S. Geological Survey Professional

Paper 437-C, 85 p.

Brandt, J.T., Bawden, G.W., and Sneed, Michelle, 1973,

Geologic factors affecting compaction of deposits in a land

subsidence area: Geological Society of America Bulletin,

v. 84, p. 3783–3802.

Bull, W.B., 1961, Causes and mechanics of near-surface subsidence in western Fresno County, California: Short Papers

in the Geologic and Hydrologic Sciences, U.S. Geological

Survey Professional Paper 424-B, p. 187-189.

Bull, W.B., and Miller, R.E., 1975, Land subsidence due to

ground-water withdrawal in the Los Banos-Kettleman City

area, California; Part 1. Changes in the hydrologic environment conducive to subsidence: U.S. Geological Survey

Professional Paper 437-E, 70 p.

Bureau of Reclamation, 1994, The Central Valley Project overview: Bureau of Reclamation History Program Research on

Historic Reclamation Projects, accessed April 16, 2009, at

http://www.usbr.gov/dataweb/html/cvpintro.html

California Climate Change Center, 2006, Our changing climate: Assessing the risks to California, accessed April 16,

2009, at http://meteora.ucsd.edu/cap/pdffiles/CA_climate_

Scenarios.pdf

California Department of Conservation, 2007, Farmland

conversion reports, accessed April 16, 2009, at http://www.

conservation.ca.gov/DLRP/fmmp/Pages/Index.aspx

California Department of Finance, 2007, E-4 population

estimates for cities, counties and the State, 2001–2007, with

2000 Benchmark, Sacramento, California, accessed April

16, 2009, at http://www.dof.ca.gov/HTML/DEMOGRAP/

ReportsPapers/Estimates/E4/E4-01-06/HistE-4.asp

California Department of Water Resources, 1993, SacramentoSan Joaquin Delta atlas: Sacramento, State of California

Department of Water Resources, 121 p.

California Department of Water Resources, 1994, California

water plan update: Bulletin 160–93, 2 v., accessed April 16,

2009, at http://rubicon.water.ca.gov/v1cwp/ause.html

California Department of Water Resources, 1998, California

water plan update: Bulletin 160–98, 3 v., accessed April 16,

2009, at http://rubicon.water.ca.gov/pdfs/b160cont.html

California Department of Water Resources, 2000, Explanations of land use attributes used in database files associated with shape files: California Department of Water

Resources, Land and Water Use Section, 11 p.

California Department of Water Resources, 2003, California’s

ground water, update 2003: California Department of Water

Resources Bulletin 118, 246 p.

California Department of Water Resources, 2005, California

water plan update 2005: Volume 1 – Strategic Plan: California Department of Water Resources, Bulletin 160-05, CD.

California Department of Water Resources, 2006, Progress

on incorporating climate change into management of

California’s water Resources: California Department of

Water Resources Technical Memorandum Report, accessed

April 16, 2009, at http://baydeltaoffice.water.ca.gov/

climatechange.cfm
118 Groundwater Availability of the Central Valley Aquifer, California

California Department of Water Resources, 2008, DWR to

use GPS technology to measure land elevations: California Department of Water Resources news release, July 16,

2008, accessed April 16, 2009, at http://www.water.ca.gov/

news/newsreleases/2008/071608gpsevaluations.pdf

California State University, Chico, 2003, The Central Valley

historic mapping project: California State University, Chico,

Department of Geography and Planning and Geographic

information Center, 25 p.

Davis, G.H., Lofgren, B.E., and Seymour, Mack, 1964, Use

of ground-water reservoirs for storage of surface water in

the San Joaquin Valley, California: U.S. Geological Survey

Water-Supply Paper 1618, 125 p.

Delta Protection Commission, 1995, Land use and resource

management plan for the primary zone of the Delta: Walnut

Grove, Delta Protection Commission, 60 p.

Dettinger, M.D., 2005, From climate-change spaghetti to

climate-change distributions for the 21st Century California: San Francisco Estuary & Watershed Science, v. 3,

issue 1, article 4, 14 p., accessed April 16, 2009, at http://

repositories.cdlib.org/jmie/sfews/vol3/iss1/art4

Dettinger, M.D., and Cayan, D.R., 1995, Large-scale atmospheric forcing of recent trends toward early snowmelt

runoff in California: Journal of Climate, v. 8, p. 606–623.

Dettinger, M.D., Cayan, D.R., Meyer, M.K., and Jeton, A.E.,

2004, Simulated hydrologic responses to climate variations

and change in the Merced, Carson, and American River

Basins, Sierra Nevada, California, 1900–2099: Climatic

Change, v. 62, p. 283–317.

Dettinger, M.D., and Earman, S., 2007, Western ground water

and climate change—Pivotal to supply sustainability or vulnerable in its own right?: Ground Water News and Views:

v. 4, no. 1, p. 4–5.

Deverel, S.J., and Rojstaczer, S.A., 1996, Subsidence of

agricultural lands in the Sacramento-San Joaquin Delta,

California: Role of aqueous and gaseous carbon fluxes:

Water Resources Research, v. 32, p. 2,359–2,367.

Domagalski, J.L., Knifong, D.L., MacCoy, D.E., Dileanis,

P.D., Dawson, B.J., and Majewski, M.S, 1998, Water quality

assessment of the Sacramento River Basin, California; environmental setting and study design: U.S. Geological Survey

Water-Resources Investigations Report 1997–4254, 31 p.

Dubrovsky, N.M., Kratzer, C.R., Brown, L.R., Gronberg, J.M.,

and Burow, K.R., 1998, Water quality in the San Joaquin–

Tulare Basins, California, 1992–1995: U.S. Geological Survey Circular 1159, 38 p., accessed April 16, 2009, at http://

pubs.usgs.gov/circ/circ1159/

Dudley, Toccoy, Spangler, Debbie, Fulton, Alan, Staton, Kelly,

Lawrence, Seth, Ehorn, Bill, and Ward, Michael, 2006,

Seeking an understanding of the ground-water aquifer

systems in the northern Sacramento Valley: An update,

accessed April 16, 2009, at http://ucce.ucdavis.edu/files/

filelibrary/2280/37336.pdf

Farrar, C.D., and Bertoldi, G.L., 1988, Region 4, Central

Valley and Pacific Coast Ranges, in Back, William, Rosenshein, J.S., and Seaber, P.R., eds., Hydrogeology: Boulder,

Colorado, Geological Society of America, Geology of North

America, v. O-2, p. 59–67.

Ferretti A., Prati C., and Rocca F., 2001, Permanent Scatterers

in SAR Interferometry: IEEE Transactions on Geoscience

and Remote Sensing, v. 39, no. 1, p. 8–20.

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.

Galloway, D.L., and Hoffmann, Jörn, 2007, The application of

satellite differential SAR interferometry-derived ground displacements in hydrogeology: Hydrogeology Journal, v. 15,

no. 1, doi: 10.1007/s10040-006-0121-5, p. 133–154.

Galloway, D.L., Jones, D.R., and Ingebritsen, S.E., eds., 1999,

Land subsidence in the United States: U.S. Geological

Survey Circular 1182, 175 p., accessed February 2, 2008, at

http://pubs.usgs.gov/circ/circ1182/

Galloway, D.L., Jones, D.R., and Ingebritsen, S.E., 2000,

Measuring land subsidence from space: U.S. Geological

Survey Fact Sheet 051–00, 4 p.

Galloway, D.L., and Riley, F.S., 1999, San Joaquin Valley,

California—Largest human alteration of the Earth’s surface:

in Galloway, D.L., Jones, D.R., and Ingebritsen, S.E., eds.,

Land Subsidence in the United States: U.S. Geological

Survey Circular 1182, p. 23–34, accessed February 2, 2008,

at http://pubs.usgs.gov/circ/circ1182/

Great Valley Center, 2005, State of the great Central Valley: Assessing the region via indicators—The economy: 

State of the Great Central Valley Indicators Series, 49 p.,

accessed April 16, 2009, at http://www.greatvalley.org/

pub_documents/2005_1_18_13_59_43_indicator_econ05_

report.pdf

Gronberg, J.A., and Belitz, K.R., 1992, Estimation of a water

budget for the central part of the western San Joaquin Valley, California: U.S. Geological Survey Water-Resources

Investigations Report 91–4192, 22 p.

Hanson, R.T., and Dettinger, M.D., 2005, Ground-water/

surface-water responses to global climate simulations, Santa

Clara-Calleguas Basin, Ventura County, California, 1950–

93: Journal of the American Water Resources Association,

v. 43, no. 3, p. 517–536.
References Cited 119

Hidalgo, H.G., Cayan, D.R., Dettinger, M.D., 2005, Sources

of variability of evapotranspiration in California: Journal of

Hydrometeorology, v. 6, p. 3–19.

Igler, David, 2001, Industrial Cowboys: Miller & Lux and the

Transformation of the Far West, 1850–1920: University of

California Press, 267 p.

Ikehara, M.E., 1994, Global Positioning System surveying to

monitor land subsidence in Sacramento Valley, Califorinia,

USA: Hydrological Sciences Journal, v. 29, no. 5,

p. 417–429.

Ikehara, M.E., 1995, Data from Woodland land-subsidence

monitoring station, Yolo County, California, water years

1988–92: U.S. Geological Survey Open-File Report 94-494,

77 p. 

Ingebritsen, S.E., and Ikehara, M.E., 1999, Sacramento-San

Joaquin Delta—The sinking heart of the state: in Galloway,

D.L., Jones, D.R., and Ingebritsen, S.E., eds., Land Subsidence in the United States: U.S. Geological Survey Circular

1182, p. 83–94.

Intergovernmental Panel on Climate Change, 2007, Climate

change 2007: The physical science basis, accessed April 16,

2009, at http://www.aaas.org/news/press_room/climate_

change/media/4th_spm2feb07.pdf

Ireland R.L., Poland, J.F., and Riley, F.S., 1984, Land subsidence in the San Joaquin Valley, California, as of 1980: U.S.

Geological Survey Professional Paper 437-I, 93 p., accessed

August 18, 2008, at http://pubs.er.usgs.gov/usgspubs/pp/

pp437I

Jacob, C.E., 1940. On the flow of water in an elastic artesian

aquifer: American Geophysical Union Trans., pt. 2,

p. 574–586.

Kern Water Bank Authority, 2007, accessed April 16, 2009, at

http://www.kwb.org/main.htm

Knowles, Noah and Cayan, D.R., 2004, Elevation dependence

of projected hydrologic changes in the San Francisco Estuary and watershed: Climatic Change, v. 62, p. 319–336.

Knowles, Noah, Dettinger, M.D., and Cayan, D.R., 2006,

Trends in snowfall versus rainfall in the Western United

States: Journal of Climate, v. 19, p. 4545–4559.

Lofgren, B.E., and Ireland, R.L., 1973, Preliminary investigation of land subsidence in the Sacramento Valley, California: U.S. Geological Survey Open-File Report, 32 p.

Londquist, C.J., 1981, Digital model of the unconsolidated

aquifer system in the Modesto Area, Stanislaus and San Joaquin Counties, California: U.S. Geological Survey WaterResources Investigations Report 81–12, 36 p.

Lund, Jay, Hanak, Ellen, Fleenor, William, Howitt, Richard,

Mount, Jeffrey, and Moyle, Peter, 2007, Envisioning futures

for the Sacramento–San Joaquin Delta: San Francisco, California, Public Policy Institute of California, 284 p.

Marshall, R.B., 1920, Outline of the Marshall plan: California State Irrigation Association, Sacramento, California,

accessed April 16, 2009, at http://www.sacramentohistory.

org/admin/photo/1740_2172.pdf

Mehl, S.W. and Hill, M.C., 2005, MODFLOW-2005, the U.S.

Geological Survey modular ground-water model––Documentation of local grid refinement (LGR): U.S. Geological

Survey Techniques and Methods 6-A12, 68 p.

Meko, D.M. and Woodhouse, C.A., 2005, Tree-ring footprint of joint hydrologic drought in Sacramento and Upper

Colorado River basins, western USA: Journal of Hydrology,

v. 308, no. 1–4, p. 196–213.

Page, R.W. and Balding, G.O., 1973, Geology and quality of

water in the Modesto–Merced area, San Joaquin Valley,

California: U.S. Geological Survey Water-Resources Investigations Report 73–6, 85 p.

Parsons, J.J., 1987, A geographer looks at the San Joaquin

Valley, 1987, Carl Sauer memorial lecture, accessed

April 15, 2008, at http://geography.berkeley.edu/

ProjectsResources/Publications/Parsons_SauerLect.html

Planert, Michael, and Williams, J.S., 1995, Ground water atlas

of the United States: Segment 1, California, Nevada: U.S.

Geological Survey Hydrologic Atlas 730-B, 1 atlas, 28 p.

Poland, J.F., 1984, Guidebook to studies of land subsidence

due to ground-water withdrawal: Studies and Reports in

Hydrology 40, prepared for the International Hydrological

Programme, Working Group 8.4, United Nations Educational, Scientific, and Cultural Organization (UNESCO),

Paris, France, 305 p., 5 appendixes, accessed April 15,

2008, at http://wwwrcamnl.wr.usgs.gov/rgws/Unesco/PDFChapters/Title.pdf

Poland, J.F., and Evenson, R.E., 1966, Hydrogeology and

land subsidence, Great Central Valley, California, Geology

of Northern California: California Division of Mines and

Geology, p. 239–247.

Poland, J.F., Lofgren, B.E., Ireland, R.L., and Pugh, A.G.,

1975, Land subsidence in the San Joaquin Valley, California, as of 1972: U.S. Geological Survey Professional Paper

437-H, 78 p.

Riley, F.S., 1969, Analysis of borehole extensometer data from

central California, in Tison, L.J., ed., Land Subsidence:

Volume 2: Proceedings of the Tokyo Symposium, September 1969, International Association of Scientific Hydrology

Publication 89, p. 423–431, accessed November 23, 2007,

at http://www.cig.ensmp.fr/~iahs/redbooks/a088/088047.pdf
120 Groundwater Availability of the Central Valley Aquifer, California

Riley, F.S., 1986, Developments in borehole extensometry,

in Johnson, I.A., Carborgnin, Laura, and Ubertini, L., eds.,

Land subsidence: International Association of Scientific

Hydrology Publication 151, p. 169–186.

Rojstaczer, S.A., and Deverel, S.J., 1993, Time dependence of

atmospheric carbon inputs from drainage of organic soils:

Geophysical Research Letters, v. 20, p. 1383–1386.

Rojstaczer, S.A., Hamon, R.E., Deverel, S.J., and Massey,

C.A., 1991, Evaluation of selected data to assess the causes

of subsidence in the Sacramento–San Joaquin Delta, California: U.S. Geological Survey Open-File Report 91-193,

16 p.

Sax, J.L., 2002, Review of the laws establishing the State

water Resources Control Board’s permitting authority over

appropriations of ground-water classified as subterranean

streams and the State water Resources Control Board’s

implementation of those laws: State water Resources Control Board, 92 p.

Schmid, Wolfgang, Hanson, R.T., Mattock III, T.M., and

Leake, S.A., 2006, 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.

Semitropic Water Storage District, 2007, Groundwater Banking, accessed April 16, 2009, at http://www.semitropic.com/

GroundwaterBanking.htm

State Water Resources Control Board, 1998, Revised notice of

public hearing, Phase 8, Bay-Delta Water Quality Control Plan: State Water Resources Control Board, accessed

April 16, 2009, at http://www.waterrights.ca.gov/baydelta/

html/6may98ntc.htm

Stewart, I.T., Cayan, D.R., and Dettinger, M.D., 2005,

Changes toward Earlier Streamflow Timing across Western

North America: Journal of Climate, Vol. 18, p. 1136–1155.

Stine, S., 1994, Extreme and persistent drought in California and Patagonia during mediaeval time: Nature, v. 369,

p. 546-549, doi:10.1038/369546a0

Swanson, A.A., 1998, Land subsidence in the San Joaquin Valley, updated to 1995, in Borchers, J.W., ed., Land subsidence case studies and current research: Proceedings of the

Dr. Joseph F. Poland Symposium on Land Subsidence,

Sacramento, Calif., October 4–5, 1995, Association of Engineering Geologists, Special Publication no. 8, p. 75–79.

Taylor, C.J., and Alley, W.M., 2001, Ground-water-level monitoring and the importance of long-term water-level data:

U.S. Geological Survey Circular 1217, 68 p.

Terzaghi, Karl, and Peck, R. B., 1948, Soil mechanics in engineering practice, New York, John Wiley & Sons, Inc. 566 p.

Thomas, H.E. and Phoenix, D.A., 1976, Summary appraisal

of the Nation’s ground-water resources—California region:

U.S. Geological Survey Professional Paper 813-E, 51 p.

Umbach, K.W., 1998, A statistical tour of California’s great

Central Valley—1998: California Research Bureau CRB98-011, accessed April 16, 2009, http://www.library.ca.gov/

crb/98/11/98011.pdf, .

U.S. Geological Survey, 2002, Concepts for national assessment of water availability and use: U.S. Geological Survey

Circular 1223, 34 p.

Williamson, A.K., Prudic, D.E., and Swain, L.A., 1989,

Ground-water flow in the Central Valley, California: U.S.

Geological Survey Professional Paper 1401-D, 127 p.

Winter, T.C., Harvey, J.W., Franke, O.L., and Alley, W.M.,

1998, Ground water and surface water a single resource:

U.S. Geological Survey Circular 1139, 77 p.

Yeh, P.J., Famiglietti, J.S., Swenson, S.C., Rodell, M.,

2006, Remote sensing of ground water storage changes

using the gravity recovery and climate experiment

(GRACE): Water Resources Research, v. 42, W12203,

doi:10.1029/2006WR005374.
Introduction 

A numerical groundwater-flow model capable of being

accurate at scales relevant to water-management decisions

was developed for the Central Valley, California. This chapter

documents (1) development of the transient three-dimensional,

finite difference numerical flow model; (2) the procedure

used to calibrate the flow model; (3) a summary of the model

results; (4) a discussion of model uncertainty and limitations;

and (5) suggestions for future work. The simulation incorporates time-varying stresses and can be used to evaluate the

effects of both climatic and anthropogenic temporal changes

in recharge and discharge on the hydrologic system between

October 1961 and September 2003.

Model Development

Examination of the existing USGS’s Central Valley

Regional Aquifer System and Analysis (CV-RASA) numerical groundwater-flow model developed by Williamson and

others (1989) indicated that updates were needed to maintain

its usefulness. The Central Valley hydrologic system has

continued to respond to the stresses imposed upon it, and new

information on the surface-water and groundwater systems

have become available. In addition to the new information,

considerable advancements in numerical hydrologic models

have occurred since the original model was developed. The

finite-difference groundwater-modeling software MODFLOW-2000 (MF2K) (Harbaugh and others, 2000; Hill and

others, 2000) incorporating an updated version of the Farm

Process (FMP) (Schmid and others, 2006b; Appendix 1) has 

made it possible to do more detailed and realistic simulations 

of hydrologic systems. This numerical modeling software

(MF2K-FMP) incorporates a dynamically integrated water

supply-and-demand accounting within agricultural areas

and areas of native vegetation, thus, enabling simulation of

surface-water and groundwater-flow. MF2K-FMP was used

to develop a model of the Central Valley hydrologic system,

and its application to the Central Valley is referred to as the

Central Valley Hydrologic Model (CVHM).

CVHM was constructed in five major phases. The first

phase was the conversion of the CV-RASA model from the

original pre-MODFLOW format into MF2K. Stan Leake (U.S.

Geological Survey, written commun., 2005) did the initial

transformation to MODFLOW-88 (McDonald and Harbaugh,

1988). As part of this study, the MODFLOW-88 version was

converted to MF2K (table C1). The original discretization and

water budget were maintained; however, new MODFLOW

packages were utilized, particularly the Subsidence package

(SUB). The second phase was the spatial re-discretization of

the model at finer spatial scales, areally and vertically. Areally,

the model grid was re-discretized from the 36 mi2 CV-RASA

model cells to 1 mi2 cells for the CVHM. Vertically, the CVRASA model had four layers representing 1,000–3,000 ft of

freshwater-bearing deposits, and the thickness of the uppermost layer ranged from 200 to 300 ft. The updated model has

10 layers and the uppermost layer is 50 ft thick. This more 

detailed vertical discretization facilitates simulation of shallow

groundwater-flow paths and compaction of fine-grained deposits. The third phase was the temporal re-discretization of the

CVHM. The CV-RASA model had two stress periods per year

representing the spring–summer growing season and the fall–

winter dormant season from 1961 through 1977. The CVHM

has monthly stress periods from 1961 through 2003. These

monthly stress periods facilitate representation of the cyclical 

nature of irrigation, urban water use, and aquifer-system storage. The fourth phase was the implementation of an alternative

water budget. The alternative water budget incorporates new

climatic, land-use, and surface-water data. Much of the surface-water diversion and delivery information was compiled

by the California Department of Water Resources (DWR)

for 21 water-balance subregions (WBSs) covering the valley

floor (C. Brush, California Department of Water Resources,

written commun., February 21, 2007). The WBSs are used

as accounting units for surface-water delivery and estimation

of groundwater pumpage. Their boundaries generally represent hydrographic rather than political subdivisions and are

described in more detail in Chapter A (fig. A5; table A1). The 

fifth and final phase was the incorporation of the texture model

into the CVHM. Table C1 summarizes the computer programs

(processes and packages) used for CVHM.

Chapter C. Numerical Model of the Hydrologic Landscape and 

Groundwater Flow in California’s Central Valley

By Claudia C. Faunt, Randall T. Hanson, Kenneth Belitz, Wolfgang Schmid, Steven P. Predmore, Diane L. 

Rewis, and Kelly McPherson
122 Groundwater Availability of the Central Valley Aquifer, California

Table C1. MODFLOW-2000 packages and processes used with the hydrologic flow model of the Central Valley, California.—Continued

Computer program 

(packages, processes, 

parameter estimation)

Function Reference

Processes and Solver

Global (GLO) and Groundwater Flow (GWF) Processes of MODFLOW-2000Setup and solve equations simulating a basic groundwater flow modelMcDonald and Harbaugh (1988), Harbaugh and others (2000), Hill and others (2000)

Preconditioned Conjugate-Gradient Package (PCG) Solves groundwater flow equations; requires convergence of heads and(or) flow rates.Hill (1990); Harbaugh and others (2000)

Farm process (FMP) Setup and solve equations simulating irrigated

agriculture.

Schmid and others (2006b)

Files

Name File (Name) Controls the capabilities of MODFLOW-2000

utilized during a simulation. Lists most of the files

used by the GLO, OBS, and FMP Processes.

Harbaugh and others (2000)

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 and others (2000)

Global File Output file for information that applies to model

simulation as a whole.

Harbaugh and others (2000)

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 and others (2000)

Discretization 

Basic Package (BAS6) Defines the initial conditions and some of the boundary conditions of the model.Harbaugh and others (2000)

Discretization Package (DIS) Space and time information. Harbaugh and others (2000)

Multiplier Package (MULT) Defines multiplier arrays for calculation of modellayer characteristics from parameter values.Harbaugh and others (2000)

Zones (ZONE) Defines arrays of different zones. Parameters may be

composed of one or many zones.

Harbaugh and others (2000)

Aquifer Parameters

Layer Property Flow Package (LPF) Calculates the hydraulic conductance between cell

centers.

Harbaugh and others (2000)

Subsidence (SUB) Simulates aquifer-system compaction and

land subsidence

Hoffman and others (2003b)

Hydrologic Flow Barriers (HFB6) Simulates a groundwater barrier by defining

a hydraulic conductance between two

adjacent cells in the same layer

Hsieh and Freckleton (1993)

Boundary Conditions

General Head Boundaries (GHB) 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 and

others (2000)

Recharge and Discharge

Multi-node Wells (MNW1) Simulates pumpage from wells with screens that span

multiple layers.

Halford and Hanson (2002)

Streamflow Routing (SFR1) Prudic and others (2004)

Output, Observations and Sensitivity

Streamflow Observations (GAGE) Prudic and others (2004)

Head Observation (HOB) Defines the head observation and weight by layer(s),

row, column, and time.

Hill and others (2000)

Observations (OBS) Generates simulated values for comparison with

observed values.

Hill and others (2000)

Hydmod (HYD) Generates simulated values for specified locations at

each time-step for subsidence, heads, and streamflow attributes.

Hanson and Leake (1998)

Sensitivity (SEN) Specifies parameter values used in other packages.

Because of calculations in the FMP Process and

SFR1 Package make MF2K sensitivity calculations invalid, this process was not used.

Hill and others (2000)

Table C1. MODFLOW-2000 packages and processes used with the hydrologic flow model of the Central Valley, California.
Model Development 123

The CVHM was adjusted during these phases, but calibrated primarily after the fifth phase, with the aid of automated parameter estimation. The parameter estimation code 

UCODE-2005 (Poeter and others, 2005) was used to calculate sensitivities and estimate parameters. The CVHM was

calibrated to water-level altitudes, water-level altitude changes

with time, streamflow losses, and subsidence. During construction and calibration of the CVHM, it became evident that

several updates and enhancements were needed within MF2K,

the FMP, and some post-processing software. The packages

that were modified or updated include the Layer-Property

Flow (LPF), Multiplier (MULT), Hydrograph Time Series

(HYDMOD), and the Streamflow Routing (SFR1) packages

(Hanson and Leake, 1998; Harbaugh and others, 2000; Prudic

and others, 2004). Additional modifications also were made to

the FMP (Schmid and others, 2006b). All of these modifications are summarized in Appendix 1 of this report and in the 

release notes and online documentation of the source code for 

MF2K with the FMP (http://water.usgs.gov/nrp/gwsoftware/

mf2k-fmp/mf2kfmp.html last updated May 6, 2006). The model

components can be described in terms of the discretization,

boundary conditions, stresses, “hydrologic landscape process,”

hydraulic properties, initial conditions, and water budget. The

next few sections of this chapter discuss the development of

these model components.

Discretization 

The CVHM encompasses the alluvial deposits of the

entire Central Valley extending from the Cascade Ranges

on the north to the Tehachapi Mountains on the south and

bounded on the east by the Sierra Nevada and on the west

by the Coast Ranges (fig. C1). The only outlet is through

Carquinez Strait, a narrow tidal strait that is part of the tidal

estuary of the Delta of the Sacramento and San Joaquin Rivers

(fig. A1). 

The finite-difference model grid used to represent the

deposits comprises a lattice of orthogonal cells. A detailed

discussion of the use of finite–difference equations to simulate

groundwater-flow is presented in McDonald and Harbaugh

(1988). Model inputs include intrinsic characteristics of

materials, such as hydraulic conductivity, represented by each

model cell. 

Spatial Discretization and Layering

The total active modeled area is 20,334 mi2 on a finitedifference grid comprising 441 rows, 98 columns, and 10

layers (table C2; fig. C1). Slightly less than 50 percent of

the cells are active. The model has a uniform horizontal

discretization of 1 × 1 mi and is oriented parallel to the valley

axis, 34 degrees west of north (fig. C1). The grid was oriented

horizontally to coincide with the original CV-RASA grid;

groups of 36 cells of the CVHM correspond to a single cell of

the CV-RASA model.

The CVHM comprises 10 layers that generally thicken

with depth (table A3). The top layer (layer 1) has an upper 

altitude of land surface and a base equal to 50 ft below land

surface. Except where the Corcoran Clay Member of the

Tulare Formation (hereafter referred to as the Corcoran Clay)

exists, the layers range in thickness from 50 to 400 ft, increasing by 50 ft with each progressively deeper layer. Where the

Corcoran Clay exists, the layers were morphed to explicitly

represent the clay with layers 4 and 5 (fig. A11). The layering

for the texture model and the flow model are the same; this is

described in more detail in Chapter A.

The bottom boundary of the CV-RASA model was specified on the basis of the base of the post-Eocene continental

deposits and the lowest altitude of freshwater (Williamson and

others, 1989). The base of these deposits averages 2,400 ft

below land surface, and ranges from less than 1,000 ft on the

margins of the valley to more than 9,000 ft below land surface

south of Bakersfield. The depth to the base of sediments saturated with freshwater (water with less than 1,000 milligrams

per liter dissolved-solids concentration) varies greatly (Planert

and Williams, 1995). In the Sacramento Valley, the base of

freshwater generally coincides with the base of continental

deposits. In the San Joaquin Valley, although the freshwater

mainly is in continental deposits, the pattern is more complex.

Freshwater also is in Tertiary marine rocks on the southeastern

side of the valley and in pre-Tertiary igneous and metamorphic rocks. The thickness of the aquifer system saturated with

freshwater in the San Joaquin Valley ranges from 100 to more

than 4,000 ft (Berkstresser, 1973; Planert and Williams, 1995).

In some local areas west and south of the Sutter Buttes and

west of Stockton, the base of freshwater is less than 500 ft

deep. 

For the CVHM, the bottom of the model was specified

on the basis of well-completion records to incorporate the part

of the aquifer system that is stressed by pumpage. The model

bottom extends to 1,800 ft below land surface, and where the

Corcoran Clay is present, to 1,500 ft below the Corcoran Clay

(fig. A11; table A3). For the most part, saline water is deeper

than the model bottom and may be as much as 2,500 ft deep 

(Berkstresser, 1973; Planert and Williams, 1995). The deeper

wells are simulated in model cells where wells are perforated

and presumably freshwater is present. The CVHM does not

simulate pumping in the lowermost model layer, 10. This

layer, which is 400 ft thick, is included in the model as a

buffer for flow.
124 Groundwater Availability of the Central Valley Aquifer, California

0

0 50 100 Kilometers

50 100 Miles

EXPLANATION

General-head boundary in layers 1 and 2

Active areas

Model cells representing—

Bedrock in some layers

San Joaquin Formation in some layers

Corcoran Clay in layers 4 and 5

Corcoran Clay over

 San Joaquin Formation

Potential horizontal flow

 barrier

Active model grid boundary

12 California Department of Water Resources

 water-balance subregions and identifier. See

 table A1 for names

Geologic section along model row 355

 (See figure A11)

A

A

A'

A'

1

2

3

4

5

6

7

8

10

11

12

13

14

15

16

17

18

19

20

21

9

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

C a s c a d e R a n g e

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

Coast Range

Tehachapi Mts

Stockton

Fault

Sutter

Buttes

White Wolf

 Fault

A

STUDY

AREA

Pacific Ocean

Sierra Nevada

San

Francisco

Central

Valley

C

A

L

I

F

O

R

N

I A

Figure C1. Central Valley Hydrologic Model grid: A, Extent of San Joaquin Formation, Corcoran Member of the Tulare Formation, 

crystalline bedrock, and horizontal flow barriers. B, Upper-most active layer.
Model Development 125

0

0 50 100 Kilometers

50 100 Miles

EXPLANATION

Active model grid boundary

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

C a s c a d e R a n g e

Coast Range

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

Tehachapi Mts

B

Uppermost active layer—

1

2

3

6

California Department of Water Resources

 water-balance subregions

Figure C1. Continued.
126 Groundwater Availability of the Central Valley Aquifer, California

The upper part of the saturated groundwater-flow system—the unconfined to semi-confined zone (layers 1–3)—was

more finely discretized in the CVHM to increase the accuracy

in simulating (1) the altitude of the water table where it is

shallow, (2) vertical hydraulic gradients, and (3) the interaction of the streams and crops with the shallow part of the

groundwater system. Increasing the layer thickness with depth

reflects a balance between the decreasing availability of data

with increasing depth and the enhanced capability to simulate groundwater-flow processes near the land surface. The

geometry and hydrogeology of the deeper units, particularly

those below the Corcoran Clay, are not well understood and,

for purposes of this study, are less critical. In addition, to 

partly correct for the inability of MF2K to simulate densitydependent flow, the hydraulic properties in the lowest layers

represent a composite of the hydraulic conductivity and viscosity. Therefore, the minimal presence of saline waters in the

model domain does not significantly affect the general flow

patterns simulated. 

During calibration, an additional modification to the

layering was added where the water table is deeper than 50 ft

(fig. B11). Where the water table is between 50 and 150 feet

below land surface, the top layer was thickened to extend to

147 feet below land surface and layer 2 was configured as a

3-ft-thick dummy layer. Where the water table was between

150 and 300 ft below land surface, layers 1 and 2 were

specified as inactive. Where the water table was deeper than

300 feet, layers 1–3 were specified as inactive.

Temporal Discretization 

The CVHM is discretized into stress periods and time

steps. In order to represent the growing season adequately, the

annual hydrologic cycle was divided into 12 monthly stress

periods. Specified inflows and outflows, including pumpage, precipitation, evapotranspiration (ET), surface-water

diversions, and water deliveries are constant within each

stress period. Variations in stresses are simulated by changing stresses from one stress period to the next. Stress periods

were further divided into two time steps for which water levels

and flows were calculated. The total simulation length was

42.5 years (or 510 monthly stress periods), from April 1961

through September 2003.

Boundary Conditions 

Boundary conditions are prescribed over the boundary of

the model domain, and are used to represent flow constraints

within the groundwater-flow system. Two general types of

boundary conditions are used in the CVHM: specified flow,

and specified head-dependent flow or general head. For the

transient simulation, the lower boundary and most of the

lateral boundaries were simulated as no-flow (specified-flow

equal to zero). A general-head boundary was used to quantify

the amount of lateral flow into and out of the groundwaterflow system at the Delta (fig. C2). Boundary conditions representing flow and head-dependent flow constraints also were

used within the model domain to simulate sources and sinks

within the flow system. These include recharge to, and discharge from, the groundwater system. Recharge to the model

includes stream leakage, precipitation, and excess applied

irrigation water. Evaporation, transpiration, pumpage for agricultural and urban uses, and groundwater inflow to streams are

the discharge mechanisms; the first three of these are specified in, or calculated by, the FMP. Although the evaporation

and transpiration are a type of flow- and head-dependent flow

boundary, they are implemented in the FMP. Because of the

complexity of the FMP, it is discussed in the next section of

this chapter. 

Table C2. Coordinates of the Central Valley Hydrologic Model grid.

[Model grid is rotated 34 degrees west; coordinates below are calculated at the cell center of the model grid using the Albers projection using Central Meridian

120 degrees west, North American Datum 1983; each cell is 1 mile by 1 mile]

Corner of 

model grid

Model 

coordinates 

X (column) 

Model 

coordinates 

Y (row)

Latitude 

(DMS)

Longitude 

(DMS)

Albers coordinates 

X (easting) 

(meters)

Albers coordinates 

Y (northing) 

(meters)

Northwest 1 1 39° 58' 34" 123° 46’ 49” –319,957 1,890,733

Northeast 98 1 40° 43' 11" 122° 12’ 45” –185,337 1,969,734

Southwest 1 441 34° 34' 54" 119° 34’ 24” 38,818 1,280,164

Southeast 98 441 35° 16' 26" 118° 04’ 36” 173,410 1,359,209
Model Development 127

EXPLANATION

Model general-head boundary cell

Active model boundary

Diversion location

Inflow location

Water-balance subregion receiving

 non-routed deliveries

Model stream cell

0

0 50 100 Kilometers

50 100 Miles

California Department of Water Resources

12 water-balance subregions and identifier

1

2

3

4

5

6

7

8

10

11

12

13

14 15

16

17

18

19

20

21

9

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Suisun

 Bay

Figure C2. Distribution of general-head boundary cells and major streams and canals with streamflow-routing cells (including location 

of inflows and diversions). The water-balance subregions are described in table A1.
128 Groundwater Availability of the Central Valley Aquifer, California

Specified-Flow Boundaries

The lower and lateral model boundaries are simulated

as no-flow and the lateral boundaries generally are no-flow.

As previously described, the lower boundary was located

1,800 ft below the land surface or 1,500 ft below the base of

the Corcoran Clay, where it exists (fig. A11). Where bedrock 

intersected the model, model cells completely within the

bedrock also were specified as no-flow (fig. C1B). Significant vertical flow at these depths or within the bedrock was

considered unlikely. Lateral boundary conditions represent the

contact between the mountain ranges and the unconsolidated

alluvial sediments of the Central Valley (fig. C1). Except at

the Delta, there are no-flow boundaries along the periphery of

the Central Valley basin representing the Sierra Nevada, Coast

Ranges, and surrounding mountains (fig. C1). 

Pumpage 

Groundwater pumpage is a major part of the groundwater

budget of the Central Valley, and is grouped into two categories for this study: agricultural and urban (which includes

municipal and industrial sources). Wells were simulated as a

combination of “farm” wells (Schmid and others, 2006b) and

multi-node wells (Halford and Hanson, 2002) (fig. C3). Farm

wells are simulated in a manner similar to the WEL package

(Harbaugh and others, 2000) and the pumpage is distributed

among each of the farm wells (Schmid and others, 2006b).

Agricultural pumpage is estimated through the FMP, whereas

urban pumpage is specified using values compiled from DWR

(C. Brush, California Department of Water Resources, written

commun., February 21, 2007).

Agricultural Pumpage

Discharge from agricultural wells rarely is metered in the

Central Valley (Diamond and Williamson, 1983), and, therefore, must be estimated by indirect means. The two most common methods are power consumption and consumptive use of

water. Because groundwater pumpage was not metered, there

can be no direct determination of the accuracy of the different 

methods of estimation. Power consumption historically has

been used to estimate agricultural groundwater pumpage in the

Central Valley, and was used for the original Central Valley

RASA studies for the 1961–1977 time period (Diamond and

Williamson, 1983). The power-consumption method involves

estimating pumpage from the amount of power consumed by

well pump motors. Although these estimates are very accurate

under ideal conditions, this accuracy generally is not achievable and is not available for the entire valley or throughout the

1961–2003 simulation period of the CVHM (Diamond and

Williamson, 1983). These data were tabulated for comparison

purposes. Significant conversion from electrical to hydrocarbon-based energy sources in the 1980s and 1990s for agricultural pumpage limits the usefulness of electrical power records

for estimating groundwater pumpage in later times.

Consumptive use of water in this context refers to all

ET by a particular crop. If consumptive use can be quantified, groundwater pumpage may be estimated by taking

into account surface-water supply, irrigation efficiency, and

effective precipitation. Irrigation efficiency, as used in this

report, is the percentage of water delivered to the WBS that

is available for consumptive use (Diamond and Williamson,

1983). The newly developed FMP for MF2K uses this method

(Schmid and others, 2006b). A disadvantage of this method is

that it includes no direct measurement of pumpage; instead, it

calculates pumpage as the residual irrigation demand. Because

most of the wells in the Central Valley are not metered, and

the power estimates are unavailable in many locations for

much of the simulation period, the magnitude and distribution

of pumpage was calculated using the FMP.

For the FMP, in each WBS, a single well was placed in

each model cell where an irrigated crop was the predominant land use for a given time frame. Because the extent of

irrigated agriculture changes through time, wells were added

and deleted accordingly in the model during the simulation

period. In general, wells were added through time because

the extent of irrigated agriculture generally increases through

time. In some areas, however, agricultural wells were replaced

by urban wells in the model as the land use changed from

agricultural to urban. In more limited areas, agricultural land

was taken out of production and replaced by native vegetation; in these cases, the wells were removed. The single well

per model cell represents the composite of all wells in each

square-mile cell. Previous studies (Diamond and Williamson,

1983; Gronberg and Belitz, 1991) show that wells within the

valley typically are at least this densely spaced.
Model Development 129

0 50 100 Miles

0 50 100 Kilometers

EXPLANATION

Area of Corcoran Clay

Model grid boundary

Pumping wells—

Perforated in layers 2 and/or 3

Perforated in upper and lower parts of system

Perforated in all or parts of layers 6 through 9

Multi-node irrigation wells perforated in layers

 2 and/or 3 and all or parts of layers 6 through 9

Multi-node urban wells perforated in layers

 2 and/or 3 and all or parts of layers 6 through 9

California Department of Water Resources

 water-balance subregion

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Figure C3. Distribution of urban and agricultural wells simulated in the Central Valley Hydrologic Model. 
130 Groundwater Availability of the Central Valley Aquifer, California

Pumpage was allocated to the layers, according to the

construction information available from DWR and USGS

files and databases. The open interval was used to identify

the layers from which pumpage occurred. Specifically, the

existing wells were analyzed, and the interval top and bottom

for each model cell was assigned, based on wells in the area.

Because pumpage was assumed to be minimal in the upper

50 ft of the system (layer 1), owing to typical construction of

surface sanitary seals in production wells, and within blankcasing intervals in production wells across the Corcoran Clay

(layers 4 and 5), pumpage was not simulated in these layers.

Furthermore, no pumpage was simulated in the lowest layer,

10. Hence, depending on completion depths, pumpage was

assumed to occur in layers 2–3 and in layers 6–9. Initially,

where wells extended across multiple layers, the MNW package was used to simulate flow through the wellbore. Because

of exceedingly long run times, only where wells are perforated

above and below the Corcoran Clay was the MNW package

used to simulate multi-aquifer pumpage and flow through the

wellbores across the clay. Outside the area of the Corcoran

Clay, a well was assigned to each model cell that was intersected by a wellbore. Because only a small amount of flow

is thought to occur through well-bore flow outside this area,

this simplification is thought to be reasonable. Estimated

agricultural pumpage was restricted to wells in model cells

corresponding to areas where active crops needed more water

than was being supplied by precipitation or surface water. The

FMP allocated the pumpage in these model cells on the basis

of their specified hydraulic properties.

A substantial amount of agricultural pumpage has

occurred in the western parts of the San Joaquin Valley and

Tulare Basin. Diamond and Williamson (1983) and Gronberg

and Belitz (1991) showed that more than 80 percent of this

pumpage came from the deeper part of the system, below the

Corcoran Clay. Because FMP distributes the amount of pumpage within a WBS evenly to all farm wells, including to multinode wells, until the well’s specified pumping capacity is

reached (Schmid and others, 2006b), another way of adjusting

the proportion of pumpage coming from the different parts of

the system was needed. The capacity of the farm wells in the

upper part of the system was set very low to approximate the

estimated low pumpage from these wells (fig. C3). In addition, 

for farm wells perforated above and below the Corcoran Clay

and simulated using the MNW package, the well skin factor

was adjusted in the upper part of the system to force more

simulated pumpage from the lower part of the well (fig. C3).

Urban Pumpage

Urban pumpage is a small percentage of the annual total

estimated pumpage in each WBS (Diamond and Williamson,

1983) (fig. B6B). As with the agricultural pumpage data, urban

pumpage data from the original USGS Central Valley RASA

studies were available for 1961–1977 (Diamond and Williamson, 1983). The amount of urban pumpage also was compiled

by DWR for C2VSIM for each WBS for the entire simulation

period (C. Brush, California Department of Water Resources,

written commun., February 21, 2007). In general, estimated

pumpage from both compilations increase with time; however,

overall, DWR’s estimates are twice as much as those of the

USGS (fig. C4). The larger estimates are most dramatic in the

Sacramento metropolitan area where the USGS compilation

may have had incomplete information (Diamond and Williamson, 1983). Because public-water suppliers are required

to report the sources and volumes of water supply to the State

(DWR), the DWR information is assumed to be more complete and accurate. Therefore, in order to have a complete and

consistent data set for the entire simulation period, DWR’s

compilation of urban pumpage is used.

The location of urban wells in the CVHM, like agricultural wells, is based on land use. Where the majority of

a corresponding model cell is occupied by urban land use, a

single well is specified in the appropriate model layer(s) to

represent the composite pumpage for urban use (municipal

and industrial pumpage) within that cell. The total amount

of urban pumpage per WBS per month is distributed evenly

within each WBS to all urban cells. To allow for flow through

the borehole, the MNW package is used to simulate all urban

groundwater pumpage.

The vertical distribution of urban pumpage is segregated

in a manner similar to the agricultural pumpage. Within the

urban parts of each WBS, urban pumpage is allocated to the

layers according to the well-completion information available

from DWR and USGS files and databases. The open-screen

interval is used to identify the layers from which pumpage

occurred. Specifically, the existing wells were analyzed and

the interval top and bottom for each cell is assigned on the

basis of the perforated intervals of wells in the area. Pumpage is assumed to be minimal in the upper 50 ft of the system

(layer 1) and within the Corcoran Clay (layers 4 and 5), and,

generally, pumpage is not simulated in these layers. Where

layer 1s thickness is increased to 147 feet, pumpage is allowed

to occur in layer 1. Hence, as with agricultural pumpage,

urban pumpage is simulated predominantly in layers 2–3 and

in layers 6–9.
Model Development 131

Recharge from and Discharge to the Delta

The Delta is the only lateral-flow boundary simulated

as a head-dependent flow boundary. This area was simulated

by the MODFLOW General Head Boundary package (GHB)

(McDonald and Harbaugh, 1988; Harbaugh and others, 2000)

(table C1); general-head boundaries can both receive water

from and contribute water to the aquifer system. General-head

boundaries were specified in layers 1–3 at all cells throughout

Suisun Bay (fig. C2). Water-level altitudes specified for the

general-head boundary were estimated to be at sea level,

and remained constant for the entire simulation period. The 

hydraulic conductance of the boundary was specified to be

relatively large, compared to that of the adjacent cells. As

a result, the texture-derived hydraulic conductivity of the

aquifer-system sediments (in the adjacent cells) controls

leakage to and from the Delta.

196119641967197019731976197919821985198819911994199720002003

YEAR

REPORTED URBAN GROUNDWATER PUMPAGE, IN THOUSANDS OF ACRE-FEET PER YEAR

Tulare Basin

Pumpage reported by U.S. Geological Survey

 [Williams and others, 1989](1961 to 1977) for—

Pumpage reported by California Department of

 Water Resources (1961 to 2003) for— 

San Joaquin Valley

Sacramento Valley and Delta

Tulare Basin

San Joaquin Valley

Sacramento Valley and Delta

200

0

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

EXPLANATION

Figure C4. Urban pumpage from U.S. Geological Survey and California Department of Water Resources data (C. Brush, California 

Department of Water Resources, written commun., February 21, 2007).
132 Groundwater Availability of the Central Valley Aquifer, California

Recharge from and Discharge to Canals and 

Streams

A large number of streams and canals dissect the Central

Valley. Forty-six key streams and canals (comprising 190

segments) were used to represent the major streams and water

conveyance features (fig. C2). These features were simulated

using the Streamflow Routing package (SFR1) (Prudic and

others, 2004) (table C1); this head-dependent boundary condition allows groundwater discharge (gaining stream reaches),

stream infiltration into the underlying aquifer (losing stream

reaches), and the diversion of water for water supply or

irrigation.

The SFR1 package also accounts for water that is routed

through stream networks. This routing capability is used in the

CVHM to route water from streams and canals as semi-routed

deliveries to WBSs through the FMP. There are 41 major river

inflows (fig. A8) and 66 diversions for irrigation simulated in

the CVHM (fig. A2, table A1). Two of the river inflows each

are divided into two additional inflows for book-keeping purposes, resulting in 43 simulated inflows. Two of the diversions

are diverted out of the model area such that there only are 64

deliveries to WBSs (table A1).

The remainder of the streamflow that does not infiltrate

as groundwater recharge or is not diverted and consumed for

agriculture flows out of the Sacramento and San Joaquin River

systems into the Delta (fig. C2). Some flow out of the Kern

River (WBS 21, fig. A2) is simulated as a loss to the American

Canal system, which represents water exported to Southern

California.

Water-Table Simulation

In MF2K, model layers can be defined as either confined

or convertible between confined and unconfined (Harbaugh

and others, 2000). Confined layers are assigned a thickness

that does not change during the simulation, regardless of the

simulated value of hydraulic head. In this study, all layers

were simulated as confined. For the CVHM, where the water

table is deeper than 50 ft (fig. B17) for the entire 1961–2003

simulation period, the base of layer 1 is specified 147 ft below

land surface, and layer 2 is specified as a dummy layer 3-ft

thick with a large vertical conductivity and small horizontal

conductivity. Where the water table is deeper than a model

layer for the entire simulation, the model cells above the

water table are inactivated. Because of the large percentage of

fine-grained sediments in the Central Valley, Williamson and

others (1989) concluded that sediments below the upper few

hundred feet should be considered “confined” in the sense that

the vertical permeabilities of the sediments are much lower

than the horizontal permeabilities. Accordingly, in the CVHM,

layers 8–10 are simulated as “confined”. Storage properties

in the upper layer are adjusted, as necessary, to represent the

unconfined part of the system (see “Storage Properties” section). Because specific-yield properties apply at the water table

and other storage properties are significantly smaller, layers

with a water table are characterized by a storage coefficient

equal to specific yield, but the saturated thickness is taken as a

fixed value. The saturated thickness is based on the thickness

of the model layer. Partly to decrease the magnitude of errors

associated with saturated thickness, the layers are designed

to be thinner near the water table and thicker with depth

(table A3). These simplifications are thought to be acceptable

for the intended use of the CVHM.

Despite simulating confined flow in the water table,

a provision was made to represent unconfined-flow conditions by allowing model cells to activate (rewet) as the water

table rose above the underlying cells; however, cell rewetting

prohibitively increased numerical instability and computation

time. Much of this instability was associated with cells where

the water table intersects the Corcoran Clay. Ultimately, this

rewetting provision was not implemented in this study, but

could be incorporated with further refinements of the CVHM

and (or) when better solvers become available.

Farm Process (FMP)

For the CVHM, the processes of evaporation, transpiration, runoff, and deep percolation to groundwater were

estimated using the FMP. The FMP allocates water, simulates

or approximates processes, and computes mass balances for

defined subregions of the model domain; in the CVHM, these

subregions, or “farms,” are defined as the WBSs.

The FMP was developed for MF2K to estimate irrigation

water allocations from conjunctively used surface water and

groundwater (fig. C5). It is designed to simulate the demand

components representing crop irrigation requirements and onfarm inefficiency losses, and the supply components representing surface-water deliveries and supplemental groundwater

pumpage. The FMP also simulates additional head-dependent

inflows and outflows such as canal losses and gains, surface

runoff, surface-water return flows, evaporation, transpiration,

and deep percolation of excess water.

The FMP is based on mass balances (Schmid and others,

2006a, b). A farm mass balance is maintained between all

inflows to and outflows from a farm, and is calculated and balanced for each simulation time step (fig. C5). A soil-water balance is calculated between inflows into the soil zone and the

ET outflow. The details of the soil-water balance are given by

Schmid and others (2006b), and are not shown on figure C5. 

The FMP dynamically integrates irrigation water demand,

surface-water and groundwater supply, and deep percolation.
Model Development 133

The FMP has the capability to estimate economically

optimal allocations through acreage optimization of water

supplied by surface-water and groundwater deliveries when

demand exceeds supply. Other non-economic droughtresponse scenarios, such as deficit irrigation and water

stacking, also are available. Combined with other MF2K

packages such as SFR1 and MNW packages, the FMP helps

to transform MF2K from a predominantly groundwater-flow

model to a more complete hydrologic model. While the FMP

contains many optional simulation features, only some of the 

available components are used for the Central Valley. More

details on all the components of the FMP can be found in

Schmid and others (2006b).

Figure C5. Flow chart of water inflows to and outflows from a “farm” as simulated by the Farm Process (FMP). 

Precipitation

Non-routed deliveries

 into farm

Semi-routed deliveries

 into farm

Groundwater well pumping

 deliveries into farm

Evaporation from groundwater

 into the farm

Evaporation from groundwater

 out of the farm

Transpiration from groundwater

 into the farm

Evaporation from irrigation

 out of the farm

Evaporation from precipitation

 out of the farm

Transpiration from irrigation

 out of the farm

Transpiration from precipitation

 out of the farm

Overland runoff out

 of the farm

Deep percolation out

 of the farm

Semi-routed deliveries

 out of the farm

Farm

p

p

srdin

srdin

wells

wells

egwin

egwin

egwout

tgwin

tgwin

Transpiration from groundwater

 out of the farm tgwout

ei

ei

ep

ep

egwout ti

ti

tp

tp

tgwout

run

run

dp

dp

nrd

nrd

srdout

srdout

Streams

Aquifer 

EXPLANATION

INFLOW

OUTFLOW
134 Groundwater Availability of the Central Valley Aquifer, California

Few applications of this new MODFLOW capability

are published because the FMP is a new process developed,

in part, as part of this project and the ongoing research of the

Groundwater Availability Program of the USGS. The first

real-world application of MF2K with FMP and SFR1 was a

micro-scale model for several hundred acres in the southern

Rincon Valley, along the Lower Rio Grande of New Mexico

within the Elephant Butte Irrigation District (Schmid and

others, 2006a). In contrast to the micro-scale application in 

the Rincon Valley, the Central Valley represents a macro-scale

application. The FMP components used in the simulation of

the hydrologic flow system of the Central Valley are summarized in this chapter. Within MF2K, the FMP was used to simulate the surface-water and groundwater inflows and outflows

needed by irrigated agriculture, native vegetation, and urban

areas. For the purposes of this report, the WBS represents the

basic accounting unit (farm) for water consumption. On the

basis of cell-by-cell estimations for each WBS, the FMP first

calculates crop-water demand as the transpiration from plantwater consumption and the related evaporation. The FMP

then determines a residual crop-water demand that cannot be

satisfied by precipitation and (or) by root uptake from groundwater. The FMP then equates this residual crop-water demand

with the crop-irrigation requirement for the cells with irrigated

crops (exclusive of any natural vegetation).

This crop irrigation requirement then is adjusted

(increased) by accounting for evaporative losses from irrigation, and other losses owing to inefficiencies, to yield a final

total farm delivery requirement (TFDR). In this report, the

TFDR is equivalent to the delivery requirement (DR) discussed in Chapter B of this report. The FMP first attempts to

satisfy the TFDR using surface water. The surface water can

be obtained from source water that is not simulated in the

stream network, referred to in this chapter as “non-routed,” or

from water routed through the stream network and delivered

to a WBS, referred to in this chapter as “semi-routed.” The

non-routed surface-water supply components have first priority and surface-water deliveries have second priority. Lastly, if

the TFDR is not met using surface water, the FMP computes

the amount of supplemental groundwater necessary to extract

from “farm” wells in order to satisfy the TFDR. The amount of

excess water from irrigation and (or) precipitation that is not

evaporated or consumed for plant growth then becomes either

overland runoff to nearby streams or groundwater recharge. As

mentioned earlier, a soil-water balance is calculated between

inflows into the soil zone and the ET outflow. Thus, the FMP

dynamically links the demand, supply, and related change in

aquifer-system storage. All of the supply and demand components then are tabulated into a WBS budget that complements

the groundwater-flow budget.

In order to do these calculations, the FMP integrates

the components of supply and demand data, which can vary

temporally. On the supply side, surface-water delivery and

”farm” well-construction data are needed to estimate the

semi-routed and non-routed surface-water deliveries and the

groundwater pumpage requirements, respectively. On the

demand side, the FMP uses soil, land use, and consumptive

irrigation requirement (CIR) to calculate demand.

The FMP dynamically simulates these supply and

demand components for a WBS within MF2K by integrating

the following computational components (fig. C5):

(1) TFDR, which depends on efficiency, changing climate

(ET and precipitation), and variable shallow groundwater

levels;

(2) Actual surface-water delivery to the WBS, which may

be driven by TFDR, but limited by canal/stream inflow rates at

the WBS’s diversion head gate, by allotments, or by semi- or

non-routed deliveries;

(3) Supplemental groundwater pumpage, which is estimated as the TFDR minus the actual surface-water delivery,

but is limited by a specified maximum farm well-pumping

capacity on a well-by-well basis; and

(4) Net recharge (deep percolation) to groundwater,

which is taken to be the sum of excess irrigation and precipitation minus the sum of surface-water runoff and ET from

groundwater.

MF2K and the FMP maintain a dual mass balance of

a WBS budget and a groundwater budget (fig. C5). Flows

between these two budgets are accommodated by head-dependent inflows and outflows, such as the actual ET from groundwater. Quantities of interest, such as TFDR, surface-water

and groundwater supply, and excess applied irrigation water

depend on these head-dependent inflows and outflows. Thus,

the use of FMP in MF2K represents the simulation of coupled

flow of water through surface-water, land-use, and groundwater processes (Schmid and others, 2006a).

Delivery Requirement

The delivery requirement is defined as consumptive use

of water by irrigated crops not met by natural precipitation

plus any inefficient use from irrigation, with respect to plant

consumption. In the CVHM, consumptive use is actual ET and

includes both plant transpiration and evaporation (Schmid and

others, 2006b). For the FMP, this consumptive use includes

components estimated from both the landscape (predominantly 

met through irrigation) and groundwater systems. Thus, the

amount of evaporation and transpiration from the groundwater

table are computed. As a result, in the FMP, ET is a function

of water-table altitude and the assumed wilting point of each

crop. 
Model Development 135

Soils

The Central Valley soils were simplified into sandy loam,

silty clay, and silt from the State Soil Geographic Database

STATSGO (U.S. Department of Agriculture Natural Resources

Conservation Service, 2005b) (fig. C6). The capillary fringe

thickness also was estimated for each soil type (fig. C6). The 

FMP associates the distributed soil types with the specified

capillary fringes and internal coefficients that allow individual

analytical solutions for the calculation of evapotranspiration

(Schmid and others, 2006a). The more detailed Soil Survey

Geographic Database (SSURGO) (U.S. Department of Agriculture Natural Resources Conservation Service, 2005a) were

not available for the entire Central Valley when this study

began. Further refinement of soils information from SSURGO

could be a future refinement of the CVHM.

Land Use

The FMP was used to estimate components of consumptive use for a wide variety of land uses, including vegetation

in irrigated or non-irrigated agriculture, fallow fields, riparian

or natural vegetation, and urban landscape settings. Similarly,

FMP was used to simulate an assortment of irrigation methods and periods of transition between applied methods. The

methods span the spectrum from flood irrigation such as for

rice and cotton, to drip irrigation of truck crops and orchards.

Although not used here, the FMP also can be used for applications with no, or nearly no, transpiration, such as for surface impoundments and spreading basins used for artificial

recharge systems.

For the Central Valley, the land-use attributes are defined

in the model on a cell-by-cell basis and include urban and 

agricultural areas, water bodies, and natural vegetation. The

land use that covered the largest fraction of each 1-mi2 model 

cell was the representative land use specified for that cell.

Producing representative maps of land use, including crops, at

regional scales is problematic because of the complex pattern

that is subject to rapid change in the dynamic environment of

modern agricultural processes. Despite the uncertainty and

complexity, land-use maps were developed for five different

time frames during the 42.5-year simulation period. Most

of these maps were based on interpreted high-altitude aerial

photography. Because land-use may vary gradationally or

discretely in time, on the basis of climate variability, urbanization, or the farmer’s free-market practices, criteria for selecting representative time frames were necessary. For this simulation, the five land-use patterns were aligned with the wet-dry

climate cycle for which they were compiled (fig. C7). 

Most of the valley floor is developed agricultural land

(table C3). This agricultural land was further subdivided

into agricultural classifications in the FMP. The agricultural

classifications are based on the 12 DWR class-1 categories

(California Department of Water Resources, 2000). The 12

class-1 categories were augmented with more general classes

for earlier years when the delineation of land use was less

detailed (table C3). In general, the 12 class-1 categories represent groups of vegetation that have similar amounts of water

consumption and similar growth cycles that drive their consumption of water. These land-use categories, herein referred

to as “virtual crops”, are defined from land-use maps for 1960,

1973, 1992, 1998, and 2000 (table C3; figs. C7–C12). For

simplicity, the land-use maps are named for the year the map 

represents. For the entire simulation period, the virtual crops

are used to drive water movement and water use for each

WBS. In total, there are 22 virtual crops. Each of the virtual

crops is represented by a number in the FMP (table C3). Many

of the virtual crops are amalgamations of the others (table C3, 

grouping of other classes). For example, virtual crops 19–22

are amalgamations of five or more virtual crops (table C3, 

grouping of other classes). Because the virtual-crop maps for

the earlier time periods are more generalized, some of the

more permanent or more established crop types mapped more 

recently are assumed to be active earlier and are embedded

in the earlier maps on the basis of the most recent land-use 

period (2000 land use). 
136 Groundwater Availability of the Central Valley Aquifer, California

EXPLANATION

Agricultural soil classes and (capillary fringe)

 used in Farm Process—

Sandy loam (9.1 feet)

Silty clay (7.1 feet) 

Silt (9.3 feet)

0 50 100 Miles

0 50 100 Kilometers

California Department of Water Resources

 water-balance subregion

Active model boundary

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Figure C6. Agricultural soils for the Central Valley, California, derived from STATSGO data (U.S. Department of Agriculture Natural 

Resources Conservation Service, 2005b). 
Model Development 137

Figure C7. Cumulative departure of precipitation, Pacific Decadal Oscillation (PDO) Index, climate windows, and time frame land-use 

maps were applied.

Time frame land-use maps were applied 

Figure C8

4/61-3/68

Figure C9

4/68-3/78

Figure C10

4/78-3/93

Figure C11

4/93-3/99

Figure C12

4/99-9/03

Fresno

Davis

Bakersfield

Pacific Decadal Oscillation (PDO) Index

 24 month moving average

Red Bluff

Annual cumulative departure of precipitation—

EXPLANATION

TIME, IN YEARS

CUMULATIVE DEPARTURE OF PRECIPITATION, IN INCHES PER YEAR

PACIFIC DECADAL OSCILLATION (PDO) INDEX, UNITLESS

1875 1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

-80

-70

-60

-50

-40

-30

-20

-10

0

40

10

20

30

50

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Dry

D

r

y

Dry W Dry Wet Dry Wet Dry Wet Wet Dry e

t

Wet Wet
138 Groundwater Availability of the Central Valley Aquifer, California Table C3. Land-use periods with acreage in square miles and percentage of different virtual crop categories. Description Irrig- ated Area (square miles) Percentage of active model area Grouping of other classes1 1961 1973 1992 1998 2000 1961 1973 1992 1998 2000 1. Water No 105 124 160 347 162 1 1 1 2 1 n/a 2 . Urban No 641 765 1,043 894 1,528 3 4 5 4 7 n/a 3. Native classes2 No 7,355 4,809 6,237 5,102 6,639 36 23 30 25 32 n/a 4. Orchards, groves, and vineyards Yes n/a 312 553 263 n/a n/a 2 3 1 n/a 10,12,16 5. Pasture/Hay Yes n/a n/a 1,799 589 n/a n/a n/a 9 3 n/a 13,14 6. Row Crops Yes n/a n/a 1,265 n/a n/a n/a n/a 6 n/a n/a 9,11,18 7.Small Grains Yes n/a n/a 634 n/a n/a n/a n/a 3 n/a n/a 11,14,17 8. Idle/fallow2 Yes 241 104 1 n/a 372 1 1 <1 n/a 2 n/a 9. Truck, nursery, and berry crops2 Yes 889 923 640 934 958 4 4 3 5 5 n/a 10. Citrus and sub-tropical3 Yes 350 269 364 204 429 2 1 2 1 2 n/a 11. Field crops2 Yes 1,483 1,561 1,103 1,568 1,602 7 8 5 8 8 n/a 12. Vineyards2 Yes 1,098 675 1,095 915 1,209 5 3 5 4 6 n/a 13. Pasture2 No 1,198 1,366 788 862 1,448 6 7 4 4 7 n/a 14. Grain and hay crops2 Yes 708 821 497 810 893 3 4 2 4 4 n/a 15. Semi-agriculture 2,3 Yes 4 n/a n/a n/a 5 0 n/a n/a n/a 0 n/a 16. Deciduous fruits and nuts2 Yes 2,075 1,010 1,921 1,683 2,344 10 5 9 8 11 n/a 17. Rice2 Yes 948 991 947 993 1,001 5 5 5 5 5 n/a 18. Cotton Yes 1,635 1,891 1,486 1,917 1,943 8 9 7 9 9 n/a 19. Developed Yes 1,803 n/a n/a n/a n/a 9 n/a n/a n/a n/a 8,9,10,11,12,13, 

14,15,16,17,18

20. Cropland and pasture Yes n/a 4,912 n/a n/a n/a n/a 24 n/a n/a n/a 9,11,13,14,17,1821. Cropland Yes n/a n/a n/a 1,542 n/a n/a n/a n/a 8 n/a 9,11,13,14,16,17,1822. Irrigated Row and Field Crops Yes n/a n/a n/a 1,910 n/a n/a n/a n/a 9 n/a 9,11,14,17,18 1Land-use category number of amalgamated crops from “descrition” in column 1.

2California Department of Water Resources class-1 categories (California Department of Water Resources, 2000).3Semi-agricullture includes livestock feedlots, dairies, and poultry farms.
Model Development 139

Virtual Crop Maps

For the simulation period of spring 1961 to spring 1968

(“1960” land-use map, fig. C8), land use was based on data

gathered by California State University, Chico (2003), by

using a geographic information system (GIS) to quantify

vegetation changes over the previous 100 years. Maps were

produced to identify major changes that have occurred in the

valley due, in part, to hydrologic alterations associated with

the Central Valley Project around 1945 and the California

State Water project around 1968. The “1960” map (represented in the simulation period from 1961 to 1968) uses 

information on the extent of urban and agricultural areas from

DWR land-use maps and native vegetation from earlier maps.

As such, it is a patchwork of sources, scales, dates, accuracies,

and completeness (California State University, Chico, 2003).

The “1960” map includes nine vegetation classes. Eight of

these classes are different types of native vegetation; one class,

“urban/agriculture” represents developed land use. This study

focuses on developed land uses. As a result, the native vegetation classes were lumped into one category for this study.

Likewise, because of the generalized agricultural and urban

classifications on this map, the best estimate for the extent

of urban land uses available was from 1973. Therefore, the

extent of urban areas was taken from the 1973 land-use map.

Likewise, the “urban/agriculture” virtual crop class, where

not identified as urban in 1973, was replaced with the virtual

crop from the 2000 land-use map. For example, where the

“urban/agriculture” class was specified and the area was not

identified as urban on the 1973 land-use map, the virtual crops

interpreted on the 2000 land-use map were embedded. This

assumes the farmer would be growing the same type of crop

in a given area over the 40-year time frame of the hydrologic

simulation. In some cases, such as with orchards, this generally is a good assumption; in other cases, the crops may have

changed several times. Approximately 36 percent of the valley

was covered by native vegetation, 61 percent was covered

by agricultural land (based on the urban and cropped land

in 2000), and 3 percent was covered by urban development

(fig. C8). The definition of early agricultural land use in the

Central Valley is an aspect of the landscape hydrologic model

that could use much improvement in future refinements of the

CVHM.

For the period of spring 1968 to spring 1978, (1973 landuse map; fig. C9) land use was interpreted using the Anderson

level II classifications (Anderson and others, 1976) and stored

in the Geographic Information Retrieval and Analysis System

(GIRAS) (U.S. Geological Survey, 1990). Data are gathered

by quadrangle, and the dates range from the mid 1970s to the

early 1980s (U.S. Geological Survey, 1990). Approximately

23 percent of the valley was covered by native vegetation,

71 percent was covered by agricultural land (24 percent was

covered by undifferentiated cropland and pasture, 7 percent

was covered by orchards and vineyards, and 40 percent was

covered by other types of agriculture, based on 2000 land-use

survey), 2 percent was covered by barren land or water, and

4 percent was covered by urban development (fig. C9). As

Gronberg and others (1998) noted, the forested land predominantly is in the Sierras and rangeland predominantly is in the

foothills of the Sierras and the Coast Ranges. Urban areas

and agricultural land predominantly are on the valley floor;

most orchards and vineyards are on the east side of the valley. Although the data are suitable for representing regional

spatial patterns of land use, there are discrepancies across 

quadrangle boundaries and detail is lacking in specific crop

types. In particular, the categorized agricultural land is limited

to four classes: (1) cropland and pasture; (2) orchards, groves,

vineyards, nurseries, and ornamental horticultural areas; (3)

confined feeding operations; and (4) other agricultural land. As

a result, these agricultural classifications were revised using

information from the 2000 land-use survey in the same manner as the 1960 virtual-crop map. Because of the large extent

and lack of definition in the “Cropland and Pasture” class,

this class was further revised to “Native” where “Native” was

identified in this area on the 1992 map or revised to the particular crop type where the crop type was identified in this area

on the 1992 map. This revision smoothed the transition from

1973 to 1992 land-use maps.

For the period spring 1978 to spring 1993 (1992 land-use

map, fig. C10), land use was based on the North American

Land Class Data 1992 (NLCD) classification, a 21-class hierarchical, modified, Anderson Land Cover Classification (U.S.

Geological Survey, 1999). The NLCD 1992 data are derived

from images acquired by Landsat’s Thematic Mapper (TM)

sensor, as well as a number of ancillary data sources. Although

the map is referred to as NLCD 1992, it represents land use

for earlier years and actually is based on imagery acquired

throughout the 1980s (U.S. Geological Survey, 1999). It is

the first national land-cover data set produced since the early

1970s, effectively replacing the GIRAS data sets. NLCD 1992

data are an improvement over GIRAS data in that the herbaceous agricultural areas (Cropland and Pasture; Anderson

Level II) are subdivided into four NLCD classes: Pasture/Hay,

Row Crops, Small Grains, and Fallow. In addition, the 30-m

resolution, raster-based NLCD was enhanced with GIRAS

data to better represent orchards and residential areas (Gilliom

and others, 2006). Approximately 30 percent of the valley

was covered by native vegetation, 64 percent was covered by

agricultural land (56 percent was covered by cropland and

pasture and 8 percent was covered by orchards and vineyards),

1 percent was covered by barren land or water, and 5 percent

was covered by urban development (fig. C10). Although the

1992 virtual crop map is much more detailed than the previous

virtual-crop maps, the agricultural classes still were generalized. As with the other maps, some of the generalized agricultural classifications were revised using information from the

2000 land-use survey. The virtual crop “orchards, vineyards,

and others” was divided into orchards, vineyards, and deciduous crops. Similarly, pasture and hay were separated, as were

the row crops that were included in field and truck crops.
140 Groundwater Availability of the Central Valley Aquifer, California

EXPLANATION

Land use—1961-68

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

Water

Urban

Native vegetation

Orchard, groves and vineyards (none shown)

Pasture/Hay (none shown)

Row crops (none shown)

Small grains (none shown)

Idle/fallow

Truck, nursery and berry crops

Citrus and subtropical

Field crops

Vineyards

Pasture

Grain and hay

Feed lots and diaries

Deciduous fruits and nuts

Rice

Cotton

Developed

Cropland and pasture (none shown)

Cropland (none shown)

Irrigated row and field crops (none shown)

Land use type—

Water-balance subregion boundaries

 and identifier—See table C1

Active model grid boundary—

 See figure C1

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

1

2

3

4

5

6

7

8

10

11

12

13

14

15

16

17

18

19 20

21

9

1 2

3

8

9

11 10

12

13

14

15

16

17

18

19

Fraction of total area by land-use type

Figure C8. Virtual crops for 1960 (modified with 2000 data), including pie chart of percentage of different virtual crops (sources: 

California Department of Water Resources, 2000; California State University, Chico, 2003;). The water-balance subregions are described 

in table A1.
Model Development 141

Figure C9. Virtual crops for 1973, including pie chart of percentage of different virtual crops (sources: U.S. Geological Survey, 1990; 

California Department of Water Resources, 2000). The water-balance subregions are described in table A1.

EXPLANATION

Land use—1968-78

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

Water

Urban

Native vegetation

Orchard, groves and vineyards

Pasture/Hay (none shown)

Row crops (none shown)

Small grains (none shown)

Idle/fallow

Truck, nursery and berry crops

Citrus and subtropical

Field crops

Vineyards

Pasture

Grain and hay

Feed lots and diaries (none shown)

Deciduous fruits and nuts

Rice

Cotton

Developed (none shown)

Cropland and pasture

Cropland (none shown)

Irrigated row and field crops (none shown)

Land use type—

Water-balance subregion boundaries

 and identifier—See table C1

Active model grid boundary—

 See figure C1

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

1

2

3

4

5

6

7

8

10

11

12

13

14

15

16

17

18

19 20

21

9

1 2

3

4

8

9

10

11

12 13

14

16

17

18

20

Fraction of total area by land-use type
142 Groundwater Availability of the Central Valley Aquifer, California

Figure C10. Virtual crops for 1992, including pie chart of percentage of different virtual crops (sources: Gilliom and others, 2006; 

California Department of Water Resources, 2000). The water-balance subregions are described in table A1.

EXPLANATION

Land use—1978-93

8 Idle/fallow (none shown)

1

2

3

4

5

6

7

9

10

11

12

13

14

15

16

17

18

19

20

21

22

Water

Urban

Native vegetation

Orchard, groves and vineyards

Pasture/Hay

Row crops

Small grains

Truck, nursery and berry crops

Citrus and subtropical

Field crops

Vineyards

Pasture

Grain and hay

Feed lots and diaries (none shown)

Deciduous fruits and nuts

Rice

Cotton

Developed (none shown)

Cropland and pasture (none shown)

Cropland (none shown)

Irrigated row and field crops (none shown)

Land use type—

Water-balance subregion boundaries

 and identifier—See table C1

Active model grid boundary—

 See figure C1

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

1

2

3

4

5

6

7

8

10

11

12

13

14

15

16

17

18

19 20

21

9

1 2

3

4

5

6 7

9

10

11

12

13

14

16

17

18

Fraction of total area by land-use type
Model Development 143

For the period of spring 1993 to spring 1999 (1998

land-use map, fig. C11), land use was obtained from the “Gap

Analysis” (Davis and others, 1998). The term “Gap Analysis” refers to the evaluation of the management status of

plant communities, vertebrate species, and vertebrate species

richness by GIS overlay of biological distribution data on a

map of existing biological reserves (U.S. Geological Survey,

1990). Base information started with 1990 satellite and aerial

photography data and was augmented extensively by local

resources and field work. The date of 1998 is assigned to this

land-use map although the land-use source information varies

through the 1990s. Approximately 25 percent of the valley was

covered by native vegetation, 69 percent was covered by agricultural land (63 percent was covered by cropland and pasture

and 6 percent was covered by orchards and vineyards), 2 percent was barren land or water, and 4 percent was covered by

urban development (fig. C11). The 1998 virtual-crop map has

agricultural distributions representative for the time period and

many individually mapped crops, such as rice, hay, orchards,

and vineyards. However, some crops, such as row and field

crops, are lumped together. Therefore, where appropriate, the

agricultural classifications were revised using information

from the 2000 virtual-crop survey, as with the previous virtual

crop maps.

For the period spring 1999 to spring 2003 (2000 landuse map, fig. C12), land use was obtained in digital format

(California Department of Water Resources, 2000). The

county land-use survey data were developed by DWR through

its Division of Planning and Local Assistance from aerial

photography and extensive field surveys. The land uses that

were compiled were detailed agricultural land uses, and lesser

detailed urban and native vegetation land uses. The agricultural classifications can be correlated to the 12 DWR class-1

categories (California Department of Water Resources, 2000).

This level of spatial detail is ideal for this study. The DWR

prepares these detailed county maps of the agricultural land

use on the valley floor every 6–7 years. Because the 2000

virtual crop map represents a composite map for land use

from the late 1990s, this type of map also lacks the temporal 

detail needed to accurately reflect the dynamics of changing

agriculture or urbanization. Although the data are suitable for

representing regional spatial patterns of land use and crop patterns, there are some discrepancies across county boundaries. 

The agricultural classes were used instead of the more detailed

crops that were identified. Approximately 32 percent of the

valley was native vegetation, 60 percent was agricultural land

(52 percent cropland and pasture, 8 percent orchards and vineyards), 3 percent was barren land or water, and 7 percent was

urban (fig. C12; table C3). The distribution of crops generally

reflects the distribution of soil texture and chemistry (Gronberg and others, 1998). About 9 percent of the irrigated land,

generally on the southwest side of the valley, is planted in

cotton, which is a salt-tolerant crop in its later growth stages.

In contrast, 11 percent of the crop land, predominantly on 

the southeast side, is planted in deciduous fruit and nut trees, 

which are intolerant of salinity and some trace elements, such

as boron, for citrus crops. Rice is grown in the finer-grained

deposits in the north, covering about 5 percent of the valley.

Together these account for about 25 percent of the total acreage of the crops in the valley. Because of the elimination of

subsidies for growing cotton, these percentages recently have

changed.

Crop-Type Data

The virtual crops provide a basis for estimating the

consumptive use of water at the land surface, a key component

of the TFDR (Schmid and others, 2006b). The TFDR largely

is determined by the CIR. The CIR is determined from the

product of ETo and an area-weighted crop coefficient on a

cell-by-cell basis; these products are summed over the uppermost active model cells within each WBS. Because many

factors affect ET (including weather parameters, soil factors,

and plant factors), it is difficult to formulate an equation that

can produce estimates of ET under different sets of conditions

(California Department of Water Resources, 2007). Therefore,

the idea of a reference crop ET was developed (California

Department of Water Resources, 2007); ET from a standardized grass surface that commonly is denoted as ETo.

Specified root depths, suction pressures for the unsaturated root zone, crop coefficients, fractions of runoff, and

fractions of transpiration and evaporation affect the consumption and movement of water for each crop category. For

the CVHM, constant values of root depths and root uptake

pressures were used for the entire simulation and were based

on values from the literature and those developed for DWR’s

C2VSIM model of the Central Valley (C. Brush, California

Department of Water Resources, written commun., February

21, 2007). These values are summarized in table C4. There is 

a positive (hydrostatic) and a negative pressure head component to the interval of heads for the root uptake interval

for anoxia, optimal, and wilting (table C4; appendix 1). The 

positive pressure is used to simulate flooded crops such as rice

and other vegetation that tolerates flooded environments. More

detailed definitions of the root uptake pressures are given by

Schmid and others (2006b).

Direct ET from the groundwater reservoir occurs when

the water table is within reach of plant roots or when the capillary fringe reaches the land surface. This form of ET almost

completely is eliminated when the water table is lowered

below the root zone.

Runoff for each crop type is poorly known. The runoff values are based loosely on those developed for DWR’s

C2VSIM model of the Central Valley (C. Brush, California

Department of Water Resources, written commun., February 21, 2007) and model calibration to surface-water flows in

stream channels and out to the Delta.
144 Groundwater Availability of the Central Valley Aquifer, California

EXPLANATION

Land use—1993-99

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

Water

Urban

Native vegetation

Orchard, groves and vineyards

Pasture/Hay

Row crops (none shown)

Small grains (none shown)

Idle/fallow (none shown)

Truck, nursery and berry crops

Citrus and subtropical

Field crops

Vineyards

Pasture

Grain and hay

Feed lots and diaries (none shown)

Deciduous fruits and nuts

Rice

Cotton

Developed (none shown)

Cropland and pasture (none shown)

Cropland

Irrigated row and field crops

Water-balance subregion boundaries

 and identifier—See table C1

Active model grid boundary—

 See figure C1

Land use type—

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

1

2

3

4

5

6

7

8

10

11

12

13

14

15

16

17

18

19 20

21

9

Fraction of total area by land-use type

1 2

3

4

5

9

10

11 12 13

14

16

17

18

21

22

Figure C11. Virtual crops for 1998, including pie chart of percentage of different virtual crops (sources: Davis and others, 1998; 

California Department of Water Resources, 2000). The water-balance subregions are described in table A1.
Model Development 145

EXPLANATION

Land use—1999-2003

Land use type—

Water-balance subregion boundaries

 and identifier—See table C1

Active model grid boundary—

 See figure C1

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

Water

Urban

Native vegetation

Orchard, groves and vineyards (none shown)

Pasture/Hay (none shown)

Row crops (none shown)

Small grains (none shown)

Idle/fallow

Truck, nursery and berry crops

Citrus and subtropical

Field crops

Vineyards

Pasture

Grain and hay

Feed lots and diaries (none shown)

Deciduous fruits and nuts

Rice

Cotton

19

20

21

22

Developed (none shown)

Cropland and pasture (none shown)

Cropland (none shown)

Irrigated row and field crops (none shown)

0 50 100 Miles

0 50 100 Kilometers

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

1

2

3

4

5

6

7

8

10

11

12

13

14

15

16

17

18

19 20

21

9

Fraction of total area by land-use type

9 11 10

12

13

14

16

17

18 1 2

3

8

Figure C12. Virtual crops for 2000, including a pie chart of percentage of different virtual crops (California Department of Water 

Resources, 2000). The water-balance subregions are described in table A1.
146 Groundwater Availability of the Central Valley Aquifer, California

Crop water demand roughly can be related to the crop

growth stage. The crop coefficient values used in this study

were based on an idealized crop growth curve. This growth

curve was divided into 12 monthly stages spanning the initial

growth stage, the rapid growth stage, the mid-season stage,

the late-season stage, and a period of no planting (fig. C13). 

Although the specific growth dates for each virtual crop vary

with the planting date and climatic zone, no allowance is made

for changes in growth dates with location in the valley. The

only change in crop coefficient value at a location is based on

a change in the virtual crop with land-use changes.

Crop coefficient values are available from several sources

(Brush and others, 2004). When available, published crop

coefficient values for the western San Joaquin Valley (Brush

and others, 2004) were used; when no published crop coefficient values were available for the San Joaquin Valley, published crop coefficient values for another similar climatic area

were used. In many cases, multiple crops were area-weighted

to produce a composite, virtual crop coefficient. For example,

the virtual crop “deciduous” is composed of almonds, walnuts,

prunes, peaches, nectarines, pistachios, apples, plums, figs,

pears, apricots, cherries, and other miscellaneous deciduous 

trees.

Other WBS and crop-related properties that were specified include the fraction of transpiration (Ftr), fraction of evaporation from precipitation (Fep), fraction of evaporation from

irrigation (Fei), and the irrigation efficiencies. These fractions

vary linearly with the respective area occupied by crops and

the area open to soil-evaporation (Schmid and others, 2006a).

Because the sum of cropped area and the exposed wetted area

is the total area, Ftr plus Fep equals one. In addition, Fei must

be less than or equal to Fep. The Ftr is assumed to be independent of whether the transpiratory consumptive use is satisfied by irrigation, precipitation, or groundwater uptake. The

fraction of the consumptive use that is transpiratory (Ftr) or

evaporative (Fep and Fei) depends highly on type of crop and

growth stage. When the vegetation cover approaches 100 percent, Ftr = 1 while Fep and Fei = 0. As a result, the fractions of

transpiration and evaporation vary by virtual crop for different

months of the year (table C5). 

Virtual-crop 

crop category 

(number)

Root depth, 

in feet

Root uptake pressure heads, in feet Fraction of 

surface-water 

runoff from 

precipitation

/irrigation 

(dimensionless)

Anoxia Lower optimal 

range

Upper optimal 

range Wilting

Water (1) 3.6 1.6 0.3 –1.0 –1.3 0.05/0.01

Urban (2) 2.0 –0.4 –0.9 –37.4 –262.5 0.01/0.01

Native classes (3) 10.6 1.6 0.4 –27.1 –377.3 0.21/0.01

Orchards, groves, and vineyards (4) 6.0 –0.4 –0.9 –22.8 –291.4 0.10/0.01

Pasture/Hay (5) 5.3 –0.4 –0.9 –37.4 –262.5 0.10/0.02

Row Crops (6) 8.3 –0.5 –1.0 –17.9 –262.5 0.10/0.06

Small Grains (7) 4.0 –0.4 –0.9 –37.4 –262.5 0.10/0.04

Idle/fallow (8) 5.3 –0.2 –0.7 –27.1 –377.3 0.06/0.01

Truck, nursery, and berry crops (9) 6.3 –0.5 –1.0 –17.9 –262.5 0.10/0.10

Citrus and subtropical (10) 4.0 –0.5 –1.0 –19.7 –262.5 0.10/0.01

Field crops (11) 4.0 –0.5 –1.0 –98.4 –405.9 0.10/0.08

Vineyards (12) 5.0 –0.5 –1.0 –23.8 –262.5 0.01/0.01

Pasture (13) 5.3 0.0 –0.9 –37.4 –262.5 0.10/0.02

Grain and hay crops (14) 4.0 –0.5 –1.0 –170.9 –525.3 0.10/0.04

Semi–agricultural (livestock feedlots, diaries,

poultry farms) (15)

3.6 –0.2 –0.7 –27.1 –377.3 0.32/0.35

Deciduous fruits and nuts (16) 6.0 –0.4 –0.9 –22.8 –377.3 0.11/0.05

Rice (17) 5.3 1.6 0.4 –5.8 –525.0 0.01/0.03

Cotton (18) 9.3 –0.2 –0.9 –91.3 –503.0 0.10/0.10

Developed (19) 5.3 –0.4 –0.9 –37.4 –262.5 0.10/0.08

Cropland and pasture (20) 4.9 –0.4 –0.9 –37.4 –262.5 0.10/0.08

Cropland (21) 6.3 –0.5 –1.0 –17.9 –262.5 0.10/0.08

Irrigated Row and Field Crops (22) 4.9 –0.4 –0.9 –37.4 –262.5 0.10/0.07

Table C4. Summary of Central Valley, California, virtual crop categories and properties.
Model Development 147

The irrigation efficiencies are defined as the consumptive

use of applied water. Inefficiency is a result of losses to runoff

and deep percolation as a result of excess irrigation and excess

precipitation (Schmid and others, 2006b). In CVHM, the

irrigation efficiencies are specified as a matrix of efficiencies

for each WBS and each crop for each of the monthly stress

periods. In this way, the efficiencies vary from crop to crop for

different WBSs and they change through time. The average

area-weighted composite efficiency, by decade, for each WBS

in the simulations tabulated in table C6. Irrigation efficiencies

are assumed to have varied in time, reflecting improvements in

irrigation application technologies, increased use of tail-water

return systems and recycling of drainage water, and changes

in the cost and availability of water (Brush and others, 2004).

In general, the efficiencies have improved through time with

technological advances in sprinkler systems, drip irrigation,

and changes in cropping patterns (California Department

of Water Resources, 1994). California Department of Water

Resources (1994) reports an average improvement in

irrigation efficiency of 10 percent during the 1980s. This

Figure C13. Monthly crop coefficients for virtual crops in the Central Valley, California (sources: Brouwer and others, 1985; Brouwer 

and Heibloem, 1986; Snyder and others, 1987a; Snyder and others, 1987b; Allen and others, 1998; Brush and others, 2004).

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

MONTH

CROP COEFFICIENT BY LAND-USE TYPE, UNITLESS

Water

Urban

Native vegetation

Orchard, groves and vineyards

Pasture/hay

Row crops

Small grains

Idle/fallow

EXPLANATION 

Land use type—

A
148 Groundwater Availability of the Central Valley Aquifer, California

particular increase in efficiency is taken into account with

fractional irrigation efficiencies that increase by 1 percent per

year in the 1980s and are reflected in efficiencies through that

time period in table C6. 

In general, irrigation efficiencies are poorly known (Williamson and others, 1989; California Department of Water

Resources, 1994; Brush and others, 2004). Williamson and

others (1989) report values averaging 59 percent, and ranging from 38 to 92 percent for the 1961–77 time period. DWR

reports overall efficiencies of 60–70 percent for parts of the

Central Valley (California Department of Water Resources,

1994). Because of the details incorporated in the FMP, some

of what typically is included as inefficiency in irrigation in

models (particularly uptake from the groundwater system)

is accounted for in the FMP. Compared to previous simulations in the Central Valley, the CVHM efficiencies specified

in the FMP typically are higher. For example, because rice is

grown in flooded fields, rice has an extremely high irrigation

efficiency of 88 percent in CVHM. Another example is WBS

14 in the central western San Joaquin Valley, which has had a

large amount of scrutiny in the past (Williamson and others,

1989; Belitz and Heimes, 1990; Belitz and others, 1993; Brush

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

MONTH

Truck, nursery and berry crops

Citrus and subtropical

Field crops

Vineyards

Pasture

Grain and hay

Feed lots and dairies

CROP COEFFICIENT BY LAND-USE TYPE, UNITLESS

EXPLANATION 

Land use type—

B

Figure C13. Continued.
Model Development 149

and others, 2004). Williamson and others (1989) report efficiencies of 83–92 percent for this general area. Brush and others (2004) report efficiencies ranging from 70 to 80 percent,

and averaging around 78 percent in this WBS between 1972

and 2000. By utilizing a completely closed pipeline system,

the Westlands Water District claims an estimated 92-percent

efficiency (California Department of Water Resources, 2003a).

CVHM has average efficiencies for this WBS increasing from

68 percent in the 1960s to 87 percent in the 2000s. WBS 14

has tile drains to drain the fields and a canal system to reuse

the water (Brush and others, 2004). As a result, the efficiencies in this area were increased by about 10 percent in the late

1970s to represent the reuse of this drainage water and canal

leakage not simulated in the CVHM. Owing to a lack of data

regarding long-term values and changes in efficiency, the efficiencies were adjusted during model calibration.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

MONTH

CROP COEFFICIENT BY LAND-USE TYPE, UNITLESS

C

Deciduous fruits and nuts

Rice

Cotton

Developed

Cropland and pasture

Cropland

Irrigated row and field crops

EXPLANATION 

Land use type—

Figure C13. Continued.
150 Groundwater Availability of the Central Valley Aquifer, California

Table C5. Summary of fractions of transpiration and evaporation by month for Central Valley virtual crops.—Continued

[Ftr, fraction of transpiration; Fep,fraction of evaporation from precipitation; Fei, fraction of evaporation from irrigation]

Virtual-crop 

crop category 

(number)

January 

(Ftr/Fep/Fei)

February 

(Ftr/Fep/Fei)

March 

(Ftr/Fep/Fei)

April 

(Ftr/Fep/Fei)

May 

(Ftr/Fep/Fei)

June 

(Ftr/Fep/Fei)

Water (1) 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00

Urban (2) 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02

Native classes (3) 0.28/0.72/0.72 0.28/0.72/0.72 0.66/0.34/0.34 0.66/0.34/0.34 0.66/0.34/0.34 0.66/0.34/0.34

Orchards, groves, and vineyards (4) 0.20/0.80/0.80 0.20/0.80/0.80 0.37/0.63/0.63 0.23/0.77/0.77 0.46/0.54/0.54 0.47/0.53/0.53

Pasture/Hay (5) 0.50/0.50/0.50 0.50/0.50/0.50 0.50/0.50/0.50 0.50/0.50/0.50 0.72/0.28/0.28 0.88/0.12/0.12

Row Crops (6) 0.11/0.89/0.89 0.11/0.89/0.89 0.11/0.89/0.89 0.09/0.91/0.91 0.36/0.64/0.64 0.46/0.54/0.54

Small Grains (7) 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00

Idle/fallow (8) 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00

Truck, nursery, and berry crops (9) 0.80/0.20/0.18 0.80/0.20/0.18 0.39/0.61/0.61 0.44/0.56/0.36 0.42/0.58/0.38 0.80/0.20/0.18

Citrus and subtropical (10) 0.27/0.73/0.73 0.27/0.73/0.73 0.46/0.54/0.14 0.46/0.54/0.14 0.46/0.54/0.14 0.46/0.54/0.14

Field crops (11) 0.01/0.99/0.99 0.01/0.99/0.99 0.01/0.99/0.99 0.15/0.85/0.85 0.15/0.85/0.85 0.94/0.06/0.06

Vineyards (12) 0.00/1.00/0.03 0.00/1.00/0.03 0.28/0.72/0.22 0.40/0.60/0.10 0.38/0.62/0.12 0.36/0.64/0.14

Pasture (13) 0.18/0.82/0.82 0.15/0.85/0.85 0.46/0.64/0.64 0.91/0.09/0.03 0.91/0.09/0.03 0.91/0.09/0.03

Grain and hay crops (14) 0.46/0.54/0.54 0.92/0.08/0.08 0.92/0.08/0.08 0.92/0.08/0.08 0.23/0.77/0.77 0.00/1.00/1.00

Semi-agricultural (15) 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00

Deciduous fruits and nuts (16) 0.10/0.90/0.90 0.10/0.90/0.90 0.10/0.90/0.90 0.50/0.50/0.50 0.50/0.50/0.50 0.97/0.03/0.03

Rice (17) 0.20/0.80/0.50 0.20/0.80/0.50 0.20/0.80/0.50 0.75/0.25/0.25 0.75/0.25/0.25 0.80/0.20/0.10

Cotton (18) 0.75/0.25/0.25 0.75/0.25/0.25 0.75/0.25/0.25 0.43/0.57/0.17 0.75/0.25/0.20 0.75/0.25/0.20

Developed (19) 0.30/0.70/0.67 0.30/0.70/0.67 0.22/0.78/0.78 0.16/0.84/0.84 0.42/0.58/0.38 0.85/0.15/0.15

Cropland and pasture (20) 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.20/0.80/0.80 0.30/0.70/0.70

Cropland (21) 0.05/0.95/0.85 0.05/0.95/0.85 0.05/0.95/0.55 0.05/0.95/0.55 0.05/0.95/0.95 0.05/0.95/0.85

Irrigated Row and Field Crops (22) 0.05/0.95/0.55 0.05/0.95/0.55 0.05/0.95/0.75 0.05/0.95/0.75 0.20/0.80/0.78 0.85/0.15/0.10

Virtual-crop 

crop category 

(number)

July 

(Ftr/Fep/Fei)

August 

(Ftr/Fep/Fei)

September 

(Ftr/Fep/Fei)

October 

(Ftr/Fep/Fei)

November 

(Ftr/Fep/Fei)

December 

(Ftr/Fep/Fei)

Water (1) 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00

Urban (2) 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02 0.25/0.75/0.02

Native classes (3) 0.66/0.34/0.34 0.66/0.34/0.34 0.66/0.34/0.34 0.66/0.34/0.34 0.66/0.34/0.34 0.28/0.72/0.72

Orchards, groves, and vineyards (4) 0.47/0.53/0.53 0.47/0.53/0.53 0.47/0.53/0.53 0.47/0.53/0.53 0.45/0.55/0.55 0.20/0.80/0.80

Pasture/Hay (5) 0.95/0.05/0.05 0.96/0.04/0.04 0.96/0.04/0.04 0.96/0.04/0.04 0.96/0.04/0.04 0.96/0.04/0.04

Row Crops (6) 0.95/0.05/0.05 0.87/0.13/0.13 0.12/0.88/0.88 0.11/0.89/0.89 0.11/0.89/0.89 0.11/0.89/0.89

Small Grains (7) 0.20/0.80/0.80 0.50/0.50/0.50 0.90/0.10/0.10 0.90/0.10/0.10 0.00/1.00/1.00 0.50/0.50/0.50

Idle/fallow (8) 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00 0.00/1.00/0.00

Truck, nursery, and berry crops (9) 0.80/0.20/0.18 0.80/0.20/0.18 0.80/0.20/0.18 0.80/0.20/0.18 0.80/0.20/0.18 0.80/0.20/0.18

Citrus and subtropical (10) 0.46/0.54/0.14 0.46/0.54/0.14 0.46/0.54/0.14 0.46/0.54/0.14 0.46/0.54/0.14 0.46/0.54/0.14

Field crops (11) 0.94/0.06/0.06 0.94/0.06/0.06 0.90/0.10/0.10 0.01/0.99/0.99 0.01/0.99/0.99 0.01/0.99/0.99

Vineyards (12) 0.36/0.64/0.14 0.36/0.64/0.14 0.36/0.64/0.14 0.36/0.64/0.14 0.36/0.64/0.14 0.38/0.62/0.12

Pasture (13) 0.96/0.04/0.04 0.91/0.09/0.03 0.91/0.09/0.03 0.46/0.64/0.64 0.15/0.85/0.85 0.15/0.85/0.85

Grain and hay crops (14) 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.16/0.84/0.84 0.35/0.65/0.65

Semi-agricultural (15) 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00 0.00/1.00/1.00

Deciduous fruits and nuts (16) 0.97/0.03/0.03 0.97/0.03/0.03 0.97/0.03/0.03 0.10/0.90/0.90 0.10/0.90/0.90 0.10/0.90/0.90

Rice (17) 0.75/0.25/0.25 0.60/0.40/0.27 0.20/0.80/0.50 0.20/0.80/0.50 0.20/0.80/0.50 0.20/0.80/0.50

Cotton (18) 0.75/0.25/0.20 0.75/0.25/0.20 0.47/0.53/0.33 0.36/0.64/0.44 0.75/0.25/0.25 0.75/0.25/0.25

Developed (19) 0.90/0.10/0.10 0.90/0.10/0.10 0.90/0.10/0.10 0.50/0.50/0.50 0.30/0.70/0.70 0.30/0.70/0.67

Cropland and pasture (20) 0.85/0.15/0.15 0.95/0.05/0.05 0.90/0.10/0.10 0.50/0.50/0.50 0.00/1.00/1.00 0.00/1.00/1.00

Cropland (21) 0.05/0.95/0.85 0.75/0.25/0.25 0.95/0.05/0.05 0.94/0.06/0.06 0.93/0.07/0.07 0.00/1.00/1.00

Irrigated Row and Field Crops (22) 0.95/0.05/0.05 0.95/0.05/0.05 0.60/0.40/0.15 0.04/0.96/0.04 0.10/0.90/0.50 0.10/0.90/0.50

Table C5. Summary of fractions of transpiration and evaporation by month for Central Valley, California, virtual crops. 

[Ftr, fraction of transpiration; Fep, fraction of evaporation from precipitation; Fei, fraction of evaporation from irrigation]
Model Development 151

Climate Data

The consumptive use of water, specifically the TFDR, is

related directly to the climate. Although several of the previously specified properties take into account yearly or monthly

variations, and some have a climatic component, the main

climatic contributors to the FMP are ETo and precipitation.

Reference Evapotranspiration (ETo)

Estimates of ETo can be derived using complex, parameter-based equations or simple, empirical equations. The main 

difficulty encountered when using parameter-based equations

is the unavailability of accurate or complete data at sufficient

spatial and temporal distributions. The detailed climatological

data required for the parameter-based equations (such as the 

Penman-Monteith equation) are not available for many sites in

California, especially prior to 1987. Empirical equations use a

small number of parameters (usually air temperature and solar 

radiation) to estimate ETo and are well suited to estimating

ETo for sites with limited climatological data. Samani (2000)

determined that temperature and radiation explain at least 80

percent of ETo. Hidalgo and others (2005) determined the seasonal cycle of ETo can be fairly accurately when approximated

from the seasonal cycle of net solar radiation (Rn) or average

air temperature. The Hargreaves–Samani equation (Hargreaves

and Samani, 1982, 1985; Hargreaves and others, 1985; Hargreaves, 1994; Hargreaves and Allen, 2003) provides a very

accurate estimate of ETo (referred to as ETh) using a simple,

reliable method with minimum data requirements and little

sensitivity to weather station aridity (Hargreaves and Allen,

2003). ETh is calculated from the daily minimum and maximum air temperatures and the extraterrestrial solar radiation:

ETh Ra T T T T = +

×





 





 −



 



0 0023  2 17 8 2 . .

max min max min

max

where,

T

T

is the maximum daily air temperature [ C],

is the mini

°

min mum daily air temperature [ C], and

is the extraterrestri

°

Ra al solar radiation 

(megaJoule/m /day). 2

Jensen and others (1997) found the Hargreaves equation

to be one of the simplest and most accurate of the empirical methods for estimating ETo. The Hargreaves method

compared well with the Food and Agriculture Organization

Penman–Monteith method (Allen and others, 1998) in most

parts of California, with the greatest differences occurring for

days with extreme values of wind speed or relative humidity

(Temesgen and others, 2005). The Hargreaves equation also

compared well with the Penman–Monteith method on a global

scale using a high-resolution monthly data set (Droogers and

Allen, 2002).

Monthly ETo values for the FMP in CVHM were estimated using the Hargreaves–Samani equation (Hargreaves

and Samani, 1982, 1985) and temperature data. Gridded

regional estimates of temperature were obtained at a 1.24-mi

spatial resolution from the Climate Source (2006). A monthly

minimum and maximum temperature value was interpolated to

the center of each 1-mi2 model cell using bilinear interpolation

of the temperature data. ETo was calculated at each active cell

for each month during the entire period of simulation using the

Hargreaves–Samani equation. Figure A2 shows the computed

average annual ETo values over the entire Central Valley.

Since the 1980s, as part of the California Irrigation

Management Information System (CIMIS), the DWR has

Waterbalance 

subregion

1960s 1970s 1980s 1990s 2000s

1 70 68 73 76 74

2 70 68 72 75 73

3 74 74 79 82 83

4 71 70 74 78 79

5 73 72 78 80 80

6 69 69 74 77 77

7 71 69 73 77 77

8 69 69 75 78 75

9 69 70 76 79 78

10 69 72 78 80 79

11 70 70 76 78 77

12 71 70 74 77 76

13 71 72 77 80 79

14 68 73 82 86 87

15 67 69 75 76 76

16 72 72 78 79 81

17 72 72 77 79 80

18 71 72 77 79 79

19 68 72 78 79 77

20 72 73 78 81 81

21 71 73 79 81 81

Table C6. Average area-weighted composite efficiency for each 

water-balance subregion of the Central Valley, California, through 

the simulation period.

[Efficiencies in percent]
152 Groundwater Availability of the Central Valley Aquifer, California

established more than 120 weather stations throughout California to measure ETo. CIMIS has been using a well-watered,

actively growing, closely clipped grass that completely shades

the soil as a reference crop. ETo values that were estimated

using the Hargreaves–Samani equation are correlated highly

with corresponding CIMIS ETo values but summer ETo values

generally were underestimated and winter ETo values generally were overestimated (table C7) (California Department

of Water Resources, 2007). Several studies have reported

methods for adjusting the constants in the Hargreaves–Samani

equation to correct for local climatological factors (Samani

2000; Droogers and Allen 2003). These methods correct differences between large ETo and ETh values but do not address

differences between small ETo and ETh values. As a result,

adjustments were made to correct for these potential errors

during calibration by multipliers on the summer and winter

crop coefficient values.

Precipitation

Precipitation for CVHM is specified through the FMP

at the uppermost active model cell for every month for the

period of simulation. Gridded regional estimates of precipitation are obtained at a 1.24-mi spatial resolution from the 

Climate Source (2006). A comparison of monthly precipitation

data from five CMIS stations in the valley (Red Bluff, Davis,

Modesto, Fresno, and Bakersfield) (fig. A3) indicates that the 

estimated precipitation from these gridded sources preserves

the total mass of precipitation measured at these stations. A

monthly precipitation rate is interpolated for the center of each 

1 mi2 model cell using bilinear interpolation of the precipitation data. A map of the average annual precipitation for the

simulation period for the model domain is shown in figure A2. 

The precipitation is applied to the uppermost active cell at an

average monthly rate. Parts of the precipitation are simulated

as evaporation and transpiration from the WBS. If excess

precipitation occurs, a part of this precipitation becomes 

runoff and the remaining part becomes deep percolation that is

groundwater recharge. The parts of runoff from precipitation

are user-specified and vary by crop type specified through the

estimation of virtual-crop properties (table C4). The values of

runoff are relatively unknown and are based on model calibration to surface-water flows in stream channels and out to the

Delta.

Surface-Water Supply

In the Central Valley, surface-water supply is simulated

using the SFR1 package (Prudic and others, 2004) as a combination of non-routed surface-water deliveries and semi-routed

conveyances from diversions. The surface-water conveyances

to and from the WBSs include a combination of transfers

representing a variety of conveyance mechanisms from natural

rivers to manmade canals and pipelines (figs. C2 and A4). In 

addition, the multiple water transfers represent a combination

of deliveries of water from private, State, and Federal sources

of water. As a result, the WBSs often have a number of

separate water transfers from multiple sources (fig. A8 and 

table A1). Water-delivery and diversion data were obtained

from DWR (C. Brush, California Department of Water

Resources, written commun., February 21, 2007). To simulate

the non-routed and semi-routed deliveries at the WBS scale,

110 diversion points from the streamflow-routing network

were specified (two diversion points were inactive during the

CVHM’s simulation period, but were included for future use).

Forty-two of the 108 active diversion points were specified

non-routed surface-water diversions in the model and were

used to move water to 12 of the WBSs without simulating the

actual process of conveyance (table A1). The remaining 66

diversion points were specified semi-routed streamflow diversions in the model and routing to the respective head gate of

the WBSs was simulated using the SFR1 (Prudic and others,

2004). Two of these diversions were delivered to areas outside

the CVHM and only one WBS did not have a semi-routed

delivery (WBS 14; table A1). In order to compile multiple 

deliveries to 18 of the WBSs, 18 “collector segments” were

added to the SFR1 streamflow-routing network. The simulated

diversions were routed through the SFR1 streamflow network

to the collector segment and then delivered to the appropriate

WBS (fig. A4). The distribution of surface water to model cells

within the WBS is subject to transmission losses that implicitly are accounted for in the model by the WBS irrigation

efficiencies.

When water transfers are specified as deliveries, they are

the first water-supply components that are used by the FMP

to satisfy the TFDR for each WBS. If more water is delivered

than the WBS demands (TFDR), the excess deliveries are

added to the furthest downstream, adjacent stream segment

of the simulated streamflow-routing network. If the TFDR is

not met by surface-water deliveries, the residual demand is

supplied by groundwater pumped from wells assigned to each

WBS.

Groundwater Supply

The groundwater supplied to each WBS is simulated by

a series of single-layer “farm wells” or through multi-aquifer

wells simulated with the MNW package (Halford and Hanson,

2002). The wells perforated above and below the Corcoran

Clay were simulated as multi-node wells (see “Agricultural 

Pumpage” section of this chapter), because the amount of 

leakage across the Corcoran Clay through well-bore flow was

of interest. Most wells simulating agricultural pumpage were

simulated using ”farm wells” in the FMP. A single well perforating and open to multiple aquifers represented in separate

model layers was simulated by specifying separate wells for

each model layer open to the well (Schmid and others, 2006b).

Agricultural groundwater pumpage requirements are estimated by the FMP after the TFDR is estimated and surfacewater imports and exports from semi-routed and non-routed
Model Development 153

Waterbalance 

subregion

January February March April May June

1 1.13 1.79 2.43 3.68 5.00 6.32

2 1.24 1.93 2.61 3.91 5.15 6.32

3 1.39 2.15 2.91 4.22 5.34 6.55

4 1.36 2.18 2.92 4.22 5.35 6.58

5 1.28 2.08 2.81 4.14 5.32 6.54

6 1.28 2.14 2.77 4.06 5.19 6.34

7 1.15 1.96 2.65 4.00 5.24 6.45

8 1.29 2.14 2.84 4.04 5.17 6.22

9 1.24 2.09 2.79 4.01 4.98 5.94

10 1.63 2.33 3.11 4.16 5.26 6.40

11 1.53 2.43 3.16 4.24 5.35 6.44

12 1.53 2.37 3.16 4.28 5.37 6.48

13 1.75 2.54 3.29 4.39 5.57 6.72

14 1.72 2.40 3.18 4.33 5.43 6.57

15 1.61 2.32 3.15 4.39 5.51 6.60

16 1.70 2.50 3.30 4.49 5.76 6.99

17 1.70 2.51 3.30 4.52 5.72 6.79

18 1.90 2.70 3.46 4.64 5.77 6.80

19 2.10 2.62 3.33 4.38 5.38 6.48

20 2.21 2.73 3.41 4.34 5.38 6.53

21 2.28 2.70 3.31 4.19 5.13 6.24

Waterbalance 

subregion

July August September October November December

1 6.79 5.98 4.71 2.62 1.34 0.91

2 6.69 5.96 4.75 2.79 1.51 1.06

3 6.97 6.23 4.98 3.02 1.78 1.35

4 7.14 6.36 5.05 3.06 1.79 1.40

5 6.92 6.15 4.86 2.94 1.68 1.18

6 6.52 5.89 4.80 2.96 1.74 1.14

7 6.74 6.00 4.76 2.82 1.60 0.98

8 6.53 5.82 4.69 2.94 1.80 1.25

9 6.09 5.45 4.54 2.88 1.75 1.10

10 6.65 5.87 4.89 3.15 1.96 1.41

11 6.93 6.16 4.99 3.20 2.10 1.72

12 6.77 6.02 4.92 3.18 2.01 1.48

13 7.16 6.40 5.30 3.46 2.20 1.73

14 6.67 5.91 4.86 3.26 2.03 1.52

15 6.74 6.04 4.93 3.34 2.05 1.42

16 7.36 6.65 5.32 3.47 2.23 1.73

17 7.20 6.61 5.23 3.51 2.26 1.76

18 7.11 6.52 5.30 3.60 2.38 1.93

19 6.58 5.99 4.98 3.40 2.24 1.83

20 6.63 6.02 5.02 3.38 2.29 1.95

21 6.33 5.76 4.88 3.32 2.28 1.96

Table C7. Average reference evapotranspiration (ETo) by month for the Central Valley, California, for 1961–2003 based on temperature 

data using the Hargreaves–Samani equation.

[ETo values in inches]

deliveries have been subtracted from the TFDR. If the TFDR

was satisfied by the surface-water deliveries, then no groundwater pumpage was estimated for that stress period. Conversely, if not enough surface water was available for supply

then groundwater from farm wells and multi-node wells was

used to satisfy the residual demand. 
154 Groundwater Availability of the Central Valley Aquifer, California

Net Recharge

The net recharge in a WBS is defined as inefficiency

losses due to excess irrigation and excess precipitation,

reduced by losses to surface-water runoff and ET from

groundwater (Schmid and others, 2006b). Alternatively, the

net recharge can be defined in terms of consumptive use: the

part of irrigation and precipitation not consumptively used

by plants reduced by losses to surface-water runoff and ET

from groundwater. The fraction of losses to surface-water

runoff depends on whether the loss is related to irrigation or

to precipitation. As in most areas, in CVHM, irrigation losses

depend on the irrigation method, which, in turn, depends on

the virtual crop type and other factors such as soil type and

slope (table C4). ET from groundwater (even for irrigated

areas) is subtracted from the potential net downward flux to

the groundwater system. Hence, net recharge to groundwater

can be defined by means of user-specified and head-dependent

parameters. This definition of net recharge is physically valid,

given the following assumptions: deep percolation beyond the

active root zone is equal to groundwater recharge, recharge

is simulated without delay and represents an instantaneous

uptake into groundwater storage, and ET from groundwater

represents an instantaneous release from groundwater storage

within any monthly stress period (Schmid and others, 2006b).

The net recharge to the groundwater is applied to the uppermost active cells in each WBS.

Hydraulic Properties

The hydraulic properties of an aquifer system govern the

transmission and storage of groundwater in the system. The

transmission properties of the Central Valley aquifer system

are represented by hydraulic conductivity (K) and thickness 

in this study. Equivalent horizontal and vertical hydraulic

conductivities are assumed to be correlated to sediment texture

(the fraction of coarse-grained sediment). This assumption is

based on the spatial correlation between saturated hydraulic

conductivity and pore-size distributions in geologic media

(Russo and Bouton, 1992). A method for estimating hydraulic

conductivity based on this assumption has been applied successfully in previous groundwater-flow models of the central

western San Joaquin Valley (Phillips and Belitz, 1991; Belitz

and Phillips, 1995; C. Brush, U.S. Geological Survey, written

commun., 2006) and northeastern San Joaquin Valley (Burow

and others, 2004; Phillips and others, 2007). The method uses

the estimated sediment texture for each cell and horizontal and

vertical hydraulic conductivity estimates for each textural end

member. 

Brush and others (C. Brush, U.S. Geological Survey,

written commun., 2006) reviewed the use of a power mean for

defining hydraulic conductivity values and found it useful. A

power mean is a mean of the form:

M x

n

x

x

p

k

p

k

n p

( ) = 



 



 =

∑

1

1

1

where 

is the value being averaged (such as hydraulic 

conductivity),

p is the averaging power-mean exponent, 

n is the number of elements being averaged, and 

x k is the element in the list. k

th

,

The horizontal hydraulic conductivity (khi) was calculated as the weighted arithmetic mean (p=1) of the hydraulic

conductivities for each cell (i) of the coarse-grained (Kc) and 

fine-grained (Kf ) lithologic end members and the distribution

of sediment texture:

 K

where

is the fraction of coarse-g

h,i = + 

  K F K F

F

c c i f f i

c i

, ,

, rained sediment 

in a cell, estimated from sediment texture 

data as described earlier in Chapter A, and

F is the fr f i, action of fine-grained sediment in a

cell (1 ) F . c i,

Because Kf is much smaller that Kc, the arithmetic mean 

largely is influenced by the K and fraction of the coarsegrained end member.

Vertical hydraulic conductivity between 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 (C. Brush, U.S. Geological Survey, written commun., 2006):

K F K F K

k

v k c k c

p

f k f

p

p

, + , + , + = + 

  1

2 12 12

1

where 

represents the layer,

K is the fraction of coarse-grained sediment v k, + 1

2

between layer midpoints, and

F is the fraction of fine f k, + 1

2

-grained sediment 

between layer midpoints.
Model Development 155

The harmonic mean is a weighted power mean with

p = –1.0. The geometric mean is a weighted power mean with

p = 0.0. Phillips and Belitz (1991) determined that vertical

conductivities could be calculated using either weighted harmonic or weighted geometric means. Belitz and others (1993)

represented the vertical conductivities with the weighted

harmonic mean. Brush and others (C. Brush, U.S. Geological Survey, written commun., 2006) calculated the vertical

conductivities as power means in which p varied between –1.0

(the harmonic mean) and 0.0 (the geometric mean). 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

result in smaller values of p. 

Figure C14 shows the relation of hydraulic conductivity

and percentage coarse-grained deposits, based on hydraulic

conductivity end members and the exponent of the power

mean. Kf is sensitive to the averaging method used. Both the

harmonic and geometric means more heavily weight the finegrained end members and, as a result, the vertical hydraulic

conductivities are much lower than the horizontal hydraulic

conductivities.

Some single-well hydraulic tests (slug tests) and some

multiple-well hydraulic tests have been done in wells in the

study area. Phillips and others (2007) report that 21 aquifer tests in the northeastern San Joaquin Valley resulted in

hydraulic conductivity estimates ranging from 6.5 to 820 ft/d.

Bertoldi and others (1991) tabulated the average horizontal

hydraulic conductivity in the Central Valley from laboratory

tests of core samples. These values ranged from 2 x 10-3 ft/d 

for silty clay to 13 ft/d for sand. Williamson and others (1989) 

estimated an average horizontal hydraulic conductivity of

6.5 ft/d for the Central Valley based on the calibration of the

CV-RASA model. They also estimated the average hydraulic

conductivity of the Sacramento Valley of about one-half the

average for the San Joaquin Valley. They attributed this difference to the relative abundance of fine-grained volcanic-derived

sediments in the Sacramento Valley. Belitz and Phillips (1995)

estimated the hydraulic conductivity of the coarse-grained end

member in the central western San Joaquin Valley (Kc) to be 

104 ft/d for the Sierran Sands and 31 ft/d for the Coast Range

sands. They estimated the fine-grained end member (Kf) to 

be 4.0×10–3 ft/d. Brush and others (C. Brush, U.S. Geological Survey, written commun., 2006) used similar values for

0.01

0.1

1

10

100

1,000

0 20 40

Exponent of power mean—

60 80 100

PERCENTAGE COARSE-GRAINED DEPOSITS

RANGE IN HYDRAULIC CONDUCTIVITY, IN FEET PER DAY

EXPLANATION

(Arithimetic mean)

(Geometric mean)

(Harmonic mean)

-0.8

-0.5

-0.3

0

0.5

1.0

-1.0

Figure C14. Relation between hydraulic conductivity and percentage coarse-grained deposits based on hydraulic conductivity end 

members and exponent of the power mean.
156 Groundwater Availability of the Central Valley Aquifer, California

the crop coefficient, however, their estimate of Kf was about

an order of magnitude lower at 4.3×10–4 ft/d (table C8). The 

Belitz and Phillips (1995) estimates are based on the harmonic

mean (p = –1.0), and the Brush and others (C. Brush, U.S.

Geological Survey, written commun., 2006) estimates are

based on the power mean (p = –0.8). The harmonic mean more

strongly weights the fine-grained end member than the power

mean with a p = –0.8 (fig. C14). 

Permeameter tests of cores from the Corcoran Clay have

resulted in vertical hydraulic conductivity measurements of

about 6.6×10–6 ft/d (Page, 1977). Johnson and others (1968)

measured vertical hydraulic conductivities of 1.0×10–5–2.0×10–

5 ft/d in cores from the Corcoran Clay. Previous investigations,

however, indicate that intra-borehole flow through the numerous wells perforated across the Corcoran Clay effectively have

short-circuited the impedance to flow through this confining

unit (Williamson and others, 1989; Belitz and Phillips, 1995).

The estimated hydraulic conductivities of the Corcoran Clay

used in previous models generally are higher than the permeameter and lab-derived results. Belitz and others (1993)

compiled initial estimates of the conductivity of the Corcoran

Clay from several modeling sources. These are tabulated in 

table C8. Based on the modeling of Williamson and others

(1989), the mean values deduced for hydraulic conductivity

for the Corcoran Clay are from 4.1×10–4 to 1.5×10–3 ft/d. Phillips and Belitz (1991) estimated that the hydraulic conductivity of the Corcoran Clay is 6.9×10–4 ft/d if harmonic averaging

is used in the vertical direction, or 9.2×10–4 ft/d if geometric averaging is used in the vertical direction. The vertical

hydraulic conductivity estimated by Belitz and Phillips (1995)

is 5.2×10–4 ft/d. Brush and others (C. Brush, U.S. Geological

Survey, written commun., 2006) used values of 3.9×10–4–

6.2×10–4 ft/d for the Corcoran Clay.

Hydraulic Conductivity

The lithologic end-member hydraulic conductivities used

in this study are Kc, Kf , and Kcorc. Parameter estimation was

used in combination with the textural model developed for the

region on the basis of the known stratigraphic units and kriged

subsurface lithology, to estimate these end-member K values.

These end members were used to estimate the horizontal and

vertical K for each cell in the CVHM. The values of hydraulic

conductivity in table C8 represent final values derived from

model calibration; calibration of the CVHM is discussed in the

“Model Calibration and Sensitivity” section of this chapter. 

The Sacramento and San Joaquin Valleys have somewhat

different depositional environments and textural compositions

that affect the end-member K values. The Sacramento Valley

is much finer grained, has a strong volcanic influence, and,

as a result, possibly has less layering of fine-grained deposits

than does the San Joaquin Valley. Conversely, the San Joaquin

Valley is known to have numerous lenticular clay deposits

(Page, 1986; Williamson and others, 1989). Therefore, the

hydraulic properties of each were estimated separately. For the

Sacramento Valley, the calibrated value of Kf is 7.5×10-2 ft/d;

Kc is 6.7×10+2 ft/d. For the San Joaquin Valley, the calibrated

value of Kf is 2.4×10–1 ft/d; Kc is 3.3×10+3 ft/d. For both valleys, the distributions of horizontal and vertical K’s are the

same as those for the sediment texture (fig. A9). These values

are significantly higher than values estimated for the western

San Joaquin Valley by Belitz and others (1993), Belitz and

Phillips (1995), and Brush and others (C. Brush, U.S. Geological Survey, written commun., 2006). Their values represent

the finer-grained deposits characteristic of the western side of

the San Joaquin Valley, whereas the values used in the CVHM

are more representative of the coarser-grained sediments of

the eastern side of the valley. In fact, a multiplier was used to

decrease the K for the western side of the San Joaquin Valley

(see “Model Parameters” section). The CVHM values are

slightly higher than the values tabulated by Bertoldi and others

(1991) and comparable with the range of values used by Williamson and others (1989) in the original CV-RASA model.

Unlike previous models where the hydraulic conductivity

was specified for the Corcoran Clay as a unit, in the CVHM

the hydraulic conductivity (Kcorc) is based on the coarse- and 

fine-grained end member values. In general, finer-grained

sediments are present in the area of the Corcoran clay. A

“clay” designation on drillers logs within the Corcoran Clay

has a much greater probability of being homogeneous and

being clay, as opposed to silt or silty clay,. Hence, the Corcoran Clay forms a relatively continuous confining unit in the

aquifer system as opposed to the clayey lenses or interbeds 

prevalent throughout the rest of the system. Decreased vertical

flow through confining units and interbeds, particularly the

Corcoran Clay, has resulted from the inelastic compaction of

fine-grained materials within the aquifer system. As a result,

the vertical hydraulic conductivity of the clays, including the

Corcoran Clay, may have been reduced by 1.5 to 6 times from

original laboratory values (Williamson and others, 1989). In

addition, Riley (1998) concluded that aquitard permeabilities

measured under no-load conditions (for example, permeameter tests) appear to have very limited applicability to field

problems, although they presumably are significant, relative to

each other (Riley, 1998).

In order to represent the impedance to flow through the

clays and the enhanced impedance caused by compaction 

and other factors affecting the Corcoran Clay, the horizontal

hydraulic conductivity of the Corcoran Clay was reduced by a

factor of 0.01 from the value estimated by the arithmetic mean

of the coarse- and fine-grained fractions. Likewise, the vertical

conductivity was reduced by a factor of 0.002, resulting in

vertical hydraulic conductivities of 100-percent fine-grained

deposits about 4.8×10–4 ft/d (0.002 * 2.4×10–1 ft/d), close to the 

laboratory values. The local short-circuiting of flow through

wells perforated across the Corcoran Clay was accomplished

using intra-borehole flow with the MNW package.

Texture generally decreases with increasing distance

from the original source of the sediments (adjacent mountain
Model Development 157

Table C8. Measured and simulated hydraulic properties.

[Kc, hydraulic conductivity of coarse end member;Kf, hydraulic conductivity of fine-grained end member; Kc vertical hydraulic conductivity of Corcoran Clay;

MNW, Multi-Node Well package; p, texture-weighted power-mean value; SY, specific yield; Ss, specific storage; ft, foot; ft/d, foot per day]

Hydraulic 

property Field and laboratory values Previously simulated values Values simulated in this study

Kc 8.2×10+2 ft/d horizontal1; 31 to 104 ft/d2,3,4 6.7×10+2 ft/d Sacramento Valley;

10 ft/d vertical5 3.3×10+3 ft/d San Joaquin Valley

Kf 2.0×10–3 ft/d vertical5 to 4.3×10–4 ft/d4; 4.7×10–2 ft/d6 7.5×10-2 ft/d Sacramento Valley;

9.8×10–6 ft/d vertical5 2.4×10-1 ft/d San Joaquin Valley

Kcorc 6.6×10–6 ft/d7 3.9 to 6.2×10–4 ft/d4

5.2×10–4 ft/d2;

6.9 to 9.2×10–4 ft/d6;

4.1×10–4 to 1.5×10–3 ft/d9

Varies with parameters in San

Joaquin Valley; Horizontal K

multiplied by factor of 1×10-2;

Vertical K multiplied by factor of

2×10-3 ; borehole leakage across

clay included in MNW (previous simulations include borehole 

leakage by increasing K)

p N/A –0.8 San Joaquin Valley4 –0.5 Sacramento Valley;

–0.8 San Joaquin Valley

Porosity (total) 0.25 to 0.655 N/A 0.25 coarse-grained deposits;

0.50 fine-grained deposits

SY 0.0 to 0.355 0.2 (lower layers) to 0.3 (upper layer)4

0.02 to 0.209

Median value of 0.23 (0.09 to 0.40;

scaled based on percentage of

coarse-grained deposits)

Ss – elastic Coarse-grained = 1.0×10-6 per ft10–15; Coarse-grained = 1.4×10–6 per ft9; Coarse-grained = 1.0×10-6 per ft;

Fine-grained = 2.0×10–6 to 7.5×10–6

per ft10–15;

Fine-grained = 4.5×10–6 per ft9 Fine-grained = 4.5×10-6 per ft;

Compressibility of water = 1.4×10–6

per ft10–15

Ss – elastic and inelastic combined =

3.0×10–6 per ft5; 8.6×10–8 per ft16

Compressibility of water = 1.4×10-6

per ft

Ss – inelastic 1.4×10–4 per ft to 6.7×10–4 per ft15 3.0×10–4 per ft9 1.4×10–4 per ft 1

Phillips and others (2007).

2

Belitz and others (1993).

3

Belitz and Phillips (1995).

4

Brush and others (2006).

5

Bertoldi and others (1991).

6

Phillips and Belitz (1991).

7

Page (1977).

8

Johnson and others (1968).

9

Williamson and others (1989). 

10Riley (1969).

11Riley (1984).

12

Helm (1974).

13Helm (1975).

14

Helm (1976).

15Helm (1977).

16Ireland and others (1984).
158 Groundwater Availability of the Central Valley Aquifer, California

ranges) and with depth. This trend is observed in the observed

aquifer-system sediments and texture model (fig. A13). 

Coarser-grained sediments were simulated near stream channels. Because the Sacramento Valley generally is finer grained

than the San Joaquin Valley, average hydraulic conductivities

in the Sacramento Valley are less than those in the San Joaquin

Valley. An exception to this trend is in the southwestern part of

the valley, where fine-grained deposits are common.

Intuitively, hydraulic conductivity decreases with depth

because, among other things, (1) the geostatic load increases,

thereby compressing deposits, and (2) older deposits tend to

be more consolidated and (or) indurated with depth (age).

Determining the value of the depth-dependence directly from

hydraulic-conductivity data can be difficult because, in most

situations, such data are scarce, depth decay is obscured by 

variability caused by other factors, and measurement error

is substantial. Taylor and others (2001) and Whittemore and 

others (1993) show that hydraulic conductivity can decline

systematically with depth. Though the most appropriate decay

function to use is not always clear, exponential decay is commonly assumed (Anderman and Hill, 2003). Analyses and

simulations indicated that depth was a significant factor in

the variability of hydraulic conductivity in the Death Valley

region (Faunt and others, 2004). Their study indicates that

the hydraulic conductivity decreases rapidly for most rocks

at depths less than 3,000 ft, particularly alluvial units. The

exponential rate of decline of hydraulic conductivity estimated for alluvial units in Death Valley was used to specify

depth-dependent hydraulic conductivity values in the CVHM.

Adjustments to these specified values were made by estimating multipliers on the specified depth-dependent hydraulic

conductivities during calibration.

Storage Properties

The hydraulic properties used to simulate the changes

in storage of water within the aquifer system comprise three

principal components:

	 1. Specific yield,

	 2. Elastic specific storage, and

	 3. Inelastic specific storage.

Specific yield (dimensionless) is unconfined storage and

represents the fraction of gravity-driven drainage of a unit

volume of saturated sediments following a decline of the water

table or filling of drained porosity by a rising water table. Specific yield is a function of sediment porosity and its moistureretention characteristics, and it cannot exceed the fractional

sediment porosity. Specific storage is the volume of water that

an aquifer system, or a specified hydrogeologic unit within

the aquifer system, releases from or takes into storage per unit

volume per unit change in head. Specific storage (units of

inverse length) is equal to the storage coefficient (dimensionless) divided by the thickness (units of length) of the aquifer

system or specified hydrogeologic unit within the aquifer system. Elastic and inelastic specific storage refers to the elastic

and inelastic compressibilities of the aquifer-system material. 

Typically, coarse-grained material deforms elastically, and

fine-grained material deforms elastically and inelastically,

depending on the state of stress (Riley, 1969).

The first two principal storage components listed above,

specific yield and the elastic specific storage, represent and

govern the reversible uptake and release of water to and from

storage. The elastic specific storage represents the component

of confined storage owing to the compressibility of water and

to the reversible compressibility of the matrix or the skeletal

framework (skeleton) of the aquifer system. The inelastic

specific storage governs the irreversible release of water from

the inelastic compaction of the fine-grained deposits or permanent reduction of pore space. The values of inelastic specific

storage are much larger than those of elastic specific storage,

and the relative magnitudes of the corresponding inelastic

and elastic storage coefficients are dependent on the relative

aggregate thickness of the fine-grained sediments in the aquifer system or specified hydrogeologic unit within the system.

Specific yield typically is orders of magnitude larger than the

elastic storage coefficient and volumetrically is the dominant

storage parameter; however, storage in fine-grained beds is a

significant source of water where aquifers are developed and

inelastic compaction occurs (Konikow and Neuzil, 2007).

Given the fine-grained nature of the Central Valley aquifer system and its extensive development, water released by the compaction of the interbeds (discontinuous beds of fine-grained

deposits) in the aquifer system most likely is a significant

source of water in the valley. The water derived from inelastic

compaction is a one-time, non-recoverable release of water

from the fine-grained deposits. The release of water owing to

compaction of fine-grained deposits results in land subsidence.

The effects of long-term pumpage on lowering groundwater

levels and compaction of the fine-grained deposits are shown

in figures B16 and B18.

In the CVHM, a combination of the LPF and SUB

packages were used to define the storage properties. The LPF

package was used to specify the compressibility of water for

all model layers and the specific yield for the upper active

layer (fig. C1B). Although all model layers are simulated as

confined, the upper active layer represents unconfined (water

table) conditions and, therefore, is assigned a specific yield.

Because porosity constrains specific yield, and because

the product of fractional porosity and water compressibility determines the storage change in a confined aquifer

system or hydrogeologic unit in the system owing to fluid
Model Development 159

compressibility, it is worth reviewing some Central Valley

aquifer-system porosity measurements and estimates. Previous

modeling studies used porosities ranging from 0.25 to 0.65

in the Central Valley (Bertoldi and others, 1991). A porosity

value of 0.25 for the channel sand and 0.35 for the mud, with

an overall weighted average porosity of 0.31, was estimated

on the basis of the hydrofacies models developed for the

Modesto and Fresno areas of the San Joaquin Valley (Burow

and others, 2004; Phillips and others, 2007). On the western

side of the San Joaquin Valley in the Grasslands Drainage

area, porosity is estimated to range from 0.31 to 0.56 with a

mean of 0.42 for fine-grained sediments (Johnson and others,

1968); coarse-grained sediments are estimated to range from

0.28 to 0.50 with a mean of 0.41 (C. Brush, U.S. Geological

Survey, written commun., 2006). These values were used for

the western San Joaquin model (C. Brush, U.S. Geological

Survey, written commun., 2006). Laboratory values of porosity range from 0.25 for silty sands to 0.65 for sands (Bertoldi

and others, 1991). 

Specific yields from previous models range from 0.02

to 0.20 for the entire Central Valley (Williamson and others,

1989) and from 0.20 to 0.30 for the Grasslands area of the

western San Joaquin (Belitz and others, 1993). Williamson and

others (1989, table 4) lists estimated aggregated specific yield

values for the saturated sediment based on lithologic descriptions from about 17,000 well logs. They estimate an average

specific yield of 0.07 for the Sacramento Valley, 0.08 for the

Delta area, and 0.10 for the San Joaquin Valley and Tulare

Basin. Laboratory values of specific yield range from less than

0.01 for clays to 0.35 for sands (Bertoldi and others, 1991).

For the uppermost active model cells in the CVHM, specific

yield was added to the compressibility of water. Specific yield

was calculated using a linear relation between the fractions of

coarse-grained deposits. Where there were no coarse-grained

deposits, the specific yield was 0.09. Where the deposits are

all coarse-grained, the specific yield was 0.40. Very few of

the values reach the extreme ends of the range. The high end

of this range is above previous measurements and estimates

and only is used for coarsest-grained deposits representing

very well-graded sand. Less than 17 percent of the uppermost

active cells have specific yield values above 0.30 and less than

5 percent have specific yield values greater than 0.40. More

than 50 percent of the model cells, where specific yield was

specified, have values between 0.20 and 0.30. The median and

average values are 0.23 and 0.24, respectively, well within

previously estimated values of specific yield (table C8). 

For the CVHM, porosity values of 0.25 and 0.50 were

used for coarse-grained and fine-grained deposits, respectively.

The products of these porosity values and the respective cellby-cell average coarse- and fine-grained fractional aggregate

thicknesses are summed and multiplied by the compressibility 

of water (1.4×10-6 per foot) to yield an aquifer-system specific

storage value for each active cell of every layer.

Storage properties defining the matrix or skeletal components of specific storage were specified in the SUB package (Hoffmann and others, 2003b). The Subsidence (SUB)

package was chosen over the Interbed Storage (IBS) package

(Leake and Prudic, 1991) because SUB allows for time-dependent drainage. Realistically, the pore pressure of low-permeability units does not equilibrate instantaneously with changing

hydraulic heads in the adjacent aquifer, as assumed in the IBS

package. A time lag occurs that is dependent on the thickness,

vertical hydraulic conductivity, and the specific storage of the

low permeability units. Although the time-dependent drainage

feature within SUB was not utilized, it was included to enable

future use. 

Both the elastic and inelastic components of skeletal

specific storage were simulated with the SUB package. The

elastic and inelastic skeletal storage coefficients were calculated as the product of the estimated elastic- and inelastic-specific storage values for coarse- and fine-grained materials and

the aggregate thicknesses of those materials in each cell. The

elastic skeletal storage coefficient of the coarse-grained deposits was estimated from the product of the aggregate thickness

of coarse-grained deposits and the difference between an estimated elastic-specific storage and the specific storage representing the compressibility of water (Hanson, 1988). Reported

values for aquifer-specific storage determined

from selected aquifer tests typically range from 1×10–7 to 

2×10–7 per foot (Riley, 1969, 1984; Helm, 1974, 1975, 1976,

1977). The average aquifer specific storage is reported to be

8.6×10–8 per foot for the San Joaquin Valley (Ireland and others, 1984). An initial elastic skeletal specific storage for aquifers of 1.0×10–6 per foot was specified in the model, based on

reported values for coarse, alluvial sediments from the Central

Valley and Arizona (Ireland and others, 1984; Hanson, 1988).

The aquifer elastic skeletal-storage coefficient was estimated

as the product of the aquifer skeletal-specific storage and the

aggregate cell-by-cell thickness of the coarse-grained deposits

for each layer. An initial elastic skeletal-specific storage for

fine-grained units of 4.5×10–6 per foot was specified initially in

the model. This value is based on reported values for alluvial

sediments from the Central Valley and Arizona (Ireland and

others, 1984; Hanson, 1988). During model calibration, this

parameter was allowed to vary and the model solution was

relatively insensitive to this parameter. Ultimately, the initial

estimated value was used in the calibrated model. In a similar

manner, the average elastic skeletal storage coefficient of the

coarse-grained deposits was estimated to be 1.0×10–6 per foot. 

The composite aquifer-system elastic skeletal storage coefficient was the sum of the elastic skeletal storage coefficients for

the coarse-grained and fine-grained deposits for each cell in

each layer. An inelastic, skeletal-specific storage of 1.37×10–

4 per foot was estimated. This value is consistent with the low

end of the range of values determined from the analysis of

extensometer and piezometer data of 1.4×10–4 per foot (Riley,

1969; Helm, 1977; Ireland and others, 1984). The inelastic

skeletal storage coefficient was estimated as the product of the

inelastic specific storage and the aggregate cell-by-cell thickness of the fine-grained deposits for each layer. With respect

to matching subsidence observations, the model solution was
160 Groundwater Availability of the Central Valley Aquifer, California

most sensitive to this parameter, and cell-by-cell parameter

values were estimated during calibration.

Critical head is another parameter used by the SUB package that strongly affects storage changes, particularly the timing of those changes (fig. B18). Critical head is the equivalent

head at which effective or intergranular stress is equal to the

pre-consolidation stress. The equivalent critical head or preconsolidation stress represents the threshold stress that determines whether changes in stress deform the granular skeleton

elastically or inelastically. For head changes (whether positive

or negative) in the range of heads greater than the critical

head, the skeleton deforms elastically. For head changes in the

range of heads less than the critical head, the mode of skeletal

deformation depends on the sense of the head change—a positive change (head increase) causes elastic deformation, and

a negative change (head decrease) causes inelastic deformation and re-establishes a new critical head. In the upper three

model layers, specified initial critical-head values were equal

to the water levels estimated for the spring of 1961 (starting

head values used in CVHM). In the lower seven model layers, the initial critical heads initially were derived from those

estimated by Williamson and others (1989). These heads are 

approximate and were interpolated from the minimum historical head values simulated in the CV-RASA model. In the final

calibration, specified initial critical heads were equal to the

head simulated in CVHM in September 1961. These values

approximate the minimum historical head value in 1961.

Hydrogeologic Units 

Because the 3D configuration of regionally extensive

hydrogeologic units generally is unavailable for the Central

Valley, only two stratigraphically defined units and the crystalline bedrock of the Sierra Nevada complex are explicitly

incorporated in the CVHM. As described in Chapter A, the 

extent and thickness of the Corcoran Clay defined by Page

(1986) and later modified by Burow and others (2004) was

used to define model layers 4 and 5 (fig. A8). Where the San

Joaquin Formation (Allegra Hosford Scheirer, U.S. Geological Survey, written commun., 2004) is present in the model

domain, model cells within its mapped extent were identified.

Similarly, model cells that coincide with the mapped extents of

crystalline rocks of the Sierra Nevada complex also were identified. The uppermost model cell in each applicable column

intersecting these crystalline rocks is zoned as upper bedrock

and all lower cells were inactived. This bedrock intersection

occurs only on the eastern edge of the model domain and

leaves the bulk of the domain undefined by specific formations

(fig. C1). The contribution of groundwater from the bedrock

was assumed negligible.

Hydrogeologic Structures 

As delineated in Chapter A, the basin is traversed by two

cross-valley faults, the Stockton Fault and White Wolf Fault

(Hackel, 1966) (fig. C1). In addition, several smaller structures

also were identified as possibly affecting groundwater-flow

during an examination of water levels throughout the valley (fig. C1). The Horizontal Flow Barrier package (Hsieh

and Freckelton, 1993) was used to simulate resistance to

flow across these two major structures and several smaller

structures (fig. C1). Although the model solution is relatively

insensitive to these features, the effectiveness of these barriers was evaluated through model calibration by estimating

parameters representing the hydraulic conductance across the

features. The only other prominent structure in the Central

Valley is the Sutter Buttes, a Pliocene and Pleistocene volcanic

plug that rises abruptly to an altitude of 2,000 ft (600 m) above

the flat valley floor (fig. C1). The Sutter Buttes is about 9 mi

in diameter and the area is represented by inactive cells within

the model domain (fig. C1).

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.

Despite the fact that the system has been under stress since the

late 1800s, sufficient historical water levels and data for estimating stresses were not available until about the 1960s. The

combined effects of irrigation and groundwater pumpage have

greatly increased the vertical head gradients, particularly in the

southwestern part of the CVHM (WBS 14, fig. A4 and B13). 

The hydrologic system was in a transient state during the early

1960s owing to the changing vertical head gradients and the

continued recovery of the potentiometric surface. As a result

of these and possibly other conditions, steady-state simulations 

using 1961 stresses and water-level altitude constraints fail to

capture the ongoing transient responses to pre-1961 stresses.

Therefore, there is little choice but to begin the simulation

with initial conditions derived from a combination of historical water-level-altitude data and model-derived initial water

levels. Like CV-RASA, the groundwater-flow simulation starts

in April 1961, for which there are sufficient data to map both

the altitude of the water table and the groundwater levels in

the confined part of the aquifer system (Williamson and others, 1989). Although the specified initial state of the system

generally is inconsistent, to some degree, with the conservation equations and properties of the CVHM, it is considered an

adequate starting point.

The initial heads for the transient simulation were specified using the approach employed for previous studies in

the San Joaquin Valley (Belitz and others, 1993; C. Brush,

U.S. Geological Survey, written commun., 2006). The 1961
Model Calibration and Sensitivity 161

heads and streamflows are used as initial conditions to begin

the simulation and calibration period of April 1961 through

September 2003. Specifically, the initial heads in the upper

three model layers representing the shallow part of the system

(the semi-confined zone) (described in Chapter B) were set

equal to the water table altitudes defined by Williamson and

others (1989; fig. B12A), and heads for layers 4–10 were set

equal to groundwater levels defined by Williamson and others

(1989; fig. B12B). Specifying hydrostatic initial conditions for

the shallow and deep zones of the aquifer system ignores the

vertical head gradients within those zones. However, the vertical head gradients were re-established in the first few months

of simulation. Because of the importance of these gradients

and the fact that the initial heads were coarse estimates, the

CVHM was allowed to run forward 1 year to dissipate the

transient effects caused by imposition of the poorly estimated 

initial heads. The resulting simulated heads were considered

representative of heads in April 1961 and were used subsequently as initial heads for calibration of the CVHM as the

April 1961 starting heads. During calibration of the CVHM, as

various model parameter values were modified, this procedure

to dissipate transient effects caused by inaccurate initial heads 

was repeated periodically.

Because the irrigation and pumpage stresses on the system change rapidly, the inconsistencies between the initially

specified conditions and the simulated initial processes and

properties generally are not problematic because the next

stress regime soon dominates the solution (Hill and Tiedeman,

2007). This study and previous studies (Belitz and Phillips,

1995; C. Brush, U.S. Geological Survey, written commun.,

2006) show that the time frame for the stabilization is on the

order of several months of simulation. As a result, comparing

observed and simulated values becomes meaningful after a

relatively short simulation period.

Model Calibration and Sensitivity

Calibration of transient-state conditions was dependent

on recharge (streamflow, farm net recharge) to, and discharge

(pumpage, streamflow, and ET) from, the aquifer system and

on hydraulic conductivity, storage, fault hydraulic characteristics, general-head boundary conductance, and streambed

hydraulic conductivity. Many of the water-budget components are specified values. Specified model inputs that were

not adjusted during calibration include precipitation, stream

inflows at lateral boundaries, urban pumpage, semi-routed and

non-routed diversions from streams and canals to WBSs, and

many properties associated with WBSs and crops simulated

in the FMP. The remaining water-budget components, calculated by the model, include streamflow gains/losses, inflow

and outflow through the Delta, evaporation, transpiration,

groundwater pumpage for agricultural uses, runoff, farm net

recharge, leakage through multi-node wells, subsidence, and

groundwater storage changes. The implementation of the

MNW package maintained the net pumpage but redistributed

groundwater-flow vertically between layers through intraborehole flow.

Calibration of the CVHM was accomplished using a

combination of trial-and-error and automated methods. For

the CVHM, UCODE-2005 (Poeter and others, 2005) was used

to help assess the ability of the CVHM to predict the effects

of changing stresses on the hydrologic system. Simulated

changes in water levels, streamflows, streamflow losses, and

subsidence through time were compared to those measured in

wells, at streamflow gages, and extensometer sites. Automated

calibration adjustments were related to the combined fitting of

the groundwater levels, groundwater level differences, streamflow losses, and subsidence measurements (locations shown

in fig. C15). The observations were all compared to simulated

values and provided a measure of model performance through

space and time. The resulting error distributions constrain the

parameter set and constitute a sensitivity analysis of these

parameters. Maps of groundwater levels were used for qualitative comparisons but were considered less reliable than timeseries data because the composite water-level altitude measurements and hand contours represent averaged conditions

in many areas where there are large vertical-head differences

within some parts of the aquifer system.

Observations Used in Model Calibration

Successful groundwater model calibration often is

dependent on multiple observation types (Hill and others,

1998). However, parameter estimation, as used in groundwater

model calibration, often uses hydraulic head data as the sole or 

highly dominant type of observation. The combination of flow

observations, or other types of data, with water-level altitude

data lead to a more accurate, rapid, and unique calibration. 

The availability and accessibility of other data types provide

important observations that greatly aid in parameter estimation. Therefore, water levels, water-level altitude changes,

and water-level and potentiometric-surface altitude maps;

streamflows; boundary flows; subsidence; groundwater pumpage; water use; and water-delivery observations were used to

constrain parameter estimates throughout the calibration of the

CVHM.
162 Groundwater Availability of the Central Valley Aquifer, California

0 50 100 Miles

0 50 100 Kilometers

EXPLANATION

Location of stream segments used

 for model calibration

Model calibration—

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Location of wells used for model

 calibration

Location of subsidence measurements

 used for model calibration

Central Valley model boundary

Water-balance subregion

Figure C15. Distribution of calibration data (groundwater levels, gains and losses of streamflow, and subsidence observations).
Model Calibration and Sensitivity 163

Water-Level Altitudes, Water-Level Altitude 

Changes, and Water-Level Altitude Maps

The first calibration target was the groundwater-level

altitudes and changes in these altitudes through time. The

USGS and DWR maintain databases of key wells in the

Central Valley that are web-accessible (http://waterdata.usgs.

gov and http://wdl.water.ca.gov/gw/, respectively). These data

were combined to form a database of available water levels

throughout the Central Valley from 1961 to 2003. More than

850,000 water-level altitude measurements from more than

21,400 wells have been made by the USGS or DWR and

have been entered into their respective databases (fig. C16). 

However, only a small proportion of these wells (590 wells)

have both sufficient construction information to determine the

well-perforation interval and water-level measurements for the

simulation period. 

For model calibration, water-level altitude data were

needed that were (1) distributed spatially (both geographically

and vertically) throughout the Central Valley, from the valley

trough to the foothills; (2) distributed temporally throughout

the simulation period (1961–2003); and (3) available during both wet and dry climatic regimes. From the available

wells records, a subset of 170 comparison wells was selected

on the basis of perforation depths, completeness of record, 

and locations throughout the Central Valley. In selecting the

appropriate wells, shallow and deep wells were paired, where

possible, and several nested or clustered monitoring wells

were included. Even though the dataset containing 170 wells

spanned the simulation period, the water-level altitude data

were not always complete during the wettest and driest periods

of the record. In order to ensure that the wet and dry years

were adequately represented in the water-level-altitude calibration dataset, an additional analysis was done on the basis of

perforation depths, water-level altitude measurements during

the wet and dry periods (1961, 1965, 1969, 1976, 1977, 1980,

1983, 1991, 1998, and 2003; fig. C7), and the location of wells

within the WBS and throughout the Central Valley. Thirty-six

additional comparison wells were identified that best represent

the wet and dry periods, for a total of 206 comparison wells

(fig. C16). Because many wells had multiple measurements

in a given monthly stress period, the minimum water-level

altitude for each month was selected, resulting in 19,725

water-level altitude observations for the 206 comparison wells.

These observations were used as calibration targets during

parameter estimation. 

In addition to simulating the measured water-level altitudes at a particular time, it is necessary to accurately simulate the trends in water-level altitudes throughout the valley.

In order to represent these trends, a set of observations were

compiled for each well based on the net change in water-level

altitude for the period of record for each observation well. For

comparison, the water-level altitudes at the observation wells

ranged from –277 to 489 feet, and the average was 104 feet.

The net change in measured water-level altitude in individual

wells ranged from –150 to 250 ft and the average was 50 ft.

The net change in simulated water-level altitude at these well

locations ranged from –102 to 297 ft and the average was

80 ft. Both simulated and observed trends remained fairly flat

in the northern part of the study area, increased dramatically in 

the deeper parts of the southwestern part of the study area and

declined somewhat in the southeastern part of the study area.

A simple method of assessing overall model fit is to

plot the simulated water-level altitude values against the

measured water-level altitudes (fig. C17). For a perfect fit,

all points should show a 1:1 relation (fall on the 1:1 diagonal line). The final model sum of squared weighted residuals (SOSWR) for water-level altitudes from the comparison

wells was 3.82×107×10 ft2 for the 42.5-year simulation period 

1961–2003. The root mean square error between measured

and simulated water-level altitudes is 0.80 (fig. C17). Given

the scale of the CVHM, simulated water-level altitudes reasonably matched measured water-level altitudes, as indicated

by an average residual of –14 ft and a standard deviation of

43 ft; the residuals ranged from –277 to 384 ft. These extremes

possibly represent errors in the databases, measurements that 

represent pumping conditions, or seasonal variations beyond

the ability of the model to simulate. More than 50 percent of

the simulated water-level altitudes are within 25 ft of observed

water-level altitudes, more than 80 percent are within 50 ft,

and more than 94 percent are within 75 ft. In general, the

errors were distributed randomly and normally (fig. C17). In 

many WBSs, the measured water-level altitudes have a greater

range in water-level altitude than the simulated water-level

altitudes. This can be attributed to matching the average, more

than the range, of water-level altitude values. Because the

agricultural pumpage is distributed throughout all wells in a

WBS, the CVHM tends to match the average more than the

range of water-level altitude values. In reality, it is likely that

some wells are pumping harder than others and would have

a larger range in water-level altitude values. More detailed

“farms,” pumpage records, well-by-well pumping capacities,

and delivery information could be used to better match the

range of water-level altitude values.

The Central Valley aquifer system represents a range

of unconfined, semi-confined, and confined conditions. As a

result, water-level altitudes measured in wells and simulated

with the CVHM represent all of these conditions. In this

report, the term water-level altitude will be used to describe

the altitude that the potentiometric surface would be at if

penetrated by a well, whether unconfined, semi-confined, or

unconfined.
164 Groundwater Availability of the Central Valley Aquifer, California

Wells selected for spatial

 and climate coverage

Wells selected for spatial

 coverage

Wells selected for wet and dry period

 coverage

Remaining wells with water-level

 measurements between 1961 and 2003

Model streams and canals

0 50 100 Miles

0 50 100 Kilometers

EXPLANATION

Active model grid boundary

Potential horizontal flow

 barrier

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Model water-balance subregion

Figure C16. Distribution of wells with water-level-altitude data for the simulation period 1961–2003, and location of wells selected for 

model calibration. Only selected wells were used for calibration; other wells are shown to display the density of wells with water-level 

measurements in simulation period.
Model Calibration and Sensitivity 165

Hydrographs comparing simulated and measured waterlevel altitudes for selected wells indicate how well the CVHM

matches measured water-level altitudes in the upper and lower

parts of the aquifer system above and below the Corcoran Clay

(fig. B13). The minimum time period for which model simulations can reproduce accurate water-level altitude fluctuations

in the groundwater-flow system (the response time of the

model) varies with the depth to groundwater. Simulated annual

water-level altitude fluctuations generally are greater than

measured annual fluctuations. The reasons for this difference

include the contrast between simulation and measurement

frequency, and the fact that measurements generally are made

after the well is turned off and, therefore, is more likely to

represent quasi-static conditions. 

The hydrographs indicate that CVHM-simulated waterlevel altitudes general fit the measured values (fig. B13). In 

both the simulated and measured water-level altitudes, the

annual fluctuations are smallest at the water table and increase

with depth below the land surface. This most likely is a result

of the variability in storage, pumpage rates, and applications

of irrigation water. The use of large WBSs, estimated pumpage rates, coarse land use, spatial and temporal crop distributions, uniform model layer thicknesses (except above and in

the Corcoran Clay), lateral discretization of the model grid,

and assumptions made in spatially distributing pumpage limit

the performance of the CVHM. Given these limitations, the

CVHM cannot be expected to accurately simulate time-series

water-level altitude data from individual wells. Thus, the goal

of the model calibration was not to match individual hydrographs, but to match the long-term change in water-level

altitudes and to minimize the SOSWR for all simulated waterlevel altitudes.

-500

-300

-100

0

100

300

500

-500 -300 -100 0 100 300 500

MEASURED WATER LEVELS, IN FEET

EXPLANATION

SIMULATED HYDRAULIC HEAD, IN FEET

1 to 1 correlation line 

California Department of Water Resources water-balance subregions (See figure C1 for location) 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

18

19

20

21

Sacramento Delta and Eastside San Joaquin Tulare

CENTRAL VALLEY

A

A

0

1,000

2,000

3,000

4,000

-150-75-60-45-30-15 0 15 30 45 60 75 150 More

FREQUENCY

RESIDUALS, IN FEET

Figure C17. Plots of simulated water-level altitude values compared with the measured water-level altitudes for the A, Entire modeled 

area (and with inset with histogram of residuals). B, Sacramento Valley. C, Delta and Eastside. D, San Joaquin Basin. E, Tulare Basin.
166 Groundwater Availability of the Central Valley Aquifer, California

A comparison of the simulated water levels suggests

the CVHM is fairly accurate in the northern part of the study

area where the system has not been stressed vigorously and

the water table generally is flat, but performs poorly in the

southeastern part of the study area (fig. C17). There was little

change in the simulated water levels in the northern part

of the study area but there were some dramatic changes in

water-level altitudes in the southwestern part of the study area

(fig. B13). 

The CVHM closely matches measured water-level altitudes during some periods of time but overestimates or underestimates water-level altitudes at other times. This result likely

is due to previously discussed assumptions about the simulated WBS budget, land use, and to the inability to represent at

sufficient spatial and temporal detail, the land use (crops), well

locations, and associated stresses throughout the simulation

period. This is evident particularly in the southernmost part

of the study area. Examination of the simulated water-level

-100 -100

-300

0

100

300

0

100

300

500

-100

-300 -100

0 100 300

0 100 300

-100

-300

0

100

300

-100

-100

-300

-500

0

100

300

500

0 100 300

-300 -100 0 100 300 500

500

MEASURED WATER LEVELS, IN FEET

EXPLANATION

SIMULATED HYDRAULIC HEAD, IN FEET

1 to 1 correlation line 

1 to 1 correlation line 

1 to 1 correlation line 

California Department of Water Resources water-balance subregions (See figure C1 for location) 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

18

19

20

21

Sacramento Delta and Eastside San Joaquin Tulare

B

B C D E

C

D

E

Figure C17. Continued.
Model Calibration and Sensitivity 167

altitudes shows that the CVHM reasonably represents seasonal

changes and major features in the climate record. The effect of

droughts during 1976–77 and 1987–91, and higher-than-normal precipitation during 1983–84 and 1998, are represented by

changes in water-level altitudes (fig. B4). These changes are

evident particularly in the highly stressed southwestern part of

the San Joaquin Valley.

Water-Table and Potentiometric-Surface Maps

Water-table and potentiometric-surface maps for the 

Central Valley aquifer system for 1961 and 1976 are available

from the CV-RASA study (fig. B12) (Williamson and others, 

1989). The potentiometric-surface maps were compared with

contoured model results to ensure similarity between hydraulic

gradients and between water-level altitudes. The simulated

water-level altitudes are in general agreement with the handcontoured water-table and potentiometric-surface maps for the

spring of 1976 (fig. C18). The water-table and potentiometricsurface maps for 1961 were used for starting water-level

altitudes for the simulation. 

The distribution of groundwater-level altitudes through

time shows the effects of the time-varying recharge and

pumpage on groundwater-flow. Cones of depression in major

pumpage centers and water-level-altitude mounds in some

fine-grained irrigation areas are evident. The water-level

altitudes in the confined part of the aquifer system have varied

significantly from year to year, declining in years of greaterthan-average groundwater pumpage and recovering in years

of reduced pumpage (fig. B13). The simulated and observed

water-level altitudes in the water table wells (layers 1 through

3) remain fairly constant throughout the 1961–2003 period,

with some variations in intensively irrigated or pumped areas

(figs. B4 and B13). The simulated hydrographs (fig. B13, wells

06b12975, 14_30806, 15_2973719_37289) show that vertical hydraulic gradients from the water table to the deeper

production zones are strongly downward around the edges of

the valley and larger during spring and summer than during

fall and winter. Conversely, the gradient generally is upward

in the vicinity of the valley trough (fig. B13, 03b06329 and 

19_38742). Seasonally, simulated water-level altitudes fluctuate several hundred feet in the deeper wells on the west side

of the San Joaquin Valley and generally fluctuate less than 5 ft

at the water table (fig. B13). Temporal variability of measured

water-level altitudes generally is dominated by irrigation. At

rivers, variability is dominated by the river stage, and a

combination of these factors influence areas nearby the rivers.

Sacramento Valley

In the Sacramento Valley, the hydraulic gradients generally are downward around the edges of the basin, while

horizontal gradients, particularly in the upper part of the

aquifer system, are toward the Sacramento River (fig. C18). 

In the Redding area, water-level altitudes seem relatively

static over time (fig. B13, WBS 1), with water-level altitudes

highest around the basin edges and decreasing toward the

south and central part of the valley (fig. C18). Water-level

altitudes in the wells in the northern Sacramento Valley have

hydrograph signatures (peaks and troughs) which, although

subdued, reflect the effect of wet, dry, and average precipitation years, and show seasonal fluctuations ranging from about

5 to 10 ft. The CVHM simulates the seasonal changes and

some of the climatic effects accurately (fig. B13). Toward the

south, water-level altitudes decline slightly, as indicated by the

potentiometric surface maps (fig. C18). These changes partly

are owing to topographic changes and may be partly owing

to increased water demands caused by population growth.

Hydraulic gradients near the Sacramento River (WBS 4)

generally are upward, toward the gaining sections of the

Sacramento River (fig. B13, well 04_07807). Measured and

simulated water-level altitudes in wells closest to the Sacramento River respond only slightly to the dry years in the mid

1970s and early 1990s and specific periods in the mid 1980s

and mid-to-late 1990s (fig. B13, well 02_05172, 03b06329,

07_13870, and 08_14958).

In the southern Sacramento Valley, numerous creeks

drain runoff from the Coast Ranges that ultimately drains into

the Sacramento River. The aquifer system in these areas is

thicker; the deeper part of the system has upward hydraulic

gradients and the shallow part of the system has downward

gradients (fig. B13). These gradients partially are in response

to pumpage. In addition, further south in the Sacramento Valley, water-level altitude changes reflect changes in pumpage

activities, particularly during droughts. Near the North Fork of

the American River, in the vicinity of the city of Sacramento,

an area of water-level altitude declines likely represents urban

pumpage (fig. C18). Although development-related stresses

have only just begun by 1976 in parts of the Sacramento Valley, the initial effects seen in one interval of the aquifer system

are not readily observable in deeper or shallower intervals

of the system (fig. C18). This may indicate that major stratigraphic units, such as the Tuscan and Tehama Formations,

form the primary aquifers in the Sacramento Valley.

Delta

Water-level altitudes in the Delta area are affected by

human activity. Though surface-water-level altitudes in the

canals, sloughs, and rivers between the island levees are above

sea level, land-surface elevations of the islands generally are

below sea level. Simulated and observed water-level altitudes

generally decline toward the Delta (fig. C18); however, the

Stockton area has a large pumpage depression that causes an

influx of water from the Delta. This depression also is evident in the simulated potentiometric-surface maps (fig. C18). 

Although vertical hydraulic gradients generally are downward,

particularly south of the Delta, they have varied over time with

changes in pumpage activities. Locally, a number of faults

(fig. C16) may affect water-level altitudes and groundwaterflow directions. Some faults were simulated in the CVHM, but

not in this area. 
168 Groundwater Availability of the Central Valley Aquifer, California

Figure C18. The simulated A, Water-table altitude in spring 1976. B, Potentiometric-surface altitude in spring 1976, for the calibrated 

transient groundwater-flow model of the Central Valley. Residuals at observation points and areal differences between hand-drawn 

altitude maps also are shown. Maps showing the simulated water-table altitude and potentiometric-surface altitude in spring of 2000 

are shown in figures B12E and B12F.

Model streams

 and canals

Model water-balance

 subregion

0 50 100 Miles

0 50 100 Kilometers

EXPLANATION

Active model grid

 boundary

Simulated water-level altitude, 

 in feet. Spring, 1976—

A

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

-160

-120

-80

-40

0

40

80

120

160

200

240

280

320

360

400

440

480

Residual (Observed minus

 Simulated), in feet—

-57.3 to -55

-54.9 to -50

-49.9 to -45

-44.9 to -40

-39.9 to -35

-34.9 to -30

-29.9 to -25

-24.9 to -20

-19.9 to -15

-14.9 to -10

-9.9 to -5

-4.9 to 5

5.1 to 10

10.1 to 15

15.1 to 20

20.1 to 25

25.1 to 30

30.1 to 35

35.1 to 40

40.1 to 45

45.1 to 50

50.1 to 55

55.1 to 60

Water-level difference, in feet,

 simulated minus estimated (Williamson

 and others, 1989)—

Equal to or less than -50.0

-49.9 to -25.0

-24.9 to 0.0

0.1 to 24.9

25.0 to 49.9

Equal to or greater than 50.0
Model Calibration and Sensitivity 169

0 50 100 Miles

0 50 100 Kilometers

B

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Model streams

 and canals

Model water-balance

 subregion

EXPLANATION

Active model grid

 boundary

Simulated potentiometric-surface

 altitude, in feet. Spring, 1976—

-160

-120

-80

-40

0

40

80

120

160

200

240

280

320

360

400

440

480

Residual (Observed minus

 Simulated), in feet—

-57.3 to -55

-54.9 to -50

-49.9 to -45

-44.9 to -40

-39.9 to -35

-34.9 to -30

-29.9 to -25

-24.9 to -20

-19.9 to -15

-14.9 to -10

-9.9 to -5

-4.9 to 5

5.1 to 10

10.1 to 15

15.1 to 20

20.1 to 25

25.1 to 30

30.1 to 35

35.1 to 40

40.1 to 45

45.1 to 50

50.1 to 55

55.1 to 60

Potentiometric-surface difference, in feet,

 simulated minus estimated (Williamson

 and others, 1989)—

Equal to or less than -50.0

-49.9 to -25.0

-24.9 to 0.0

0.1 to 24.9

25.0 to 49.9

Equal to or greater than 50.0

Figure C18. Continued.
170 Groundwater Availability of the Central Valley Aquifer, California

San Joaquin Valley 

Data from wells close to streams and rivers in the San

Joaquin Valley exhibit a connection between the groundwater

and surface-water systems. Along the edges of the basins,

streams and rivers generally are losing water to the aquifer

system; those near or along the axis of the valley generally are

gaining. The potentiometric-surface maps show these relationships (fig. C18). 

Water-level altitudes throughout most of the San Joaquin

Valley are influenced by the Corcoran Clay, which acts as a

confining unit, forming a confined part of the aquifer system.

The vertical hydraulic gradients in the area of the Corcoran

Clay generally are downward (fig. B13). This downward

gradient is a result of the combined effect of a large amount

of pumpage from the lower part of the aquifer system, and

recharge of excess irrigation water to the shallow part of the

system. Seasonal fluctuations range from about 15 to 100 ft,

depending on the amount of pumpage (fig. B13). There was

a substantial water-level-altitude decline associated with the

1987–92 drought and its after effects; pumpage increased

during this period and water-level altitudes declined as much

as 300 feet. As surface-water deliveries ramped up after the

drought, water-level altitudes recovered to pre-drought levels

(fig. B13). 

In the San Joaquin Valley, water-level altitudes in the

confined part of the aquifer system may be affected by variations in precipitation in the recharge area near the foothills

and by drawdown cones from pumping wells. Both simulated and observed vertical hydraulic gradients on the eastern

and western side of the San Joaquin Valley are downward.

Measurements and other modeling in the Stanislaus to Merced

River and Fresno areas indicate downward hydraulic gradients

(Phillips and others, 2007). The measurements include multilevel piezometers (above and below the Corcoran Clay, where

present) within, west of, and east of Modesto; near the Merced

River; and in the mid-alluvial fan area near Fresno.

Water-level altitudes in the confined part of the aquifer

system in the Chowchilla area are much different from those

in the Merced area north of the Pre-Quaternary Fault; this

suggests the fault may impede flow in the confined part of the

aquifer system (figs. C16 and C18). Water-level altitudes near

the city of Fresno generally have been declining, which until

very recently was the largest metropolitan area in the U.S.

entirely dependent on groundwater (figs. B13 and C18).

Extreme examples of changes in development and the

effects on water-level altitudes occurred in the western San

Joaquin Valley. These changes are replicated in the potentiometric-surface maps and hydrographs (figs. B13 and C18). 

Groundwater pumpage reached a sustained maximum, and

confined water-level altitudes generally reached a low during the 1960s. Pumpage in this area, predominantly from the

lower part of the aquifer system, caused downward flow from

the upper to the lower zone throughout the simulation period.

Tulare Basin

The Corcoran Clay extends throughout most of the Tulare

basin. As on the west side of the San Joaquin Valley, waterlevel altitudes are heavily influenced by agricultural practices

and the presence of the Corcoran Clay. On the eastern side of

the Tulare basin, surface water is more available and groundwater pumpage has been less. Long-term, as well as seasonal,

fluctuations in groundwater-level altitudes reflect changes in

pumping activities in response to wet and dry years. Longterm water-level altitudes for several wells (fig. B13) indicate a 

downward vertical hydraulic gradient from the 1960s through

1972, and upward vertical gradients from 1973 through 1993.

In the southern part of the basin, the confined part of the

aquifer system has a higher water-level altitude, indicating

an upward gradient. Wells located outside the extent of the

Corcoran Clay (fig. B13) had declining water-level altitudes

and several vertical hydraulic gradient reversals during the

simulation period. On the southeastern side of the basin, 

simulated water-level altitudes decline at a faster rate than the

observed rate of water-level altitude decline.

Streamflow Observations

Quantitative observations of streamflow gains and losses

were available for 57 reaches of 20 major stream systems in

the Central Valley for water years 1961–77 (Mullen and Nady,

1985). These observations were included in UCODE-2005’s

parameter estimation process and in the model-fit statistics.

Water budgets for the Sacramento River were not included

because estimates of gains (and losses) to groundwater are

subject to larger errors in the Sacramento River than for other

streams (Mullen and Nady, 1985). The 1961–77 period during

which these data were collected reflects the cyclical characteristics of water supplies in the Central Valley and includes

reliable records during a series of wet and dry years (Mullen

and Nady, 1985). Phillips and others (2007) reported streamflow losses from the tributaries to the San Joaquin River in

the Modesto area. Their work shows streamflow losses in the

upper reaches and gains in the lower reaches.

Measured and simulated streamflow gains and losses

are shown in figure C19. The simulated gaining and losing

reaches generally are consistent with the observations from

Mullen and Nady (1985) and the gaining and losing sections

in the Modesto area (Phillips and others, 2007). The results

of the simulation show gaining sections of the San Joaquin

River with much larger gains than measured. The results of

the simulation show the southern part of the Kern River as

gaining when measurements indicate that this stream reach is

losing water. High streambed hydraulic conductivities were

assigned in the lower reaches of the Kern River to represent

the artificial recharge area of the Kern Water Bank. The simulated gaining reaches may indicate that simulated water-level

altitudes in the shallow part of the aquifer system are too high

in this area. 
Model Calibration and Sensitivity 171

Stony Creek

Cache Creek

Putah Creek

Kern River

Kaweah

River

Kings River

Fresno Slough

San Joaquin

Fresno

San Joaquin

Merced

Tuolumne

Stanislaus

EXPLANATION

Active model grid boundary

Model stream cells

0 50 100 Miles

0 50 100 Kilometers

GAIN LOSS

A

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Model water-balance subregions

Groundwater/surface-water interaction,

 in acre-feet per year— STREAMFLOW

-54.97 to -44.42

-44.39 to -14.81

-14.78 to -1.48

-1.45 to 0

0.03 to 1.48

1.51 to 14.81

14.84 to 44.42

44.45 to 113.07

Out of groundwater

system

Into groundwater

system

Figure C19. Distribution of stream gain/loss segments used for model calibration. A, Measured gaining and losing reaches for selected 

stream reaches for 1961–1977. B, simulated gaining and losing reaches for selected stream reaches for 1961–1977 for the Central Valley, 

California.
172 Groundwater Availability of the Central Valley Aquifer, California

Stony Creek

Cache Creek

Putah Creek

Kern River

Kaweah

River

Kings River

Fresno Slough

San Joaquin

Fresno

San Joaquin

Merced

Tuolumne

Stanislaus

EXPLANATION

Active model grid boundary

0 50 100 Miles

0 50 100 Kilometers

GAIN LOSS

B

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Model water-balance subregions

Groundwater/surface-water interaction,

 in acre-feet per year

Less than -500

-500 to -100

-99.9 to -25

-24.9 to 0

0.1 to 25

25.1 to 100

100.1 to 500

Greater than 500

STREAMFLOW

Out of groundwater

system

Into groundwater

system

Figure C19. Continued.
Model Calibration and Sensitivity 173

Simulated streamflows at various gaging stations

throughout the valley were monitored during calibration

although, unlike streamflow gains and losses, these streamflow

values were not a specific calibration target. The simulated

streamflow generally matches measured streamflow, except in

some low-flow situations.

Another model calibration constraint was the historical reported streamflow diversions from the 66 simulated

diversions in the streamflow-routing network. Although not a

specified calibration target used in the parameter estimation,

the CVHM satisfies this constraint. This indirectly suggests

that the streambed hydraulic conductivities for the stream segments upstream of these diversions allowed sufficient water to

be conveyed by the various rivers to the points of diversion in

the streamflow network.

The simulated streamflow gains and losses are approximately 1.5–2 times larger than those simulated by the

CV-RASA model (Williamson and others, 1989). However,

the net volume rate of streamflow gains and losses is a loss of

300,000 acre-ft per year. This net loss rate is of similar magnitude to the net loss rate simulated by the CV-RASA model

(fig. B1), and represents a small part of the overall groundwater budget. The differences in magnitude of the gains, losses,

and overall net rates likely are a result of the more detailed

discretization of the shallow part of the aquifer system in the

CVHM. The fact that simulated water-level altitudes in the

shallow system are too high in places and that the net gain-loss

rates compare well suggests that any additional groundwaterflow to streams flows quickly from streams to areas of lower

groundwater level altitudes.

Boundary Flow Observations

Although little is known about the actual volume of

groundwater discharge through the Delta, it is thought to be

negligible, compared to the rest of the water budget (Williamson and others, 1989). The CVHM simulation indicates

that less than 1 percent of the water is leaving the groundwater

system through the general head boundaries (GHBs).

Flow from small watersheds surrounding the valley

is poorly understood and, therefore, is not specified in the

CVHM. This is a potential source of model error. In DWR’s

model (C. Brush, California Department of Water Resources,

written commun., February 21, 2007), these small-watershed

inflows average about 1 million acre-ft per year and account

for less than 5 percent of the influx to the system. Brush

(C. Brush, California Department of Water Resources, written

commun., February 21, 2007) reports that these inflows are

based on values published by Nady and Larragueta (1983).

Although small, if these fluxes are correct, they represent

a part of the total loss of storage simulated in the CVHM.

Because simulated water-level altitudes are particularly low

in the southeastern part of the Central Valley and DWR’s estimated input to that area is relatively large, incorporating these

inflows in the CVHM could improve model fit.

Subsidence Observations

Measured compaction from extensometers in the valley

also was used as a calibration target. Subsidence monitoring

observations can provide valuable information about hydrologic parameters such as elastic and inelastic skeletal specific

storage (Riley, 1969; Hanson, 1988; Leake, 1990; Sneed and

Galloway, 2000; Burbey, 2001; Larson and others, 2001;

Sneed, 2001; Hoffmann and others, 2003a; Phillips and others,

2003, Pavelko, 2004; Halford and others, 2005). The CVHM

was adjusted to fit the range of measured compaction at the

extensometer sites utilizing UCODE-2005 and manual calibration. The calibration target was the measured compaction

from several extensometers in the region (figs. C15 and C20). 

Monthly simulation of stresses and associated water-level altitude changes improved the temporal resolution of simulated

compaction over that in the CV-RASA model. A good match

between simulated and measured compaction at extensometer

sites was achieved though delayed drainage (and repressurizing) of aquitards was not simulated. In some areas, more

seasonal variability in water-level altitudes is being simulated

and, as a result, elastic rebound is overestimated (fig. C20). 

The trend of recovery of the land surface at some extensometers is not seen in the CVHM simulation, indicating some

error in simulated water-level altitude or elastic properties

and (or) the presence of delayed repressurization of aquitards. The simulated subsidence correlates well with measured

climatic changes and surface-water deliveries. For example,

the simulated subsidence shows multi-year elastic deformation

related to the droughts (1976–77; 1980s) (fig. C21). Uplift also 

is evident in many of the plots (fig. C21). 

Subsidence simulated by the revised CVHM was compared to estimated-subsidence maps. The simulated subsidence 

shows a similar spatial pattern of deformation, with respect to

the spatial distribution derived from the historical subsidence

(figs. C20 and C21). As would be expected, the areal distribution and amount of subsidence generally has increased with

time, particularly through the last major drought, 1987–92.

The simulated subsidence is much larger than the measured

subsidence in some areas, especially near township 24S/range

26E (fig. C21). In part, this subsidence likely is caused by the 

larger simulated water-level altitude declines, as compared to

the measured declines, in these areas. 
174 Groundwater Availability of the Central Valley Aquifer, California

1

1

1

2 2

2

3

3

3

3

4

4

4

4

5

6

7 7

8

5

1

1

1

2

3 4

-1

Model streams and canals

0 50 100 Miles

0 50 100 Kilometers

EXPLANATION

Active model grid boundary

Subsidence, in feet, from 1961 to 1975,

 (Williamson and others, 1989). Negative

 values indicate uplift—

-1 to 0

1

2 to 5

6 to 10

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Model water-balance subregion

Figure C20. Distribution of historical subsidence, estimated from 1961 to 1977 extensometer data, Central Valley, California (modified 

from Williamson and others, 1989).
Model Calibration and Sensitivity 175

11N/1E-24Q8

26S/23E-16H2, 3

24S/26E-34F1

24S/26E-36A2

25S/26E-1A2

23S/25E-16N1,3,4

22S/27E-30D2

12S/12E-16H2,3

14S/12E-12H1

16S/15E-34N1

32S/28E-20Q1

1

1

1

1 1 1

1

1

1

5

5

5

5

5

5

5

5

5

5

5

10

10

10

10

10

10

10

15S/16E-31N3

15S/13E-11D2

14S/13E-11D6

13S/15E-35D5

18S/19E-20P2

20S/18E-6D1

20S/18E-6D1

18S/16E-33A1

18S/16E-33A1

17S/15E-14Q1

13S/15E-31J3

Model streams and canals

EXPLANATION

Active model grid boundary

A

Subsidence observation locations

Simulated subsidence 1962-2003, in feet

1

5

10

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Model water-balance subregion

0 50 100 Miles

0 50 100 Kilometers

Figure C21. A, Distribution of total simulated subsidence for water years 1962 through 2003, and locations of subsidence measurements. 

B, Aggregate compaction measured at, and simulated subsidence for, selected extensometer locations, Central Valley, California.
176 Groundwater Availability of the Central Valley Aquifer, California

B

1960 1970 1980 1990 2000 2010

SIMULATED SUBSIDENCE, IN FEET

1960 1970 1980 1990 2000 2010

EXPLANATION

Simulated subsidence Measured subsidence

11N/1E-24Q8

12S/12E-16H2

12S/12E-16H3

13S/15E-31J3

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

15S/13E-11D2

14S/12E-12H1

14S/13E-11D6

7

0

10

20

26

WATER YEAR WATER YEAR

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

13S/15E-35D5

7

0

10

20

26

7

0

10

20

26

Figure C21. Continued.
Model Calibration and Sensitivity 177

Figure C21. Continued.

23S/25E-16N1

B (Cont.)

1960 1970 1980 1990 2000 2010

SIMULATED SUBSIDENCE, IN FEET

1960 1970 1980 1990 2000 2010

EXPLANATION

Simulated subsidence Measured subsidence

18S/19E-20P2

22S/27E-30D2

20S/18E-6D1

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

WATER YEAR WATER YEAR

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

7

0

10

20

26

7

0

10

20

26

17S/15E-14Q1

18S/16E-33A1

16S/15E-34N1

15S/16E-31N3
178 Groundwater Availability of the Central Valley Aquifer, California

Figure C21. Continued.

B (Cont.)

1960 1970 1980 1990 2000 2010

SIMULATED SUBSIDENCE, IN FEET

1960 1970 1980 1990 2000 2010

EXPLANATION

Simulated subsidence Measured subsidence

25S/26E-1A2

26S/23E-16H3

32S/28E-20Q1

23S/25E-16N4

24S/26E-34F1

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

7

0

10

20

26

WATER YEAR WATER YEAR

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

7

0

10

20

26

7

0

10

20

26

23S/25E-16N3

24S/26E-36A2

26S/23E-16H2
Model Calibration and Sensitivity 179

When aquifer systems are developed, groundwater

released from storage in fine-grained deposits often contributes a significant amount of the water supplied to pumping

wells (Konikow and Neuzil, 2007). Because the fine-grained

deposits constitute an average 64 percent of the unconsolidated sediments in the Central Valley (Chapter B), they likely 

represent a principal source of groundwater released from storage in the valley. In the original CV-RASA, many of the finegrained units were simulated on the basis of the percentage

of fine-grained deposits in a model cell. The confining units

simply were represented by specifying impedance (reduced

vertical conductance) between adjacent model layers, that is,

without having the capacity to store groundwater. Although

the original CV-RASA study did quantify storage losses attributed to compaction, storage changes in these confining units,

such as the Corcoran Clay, were not simulated by the CVRASA model and were not computed as a distinct component

of the groundwater budget. The CV-RASA study and previous

studies attributed most of the water derived from compaction to that of fine-grained interbeds and not from regional

confining beds such as the Corcoran Clay (Ireland and others,

1984; Williamson and others, 1989). Because the fine-grained

interbedded deposits and the confining units in the aquifer

system are simulated explicitly in the CVHM, a more accurate

accounting of the sources of water produced is now available.

Simulated compaction in the CVHM corroborates the previous

findings; since predevelopment, compared to storage losses

from the fine-grained interbeds, a relatively significant volume

of water has not yet been released from storage in the

Corcoran Clay.

Pumpage Observations

Although not a defined calibration target, where available, agricultural pumpage estimates from power records (Diamond and Williamson, 1983) were compared with agricultural

pumpage estimates from the CVHM (fig. C22). As mentioned

previously, the USGS estimated groundwater pumpage from

electric power consumption in the Central Valley prior to

1980. Estimates from these power records were compiled for

the original CV-RASA study by township for the period 1961–

77. Missing data during this period were computed by means

of multiple regression models for each township by Diamond

and Williamson (1983). Next, the agricultural groundwater

pumpage per township was summed for each WBS. Although

these power record estimates are reported to be accurate where

they are complete (Diamond and Williamson, 1983), the data

are not comprehensive for the Central Valley. Because these

power-record based pumpage estimates were not available for

the entire Central Valley, the estimates are considered minimum values. For the 1961–77 period, the estimated pumpage

was compared to the CVHM simulated pumpage values for

the entire Central Valley (fig. C22). Comparison of simulated

and estimated pumpage for this period shows good general

agreement between these values (fig. C22). In particular, the 

trends are matched. The simulation matches closely in drier 

periods and is simulated at lower rates in wet periods.

For the 1960s and 1970s, the CVHM estimates that an

average of approximately 20 million acre-ft of irrigation water

was required annually, about one-half from groundwater and

one-half from surface-water. These values closely match

the proportions and total 21 million acre-ft of annual farm 

delivery requirement, as reported by Williamson and others

(1989).

The development of groundwater involved the construction of about 100,000 irrigation wells, many with long

intervals of perforated casing that provide a hydraulic connection between permeable zones within the aquifer system.

The vertical leakage of the aquifer system was increased

substantially because of the hydraulic connection provided

by the wells completed in multiple zones (fig. C13) (Page

and Balding, 1973; Londquist, 1981; Williamson and others,

1989). This vertical flow through wellbores occurs through

both pumped and non-pumped wells. Davis and others (1964)

estimated that about 100,000 acre-ft/yr flowed between these

zones through wells in the western part of the San Joaquin

Valley in the early 1960s. The CVHM suggests that between

1961 and 2003, 400,000 acre-ft/yr flowed into the lower

aquifer through well-bore flow (fig. C13). This most likely is a 

low estimate, because, for computational reasons, only urban

wells and agricultural wells penetrating both above and below

the Corcoran Clay were simulated using MNW. Williamson

and others (1989) suggest that more intra-borehole flow may

occur through wells in the rest of the Central Valley than

intra-borehole flow across the Corcoran Clay. Unfortunately,

given its current configuration, the CVHM cannot confirm or

contradict this.

Water-Use Observations

As with pumpage estimates, water-use data were not a

defined calibration target. Few data exist for water use and

most of the factors used in its calculation are indirect measurements, such as temperature or crop coefficient values, or are

estimates, such as percentage of runoff. However, as part of

the CV-RASA, Williamson (1982) calculated the evapotranspiration of applied water (ETaw) for the Central Valley during

1957–78. Table C9 lists Williamson’s estimated ETaw values,

along with CVHM simulated values, for the Central Valley.

The simulated values of ETaw (which do not include uptake of

groundwater) fall within the range of values estimated by Williamson (1982). Of note, the average ETaw value simulated, in

some cases, is less than those estimated by Williamson (1982). 

This may be the result of changes in crop types, increased

irrigation efficiencies, and groundwater uptake simulated

in the 1962–2003 time period. The estimated and simulated

ETaw generally increase toward the south. Although the area

of irrigated agriculture increased (table C3), the amount of 
180 Groundwater Availability of the Central Valley Aquifer, California

Basin

Area 

(square 

miles)

Estimated unit (average) 

ETaw 1957–1961 ETaw 1966-–1973 ETaw 1968–1978

Simulated ETaw 

 average 

(1962–2003)

Sacramento Valley 5,981 2.4 (2.49) 2.3 (2.84) 1.9 (3.04) 1.8 (2.20)

Delta and Eastside Streams 2,388 2.2 (1.86) 2.1 (1.96) 1.8 (1.91) 1.9 (1.00)

San Joaquin Valley 3,782 2.5 (2.76) 2.0 (2.48) 2.3 (3.16) 2.3 (2.90)

Tulare Basin 7,780 2.1 (4.46) 2.2 (6.390) 2.2 (7.13) 2.3 (8.20)

Central Valley 19,389 2.3 (11.57) 2.1 (13.67) 2.1 (15.24) 2.1( 14.30)

Table C9. Estimated and Central Valley Hydrologic Model-simulated average and unit evapotranspiration of applied water (ETaw) for 

the Central Valley, California. 

[Unit ETaw values in feet; Average ETaw values in millions of acre-feet per year; source of estimates, Williamson (1982)]

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry

Wet Dry Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

YEAR

PUMPAGE, IN MILLIONS OF ACRE-FEET PER YEAR

Estimated from

 power usage

Simulated agricultural

4

0

8

12

16

EXPLANATION

Figure C22. Agricultural pumpage from 1961–77 estimated from power records (Diamond and Williamson, 1983) compared to Central 

Valley Hydrologic Model simulated agricultural pumpage for the Central Valley, California.
Model Calibration and Sensitivity 181

ETaw increased at a smaller rate, most likely the result of

using lower water-use crops (Williamson, 1982). In the Tulare

Basin, the increase in ETaw is attributed to the delivery of

surface water for irrigation by the California Aqueduct,

beginning in 1967 (Williamson, 1982).

Water-Delivery Observations

Water-delivery data were not a calibration target, but

were used to refine the CVHM. In some areas and in many

years, particularly in wetter years, available surface-water

deliveries exceeded the used surface-water deliveries. This

was particularly evident on the west side of the San Joaquin

Valley (WBS 14) (fig. C23A). During these years, a significant

part of the unused available deliveries occurred during the

winter months (fig. C23B). These winter-month deliveries are

used partly for double cropping and partly for on-farm storage

and pre-wetting of fields. Double cropping was estimated by

adjusting the crop-coefficients to have a second peak during

the winter (fig. C25). 

On-farm storage of surplus water deliveries and its

delayed release is not simulated currently in the FMP. This

modification will require adding ‘farm-water storage’ to the

farm mass balance (Schmid and others, 2006a). Because these

values are important on the heavily irrigated Central Valley’s

west side (WBS 14), a method was developed for simulating

the use of some of these deliveries for storing and pre-wetting

the soils. In general, truck crops and cotton are grown in this

area; therefore, the crop-coefficients were adjusted for both

cotton and truck crops. In order to preserve mass balance of

water usage, the adjustment was made keeping the average

total ET constant for WBS 14. For example, a percentage

of the total ET was taken from the initial summer growing

months (April through July). This volume of water then was

added equally to December through February by adjusting the

crop coefficient values for each month (fig. C25). Based on the

best match with deliveries, it was estimated that for WBS 14,

about 20 percent of the volume of ET was used for on-farm

storage and pre-wetting of soils.

Model Parameters 

Following Hill and Tiedeman (2007), the term “parameter” is used to define model inputs. Because the CVHM

includes many complex processes that require that parameters

be distributed widely in space and time, the potential number

of model parameters that could be estimated is large and computationally prohibitive. 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 best guesses and a systematic

application of the parameter estimation method to narrow

the range of possible solutions to produce simulated values

that best matched the measured observations. Many of the

parameters were defined beforehand, and about 50 parameters

were estimated during the automated calibration process, with

less than ten estimated at any one time. These parameters 

included hydraulic properties of the aquifer system, streambed 

hydraulic conductivities, and parameters related to the FMP

(table C10). Additional parameters could be estimated and

others could be added, as needed. However, longer model

execution times pose a practical limit on the number of estimated parameters.

Initial input parameters were adjusted within ranges of

reasonable values to best fit hydrologic conditions measured

in the aquifer system, including measured water-level altitudes and associated long-term trends, estimated streamflow

losses, and subsidence. Because the CVHM utilized the SFR1

package and FMP process, MF2K could not be used directly

for sensitivity analyses and parameter estimation. A separate

code for sensitivity analyses, such as UCODE or PEST must

be used. A combination of UCODE-2005 (Poeter and others, 2005) and manual adjustments were used to conduct the

parameter estimation and sensitivity analyses.

As described in the “Hydraulic Properties” section 

of this report, Kh and Kv were estimated for every cell in

the CVHM on the basis of sediment texture, end-member

hydraulic conductivity values (Kc and Kf ), and averaging

method (weighted arithmetic average for Kh; power mean for

Kv). These end-member hydraulic conductivity values were

adjusted by UCODE-2005 to minimize model error. During

initial calibration efforts, the same end-member hydraulic conductivities and averaging-method parameter (the p value used

in the power mean) were used for the entire Central Valley. As

calibration progressed, it became evident that more hydraulic

parameters would be necessary to represent the system with

sufficient accuracy. A Kc and Kf were defined separately for

each of the Sacramento and San Joaquin Valleys because of

the somewhat different depositional environments in the two

valleys (Chapter A). In addition, a separate power mean was

used for these two areas. A relatively larger value of the power

mean (p = –0.5 versus –0.8) was estimated in the Sacramento

Valley to represent the less layered nature of the aquifer

system there (fig. C14). The estimated end-member hydraulic 

conductivities for the Sacramento and San Joaquin Valleys

were similar and the Kv differed according to the power mean

(table C10).
182 Groundwater Availability of the Central Valley Aquifer, California

"Variable"

to

Dry

"Variable"

to

Dry

"Variable"

to

Wet

Dry Wet Dry Wet

A

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

SURFACE-WATER DELIVERIES, IN MILLIONS OF ACRE-FEET PER YEAR

0.4

0

0.8

1.2

1.6

Deliveries taken (modeled) Deliveries available

100

0

50

150

200

250

300

1977 1978 1979 1980 1981 1982 1983 1984

B

YEAR

WATER YEAR

EXPLANATION

SURFACE-WATER DELIVERIES, IN THOUSANDS OF ACRE-FEET PER YEAR

Figure C23. A, Annual water deliveries. B, monthly water deliveries from the mid 1970s to mid 1980s for water-balance subregion 14.
Model Calibration and Sensitivity 183

As calibration continued, it became evident that too much

flow was occurring through the lower parts of the aquifer

system. Because the texture model does not incorporate depth

decay of horizontal and vertical hydraulic conductivity, a set

of multipliers were included in the CVHM to represent these

changes. Initially, the exponential rate of decline of hydraulic

conductivity estimated for alluvial units in Death Valley was

used to decrease the hydraulic conductivity values. Although

the multipliers varied slightly by location and for horizontal

and vertical K, the values generally are 1.0 for the upper three

model layers and 0.5, 0.37, 0.24, 0.15, and 0.08 for layers 

6–10, respectively. Adjustments to this rate of decline were

made by estimating multipliers on the hydraulic conductivity

with depth during calibration (table C10). 

The streambed hydraulic conductivity parameters also

were estimated. Seven streambed hydraulic conductivity zones

were identified (fig. C26 and table C10) and low streambed

hydraulic conductivities were used as initial estimates and

adjusted during calibration (table C10). The GAGE package was used to calculate flow at upstream and downstream

gage points (fig. C26). Yearly streamflow losses between gage

points were used to estimate streambed hydraulic conductivities that reproduced the average annual streamflow losses for

1961–77. Streamflow depths were not calibrated and were

specified at 3 ft.

A multiplier on the range of specific-yield values was

adjusted during calibration. This value was modified to scale

the range in specific yield and minimize the error for all

observations. Parameters representing both the elastic and

inelastic storage properties also were estimated. Adjustments

to the elastic and inelastic storage properties allowed for better matches to subsidence data. The skeletal inelastic specific

storage was estimated directly (table C10). A multiplier on the

elastic specific storage for both the coarse and fine-grained end

members also was estimated (table C10). Trends in subsidence that are inaccurately simulated indicate some error in the 

simulated water-level altitude, the elastic properties, or both.

Elastic specific storage appears to be the most difficult parameter to estimate accurately. 

In order to match water-level altitudes in the western

San Joaquin Valley, wells penetrating the Corcoran Clay and

perforated above and below the clay were simulated as multinode wells. During calibration, the skin factor in the MNW

package was found to affect the interlayer flow and related

water-level altitude difference between layers. This value was

adjusted manually to best match observed water-level altitudes. The skin factor representing resistance to flow between

the wellbore and the aquifer system was decreased both above

and below the Corcoran Clay, but more so below the Corcoran

Clay.

0.00

Single cropped

Double cropped

0.20

0.40

0.60

0.80

1.00

1.20

JAN FEB MAR APR MAY JUN

MONTH

EXPLANATION

JUL AUG SEP OCT NOV DEC

CROP COEFFICIENT, UNITLESS

Figure C24. Single and double cropped crop coefficient values for truck crops.
184 Groundwater Availability of the Central Valley Aquifer, California

0

2

4

6

8

10

12

JAN FEB MAR APR MAY JUN

MONTH

JUL AUG SEP OCT NOV DEC

EVAPOTRANSPIRATION, IN INCHES

A

TRUCK CROPS

Crop evapotranspiration

Adjusted evapotranspiration

12

JAN FEB MAR APR MAY JUN

MONTH

EXPLANATION

JUL AUG SEP OCT NOV DEC

EVAPOTRANSPIRATION, IN INCHES

B

COTTON CROPS

0

2

4

6

8

10

Figure C25. Original and adjusted crop coefficient values for truck and cotton crops.
Model Calibration and Sensitivity 185

Table C10. Parameter values estimated for the Central Valley Hydrologic Model.—Continued

[WBS, water-balance subregion]

Parameter 

name

Parameter 

description Value Units

Estimated 

using 

automated 

methods

Composite 

scaled 

sensitivity

Parameter 

group

Land-use Properties

NAT_ROOT Rooting depth of natural vegetation 10.55 feet yes 1.37 Farm Process

properties

KC_S Multiplier for crop coefficients in

summer growing season

(April–September)

0.98 multiplier yes 1.18102 Farm Process

properties

KC_W Multiplier for crop coefficients in

winter dormant season

(October–March)

1.18 multiplier yes 2.20101 Farm Process

properties

Runoff

H2O_PRO Percent runoff from precipitation

for water land-use class

5.00 percentage yes 1.671010 Farm Process

properties

URB_PRO Percent runoff from precipitation

for urban land-use class

1.50 percentage yes 1.23 Farm Process

properties

NAT_PRO Percent runoff from precipitation

for natural vegetation land-use

class

20.71 percentage yes 3.76 Farm Process

properties

PRECIP_RO Percent runoff from precipitation

for various land-use classes

10.25 percentage yes 1.41 Farm Process

properties

IDL_PRO Percent runoff from precipitation

for idle land-use class

6.04 percentage yes 1.38 Farm Process

properties

VIN_PRO Percent runoff from precipitation

for vineyards land-use class

1.30 percentage yes 3.64 Farm Process

properties

SEM_PRO Percent runoff from precipitation

for semi-agriculture land-use

class

32.29 percentage yes 6.67109 Farm Process

properties

DEC_PRO Percent runoff from precipitation for

deciduous land-use class

10.67 percentage yes 1.34 Farm Process

properties

RIC_PRO Percent runoff from precipitation for

rice land-use class

10.62 percentage yes 3.52 Farm Process

properties

CIT_IRO Percent runoff from irrigation for

citrus land-use class

1.00 percentage yes 1.23 Farm Process

properties

PAS_IRO Percent runoff from irrigation for

pasture land-use class

1.72 percentage yes 2.671011 Farm Process

properties

ROW_IRO Percent runoff from irrigation for

row crop land-use class

6.08 percentage yes 1.01 Farm Process

properties

SGR_IRO Percent runoff from irrigation for

grains and small grain land-use

class

4.46 percentage yes 1.23 Farm Process

properties

TRK_IRO Percent runoff from irrigation for

truck crop land-use class

10.00 percentage yes 1.40 Farm Process

properties

FLD_IRO Percent runoff from irrigation for

field crops land-use class

7.72 percentage yes 1.24 Farm Process

properties

VIN_IRO Percent runoff from irrigation for

vineyards land-use class

1.20 percentage yes 1.40 Farm Process

properties

DEC_IRO Percent runoff from irrigation for

deciduous land-use class

4.82 percentage yes 3.66 Farm Process

properties

RIC_IRO Percent runoff from irrigation for

rice land-use class

3.00 percentage yes 1.001010 Farm Process

properties

COT_IRO Percent runoff from irrigation for

cotton land-use class

10.16 percentage yes 2.001010 Farm Process

properties

UDE_IRO Percent runoff from irrigation for

other land-use class

7.76 percentage yes 1.28 Farm Process

properties

Table C10. Parameter values estimated for the Central Valley Hydrologic Model.

[WBS, water-balance subregion]
186 Groundwater Availability of the Central Valley Aquifer, California

Table C10. Parameter values estimated for the Central Valley Hydrologic Model.—Continued

[WBS, water-balance subregion]

Parameter 

name

Parameter 

description Value Units

Estimated 

using 

automated 

methods

Composite 

scaled 

sensitivity

Parameter 

group

Soil Properties

CAPFR_SICL Length of capillary fringe for

silty-clay

9.08 feet yes 3.51 Farm Process

properties

CAPFR_SILT Length of capillary fringe for silt 7.10 feet yes 1.12 Farm Process

properties

CAPFR_SALO Length of capillary fringe for

sandy-loam

9.32 feet yes 3.81 Farm Process

properties

Irrigation efficiency

EFF_MLT1 Multiplier on irrigation efficiency for

April 1961 through September

1964

0.971 multiplier yes 2.47101 Farm Process

properties

EFF_MLT2 Multiplier on irrigation efficiency for

October 1964 through September

1977

0.991 multiplier yes 1.531010 Farm Process

properties

EFF_MLT3 Multiplier on irrigation efficiency for

October 1977 through March 1978

1.030 multiplier no 6.75101 Farm Process

properties

EFF_MLT4 Multiplier on irrigation efficiency for

April 1978 through March 1980

1.040 multiplier no 2.46 Farm Process

properties

EFF_MLT5 Multiplier on irrigation efficiency for

April 1980 through March 1982

1.050 multiplier no 2.42 Farm Process

properties

EFF_MLT6 Multiplier on irrigation efficiency for

April 1982 through March 1984

1.060 multiplier no 1.86 Farm Process

properties

EFF_MLT7 Multiplier on irrigation efficiency for

April 1984 through March 1990

1.065 multiplier no 3.06 Farm Process

properties

EFF_MLT8 Multiplier on irrigation efficiency for

April 1990 through March 1996

1.070 multiplier no 6.66 Farm Process

properties

EFF_MLT9 Multiplier on irrigation efficiency for

April 1996 through September

2003

1.074 multiplier yes 1.32101 Farm Process

properties

EFF_75G Irrigation efficiency for generally less

known regions and months

60.30 percentage yes 4.51 Farm Process

properties

EFF_RICE Irrigation efficiency for rice 80.00 percentage yes 1.21 Farm Process

properties

Hydraulic Conductivity

KC Hydraulic conductivity of coarsegrained deposits in San Joaquin

Valley and Tulare Basin

672.93 feet/day yes 4.091011 Hydraulic conductivity

KF Hydraulic conductivity of finegrained deposits in San Joaquin

Valley and Tulare Basin

0.24 feet/day yes 1.26101 Hydraulic conductivity

KC_SAC Hydraulic conductivity of coarsegrained deposits in Sacramento

Valley

4921.50 feet/day yes 2.71 Hydraulic conductivity

KF_SAC Hydraulic conductivity of finegrained deposits in Sacramento

Valley

0.08 feet/day yes 4.42 Hydraulic conductivity

HK_BEDRX Horizontal hydraulic conductivity of

Bedrock

1.57 feet/day yes 4.17107 Hydraulic conductivity

HK_SJ Horizontal hydraulic conductivity of

San Joaquin Formation

0.17 feet/day yes 1.33 Hydraulic conductivity

multipliers

HK_QPC Horizontal hydraulic conductivity of

the dissected uplands

0.68 feet/day yes 1.40101 Hydraulic conductivity

VK_BEDRX Vertical hydraulic conductivity of

bedrock

1.57 feet/day yes 1.26 Hydraulic conductivity

multipliers
Model Calibration and Sensitivity 187

Table C10. Parameter values estimated for the Central Valley Hydrologic Model.—Continued

[WBS, water-balance subregion]

Parameter 

name

Parameter 

description Value Units

Estimated 

using 

automated 

methods

Composite 

scaled 

sensitivity

Parameter 

group

VK_SJ Vertical hydraulic conductivity of

San Joaquin Formation

0.17 feet/day yes 2.64 Hydraulic conductivity

multipliers

VK_QPC Vertical hydraulic conductivity of

the dissected uplands

0.68 feet/day yes 1.93 Hydraulic conductivity

multipliers

Multiplier on hydraulic properties of Corcoran Clay

HK_CC_MULT Multiplier on horizontal hydraulic

conductivity of Corcoran Clay

0.010 multiplier yes 1.35 Hydraulic conductivity

multipliers

VK_CC_MULT Multiplier on vertical hydraulic

conductivity of Corcoran Clay

0.002 multiplier yes 7.45 Hydraulic conductivity

multipliers

Horizontal Conductivity Depth Decay

HK_UN_VF Multiplier for layers 1–3 Sacramento

Valley

1.000 multiplier no 2.35 Hydraulic conductivity

multipliers

HK_US_VF Multiplier for layers 1–3 San Joaquin

Valley

1.000 multiplier no 1.831011 Hydraulic conductivity

multipliers

HK_UWS_VF Multiplier for layers 1–3 Western

San Joaquin Valley (WBS 10 and

14)

0.181 multiplier yes 2.081010 Hydraulic conductivity

multipliers

HK_LN6_VF Multiplier for layer 6 Sacramento

Valley

0.914 multiplier yes 1.46 Hydraulic conductivity

multipliers

HK_LS6_VF Multiplier for layer 6 San Joaquin

Valley

0.614 multiplier yes 3.841011 Hydraulic conductivity

multipliers

HK_WS6_VF Multiplier for layer 6 Western San

Joaquin Valley (WBS 10 and 14)

0.100 multiplier no 9.59 Hydraulic conductivity

multipliers

HK_LN7_VF Multiplier for layer 7 Sacramento

Valley

0.370 multiplier no 4.19101 Hydraulic conductivity

multipliers

HK_LS7_VF Multiplier for layer 7 San Joaquin

Valley

0.240 multiplier no 1.001011 Hydraulic conductivity

multipliers

HK_WS7_VF Multiplier for layer 7 Western San

Joaquin Valley (WBS 10 and 14)

0.075 multiplier no 8.59 Hydraulic conductivity

multipliers

HK_LN8_VF Multiplier for layer 8 Sacramento

Valley

0.240 multiplier no 4.17101 Hydraulic conductivity

multipliers

HK_LS8_VF Multiplier for layer 8 San Joaquin

Valley

0.180 multiplier no 4.171010 Hydraulic conductivity

multipliers

HK_WS8_VF Multiplier for layer 8 Western San Joaquin Valley (WBS 10 and 14)

0.060 multiplier no 4.36 Hydraulic conductivity

multipliers

HK_LN9_VF Multiplier for layer 9 Sacramento

Valley

0.150 multiplier no 1.52 Hydraulic conductivity

multipliers

HK_LS9_VF Multiplier for layer 9 San Joaquin

Valley

0.040 multiplier no 1.84 Hydraulic conductivity

multipliers

HK_WS9_VF Multiplier for layer 9 Western San

Joaquin Valley (WBS 10 and 14)

0.040 multiplier no 3.14 Hydraulic conductivity

multipliers

HK_LN10_VF Multiplier for layer 10 Sacramento

Valley

0.080 multiplier no 1.001011 Hydraulic conductivity

multipliers

HK_LS10_VF Multiplier for layer 10 San Joaquin

Valley

0.020 multiplier no 1.73 Hydraulic conductivity

multipliers

HK_WS10_VF Multiplier for layer 10 Western San

Joaquin Valley (WBS 10 and 14)

0.020 multiplier no 1.421011 Hydraulic conductivity

multipliers
188 Groundwater Availability of the Central Valley Aquifer, California

Table C10. Parameter values estimated for the Central Valley Hydrologic Model.—Continued

[WBS, water-balance subregion]

Parameter 

name

Parameter 

description Value Units

Estimated 

using 

automated 

methods

Composite 

scaled 

sensitivity

Parameter 

group

Vertical Conductivity Depth Decay

VK_UN_VF Multiplier for layers 1–3 Sacramento

Valley

1.000 multiplier no 1.61 Hydraulic conductivity

multipliers

VK_US_VF Multiplier for layers 1–3 San Joaquin

Valley

1.000 multiplier no 5.94 Hydraulic conductivity

multipliers

VK_UWS_VF Multiplier for layers 1–3 Western

San Joaquin Valley (WBS 10 and

14)

0.180 multiplier no 2.18 Hydraulic conductivity

multipliers

VK_LN6_VF Multiplier for layer 6 Sacramento

Valley

0.663 multiplier yes 1.67 Hydraulic conductivity

multipliers

VK_LS6_VF Multiplier for layer 6 San Joaquin

Valley

0.463 multiplier yes 5.34 Hydraulic conductivity

multipliers

VK_WS6_VF Multiplier for layer 6 Western San

Joaquin Valley (WBS 10 and 14)

0.150 multiplier no 1.61 Hydraulic conductivity

multipliers

VK_LN7_VF Multiplier for layer 7 Sacramento

Valley

0.370 multiplier no 1.40 Hydraulic conductivity

multipliers

VK_LS7_VF Multiplier for layer 7 San Joaquin

Valley

0.200 multiplier no 2.14 Hydraulic conductivity

multipliers

VK_WS7_VF Multiplier for layer 7 Western San

Joaquin Valley (WBS 10 and 14)

0.100 multiplier no 1.53 Hydraulic conductivity

multipliers

VK_LN8_VF Multiplier for layer 8 Sacramento

Valley

0.240 multiplier no 1.62 Hydraulic conductivity

multipliers

VK_LS8_VF Multiplier for layer 8 San Joaquin

Valley

0.150 multiplier no 1.78 Hydraulic conductivity

multipliers

VK_WS8_VF Multiplier for layer 8 Western San

Joaquin Valley (WBS 10 and 14)

0.060 multiplier no 1.66 Hydraulic conductivity

multipliers

VK_LN9_VF Multiplier for layer 9 Sacramento

Valley

0.150 multiplier no 1.94 Hydraulic conductivity

multipliers

VK_LS9_VF Multiplier for layer 9 San Joaquin

Valley

0.040 multiplier no 4.171010 Hydraulic conductivity

multipliers

VK_WS9_VF Multiplier for layer 9 Western San

Joaquin Valley (WBS 10 and 14)

0.040 multiplier no 1.51 Hydraulic conductivity

multipliers

VK_LN10_VF Multiplier for layer 10 Sacramento

Valley

0.080 multiplier no 4.001011 Hydraulic conductivity

multipliers

VK_LS10_VF Multiplier for layer 10 San Joaquin

Valley

0.020 multiplier no 2.52 Hydraulic conductivity

multipliers

VK_WS10_VF Multiplier for layer 10 Western San

Joaquin Valley (WBS 10 and 14)

0.020 multiplier no 1.251010 Hydraulic conductivity

multipliers
Model Calibration and Sensitivity 189

Table C10. Parameter values estimated for the Central Valley Hydrologic Model.—Continued

[WBS, water-balance subregion]

Parameter 

name

Parameter 

description Value Units

Estimated 

using 

automated 

methods

Composite 

scaled 

sensitivity

Parameter 

group

Conductance

DELTA Conductance of Delta Sediments 3281.00 feet/day no 1.36 Delta

Stream Bed Hydraulic Conductivity

K_SACRIV Hydraulic conductivity of the stream

bed of the northern Sacramento

River

0.04 feet/day yes 1.70 Streambed hydraulic

conductivity

K_SACRIV Hydraulic conductivity of the stream

bed of the Sacramento River

0.16 feet/day yes 1.101011 Streambed hydraulic

conductivity

K_SJRIV Hydraulic conductivity of the stream

bed of the San Joaquin River

0.48 feet/day yes 4.58 Streambed hydraulic

conductivity

K_SO_RIV Hydraulic conductivity of the stream

bed of the river channel in trough

of valley south of San Joaquin

River

2.71 feet/day yes 1.49 Streambed hydraulic

conductivity

K_TRIB_NE Hydraulic conductivity of the stream

bed of the Sacramento Valley

tributaries east of Sacramento

River

0.33 feet/day yes 1.21 Streambed hydraulic

conductivity

K_TRIB_NW Hydraulic conductivity of the stream

bed of the Sacramento Valley

tributaries west of Sacramento

River

5.64 feet/day yes 2.371011 Streambed hydraulic

conductivity

K_TRIB_SE Hydraulic conductivity of the stream

bed of the San Joaquin Valley and

Tulare Basin Tributaries east of

valley trough

3.31 feet/day yes 1.331010 Streambed hydraulic

conductivity

K_TRIB_SW Hydraulic conductivity of the stream

bed of the San Joaquin Valley and

Tulare Basin tributaries west of

valley trough

0.19 feet/day yes 1.40 Streambed hydraulic

conductivity

Storage Properties

SY_MULT Multiplier on specific yield values

based on percentage of coarsegrained deposits

1.33 multiplier yes 5.93 Storage properties

PHI_C Porosity of the coarse-grained

deposits

25.00 percentage no 1.26 Storage properties

PHI_F Porosity of the fine-grained

deposits

50.00 percentage no 2.371011 Storage properties

SS_QPC_MULT Multiplier on storage values of the

dissected uplands

1.00 multiplier yes 3.33109 Storage properties

SUB_E Multiplier on elastic storage

coefficients

1.00 multiplier yes 5.73 Storage properties

SUB_I Inelastic storage coefficient 1.37E-04 per foot yes 6.67109 Storage properties
190 Groundwater Availability of the Central Valley Aquifer, California

0 50 100 Miles

0 50 100 Kilometers

EXPLANATION

(K_SACRIVN)

(K_TRIB_NWB)

(K_SACRIV)

(K_TRIB_SW)

(K_TRIB_NE)

(K_OTHER1)

(K_SJRIV)

(K_TRIB_SEB)

(K_TRIB_NEB)

(K_SO_RIV)

(K_TRIB_SE)

(K_TRIB_NW)

(K_OTHER2)

(K_TRIB_SEC)

Shaded relief derived from U.S. Geological Survey

National Elevation Dataset, 2006. Albers Equal Area Conic Projection

Active model grid boundary

Model water-balance subregions

Inflow locations

Stream cell hydraulic conductivity,

 in feet per day—

0.0413406

0.0564332

0.16405

0.193579

0.326131

0.3281

0.479026

0.588939

1.29271

2.71358

3.31381

5.63676

32.81

328.133

Figure C26. Distribution of cells used for streams, colored by streambed hydraulic conductivity values for cells estimated during 

calibration.
Simulation Results and Budget 191

Sensitivity Analysis 

The CVHM was extremely sensitive to changes in certain

hydraulic properties. In particular, the CVHM failed convergence when some of the model parameters were perturbed out

of the range of the final set of specified values. This, in part,

limited the use of systematic parameter-estimation techniques 

to estimate model parameters and related sensitivities. However, it was generally possible to use systematic parameterestimation techniques based on perturbation approaches for 

selected parameters. The sensitivity process in UCODE-2005

identifies the sensitivity of computed values at the locations

of measurements to changes in model parameters, and was

used to identify the parameters to include in the parameter 

estimation and to adjust during calibration. More than 100

parameters related to hydraulic properties, storage properties,

streambed hydraulic conductivities, and various parameters in

the FMP were included in the sensitivity process. Composite

scaled sensitivity (CSS) values are used here to show relative sensitivity; the definition and derivation are described in

Hill and others (2000). Although more than 100 parameters

were identified, 24 parameters had relatively large and similar

CSS values. Very few of the parameters actually were estimated using automated calibration (table C10). Results of the

UCODE-2005 sensitivity process indicate that the CVHM

was sensitive to a variety of different types of parameters

(fig. C27). The CVHM was most sensitive to the hydraulic

conductivity of the coarse-grained fraction in the San Joaquin

Valley (Kc). Several multipliers on different hydraulic conductivity parameters also were fairly sensitive. Runoff from

irrigation from pastures (PAS_IRO) had one of the highest

CSS values (fig. C27). Some streambed hydraulic conductivities and storage properties also were relatively sensitive.

The CVHM also was extremely insensitive to changes in

certain hydraulic properties. Streambed hydraulic conductivities of the eastern tributaries generally are insensitive. Because

of the relatively low estimated values, the majority of the multipliers on the vertical hydraulic conductivity were relatively

insensitive. Except for the cases shown on fig. C27, in general,

the fractions of runoff from precipitation and irrigation were

insensitive. Multipliers on efficiencies since the late 1970s

also are generally insensitive. As would be expected, many

parameters were insensitive within a certain range. When

they were moved outside this range, they became much more

sensitive. As a result, the sensitivities of parameters changed

through the calibration process.

Simulation Results and Budget

A quantitative understanding of a basin’s water balance

provides key information about groundwater availability

(Grannemann and Reeves, 2005). The water balance for the

Central Valley was compiled and summarized by describing the components of the groundwater budget through the

42-year simulation. This chapter focuses on the simulation of 

the groundwater part of the budget and its linkages to surface

water and the farm budget. More detailed discussions of the

budget and groundwater availability, based on the simulation,

are the focus of Chapter B.

In terms of the post-development groundwater budget,

withdrawals from pumpage are balanced by a combination

of increased recharge from irrigation, decreased discharge to

streams, decreased evaporation, and release of groundwater

from storage. The aquifer receives recharge from precipitation,

streamflow losses, and agricultural return flow. Groundwater

leaves the system predominantly through wells, ET, discharge

to streams, and (to a small degree) discharge to the Delta.

Depending on the magnitude, distribution, and timing of

recharge and discharge, groundwater is both released from and

taken into storage. Storage depletion often is accompanied by

aquifer-system compaction and land subsidence. 

Recharge from a combination of precipitation and excess

applied irrigation water, and groundwater pumpage, are the

dominant hydrologic stresses on the groundwater system

(table C3; figs. C28 and C29). Groundwater-level altitudes

respond to these stresses. Groundwater pumpage has altered

groundwater-flow rates and directions, reduced flow to

streams, captured water from streams and other parts of the

aquifer system, and altered groundwater quality. From 1961 to

2003 there has been a net depletion of groundwater in storage

in the Central Valley.

Figure C30 shows an example of how the TFDR is met

by a combination of surface-water deliveries and pumpage

that varies through the irrigation cycle. The TFDR first is met

by non-routed surface-water deliveries and then semi-routed

surface-water deliveries. When the TFDR exceeds the total

surface-water deliveries, groundwater is pumped to meet the

remaining agricultural water demand. When the surface-water

deliveries exceed the TFDR, the excess deliveries are returned

to the surface-water system.
192 Groundwater Availability of the Central Valley Aquifer, California

Recharge and Discharge

Delivery of surface water for irrigation, combined with

pumpage for irrigation and public supply, have greatly altered

the amount and distribution of recharge and discharge in the

Central Valley. Prior to development, recharge and discharge

were about 2 million acre-ft per year (fig. A23). As expected,

simulated groundwater-flow to the Delta (General Head

Boundary) was negligible (fig. C31). Since irrigated agriculture began in the late 1800s, the amount of groundwater

pumped for irrigation has been greater than the natural (predevelopment) recharge and discharge. During the period from

1962 to 2003, an average of 18.7 million acre-ft of irrigation

water was required annually, about one-half from groundwater and one-half from surface-water (tables C11 and C12 and 

fig. C29). It is important to recognize that that not all irrigation

water necessarily is consumed by ET; some water runs off

and returns to the surface-water flow system and some returns

to the groundwater system by deep percolation and other

mechanisms. 

KC

KC_S

KC_W

PAS_IRO

COT_IRO

RIC_IRO

H2O_PRO

EFF_MLT2

SEM_PRO

EFF_MLT1

K_TRIB_SE

K_TRIB_NW

K_SACRIVN

PHI_F

SUB_I

SS_QPC_MULT

HK_US_VF

1012

1010

108

106

104

102

100

COMPOSITE SCALED SENSITIVITY, UNITLESS

PARAMETER IDENTIFIER

Hydraulic conductivity

Parameter groups—

Hydraulic conductivity multipliers

Farm process properties

Storage properties

Streambed conductance

VK_LN10_VF

HK_LS6_VF

HK_WS10_VF

HK_LN10_VF

HK_LS7_VFHK_LS8_VF

HK_LN7_VFHK_LN8_VF

HK_QPC

HK_UWS_VF

VK_LS9_VF

HK_BEDRX

VK_WS10_VF

EXPLANATION

Figure C27. Relative composite sensitivity of computed water-level altitudes, flows, and subsidence information at calibration points to 

changes in parameters. Composite scaled sensitivity values are used here to show relative sensitivity; the definition and derivation are 

described in Hill and others (2000). The top 30 composite scaled sensitivities are plotted.
Simulation Results and Budget 193

INFLOWS

40%

10%

17%

23%

7% 3%

p

nrd

srdin

wells

egwin

tgwin

8%

12%

7%

8% 29%

3%

3%

30%

ei

ep

egwout

ti

tp

tgwout run

dp

OUTFLOWS

-15

-10

-5

5

0

10

15

20

FLOW RANGE, IN MILLIONS OF ACRE-FEET PER YEAR

Precipitation

Non-routed deliveries

 into farm

Semi-routed deliveries

 into farm

Groundwater well pumping

 deliveries into farm

Evaporation from groundwater

 into the farm

Evaporation from groundwater

 out of the farm

Transpiration from groundwater

 into the farm

Evaporation from irrigation

 out of the farm

Evaporation from precipitation

 out of the farm

Transpiration from irrigation

 out of the farm

Transpiration from precipitation

 out of the farm

Overland runoff out

 of the farm

Deep percolation out

 of the farm

p

srdin

wells

egwin

egwout

tgwin

Transpiration from groundwater

 out of the farm tgwout ei

ep

ti

tp

run

dp

nrd

Figure C28. Average annual components of farm budget for water years 1962–2003.
194 Groundwater Availability of the Central Valley Aquifer, California

Recharge and discharge from the various budget components changes through the 1962–2003 time period as a result

of climatic changes, land-use changes, and farming practices

(tables C11 and C12 and figs. C28, C29, and C31). Between

1962 and 2003, figure C29 shows that groundwater pumpage

and surface-water deliveries for irrigation are a larger component of recharge to the landscape system than precipitation.

As a result, percolation of irrigation water past crop roots has

replaced infiltration of precipitation and stream water as the

primary mechanism of recharge. With increases in irrigation

efficiencies, however, the recharge from groundwater pumpage and surface-water deliveries (excess irrigation water),

relative to precipitation, has declined (fig. C28). Recharge

rates from precipitation are thought to have not changed significantly from pre-development times (Williamson and others,

1989). However, because the shallow part of the system now

is simulated in more detail, the streamflow gains and losses are

of much larger magnitude than previous estimates by Williamson and others (1989) (fig. C31A). Discharge of water through

wells and the resulting loss of storage have replaced natural

ET as the primary mechanism of discharge (fig. C31). 

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

YEAR

WATER FLOW, IN MILLIONS OF ACRE-FEET PER YEAR

Flow into landscapesystem

Flow out of landscape

system

EXPLANATION

-60

-40

-20

20

0

40

60

Deep percolation

Runoff

Evapotranspiration Evapotranspiration from groundwater

Groundwater pumpage

Surface-water deliveries

Precipitation

Figure C29. Farm budget changes through time for the Central Valley, California.
Simulation Results and Budget 195

"Variable"

to

Dry Wet Dry Wet

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982

WATER YEAR

EXPLANATION

AESTIMATED WATER USAGE,

IN MILLIONS OF ACRE-FEET PER YEAR

ESTIMATED WATER USAGE,

IN MILLIONS OF ACRE-FEET PER YEAR

0

6

5

4

3

2

1

B

1977 1978 1979 1980 1981 1982 1983 1984 1985

WATER YEAR

0

6

5

4

3

2

1

Total farm delivery requirements

Routed surface-water deliveries

Non-routed surface-water deliveries

Groundwater pumpage

Figure C30. Farm budget changes through time for water-balance subregion 13, western San Joaquin Valley, Central Valley, California. 

A, Water years 1962–2003. B, Water years 1977–1985.
196 Groundwater Availability of the Central Valley Aquifer, California

The sources of irrigation water vary greatly from year

to year and month to month (fig. C30). Dry periods generally

lead to increased pumpage from wells and associated losses

from storage, and reduced recharge associated with fallowing

and changes to lower water-use crops. Wet periods have the

opposite effects. 

Water pumped from the aquifer system may or may not 

be replenished quickly. In some areas, groundwater that is

pumped can be replenished annually during the non-irrigation

season by recharge from precipitation and streams. In other

areas, replenishment only occurs in years of abundant precipitation. In still other areas, most notably the southwestern San

Joaquin Valley and Tulare basin, pumpage caused substantial

water-level altitude declines and subsidence during periods

of drought. Recently, artificial recharge projects have been

implemented in the Tulare basin. Examples of these recharge

projects are the recharge ponds developed by the Kern Water

Bank (Kern Water Bank Authority, 2007) and in-lieu recharge

projects started by SemiTropic Water Storage District (Semitropic Water Storage District, 2007). The potential effects of

these projects are discussed in more detail in later sections of

this report.

"Variable"

to

Dry

Wet Dry Wet

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

WATER YEAR

ESTIMATED WATER USAGE,

IN MILLIONS OF ACRE-FEET PER YEAR

0

6

5

4

3

2

1

A (continued)

Figure C30. Continued.
Simulation Results and Budget 197

Aquifer-System Storage

During the simulation period (1961–2003) there has been

a depletion of millions of acre-ft of aquifer-system storage

(table C12 and fig. C31). Generally, the water removed from

storage may be replaced by precipitation, stream leakage,

excess applied irrigation water, artificial recharge, or any combination of the above (fig. C31). However, the withdrawals

also have caused the permanent loss of storage by the inelastic

compaction of fine-grained sediments. About 10 million

acre-ft of water are taken into and released from groundwater

storage annually (table C12 and fig. C31). Year-to-year (and

season-to-season) changes in storage reflect groundwater

pumpage and the availability of precipitation and surface

water. The difference between simulated annual groundwater discharge and recharge indicates an overall average net

removal of about 1 million acre-ft/yr of groundwater from

storage.

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

YEAR

WATER FLOW, IN MILLIONS OF ACRE-FEET PER YEAR -40

-30

-20

-10

20

10

0

30

40

EXPLANATION

Landscape recharge

General-head boundary

Farm wells

Pumpage

Stream leakage

Subsidence

Storage

Landscape recharge

General-head boundary

Pumpage

Stream leakage

Subsidence

Storage

A

Flow into groundwatersystem

Flow from groundwater

system

Groundwater Surface water

Figure C31. Groundwater budget changes through time for the Central Valley. A, All values. B, Net values.
198 Groundwater Availability of the Central Valley Aquifer, California

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

YEAR

WATER FLOW, IN MILLIONS OF ACRE-FEET PER YEAR -20

-15

-10

-5

10

5

0

15

20

EXPLANATION

Specific yield and

 compressibility of water

General-head boundary

Elastic and inelastic storage

Stream leakage

Landscape recharge

Pumpage

B

Flow into groundwatersystem

Flow out of groundwater

system

Figure C31. Continued.
Simulation Results and Budget 199

Year Precipitation Surface-water 

deliveries

Groundwater 

pumpage

Evapotrans- piration Runoff

Evapotranspiration from 

groundwater

Deep percolation

1962 14,281,000 10,808,000 11,271,000 25,505,000 1,310,000 3,522,000 12,967,000

1963 17,119,000 9,625,000 9,096,000 26,300,000 1,097,000 3,056,000 11,391,000

1964 9,877,000 9,079,000 12,156,000 24,584,000 795,000 3,484,000 9,108,000

1965 15,590,000 9,915,000 9,355,000 25,448,000 1,221,000 3,337,000 11,425,000

1966 10,876,000 9,619,000 12,918,000 24,920,000 1,071,000 3,663,000 10,976,000

1967 19,718,000 10,583,000 8,743,000 25,748,000 1,652,000 3,514,000 15,012,000

1968 11,127,000 9,322,000 12,734,000 26,118,000 820,000 3,639,000 9,798,000

1969 23,300,000 11,002,000 8,231,000 25,937,000 2,015,000 4,039,000 18,433,000

1970 18,345,000 10,261,000 10,738,000 26,122,000 1,675,000 4,342,000 15,723,000

1971 14,102,000 9,701,000 10,569,000 25,198,000 1,159,000 4,114,000 11,973,000

1972 8,042,000 10,200,000 12,875,000 25,015,000 716,000 4,139,000 9,394,000

1973 21,343,000 11,136,000 8,253,000 25,934,000 1,772,000 4,043,000 16,914,000

1974 17,548,000 11,581,000 7,854,000 26,366,000 1,311,000 3,966,000 13,154,000

1975 14,527,000 11,279,000 7,925,000 25,525,000 1,024,000 3,927,000 10,995,000

1976 9,301,000 9,560,000 10,558,000 25,581,000 453,000 3,894,000 7,177,000

1977 7,152,000 7,034,000 15,551,000 25,389,000 436,000 3,445,000 7,279,000

1978 24,651,000 10,546,000 6,768,000 27,474,000 1,755,000 3,521,000 16,088,000

1979 14,134,000 12,364,000 7,661,000 26,325,000 941,000 3,857,000 10,657,000

1980 18,453,000 11,735,000 6,230,000 26,189,000 1,257,000 3,616,000 12,472,000

1981 12,350,000 11,440,000 9,108,000 26,510,000 810,000 3,965,000 9,435,000

1982 22,260,000 11,133,000 4,956,000 26,990,000 1,386,000 3,360,000 13,176,000

1983 28,589,000 10,166,000 4,341,000 26,664,000 2,050,000 3,532,000 17,703,000

1984 13,603,000 12,098,000 7,258,000 25,091,000 1,148,000 4,640,000 11,233,000

1985 11,972,000 10,760,000 7,647,000 25,435,000 700,000 4,055,000 8,204,000

1986 19,712,000 11,273,000 5,836,000 26,435,000 1,324,000 3,809,000 12,738,000

1987 10,048,000 10,509,000 8,843,000 25,282,000 581,000 4,171,000 7,620,000

1988 12,796,000 9,390,000 9,815,000 26,129,000 792,000 3,880,000 8,850,000

1989 12,632,000 9,663,000 9,830,000 26,131,000 741,000 3,549,000 8,662,000

1990 10,121,000 8,356,000 11,690,000 25,993,000 487,000 3,331,000 6,919,000

1991 11,763,000 8,009,000 12,549,000 25,424,000 793,000 3,436,000 9,395,000

1992 13,684,000 7,889,000 12,229,000 26,666,000 821,000 3,334,000 9,546,000

1993 22,192,000 10,538,000 6,958,000 26,430,000 1,645,000 3,262,000 14,726,000

1994 11,690,000 9,676,000 9,522,000 26,196,000 617,000 3,332,000 7,295,000

1995 24,818,000 10,345,000 5,737,000 25,428,000 1,913,000 3,366,000 16,739,000

1996 17,151,000 11,843,000 6,485,000 25,973,000 1,240,000 3,814,000 11,969,000

1997 16,299,000 11,567,000 6,644,000 24,859,000 1,294,000 4,120,000 12,347,000

1998 30,177,000 8,946,000 4,535,000 25,846,000 2,139,000 3,190,000 18,646,000

1999 12,511,000 10,006,000 4,831,000 22,538,000 757,000 3,969,000 7,885,000

2000 15,602,000 10,116,000 4,887,000 23,765,000 1,019,000 4,157,000 9,822,000

2001 13,197,000 9,099,000 6,127,000 24,232,000 673,000 3,925,000 7,316,000

2002 12,868,000 9,425,000 7,137,000 23,175,000 943,000 4,050,000 9,238,000

2003 16,050,000 9,571,000 5,643,000 24,252,000 1,009,000 3,695,000 9,554,000

Average 15,752,000 10,171,000 8,621,000 25,598,000 1,128,000 3,740,000 11,427,000

Table C11. Simulated farm budget for the Central Valley, California, in acre-feet per year. 

Typical (1975) highlighted in bold italic, dry (1990) highlighted in bold, and wet (1998) years highlighted in italic. 
200 Groundwater Availability of the Central Valley Aquifer, California

Table C12. Simulated groundwater budget for the Central Valley, California, in acre-feet per year—Continued.

[Values in this table are relative to flow in and out of the groundwater system as modeled. Typical (1975) highlighted in bold italic, dry (1990) highlighted in

bold, and wet (1998) years highlighted in italic. GHB, General Head Boundary; IN, into groundwater system; OUT, out of groundwater system; NET, IN minus

OUT] 

 Year

Specific yield and 

compressibility 

of water IN

GHB IN

Elastic and inelastic 

storage IN

Stream 

leakage IN

Farm 

recharge IN

Pumpage

 IN1

1962 8,613,000 57,000 3,150,000 3,791,000 12,166,000 439,000

1963 6,688,000 32,000 2,582,000 2,619,000 10,597,000 448,000

1964 8,424,000 58,000 3,320,000 1,957,000 8,299,000 451,000

1965 6,990,000 49,000 2,559,000 2,715,000 10,638,000 452,000

1966 8,331,000 63,000 3,369,000 2,171,000 10,197,000 465,000

1967 6,527,000 37,000 2,389,000 3,284,000 14,196,000 463,000

1968 8,849,000 59,000 3,206,000 1,684,000 8,957,000 464,000

1969 8,351,000 35,000 1,950,000 5,879,000 17,582,000 479,000

1970 9,620,000 23,000 2,515,000 2,425,000 14,898,000 565,000

1971 8,774,000 25,000 2,326,000 1,891,000 11,129,000 558,000

1972 9,570,000 56,000 2,859,000 1,445,000 8,511,000 557,000

1973 7,424,000 26,000 1,839,000 2,919,000 16,122,000 490,000

1974 7,262,000 20,000 1,643,000 2,398,000 12,247,000 463,000

1975 7,667,000 29,000 1,664,000 1,901,000 10,189,000 436,000

1976 9,005,000 60,000 2,099,000 1,224,000 6,313,000 430,000

1977 10,460,000 102,000 3,827,000 1,347,000 6,436,000 477,000

1978 7,231,000 46,000 1,409,000 4,261,000 15,387,000 478,000

1979 7,636,000 42,000 1,429,000 1,719,000 9,930,000 395,000

1980 6,741,000 29,000 1,195,000 2,600,000 11,765,000 374,000

1981 8,560,000 47,000 1,693,000 1,405,000 8,731,000 367,000

1982 5,916,000 21,000 995,000 2,781,000 12,536,000 343,000

1983 5,928,000 8,000 827,000 4,266,000 17,069,000 301,000

1984 9,222,000 12,000 1,340,000 1,825,000 10,613,000 313,000

1985 8,734,000 22,000 1,457,000 1,387,000 7,597,000 356,000

1986 7,199,000 17,000 1,177,000 2,712,000 12,106,000 340,000

1987 9,340,000 25,000 1,782,000 1,062,000 7,085,000 338,000

1988 9,462,000 34,000 2,136,000 1,211,000 8,202,000 344,000

1989 8,803,000 42,000 2,198,000 1,300,000 8,065,000 362,000

1990 9,428,000 62,000 2,991,000 1,113,000 6,292,000 394,000

1991 10,969,000 67,000 3,188,000 3,908,000 8,830,000 470,000

1992 9,813,000 67,000 3,153,000 1,324,000 8,941,000 497,000

1993 7,367,000 40,000 1,589,000 2,339,000 14,114,000 425,000

1994 8,959,000 55,000 2,310,000 1,417,000 6,773,000 381,000

1995 10,261,000 26,000 1,337,000 8,331,000 16,185,000 414,000

1996 8,420,000 20,000 1,237,000 2,296,000 11,452,000 312,000

1997 9,249,000 22,000 1,277,000 3,735,000 11,851,000 299,000

1998 8,831,000 11,000 1,017,000 7,460,000 18,138,000 305,000

1999 8,626,000 17,000 1,133,000 1,715,000 7,381,000 248,000

2000 8,730,000 20,000 1,245,000 1,862,000 9,176,000 264,000

2001 9,324,000 25,000 1,622,000 1,680,000 6,716,000 283,000

2002 9,699,000 26,000 1,877,000 2,011,000 8,686,000 316,000

2003 8,533,000 24,000 1,560,000 2,197,000 8,950,000 305,000

Average 8,465,000 37,000 2,011,000 2,561,000 10,739,000 401,000

Table C12. Simulated groundwater budget for the Central Valley, California, in acre-feet per year.

[Values in this table are relative to flow in and out of the groundwater system as modeled. Typical (1975) highlighted in bold italic, dry (1990) highlighted in

bold, and wet (1998) years highlighted in italic. GHB, General Head Boundary; IN, into groundwater system; OUT, out of groundwater system; NET, IN minus

OUT] 
Simulation Results and Budget 201

Table C12. Simulated groundwater budget for the Central Valley, California, in acre-feet per year—Continued.

[Values in this table are relative to flow in and out of the groundwater system as modeled. Typical (1975) highlighted in bold italic, dry (1990) highlighted in

bold, and wet (1998) years highlighted in italic. GHB, General Head Boundary; IN, into groundwater system; OUT, out of groundwater system; NET, IN minus

OUT] 

 Year

Specific yield and 

compressibility of 

water OUT

GHB OUT

Elastic and inelastic 

storage OUT

Stream 

leakage OUT

Farm recharge 

OUT2

Pumpage 

OUT3

Farm wells 

OUT

1962 10,634,000 105,000 1,153,000 1,850,000 2,648,000 1,628,000 10,185,000

1963 8,192,000 120,000 1,007,000 1,738,000 2,209,000 1,513,000 8,170,000

1964 4,694,000 53,000 809,000 1,689,000 2,618,000 1,720,000 10,911,000

1965 8,013,000 98,000 1,058,000 1,729,000 2,493,000 1,548,000 8,449,000

1966 5,811,000 65,000 864,000 1,667,000 2,807,000 1,815,000 11,548,000

1967 11,462,000 132,000 1,259,000 1,983,000 2,630,000 1,468,000 7,948,000

1968 4,601,000 56,000 793,000 1,814,000 2,729,000 1,986,000 11,220,000

1969 17,398,000 263,000 1,537,000 2,905,000 3,122,000 1,738,000 7,290,000

1970 11,241,000 182,000 1,164,000 2,558,000 3,439,000 2,151,000 9,297,000

1971 6,685,000 126,000 1,030,000 2,361,000 3,197,000 2,158,000 9,129,000

1972 3,466,000 46,000 875,000 1,976,000 3,168,000 2,328,000 11,119,000

1973 12,662,000 165,000 1,406,000 2,333,000 3,169,000 1,962,000 7,109,000

1974 8,543,000 154,000 1,201,000 2,457,000 2,999,000 1,953,000 6,708,000

1975 6,496,000 115,000 1,125,000 2,361,000 3,060,000 1,963,000 6,752,000

1976 2,127,000 39,000 892,000 1,922,000 2,945,000 2,154,000 9,037,000

1977 1,918,000 10,000 531,000 1,552,000 2,527,000 2,502,000 13,593,000

1978 14,447,000 164,000 1,564,000 2,131,000 2,736,000 1,862,000 5,889,000

1979 6,590,000 86,000 935,000 1,982,000 3,040,000 1,984,000 6,516,000

1980 9,350,000 142,000 1,081,000 2,146,000 2,845,000 1,885,000 5,242,000

1981 4,987,000 61,000 787,000 1,963,000 3,168,000 2,173,000 7,641,000

1982 10,469,000 182,000 1,122,000 2,225,000 2,660,000 1,799,000 4,123,000

1983 15,728,000 331,000 1,134,000 3,009,000 2,858,000 1,737,000 3,583,000

1984 7,235,000 232,000 666,000 3,051,000 3,966,000 2,169,000 5,988,000

1985 4,195,000 118,000 772,000 2,504,000 3,400,000 2,247,000 6,298,000

1986 9,712,000 186,000 1,006,000 2,597,000 3,133,000 2,070,000 4,825,000

1987 3,336,000 94,000 673,000 2,203,000 3,586,000 2,398,000 7,321,000

1988 4,666,000 68,000 778,000 1,957,000 3,182,000 2,481,000 8,238,000

1989 4,546,000 55,000 756,000 1,775,000 2,894,000 2,505,000 8,221,000

1990 2,779,000 33,000 582,000 1,641,000 2,635,000 2,724,000 9,871,000

1991 8,216,000 99,000 728,000 2,041,000 2,785,000 2,847,000 10,702,000

1992 5,346,000 49,000 856,000 1,547,000 2,644,000 2,820,000 10,513,000

1993 11,676,000 116,000 1,325,000 1,881,000 2,584,000 2,290,000 5,992,000

1994 3,835,000 52,000 818,000 1,686,000 2,728,000 2,627,000 8,133,000

1995 20,424,000 384,000 1,475,000 4,125,000 2,756,000 2,398,000 4,970,000

1996 8,814,000 231,000 933,000 2,510,000 3,245,000 2,475,000 5,509,000

1997 10,716,000 249,000 1,012,000 2,610,000 3,568,000 2,645,000 5,614,000

1998 20,861,000 454,000 1,472,000 4,126,000 2,649,000 2,347,000 3,833,000

1999 4,894,000 240,000 688,000 2,793,000 3,422,000 3,130,000 3,938,000

2000 7,112,000 195,000 732,000 2,411,000 3,468,000 3,306,000 4,052,000

2001 4,471,000 137,000 636,000 2,166,000 3,284,000 3,869,000 5,068,000

2002 6,189,000 130,000 710,000 2,086,000 3,448,000 4,053,000 5,979,000

2003 6,966,000 137,000 879,000 2,041,000 3,046,000 3,768,000 4,712,000

Average 8,131,000 142,000 972,000 2,240,000 2,988,000 2,314,000 7,410,000
202 Groundwater Availability of the Central Valley Aquifer, California

Table C12. Simulated groundwater budget for the Central Valley, California, in acre-feet per year—Continued.

[Values in this table are relative to flow in and out of the groundwater system as modeled. Typical (1975) highlighted in bold italic, dry (1990) highlighted in

bold, and wet (1998) years highlighted in italic. GHB, General Head Boundary; IN, into groundwater system; OUT, out of groundwater system; NET, IN minus

OUT] 

 Year

Net specific 

yield and compressibility 

of water

Net GHB

Net elastic 

and inelastic 

storage

Net stream 

leakage

Net farm 

recharge Net Pumpage4

1962 –2,021,000 –48,000 1,997,000 1,941,000 9,519,000 –11,373,000

1963 –1,504,000 –89,000 1,575,000 881,000 8,388,000 –9,236,000

1964 3,730,000 4,000 2,511,000 268,000 5,681,000 –12,180,000

1965 –1,023,000 –49,000 1,500,000 986,000 8,145,000 –9,544,000

1966 2,521,000 –2,000 2,505,000 504,000 7,389,000 –12,898,000

1967 –4,935,000 –94,000 1,129,000 1,301,000 11,566,000 –8,953,000

1968 4,247,000 2,000 2,413,000 –130,000 6,229,000 –12,742,000

1969 –9,047,000 –229,000 413,000 2,974,000 14,460,000 –8,550,000

1970 –1,621,000 –159,000 1,351,000 –134,000 11,459,000 –10,883,000

1971 2,089,000 –102,000 1,296,000 –470,000 7,932,000 –10,729,000

1972 6,103,000 10,000 1,984,000 –531,000 5,343,000 –12,890,000

1973 –5,238,000 –139,000 433,000 586,000 12,953,000 –8,581,000

1974 –1,282,000 –134,000 442,000 –59,000 9,247,000 –8,198,000

1975 1,171,000 –86,000 539,000 –461,000 7,129,000 –8,279,000

1976 6,878,000 22,000 1,208,000 –698,000 3,368,000 –10,762,000

1977 8,542,000 92,000 3,296,000 –205,000 3,909,000 –15,618,000

1978 –7,216,000 –117,000 –155,000 2,130,000 12,651,000 –7,272,000

1979 1,046,000 –44,000 494,000 –263,000 6,890,000 –8,104,000

1980 –2,609,000 –113,000 114,000 454,000 8,920,000 –6,752,000

1981 3,573,000 –15,000 906,000 –557,000 5,562,000 –9,447,000

1982 –4,553,000 –161,000 –127,000 556,000 9,877,000 –5,579,000

1983 –9,800,000 –323,000 –307,000 1,258,000 14,211,000 –5,020,000

1984 1,987,000 –220,000 674,000 –1,225,000 6,647,000 –7,844,000

1985 4,539,000 –96,000 685,000 –1,117,000 4,197,000 –8,188,000

1986 –2,514,000 –170,000 171,000 116,000 8,973,000 –6,556,000

1987 6,004,000 –69,000 1,109,000 –1,141,000 3,499,000 –9,381,000

1988 4,796,000 –35,000 1,358,000 –745,000 5,020,000 –10,375,000

1989 4,257,000 –13,000 1,442,000 –475,000 5,171,000 –10,365,000

1990 6,649,000 29,000 2,409,000 –528,000 3,657,000 –12,201,000

1991 2,753,000 –32,000 2,460,000 1,868,000 6,045,000 –13,078,000

1992 4,467,000 18,000 2,297,000 –223,000 6,297,000 –12,836,000

1993 –4,309,000 –76,000 264,000 459,000 11,531,000 –7,857,000

1994 5,124,000 3,000 1,492,000 –269,000 4,045,000 –10,379,000

1995 –10,163,000 –358,000 –139,000 4,206,000 13,428,000 –6,954,000

1996 –394,000 –211,000 303,000 –214,000 8,206,000 –7,672,000

1997 –1,467,000 –227,000 266,000 1,125,000 8,283,000 –7,960,000

1998 –12,030,000 –443,000 –455,000 3,334,000 15,489,000 –5,874,000

1999 3,732,000 –222,000 445,000 –1,078,000 3,959,000 –6,820,000

2000 1,619,000 –175,000 513,000 –549,000 5,708,000 –7,095,000

2001 4,853,000 –111,000 986,000 –486,000 3,432,000 –8,654,000

2002 3,510,000 –103,000 1,168,000 –75,000 5,237,000 –9,717,000

2003 1,566,000 –113,000 680,000 155,000 5,904,000 –8,175,000

Average 334,000 –105,000 1,039,000 321,000 7,751,000 –9,323,000 1

Pumpage IN refers to flow into the borehole from the aquifer system.

2

Farm recharge OUT is equivalent to evapotranspiration from groundwater.

3

Pumpage OUT includes urban pumpage and farm pumpage through multi–node wells.

4

Net Pumpage = Pumpage IN – Pumpage OUT – Farm Wells OUT.
Model Uncertainty and Limitations 203

Model Uncertainty and Limitations 

The goal of this modeling activity was to develop a

model capable of being accurate at scales relevant to watermanagement decisions. The intent of developing the CVHM

was not to reproduce every detail of the hydrologic system,

but to portray its general characteristics. Although the CVHM

doesn’t completely represent all parts of the system, it is relevant for developing a better understanding of the flow system

at a regional scale and contains sufficient fundamental detail

to facilitate addition of more detailed features that may be 

relevant at a sub-regional scale.

Though the development of the CVHM has employed

some of the latest modeling methods available at the time

of the study, the use of numerical models to simulate hydrologic systems still has inherent limitations. Limitations of the

modeling software, data limitations, assumptions made during

model development, conceptual model error (Bredeheoft,

2005), and results of model calibration and sensitivity analysis

all are factors that constrain the appropriate use of hydrologic

models, including the CVHM. Differences between simulated and actual hydrologic conditions arise from a number

of sources and are known collectively as model error (Walter

and Whealan, 2005). One component of model error relates to 

discretization. The CVHM represents the hydrologic system

as a series of discrete spatial units, through which intrinsic

properties and stresses were simulated as locally uniform. For

example, spatially the CVHM is discretized into 1-mi2 model 

cells and features smaller cells that are not simulated. In reality, the model is likely only to represent features accurately 

at a scale of approximately 5 mi2. Temporally, the CVHM is

discretized into a series of discrete, monthly stress periods

during which hydrologic stresses (user-specified inflows and

outflows) are constant. Temporal discretization introduces

additional sources of model inaccuracy. These were minimized

by choosing monthly stress periods, which are an appropriate

temporal interval to address the changes in irrigated agriculture and disparities between supply and demand components

of the hydrologic budget. Model errors also may arise from

the numerical solution that is based on head and flow closure

criteria. These errors were minimized by constraining acceptable model solutions to mass-balance residuals of less than 

0.1 percent of the total mass of flow in the Central Valley.

An additional component of model error arose from how

accurately model-input values represent the actual hydrologic

system. The degree to which the CVHM simulation provides a

reasonable representation of the hydrologic system was evaluated by comparing simulated hydrologic conditions with those

observed in the field. The CVHM’s performance and accuracy

are constrained primarily by groundwater-level altitudes,

streamflow losses, the database used to synthesize the texture

distribution, and subsidence values. These comparisons were

used to ensure that the simulation of the regional hydrologic

system is consistent with historical measurements of responses

to stresses throughout the Central Valley.

Although simplifying assumptions were made of an

inherently complex, developed hydrologic system in developing the CVHM model, this perhaps is the most detailed model

of the entire Central Valley that has been developed to date.

The CVHM solves for average conditions within each model

cell, these cells range in volume from about 32,000 acre-ft

near the land surface to 256,000 acre-ft at depth. Within each 

of these cells, the hydraulic properties are interpolated or 

extrapolated from measurements and (or) estimated during

model calibration. Long-term hydrographs indicate that the

water-level altitudes have been changing with time. Because

the initial conditions specified in the CVHM were derived

from a period of transient groundwater-flow, errors related to

these transients may be significant in places during the early

part of the simulation. However, discrepancies in the initial

conditions are quickly dissipated with time. Based on sensitivity tests of the initial heads (±5 ft), errors associated with

misspecification of the initial condition are negligible after the

first 6 months. Thus, care must be taken in interpreting CVHM

results and analyses that depend on the early part of the simulation. As a result, model output only was used for analyses

after this first 6-month stabilization period.

Several elements of the CVHM remain uncertain and will

require additional investigation. The hydrologic stresses in the

CVHM are a combination of measured values, adjustments to

represent conceptualizations of the system, and values specified through the GHB, HFB, LPF, MNW, MULT, and SFR1

packages and the FMP. Hydrologic features that remain uncertain include hydraulic properties, the location and properties 

of flow barriers, and critical-head distributions. Hydraulic

properties that remain uncertain include horizontal and vertical

hydraulic conductivities and storage properties.

The CVHM model-layering is, in part, uncertain because

it is based on layers that were derived by using a uniform

thickness with depth except where the Corcoran Clay is present. Although the sequence stratigraphic and facies changes

may be somewhat depth dependent, the model layers generally

do not coincide with the actual glacial cycles of sedimentation sequences in the alluvial deposits. The representation of

sedimentary layering and additional formations might improve

the model accuracy. However, the inclusion of the Corcoran

clay is a unique feature that was not explicitly present in other

models of the Central Valley. The representation of sedimentary layering and formations needs to be improved before the

CVHM is suitable for particle-tracking simulations or for simulating solute-transport. The implicit inclusion of the volcanic

units and the sedimentary Tuscan and Tehama Formations

in the Sacramento Valley inadequately defines the sequence

stratigraphic detail and the geologically controlled flow paths

in the groundwater-flow system. In addition, depth decay was

implemented during calibration but remains an uncertain feature. In some areas, the change in saturated thickness as well

as changes in porosity and specific yield with depth may affect

the CVHM.
204 Groundwater Availability of the Central Valley Aquifer, California

The application of the SFR1 package makes several

assumptions that may affect the accuracy of the streamflow

infiltration, streambed hydraulic conductivity, and the related

groundwater/surface-water interactions. In particular, the

assumption of a linear change in streambed altitude may be a

poor approximation. Errors in this approximation may result

in an over or under estimation of streambed infiltration. In

addition, the assumption that the streamflow stage-discharge

relation remains constant over entire stream segments with

a wide variety of geomorphic conditions may lead to model

error in streamflow infiltration and stream-bed conductivities.

Finally, the distribution of streambed hydraulic conductivities

is poorly defined. Uncertainty exists in surface-water inflows,

surface-water deliveries, and surface-water diversions. These

uncertainties can have a significant effect on the accuracy of

the CVHM and can represent thousands of acre-feet of error

per month. As a result, these uncertainties may indirectly

affect the amount of groundwater pumpage required to satisfy

irrigation requirements.

The effectiveness of simulated barriers used in the HFB

package to represent selected fault systems that may be partial

barriers to groundwater-flow remains uncertain. Limited

water-level altitude observations across faults were used to

constrain flow-barrier conductances during model calibration.

Additional water-level altitude observations would be required

to constrain these features. 

Overall, the application of multi-aquifer pumpage to

dynamically redistribute the pumpage between layers temporally and spatially still is a significant improvement over

other models of the Central Valley. Despite this improvement,

multi-aquifer pumpage, which is simulated by a combination

of farm wells and the MNW package has several elements

of significant uncertainty. The use of virtual wells instead of

actual wells, and the lack of pumping capacity information

to locally constrain non-uniform pumpage, may affect the

accuracy of the model on a local basis. A large component

of multi-node well pumpage uncertainty is the distribution

of wellbore properties such as well depths, well radii, pumping capacities of individual wells, and the skin factor used to

represent friction and entrance losses. In particular, the value

of the skin factor is uncertain. This skin factor promotes or 

inhibits wellbore flow between model layers. As with agricultural pumpage, urban pumpage is not metered for the major

urban centers. The estimates of urban pumpage are based on

population. Finally, all the agricultural wells outside the extent

of the Corcoran Clay could not be simulated feasibly as multiaquifer wells. Both the MNW package and the FMP can have

extremely non-linear solutions. When simulated together, the

solutions were unfeasibly slow and unstable. As a result, these

wells were simulated as single-aquifer wells with the FMP and

intra-borehole flow between model layers was not simulated in

these areas.

Some of the inputs to the FMP that are necessary to

calculate water use were estimated for some regions of

the Central Valley but remain uncertain in other areas. For

example, the temporal and spatial distribution of land use

and crop distributions are very coarse. Temporally, land-use

distributions were based on only five multi-year maps. Many

of the stresses related to these land uses varied throughout the

simulation period, driven by climatic conditions as well as

cultivation periods. Hence, the changes simulated by the FMP

with these few land-use estimates are simulated seasonally

and by climatic-driven events that can be yearly or multi-year

in length. Anthropogenic changes are incorporated minimally

through land-use changes and surface-water deliveries. The

CVHM includes some double cropping and Kc values that

reflect the winter versus summer growing periods. The growing periods for some crops vary annually and generally are

uncertain. This especially is true with climatic changes, where

wet spring seasons may delay planting, dry spring seasons

may require additional supplemental irrigation, and additional

warm months may allow for prolonged growing seasons. In

addition to Kc values, consumptive use is based on ETo. In

the CVHM, these ETo values are estimated from empirical

equations based on temperature data. Limitations of the FMP

include its inability to simulate soil moisture storage and “onfarm” storage. This limitation is most important during months 

when fields are rewetted prior to cultivation and in areas of

natural vegetation during prolonged droughts. This limitation

may have little effect on areas where repetitive agricultural

irrigation minimizes the changes in soil moisture or where

the water table is near the surface and groundwater uptake is

occurring.

Some of the boundary conditions of the CVHM are

incomplete, which may be a minor source of model error.

Though most surface inflow occurs through the 43 rivers simulated in CVHM, some minor intermittent flows from smaller

watersheds surrounding the valley is poorly understood and is

not specified in the CVHM. Recent work by DWR (C. Brush,

California Department of Water Resources, written commun.,

February 21, 2007) and estimates from Nady and Larragueta

(1983) report that these influxes make up less than 2 percent

of the influx to the system. However, this may be a local

source of model error and may affect the local accuracy of the 

CVHM. In addition, the CVHM does not simulate any surface-water bodies, such as the intermittently wet Tulare Lake,

Kern Lake, and Buena Vista Lake bed areas. The CVHM also

does not simulate the emergent artificial recharge projects in

the Tulare Basin. These water banks now recharge thousands

of acre-ft/year (Kern Water Bank Authority, 2007; Semitropic

Water Storage District, 2007).
Future Work 205

Not all of the major canals were simulated within the

streamflow routing network. In addition, land subsidence

can reduce the gradient and elevation on canals and streams,

which may result in a reduction in conveyance and freeboard

capacity. Although this subsidence was simulated, the effects

of subsidence on streamflow and canal conveyance were

not simulated. The total surface-water delivery system that

represents water passed through the Delta, and then pumped

into the major canals, was not accounted for in the streamflowrouting network. These deliveries were simulated as nonrouted deliveries. In addition, the component of diversions that

was used for maintenance of habitat or maintenance of water

rights was not accounted for in the model, neither within the

streamflow routing network nor as surface-water deliveries

through the FMP water-rights features. These omissions may

result in reported streamflow diversions being much larger

than the crop irrigation demand estimated with the FMP for

some WBSs. Drainage pumpage in the San Joaquin Valley,

particularly WBS 14, and the Delta also is not accounted for

in the CVHM. Omission of this pumpage may be part of the

inaccuracy water-level altitudes in these regions. Simulation

results indicate that flows across these poorly constrained

boundaries make up a small part of the groundwater budget

(table C12). The boundary flows may be significant locally but

insignificant in the regional groundwater budget because these

flows are simulated partially in the streamflow and landscape

budgets.

The accuracy of CVHM results is related strongly to

the quality and spatial distribution of input data and of 

measurements of the system (such as water-level altitudes,

subsidence, and streamflow) that are used to constrain the

calibration. The CVHM may not accurately simulate locally

intense drawdowns in the potentiometric surfaces. This limitation is a function of several factors, including the allocation

of relatively uniform pumpage per virtual well in cells where

crops were grown within each WBS, in cell size, and limited

unsaturated zone processes simulated with the FMP where

the water table is deep. For example, the CVHM does not

adequately simulate the magnitude of the subsidence and

water-level altitudes on the southern-most part of the domain.

Water-level altitude declines and the amount of subsidence

both are over estimated. This most likely is a result of limitations in the methods used to represent pumpage distribution

with virtual wells and no spatially varying pumping capacities

or local detail of irrigation schedules within a WBS, unknown

hydraulic properties, and fluxes from the edges of the domain.

Another example is the match with depth of simulated

water-level altitudes. The difference between simulated and

measured water-level altitudes generally increases with depth

below the land surface.

A related topic is the scarcity of information in the

textural database with increasing depth below the land surface

and, therefore, the decreasing accuracy of the simulated

texture distribution with depth used to estimate the hydraulic

conductivities. In addition, the texture method assumes that

all coarse/fine-grained units of the same percentage have the

same hydraulic conductivity over very large regions. This may

not necessarily be true even though the texture estimates were

completed sub-regionally to reflect more localized depositional features. 

The use of relatively large WBSs facilitated computation

of surface-water deliveries but may have degraded the local

variability of changes in water use and groundwater storage. In

particular, the composite surface-water deliveries and coarse

land-use and crop distributions are used to estimate pumpage without spatially varying pumpage rates, based on local

irrigation needs or pumping capacities. As a result, the CVHM

is best suited to quantify the conceptual understanding of the

flow system and to quantify and analyze the responses at the

scale of these 21 WBSs.

Given these limitations, the CVHM cannot be expected

to accurately simulate time-series data from individual wells,

but can be expected to represent the longer term changes

and larger spatial trends in groundwater storage. Thus, the

goal of the model calibration was not to match individual

hydrographs, subsidence records, and streamflow losses, but

to match general trends and to minimize the SOSWR for

all simulated water-level altitudes, changes in water-level

altitudes, land subsidence, and streamflow losses. Despite

these limitations, the CVHM does an adequate job of matching water-level altitudes, changes in water-level altitudes,

land subsidence, and streamflow. Furthermore, the CVHM

adequately represents groundwater conditions for the entire

Central Valley and is capable of simulating regional and subregional groundwater-flow and land subsidence.

Future Work

The performance, utility, detail, and accuracy of the 

CVHM could be improved in several ways. These improvements can be classified as limitations to address and as potential enhancements. Limitations that could be improved can be

further categorized into simulation-code features, conceptual

features, and input and comparison data. Simulation-code features include potential improvements to the SUB, LPF, MNW,

and SFR packages, FMP, and the numerical solver. These

enhancements may create more accurate or realistic simulations. In order for these simulations to be as accurate and realistic as possible, the CVHM will need to be updated frequently

with new data and new capabilities. Doing this would make

the model more dynamic and allow it to overcome some of the

limitations of the current state of the model. The simulation 

of subsidence in the CVHM could be enhanced by both data

updates and code enhancements. First, the simulation possibly

could be improved by incorporating hydrodynamic lag in the

simulation of aquifer-system compaction. Thick interbeds 

and the Corcoran Clay both should be included. Overestimated water-level altitudes and subsidence in the western San

Joaquin Valley could be related to the limitations of simulating

the instantaneous release from storage and compaction. For
206 Groundwater Availability of the Central Valley Aquifer, California

example, delay interbeds could be assigned in the SUB package and the model re-layered with the Corcoran Clay split into

four or more model layers to approximate delayed drainage in

the confining unit. Secondly, geostatic load changes owing to

recharge to the water table from irrigation return flows could

be included through the addition of the new SUB-WT package (Leake and Galloway, 2007). A potential enhancement to

the SUB package would be the ability to separate the water

derived from elastic and inelastic compaction through the SUB

package. Because a large part of the water pumped historically

into the San Joaquin Valley was derived from inelastic compaction, the ability to make this distinction will allow waterresource managers to better assess the effects of sustained

pumpage during droughts or from climate change.

The representation of sedimentary layering and formations could be improved, particularly the volcanic units and

the sedimentary Tuscan and Tehama Formations in the Sacramento Valley. Additional estimates of horizontal hydraulic

conductivity could be obtained from additional slug tests at

monitoring-well sites.

In the CVHM, the model layers all are simulated as having a constant saturated thickness. This simplification results

in an over estimation of transmissivity in areas with relatively

large water-level altitude declines. Similarly, the transition

from confined to unconfined storage also would affect the

estimates and magnitude of changes in groundwater storage and their potential effects on streamflow infiltration and

recharge. In the future, allowing the water table to fluctuate in

the uppermost layers utilizing the wetting and drying capabilities of MODFLOW may result in a more accurate simulation. Although not a large source of error, allowing the layers

to convert between confined and unconfined storage would

result in a more accurate flow solution, provided that the

drying and rewetting algorithms provided a smooth transition

with improved solvers such as the Newton-Raphson solution

schemes (Richard Niswonger, U.S. Geological Survey, written

commun., 2008).

Recharge was applied to the water table without accounting for delays associated with travel through the unsaturated

zone. Unsaturated zones range from a few feet in the San

Joaquin Valley to more than 200 feet in the southern San

Joaquin Valley. Transient storage in the unsaturated zone is

not accounted for in the CVHM. This storage may play an

important role in the water budget if artificial recharge projects

are simulated. Linkage to the Unsaturated Zone Flow (UZF1)

package would allow for the simulation of these delayed

recharge features below the root zone (Niswonger and others, 2006). In addition, linkages between the FMP and SFR

would facilitate the simulation of artificial recharge projects.

The linkage to the UZF1 package also would facilitate a more

realistic simulation of runoff during exceptionally wet periods

when rainfall greatly exceeds the saturated vertical hydraulic

conductivity.

Transition to the new SFR2 package (Niswonger and

Prudic, 2005) would facilitate unsaturated infiltration under

streambeds in the alluvial fan regions and would allow for a

more realistic representation of reach elevations. The SFR2

package allows altitudes to vary by reach. This altitude representation, along with connections to the Lake (LAK) (Merritt

and Konikow, 2000), UZF1 packages, and FMP would allow

a more complete and detailed simulation of the surface-water

deliveries. Combined with the optional connection of SFR2

and FMP with the SUB package, the SFR2 can be used to

simulate canals and streamflows that also are affected by land

subsidence owing to groundwater and petroleum production

and related changes in runoff from precipitation and irrigation

from FMP.

The magnitude and extent of surface-water/groundwater

interactions are poorly defined. These interactions could be

better quantified through field studies and more detailed classification of streambed sediments or temperature profiles. The

CVHM also could be improved by including more-detailed

estimates of the extent of leakage from canals, the nature of

inflow and outflow that could affect seawater intrusion along

the San Francisco Bay and the distribution and application of

artificial recharge.

Changes in soil-water storage are less important to longterm, large-scale models and are less important in regions

where irrigation from agriculture is frequent (Schmid and others, 2006a), but can make a difference for regions where prewetting of soils prior to the irrigation season are required or

for natural vegetation that is subjected to prolonged droughts.

The connection of FMP to UZF1 may address some of these

issues but an additional discrepancy may remain between a

transient ET and fluxes into the root zone without soil moisture storage for shorter time frames. Adding soil moisture

storage to the FMP may allow for a transition of additional ET

into the pre-wetting phase for some types of crops that would

occur in these settings.

FMP does not simulate “on-farm” storage of delivered

water and its delayed use or reuse for irrigation. This likely is

an issue where WBSs have local reservoirs or reuse of water.

To provide this feature, an ‘on-farm-water storage’ will need

to be added to the FMP mass balance (Schmid and others,

2006a). 

A potential enhancement to the CVHM would be the

improvement of the accuracy of input data. In particular, landuse data, crop acreage, amount and area of double cropping,

and ETo could be improved. Updates to the land-use distributions could reflect other factors such as urbanization and

economic factors such as migrating to more profitable truck

crops, to vineyards, and to orchards. In theory, the land-use

distributions could be based on land-use distributions that 

change monthly or seasonally to capture more of the changes

in supply and demand. Finally, the ETo estimates in the

CVHM could be improved, particularly where CIMIS data are

available. Furthermore, use of remotely sensed ET data, particularly MODIS, could be used to replace the current methods

for calculating consumptive use.
References Cited 207

The lack of information regarding the rate and the spatial and temporal distribution of groundwater pumpage is a

significant shortcoming; in particular, the depth from which

water is being pumped is lacking. Construction information on

municipal and agricultural wells and wellbore profiles would

help to refine the uncertain distribution of multi-aquifer pumpage that was simulated within CVHM. In particular, the value

of the skin factor is uncertain. The skin factor could be refined

spatially if additional wellbore flow or head-difference data

were available to better constrain this parameter during model

calibration. Urban pumpage potentially could be improved

by including the dynamic use of water in urban settings that

is subject to climate variability, variable efficiencies, and

non-uniform growth. Simulating all the agricultural wells

with multi-node wells with the FMP and intra-borehole flow

between model layers will allow more accurate simulations.

The scale of spatial distribution of deliveries used in the

CVHM is equally limiting. If deliveries were known in greater

spatial detail, groundwater pumpage could be estimated in

greater detail. For example, the Eastside Water District, southeast of Modesto, is a large water district that has no surfacewater rights and, therefore, has experienced development of

a large cone of depression (Phillips and others, 2007). This

is not reflected in the CVHM because of the spatial scale of

delivery data.

The CVHM is designed to be readily updateable to allow

for coupling with forecasts from Global Climate Models

(GCMs). Implementation of the FMP using GIS facilitates the

use of remotely sensed ET data and, therefore, allows for the

spatial and temporal input data for the CVHM to be updated

more efficiently. This capability, in turn, facilitates using the

CVHM with climate forecasts derived from GCMs. The input

to the crop-based water budget is consistent with output from

the GCMs. This consistency will provide the State of California and other stakeholders an ability to forecast the potential 

supply of surface-water deliveries, associated demand for

groundwater, and, ultimately, the change in groundwater storage in the Central Valley.

In the future, coupling CVHM with optimization tools

would improve the ability to identify and quantitatively evaluate water-management strategies. With the aid of the GIS, the

CVHM could be used as a platform to connect simulation of

groundwater/surface-water flow with the water allocation/

optimization model called CALSIM (California Department

of Water Resources, 2003b), and to transition to the more

detailed water accounting units if they become available. Likewise, the CVHM could be used for evaluation of subregional

issues, such as exportation of water from the Sacramento

Valley to Southern California or the upcoming restoration of

the salmon habitat of the San Joaquin River. These types of

subregional issues could be assessed using the CVHM as-is, or

by nesting finer-gridded subregional models within the CVHM

that are linked dynamically using the embedded-model

technology of the local grid refinement (LGR) package in

MODFLOW (Mehl and Hill, 2005) or similar technology.

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

Anderman, E.R. and Hill, M.C., 2003, MODFLOW-2000,

The U.S. Geological Survey modular ground-water flow

model—Three additions to the hydrogeologic-unit flow

(HUF) package—Alternative storage for the uppermost

active cells (STYP parameter type), flows in hydrogeologic

units, and the hydraulic conductivity depth-dependence

(KDEP) capability: U.S. Geological Survey Open-File

Report 03–347, 36 p., accessed April 16, 2009, at http://

water.usgs.gov/nrp/gwsoftware/modflow2000/modflow2000.

html 

Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E.,

1976, A land use and land cover classification system for

use with remote sensor data: U.S. Geological Survey Professional Paper 964, 28 p.

Belitz, Kenneth, and Heimes, F.J., 1990, Character and evolution of the ground-water flow system in the central part of

the western San Joaquin Valley, California: U.S. Geological

Survey Water-Supply Paper 2348, 28 p.

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 ground-water flow in the central

part of the Western San Joaquin Valley, California: U.S.

Geological Survey Water-Supply Paper 2396, 69 p.

Berkstresser, C.F., Jr., 1973, Base of fresh ground-water

—approximately 3,000 micromhos—in the Sacramento

Valley and Sacramento–San Joaquin Delta, California: U.S.

Geological Survey Water-Resources Investigations 40-73, 1

map.

Bertoldi, G.L., Johnston, R.H., and Evenson, K.D., 1991,

Ground water in the Central Valley, California—A summary

report: U.S. Geological Survey Professional Paper 1401-A,

44 p.

Bredehoeft, John, 2005, The Conceptualization Model Problem—Surprise: Hydrogeology Journal, v. 13, p. 37–46.
208 Groundwater Availability of the Central Valley Aquifer, California

Brouwer, C., Goffeau, A., and Heibloem, M., 1985, Irrigation

water management: Training manual no. 1—Introduction

into Irrigation: Food and Agriculture Organization of the

United Nations, Land and Water Development Division,

91 p.

Brouwer, C. and Heibloem, M., 1986, Irrigation Water Management: Training manual no. 3–Irrigation Water Needs:

Food and Agriculture Organization of the United Nations,

Land and Water Development Division, 89 p.

Brush, C.F., Belitz, Kenneth, and Phillips, S.P., 2004, Estimation of a water budget for 1972–2000 for the Grasslands

Area, Central Part of the Western San Joaquin Valley,

California: U.S. Geological Survey Scientific Investigations

Report 2004–5180, 51 p.

Burbey, T.J., 2001, Stress–strain analyses for aquifer-system

characterization: Ground Water, v. 39, no. 1, p. 128–136.

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. Geological Survey Scientific-Investigations Report 2004–5232, 54 p.

California Department of Water Resources, 1994, California

water plan update: Bulletin 160-93, California Department

of Water Resources, Sacramento, California.

California Department of Water Resources, 2000, Explanations of land use attributes used in database files associated

with shape files: Land and Water Use Section, 11 p.

California Department of Water Resources, 2003a, California’s groundwater, Update 2003: California Department of

Water Resources Bulletin 118, 246 p.

California Department of Water Resources, 2003b, CALSIM

II simulation of historical SWP/CVP operations, California:

Department of Water Resources Technical Memorandum

Report November 2003.

California Department of Water Resources, 2007, California

Irrigation Management Information System, accessed April

16, 2009, at http://wwwcimis.water.ca.gov/cimis/welcome.

jsp

California State University, Chico, 2003, The Central Valley

historic mapping project: California State University, Chico,

Department of Geography and Planning and Geographic

information Center, 25 p.

Climate Source, 2006, Precipitation data from PRISM data,

accessed April 16, 2009, at http://www.climatesource.com/ 

Davis, G.H., Lofgren, B.E., and Mack, Seymour, 1964, Use

of ground-water reservoirs for storage of surface water in

the San Joaquin Valley, California: U.S. Geological Survey

Water-Supply Paper 1618, 125 p.

Davis, F.W., Stoms, D.M., Hollander, A.D., Thomas, K.A.,

Stine, P.A., Odion, D., Borchert, M.I., Thorne, J.H., Gray,

M.V., Walker, R.E., Warner, K., and Grace, J., 1998, The

California Gap Analysis Project—Final Report: University

of California, Santa Barbara, California, accessed April 16,

2009, at http://www.biogeog.ucsb.edu/projects/gap/gap_rep.

html 

Diamond, J., and Williamson, A.K., 1983, A summary of

ground-water pumpage in the Central Valley, California,

1961–1977: U.S. Geological Survey Water-Resources

Investigations Report 83-4037, 70 p.

Dimitrakopoulos, Roussos, and Desbarats, A.J., 1993, Geostatistical modeling of grid block permeabilities for 3D

reservoir simulators: Reservoir Engineering, v. 8, p. 13–18.

Droogers, Peter, and Allen, R.G., 2002, Estimating reference

evapotranspiration under inaccurate data conditions: Journal

of Irrigation and Drainage Systems, v. 16, p. 33–45.

Faunt, C.C., Blainey, J.B., Hill, M.C., D’Agnese, F.A., and

O’Brien, G.M., 2004, F. Transient flow model, in Belcher,

W.R., ed., Death Valley regional ground-water flow system,

Nevada and California—Hydrogeologic framework and

transient ground-water flow model: U.S. Geological Survey

Scientific-Investigations Report 2004–5205, p. 257–352,

accessed April 16, 2009, at http://water.usgs.gov/pubs/

sir/2004/5205/

Gilliom, R.J., Barbash, J.E., Crawford, C.G., Hamilton, P.A.,

Martin, J.D., Nakagaki, Naomi,Nowell, L.H., Scott, J.C.,

Stackelberg, P.E., Thelin, G.P., Wolock, D.M., 2006, Pesticides in the nation’s streams and ground water, 1992–2001:

U.S. Geological Survey Circular 1291, 180 p.

Granneman, N.G. and Reeves, H.W., 2005, Great Lakes Basin

water availability and use: U.S. Geological Survey Fact

Sheet 2005–3113, 4 p.

Gronberg, J.M., and Belitz, K.R., 1991, Estimation of a water

budget for the central part of the western San Joaquin Valley, California: U.S. Geological Survey Water-Resources

Investigations Report 91-4192, 22 p.

Gronberg, J.M., Dubrovsky, N.M., Kratzer, C.K., Domagalski,

J.L., Brown, L.R., and Burow, K.R., 1998, Environmental

setting of the San Joaquin–Tulare Basins, California: U.S.

Geological Survey Water-Resources Investigations Report

97–4205, 45 p.

Hackel, Otto, 1966, Summary of the geology of the Great Valley, p. 217–238, in Bailey, E.H., ed., Geology of Northern

California: California Division of Mines and Geology Bulletin 190, 508 p.
References Cited 209

Halford, K.J. and Hanson, R.T., 2002, User guide for the

drawdown-limited, Multi-Node Well (MNW) Package for

the U.S. Geological Survey’s modular three-dimensional

finite-difference ground-water flow model, versions MODFLOW-96 and MODFLOW-2000: U.S. Geological Survey

Open-File Report 02–293, 33 p., accessed April 16, 2009, at

http://pubs.usgs.gov/of/2002/ofr02293/text.html

Halford, K.J., Laczniak, R.L., and Galloway, D.L., 2005,

Hydraulic characterization of over pressured tuffs in central

Yucca Flat, Nevada Test Site, Nye County, Nevada: U.S.

Geological Survey Scientific Investigations Report 2005–

5211, 36 p.

Hanson, R.T., 1988, Aquifer-system compaction, Tucson

Basin and Avra Valley, Arizona: U.S. Geological Survey

Water-Resources Investigation Report 88–4172, 69 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 finite-difference ground-water flow model: U.S. Geological Survey

Water-Resources Investigations Report 98-564, 57 p.

Harbaugh, A.W., Banta, E.R., Hill, M.C., and McDonald,

M.G., 2000, MODFLOW-2000: U.S. Geological Survey

Modular Ground-Water Model—User Guide to Modularization Concepts and the Ground-Water Flow Process: U.S.

Geological Survey Open-File Report 00–92, 121 p.

Hargreaves, G.H., 1994, Defining and using reference evapotranspiration: Journal of Irrigation and Drainage Engineering, v. 120, no. 6, p. 1132–1139.

Hargreaves, G.L., and Allen, R.G., 2003, History and evaluation of the Hargreaves evapotranspiration equation: Journal of Irrigation and Drainage Engineering, v. 129, no. 1,

p. 53–63.

Hargreaves, G.H., and Samani, Z.A., 1982, Estimating potential evapotranspiration: Journal of Irrigation and Drainage

Engineering, v. 108, no. 3, p. 225–230.

Hargreaves, G.H., and Samani, Z.A., 1985, Reference crop

evapotranspiration from temperature: Applied Engineering

in Agriculture, v. 1, no. 2, p. 96–99.

Hargreaves, G.L., Hargreaves, G.H., and Riley, J.P., 1985,

Irrigation water requirements for Senegal River Basin: Journal of Irrigation and Drainage Engineering, v. 111, no. 3,

p. 265–275.

Helm, D.C., 1974, Evaluation of stress-dependent aquitard

parameters by simulating observed compaction from known

stress history: Berkeley, California, University of California, doctoral dissertation, 175 p.

Helm, D.C., 1975, One-dimensional simulation of aquifersystem compaction near Pixley, California, 1, Constant

parameters: American Geophysical Union, Water Resources

Research, v. 11, no. 3, p. 465–478.

Helm, D.C., 1976, One-dimensional simulation of aquifer-system compaction near Pixley, California, 2, stress-dependent

parameters: American Geophysical Union, Water Resources

Research, v. 12, no. 3, p. 375–391.

Helm, D.C., 1977, Estimating parameters of compacting

fine-grained interbeds within a confined aquifer system

one-dimensional simulation of field observations, in International Symposium on Land Subsidence, 2d, Anaheim,

California, 1976, Proceedings: International Association of

Hydrological Sciences Publication 121, p. 145–156.

Hidalgo, H.G., Cayman, D.R., Hettinger, M.D., 2005, Sources

of variability of evapotranspiration in California: Journal of

Hydrometeorology, v. 6, p. 3–19.

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.

Hill, M.C., Cooley, R.L., and Pollock, D.W., 1998, A controlled experiment in ground-water flow model calibration:

Ground Water, v. 36, no. 3, p. 520–535.

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.

Hoffmann, Jon, Galloway, D.L., and Secker H.A., 2003a,

Inverse modeling of interbred storage parameters using land

subsidence observations, Antelope Valley, California: Water

Resources Research, 39, doe: 10.1029/2001WR001253.

Hoffmann, Jon, Leake, S.A., Galloway, D.L., and Wilson,

A.M., 2003b, MODFLOW-2000 ground-water model—

User guide to the subsidence and aquifer-system compaction (SUB) package: U.S. Geological Survey Open-File

Report 03–233, 46 p.

Hsieh, P.A., and Freckelton, 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-flow model: U.S. Geological

Survey Open-File Report 92-477, 32 p.

Ireland R.L., Poland, J.F., and Riley, F.S., 1984, Land subsidence in the San Joaquin Valley, California, as of 1980: U.S.

Geological Survey Professional Paper 437-I, 93 p.
210 Groundwater Availability of the Central Valley Aquifer, California

Jensen, D.T., Hargreaves, G.H., Temesgen, B., and Allen,

R.G., 1997, Computation of ETo under no ideal conditions:

Journal of Irrigation and Drainage Engineering, v. 123,

no. 5, p. 394–400.

Johnson, A.I., Muston, R.P., Morris, D.A., 1968, Physical and

hydrogeologic properties of water-bearing materials in subsiding areas in central California: U.S. Geological Survey

Professional Paper 497-A, 71p.

Kern Water Bank Authority, 2007, accessed April 16, 2009, at

http://www.kwb.org/main.htm 

Konikow, L.F. and Neuzil, C.E., 2007, A method

to estimate groundwater depletion from confining layers, Water Resources Research, 43, W07417,

doi:10.1029/2006WR005597.

Larson, K.J., Basagaolu, H., and Mario, M., 2001, Numerical

simulation of land subsidence in the Los Banos-Kettleman

City area, California: University of California Davis Water

Resources Center Technical Completion Report Contribution no. 207, 83 p. 

Laudon, Julie, 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., 1990, Interbed storage changes and compaction

in models of regional ground-water flow: Water Resources

Research, v. 26, no. 9, p. 1939–1950.

Leake, S.A., and Galloway, D.L., 2007, MODFLOW groundwater model—user guide to the Subsidence and AquiferSystem Compaction Package (SUB-WT) for Water-Table

Aquifers: U.S. Geological Survey Techniques and Methods,

Book 6, Chap. A23, 42 p.

Leake, S.A., and Prudic, D.E., 1991, Documentation of a computer program to simulate aquifer-system compaction using

the modular finite-difference ground-water flow model: U.S.

Geological Survey Techniques of Water-Resources Investigations, book 6, chap. A2, 68 p.

Londquist, C.J., 1981, Digital model of the unconsolidated

aquifer system in the Modesto Area, Stanislaus and San Joaquin Counties, California: U.S. Geological Survey WaterResources Investigations Report 81-12, 36 p.

McDonald, M.G., and Harbaugh, A.W., 1988, A modular

three-dimensional finite-difference ground-water flow

model: U.S. Geological Survey Techniques of WaterResources Investigations Report, book 6, chap. A1, 586 p.

Mehl, S.W., and Hill, M.C., 2005, MODFLOW-2005, The

U.S. Geological Survey modular ground-water model—

Documentation of shared node local grid refinement (LGR)

and the boundary flow and head (BFH) package: U.S. Geological Survey Techniques and Methods 6-A12, 68 p.

Merritt, L.M., and Konikow, L.F., 2000, Documentation of a

computer program to simulate lake-aquifer interaction using

the MODFLOW ground-water flow model and the MOC3D

solute-transport model: U.S. Geological Survey WaterResources Investigations Report 00-4167, 146 p.

Mullen, J.R., and Nady, Paul, 1985, Water budgets for major

streams in the Central Valley, California, 1961–77: U.S.

Geological Survey Open-File Report 85-401, 87 p.

Nady, Paul, and Larragueta, L.L., 1983, Estimated average

annual streamflow into the Central Valley of California:

U.S. Geological Survey Hydrologic Investigations Atlas

HA-657.

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, Book 6, Chap.

A13, 47 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, 74 p.

Page, R.W., 1977, Guide for data collection to calibrate a

predictive digital ground-water model of the unconfined

aquifer in and near the city of Modesto, California: U.S.

Geological Survey Water-Resources Investigations Report

76-41, 46 p.

Page, R.W., 1986, Geology of the fresh ground-water basin

of the Central Valley, California, with texture maps and sections: U.S. Geological Survey Professional Paper 1401-C,

54 p.

Page, R.W. and Balding, G.O., 1973, Geology and quality of

water in the Modesto–Merced area, San Joaquin Valley,

California: U.S. Geological Survey Water-Resources Investigations Report 73-6, 85 p.

Pavelko, M.T., 2004, Estimates of hydraulic properties from

a one-dimensional numerical model of vertical aquifersystem deformation, Lorenz Site, Las Vegas, Nevada: U.S.

Geological Survey Water-Resources Investigations Report

03–4083, 35 p., accessed April 16, 2009, at http://pubs.usgs.

gov/wri/wri034083/ 
References Cited 211

Phillips, S.P., and Belitz, Kenneth, 1991, Calibration of a

textured-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., Carlson, C.S., Metzger, L.F., Howled, J.F.,

Galloway, D.L., Sneed, Michelle, Ichihara, M.E., Hunt,

K.W., and King, N.E., 2003, Analysis of tests of subsurface

injection, storage, and recovery of freshwater in Lancaster,

Antelope Valley, California: U.S. Geological Survey WaterResources Investigations Report 03–4061, 122 p., accessed

April 16, 2009, at http://ca.water.usgs.gov/pubs/wrir_03-

4061.pdf

Phillips, S.P., Green, C.T., Burow, K.R., Shelton, J.L., and

Rewis, D.L., 2007, Simulation of multistage ground-water

flow in part of the Northeastern San Joaquin Valley, California: U.S. Geological Survey Scientific-Investigations Report

2007–5009, 43 p.

Poeter, E.P., Hill, M.C., Banta, E.R., Mehl, Steffen, and Christensen, Steen, 2005, UCODE_2005 and six other computer

codes for universal sensitivity analysis, calibration, and

uncertainty evaluation: U.S. Geological Survey Techniques

and Methods 6-A11, 283 p.

Prudic, D.E., Konikow, L.F., and Banta, E.A., 2004, A new

streamflow-routing (SFR1) package to simulate streamaquifer interaction with MODFLOW-2000: U.S. Geological

Survey Open-File Report 04–1042, 95 p.

Riley, F.S., 1969, Analysis of borehole extensometer data from

Central California, in Tyson, L.J., ed., Land Subsidence:

Tokyo, International Association of Hydrological Sciences

Publication 89, v. 2, p. 423–431.

Riley, F.S., 1984, Developments in borehole extensometer, in

International Symposium on Land Subsidence, 3d, Venice, Italy, 1984, Proceedings: International Association of

Hydrological Sciences Publication 151, p. 169–186.

Riley, F.S., 1998, Mechanics of aquifer systems—The scientific legacy of Dr. Joseph F. Poland, in Borchers, J.W., ed.,

Land subsidence case studies and current research: Proceedings of the Dr. Joseph F. Poland Symposium on Land

Subsidence, Association of Engineering Geologists Special

Publication no. 8, p. 13–27.

Russo, David, and Bouton, Moshe, 1992, Statistical analysis

of spatial variability in unsaturated flow parameters: Water

Resources Research, v. 28, no. 7, p. 1911–1925.

Samani, Z.A., 2000, Estimating solar radiation and evapotranspiration using minimum climatological data: J. Irrigation and Drainage Engineering, v. 126, no. 4, p. 265–267.

Schmid, Wolfgang, Hanson, R.T., and Mattock III, T.M.,

2006a, Overview and advances of the farm process for

MODFLOW-2000: Managing Groundwater Systems

—Conference Proceedings, International Ground Water

Modeling Center, Golden, Colorado, May 21–24, 2006 , p.

23–27

Schmid, Wolfgang, Hanson, R.T., Mattock III, T.M., and

Leake, S.A., 2006b, 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.

Semitropic Water Storage District, 2007, accessed April 16,

2009, at http://www.semitropic.com/index.htm

Sneed, Michelle, 2001, Hydraulic and mechanical properties

affecting ground-water flow and aquifer-system compaction, San Joaquin Valley, California: U.S. Geological Survey

Open-File Report 01-35, 26 p.

Sneed, Michelle, and Galloway, D.L., 2000, Aquifer-system

compaction and land subsidence: Measurements, analyses,

and simulations - the Holly Site, Edwards Air Force Base,

Antelope Valley, California: U.S. Geological Survey WaterResources Investigations Report 00-4015, 65 p., accessed

April 16, 2009, at http://ca.water.usgs.gov/archive/reports/

wrir004015/

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., Leaflet 21427, Cooperative Extension of the University of California Division of Agriculture and Natural

Resources, 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., Leaflet 21428, Cooperative Extension of the University of California, Division of

Agriculture and Natural Resources, 8 p.

Taylor, A., Hume, P., Hughes, A., and Ruston, K.R., 2001,

Representation of variable hydraulic conductivity with

depth in MODFLOW: In: Proceedings of MODFLOW 2001

and Other Modeling Odysseys Conference, Golden, CO,

Sep. 11–14, v. I, p. 24–30.

Temesgen, Berkeley, Echoing, Simon, Davidoff, Baryohay,

and Frame, Kent, 2005, Comparison of some reference

evapotranspiration equations for California: Journal of Irrigation and Drainage Engineering, v. 131 no. 1, p. 73–84.
212 Groundwater Availability of the Central Valley Aquifer, California

U.S. Department of Agriculture Natural Resources Conservation Service, 2005a, Soil Survey Geographic (SSURGO)

Database: USDA Natural Resources Conservation Service,

accessed April 16, 2009, at htttp://www.soils.usda.gov/

survey/geography/ssurgo/ 

U.S. Department of Agriculture Natural Resources Conservation Service, 2005b, State Soil Geographic (STATSGO)

Database: USDA Natural Resources Conservation Service,

accessed April 16, 2009, at http://www.soils.usda.gov/

survey/geography/statsgo/

U.S. Geological Survey, 1990, Land use and land cover digital

data from 1:250,000- and 1:100,000-scale maps. Data User

Guide 4, Reston, Virginia.

U.S. Geological Survey, 1999, National land cover digital data, accessed April 16, 2009, at http://edc.usgs.gov/

products/landcover/nlcd.html 

Walter, D.A. and Whealan, A.T., 2005, Simulated water

sources and effects of pumping on surface and ground

water, Sagamore and Monomoy flow lenses, Cape Cod,

Massachusetts: U.S. Geological Survey Scientific

Investigations Report 2004–5181, 85 p.

Whittemore, D.O., Macfarlane, P.A., Doveton, J.H., Butler,

J.A., Jr., Chu, T.M., Bassler, R., Smith, M., Mitchell, J.,

and Wade, A., 1993, The Dakota Aquifer Program Annual

Report, FY92, Kansas Geological Survey, Open-File Report

93-1, 170 p.

Williamson, A.K., 1982, Evapotranspiration of applied water,

Central Valley, California, 1957–78: U.S. Geological Survey Water-Resources Investigations Report 81-45, 56 p.

Williamson, A.K., Prudic, D.E., and Swain, L.A., 1989,

Ground-water flow in the Central Valley, California: U.S.

Geological Survey Professional Paper 1401-D, 127 p.
Introduction

The modification of selected packages was required to

align the functionality of MODFLOW-2000 with the hydrologic and geologic architecture of the Central Valley. The

packages that were modified or updated include the LayerProperty Flow (LPF), the Multiplier (MULT), Hydrograph

Time Series (HYDMOD) (Hanson and Leake, 1998), and the

Streamflow Routing (SFR1) (Prudic and others, 2004) packages. Additional improvements also were made to the Farm

Process (Schmid and others, 2006). All of these modifications

are summarized in the release notes and online documentation of the source code for MODFLOW-2000 version 1.15.03

(MF2K) with the Farm Process. (http://water.usgs.gov/nrp/

gwsoftware/mf2k-fmp/mf2kfmp.html).

Layer-Property Flow Package (LPF)

The modifications to the LPF package remove the vertical

leakance correction for conditions in which a partially saturated cell is immediately below a fully or partially saturated

cell (Harbaugh and others, 2000, p. 31–33; McAda and Barroll, 2002) and are implemented for MF2K. The vertical leakance correction in MF2K adds an additional nonlinear term

to the model, which simulates perched conditions within an

aquifer system (McAda and Barroll, 2002). The modified version of the LPF package, incorporating changes documented

by McAda and Barroll (2002), reduced simulated perching of

the water table in areas where it has not been measured.

Multiplier Package (MULT)

The MULT package was modified to include exponentiation as an additional binary operator that could be performed 

on scalars or arrays, as specified in the MULT package input.

The ability to perform exponentiation facilitates the expression

of power functions for calculating vertical hydraulic conductivities. The distribution of vertical hydraulic conductivity

now can uniformly grade between the harmonic and geometric

mean by the specification of the power function to multiplier

arrays that are based on sedimentary textural data estimated on

a cell-by-cell basis (Chapter C, this volume). This approach

first was recognized by Belitz and others (1993) and then

implemented externally in the development of the revised

groundwater-flow model for the central part of the western

San Joaquin Valley within the Central Valley (C. Brush, U.S.

Geological Survey, written commun., 2006). With this modification, the estimation of power functions of hydraulic conductivity distributions can be performed internally with MF2K.

This resulted in modification of the subroutine GLO1BAS6RP.

The exponentiation imposes absolute value on the operand;

therefore, the operand must be greater or equal to zero. The

exponentiation operator can be positive, negative, or zero.

To use exponentiation in the MULT package, the user simply

uses the “^” as a binary operator similar to the other binary 

operators provided by the MULT package—addition, subtraction, multiplication, and division (Harbaugh and others, 2000,

p. 47–48). The MULT package does not restrict the number of

binary operations specified by the user. The specified operations are performed in order from left to right. If exponentiation needs to occur prior to other mathematical operations, 

the user should make this binary operation a separate input 

command prior to other mathematical operations.

Time-Series Package (HYDMOD)

The modifications to the HYDMOD package allow the

capture of time series from the Subsidence package (SUB)

(Hoffmann and others, 2003) and from the SFR1 packages

(Prudic and others, 2004). Input specifications of locations

for retrieval of time-series data in HYDMOD are the same for

SUB as originally specified for the Interbed Storage package and the same for SFR1 as the first Streamflow Routing

package (STR1). However, the input item that specifies the

package that time-series data are retrieved from (PCKG) is

specified as ‘SUB’ for the Subsidence package and as ‘SFR1’

for SFR1 and STR1. Note that time series of compaction and

subsidence for non-delay interbeds is available with these

modifications and time series for delayed compaction currently are not available.

Appendix 1. Supplemental Information—Modifications to 

Modflow-2000 Packages and Processes

By Wolfgang Schmid and R.T. Hanson
214 Groundwater Availability of the Central Valley Aquifer, California

Streamflow Routing Package (SFR1)

The modifications to SFR1 (version 1.4) (Prudic and others, 2004) includes an additional option to compute streambed 

elevation for canal reaches (reaches of SFR diversion segments), which allows the streambed slope to follow the slope

of ground surface at a defined depth. Details of these changes

were documented in Schmid and others (2006). These changes

to SFR1 are independent of the linkage between the Farm

Process and SFR1.

The Farm Process (FMP1)

Additional improvements made to the Farm Process

include:

	 1. Root uptake under variably saturated conditions;

	 2. Matrix of on-farm efficiencies not only by farm,

but also by crop;

	 3. Nonirrigated vegetation;

	 4. Additional consumptive use types that include

the use of crop coefficients and reference

evapotranspiration;

	 5. A modification to the specification of a point of

diversion for semi-routed deliveries;

	 6. Semi-routed return flows to specified reaches of

the stream network;

	 7. Restrictions on farm-well pumping, where no

irrigation requirement exists; and

	 8. Additional budget components.

With the ability to simulate root-zone pressure heads that

span from negative to positive values, the FMP now can simulate the growing of crops that take up water from saturated

conditions such as rice or some types of riparian vegetation.

A more distributed specification of on-farm irrigation efficiencies allows the user to specify efficiencies as a matrix by farm

and by crop for the entire simulation or each stress period. The 

addition of an input flag to the consumptive-use data allows

the differentiation between irrigated vegetation and nonirrigated vegetation. Nonirrigated vegetation can represent dryland farming or natural vegetation settings such as rangeland,

forests, or riparian settings. This flag prevents irrigation water

from surface-water deliveries and groundwater pumpage from

being applied to the nonirrigated vegetation. The expansion of

the consumptive-use features allows the specification of consumptive use as the product of the crop coefficients and reference evapotranspiration for each crop for the entire simulation

or for each stress period. This, in turn, allows the simulation

of demand that is in alignment with the growth stages of each

crop group the user is simulating. Locations along the streamflow network, where runoff from farms can be returned to

the streamflow network, now can be specified. The additional

restriction of farm wells now allows for restricting pumpage to

farm wells that are located in cells, where vegetation requires

irrigation water. The additional budget components facilitate

output of a detailed time series of all the inflows to and outflows from each farm (water-balance subregion). The inputdata options and specifications for each of these new features

are described in the following input-data instructions.

Concepts and Input Instructions for 

New FMP1 Features

New FMP1 features that are add-on options to existing FMP1 features are described below in the order of their

occurrence within the FMP input instructions in the FMP1

user guide (Schmid and others, 2006, p. 65 and 66). The following table highlights, in yellow, the position of changed or

new items within the existing numbering scheme of data-input

items. Changes to existing flags or parameters are displayed

in blue fonts and new flags or parameters are displayed in red

fonts. For explanations of the table, for example, for footnotes

indicating which Array-Reading Utility Modules was used,

refer to the user guide (Schmid and others, 2006, p. 64–69).
The Farm Process (FMP1) 215

Data for each Simulation

Item No. Input instruction for each item 

0 [#Text] read if ‘#’ is specified (can be repeated multiple times)

1 [PARAMETER NPFWL MXL] read if word ‘PARAMETER’ is specified

2 MXACTW NFARMS NCROPS NSOILS IRTFL ICUFL IPFL IFTEFL IIESWFL IEFFL IEBFL IROTFL IDEFFL {IBEN}

{ICOST} ICCFL INRDFL {MXNRDT} ISRDFL IRDFL ISRRFL IALLOT {PCLOSE} IFWLCB IFNRCB ISDPFL

{IOPFL} {IPAPFL} IFBPFL {Option} read 

3 [PARNAM PARTYP PARVAL NLST] read if NPFWL > 0 Repeat items 3 + 4 NPFWL times

4 [Layer Row Column Farm-Well-ID Farm-ID QMAXfact] [xyz] read* 

NLST times with [3] if NPFWL > 0

Repeat items 3 + 4 NPFWL times

5 GSURF(NCOL,NROW) read* with [2]

6 FID(NCOL,NROW) read* with [1]

7 [Farm-ID OFE] or [Farm-ID OFE(FID,CID1

), OFE(FID,CID2), ... , OFE(FID,CIDNCROPS)]

read* NFARMS times with [5] if IEFFL = 1

8 SID(NCOL,NROW) read* with [1]

9 Soil-ID CapFringe [A-Coeff B-Coeff C-Coeff D-Coeff E-Coeff], or

Soil-ID CapFringe [Soil-Type] (parameters in brackets only if ICCFL = 1) read* NSOILS times with [6]

10 [CID(NCOL,NROW)] read* with [1] if IROTFL >= 0

11 [Crop-ID ROOT] read* NCROPS times with [4] if IRTFL = 1

12 [Crop-ID FTR FEP FEI] read* NCROPS times with [5] if IFTEFL = 1

13 [Crop-ID FIESWP FIESWI] read* NCROPS times with [5] if IIESWFL = 1

14 [Crop-ID PSI(1) PSI(2) PSI(3) PSI(4)] read* NCROPS times with [5] if ICCFL = 1

15 [Crop-ID BaseT MinCutT MaxCutT C0 C1 C2 C3 BegRootD MaxRootD RootGC {NONIRR}] read* NCROPS times with [5] if

IRTFL = 3, or ICUFL = 3, or IPFL = 3

16 [TimeSeriesStep MaxT MinT Precip ETref] read* LENSIM times with [5] if IRTFL = 3, or ICUFL = 3, or IPFL = 3 (LENSIM

= length of simulation expressed as total number of time-series steps; length of time-series step defined by ITMUNI in the

Discretization File)

17 [Crop-ID IFALLOW] read* NCROPS times with [7] if IDEFFL = -2

18 [Crop-ID WPF-Slope WPF-Int Crop-Price] read* NCROPS times with [5] if IDEFFL > 0 and if IBEN = 1

19 Farm-ID GWcost1 GWcost2 GWcost3 GWcost4 SWcost1 SWcost2 SWcost3 SWcost4] read* NFARMS times with [5] if

IDEFFL > 0 and ICOST = 1

20 [Farm-ID Row Column Segment Reach] read* NFARMS times with [7] if ISRDFL = 1

New Item [Farm-ID Row Column Segment Reach] read* NFARMS times with [7] if ISRRFL = 1
216 Groundwater Availability of the Central Valley Aquifer, California

Data for each Stress Period

Item No. Input instruction for each item 

21 ITMP NP read

22 [Layer Row Column Farm-Well-ID Farm-ID QMAX] [xyz] read* ITMP times with [3] if ITMP > 0

23 [Pname] read NP times if NP > 0

24 [Farm-ID OFE] or [Farm-ID OFE(FID,CID1

), OFE(FID,CID2), ... , OFE(FID,CIDNCROPS)]

read* NFARMS times with [5] if IEFFL = 2

25 [CID(NCOL,NROW)] read* with [1] if IROTFL = -1

26 [Crop-ID ROOT] read* NCROPS times with [4] if IRTFL = 2

27 [Crop-ID CU {NONIRR}] read* NCROPS times with [4] if ICUFL = 2

New item ETR(NCOL,NROW) read with [1] if ICUFL = 1 or -1

28 [Crop-ID FTR FEP FEI] read* NCROPS times with [5] if IFTEFL = 2

29 [Crop-ID FIESWP FIESWI] read* NCROPS times with [5] if IIESWFL = 2

30 [PFLX(NROW,NCOL)] read* with [2] if IPFL = 2

31 

[Crop-ID WPF-Slope WPF-Int Crop-Price] read* NCROPS times with [5] if IDEFFL > 0 and if IBEN = 2.

32 [Farm-ID GWcost1 GWcost2 GWcost3 GWcost4 SWcost1 SWcost2 SWcost3 SWcost4] read* NFARMS times with [5] if

IDEFFL > 0 and ICOST = 2.

33 [Farm-ID (NRDV NRDR NRDU)1

, (NRDV NRDR NRDU)2, ... , (NRDV NRDR NRDU)MXNRDT] read* NFARMS times with

[5] if INRDFL = 1. A maximum number of MXNRDT types of nonrouted deliveries is read for each farm. One set of variables

NRDV, NRDR, and NRDU is read for a certain unranked type t of a nonrouted delivery by (NRDV NRDR NRDU)t .

34 [Farm-ID Row Column Segment Reach] read* NFARMS times with [7] if ISRDFL = 2

New Item [Farm-ID Row Column Segment Reach] read* NFARMS times with [7] if ISRRFL = 2

35 [ALLOT] read if IALLOT = 1

36 [Farm-ID CALL] read* NFARMS times with [5] if IALLOT = 2
The Farm Process (FMP1) 217

Root Uptake Under Variably Saturated 

Conditions (PSI specified in Item 14)

The currently released version of the FMP allows only

the simulation of transpiration uptake from unsaturated root 

zones. A water level rising into the root zone causes saturated

conditions, under which the stress response of natural vegetation or crops becomes zero. A new concept was developed

that allows the simulation of natural vegetation or crops (for

example, rice and willow trees) that do not reduce their uptake

as a result of anoxic conditions in the unsaturated zone and

maintain maximum uptake even under saturated conditions

until, eventually, they reduce their uptake as positive pressure

heads increase. We will review the current concept before

elaborating on the new concept. The review of the current

concept makes reference to text and graphs in the FMP1 user

guide (Schmid and others, 2006). Therefore, the reader is

referred to the user guide, which is available under (http://

water.usgs.gov/nrp/gwsoftware/mf2k-fmp/mf2kfmp.html).

Current Concept of Root Uptake from 

Unsaturated Conditions

The FMP assumes that the actual transpiration of crops is

reduced proportionally to the reduction of the total root zone,

TRZ, to an active root zone by wilting and anoxia. Prior to

the current FMP modifications, this concept extended only

to crops, whose stress response to water does not allow any

uptake from saturated conditions.

The conceptualization of root uptake from an unsaturated

root zone in FMP is described in detail in Schmid and others (2006, p. 11, 15, 46, and 76) and in Schmid (2004, p. 80,

83, 84, and 86). Varying hydraulic pressure heads within a

root zone impose different levels of stress on a crop resulting

in water uptake ranging between a maximum and zero. The

functionality between dimensionless water uptake, α, (0 ≤ α ≤

1) and pressure head, ψ, is called a water stress response function. Such a crop-specific water stress response function can

be defined by four negative critical pressure heads at which

water uptake ceases as a result of either anoxia or wilting (ψ1 

ψ4) or at which water uptake is at its maximum (ψ2, ψ3). The 

FMP simplifies this stress response function to a step function,

where water uptake is considered at maximum between the

averages of ψ1 and ψ2, and of ψ3 and ψ4(Schmid and others,

2006, fig. 8A, p. 15). These averages then are compared with

pressure heads found by an analytical solution of the vertical

pressure head configuration across the root zone (Schmid and

others, 2006, fig. 8B, p. 15). In the FMP, regions of the root

zone with negative pressure heads smaller than the average of

ψ4 and ψ3 or greater than the average of ψ2 and ψ1 are considered inactive wilting and anoxia zones, respectively (WZ,

AZ) (Schmid and others, 2006, fig. 8B, p. 15). For a water

level at the bottom of the root zone (hb), the residual active

unsaturated root zone (AURZ) is equal to the TRZ minus WZ

and AZ. As the groundwater level rises, the vertical pressure

head distribution is shifted upward. The WZ at the top end of

the pressure head distribution becomes gradually eliminated

and the active root zone remains constant until the water level

reaches a point, where the depth of the WZ is zero (water level

at that point = hw0). For water levels rising beyond this point,

the AURZ is reduced linearly until the top of the anoxia fringe

above the water level reaches the ground-surface elevation

(GSE). At this position of the water level, transpiration reaches

extinction (water level at that point = hux).

AURZ(h) = TRZ AZ WZ, h h h 

GSE AZ h, h h h

0 h u

b w0

w0 ux

≥

≥

≥ x

(1)

For water levels at or above the bottom of the root zone,

the root uptake from groundwater from unsaturated conditions

can be formulated as:

T h T AURZ h TRZ

T

gw act unsat c pot

c pot

− − −

−

( ) = , / ( )

with : 

 = potentialtranspiration;

= actual transpiration 

(root

Tgw− − act unsat

uptake) from groundwater.

(2)

Expanded Concept of Root Uptake from Variably 

Saturated Conditions

An expansion of the current concept was needed because

certain crops and riparian vegetation (for example, rice and

willow trees) do not reduce their uptake as a result of anoxic

conditions in the unsaturated zone. However, they eventually

do reduce their uptake as positive pressure heads increase in

the saturated root zone or, for ponding conditions, above the

ground-surface elevation.

For deep root zones and for groundwater levels ranging within the root zone, particular cases are possible, where

uptake from both unsaturated and saturated conditions occurs. 

Above the groundwater level, zero or full uptake may occur

from unsaturated conditions within the WZ and the AURZ,

respectively. For crops characterized by positive critical pressure heads ψ1 and ψ2

, the AURZ is not restricted by anoxia

(AZ = 0) (fig. 1-1, above groundwater level). Below the

groundwater level, full or reduced uptake may occur from

saturated conditions within the active saturated root zone

(fig. 1-1, below groundwater level):
218 Groundwater Availability of the Central Valley Aquifer, California

Wilting Zone with

zero uptake (WZ) 

Active Unsaturated

Root Zone with full

uptake (AURZ) 

Active Saturated

Root Zone with

full uptake

(ASRZ1)

Active Saturated

Root Zone with

reduced

uptake

(ASRZ2)

Inactive

Saturated

Zone below

Root Zone

with zero uptake

Inactive Saturated

Zone with zero

uptake due to

High Pressure (> 1)

Depth below Surface

and Anoxia

 = 1

 = 0

Anoxia &

High

Pressure

Zero uptake Full uptake

User specified stress response function, 

Simplified stress response function in FMP

Pressure head distribution in unsaturated root 

zone: | | = | |(d) solved by analytical solution

Hydrostatic pressure heads in saturated root 

zone: = h - z = head minus elevation

Unsaturated Zone within Root Zone

Saturated Zone within Root Zone

Saturated Zone below Root Zone

0 1

2 | |4 | |3 1

ln (| |), for < 0 Soil Water Pressure Head , for 0

| | =

| |(d)

= h - z

Stress Response

Stress Response

Depth below Surface

Ground

Surface

Bottom of

Root Zone

Groundwater

Level

Wilting

Figure 1-1. Evaluation of active and inactive parts of a variably saturated root zone in FMP (Concept of stress response, α, as a 

function of pressure heads, ψ, varying over depth, d).
The Farm Process (FMP1) 219

Full uptake from saturated conditions occurs for a

region of positive pressure heads within the root zone ranging

between zero at the groundwater level and the user-specified

pressure head ψ2. This region of the root zone is defined as

Active Saturated Root Zone 1, ASRZ1. Within this zone, the

stress response to water uptake, a, is equal to 1, meaning full

uptake is possible. For water levels rising above the GSE (not

displayed in fig. 1-1), the ASRZ1 extends from the GSE to

where the critical pressure head ψ2 is found.

Reduced uptake from saturated conditions occurs for a

region of positive pressure heads ranging between ψ2 (full 

uptake) and the lesser of ψ1 (zero uptake) and the pressure

head at the bottom of the root zone. This region of the root

zone is defined as the Active Saturated Root Zone 2, ASRZ2.

Within this zone, the stress response to water uptake, α, is

taken to be equal to the average of stress responses, , owing

to pressure heads that are found within the root zone between

ψ2 and the lesser of ψ1 and the pressure head at the bottom of 

the root zone. Figure 1-1 displays a case where ψ1 is not found 

within but is found below the bottom of the root zone. That

is, in this case, ASRZ2 is not bound by ψ1 but by the pressure 

head at the bottom of the root zone, where the stress response

owing to this pressure head is not yet zero.

For water levels at and above the bottom of the root zone,

the uptake from saturated conditions is formulated as 

(T-notations see eq. 2):

T h gw− − act sat ( ) = T A c− pot i( ( SRZ h 1 2 ) + ASRZ ( ) h h i ( )) / TRZ (3)

For water levels at and above the bottom of the root zone,

the total uptake is formulated as:

T h T AURZ h ASRZ h

ASRZ h h TRZ

gw−act ( ) = c− pot ( ( ) + ( ) +

( ) ( ))

i

i

1

2  / (4)

ASRZ1, ASRZ2, and  depend on the vertical location

of the hydrostatic pressure heads ψ1 and ψ2. Because ψ1 and 

ψ2 move vertically up or down as the water level rises or falls,

the terms ASRZ1, ASRZ2, and  depend on the simulated 

groundwater level and, therefore, are head-dependent terms.

In order to avoid the term ASRZ2( ) h h i ( ) becoming nonlinear in head, we evaluate ā based on the head of the previous iteration (k-1), while ASRZ2 is related to the head of the

current iteration (k):

T h T AURZ h ASRZ h

ASRZ h h

gw act

k

c pot

k k

k k

− −

−

( ) = ( ( ) + ( ) +

( ) ( ))

i

i

1

2 1  / TRZ

(5)

Schmid and others (2006) explained how Tgw-act-unsat can 

be split into non-head-dependent and head-dependent terms. 

Similarly, Tgw-act-sat, is separated into terms either dependent 

or not dependent on the head of the current iteration. While 

figure 1-1 demonstrates a situation for a particular water-level

elevation, figure 1-2 illustrates the conceptual approximation

to the change of all transpiration and evaporation components

with varying groundwater level (fig. 1-2, Example 1). Only the

transpiration components are discussed in this appendix, the

evaporation components are explained in the FMP user guide

(Schmid and others, 2006) and as no expansions to them were

made here.

This concept allows the simulation of water uptake

and irrigation requirements of natural vegetation or crops

(for example, rice, willows) rooting in soils that are fully or

partially saturated by the groundwater rising into the root

zone or even above ground surface (for example, in alluvial

valleys). Under such conditions, irrigation only is required for

vegetation specified as irrigated crops for special cases, where

uptake from groundwater does not fully satisfy the potentially

possible transpiration.

Depending on where the water level is positioned (above,

within, or below the root zone), this new concept of FMP

considers five different cases of combinations of up to four

transpiration components. These components are fed either by 

capillary rise from groundwater (unsaturated root zone), by

direct uptake from groundwater (saturated root zone), by irrigation, or by precipitation. For instance, for Case 3 (fig. 1-2, 

Example 1), the water level rises only slightly above the bottom of the root zone and wilting still might occur in the drying

top soil. Transpiration is fed by groundwater uptake from the

unsaturated and saturated part of the root zone. The deficit

between the transpiration from groundwater and the maximum

possible transpiration may be supplemented by precipitation 

or irrigation. However, if the water level rises further (fig. 1-2, 

Example 1, Case 2), all possible transpiration will occur by

groundwater uptake from the unsaturated and saturated root

zone. Finally, when the water level rises above the ground

surface and ponding occurs, then uptake only will take place

from the saturated, inundated root zone (fig. 1-2, Example 1,

Case 1).

Examples 1, 2, and 3 (fig. 1-2) show how the total

transpiration uptake from the saturated root zone (light green

curve) is composed of the uptake from the fully active and

partially active part of the saturated root zone. The uptake

from the fully active root zone (light blue curve) is a piecewise linear approximation. The uptake from the partially

active root zone (purple curve) depends on the product of two

head-dependent terms, the depth of this zone and the average

stress response,  , within that zone. Therefore, as shown in

figure 1-2, this part of the uptake is nonlinear with changing

head (eq. 4). For select positive ψ1 and ψ2 values, the range

of positive pressure heads with reduced uptake (ψ1 – ψ2) may 

be less than the thickness of the total root zone. In this case,

the “partial uptake zone,” ASRZ2, and the average stress

response,  , within that zone may remain constant with a

moving water level, as long as the elevation where ψ2 is found 

(head - ψ2) is less than the ground-surface elevation, and as

long as the elevation where ψ1 is found (head - ψ2) is greater

than the bottom of the root zone.


220 Groundwater Availability of the Central Valley Aquifer, California

Capillary 

Fringe

Tc-pot

c-pot

c-pot c-pot

Root 

zone

gw -Extinction

Egw -Extinction

Capillary 

Root

Zone

Fringe

Flux

Ground

Surface

water level

E

water level

waterlevelwaterlevel

Root

Zone

Root

Zone

2

1

2

1 Case 1: Transpiration =

Uptake from saturated root zone 

Case 2: Transpiration =

Uptake from saturated root zone plus

Capillary uptake from unsat. root zone

Case 3: Transpiration =

Uptake from saturated root zone plus

Capillary uptake from unsat. root zone

plus uptake from irrigation and

precipitation

Case 4: Transpiration =

Capillary uptake from unsat. root zone

plus Uptake from irrigation and

precipitation

Case 5: Transpiration =

Uptake from irrigation and

precipitation 

T

Flux

2

1

2

1

T Tc-pot Flux

2

1

2

1

water level

water level

Example 1:

Root zone = 30 inches

 = 20 inches

 = 10 inches

1

2

Example 3:

Root zone = 30 inches

 = 40 inches

 = 10 inches

1

2

Example 2:

Root zone = 30 inches

 = 30 inches

 = 10 inches

1

Example 1:

 = 30 inches 1

2

Ground

Surface

Transpiration from fully active

portion of the saturated

rootzone (ASRZ1) 

Potential Transpiration

Ec-pot Potential Evaporation

Ei Evaporation fed by irrigation

Ep Evaporation fed by precipitation

Positive pressure head with

maximum transpiration

2 Positive pressure head with

zero transpiration

Ti Transpiration fed by irrigation

Tp Transpiration fed by precipitation

Tgw-sat Transpiration fed by groundwater

uptake in saturated root zone

Egw-sat Surface Evaporation from 

ponding groundwater

Egw-unsat Surface Evaporation fed by 

capillary rise from groundwater

Tgw-unsat Transpiration fed by capillary

groundwater uptake in unsaturated root zone

Transpiration from partially

active portion of the

saturated rootzone (ASRZ2) 

Total Transpiration from the

saturated root zone 

Tgw-unsat

Components of Transpiration and

Evaporation with varying Water Level

Head-dependent Combinations

of Transpiration Components

Subcomponents of Transpiration 

from Saturated Conditions

Subcomponents of Transpiration 

from Saturated Conditions

Subcomponents of Transpiration 

from Saturated Conditions

EXPLANATION

1

Tc-pot

Ti Tp Ei

Ep

Egw

Tgw-sat

T

Figure 1-2. Conceptualization to the change of transpiration uptake from a saturated root zone with varying water level (Three 

examples with different ψ1 values; example 1 at top includes conceptualization of all transpiration and evaporation components with 

varying water level).
The Farm Process (FMP1) 221

Input Instructions

Until now, the use of the FMP was limited to natural vegetation and crops that consume water only from unsaturated

conditions. The original input requirements for crop-specific

stress response functions required absolute values of negative

pressure heads, between which, root uptake is at maximum

and at which uptake becomes zero due to wilting and anoxia.

For new FMP1 features, the parameter and input item number

of the FMP input instructions are referenced in parenthesis

after the section titles.

The new, expanded concept of consumptive use allows

the simulation of natural vegetation or crops that take up water

from parts of the root zone with both negative and positive

pressure heads. Therefore, the user must be able to enter any 

pressure head at which root uptake is at maximum or zero. For

saturation-intolerant natural vegetation or crops, the user is

required to enter negative pressure head values for the stressresponse function for unsaturated conditions. For natural

vegetation or crops that tolerate saturated conditions, ψ1 and 

ψ2 have to be positive pressure heads. These values describe

a linear decrease of uptake from optimal (at ψ2) to zero (at

ψ1) due to anoxia and increasing pressure. The critical pressure heads ψ3 and ψ4 remain negative, since they represent the

diminishing of uptake from maximum (at ψ3) to zero (at ψ4) 

due to wilting in drying, unsaturated conditions.

The input instructions for item 14 (Schmid and others,

2006, p. 76) have changed to:

PSI(1)	 Negative or positive pressure head, at which root

	 uptake becomes zero due to anoxia or high

	 pressure [L]

PSI(2)	 Negative or positive pressure head, at which

	 root uptake is at maximum and from which uptake

	 decreases with rising pressure head due to anoxia [L]

PSI(3)	 Negative pressure head, at which root uptake is at

	 maximum and from which uptake decreases with

	 falling pressure head due to wilting [L]

PSI(4)	 Negative pressure head, at which root uptake

	 becomes zero due to wilting [L]

Matrix of On-Farm Efficiencies (OFE specified 

in Items 7 or 24)

In addition to just reading one value of on-farm efficiency

per farm, a matrix of efficiencies for any farm and any crop

now can be read (rows: farm ID; columns: crop type ID). The

user also may specify efficiencies varying from crop to crop

for some farms while specifying one efficiency (not varying by crop) for other farms. In the latter case, the efficiency

entered in column 2 of item 7 or item 24 (column 1 = farm ID)

is assumed to be valid for all other crops and the matrix fields

do not have to be filled for other crops.

Input Requirements

For a matrix of on-farm efficiencies, the required data

input is as follows:

[FID OFE(FID,CID=1), OFE(FID,CID=2), ... ,

OFE(FID,CID=NCROPS)] read NFARMS times for each

simulation if IEFFL = 1 (item 7) or for each stress period if

IEFFL = 2 (item 24). (FID= Farm ID; CID = Crop-ID)

In matrix form (NFARMS = number of farms;

NCROPS = number of crops):

Column 1 Column 2 Column 3 ... Column

NCROPS+1

Row 1 1 OFE1,1 OFE1,2 ... OFE1,NCROPS

Row 2 2 OFE2,1 OFE2,2 ... OFE2,NCROPS

... ... ... ... ... ...

Row

NFARMS

NFARMS OFENFARMS,1 OFENFARMS,2 ... OFENFARMS,NCROPS

CAUTION: Comments for each farm neither can be

entered to the right of efficiencies specified by crop nor to the

right of just one value per farm in column 2. If the user enters

a comment, the following input error is printed to the list file:

ERROR CONVERTING “...” TO A DP-REAL NUMBER IN

LINE ... .

Data Output

For each farm, an output “composite efficiency” is

printed together with the farm demand and supply budget for

each iteration, each time step, or selected time steps either to 

the list file, to an ASCII file called FDS.OUT, or to a binary

file as specified by the Farm Supply and Demand Print Flag,

ISDPFL. This “composite efficiency” is an area-weighted

average of either specified efficiency values (IEBFL=1) or

simulated head-dependent efficiencies (IEBFL=2,3) of all

model cells in a farm, weighted by the area of each cell.
222 Groundwater Availability of the Central Valley Aquifer, California

Non-Irrigation Crops (NONIRR Specified in 

Items 15 or 27)

Non-irrigation crops are designated by NONIRR=1

in column 3 of the data list in item 27 (ICUFL≤2). This is

required if, a potential crop evapotranspiration flux value

for each crop is specified for each stress period in item

27 (ICUFL=1,2). This also is required if, for each crop, a

potential crop evapotranspiration flux value is derived as the

product of crop coefficients specified in item 27 for each stress

period and a reference evapotranspiration-inserted item after

item 27 for each stress period (ICUFL=–1). Non-irrigation

crops are designated by NONIRR=1 in column 12 of the crop

list in item 15 (ICUFL=3), if, for each crop, a potential crop

evapotranspiration flux value is derived for each time step

from time-series of climate data and growing-degree-day coefficients as explained in Schmid and others (2006, p. 47–48).

For irrigated crops, NONIRR=0 or no data entry is required in

column 3 of item 27 (ICUFL= 2), or in column 12 of item 15

(ICUFL=3), respectively.

The Farm Process does not calculate an irrigation requirement or excess irrigation return flows for non-irrigation crops.

However, it does account for transpiration and evaporation

parts supplied by precipitation and groundwater uptake, as

well as for excess precipitation runoff-return flows and deep

percolation.

For Non-irrigation crops, the required data input is as

follows:

[Crop-ID CU NONIRR] read NCROPS times if ICUFL =

–1, 1, or 2 (item 27)

Column 1 Column 2 Column 3

Row 1 1 CU1 NONIRR1

Row 2 2 CU2 NONIRR2

... ... ... ...

Row NCROPS NCROPS CUNCROPS NONIRRNCROPS

Or

[Crop-ID BaseT MinCutT MaxCutT C0 C1 C2 C3 BegRootD MaxRootD RootGC NONIRR] read NCROPS times if

IRTFL = 3, or ICUFL = 3, or IPFL = 3 (item 15)

Column 1 Column 2 Column

3–11

Column 12

Row 1 1 BaseT1 ... NONIRR1

Row 2 2 BaseT2 ... NONIRR2

... ... ... ... ...

Row NCROPS NCROPS BaseTNCROPS ... NONIRRNCROPS

Consumptive Use Options (ICUFL Specified in 

Item 2; ETR Specified as New Item)

1. If ICUFL=3, then a time series of crop-specific crop

coefficients (Kc) is calculated. A time series of reference

evapotranspiration (ETo) is read and available to be used

for multiplication with a Kc value (Kc*ETo=ETc-pot) and 

for fallow cells (ICID(IC,IR)=–1). The ETo is assumed to

be 100 percent evaporative for fallow cells.

2. If ICUFL=2, then a list of crop specific consumptive use

fluxes is read for every stress period (crop consumptive

use = potential crop evapotranspiration). No fallow cells

(ICID(IC,IR)=–1) can be used. In this case, no ETo is read

because cropped cells (ICID(IC,IR>0) are associated with

a lumped consumptive user for the entire stress period.

3. NEW: If ICUFL=1, then a list of crop-specific consumptive use fluxes is read as item 27 for every stress period

(crop consumptive use = potential crop evapotranspiration). An ETo is assigned to optional fallow cells

(ICID(IC,IR)=-1). In this case, a constant or a 2D array of

ETo is read directly after item 27 (Crop-ID, Consumptive

Use). The ETo is assumed to be 100 percent evaporative

for fallow cells.

4. NEW: If ICUFL=-1, then a list of crop-specific crop coefficients (Kc

) are read as item 27 for every stress period.

A constant or a 2D array of ETo is read directly after

item 27 (Crop-ID, Kc) in order to be multiplied with a Kc

value (Kc*ETo=ETc-pot) and to be assigned to optional for

fallow cells (ICID(IC,IR)=-1). The ETo is assumed to be

100 percent evaporative for fallow cells.

For ETo, the required data input after item 27 is as follows:

ETR(NCOL,NROW) read by utility module U2DREL

as constant or as 2D real array if ICUFL = 1 or –1 (new item

after item 27).

Semi-Routed Delivery (ISRDFL in Item 2; REACH 

in Items 20 or 34)

Previously in the FMP, a stream-reach number required

to specify a point of diversion for a semi-routed delivery was

defined as a sequential number of a reach from zero to the

total number of reaches that are active during the simulation,

as specified in the SFR1 input file (NSTRM, item 1, Prudic

and others, 2004, p. 40). This option allowed defining the

diversion point uniquely by just the reach number (option 5

in Schmid and others, 2006, pages 54, 61, and 78) but made it

difficult for the user to keep track of which segment the reach

belongs to. The reach number of the diversion point is now

aligned in FMP with the reach numbering scheme per segment

in the SFR1 input instructions (IREACH, item 2, Prudic and

others, 2004, p. 41). A unique definition of a diversion point

for complex cases, where multiple reaches may exist within

one model cell, now requires the entry of both a segment number and reach number. Therefore, option 5 became obsolete 

and was deleted.
The Farm Process (FMP1) 223

Semi-Routed Surface-Water Runoff-Return Flow 

(ISRRFL in Item 2 and ROW COLUMN SEGMENT 

REACH Specified as New Item)

A “Semi-Routed Surface-Water Runoff-Return flow”

Flag, ISRRFL, is available to simulate surface-water runoffreturn flow from excess irrigation and (or) excess precipitation that is allocated as non-routed return flow to a point of

recharge at a specified reach of the stream network and then

routed further downstream. The ISRRFL flag was added to

the existing surface-water flags after the delivery-related flags

and before the water-rights related flags. That is, it is inserted

between flags IRDFL and IALLOT:

ISRRFL	Semi-Routed Runoff-Return flow Flag:

	 0	 =	 No locations along the stream network

	 	 	 are specified for any farm, where semi-routed

	 	 	 runoff-return flow is recharged into the stream

	 	 	 network. Runoff either is automatically

	 	 	 prorated over tributary-segment reaches

	 	 	 crossing through or adjacent to a farm, or

	 	 	 automatically recharged into one tributary-

	 	 	 segment reach nearest to the lowest elevation

 of the farm.

	 1or 2 = For each farm, a location is specified

	 	 	 anywhere along the stream network, where

	 	 	 semi-routed runoff-return flow is recharged.

	 	 	 A farm-related list of row and column

	 	 	 coordinates or segment and reach numbers

	 	 	 for a point of runoff-return flow recharge is

	 	 	 read (only if SFR1 is specified in Name

	 	 	 File).

	 1	 =	 List of row and column coordinates or segment

 and reach numbers is read for the entire 

 simulation.

	 2	 =	 List of row and column coordinates or segment

 and reach numbers is read for each stress 

 period.

If ISRRFL=0, the FMP attempts by first priority to automatically detect tributary-segment reaches adjacent or within

a farm. If none of these are found, then, by second priority, 

the FMP automatically locates one remote reach of a tributary

segment that is situated nearest to the lowest elevation of the

farm. If tributary-segment reaches are adjacent or within a

farm, then the surface-water runoff of a farm is prorated over

these reaches weighted by their reach lengths. We define this

form of return flow as “automatic fully routed return flow.”

If, instead, a remote tributary-segment reach was detected,

then the total runoff of a respective farm was recharged into

the tributary at this reach. This form of return flow is called

“automatic semi-routed return flow.”

Unlike reaches receiving automatic fully or semi-routed

return flow (ISRRFL=0), reaches receiving the new “specified

semi-routed return flow” can be located on any type of segment. Notice that multiple farms may discharge into the same

runoff-return flow reach.

A list of row and column coordinates, segment and reach

numbers of a return flow reach has to be specified to receive

semi-routed return flow from a specific farm. This farmspecific list is read as a new item after current item 20 or 34

(list of coordinates for semi-routed deliveries), depending on

whether these data are entered for the entire simulation or for

each stress period. If ISRRFL=1 and a semi-routed runoffreturn flow location is specified for a particular farm, then a

potentially existing automatic, fully routed runoff-return flow

is disabled. For a farm, where no runoff-return flow location is

known, zero coordinates and zero segment and reach numbers

have to be entered. If ISRRFL=1 and no return-flow location

is specified for a farm, then the FMP applies just for that particular farm either “automatic fully routed runoff return flow”

or “automatic semi-routed runoff return flow,” as described

above for all farms if ISRRFL=0. Clearly, setting ISRRFL=1

allows the user to specify return-flow locations for farms

where they are known, but still assures invoking automatic

features of the FMP for other farms to find stream reaches that

receive the return flow. This warrants that simulated crop-inefficient losses from a farm to surface-water runoff are indeed

sent as return flow from the respective farm to the stream network. This prevents loosing mass to an open system. Simulating runoff outflows from farms without reallocating them back

into the stream network would require the assumption that the

runoff leaves the model domain. In the FMP, this assumption

applies only if the SFR1 package is not specified in the MF2K

Name File.

Farm-Related Data List for Semi-Routed Runoff-Return 

Flow Locations (New Item Added After Items 20 or 34):

Farm-ID	 Farm identity to which the parameters below

are attributed

Row 	 Row number of point of recharge of runoffreturn flow

Column 	 Column number of point of recharge of runoffreturn flow

Segment	 Number of stream segment, in which the runoffreturn flow reach is located. This number must

be equal to the segment number of the identical

stream reach, as specified in column 4 of the

data list in the SFR1 input file for the entire

simulation.

Reach	 Number of reach within a segment, into which

the runoff-return flow is recharged. This number

must be equal to the sequential number of a 

reach within a particular stream segment.
224 Groundwater Availability of the Central Valley Aquifer, California

Four options of data input are available in order to

uniquely identify the point of recharge of runoff-return flow

within a cell:

Row Column Segment Reach Comments

x x x x full set of information is availablemaximum information

x x x __ if more than one 

segment pass

through the cell

identification of

location by 

coordinates

x x __ __ if just one segment

passes through

the cell

0 0 x x if more than one 

segment pass

through the cell

identification

of location 

by reach 

and segment

numbers

x = data input; 0 = input of zero; __ = no input or zero input

Additional Auxiliary Variable (AUX NOCIRNOQ 

Specified in Item 2)

The specification of the optional flag “AUX

NOCIRNOQ” for {option} in item 2 will prompt the FMP1 to

limit the distribution of farm pumpage to farm wells, whose

row and column coincides with a top layer cell with a current

irrigation requirement from active crops. “NOCIRNOQ” is

“no crop irrigation requirement (CIR), no pumping (Q).” This

feature is implemented by setting the maximum capacity of

select farm wells to zero if, during a particular time step, no

crop irrigation requirement of the top layer cell exists. At each

new time step, the maximum capacity of such a select well

will be reset to the default value. If some wells of a farm are

deactivated, the remaining active wells will receive a higher

demand to satisfy the pumping requirement.

The variable “AUX NOCIRNOQ” has to be specified in

the entry line that contains all FMP1 flags (item 2) after the

optional auxiliary variable “AUX QMAXRESET,” if set, and

before optional flag “NOPRINT.” The optional flag “AUX

NOCIRNOQ” requires FMP1 to read an auxiliary variable

[xyz] either from column 7 of the farm-wells list (items 4 or

22) if “AUX QMAXRESET” is not set, or from column 8 of

the farm-wells list (items 4 or 22) if “AUX QMAXRESET” is

set. The auxiliary variable for “AUX NOCIRNOQ” is a binary

parameter that specifies wells selected for the NOCIRNOQ

option. If a “1” is read, then the respective well is selected and

its maximum capacity is set equal to zero if no crop irrigation

requirement of the top layer cell exists during a particular time

step. The maximum capacity of such a select well is reset to

the default value at each new time step.

Farm Budget Output Options (IFBPFL Specified 

in Item 2)

A Farm Budget print flag IFBPFL may be entered in item

2 after mandatory flag ISDPFL and between optional flags

{IPAPFL} and {Option}. The IFBPFL flag allows for the following print option:

IFBPFL = 1 A compact list of Farm Budget components (flow rates and cumulative volumes into and out of a

farm) is saved on ASCII file “FB_COMPACT.OUT” for all

time steps:

PER Stress period

STP Time step

DAYS Time unit chosen in discretiza

tion file

FID Farm ID

Flow rates into a farm:

Q-p-in Precipitation

Q-sw-in Surface-water inflow

Q-gw-in Groundwater inflow

Q-ext-in External deliveries

Q-in-tot Total inflows

Flow rates out of farm:

Q-et-out Evapotranspiration outflow

Q-ineff-out Inefficient losses

Q-sw-out Surface-water outflow (excess nonrouted deliveries back into canal)

Q-gw-out Groundwater outflow (excess nonrouted deliveries injected into farm

wells)

Q-tot-out Total outflows

Q-in-out Inflows minus Outflows

Q-Discrepancy[%] Percent discrepancy
References Cited 225

Cumulative volumes into and out of a farm are denoted

by “V” analogous to “Q” for flow rates (e.g.: V-p-in = cumulative precipitation into a farm)

IEBPFL=2 A detailed list of Farm Budget components

(flow rates and cumulative volumes into and out of a farm) is

saved on ASCII file “FB_DETAILS.OUT” for all time steps:

PER Stress period

STP Time step

DAYS Time unit chosen in discretization file

FID Farm ID

Flowrates into a farm:

Q-p-in Precipitation

Q-nrd-in Non-routed deliveries

Q-srd-in Semi-routed deliveries

Q-rd-in Fully routed deliveries

Q-wells-in Groundwater well pumping deliveries

Q-egw-in Evaporation from groundwater into the

farm

Q-tgw-in Transpiration from groundwater into

the farm

Q-ext-in External deliveries

Q-in-tot Total inflows

Flowrates out of a farm:

Q-ei-out Evaporation from irrigation out of the

farm

Q-ep-out Evaporation from precipitation out of

the farm

Q-egw-out Evaporation from groundwater out of

the farm

Q-ti-out Transpiration from irrigation out of the

farm

Q-tp-out Transpiration from precipitation out of 

the farm

Q-tgw-out Transpiration from groundwater out of

the farm

Q-run-out Overland runoff out of the farm

Q-dp-out Deep percolation out of the farm

Q-nrd-out Non-routed deliveries from the farm

Q-srd-out Semi-routed deliveries out of the farm

(in form of excess non-routed deliveries recharged back into ‘remote’

head-gate)

Q-rd-out Fully routed deliveries out of the farm

(in form of excess non-routed deliveries recharged back into a head-gate

within or adjacent to the farm)

Q-wells-out Injection from farm into farm-wells

(excess non-routed deliveries injected into farm-wells)

Q-tot-out Total outflows

Q-in-out Inflows minus outflows

Q-Discrepancy[%] Percent discrepancy

The FMP Farm Budget has to be viewed as a ‘reference interface’ on the ground surface rather than a ‘reference

volume.’At this stage, the Farm Budget does not include soilwater storage or on-farm water storage.

References Cited

Belitz, Kenneth, Phillips, S.P., and Gronberg, J.M., 1993,

Numerical simulation of ground-water flow in the central

part of the Western San Joaquin Valley, California: U.S.

Geological Survey Water-Supply Paper 2396, 69 p.

Brush, C.F. Belitz, Kenneth, Phillips, S.P., Burow, K.R., and

Knifong, D.L., 2006, MODGRASS: Update of a groundwater flow model for the Central Part of the Western San

Joaquin Valley, California: U.S. Geological Survey Scientific Investigations Report 2005-5290, 81 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 finite-difference ground-water flow model: U.S. Geological Survey

Open-File Report 98-564, 57 p, accessed April 16, 2009 at

http://pubs.er.usgs.gov/usgspubs/ofr/ofr98564

Harbaugh, A.W., Banta, E.R., Hill, M.C., and McDonald,

M.G., 2000, MODFLOW-2000: U.S. Geological Survey

Modular Ground-Water Model—User Guide to Modularization Concepts and the Ground-Water Flow Process: U.S.

Geological Survey Open-File Report 00-92, 121 p.

Hoffmann, J., Leake, S.A., Galloway, D.L., and Wilson, A.M.,

2003, MODFLOW-2000 ground-water model—user guide

to the subsidence and aquifer-system compaction (SUB)

package: U.S. Geological Survey Open-File Report 03–233,

46 p. 

McAda, D.P., and Barroll, P., 2002, Simulation of groundwater flow in the middle Rio Grande Basin between Cochiti

and San Acacia, New Mexico: U.S. Geological Survey

Water-Resources Investigations Report 02–4200, 88 p.

Prudic, D.E., Konikow, L.F., and Banta, E.A., 2004, A new

Streamflow-Routing (SFR1) Package to simulate streamaquifer interaction with MODFLOW-2000: U.S. Geological

Survey Open-File Report 04-1042, 95 p.

Schmid, W., 2004, A Farm Package for MODFLOW-2000:

Simulation of Irrigation Demand and Conjunctively Managed Surface-Water and Ground-Water Supply; PhD Dissertation: Department of Hydrology and Water Resources,

The University of Arizona, 278 p.

Schmid, W., Hanson, R.T., Maddock III, T.M., and Leake,

S.A., 2006, User’s guide for the Farm process (FMP) for

the U.S. Geological Survey’s modular three-dimensional

finite-difference ground-water flow model, MODFLOW-2000: U.S. Geological Survey Techniques and

Methods 6-A17, 127 p.

Manuscript appproved on April 16, 2009

Prepared by the USGS Publishing Network,

Publishing Service Center, Sacramento, California

For more information concerning the research in this report, contact 

the

California Water Science Center Director,

U.S. Geological Survey, 6000 J Street

Sacramento, California 95819

http://ca.water.usgs.gov
Groundwater Availability of 

the Central Valley Aquifer, California

Faunt — Groundwater Availability of the Central Valley Aquifer, California Professional Paper 1766

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