U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2014–5150

Version 1.1, May 2015

Prepared in cooperation with Santa Barbara County Department of Public Works Water Agency

Hydrologic Models and Analysis of Water Availability in 

Cuyama Valley, California
Cover. Looking northwest from the south side of Cuyama Valley to the Caliente Range. Photograph taken by Randall T. Hanson, 

U.S. Geological Survey, September 19, 2009.
Hydrologic Models and Analysis of Water 

Availability in Cuyama Valley, California

By R.T. Hanson, Lorraine E. Flint, Claudia C. Faunt, Dennis R. Gibbs, and 

Wolfgang Schmid

In cooperation with Santa Barbara County Department of Public Works Water 

Agency

Scientific Investigations Report 2014–5150

Version 1.1, May 2015

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

First release: 2014

Revised: May 2015 (ver 1.1)

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Suggested citation:

Hanson, R.T., Flint, L.E., Faunt, C.C., Gibbs, D.R., and Schmid, W., 2015, Hydrologic models and analysis of water 

availability in Cuyama Valley, California (ver. 1.1, May 2015): U.S Geological Survey Scientific Investigations Report 

2014–5150, 150 p., http://dx.doi.org/10.3133/sir20145150.

ISSN 2328-0328 (online)
iii

Contents

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

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

Purpose and Scope ..............................................................................................................................2

Approach................................................................................................................................................2

Description of the Study Area .....................................................................................................................2

Hydrologic and Water-Balance Subregions ....................................................................................3

Geologic Framework ............................................................................................................................3

Hydrogeologic Units.............................................................................................................................3

Faults and the Groundwater Flow System........................................................................................4

Hydrogeologic Framework..................................................................................................................4

Three-Dimensional Model of Grain-Size Distribution............................................................5

Hydrologic System.........................................................................................................................................6

Climate ...................................................................................................................................................6

Effects of Water Use on the Landscape ...........................................................................................6

Surface Water .....................................................................................................................................7

Groundwater..........................................................................................................................................7

Model Development ......................................................................................................................................7

Water-Balance Model..........................................................................................................................8

Estimation of Recharge and Runoff ..........................................................................................8

Calibration and Comparison With Measured Streamflows..................................................8

Development of BCM Results for CUVHM model...................................................................9

Integrated Hydrologic Model—CUVHM ..........................................................................................9

Discretization ......................................................................................................................................10

Spatial Discretization and Layering........................................................................................10

Temporal discretization ............................................................................................................10

Initial Conditions and Recent Conditions ..............................................................................11

Boundary Conditions ........................................................................................................................11

No-Flow Boundaries .................................................................................................................11

General-Head Boundaries .......................................................................................................11

Surface-Water Inflows and Outflows .............................................................................................11

Groundwater Pumpage .....................................................................................................................12

Agricultural Supply....................................................................................................................12

Water Supply ............................................................................................................................13

Landscape Use and Movement of Water.......................................................................................13

Delivery Requirement.......................................................................................................14

Soils ................................................................................................................................14

Land Use.............................................................................................................................14
iv

Land-Use maps ........................................................................................................15

Crop-Type Data.........................................................................................................16

Climate Data ......................................................................................................................17

Precipitation .............................................................................................................17

Reference Evapotranspiration (ETh) .....................................................................18

Groundwater Agricultural Supply ...........................................................................................18

Net Recharge .............................................................................................................................18

Aquifer Characteristics......................................................................................................................18

Textural Analysis........................................................................................................................19

Hydraulic properties..................................................................................................................19

Hydraulic Conductivity of Lithologic End Members....................................................20

Storage Properties ...........................................................................................................21

Hydrogeologic Structures ...............................................................................................22

Initial Conditions ........................................................................................................................22

Model Calibration and Sensitivity .............................................................................................................23

Observations Used In Model Calibration........................................................................................24

Groundwater Observations......................................................................................................24

Water-Level Maps ............................................................................................................26

Land-Subsidence Observations ..............................................................................................26

Pumpage Observations.............................................................................................................26

Model Parameters..............................................................................................................................26

Farm Process Parameters........................................................................................................27

Hydraulic Parameters ...............................................................................................................27

Streamflow Properties..............................................................................................................28

Multi-Aquifer Well Parameters ...............................................................................................28

General-Head Boundary Parameters.....................................................................................28

Horizontal Flow Barrier Parameters.......................................................................................28

Subsidence Parameters ...........................................................................................................28

Sensitivity Analysis.............................................................................................................................28

Model Uncertainty, Limitations, and Potential Improvements .............................................................29

Hydrologic Budget and Flow Analysis .....................................................................................................31

Projection of Potential Water Availability................................................................................................32

Suggestions for Future Work .....................................................................................................................34

Summary and Conclusions.........................................................................................................................34

References Cited .........................................................................................................................................36

Figures ...........................................................................................................................................................41

Tables .........................................................................................................................................................126

Contents—Continued
v

Figures

1. Maps showing A, Cuyama Valley watershed and groundwater basin, and 

B,detailed location map with the active hydrologic model grid, groundwater

basin, and major rivers, Cuyama Valley, California...............................................................42

2. Maps showing A, groundwater hydrologic subregions and related geologic

structures; B, simplified Cuyama major groundwater regions; and C, groups of

landscape water-balance subregions for 1943–2010 in Cuyama Valley, California ........44

3. Map showing generalized A, outcrops of geologic units and major faults within

model grid; B, axial hydrogeologic cross-section (A–A’); and C, transverse

hydrogeologic cross-section (B–B’) of Cuyama Valley, California.....................................48

4. Maps showing extent and percentage of coarse-grained deposits for the 

A, Recent Alluvial aquifer; B, Older Alluvial aquifer; and C, Morales Formation

aquifer of Cuyama Valley, California........................................................................................51

5. Graph showing cumulative departure of precipitation along with wet-dry periods,

land-use map periods, periods of application for land-use, land-ownership and

related farm wells, and selected crop attributes for Cuyama Valley, California..............54

6. Maps showing average annual A, precipitation, and B, potential

evapotranspiration for the simulation period for Cuyama Valley, California ....................55

7. A, Timeline showing history of water and land-use development

through time, and B, graph showing population growth for Cuyama Valley,

California ......................................................................................................................................57

8. Map showing distribution of streams with streamflow routing cells and

segments, and location of inflows, Cuyama Valley, California............................................58

9. Map showing distribution of agricultural, urban-supply, and domestic wells,

Cuyama Valley, California..........................................................................................................59

10. Graph showing estimated groundwater pumpage from municipal and domestic

wells, Cuyama Valley, California ..............................................................................................60

11. Map showing the generalized distribution of bedrock, subwatersheds and

related inflow points used with the Basin Characterization Model

recharge-runoff estimates for Cuyama Valley.......................................................................61

12. Graph showing comparison of Basic Characteristics Model simulated potential

evapotranspiration using the Priestley-Taylor approach with measured

reference evapotranspiration from California Irrigation Management

Information System station in Cuyama Valley, California ....................................................62

13. Graphs showing comparisons of basin discharge, estimated by using the Basin

Characterization Model, with measured streamflow for gaged basins in the

Cuyama Valley model domain...................................................................................................63

14. Graphs showing annual streamflow for all Streamflow Routing inflows in

Cuyama Valley, California, A, for 1939–2009, and B, in comparison to precipitation.......64

15. Maps showing A, drawdown contours for 1947–66; and groundwater-level

contours for B, summer 1966; C, spring 2010; and D, summer 2010 for Cuyama

Valley, California .........................................................................................................................65
vi

16. Map showing distribution of model cells representing no-flow, groundwater 

underflow, springs, streams, and horizontal groundwater flow barrier boundaries

in the Cuyama Valley, California...............................................................................................69

17. Map showing agricultural soils for the Cuyama Valley simplified from Soil

Survey Geographic Database...................................................................................................70

18. Maps showing early periods of land-use groups discretized to the model 

grid, and pie chart of percentages of total land use over the modeled area for 

A, 1952; B, 1959; C, 1966; and D, 1977 for Cuyama Valley, California ..................................71

19. Maps showing land-use groups discretized to the model grid, and pie chart 

of percentages of total land use over the modeled area for A, 1984; B, 2000; and 

C, 2002 for Cuyama Valley, California ......................................................................................75

20. Maps showing 2004–06 periods of land-use groups discretized to the model

grid, and pie chart of percentages of total land use over the modeled area for

A, 2004; B, 2005; and C, 2006 for Cuyama Valley, California .................................................78

21. Maps showing 2007–09 periods of land-use groups discretized to the model

grid, and pie chart of percentages of total land use over the modeled area for

A, 2007; B, 2008; and C, 2009 for Cuyama Valley, California .................................................81

22. Maps showing A, actual major categories of land-use for 2010; B, equivalent

land-use groups discretized to the model grid, and pie chart of percentage of

total land use over the entire model area; and C, changes in percentages of

selected land use through time, Cuyama Valley, California ................................................84

23. Graphs showing monthly crop coefficients for A, orchards; B, grains and hay; 

C, vegetables; D, general land use; and E, native vegetation in the Cuyama Valley,

California ......................................................................................................................................87

24. Maps showing A, locations of wells with pumping tests, and the distribution 

of parameter zones used for model calibration of hydraulic properties for 

B, model layer 1; C, model layer 2; and D, model layer 3 in the Cuyama Valley,

California ......................................................................................................................................88

25. Map showing calibration data sites of wells for groundwater levels and

water-level differences for the Cuyama Valley Hydrologic Model, Cuyama

Valley, California .........................................................................................................................92

26. Graphs showing simulated and measured hydrographs for selected wells in 

A, Main-zone; B, Ventucopa Upland; and C, Sierra Madre Foothills subregions,

Cuyama Valley, California..........................................................................................................93

27. Graphs showing A, histogram of distribution of water-level residuals for the

Cuyama Valley hydrologic model model, B, correlation graph by subregions of

measured versus simulated water levels, and C, correlation between simulated

and measured vertical water-level differences for selected wells, Cuyama

Valley, California .......................................................................................................................102

Figures—Continued
vii

28. Maps showing comparison of the contoured measured water levels with

simulated water levels A, for fall 1966; B, for spring 2010; and C, for fall 2010,

Cuyama Valley, California........................................................................................................105

29. Graphs showing historical subsidence as A, map of seasonal InSAR with 

graphs of simulated and measured time series for selected locations of relative 

land-surface deformation from Plate-Boundary Observation sites and Point 

InSAR targets, and B, simulated total subsidence 1950–2010 for the calibrated 

hydrologic flow model, Cuyama Valley, California ..............................................................108

30. Graph showing reported and simulated agricultural pumpage for Cuyama

Valley, California .......................................................................................................................110

31. Graph showing relative composite sensitivity of computed observations at

calibration points to changes in selected parameters from analysis with PEST ..........111

32. Stacked bar charts showing hydrologic budget for the landscape with 

A, the temporal distribution of total landscape inflows and outflows; and 

B, pie chart showing average annual components of farm budget of the 

simulated landscape flow system within the Cuyama Valley Hydrologic Model, 

Cuyama Valley, California........................................................................................................112

33. Graphs showing A, the simulated net flow of groundwater in the hydrologic cycle; 

B, pie chart showing average annual components of simulated groundwater flow; 

C, the cumulative change in storage; and D, changes in groundwater storage,

Cuyama Valley, California........................................................................................................114

34. Chart showing the percentage of simulated groundwater pumpage for the

water years 1950–2010 for all three model layers, Cuyama Valley, California ...............118

35. Stacked bar charts showing A, percentage of total recharge by 

aquifer model layers; and B, Net downward flow between model layers, 

Cuyama Valley, California........................................................................................................119

36. Maps showing A, projected simulated water levels; and B, the difference in

water levels between projection of simulated water levels in fall 2071 and

simulated water levels in fall 2010 for the hydrologic flow model of Cuyama

Valley, California .......................................................................................................................121

37. Three projected scenarios showing projected A, cumulative change in net 

groundwater storage, B, potential groundwater levels at CVKR and CVBR 

monitoring sites, and C, potential land subsidence near Cuyama,

Cuyama Valley, California........................................................................................................123

Figures—Continued
viii

Tables

1. Summary of groundwater regional zones and subregions for the Cuyama Valley

Hydrologic Model, Cuyama Valley, California......................................................................127

2. Summary of climate periods for the Cuyama Valley Hydrologic Model, Cuyama

Valley, California .......................................................................................................................128

3. Scaling coefficients for estimation of streamflow for MODFLOW Streamflow

Routing from recharge and runoff maps developed by the Basin

Characterization Model for ungaged basins in three geologic types in Cuyama

Valley...........................................................................................................................................129

4. Streamgages used for Basin Characterization Model calibration with

calibration statistics for Cuyama Valley, California ............................................................130

5. Summary of One-Water Hydrologic Flow Model Packages and processes used

with the hydrologic flow model of Cuyama Valley, California ...........................................131

6. Coordinates of the hydrologic flow model of Cuyama Valley, California.........................132

7. Percentage of different virtual crop categories in Cuyama Valley Hydrologic

Model for selected land-use periods ....................................................................................133

8. Summary of Cuyama Valley Farm process virtual-crop crop category,

crop-index number, and select properties for the Cuyama Valley Hydrologic

Model, Cuyama Valley, California ..........................................................................................134

9. Summary of fractions of transpiration and evaporation by month, for Cuyama

Valley crop categories .............................................................................................................135

10. Irrigation efficiency, by month, for each crop of the Cuyama Valley, California............138

11. Summary of reference evapotranspiration comparisons between

Pennman-Montieth from California Irrigation Management Information System

stations and Priestley-Taylor estimates from regional climate data, Cuyama

Valley, California .......................................................................................................................139

12. Summary of hydraulic properties estimated from the Cuyama Valley hydrologic

model calibration ......................................................................................................................140

13. Summary of parameter zones and related property parameter names used to

calibrate horizontal hydraulic conductivity, vertical hydraulic conductivity, and

aquifer specific storage and specific yield in the Cuyama Valley hydrologic

model, Cuyama Valley, California .........................................................................................141

14. Summary of selected parameter values estimated for the Cuyama Valley

hydrologic model, Cuyama Valley, California ......................................................................142

15. Summary of streambed conductivity parameters and current values, Cuyama

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

16. Summary of groundwater-flow budgets for selected periods, Cuyama Valley,

California ....................................................................................................................................149
ix

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

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)

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

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)
x

Abbreviations

BCM Basin Characteristics Model

CADWR California Department of Water Resources

CIR Consumptive irrigation requirement

CUVHM Cuyama Valley Hydrologic Model

SBDPWWA Santa Barbara Department of Public Works Water Agency 

ENSO El Nino-Southern Oscillation

ETh

, ETo Reference evapotranspiration

ETact Actual evapotranspiration

Kc Crop coefficient

Kh Horizontal hydraulic conductivity

Kv Vertical hydraulic conductivity

MF2K5 MODFLOW-2005

OWHM One Water Hydrologic Flow Model 

NAMS/PE North American Monsoon-Pineapple Express

PDO Pacific Decadal Oscillation

PEST Parameter Estimation Program 

SOSWR Sum of squared weighted residual

TFDR Total farm delivery requirement

USGS U.S. Geological Survey

WBS Water-balance subregions
xi

Acknowledgments 

The authors acknowledge Alan Flint, Chris Harich, Donald Martin, Sarah Falk, and Katherine Paybins, 

who contributed to the technical content of this study. The authors also thank Larry Schneider of the 

U.S. Geological Survey for completion of the technical illustrations in this document.
xii

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Hydrologic Models and Analysis of Water Availability in 

Cuyama Valley, California

By R.T. Hanson, Lorraine Flint, Claudia C. Faunt, Dennis Gibbs, and Wolfgang Schmid

Abstract

Changes in population, agricultural development 

practices (including shifts to more water-intensive crops), and 

climate variability are placing increasingly larger demands 

on available water resources, particularly groundwater, in 

the Cuyama Valley, one of the most productive agricultural 

regions in Santa Barbara County. The goal of this study was to 

produce a model capable of being accurate at scales relevant 

to water management decisions that could be considered in 

the evaluation of the sustainable water supply. The Cuyama 

Valley Hydrologic Model (CUVHM) was designed to simulate 

the most important natural and human components of the 

hydrologic system, including components dependent on 

variations in climate, thereby providing a reliable assessment 

of groundwater conditions and processes that can inform water 

users and help to improve planning for future conditions. 

Model development included a revision of the conceptual 

model of the flow system, construction of a precipitationrunoff model using the Basin Characterization Model (BCM), 

and construction of an integrated hydrologic flow model 

with MODFLOW-One-Water Hydrologic Flow Model 

(MF-OWHM). The hydrologic models were calibrated to 

historical conditions of water and land use and, then, used to 

assess the use and movement of water throughout the Valley. 

These tools provide a means to understand the evolution 

of water use in the Valley, its availability, and the limits of 

sustainability.

The conceptual model identified inflows and outflows 

that include the movement and use of water in both natural 

and anthropogenic systems. The groundwater flow system 

is characterized by a layered geologic sedimentary sequence 

that—in combination with the effects of groundwater 

pumping, natural recharge, and the application of irrigation 

water at the land surface—displays vertical hydraulic-head 

gradients. Overall, most of the agricultural demand for 

water in the Cuyama Valley in the initial part of the growing 

season is supplied by groundwater, which is augmented 

by precipitation during wet winter and spring seasons. In 

addition, the amount of groundwater used for irrigation varies 

from year to year in response to climate variation and can 

increase dramatically in dry years. Model simulation results, 

however, also indicated that irrigation may have been less 

efficient during wet years. Agricultural pumpage is a major 

component to simulated outflow that is often poorly recorded. 

Therefore, an integrated, coupled farm-process model is used 

to estimate historical pumpage for water-balance subregions 

that evolved with the development of groundwater in the 

Valley from 1949 through 2010. The integrated hydrologic 

model includes these water-balance subregions and delineates 

natural, municipal, and agricultural land use; streamflow 

networks; and groundwater flow systems. The redefinition 

of the geohydrologic framework (including the internal 

architecture of the sedimentary units) and incorporation of 

these units into the simulation of the regional groundwater 

flow system indicated that faults have compartmentalized 

the alluvial deposits into subregions, which have responded 

differently to regional groundwater flow, locations of recharge, 

and the effects of development. The Cuyama Valley comprises 

nine subregions grouped into three regional zones, the Main, 

Ventucopa Uplands, and Sierra Madre Foothills, which are 

fault bounded, represent different proportions of the three 

alluvial aquifers, and have different water quality.

The CUVHM uses MF-OWHM to simulate and assess 

the use and movement of water, including the evolution of 

land use and related water-balance regions. The model is 

capable of being accurate at annual to interannual time frames 

and at subregional to valley-wide spatial scales, which allows 

for analysis of the groundwater hydrologic budget for the 

water years 1950–2010, as well as potential assessment of the 

sustainable use of groundwater. 

Simulated changes in storage over time showed that 

significant withdrawals from storage generally occurred 

not only during drought years (1976–77 and 1988–92) 

but also during the early stages of industrial agriculture, 

which was initially dominated by alfalfa production. Since 

the 1990s, agriculture has shifted to more water-intensive 

crops. Measured and simulated groundwater levels indicated 

substantial declines in selected subregions, mining of 

groundwater that is thousands to tens of thousands of years 

old, increased groundwater storage depletion, and land 

subsidence. Most of the recharge occurs in the upland regions 

of Ventucopa and Sierra Madre Foothills, and the largest 

fractions of pumpage and storage depletion occur in the 

Main subregion. The long-term imbalance between inflows 

and outflows resulted in simulated overdraft (groundwater 
2 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

withdrawals in excess of natural recharge) of the groundwater 

basin over the 61-year period of 1949–2010. Changes in 

storage varied considerably from year to year, depending 

on land use, pumpage, and climate conditions. Climatically 

driven factors can greatly affect inflows, outflows, and 

water use by more than a factor of two between wet and dry 

years. Although precipitation during inter-decadal wet years 

previously replenished the basin, the water use and storage 

depletion have lessened the effects of these major recharge 

events. Simulated and measured water-level altitudes indicated 

the presence of large areas where depressed water levels have 

resulted in large desaturated zones in the younger and Older 

Alluvium layers in the Main-zone subregions. The results 

of modeled projection of the base-case scenario 61 years 

into the future indicated that current supply-and-demand are 

unsustainable and will result in additional groundwater-level 

declines and related storage depletion and land subsidence. 

The reduced-supply and reduced-demand projections 

reduced groundwater storage depletion but may not allow for 

sustainable agriculture under current demands, agricultural 

practices, and land use.

Introduction

Cuyama Valley is north of Sierra Madre Mountains 

in south-central California (fig. 1) and is one of the most 

productive agricultural regions in Santa Barbara County. 

Increases in population in the Valley and transitions to crops 

that consume additional water have increased the demand for 

water within Cuyama Valley groundwater basin (CUVGB). 

Although a small amount of urban supply is provided by 

groundwater, irrigated agriculture is solely supplied by 

groundwater pumpage. The aquifers in the Valley have been 

subject to overdraft (groundwater withdrawals in excess of 

natural recharge) since the 1950s (Singer and Swarzenski, 

1970), and more recently, land subsidence related to increased 

and sustained groundwater pumpage has occurred (Everett and 

others, 2013). The water levels throughout most of the central 

parts of Cuyama Valley have not substantially recovered 

since the onset of industrial agriculture in the 1970s. As a 

part of a resource assessment process, the U.S. Geological 

Survey (USGS) undertook the study described in this report 

in cooperation with the Santa Barbara Department of Public 

Works Water Agency (SBDPWWA) to better understand the 

hydrologic budget and limits of availability and sustainability. 

The purpose of the study was to quantify the water 

availability of the Cuyama Groundwater Basin under varying 

cultural and climatic scenarios to inform regional stakeholders’ 

potential constraints of water-supply availability options for 

the aquifer system, which is the sole source of water supply 

for the basin. A regional hydrologic flow model capable 

of being accurate at scales relevant to water management 

decisions was developed with the SBDPWWA for the Cuyama 

Valley, California. 

Purpose and Scope

This report documents (1) an analysis of the conceptual 

model of the hydrologic system of the Cuyama Valley, 

(2) the description of the hydrologic features used in the 

hydrologic flow models of the Valley groundwater system, 

(3) development and calibration of a three-dimensional 

(3D) regional flow model, and (4) an analysis of water 

availability with respect to current water and land use and 

potential climate variability and change. Because the regional 

hydrologic model incorporates time-varying inflows and 

outflows, the model can be used to evaluate the basin-scale 

effects of temporal changes in groundwater recharge and 

pumping. Overall, the development of the geohydrologic 

and hydrologic models, data networks, and hydrologic 

analyses provide a basis for assessing water availability 

and formulating and assessing water-resource management 

strategies.

Approach

The creation of the first set of hydrologic models of 

Cuyama Valley for this study required the updating of the 

conceptual model, the geohydrologic framework, and the 

estimation of the components of the hydrologic cycle. The 

conceptual model was realigned with recent information 

about the framework of recharge, land use, and streamflow 

infiltration (Everett and others, 2013; Sweetkind and others, 

2013). Refinement of the geohydrologic framework required 

the remapping of geologic surfaces and reconciliation 

of recent geologic information available from wells and 

investigations (Sweetkind and others, 2013). 

The Cuyama Valley Hydrologic Model (CUVHM) 

was constructed on the basis of the new conceptual and 

geohydrologic models to simulate the flow and use of water 

for the period September 1949 through December 2010. This 

model includes new layering, inflows and outflows, and more 

detailed representation of the current land cover/land use and 

vegetation. The new valley-wide model (fig. 1B) includes 

estimates of runoff from the surrounding watersheds simulated 

by using the Basin Characterization Model (BCM) (Flint 

and Flint, 2012), a regional-scale precipitation-runoff model 

(fig. 1A).

Description of the Study Area 

Cuyama Valley is a high desert watershed with a surfacewater drainage area of about 690 square miles (mi2

) and an 

underlying main alluvial basin covering about 230 mi2 that 

straddles the northeastern part of Santa Barbara County and 

parts of San Luis Obispo, Ventura, and Kern Counties (the 

Cuyama River forms part of the county boundary) within the 

CUVGB (figs. 1A, 1B). This high desert watershed trends 

northwesterly from the Sierra Madre Mountains on the south 
Description of the Study Area 3

to the Caliente Range on the north (fig. 1A). Land-surface 

elevations in the watershed range from 800 feet (ft) above 

NAVD88 near Twitchell Reservoir to greater than 8,000 ft at 

Mt. Pinos, and land surface elevations within the groundwater 

basin proper range from about 1,950 ft to 3,600 ft above 

NAVD88. The valley is drained by the Cuyama River and 

its tributaries, of which Santa Barbara Creek is the largest 

(fig. 1B). The valley has been developed predominantly for oil 

production since the 1950s and for agriculture since the 1930s 

but also contains the towns of Cuyama and New Cuyama and 

other small towns (fig. 1B). The CUVGB encompasses about 

230 mi2, of which about 30 percent is used for agriculture, 

about 69 percent is natural vegetation, and one percent is 

urban land as of 2010. The residents of the valley rely almost 

exclusively on groundwater for their drinking-water supply 

and for irrigation (Gibbs, 2010). As a result, the aquifer is 

susceptible to overdraft (groundwater pumpage in excess 

of recharge) and related secondary effects such as land 

subsidence and poor water quality when outflows (including 

pumpage) exceed inflows for an extended period of time.

Hydrologic and Water-Balance Subregions

The assessment and analysis of groundwater availability 

relative to the components of the hydrologic cycle required 

the division of Cuyama Valley into subregions that can be 

analyzed individually with respect to supply-and-demand 

components. This study also required a more precise 

delineation of the groundwater basin. The delineation 

described by the California Department of Water Resources 

(2003) includes several extraneous regions that are not part 

of the main regional aquifer systems within Cuyama Valley. 

Thus, the extent of the groundwater basin was redefined as a 

part of this study (fig. 1B). The basin was further divided into 

nine groundwater hydrologic subregions (fig. 2A, table 1). 

These subregions separate the aquifers into regions that: 

are fault bounded; represent different proportions of the 

three alluvial aquifer systems; have different water-quality 

characteristics; and where the response to the use, movement, 

and consumption of water is similar in specific parts of the 

aquifers but differ from the responses in the other subregions. 

In this context, these subregions of Cuyama Valley may 

be considered a collection of subbasins that are partially 

hydraulically connected, but have different hydrologic 

features or hydraulic properties and consequently respond 

differently to natural and anthropogenic stresses. To facilitate 

regional water-availability analysis, these nine subregions 

were grouped into three simplified major regional zones that 

represent the Main zone, Ventucopa Uplands, and Sierra 

Madre Foothills (fig. 2B).

The valley also was divided into multiple wateraccounting units called water-balance subregions (WBS), to 

create the associations between demand for water for irrigation 

and supply from wells that link the supply-and-demand 

components driven by changing land use and land ownership 

(fig. 2C). These subregions comprise a combination of private 

and public lands from which data can be used to estimate 

the water-balance components of land use, streamflow, and 

groundwater flow relative to the use and movement of water 

at the land surface. The increase in the number—from 2 in 

1949 to 83 in 2010—reflect the historical development of the 

valley across the landscape. The changing number of WBS 

generally represents changes in land ownership and use that 

occurred during 10 different periods within the 61 years of 

simulation. Superimposed on these WBS are cell-by-cell 

distributions of changes in land use that include different 

natural vegetation, urban, and agricultural uses throughout the 

valley (described later in the “Model Development” section). 

The most recent WBS are based on land-use parcels of 2010 

and were sequentially changed for earlier periods to provide 

a logical progression of land-use and ownership changes over 

the 61-year simulation period (1949–2010). These WBS are 

also combined with the nine groundwater subregions for the 

purposes of water-supply analysis and are generally coincident 

with those subregions (fig. 2A).

Geologic Framework

 The Cuyama Valley is a down-faulted block or graben 

that is bordered on the north by the Morales and Whiterock 

Faults and on the south by the South Cuyama and Ozena 

Faults (fig. 3A). The eastern part of the valley is underlain by 

the Cuyama syncline, with a strike parallel to the elongation 

of the valley, which plunges toward the northwest. The north 

limb of this fold is truncated against the Morales Fault (Singer 

and Swarzenski, 1970).

Hydrogeologic Units

The hydrogeologic framework of Cuyama Valley was 

developed through a reevaluation and synthesis of geologic 

information from previous studies, which resulted in a 

simplified grouping of geologic units into hydrogeologic 

units (Sweetkind and others, 2013). Geologic units within the 

Cuyama Valley groundwater basin include unconsolidated 

Pleistocene and Holocene alluvial deposits and fluvial deposits 

of the Cuyama River drainage, and the underlying, partly 

consolidated nonmarine Morales Formation of Pliocene 

to Pleistocene age (Upson and Worts, 1951; Singer and 

Swarzenski, 1970). These deposits unconformably overlie a 

late Cretaceous to middle Cenozoic succession of consolidated 

marine and nonmarine sedimentary rocks, which themselves 

overlie crystalline granitic and gneissic rocks (Hill and others, 

1958; Dibblee, 1982; Lagoe, 1987; Bazeley, 1988; fig. 3A). 

Previous USGS studies of Cuyama Valley (Upson and Worts, 

1951; Singer and Swarzenski, 1970) delineated aquifers in 

the saturated parts of the Recent and Older Alluvium, units 

that historically have yielded most of the water pumped in the 

study area. Since these studies were completed, water levels 
4 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

have declined in some areas into the deeper units such as the 

Morales Formation. The hydrogeologic framework that was 

used to represent the three discrete hydrologic model layers 

as determined by Sweetkind and others (2013) is illustrated in 

figure 3A:

1. Recent Alluvium aquifer—one layer of the younger 

alluvial deposits representing an alluvial deposit layer. 

2. Older Alluvium aquifer—one layer of the older alluvial 

deposits.

3. Morales Formation aquifer—one layer representing the 

uppermost units of the Morales Formation.

Collectively, these aquifers are variable in areal extent and 

range in thickness from a few feet up to thousands of feet. The 

outcrops and extent of these units are superimposed onto the 

BCM and the CUVHM active model grids (fig. 3A). 

Faults and the Groundwater Flow System

Faults of hydrologic significance occur at the basin 

margins, where fault offset juxtaposes basin-fill sediments 

against older consolidated rocks, and within the basin, 

where basin-fill units of differing water-transmitting ability 

are juxtaposed. Faults within the basin fill have been 

recognized previously as being associated with historical 

surface springs or lateral changes in groundwater elevations 

(Singer and Swarzenski, 1970). Sweetkind and others (2013) 

identified three faults within the basin that offset the basinfilling deposits and are associated with known water-level 

changes (Upson and Worts, 1951; Singer and Swarzenski, 

1970): the thrust faults that bound Turkey Trap Ridge and 

Graveyard Ridge, the Santa Barbara Canyon Fault, and the 

Rehoboth Fault (fig. 3A). Upson and Worts (1951) reported 

the presence of springs and seeps along the base of Turkey 

Trap and Graveyard Ridges in 1946. Singer and Swarenski 

(1970) reported water-level drawdowns of 80 to 100 feet in 

the area near these ridges and indicated that water removed 

by pumping from this region is slow to replenish because 

faults restrict movement of water. The impediment to flow 

might be related to the hydraulic properties of the fault 

itself or fault juxtaposition of older, slightly less permeable 

material. A fault (or fault zone), here called the Santa Barbara 

Canyon Fault (SBCF; fig. 3A), was suggested by Singer and 

Swarzenski (1970) as the cause of a steep hydraulic gradient 

in the southeastern part of Cuyama Valley, where water levels 

in the vicinity of Ventucopa are at least 100 ft higher than 

water levels a couple miles to the northwest. The relatively 

small amount of vertical offset on the Santa Barbara Canyon 

Fault indicates that changes in water levels across this fault 

documented in previous studies are caused by distinct faultzone properties, rather than juxtaposition of units of differing 

water-transmitting ability. Another fault, here called the 

Rehoboth Fault (fig. 3A), is inferred from lateral water-level 

changes in the west-central part of the valley. The other major 

faults in Cuyama Valley (figs. 2A, 3A), such as the Russell, 

Morales, South Cuyama, Ozena, and Whiterock Faults, are 

represented as no-flow groundwater boundaries along the 

outer edge of the alluvial basin. 

Hydrogeologic Framework

A digital 3D hydrogeologic framework model of the 

alluvial basin was developed and is described in detail by 

Sweetkind and others (2013). The framework model uses 

information from a variety of datasets, including existing 

lithologic and electrical geophysical logs from oil and gas 

wells and water wells, cross sections, and geologic maps, 

to delineate the volumes of the aquifer system bounded by 

faults and relevant depositional or formational boundaries. 

The model is the digital representation of the interpreted 

geometry and thickness of subsurface geologic units and 

the geometry of folds and faults that bound the basin and lie 

within it. Specifically, the model was constructed to represent 

the subsurface geometry of the Recent Alluvial aquifer, the 

Older Alluvial aquifer, the Morales Formation aquifer, and a 

composite pre-Morales Formation bedrock unit. This model 

provides the fundamental hydrogeologic framework for the 

subsequent development of a transient numerical model of 

groundwater flow in the study area. 

The framework model may be explored and visualized by 

slicing the model volume at any chosen location (for example, 

figs 3B, C). Two sections were cut from the framework model 

along the same two section lines as published by Singer and 

Swarzenski (1970). One section (A–A’, fig. 3B) is aligned 

roughly east-west , parallel to the trace of the interbasin thrust 

faults that bound the Turkey Trap Ridge and Graveyard Ridge, 

and a second (B–B’, fig. 3C) is a roughly north-south section 

transverse to the major structural grain of the basin. Together 

with the map, the sections show the extent and thickness of the 

aquifers. The sections show the thickness of Recent Alluvial 

aquifer in the axis of the valley, underlain by Older Alluvial 

aquifer. The Older Alluvial aquifer dominates the southern part 

of the valley, beneath its outcrop exposures, with the Morales 

Formation aquifer underlying it. The Morales Formation 

aquifer predominates in the Cuyama Badlands area, where it is 

virtually the only permeable stratigraphic unit except for thin 

Recent Alluvium along the trace of the Cuyama River channel. 

The Morales Formation aquifer is also exposed at the ground 

surface in the western part of the valley, where it is locally 

overlain by thin deposits of alluvium in the channel of the 

Cuyama River. The effect of fault offset is not obvious at the 

scale of figure 3A, except for the appearance of Older Alluvial 

aquifer at land surface at Graveyard Ridge and Turkey Trap 

Ridge.
Description of the Study Area 5

Three-Dimensional Model of Grain-Size 

Distribution

An analysis of variability of lithology and grain size 

was conducted for the three principal basin-filling units, the 

Recent Alluvial aquifer, Older Alluvial aquifer, and Morales 

Formation aquifer. The details of this analysis are documented 

by Sweetkind and others (2013). Textural variability in the 

basin-filling units is ultimately a function of the sedimentary 

facies, environment of deposition, and depositional history 

of the basin. Textural data such as grain size, sorting, and 

bedding characteristics form the geologic basis for estimating 

the hydraulic properties within the numerical hydrologic-flow 

model. 

The spatial distribution and the characteristics of the 

sediments forming the three aquifers are related to the 

Pliocene and Pleistocene tectonic evolution and uplift of 

the basin, the progressive narrowing of the valley, and the 

gradually increasing channelization of the Cuyama River 

drainage. The Morales Formation is a widespread unit that was 

deposited prior to the constriction of the basin by encroaching 

thrust faults. As a result of tectonic uplift, the previously 

deposited Morales Formation was exposed and eroded. 

Streams deposited and reworked sediment from the Morales 

Formation into a narrower basin that resulted in the deposition 

of the Older Alluvial aquifer. The Recent Alluvial aquifer is 

confined to the center of Cuyama Valley and alluvial channels 

tributary to the Cuyama River. Textural variations in the 

Recent Alluvial aquifer appear to be primarily climate-driven 

and reflect regional rainfall variations that control stream 

incision and aggradation. 

Sediment grain size, a textural parameter commonly 

reported in oil-well and water-well data as well as in outcrop 

observations, was analyzed and modeled. Boulders, gravels 

and sands are considered coarse-grained, whereas silts and 

clays are considered fine-grained. As part of a statistical 

and geostatistical analysis, the percentage of coarse-grained 

sediment was calculated for the entire thickness of each 

aquifer for all 218 available wells. Percent coarse-grained 

sediment was calculated as the total thickness of coarsegrained intervals divided by the total thickness of the aquifer. 

The global mean percentage of coarse-grained texture is 

34 percent, with the Recent Alluvial aquifer being significantly 

more coarse-grained than the Older Alluvial aquifer or the 

Morales Formation aquifer.

Initially, the interpreted grain-size and bedding-frequency 

parameters derived from data from the oil and gas exploration 

boreholes were used to construct a 3D model of textural 

variations within the basin by extrapolating data away from 

boreholes using a nearest-neighbor 3D-gridding process for 

a cell size of 500 meters (m) horizontally and 10 m vertically 

(Sweetkind and others, 2013). Using geostatistical methods, 

this model was refined to a higher resolution 250-meter 

grid for producing a series of plan-view estimates of texture 

variation of grain-size variability for each aquifer that is 

coincident with the gridding of the hydrologic model (fig. 4). 

The two-dimensional (2D) kriged estimates of percentage of 

coarse-grained texture highlight textural distributions within 

and between the aquifers.

The spatial patterns of the percentage of coarse-grained 

texture for each aquifer show significant heterogeneity in 

the texture of the sediments, which reflects the depositional 

environment and the geomorphic evolution of the region 

since Pliocene time. The texture model of the Recent Alluvial 

aquifer has the highest percentage of coarse-grained deposits 

(fig. 4A). It is coarsest in the eastern part of the valley, 

becomes finer grained with distance downstream to the west, 

and, although not evident at the scale of these maps, is also 

coarsest in the vicinity of the active Cuyama River channel. 

The coarse-grained nature of the Recent Alluvial aquifer 

reflects a number of factors, including the short distances 

between the sediment sources in the surrounding uplands and 

the sites of sediment deposition as well as the high-energy 

nature of Cuyama River and tributary creeks that transport 

sediments during winter storms and summer monsoonal rains. 

The spatial structure of the kriged textural model for the 

Recent Alluvial aquifer can be attributed to the alignment of 

the active drainages, whereas the textural models of the older 

aquifers are less correlated to modern topography. 

The texture model for the Older Alluvial aquifer differs 

in spatial structure from the Recent Alluvial aquifer in being 

overall much finer grained and generally unrelated to the 

modern active drainages (fig. 4B). The Older Alluvial aquifer 

is moderately coarse-grained in the eastern half of Cuyama 

Valley, but transitions to fine-grained at the western end of 

the valley. Much of (the) Older Alluvial aquifer is derived 

from erosional reworking of uplifted parts of the Morales 

Formation. The Older Alluvial aquifer is generally coarser 

than the Morales Formation aquifer and has more numerous 

medium- and coarse-grained lenses that probably represent 

alluvial channel deposits. 

The Morales Formation aquifer is much finer-grained 

than the overlying units (fig. 4C). This aquifer has relatively 

few coarse-grained intervals and is characterized by relatively 

fine-grained material, particularly in the axis of the valley, 

where Older Alluvial aquifer contains some of the coarsest 

intervals. The Morales Formation aquifer is particularly fine 

grained in the western half of Cuyama Valley, where surface 

geologic mapping identifies a lacustrine facies in this unit 

(Upson and Worts, 1951; Dibblee and Minch, 2005; DeLong 

and others, 2008). However, the Morales Formation aquifer 

becomes more coarse-grained along the southern flank of 

the valley and to the southeast, perhaps reflecting available 

sediment supply from uplifting areas outside the valley at the 

time of deposition.
6 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Hydrologic System

The conceptual model for the hydrologic cycle starts 

with inflows from precipitation and streamflow. Streamflow 

enters Cuyama Valley through the Cuyama River and as 

runoff from the side slopes and local stream networks that 

drain the surrounding mountains. Infiltration of runoff along 

with percolation of some precipitation and irrigation below 

the root zone contribute to groundwater recharge. Additional 

underflow of groundwater occurs along the Cuyama River 

channel as inflows at the eastern and outflows at the western 

boundaries of the valley in all three aquifers (fig. 2A). Outflow 

also occurs as evapotranspiration from natural vegetation, 

urban landscapes, and irrigated agriculture. Additional outflow 

occurs as groundwater pumpage for agricultural, urban, and 

domestic uses. These natural and man-made inflows and 

outflows represent the supply-and-demand components of 

water use within the hydrologic cycle in Cuyama Valley. Since 

the 1990s, the developed hydrologic system now also includes 

the pumpage of water in one groundwater subregion that is 

exported to adjacent subregions for irrigation use.

Climate 

The climate of the Cuyama Valley is arid, with hot 

summers and cool winters. The record of cumulative departure 

from the mean of precipitation for the late 1940s or 1950s 

(depending on when records were available) to 2010 shows 

that major and minor wet periods and dry periods are typical 

of the climate variability for Cuyama Valley (figs. 5, 6A). 

The map of average annual precipitation indicates that higher 

precipitation occurs within the large mountain-front inland 

regions (fig. 6A). 

On figure 5, 16 wet and dry periods are shown, and 15 

major wet and dry periods are coincident with the period 

of simulation and related stress periods from October 1949 

through December 2010 (fig. 5; table 2). Average rainfall 

ranges from about 7 inches per year on the valley floor to 

about 15 inches per year in the eastern part of Cuyama Valley 

(Gibbs, 2010; fig. 6A).

Time-series analysis of the residuals from the cumulative 

departure of precipitation from the Santa Barbara Canyon 

(Reyes Ranch) long-term hydrologic time series from 

Cuyama Valley suggest a significant influence in climate 

variability. The estimated periodicities include 6 percent 

of the oscillations coincident with the El Nino-Southern 

Oscillation (ENSO, 2–6 years), 0 percent of cycles from the 

North American Monsoon-Pineapple Express (NAMS/PE, 

7–10 years), and 94 percent of the variation from the Pacific 

Decadal Oscillation (PDO, 10–30 years) (Hanson and others, 

2006; Dickinson and others, 2014). This long-term record 

shows periods of 27 years (55 percent of the variation), 

22 years (36 percent of the variation), 13.5 years (3 percent 

of the variation) (PDO), and 2–6 years (ENSO) that explain 

variation in precipitation (fig. 5). Thus, almost all of the 

variation in precipitation and streamflow occurs in the longer 

climate cycles. No records of streamflow or groundwater 

levels are long enough for estimation of climate cycles. The 

longer cycles will be important periods for the evaluation of 

interdecadal sustainability of the water resources.

The average annual reference evapotranspiration (ETh) 

values show the orographic effects similar to those in the 

precipitation values. The ETh in the Cuyama Valley transitions 

from values of about 55 to 56 inches per year (in/yr) at the 

base of the Caliente Mountains to lower values of about 

45 to 53 in/yr toward the south end of the Cuyama Valley 

near Ventucopa (fig. 6B). Values of ETh in the inland areas of 

Cuyama Valley consistently range from 53 to 55 in/yr with 

very little variation (fig. 6B). Variations in ETh are higher in 

the southeastern part of Valley, where they range from 45 to 

57 in/yr due to shading effects from the rugged terrain.

Effects of Water Use on the Landscape

An integral part of the hydrologic system is the use and 

movement of water across the landscape, which in this study 

includes the shallow subsurface defined by the root zone. This 

includes the evolution of the development and use of land in 

Cuyama Valley, from the tracts of the Spanish land grants to 

modern agriculture, urbanization, and industry. Several major 

periods of development occurred in Cuyama Valley, including 

the transformation of the land grants into cattle ranches with 

the eventual need for alfalfa, the introduction of the petroleum 

industry and founding of the town of Cuyama by the Atlantic 

Richfield Company (ARCO) , and the introduction of largescale agriculture with orchards, vineyards, and organic 

farming (fig. 7). Also farming has evolved from the planting 

of predominantly potatoes and alfalfa during the 1940s–1970s 

to a doubling of the acreage of grain crops and a tripling of 

the acreage in carrot crops by the mid-1980s (fig. 7A). Carrot 

and grains represent more than half of the crops grown in the 

Cuyama Valley in recent decades (fig. 7A).

Population growth in Cuyama Valley was estimated from 

census tract data (U.S. Census Bureau, 2014a, b) and showed 

a steady increase from just over 1,000 inhabitants to more 

than 8,600 inhabitants from 1950 through 2010 (fig. 7). The 

town of Cuyama was established along with the discovery 

and development of petroleum resources (fig. 7). Cuyama, 

along with New Cuyama and the smaller town of Ventucopa, 

represent the three clusters of housing in the valley. These 

urban clusters represent less than 1 percent of the land on 

the valley floor. The towns of Cuyama and New Cuyama are 

served water from the Cuyama Community Service District 

supply wells, while the schools and other residents are served 

water by their own local wells. 
Model Development 7

The evolution of the landscape occurred as a combination 

of changes in land use and related land ownership in Cuyama 

Valley. For the purpose of modeling the hydrologic system, 

temporal changes in the land ownership were represented by 

using a sequence of 10 different periods over the 61 years of 

historical simulation 1949–2010 (figs. 2C, 5). These periods 

were first defined for 2010 on the basis of current land use and 

ownership and then discretized from recent years to past years 

to represent the multi-year periods of 1943–50, 1951–59, 

1960–69, 1970–79, 1980–85, 1986–92, 1993–95, 1996–2000, 

2001–09, and 2010 (fig. 5). The changing water-balance 

subregions (WBS; fig. 2C) reflect the evolution of land 

ownership and land-use that required groundwater pumpage, 

as well as regions of native vegetation using precipitation only, 

and urban and domestic areas served by separate specified 

sources of groundwater pumpage. The more detailed land-use 

changes that cover 14 periods (fig. 5) are described later in the 

“Land Use” section.

Surface Water 

Streamflow infiltration together with deep percolation of 

precipitation, is a major source of natural recharge in Cuyama 

Valley. Streamflow within the valley occurs primarily from 

runoff that originates from rainfall and snowmelt in upstream 

tributary drainages, entering the valley through the Cuyama 

River and Reyes Creek and other ungaged tributaries. During 

occasional large storms that can result in flood flows, runoff 

is also generated within Cuyama Valley and flows through 

the tributaries to the Cuyama River (fig. 8). Streamflow is 

currently measured at two gages that record the flow into 

Cuyama Valley: the Cuyama River near Ventucopa (11136500, 

1937–58; 11136501, 2002–10); and, Santa Barbara Canyon 

Creek near Ventucopa (11136600, 2002–10; fig. 8). There is no 

downstream gage to measure outflow prior to the streamgage 

at Buckhorn and inflow to Twitchell Reservoir (fig. 1), 

which include flows from other large tributary watersheds 

downstream of Cuyama Valley. The remainder of the tributary 

canyons and outflow from the Valley along the Cuyama River 

remain ungaged with the exception of occasional flood-flow 

measurements. 

Groundwater

Under predevelopment conditions, groundwater flowed 

from the foothills of the surrounding mountains of the Cuyama 

Valley toward the Pacific Ocean. Under developed conditions, 

pumpage in excess of recharge has occurred for decades, 

altering the predevelopment flows in response to groundwater 

storage depletion and regional cones of depression (or 

drawdown) in groundwater levels in the center of the valley. 

Groundwater levels in these persistent depressions show 

additional seasonal declines that are driven by a combination 

of agricultural and water-supply pumpage. Groundwater 

inflows include recharge from infiltration of precipitation, 

streamflow (figs. 6A, 8), and applied water from irrigation. 

Additional inflow occurs as underflow across the southeastern 

boundary of the valley, beneath the stream channel of the 

Cuyama River and Reyes Creek. Outflow from groundwater 

includes pumpage, base flow or rejected recharge along 

streams, evapotranspiration, and subsurface underflow to the 

west from the aquifer systems (fig. 9).

Development of groundwater in the Cuyama Valley 

has resulted in the construction and pumpage of several 

hundred wells between 1949 and 2010. This includes about 

120 domestic wells, two municipal-supply wells, and more 

than 100 agricultural irrigation wells (fig. 9). Total pumpage 

for water supply grew from less than 50 acre-feet (acre-ft) 

prior to 1982 to more than 150 acre-ft from 1983 to 2010, 

with an increase around 1982, which was coincident with the 

increase in population in the valley (fig. 10). The domestic 

pumpage was estimated on the basis of population growth and 

an assumed consumption of about 0.54 acre-ft per year per 

land parcel for each “domestic” (household) well. A minor 

amount of the increase can also be attributed to the increase in 

rural residential (domestic) pumpage between 2000 and 2010 

(fig. 10). Most of the drinking-water supply is pumped by the 

Cuyama Valley Community Service District. For the period 

1949–2010, the overall distribution of pumpage for drinkingwater supply is estimated to be about 88 percent urban, and 

12 percent domestic. Temperature difference logs indicated 

that all three aquifers are contributing to groundwater flow and 

pumpage in various parts of the valley (Everett and others, 

2013).

Model Development

Two hydrologic models were developed for the Cuyama 

Valley watershed. One is a water-balance model representing 

the watersheds in the mountains surrounding the valley 

that was developed by using the Basin Characterization 

Model (BCM) (Flint and Flint, 2012; Flint and others, 2012; 

Thorne and others, 2012). Simulations made with this model 

provided runoff estimates for all of the ungaged ephemeral 

streams and arroyos that form a drainage network that carries 

mountain-front recharge from streamflow infiltration of flood 

flows along the boundary of the alluvial groundwater basin. 

The second model, referred to herein as the Cuyama Valley 

Hydrologic Model (CUVHM), is an integrated hydrologic 

model that was developed using an integrated hydrologic 

flow model with MODFLOW-One-Water Hydrologic Flow 

Model (MF-OWHM) (Schmid and others, 2006a, b; Schmid 

and Hanson, 2009, 2013; Hanson and others, 2010, 2014) 

to simulate the use and movement of water throughout the 

groundwater basin.
8 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Water-Balance Model

Estimation of Recharge and Runoff

Rainfall-runoff models require streamflow data for 

calibration and, then, can be used to simulate flow at gaged 

and ungaged locations. Rainfall-runoff models do not provide 

an estimate of spatially distributed recharge to complement 

runoff estimates, but do provide a more complete picture of 

the hydrologic processes in data-sparse basins. The Basin 

Characterization Model (BCM) is a grid-based, regional 

water-balance model that can provide process-based estimates 

of recharge and runoff for ungaged locations. BCM was 

used in this study to provide flow boundary conditions 

for the CUVHM. The BCM model domain includes the 

144 subwatersheds that surround and drain into the alluvial/

structural valley (fig. 11).

BCM is a distributed parameter water-balance model 

that performs a multi-year simulation of surface and 

shallow subsurface hydrologic processes. The water balance 

calculations are performed at a monthly time step and 

independently at an evenly distributed 270 square meters (m2) 

grid cell spacing. The model inputs include (1) topography, 

soil properties, and geology datasets, which are virtually static 

with time; (2) monthly gridded precipitation and temperature 

datasets (Parameter–Elevation Regressions on Independent 

Slopes Model, PRISM; Daly and others, 2008; 800-m transient 

dataset); and (3) monthly gridded potential evapotranspiration 

(PET). The monthly gridded PET is simulated using an hourly 

energy-balance calculation that is based on solar radiation, 

air temperature, and the Priestley–Taylor equation (Flint 

and Childs, 1987) to calculate potential evapotranspiration 

(Flint and Childs, 1991). Clear sky PET is calculated using a 

solar radiation model that incorporates seasonal atmospheric 

transmissivity parameters and site-specific parameters of 

slope, aspect, and topographic shading. Hourly PET is 

averaged to a monthly rate and cloudiness corrections are 

made using cloudiness data from National Renewable Energy 

(2014). Modeled PET for the southwestern United States was 

calibrated to the measured PET rates from California Irrigation 

Management Information System (CIMIS) (California 

Department of Water Resources, 2007) and Arizona 

Meterological Network (University of Arizona, 2012) stations, 

and is comparable to the estimates from Cuyama Valley 

CIMIS station No. 88 (figs. 6B, 12). No error analysis was 

made for the PET. There is a bias in the comparison to CIMIS 

measured ETo on the valley floor equivalent of approximately 

−10 percent (BCM estimates are lower than measured at the 

CIMAS station), or approximately −0.8 inches per month for 

the months with the highest PET, and less than −0.1 inches per 

month for low PET months (fig. 12).

For the Cuyama Valley, the precipitation, air temperature, 

and monthly PET maps were combined with maps of 

elevation, bedrock permeability (estimated on the basis of 

geology (Jennings, 1977) and iteratively modified in the model 

calibration process), and soil-water storage from the SSURGO 

soil databases (U.S. Department of Agriculture Natural 

Resources Conservation Service, 2006). Once available 

monthly water is calculated, if available water exceeds total 

soil storage, this excess water becomes runoff, and the amount 

of water between total soil storage and field capacity storage 

becomes potential recharge. If available water is less than total 

soil storage but greater than field capacity, the water exceeding 

field capacity becomes potential recharge. If potential recharge 

is greater than bedrock permeability (K), then recharge equals 

K and potential recharge that exceeds K becomes runoff, 

or else it will recharge at K until it reaches field capacity. 

Any water less than field capacity will be lost to actual 

evapotranspiration at the rate of PET for that month until it 

reaches wilting point. Additional details of model operation 

and input and output datasets can be found in Thorne and 

others (2012).

Calibration and Comparison With Measured 

Streamflows

The BCM is calibrated to partition excess water into 

recharge and runoff by comparing simulation results for runoff 

with measured surface-water flow and iteratively changing 

K until a reasonable match is achieved. This was done for 

seven basins (fig. 11) with varying amounts of impairment 

(regulated flow) and representing three main geologic units, 

sandstone, conglomerate, and alluvium (fig. 11, table 3). 

Finally, basin discharge was calculated from recharge and 

runoff accumulated from grid cells upstream of “pour points,” 

to more accurately reflect stream channel losses and gains 

between stream gages and to create surface-water flow 

recession and baseflow that can extend throughout the dry 

season (Flint and others, 2012). The “pour points” represent 

locations where outflow from each of the surrounding 

watersheds flows into the valley. The portions of the recharge 

and runoff estimated by BCM simulations then become the 

inflow at 144 pour-point locations within the streamflow 

network that is simulated by MF-OWHM in the CUVHM 

model (fig.8). The fractions of recharge and runoff that are 

ultimately used within CUVHM were adjusted for the two 

largest inflows along the Cuyama River and Santa Barbara 

Creek during BCM calibration.

The BCM was calibrated against selected monthly 

streamflows at seven USGS streamgages (fig. 13, table 4). 

Comparisons of BCM-estimated basin discharge and measured 

streamflow indicate a relatively good match with BCM 

results. By adjusting the parameter controlling baseflow, the 

total measured streamflow volume for the period of record 

for each streamgage was matched exactly by BCM estimates. 

Calibration statistics indicate relatively good goodness-of-fit 

on the basis of the Nash-Sutcliffe Efficiency (NSE) statistic, 

and monthly and annual r2 values (table 4). The majority of the 

runoff is derived from the watersheds that drain the Cuyama 

River and Santa Barbara Canyon, with lesser amounts of 

storm flows from other ungaged creeks such as Aliso, Apache, 

Quatal, Berringer, and Reyes Creeks.
Model Development 9

Development of BCM Results for CUVHM model

The average annual areal recharge for 1980–2009 

ranges from 0 to 11.8 in/yr. The relative proportions of 

(1) shallow subsurface flow from recharge that becomes 

baseflow, (2) runoff that becomes streamflow, and (3) runoff 

that become deep recharge to the mountain-block or alluvialbasin areas were calculated and are indicated in table 4. These 

were used to develop scaling factors for 144 ungaged basins 

surrounding the fault-defined valley in two main geologic 

types, and 13 pour points within the alluvial valley. The first 

two columns in table 3 indicate the scaling coefficients used 

to distribute the total potential stream inflow estimates for 

the MF-OWHM SFR Package that were the initial estimates 

of inflow used for model calibration. The third column is an 

estimate of the recharge upstream of each basin’s pour point 

that becomes mountain block recharge. It was assumed that no 

mountain block recharge would cross the fault boundaries that 

surround most of the valley and would discharge upgradient 

of the fault. Therefore only the scaling factors for the SFR 

recharge and SFR runoff were used and selectively adjusted 

to estimate the fractions of runoff and rejected recharge that 

become inflow along the mountain fronts during the CUVHM 

model calibration for the largest contributing drainages, 

the Cuyama River and Santa Barbara Canyon Creek. The 

scaling factor for each column of table 3 was multiplied by 

the accumulated recharge or runoff for each subwatershed for 

each geologic type and summed to provide the SFR boundary 

condition for each of the 144 basins as a monthly recharge 

and a runoff flow. Average annual streamflow applied to SFR 

boundaries is approximately 1,500 acre-ft, ranging from 0 to 

120,000 acre-feet per year (acre-ft/yr) (fig. 14A). Annual 

streamflow exceeds 10 acre-ft in only 14 of 144 basins for 

any of the last 40 years, and with the exception of the two 

largest basins in the southeastern conglomerates, all are on the 

southern side of the valley, an area dominated by sandstones. 

These 14 basins contribute more than 60 percent of the total 

streamflow.

The Cuyama Valley is classified as semiarid, which 

means that average annual precipitation is between 20 and 

50 percent of potential evapotranspiration, indicating 

little potential for runoff or recharge. However, recharge 

in a semiarid basin does not occur on the basis of average 

annual conditions. In certain areas of a basin, such as 

at higher elevations on the southern slopes of Cuyama 

Valley, precipitation in some months can exceed potential 

evapotranspiration and soil storage, and runoff and (or) 

recharge can occur. Note that there is commonly little 

streamflow in the Cuyama Valley (fig. 14A), and significant 

streamflow (greater than 10,000 acre-ft/yr) occurs in only 

23 of 71 years (1939–2010), or about 32 percent of the 

time. The relation of streamflow and especially recharge to 

precipitation is nonlinear in arid and semiarid environments 

(Flint and others, 2012), which is confirmed in Cuyama Valley 

(fig. 14B). 

For application to the CUVHM, the monthly streamflows 

developed through simulations with the BCM for the 144 pour 

points are used as inflow rates for the monthly periods and 

provide the intermittent inflows along the outer boundary 

of the active CUVHM model area. The overall estimate of 

gaged and ungaged inflow for the period 1950–2010 averaged 

29,500 acre-ft/yr, with about 19,100 acre-ft/yr as runoff 

(65 percent) and 10,400 acre-ft/yr as recharge (35 percent) 

for the watersheds surrounding and draining into the valley. 

Recharge occurring as underflow (mountain-block recharge) 

was considered negligible, because faults bound most of the 

valley and the age of many groundwater samples from wells 

along the mountain-fronts are thousands to tens of thousands 

of years old (Everett and others, 2013). Consequently, the 

BCM recharge as groundwater underflow into the valley 

(mountain-block recharge) was considered to discharge locally 

through ET or additional baseflow as rejected mountain-front 

recharge. The reader is referred to BCM documentation for 

more details on limitations associated with monthly stress 

periods (Flint and Flint, 2012; Flint and others, 2012; Thorne 

and others, 2012).

Integrated Hydrologic Model—CUVHM 

The Cuyama Valley Hydrologic Model, or CUVHM, 

was developed to (1) characterize the historical conditions for 

the analysis of the use and movement of water throughout the 

valley, and (2) provide a tool for stakeholders to address water 

availability and water-use issues in the valley. In order to 

maintain the usefulness of the CUVHM, periodic updates will 

be required as changing conditions in the actual hydrologic 

system continue to respond to the stresses imposed upon it, 

and as new information on the surface-water and groundwater 

systems become available. The CUVHM is a numerical 

hydrologic flow model developed with the finite-difference 

hydrologic modeling software One Water Hydrologic Flow 

Model (MF-OWHM) (Hanson and Schmid, 2013; Hanson and 

others, 2014a, b) that includes MODFLOW-2005 (MF2K5) 

(Harbaugh and others, 2000; Hill and others, 2000; Harbaugh, 

2005) and incorporates an updated version of the Farm 

Process (FMP3) (Hanson and others, 2014b). The MF-OWHM 

is the newest version of MODFLOW-2005 with the Farm 

Process (Schmid and others, 2006a, b; Schmid and Hanson, 

2009) that incorporates a dynamically integrated water supplyand-demand accounting within agricultural areas and areas of 

native vegetation. The MF-OWHM enables a more-detailed 

and realistic simulation of hydrologic systems than do earlier 

versions of MODFLOW. The MF-OWHM code incorporates 

the simulation of conjunctive use with linkages of supplyconstrained and demand-driven use and movement of water 

across the landscape, surface-water, and groundwater flow 

systems throughout the Cuyama Valley (Hanson and others, 

2010, 2014b; Hanson and Schmid, 2013).
10 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

The CUVHM was constructed in three major 

phases. The first phase was the collection of new data and 

compilation of existing data (Everett and others, 2013). The 

geohydrologic framework model was then developed on the 

basis of work in previous studies and analysis of new data 

(Sweetkind and others, 2013). This framework was further 

modified to include the inflow and outflows of the updated 

conceptual model, geohydrologic model development to 

determine the distribution of hydraulic properties, and finally, 

development of the hydrologic models. These components 

of model development were completed iteratively during 

the development and calibration of the model. The final 

components of MF-OWHM (processes and packages) used for 

the CUVHM are summarized in table 5.

Input parameters to the CUVHM were adjusted during 

implementation of these model development phases. Input 

parameters to the CUVHM were adjusted, with the aid 

of trial-and-error and automated parameter estimation 

calibration. The parameter estimation codes UCODE-2005 

(Poeter and others, 2005) and PEST (Doherty, 2004, 2010a, 

b, c; Doherty and Hunt, 2010) were used to help with the 

calculation of sensitivities and parameter estimation. The 

model was calibrated to heads (groundwater levels), head 

differences, head changes with time, and land subsidence. 

During construction and calibration of the model, it became 

evident that several updates and enhancements were needed 

within MF2K5, the FMP, and some post-processing software. 

These updates and enhancements are summarized in the 

documentation of MF-OWHM (Hanson and others, 2014a, 

b). The CUVHM model components can be grouped in terms 

of the discretization and boundaries, land-use, streamflow, 

aquifer characteristics, initial conditions, and water budgets. 

The next few sections of the report describe the model 

components within these groups.

Discretization 

The CUVHM domain includes the major alluvial deposits 

of the entire Cuyama Valley. The valley extends from east of 

Ventucopa and the confluence of Reyes Creek with Cuyama 

River to the narrows along Cuyama River northwest of New 

Cuyama, to the headlands of the foothills of the Sierra Madre 

Mountains on the southwest and west, and is bounded on the 

northeast by Caliente Range and Cuyama badlands (fig. 2A). 

The finite-difference model grid used to represent the land 

surface and subsurface alluvial deposits consists of a series 

of orthogonal square model cells. Spatial and temporal 

discretizations are held to uniform increments throughout 

space and time. 

Spatial Discretization and Layering

The total active modeled area is 164 mi2 on a finitedifference grid consisting of 135 rows, 300 columns 

(40,500 cells), and 3 layers having a varying number of 

active cells per layer, for a total of 15,577 active model cells 

(figs. 1B, 3A). In the horizontal dimension, about 17 percent of 

the cells (6,813 cells) are used to define the active part of the 

hydrologic model grid. The model has a uniform horizontal 

discretization of 15.4 acres per cell (820.2 ft by 820.2 ft 

equal to 250 m by 250 m) and is oriented subparallel to the 

tectonic structure of the Cuyama Valley and to the Cuyama 

River, 33 degrees west of due north (fig. 1B). This cell size 

was chosen to be comparable to the typical land parcel size 

and to facilitate the future linkage of the CUVHM model with 

remotely sensed land-use data for potential updates of land use 

and other landscape properties. The bounding coordinates for 

the total model grid are summarized in table 6.

The model includes three layers that are aligned with 

the hydrostratigraphic units described previously (Sweetkind 

and others, 2013). The top of the model is represented by 

the altitude of the land surface and is a composite of model 

layers 1, 2, and 3. The uppermost, Recent Alluvial aquifer 

model layer (layer 1) ranges in thickness from an assumed 

minimum of 16 ft (5 m) to an estimated maximum of about 

633 ft (193 m). The second layer is coincident with the Older 

Alluvial aquifer system and ranges in thickness from an 

assumed minimum of 16 ft (5 m) to an estimated maximum 

of about 1,350 ft (411 m). The third layer is coincident with 

the extent of the upper portion of the Morales Formation and 

ranges in thickness from an assumed minimum of 16 ft (5 m) 

to an estimated maximum of about 4,710 ft (1,436 m).

Temporal Discretization 

In order to adequately represent the dynamics of 

changing precipitation and streamflow, as well as the 

dynamics of the growing season, including the irrigation 

supply and demand components, the CUVHM is discretized 

into monthly stress periods and bimonthly time steps. Periods 

of user-specified (or BCM simulated) model inflows and 

outflows and boundary heads are referred to as stress periods. 

A model stress period is an interval of time in which the userspecified inflows and outflows are held constant. Variations 

in stresses are simulated by changing inflows and outflows 

and boundary heads from one stress period to the next. These 

inflows, outflows and boundary heads that include pumping, 

precipitation, reference evapotranspiration (ETh), stream 

inflows, irrigation, and underflow beneath the Cuyama River 

are assumed to be constant within each stress period. Stress 

periods are further divided into bimonthly (approximately 

15-day) time steps, which are units of time for which water 

levels and flows are calculated throughout all model cells. The 

total simulation period was 61.25 years (or 735 monthly stress 

periods) from October 1949 through December 2010.
Model Development 11

Initial Conditions and Recent Conditions 

Initial conditions are the distribution of water levels 

at every active cell within each of the three model layers 

estimated for 1947 and assumed to apply to October 1949. 

Data for 1947–66 drawdowns (fig. 15A) were used because 

more data were available for 1947 than for 1949, and any 

water-level changes during those 2 years early in development 

are assumed to have been negligible. Also, because very little 

data are available for the late 1940s, water-level data for years 

1938–1955 were used to create the 1947 composite waterlevel contour map. The spring 1966 water-level contour map 

from Singer and Swarzenski (1970; fig. 15B) was used to 

help identify spatial trends in water levels on the 1947 map. 

A map of drawdown between 1947 and 1966 was developed 

by Singer and Swarzenski (1970). In order to check the 

accuracy of the 1947 map having more limited data, the 

contour maps were converted to raster grids, and the spring 

1966 water-level and the 1947–66 drawdown raster grids 

were differenced. A good match was found with the Singer 

and Swarzenski (1970) water-level change map. In this study, 

all model layers were simulated as confined yet still represent 

the drawdown and evolution of the large cones of depression 

in the water table in the central subregions of the Cuyama 

Valley. For the parts of model layers that represent areas of the 

aquifers that are actually unconfined, the saturated thickness 

is held constant during declining or rising water levels. 

Though all layers are treated as confined in the model during 

the simulation, only parts of model layers 2 and 3 actually 

remain confined while other parts remain unconfined. Storage 

properties in the outcrop subregions (fig. 3A) of the uppermost 

layers (1, 2, or 3) are represented by specific yield and aare 

coincident with the unconfined portion of the system (see 

“Storage Properties” section). The regions of large water-level 

declines and related large unsaturated zones in the central 

zones of the valley are illustrated by the water-level maps 

from summer 1966 (fig. 15B), and from spring and summer of 

2010 (fig. 15C, D). The geologic cross sections indicate that, 

after sustained groundwater-level declines between 1966 and 

2008, portions or all of the shallower zones of these aquifers 

were drained (figs. 3B, C). 

Boundary Conditions 

Boundary conditions are applied at some model cells 

to simulate the inflows and outflows from the active model 

region as groundwater underflow (both inflows and outflows) 

and aquifer interaction along intermittent streams, as well 

as interaction with landscape processes (figs. 8 and 16). 

Two general types of boundary conditions are used in the 

model: no-flow and general-head. Inflows and outflows 

simulated across the hydrologic boundaries include recharge 

to and discharge from the groundwater system as well as 

interdependent flows between the groundwater, streams, 

and landscape processes such as ET and irrigation. The 

intermittent stream-aquifer interaction and landscape process 

interactions are discussed in later sections.

No-Flow Boundaries

No-flow boundaries were used for the bottom of the 

model and the lateral boundaries that are coincident with 

faults. The lower boundary was limited to the bottom of the 

Morales Formation or a total thickness for the formation of 

300 m (980 ft), which is deeper than the deepest supply wells. 

Lateral no-flow boundaries represent the contact between 

the low-permeability rocks and thrust faults that bound the 

foothills and the unconsolidated alluvial sediments of Cuyama 

Valley (figs. 3A, 16). 

General-Head Boundaries

The upstream northern and downstream regions of 

the Cuyama River are lateral hydrologic boundaries of the 

groundwater flow system that are simulated as head-dependent 

flow boundaries (figs. 3A, 16). These regions were simulated 

by using the General Head Boundary Package (GHB) of 

MODFLOW (Harbaugh, 2005). General-head boundaries 

were specified for model cells in layers 1 through 3 for the 

inflow region with spatially and temporally constant boundary 

heads and cell-specific hydraulic conductance. The hydraulic 

conductances of the lateral boundary cells were initially based 

on the texture-derived hydraulic conductivity of the aquifer 

sediments (described in the section “Aquifer Characteristics”). 

Hydraulic conductances were adjusted during model 

calibration.

Surface-Water Inflows and Outflows 

Surface-water inflows and outflows were simulated with 

a streamflow routing network comprising 708 individual 

stream segments that represent the Cuyama River and its 

major and minor tributaries. This network was used to 

simulate the inflows from 144 major and minor drainages 

from the surrounding mountains, streamflow infiltration, and 

occasional outflows along the Cuyama River network (fig. 8). 

Additional stream inflow also was specified from the discharge 

of the waste-water treatment plant for the period 1938–2010. 

These features were simulated by using the Streamflow 

Routing Package (SFR2) (Prudic and others, 2004; Niswonger 

and Prudic, 2005); the head-dependent boundary condition 

used in SFR2 allows for streamflow routing, the capture and 

conveyance of overland runoff, streamflow infiltration into 

the aquifer (losing stream reaches), and any potential base 

flow as groundwater discharge to streams (gaining stream 

reaches). Runoff estimated by FMP is redirected to the 

streamflow networks and provides a substantial component 

of groundwater recharge and streamflow during the wettest 

months. Each of the major and minor drainages is represented 

by a collection of stream cells (referred to as reaches). The 

cells or reaches are combined between tributary points to 

form a collection of cells or reaches known as a segment. The 

stage-discharge relations were assumed to be constant for each 

segment in the SFR stream network. The details on how the 
12 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

relation is specified are given in the SFR manual (Niswonger 

and Prudic, 2005). The streambed elevations for the beginning 

and end of each segment are specified, along with the stream 

channel width, streambed thickness, and the vertical hydraulic 

conductivity of reaches within each segment (fig. 8).

In addition to intermittent and ephemeral streamflows, 

and about 9 springs and groups of seeps historically 

discharged shallow groundwater in Cuyama Valley prior to the 

1970s (Singer and Swarzenski, 1970). Prior to groundwater 

development, these springs flowed at rates from 0.01 cubic 

feet per second (ft3/sec) (5 gallons per minute, gpm) to as 

much as 1.9 ft3/sec (860 gpm) along the outcrop boundaries 

that are aligned with the Turkey Trap and Graveyard Ridge 

Faults in the center of the valley along the northwestern 

segments of the Cuyama River channel (fig. 16). These springs 

and seeps are no longer flowing since the 1970s.

Groundwater Pumpage 

Groundwater pumpage is a major component of the 

hydrologic budget of Cuyama Valley, and is grouped into 

two categories of pumpage for this study: agricultural and 

water supply. Agricultural pumpage includes water withdrawn 

from all farm wells used to supply water for irrigation, and 

water supply includes groundwater withdrawn for municipal, 

domestic/rural residential and industrial uses. Farm wells were 

simulated as a combination of single-aquifer wells (Schmid 

and others, 2006a) and multi-aquifer wells. Farm wells that 

are single-aquifer wells are simulated in a similar manner 

as used in the WEL Package (Harbaugh and others, 2000), 

while multi-aquifer wells are simulated by the multi-node 

well (MNW) Package (Halford and Hanson, 2002). The total 

pumpage for each WBS (that is, virtual farm) is distributed 

among each of the farm wells (both single-aquifer wells and 

multi-aquifer wells) that collectively supply groundwater 

to that WBS needed for irrigation for each monthly stress 

period (fig. 2C). The distribution of pumpage between wells 

is based on the average pumping rate up to the maximum 

yield of each well (Schmid and others, 2006a). Agricultural 

pumpage is estimated within FMP of the MF-OWHM model. 

Pumpage from wells used for municipal and domestic supply 

is specified on the basis of reported and estimated values. A 

select number of farm wells and municipal wells are simulated 

as multi-aquifer (MNW) wells that derive water from up to 

three aquifer model layers. Because some wells in the valley 

were not located in the DWR well-permit database, additional 

“virtual wells” or “other agricultural wells” (fig. 9) were 

simulated were simulated to satisfy simulated delivery of 

groundwater to selected WBSs. In this report, a virtual well is 

one for which there is no specific information available for the 

existing well.

Agricultural Supply

Because pumpage from agricultural wells has never been 

metered in the Cuyama Valley, those values must be indirectly 

estimated for simulating and analyzing water use. The two 

most common methods of indirectly estimating pumpage 

are through analysis of data for power consumption by well 

pumps and data for consumptive use of water. Because many 

wells are driven by either electric or diesel power sources, 

and because of the inherent complexity of accounting for 

additional uses for electricity on a farm by farm basis, 

the use of electric power records is considered unreliable 

for estimating agricultural pumpage here. Consumptiveuse estimates are also considered unreliable because this 

method does not account for the combined consumption of 

precipitation and water applied for irrigation and does not 

capture the variability in consumption with changing climate. 

The estimation of agricultural pumpage through application of 

FMP provides physically-based, dynamic, and linked pumpage 

estimates as an alternative to these other indirect methods 

(Hanson and others, 2014b; Schmid and Hanson, 2009). 

Pumpage for agricultural supply is estimated as a 

combination of crop irrigation requirement and inefficient 

losses required to satisfy the total farm delivery requirement 

for all wells that deliver water to a particular WBS. Inefficient 

losses include those from in-farm conveyance of irrigation 

water, as well as potential losses from runoff and deep 

percolation below the root zone during irrigation. The crop 

irrigation requirement in this context refers to all evaporation 

and transpiration of water by a particular crop within a 

model cell, and is a part of the total consumptive use. Total 

consumptive use is the water consumed by evaporation and 

transpiration from all sources of water. Groundwater pumpage 

needed to satisfy the total farm delivery requirement can 

be estimated by taking into account any potential surfacewater supply, the efficiency of irrigation, additional effective 

precipitation, fractions of transpiration and evaporation within 

each model cell, and the fractions of inefficient losses to runoff 

and deep percolation. Because all irrigation in Cuyama Valley 

is supplied by groundwater pumpage, surface-water supplies 

are not simulated. Unmetered pumpage is estimated through 

consumptive use by the FMP on the basis of a suite of land-use 

estimates applied to selected periods of the entire simulation 

period (table 2). Data from as many as 94 actual farm wells 

(fig. 9) were used for simulating pumpage for irrigation and 

the number of active wells for any given month varies through 

time on the basis of reported drill dates and destruction dates. 

There is no known reported agricultural pumpage data for 

Cuyama Valley that can be used as corroborative observations 

for calibration of simulated pumpage.

Pumpage for each well was allocated to the model layers 

on the basis of the construction information available. The 

open-screen interval was used to identify the model layers 

from which water was withdrawn, with the model assuming 

full penetration of each layer. If no construction information 

was available for “real” wells, or virtual wells were needed 

for irrigation, top and bottom model layer for each well were 

assigned on the basis of data from other wells in the area. The 

FMP allocated pumpage on a well-by-well basis, using the 

average fraction of total required pumpage within a particular 

WBS up to the pumping capacity of each well’s screened 

interval that supplies water. The capacity of the farm wells 

ranges from several hundred to several thousand gallons per
Model Development 13

minute, and the casing diameters range from 6 to 16 inches. 

However, during model calibration pumping capacities were 

set to a larger value to insure that supply would meet demand. 

In addition, the deficit irrigation scenario was used with FMP 

to reduce demand to available supply, and virtual wells were 

used for farms with simulated demand that did not have a 

known well a priori.

Water Supply 

Pumpage information for municipal and industrial 

(M and I) uses and for domestic water supply was based on 

available reported monthly to annual pumpage on a wellby-well basis. As many as 17 wells, including the 2 Cuyama 

Community Service District (CCSD) production wells, were 

used to represent M and I wells at various periods during the 

61-year simulation. The actual locations of municipal-supply 

wells were used in the model. The MNW Package is used to 

simulate municipal-supply groundwater pumpage. The openscreen interval or total depth was used to identify the model 

layers from which pumping occurred. 

For domestic wells, either actual locations were used, or, 

if the actual locations were unknown for a select land parcel, 

the parcel was assigned a single virtual well (fig. 9). The well 

package was used to simulate the domestic pumpage from 

single aquifer model layers. The number of the domestic 

wells varies for each stress period. Drilling and destruction 

dates were used when available, or otherwise, wells were 

assumed to be present for the entire period of simulation. 

Total domestic pumpage was estimated to range from about 

8 to 37 acre-ft/yr from as many as 95 domestic wells (figs. 9

and 10). Domestic pumpage was estimated on the basis of 

an assumed consumption rate of 0.25 to 0.94 acre-ft/yr and 

averaged about 0.54 acre-ft/yr per well (fig. 10). Overall, the 

combined M and I and domestic pumpage is minor compared 

to agricultural pumpage, but is important locally. For example, 

the CCSD wells supplied between 165 and 206 acre-ft/yr for 

the period 1998 to 2007 (U.S. Wilson, Cuyama Community 

Service District, written commun., 2008).

Landscape Use and Movement of Water 

The FMP provides coupled simulation of the groundwater 

and surface-water components of the hydrologic cycle for 

irrigated and non-irrigated areas. A dynamic allocation of 

groundwater recharge and groundwater pumping is simulated 

on the basis of residual crop-water demand after surfacewater deliveries and root uptake from shallow groundwater. 

The estimation of irrigation pumpage in FMP is dependent 

on contributions of water from precipitation and variable 

irrigation efficiencies and is also connected to irrigation 

inefficiency losses as return flows (deep percolation and runoff 

combined). The FMP not only estimates supply and demand, 

movement, and consumption of agricultural irrigation water, 

but also estimates these components for natural vegetation and 

for landscape irrigation in urban areas. Thus, the use of FMP 

in MF-OWHM represents the simulation of fully coupled flow 

of water through surface-water, land-use, and groundwater 

processes and is also dependent on atmospheric conditions 

through precipitation and reference evapotranspiration 

(Schmid and others, 2006b; Schmid and Hanson, 2009; 

Hanson and others, 2014b). MF-OWHM simulates the demand 

components representing crop irrigation requirements that are 

subject to crop and farm-specific inefficiency losses, and the 

supply components representing precipitation, direct uptake 

from groundwater, and irrigation from pumped groundwater. 

Soil moisture is not considered a significant source or storage 

component of the water budget in well managed, irrigated 

agriculture. The FMP also simulates additional headdependent inflows and outflows from the landscape, such as 

a monthly approximation of surface runoff from precipitation 

and surface-water return flows to the streamflow network, and 

groundwater recharge by way of deep percolation of water in 

excess of actual evapotranspiration (ETact) and runoff (Schmid 

and others, 2006a, b; Schmid and Hanson, 2009). 

Inflows and outflows throughout the WBSs on the 

landscape are simulated by FMP as mass balances within each 

WBS and are calculated and balanced for each simulation 

time step. The following summarizes how FMP accounts for 

inflows and outflows for each WBS; more details can be found 

in the FMP and MF-OWHM documentation step (Schmid 

and others, 2006a, b; Schmid and Hanson, 2009; Hanson and 

others, 2014). The FMP dynamically integrates irrigation 

water demand from evapotranspiration with water supply and 

inefficiency losses. FMP allocates water, simulates processes, 

and computes the surface-water and groundwater inflows and 

outflows for each WBS in the active model domain induced by 

irrigated and non-irrigated agriculture and natural vegetation. 

On the basis of cell-by-cell estimations for each WBS, the 

FMP first calculates water demand as the transpiration from 

plant-water consumption and the related evaporation. The 

FMP then determines a residual water demand that cannot be 

satisfied by precipitation and (or) by root uptake from shallow 

groundwater near the root zone. Next, the FMP equates this 

residual water demand with the irrigation requirement for 

the cells with irrigated crops (that is, exclusive of any natural 

vegetation), which is called the crop irrigation requirement 

(CIR).

The CIR is then adjusted (increased) by accounting 

for evaporative losses from irrigation and other inefficiency 

losses to yield a final total farm delivery requirement (TFDR). 

For Cuyama Valley, where groundwater is the sole source of 

water used for irrigation, FMP attempts to satisfy the TFDR 

using only pumped groundwater. The amount of excess water 

from irrigation and (or) precipitation that is not effectively 

used for crop growth or is otherwise “lost” as described 

above then becomes either overland runoff to nearby streams 

or groundwater recharge as deep percolation below the 

root zone. Thus, the FMP dynamically links the demand, 

supply, and related change in aquifer storage. All of the 

supply and demand components are then tabulated into WBS 

landscape budgets that complement the groundwater-flow and 

streamflow budgets that collectively represent the hydrologic 

cycle within Cuyama Valley. 
14 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

In order to estimate the inflows and outflows, the FMP 

integrates various components of supply and demand data 

that can be specified over time or held constant for the entire 

simulation. The FMP requires soil, crop, and climate data 

to compute consumptive use and the groundwater pumping 

capacity of all wells that serve a WBS.

The FMP dynamically simulates these supply and 

demand components for a WBS within MF-OWHM by 

integrating the following computational components specific 

to Cuyama Valley’s hydrologic setting:

1. TFDR, which is largely dependent on the CIR but 

also depends on efficiency, changing climate (ET and 

precipitation), and variable aquifer head.

2. Supplemental groundwater pumpage, which is estimated 

as the TFDR, but is limited by a specified maximum 

WBS well-pumping capacity on a well-by-well basis.

3. 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 (Schmid and others, 2006a, p. 20). 

(Groundwater discharge to streams is accounted for by 

SFR2).

 The MF-OWHM code maintains a mass balance of the 

landscape for each WBS, for the streamflow network, and for 

the groundwater-flow system. Flows between these budgets 

are accommodated by head-and flow-dependent inflows and 

outflows, such as the actual ET, runoff and infiltration, or 

transpiration 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. 

For the CUVHM, the processes of evaporation, 

transpiration, runoff, deep percolation to groundwater, and 

groundwater pumpage were estimated using MF-OWHM. The 

simulated deliveries and groundwater pumpage reflect climatic 

differences, differences in agricultural practices among defined 

WBSs, changes in the water-delivery system, and changes in 

the distribution of the WBSs that reflect changing land use 

and water usage during the 1939–2010 simulation period. 

The CUVHM model provides a detailed transient analysis 

of changes in groundwater availability in relation to climatic 

variability, urbanization, land use, WBS, and changes in 

irrigated agriculture.

Delivery Requirement

The TFDR is determined as the sum of consumptive use 

of all WBS cells for irrigated crops and inefficient losses of 

applied irrigation water with respect to plant consumption. 

In order to calculate the components of the water budget, 

the FMP also requires estimates of both the irrigation and 

groundwater components and ET as a whole. Consumption 

of water by individual crops in each WBS is simulated with 

steady-state transpiration, varying with changing water level, 

which is approximated in FMP by an analytical solution. Thus, 

the amount of evaporation and transpiration from the water 

table are both a function of soil type, water-table altitude, the 

root depth of each crop type, and the user-specified anoxia 

and wilting point of each crop. As mentioned previously, soil 

moisture is not accounted for directly other than by a capillary 

fringe based on soil type. Therefore, the TFDR requires soil, 

land use (specifically distribution of crop types), and climate 

data to compute consumptive use on a cell-by-cell basis.

Soils

The CUVHM soils were simplified into four categories—

sand, sandy loam, silty clay, and silt—on the basis of data 

from the Soil Survey Geographic Database (SSURGO; U.S. 

Department of Agriculture Natural Resources Conservation 

Service, 2005, 2006; fig. 17). The capillary fringe was also 

estimated for each soil type, and ranges from 4 to 6 feet thick. 

These soil attributes are used for the entire simulation period 

and the cell-by-cell distribution is independent of the crop 

and WBS. The FMP associates the distributed soil types with 

the specified capillary fringes and internal coefficients that 

allow individual analytical solutions for the calculation of ET 

(Schmid and others, 2006a). 

Land Use

The FMP can be used to estimate components of 

consumptive water 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. FMP also can be used to simulate an 

assortment of irrigation settings that span the spectrum from 

flooded fields such as rice and cotton, to drip irrigation of 

truck crops, vineyards, and orchards. Applications with zero 

transpiration, such as artificial recharge systems (including 

Aquifer Storage and Recovery, or ASR, systems) also can be 

simulated with FMP (Hanson and others, 2010, 2014a).

For the Cuyama Valley, the land-use attributes are defined 

on a cell-by-cell basis and include urban and agricultural 

areas, as well as areas of natural vegetation. The land use 

that covered the largest fraction of each cell was used as the 

use representative of that cell. The CUVHM model employs 

a standardized land-use category system that combines the 

classification systems for agricultural and native vegetation 

as well as generalized land uses from historical maps. This 

system combines the USGS National Land Cover Database 

(NLCD) (Anderson and others, 1976; Homer and others, 

2012), the USDA National Vegetation Classification System 

(NVCS) (Brohman and Bryant, 2005; Federal Geographic 

Data Committee, 2008), and the U.S. Forest Service 

CALVEG (“Classification and Assessment with Landsat of 

Visible Ecological Groupings”) system (U.S. Department of 

Agriculture, 2007). The CUVHM has 41 land-use categories 

that represent 41 agricultural, urban vegetation, native 

vegetation, general, and non-vegetation land uses. This 

includes a split in crop attributes for the period prior to 1993 

and for 1993–2010. Crops that are represented at various 

land-use periods include 8 hay and grain crops, 8 vegetable 
Model Development 15

crops, 10 orchard crops, 4 natural vegetation types, 4 nonvegetation land uses, and 5 generalized land-use categories 

(table 7). Constructing maps of land use, including crops, is 

problematic because of the complex pattern that is subject 

to rapid change in the dynamic environment of modern 

agriculture. Despite the uncertainty and complexity, land-use 

maps were developed for 14 different periods during the entire 

period of simulation. Most of the more recent maps (2007–10) 

were based on interpreted high-altitude aerial photography that 

is supplemented with published land-use maps and CropScape 

images (Mueller and others, 2011; U.S. Department of 

Agriculture, 2012) and confirmed with the NAIP photo 

imagery. Land-use changes may occur gradually or rapidly 

in response to changes in climate, urbanization, zoning, or 

farming practices. This required making decisions as to how 

and when to assign land-use changes to the modeled domain. 

For this simulation, the seven land-use patterns were generally 

aligned with the wet-dry climate cycle for which they were 

compiled (table 2, fig. 5). 

From 13 to 34 percent of the valley floor is developed 

land that is not native trees or shrub land (table 7). Most of 

this land is agricultural land that was further subdivided into 

agricultural classifications. The agricultural categories were 

augmented with more general classes for earlier years, when 

the delineation of land use was less detailed. In general, the 

class-1 categories represent groups of vegetation that have 

similar amounts of water consumption and similar growth 

cycles that drive their consumption of water. Because of the 

interest by water managers in water use by vegetables and 

by orchard and field crops, selected varieties that are grown 

in Cuyama Valley were simulated individually when their 

distribution was available from the land-use maps. These 

land-use categories were then defined on the basis of land-use 

maps and these groups of similar crops are herein referred 

to as “virtual crops” (table 7, figs. 5, 18–22). For the entire 

simulation period, these virtual crops were used to drive 

the use and demand for water for each WBS. Each of the 

virtual crops was represented by an index number in the FMP 

(table 7). Many of the virtual crops were amalgamations of 

the multiple crop types (table 7, grouping of other classes). 

For example, virtual crops such as “Irrigated Row and 

Vegetables Crop” or “Field Crops” were amalgamations of 

other more detailed virtual crops. Because the virtual crop 

maps for the earlier periods were more generalized, some of 

the more permanent or more established land cover, such as 

“native vegetation” and orchard crops, which were mapped 

more recently, were assumed to be active earlier and were 

embedded in the earlier land-use maps on the basis of the most 

recent land-use period (2010). The land-use periods simulated 

are the multi-year periods of 1949–55, 1955–62, 1962–76, 

1976–79, 1980–84; biannually for 1985–86, 1987–88, 

1989–90, 1991–92, 1993–94, 1995–96, 1997–98, 1999–2000, 

2001–02, 2003–04; and annually from 2005–10 (fig. 5).

Land-Use Maps

For the period 1945–79, land use was based on the 

Anderson level II classifications (Anderson and others, 

1976) for the 1977 land-use map (fig. 18), and stored in 

the Geographic Information Retrieval and Analysis System 

(GIRAS) (U.S. Geological Survey, 1990). Data were compiled 

by geographic quadrangle from the mid-1970s to the early 

1980s. The original 1977 land-use map includes 22 vegetation 

classes that matched 8 of the CUVHM virtual crops (fig. 18). 

Five of these classes are different types of native vegetation, 

and six classes represent developed land uses. Because of 

this generalized classification, the agricultural virtual crop 

classes were replaced with the virtual crop of identical extent 

from the 2000 virtual-crop map. For example, where only 

“cropland” was specified in 1977, the virtual crops interpreted 

on the 2000 virtual crop map were embedded. This assumes 

the farmer would be growing the same type of crop in a given 

area through the period of the hydrologic simulation. For 

some crops, such as for orchards, this is generally a good 

assumption; for other crop types, however, the type of crop 

may have changed several times. Despite the general nature of 

the map, it shows that approximately 66 percent of the valley 

was covered by native vegetation, 34 percent was agricultural 

land and less than 1 percent was urban land use (fig. 18D;

table 7). Because earlier land-use maps were not available, 

land-ownership parcels were used to define the evolution from 

native vegetation to agricultural land use (fig. 18A–C).

For the period 1980–94, land use was based on the 

NLCD land-use map (fig. 19A). The NLCD classification, 

is a 21-class hierarchical, modified, Anderson Land Cover 

Classification (U.S. Geological Survey, 1999). The NLCD 

data are derived from images acquired by Landsat’s Thematic 

Mapper (TM) sensor, and several ancillary data sources. The 

NLCD is based on imagery acquired throughout the 1980s 

(U.S. Geological Survey, 1999). It is the first national landcover dataset produced since the early 1970s, effectively 

replacing the GIRAS datasets. Despite the availability of more 

recent datasets, however, many of the land-use categories were 

more general than those in the original 1977 land-use map. 

Therefore, the general land-use categories were replaced with 

the more detailed classifications from the 1977 land-use map 

(fig. 18A). 

For the period 1995 through 2000, land use was assigned 

on the basis of land-use data for 2000 (fig. 19B), which were 

obtained in digital format from the California Department of 

Water Resources (2000). The county land-use survey data 

were developed by CADWR, through its Division of Planning 

and Local Assistance, from aerial photography and extensive 

field surveys. The land uses that were compiled were detailed 

agricultural uses and less detailed urban and native vegetation 

land uses. The agricultural classifications can be correlated 

to the 12 CADWR class-1 categories (California Department 

of Water Resources, 2000). Such level of spatial detail is 

ideal for this study, because the crop types are aggregated 

into classes that have similar water-use characteristics. The 
16 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

CADWR prepares these detailed county maps of agricultural 

land use every 6–7 years. Because the virtual-crop map for 

2000 represents a composite map of 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. The land use was grouped into 

14 classes, and the crop that covers the majority of each 

model cell was identified as the virtual crop for that cell. 

Upland areas omitted from the CADWR maps were classified 

as native vegetation. For the period 2001–2002, land-use 

parcels were used to define the change in agricultural land 

use (fig. 19C). For all these maps, approximately 65 percent 

of the valley was covered by native vegetation, 34 percent 

was agricultural land, and less than 1 percent was urban land 

(fig. 19; table 7).

For the period of 2004–09, land use was assigned on 

the basis of the use in 2000 (California Department of Water 

Resources, 2000; figs. 20, 21). As in prior years, land-use 

parcels were used to define the change in agricultural land use. 

The spatial distribution is similar to that in 2000, with only 

small local changes. Approximately 65 percent of the valley 

was covered by native vegetation, 34 percent was agricultural 

land, and less than 1 percent was urban land (figs. 20, 21;

table 7). 

For 2010, land-use data were obtained in digital format 

from the CropScape (U.S. Department of Agriculture, 2012; 

fig. 22). These data were based on parcel maps and show 

more detailed crop distributions than the CADWR land-use 

maps (California Department of Water Resources, 2000). 

These data, however, do not cover the entire valley and were 

supplemented and modified with the CADWR land-use 

maps (California Department of Water Resources, 2000) in 

areas where the data were missing. The spatial distribution 

of different land use is similar to that of 1997 and 2000, with 

only small local changes. Approximately 65 percent of the 

valley was covered by native vegetation, 35 percent was 

agricultural land, and less than 1 percent was urban land (fig. 

22). The actual land use (fig. 22A) and the model discretized 

land use (fig. 22B) are shown for this most detailed land-use 

cover to demonstrate the alignment of actual and modeled 

land use over the active model area. Overall, the changes in 

total land use include a small decrease in natural vegetation, 

a small increase in total percentage of agricultural land use, 

but multiple changes in the types of crops grown on that 

agricultural land. 

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, 2006a). The 

TFDR is largely determined by the consumptive irrigation 

requirement (CIR). The CIR is determined from the product 

of a reference ET (ETh) and an area-weighted crop coefficient 

(Kc) on a cell-by-cell basis; these products are summed over 

all cells within each WBS. Because so many factors affect 

ET (including weather conditions, soil properties, and plant 

characteristics), it is difficult to formulate an equation that 

can produce estimates of ET under different sets of conditions 

(California Department of Water Resources, 2013). Therefore, 

the reference crop ET was developed (California Department 

of Water Resources, 2013). The reference ET from a uniform 

(evenly mowed) grass surface is commonly denoted as ETo or 

ETh from the CIMIS station 88 (fig. 6B). 

Specified root depths, suction pressures for the 

unsaturated root zone, Kcs, and fractions of transpiration and 

evaporation affect the consumption and movement of water 

for each crop category (Schmid and Hanson, 2009). For the 

CUVHM, the root depths and root uptake pressures were held 

constant for the entire simulation and are based on values from 

the literature (table 8). Pressure heads for suction pressures in 

the root zone are a range of negative (unsaturated) pressures 

for agriculture and native vegetation such as grasses, shrubs, 

and trees.

Direct transpiration (T) and evaporation (E) from 

groundwater occur at a rising water table when the top of 

the capillary fringe above the water table reaches the bottom 

of the root zone of plants or when the top of the capillary 

fringe above the water table reaches the land surface, 

respectively. For changing water tables, the direct T and E 

from groundwater are eliminated when the top of the capillary 

fringe above the water table reaches the land surface or when 

the top of the capillary fringe above the water table falls below 

the land surface (Schmid and others, 2006a).

Crop water demand, which is the product of the Kc values 

and a crop stress coefficient, can be related to crop growth 

stages. The Kc values used in this study were based on an 

unstressed crop growth curve. This growth curve was divided 

into twelve 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. 23). Although the 

specific growth dates for each virtual crop vary depending on 

the planting date and climatic zone, growth dates are assumed 

to be spatially uniform throughout the valley. The only change 

in Kc value at a given location is based on a change in virtualcrop type with land-use changes and with changes in the crop 

stress coefficient for different wet- and dry-year seasons. 

The Kc values were derived from several sources 

(figs. 23A, B, C, D, E). When available, published Kc values 

for similar coastal areas were used (Brouwer and others, 1985; 

Brouwer and Heibloem, 1986; Snyder and others, 1987a, 

b; Allen and others, 1998). If no published Kc values were 

available for similar coastal areas, published Kc values for the 

western San Joaquin Valley compiled by Brush and others 

(2004), for turf grass (Gibeault and others, 1989), and for 

various Central coast field and vegetable crops (Snyder and 

Schullbach, 1992) were used. In many cases, multiple crops 

were area-weighted to produce a composite virtual Kc value. 

The Kc values were divided into two periods of agriculture, 
Model Development 17

representing an early period of more traditional seasonal 

agriculture in the Cuyama Valley (1949–92) and a more recent 

period of more intensified agriculture (1993–2010). The 

transition between these periods of agriculture was placed 

at the end of the last multi-year drought (1984–92). Finally, 

the Kc values were multiplied by a crop-stress coefficient 

(Schmid and Hanson, 2009), the values of which depended on 

climatic conditions and other factors. The climatic stress on 

irrigated agriculture can vary by more than 20 percent between 

wet and dry seasons (Hanson and others, 2010). Eight stress 

coefficients were used to represent the wet- and dry-year 

seasons. These stress coefficients were adjusted during model 

calibration.

Other WBS and crop-related properties that were 

specified include the fraction of transpiration (Ftr), fraction 

of evaporation from precipitation (Fep), and fraction of 

evaporation from irrigation (Fei), and the irrigation efficiencies. 

These fractions (Ftr, Fep, and Fei) vary linearly with the 

respective area occupied by crops and the area open to soilevaporation (Schmid and others, 2006a). Because the cropped 

area and the exposed wetted area amount to the entire 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 reaches nearly 100 percent, 

then Ftr = 1, with Fep and Fei = 0. As a result, the fractions of 

transpiration and evaporation vary by virtual crop for different 

months of the year (table 9).

Irrigation efficiency is defined as the fraction of 

applied water actually consumed. The applied water that 

is not consumed, as a result of excess irrigation and excess 

precipitation, becomes losses to runoff and deep percolation 

(Schmid and others, 2006a). In the CUVHM, the irrigation 

efficiencies are specified as a matrix of efficiencies for each 

WBS and each crop for each of the monthly stress periods 

(Schmid and Hanson, 2009). In this way, the efficiencies 

differ from crop to crop for different WBSs and can change 

through time. The range in irrigation efficiency for each crop 

or crop group is tabulated in table 10. Irrigation efficiencies 

are assumed to have varied in time, reflecting improvements in 

irrigation application technologies 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 irrigation systems, changes in cropping patterns, 

and better leveling of the fields (California Department 

of Water Resources, 1994). The increase in efficiency is 

taken into account during calibration by applying fractional 

irrigation efficiencies that were estimated to increase through 

time.

In general, irrigation efficiencies are poorly known 

(California Department of Water Resources, 1994; and Brush 

and others, 2004). The CUVHM efficiencies specified in 

the FMP are typically quite variable, with lower values in 

wet seasons and in early years with less efficient means of 

irrigation and higher values in dry seasons and in more recent 

years with improved irrigation methods. However, irrigation 

efficiencies also can vary between seasons, and this variability 

can differ between wet-year and dry-year periods. Thus, 

irrigation efficiencies were also scaled on the basis of wet- and 

dry-year seasons. These scale factors were adjusted during 

model calibration.

Climate Data

The consumptive use of water, specifically the TFDR, 

is directly related to the climate. Although several of the 

properties specified previously take into account yearly or 

monthly variations, and some have a climatic component, the 

main climatic contributors to the FMP are precipitation and 

potential or reference evapotranspiration (ETh). In constructing 

the CUVHM, climate data were developed for precipitation 

and potential evapotranspiration and distributed spatially and 

temporally for all months and active model cells (Hanson and 

others, 2012; Flint and Flint, 2012).

Precipitation

Precipitation for the CUVHM is specified through the 

FMP at the uppermost active cells across the entire active 

model grid. For each month of the entire period of simulation 

the total monthly precipitation is specified at an equivalent 

average daily rate. Gridded regional estimates of precipitation 

and temperature are obtained at a 800-m spatial resolution 

from the Parameter-elevation Regression on Independent 

Slopes Model (PRISM, www.prism.oregonstate.edu) (Daly 

and others, 2008), transient monthly dataset, downscaled 

to a 270-m grid resolution (Flint and Flint, 2012). PRISM 

uses instrumental observations and a digital elevation model, 

making adjustments for features such as elevation, aspect, 

slope, and rain shadows. Flint and Flint (2012) downscaled the 

PRISM precipitation estimates from 800-m to 270-m using a 

gradient-inverse-distance-squared approach that incorporates 

northing, easting, and elevation. A monthly precipitation 

rate was bilinearly interpolated from the 270-meter monthly 

raster estimates to the center of each 15-acre model cell 

of the rotated model grid, and varies month to month with 

the general distribution reflected by the long-term average 

(fig. 6A).

Portions of the precipitation are simulated as 

consumption through evaporation and transpiration from 

the WBS on a cell-by-cell basis. If precipitation in excess 

of ET occurs, a portion of this precipitation becomes runoff 

and the remaining portion becomes deep percolation as 

natural groundwater recharge from precipitation or artificial 

groundwater recharge from excess irrigation. The portions 

of runoff from precipitation vary by land-use type specified 

through the estimation of virtual-crop properties (table 8). 

Certain types of crops have additional runoff, such as some 

pistachio orchards on which a plastic mulch is applied. 

Larger fractions of runoff for irrigation and precipitation were 

specified for these types of agricultural practices.
18 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Reference Evapotranspiration (ETh)

Estimates of ETh can be derived by using either complex 

parameter-based equations or simpler empirical equations. 

The main difficulty encountered in the use of parameterbased equations is the lack of accurate or complete data 

with a sufficient spatial and temporal distribution for the 

parameters and the general requirement to make estimates 

on a daily basis. In addition, 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 the operation of the California 

Irrigation Management Information System (CIMIS) stations 

started in 1987. For the CUVHM, ETh was developed on the 

basis of an hourly energy-balance calculation that is based 

on solar radiation, air temperature, and the Priestley–Taylor 

equation (Flint and Childs, 1987) to calculate potential 

evapotranspiration (ETh; Flint and Childs, 1991). Clearsky ETh

 is calculated using a solar radiation model that 

incorporates seasonal atmospheric transmissivity parameters 

and site parameters of slope, aspect, and topographic shading 

(to define the percentage of sky exposing every grid cell) 

(Flint and Flint, 2007). Hourly ETh is aggregated to a monthly 

rate and cloudiness corrections are made using cloudiness 

data from NREL (National Renewable Energy Lab, 2014). 

Modeled ETh for the southwestern United States was then 

calibrated to the measured ETh rates from CIMIS and Arizona 

Meterological Network (AZMET) stations (Flint and Flint, 

2007). 

One CIMIS station has been operated in Cuyama Valley 

since 1989 (Cuyama Station No. 88; fig.6B). The Cuyama 

Valley station has an average annual ETh of 60.8 inches. 

The comparison with simulated potential ET (ETh) is shown 

for CIMIS Station No. 88 (fig. 12). Simulated ETh has an 

average annual value of 60.0 inches and underestimates 

measured ETh for all months, with a standard error of the 

regression of 0.37 inches/month for the entire year. Monthly 

differences between measured ETh and simulated ETh range 

from 2 percent to 14 percent, with the highest differences in 

the summer months (table 11). When forced through zero, 

the regression equation has a slope of 1.1097, indicating an 

underestimation of the evapotranspiration in general relative to 

the CIMIS data.

Groundwater Agricultural Supply

The groundwater supplied to each WBS is simulated by a 

series of single-model-layer “farm wells” or through multiaquifer wells simulated with the MNW1 Package (Halford 

and Hanson, 2002). The multi-aquifer farm wells that are 

simulated by MNW1 were reduced to a single priority well in 

each cell when more than one multi-aquifer well occurred in 

same cell. The priority for the multi-aquifer farm wells was 

given to wells with more than 10 percent screened interval 

in more than one layer, largest capacity, and longest history 

of potential pumpage. All remaining wells were simulated as 

single-aquifer farm wells through the farms-wells feature in 

the FMP. In addition, any multi-aquifer farm wells that did not 

include more than about 10 percent of the second model-layer 

thickness were also treated as single-aquifer farm wells. This 

resulted in as many as 103 single-aquifer farm wells and 29 

multi-aquifer farms wells. 

 Agricultural groundwater pumpage requirements are 

estimated by the FMP after water supplied by precipitation 

is subtracted from the total actual ET on a cell-by-cell basis. 

The remainder of the water needed for agricultural land-use 

is the crop irrigation requirement that is summed on a cellby-cell basis within each WBS as the TFDR which is the 

CIR combined with other potential losses from inefficient 

irrigation. The TFDR that is required from groundwater 

pumpage is estimated from this sole-source aquifer. This 

allows a way to simulate an estimate of historical unmetered 

pumpage for the period 1949–2010.

Net Recharge

The net recharge in a WBS is defined as losses after 

consumption due to excess irrigation and excess precipitation, 

reduced by losses to surface-water runoff and ET from 

groundwater (Schmid and others, 2006a). The fraction of 

losses to surface-water runoff depends on whether the runoff 

is related to irrigation or to precipitation. Losses based on 

irrigation depend on different irrigation methods, which, in 

turn, depend on the virtual crop type and related fractions of 

runoff from precipitation and irrigation (table 8) as well as 

other factors such as soil type and irrigation efficiency. The 

ET from groundwater is subtracted from the potential net 

downward flux to the uppermost aquifer. Hence, net recharge 

to groundwater can be affected by both user-specified and 

head-dependent parameters. This definition of net recharge 

requires the following assumptions: deep percolation below 

the active root zone is equal to groundwater recharge, ET 

from groundwater equals an instantaneous outflow from 

aquifer storage within any time step, and the net change in 

soil moisture storage for well managed (irrigated) agricultural 

areas for periods of weeks to months is negligible (Schmid 

and others, 2006a). The net recharge to the aquifers is applied 

to each uppermost active model cell in each WBS.

Aquifer Characteristics

The unconsolidated alluvial deposits and the Morales 

Formation form a three-layered aquifer system within the 

regional aquifer system defined by the three hydrogeologic 

units in the Cuyama Valley. Each aquifer can be characterized 

by variations in hydraulic properties, which are based on the 

textural distribution of coarse and fine-grained sediments 

and zones representing subregions in which the sediments 

accumulated in particular depositional environments. The 

hydraulic properties represent the ability for the aquifer to 

transmit water and to store or release water and are functions 
Model Development 19

of depositional environment and lithology. Variations in 

depositional environments and lithology cause differences 

in grain size, grain shape, grain orientation, and the degree 

of sorting. This causes considerable spatial variation in the 

hydraulic properties of the deposits. Thus, variable lithology 

and depositional environments determine spatial variation 

in the hydraulic properties of the deposits. The hydraulic 

water-transmitting properties of the aquifer sediments are 

represented by horizontal (Kh) and vertical (Kv) hydraulic 

conductivity and hydraulic storage properties of the 

hydrogeologic units that constitute the aquifer system are 

represented by hydraulic conductivity and the storativity, 

respectively. The relation between hydrogeologic units in 

the aquifer system, lithology, and hydraulic properties has 

been developed in many previous studies that include both 

the properties of the aquifers and those of any fine-grained 

interbeds or confining units (Hanson and others, 1990, 2003, 

2004, 2014a, b; Laudon and Belitz, 1991; Phillips and Belitz, 

1991; Hanson and Benedict, 1993; Leighton and others, 1994; 

Fio and Leighton, 1995; Belitz and Phillips, 1995; Burow and 

others, 2004, Phillips and others, 2007; and Faunt and others, 

2009a, b). 

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 storativity. The 

textural distribution was used to define the vertical and lateral 

hydraulic conductivity and storage property distributions for 

the hydrologic model (Sweetkind and others, 2013). As in 

many of the previous studies identified above, the textural 

distribution was based on drillers’ and geophysical logs. The 

primary variable selected for the textural analysis was the 

percentage of coarse-grained sediment, with the complement 

being the percentage of fine-grained sediment. 

Based on the distribution of texture in the Cuyama Valley 

and the reanalysis of the hydrogeology (Sweetkind and others, 

2013) the groundwater system was split into three aquifers. 

Within each hydrogeologic model layer, the fraction of coarseand fine-grained sediments within the thickness of each layer 

was estimated on a cell-by-cell basis. Texture was estimated 

at the model-cell centers of the model grid for each of the 

model layers that are coincident with the hydrogeologic units. 

The fraction of coarse- and fine-grained sediments within the 

thickness of each layer was estimated on a cell-by-cell basis. 

Hydraulic Properties

Estimates of textural-based hydraulic properties were 

segregated into three hydrogeologic units that were delineated 

on the basis of the distribution of sediment texture derived 

from drillers’ logs, geologic logs, and geophysical logs. The 

hydraulic properties of an aquifer are its transmission and 

storage properties. The transmission properties of the Cuyama 

Valley aquifer are represented by the hydraulic conductivity 

(K) in this study. Equivalent horizontal and vertical hydraulic 

conductivities are assumed to be correlated to sediment texture 

(the fraction of coarse-grained and fine-grained sediment). The 

method uses the estimated binary sediment texture for each 

model cell and horizontal and vertical hydraulic conductivity 

estimates for each textural end member. 

Faunt and others (2009a) identify the power mean as 

useful for defining hydraulic conductivity values. In addition, 

their work also includes a review of the literature that 

describes the use of power mean for estimating hydraulic 

conductivity. A power mean is a mean of the following form:

M x

n

x p

k

p

k

n p

( ) = 



 



 =

∑

1

1

1

(1)

where

p is the averaging power-mean exponent, 

n is the number of elements being averaged, and 

Xk is the kth element in the list.

The horizontal hydraulic conductivity (Kh, i) was 

calculated as the weighted arithmetic mean (also equivalent 

to the power mean to the zero power) of the hydraulic 

conductivities of the coarse-grained (Kc) and fine-grained 

(Kf ) lithologic end members and the distribution of sediment 

texture for each (i

th) model cell:

K K h i, , = + c c F K i f Ff i, 

  (2)

where 

Fc, i is the fraction of coarse-grained sediment in a 

cell, estimated from sediment texture data, 

as described in the previous section, and

Ff, i is the fraction of fine-grained sediment in a 

cell (1 – Fc, i ).

Because Kf is much smaller than Kc, the arithmetic mean 

heavily weights the coarse-grained end member for horizontal 

hydraulic conductivity. 

Vertical hydraulic conductivity between model layers 

(Kv, k + 1⁄2) was calculated as the pth weighted power mean of 

the hydraulic conductivities of the coarse-and fine-grained 

lithologic end members, and k is the model-layer number 

(Faunt and others, 2009b):

K F K F K v k c k c

p

f k f

p p

, , + + , + = + 

  1

2 12 12

1

(3)

where 

Fc, k + 1⁄2 is the fraction of coarse-grained sediment 

between layer midpoints, and

Ff, k + 1⁄2 is the fraction of fine-grained sediment 

between layer midpoints of kth element in 

the list.
20 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

The harmonic mean is a weighted power mean with 

the exponent p = −1.0 in eqn. 3 and results in increased 

vertical anisotropy. The geometric mean is a weighted 

power mean with p = 0.0 in eqn. 3 and results in decreased 

vertical anisotropy. 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. The vertical conductivities can 

be represented as power means in which p varied between 

−1.0 (the harmonic mean) and 0.0 (the geometric mean) 

(Faunt and others, 2009b; Hanson and others, 2014a). The 

relation between hydraulic conductivity and percentage of 

coarse-grained deposits based on hydraulic conductivity 

end members and exponent of the power mean is nonlinear. 

The resulting value is a function of the power mean and as 

a result is sensitive to the power used (averaging method). 

Both the harmonic and geometric means weight the finegrained end members more heavily, and as a result, the vertical 

hydraulic conductivities are much lower than the horizontal. 

Dimitrakopoulos and Desbarats (1993) determined that the 

value of p depended to some extent on the size and thickness 

of the grid blocks used to discretize the model domain; smaller 

grid cells resulted in smaller values of p. The exponent p was 

specified for each model layer and adjusted during model 

calibration. The resulting Kf values of the exponent, p were 

−0.9 for the Recent Alluvium aquifer (layer 1), −0.5 for the 

Older Alluvium aquifer (layer 2), and −0.7 for the Morales 

Formation aquifer (layer 3).

Data from aquifer tests in the Cuyama Valley that 

generally represent short-term pump tests that were compiled 

and used to provide selected transmissivity and hydraulic 

conductivity values (Everett and others, 2013; fig 24A). The 

estimated hydraulic conductivity values from these tests 

ranged from 0.3 to 39 feet per day (ft/d) from an estimated 

transmissivity derived from Jacob’s method (Jacob, 1946). 

Additional estimates of hydraulic conductivity for the three 

aquifers include slug tests from the three multiple-well 

monitoring sites and range between 1.6 and 28 ft/d (Everett 

and others, 2011, 2013). These estimates were used as 

additional observations during model calibration to constrain 

the hydraulic conductivity of the model layers in select regions 

(table 12).

Hydraulic Conductivity of Lithologic End Members 

 Parameter estimation, in combination with the texture 

model developed for the region on the basis of the known 

stratigraphic units and kriged subsurface texture based 

on reported lithology, was used to estimate Kc and Kf, the 

end-member hydraulic conductivities (Sweetkind and 

others, 2013). These end members were used to estimate the 

horizontal and vertical K for each cell in the model, which 

are then related to zonal subareas (table 13; figs. 24B–D) 

that are used to estimate final values derived from model 

calibration. The Layer Property Flow Package (LPF) is used 

to simulate the hydraulic properties and groundwater flow 

process for the application of MF-OWHM to Cuyama Valley’s 

aquifer systems. The final parameters from model calibration 

representing hydraulic properties and related scale factors 

are included in the summary of parameter values in table 14

discussed in the section “Model Calibration.”

The hydrostratigraphic layers of the aquifer system in 

Cuyama Valley formed in somewhat different depositional 

environments and have textural compositions that affect the 

end-member K values. In the model, each of these layers was 

further subdivided into subareas that helped facilitate model 

calibration and better represent subareas that are different 

depositional environments (table 12; fig. 24). These also were 

used to define the subareas distribution of vertical hydraulic 

conductivity (fig. 24). In subareas where the Older Alluvium 

(layer 2) was estimated to be missing between the Recent 

Alluvium and Morales Formation, the hydraulic properties 

are represented by assumed values that allow communication 

between the Recent Alluvium and the Morales Formation 

model layers.

Because the hydraulic properties differ for each of the 

hydrostratigraphic units, they were estimated separately. The 

parameters used to control these subareas within each model 

layer represent unconfined aquifers in the outcrop areas as well 

as subareas of confined aquifers where the Older Alluvium or 

Morales Formation underlie the other aquifers. In addition, the 

subareas where the Older Alluvium aquifer is missing is also 

treated separately and represent subareas where the hydraulic 

properties allow the surrounding units to communicate. 

Therefore, the hydraulic properties of each of these subareas 

were estimated with separate model parameters during model 

calibration (table 13). The estimated values of Kf range from 

3.9x10–03 ft/d for the alluvial aquifer layer to 2.9x10–03 ft/d for 

the Morales Formation; Kc range from 20.3 ft/d for the alluvial 

aquifers to 0.8 ft/d for the sediments of the Morales Formation 

layer. For each unit, the distributions of horizontal and vertical 

K’s vary with the distribution of sediment texture within each 

zone of each layer (figs. 4, 24). During calibration, a multiplier 

was used for each zone, and the final range in vertical and 

horizontal hydraulic conductivities was calculated based on 

this formulation (fig. 24; tables 12, 13, 14). 

Unlike previous analysis of the valley in which the 

hydraulic conductivity was not differentiated for the various 

aquifers, the recent and Older Alluvium were delineated as 

separate units with separate estimates and zonation of the 

coarse- and fine-grained end-member values of the hydraulic 

conductivity. Hydraulic conductivities generally decrease 

with depth and with increasing distances from the original 

source of the sediments (eroded and (or) transported from 

the adjacent mountain ranges and river channels), which is 

consistent with the fining down and fining toward the center 

sequences observed in the aquifer sediments and textural 

model (Sweetkind and others, 2013). In several subregions, 

however, smaller values of hydraulic conductivity have 

been estimated at depth because of fine-grained textures and 

secondary alteration such as cementation (Everett and other, 

2013; Sweetkind and others, 2013). Coarser grained sediments 
Model Development 21

were simulated near stream channels in the alluvium in the 

outcrop parts of all three layers. The hydraulic property zones 

used in CUVHM also were aligned with the textural zones and 

with the internal fault boundaries.

Storage Properties

The hydraulic properties used to simulate the changes 

in storage of water within the saturated parts of the aquifer 

system consist of three components (Hanson, 1988):

1. Specific yield.

2. Elastic specific storage. 

3. Inelastic specific storage.

The first two components, specific yield and the elastic 

specific storage, represent and govern the reversible uptake 

and release of water to and from storage. Specific yield is 

unconfined storage and represents gravity-driven draining 

or filling (resaturation) of sediments with changes of the 

water table. The elastic storage coefficient represents the 

component of confined storage because of the compressibility 

of water and the reversible compressibility of the matrix or 

the skeletal framework of the aquifer system (Jacob, 1940; 

Hanson, 1988). The inelastic storage coefficient governs the 

irreversible release of water from the inelastic compaction 

of the fine-grained deposits or permanent reduction of pore 

space, which can also lead to land subsidence. Changes in 

inelastic storage in fine-grained beds is beginning to occur. 

Because this is potentially a significant source of water, as 

a result of the relatively large water-level declines in the 

Main-zone subregions in the Cuyama Valley, the estimation 

of water derived from inelastic compaction was included as 

a feature in this hydrologic model. Given the fine-grained 

nature of parts of the three aquifers in the Cuyama Valley, the 

elastic components of storage for the coarse and fine-grained 

sediments were simulated separately with the Subsidence 

Package (SUB) in MF-OWHM. Thus, separate values of 

elastic storage for coarse and fine-grained sediments were used 

to simulate elastic specific storage for the aquifers and finegrained interbeds that were applied to all layers. Specific yield 

typically is orders of magnitude larger than specific storage 

and is volumetrically the dominant storage parameter for the 

outcrop regions of all three aquifers.

The Layer Property Flow Package (LPF) and SUB 

were collectively used to define storage properties in each 

of the aquifers represented in the model. The LPF and 

multiplier (MULT) Packages were used to calculate and 

specify the aquifer-storage components, which included the 

compressibility of water for all model layers and the specific 

yield for the portions of the uppermost active layers (layers 1, 

2, 3; fig. 3A). The SUB Package was used to specify the 

specific storage related to the skeletal elastic compressibility 

of the coarse and fine-grained portions of the aquifers and the 

inelastic compressibility of the fine-grained portions of the 

aquifers. The resulting equation for the composite storage is 

represented (Hanson and others, 2014) as follows:

S S S Sy

* = + ' + (4)

where 

S* is the total storage of the aquifer layer,

S is the elastic storage of the coarse-grained 

component, 

S’ is the elastic and inelastic storage of the finegrained component, and

Sy is the specific yield from the water table 

drainage for the unconfined portions of an 

aquifer.

Both S and S’ can be further represented by its respective 

components:

S b S g n b = ∗ s = + ρ α( ) β ∗ (5)

where

	 ρg is the weight of water,

α is the compressibility of the coarse- or finegrained matrix material,

n is the total porosity of the coarse- or finegrained material, 

b is the fractional thickness of the total modellayer thickness of the coarse- or finegrained material, and 

β is the compressibility of water.

The aquifer-system specific storage for each model 

layer on a cell-by-cell basis can be further subdivided into its 

components for coarse- and fine-grained material, resulting in 

a complete equation of storage based on textural fractions of 

total porosity and the matrix compressibility:

S S S g n Fc n Ff s s = + Fc s Ff F = +c Fc I + + Ff Ff I 

  ρ α( ) β α * ( β) * (6)

where 

total porosity, nT = nFc +nFf ,

is the sum of the coarse and fine-grained 

fractions of porosity, with nFc= nFc× Fc I and 

nFf = nFf × Ff I

αFc and αFf are the compressibility of the coarse or finegrained matrix material, respectively;

Fc I is the fraction of coarse-grained sediment in 

cell (I,J); and

Ff I is the fraction of fine-grained sediment in the 

i

th model cell (1 – Fc I).

Although all model layers are simulated as convertible 

from confined to unconfined, portions of uppermost active 

model layer represent unconfined conditions, and are therefore 

assigned a specific yield. Specific yield, which is a function 

of sediment porosity and moisture-retention characteristics, 

cannot exceed sediment porosity. The zones used to specify 

the subareas of the storage properties are similar to the layers 

used for the other hydraulic properties (tables 13, 14; fig. 24) 

except for the unconfined subareas of the uppermost layers.
22 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

The compressibility of water as well as the 

compressibility of the aquifer skeleton is dependent on the 

specified porosities for the coarse- and fine-grained fractions 

of each hydrostratigraphic unit (model layer). The estimated 

total porosities from selected core samples from alluvial 

sediments in nearby Santa Clara Valley ranged from 23 to 

43 percent, and the effective porosity ranged from 22 to 

40 percent on the basis of laboratory tests of selected cores 

(Newhouse and others, 2004). For this model, porosity values 

range from 20 percent for the coarse-grained sediments of 

the Recent Alluvium to 29 percent for fine-grained sediments 

within the Morales Formation aquifer sediments (table 14). 

The products of these average porosity and the respective cellby-cell average coarse- and fine-grained fractional aggregate 

thicknesses are summed and multiplied by the compressibility 

of water (1.4x10–6 ft–1) to yield one part of the composite 

aquifer specific storage value for each active cell of every 

layer. 

Specific yield was specified for all active cells of each 

layer where those model cells represent the uppermost 

model cell and unconfined conditions. Specific yield was 

calculated by using a linear relationship between the fraction 

of coarse grained deposits, between 0 and 1, and an upper 

maximum estimated specific yield value ranging from 0.14 

for the alluvial to 0.25 for the Morales Formation (table 14). 

During calibration, a multiplier was used for each zone and to 

determine the final range in specific yield (fig. 24; tables 12, 

13, 14). 

Hydrogeologic Structures 

The subregions of the Main zone are bounded by faults. 

The faults along the edges of these zones delineate the no-flow 

boundaries of the active flow region. The Morales, Graveyard, 

Turkey Track, Santa Barbara Canyon, and Rehoboth Faults 

subdivide and compartmentalize the Main-zone subregions 

from the bounding subregions of the Sierra Madre Foothills 

and Ventucopa zones (Sweetkind and others, 2013; fig. 2A). 

These interior faults separate the Cuyama Valley into a 

set of subregions that respond differently to climate and 

water-resource development. The Horizontal Flow Barrier 

Package (Hsieh and Freckelton, 1993) was used to simulate 

resistance to flow across these structures. The effectiveness 

of these faults as partial flow barriers was then estimated by a 

parameter representing the conductance of the vertical model 

cell faces aligned with the fault trace (table 14). All faults 

were essentially barriers to groundwater flow, although some 

leakage occurs across the Rehoboth Fault (fig. 3A).

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 1940s, historical water levels and other data sufficient 

for estimating stresses are not available until about the 1960s 

(figs. 25, 26). The combined effects of groundwater pumping 

for irrigation and water supply have greatly depressed the 

groundwater levels in the Main zone. The pumpage has also 

increased the vertical head differences in some parts of the 

Main zone; however, the vertical head difference remains 

small in other regions, such as Ventucopa. In addition, head 

differences vary seasonally, ranging from about 40 ft in a 

downward gradient direction during the pumping season to 

about 8 ft of upward gradient during the nonpumping winter 

months at the multiple-well monitoring site (CVKR) in the 

Main-zone subregions. There are almost no head differences 

less than 5 ft away from the main regions of pumping (CVFR; 

fig. 26A; Everett and others, 2011, 2013). While the effects 

of climate variability may preclude the occurrence of true 

steady-state conditions for this hydrologic system, prior to 

development that started in the 1940s the basin was virtually 

full and in a quasi-steady-state condition, responding to 

changes driven by the natural cycles of climate variability. 

Initially, groundwater levels may have been shallow in many 

parts of the basin, as evidenced by the presence of cottonwood 

riparian areas along some reaches of the Cuyama River and 

the discharge of springs along the Graveyard and Turkey 

Track Faults that separate the Main zone ssubregions near 

the Cuyama River (fig. 2A). As a result of these subsurface 

conditions combined with the exceptionally wet climatic 

conditions for the initial years (1939–45), which reflect 

regional climate variability in the years prior to the simulation, 

the initial conditions used in the model do not represent 

steady-state conditions but rather estimates of hydrologic 

conditions in 1949. These initial conditions were derived 

from a combination of land-surface data and model-derived 

initial water levels. The groundwater flow simulation starts 

in October 1949, for which there are no data to map the 

undifferentiated groundwater levels throughout the regional 

aquifer system. Thus, initial heads were further refined by 

periodically using the simulated heads from the end of the 

first year (October 1950) of simulation as initial heads during 

calibration. This substitution was made in concert with scaling 

parameters of the overall elevation of initial water levels that 

helped refine the initial heads for all three model layers during 

parameter estimation.

The range in water levels over which elastic and inelastic 

compaction occur is controlled by the previous maximum 

stresses imposed on the aquifer system from the history 

of geologic loading and water-level declines (Terzaghi 

and Peck, 1948; Riley, 1969), as well as secondary effects 

such as cementation. 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) that 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 system. A head decline below the previous 

critical head establishes a new critical head so that any 
Model Calibration and Sensitivity 23

subsequent head increase results in elastic expansion of the 

aquifer system. The specification of the critical heads that 

control the transition from elastic to inelastic compaction 

within the fine-grained deposits of the aquifers is initially 

unknown for each aquifer. This threshold in pressure 

can typically vary from 50 to more than 200 feet below 

predevelopment water levels for alluvial basins and usually 

represents sediments that are overconsolidated because of 

geologic and hydrologic stresses and secondary lithification 

(Holzer, 1981; Hanson and others, 1990, 2003; Hanson and 

Benedict, 1993). Because these are initially unknown, the 

critical heads were specified as a constant depth below land 

surface and were modified during model calibration with 

respect to recent land subsidence observations.

When the simulation is started, the simulated heads and 

flows change in response to the initially specified and ongoing 

inflows and outflows. Because the irrigation and pumping 

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). As a result, comparing observed 

and simulated values becomes meaningful after a relatively 

short simulation time. This study and previous studies (Belitz 

and Phillips, 1995; Faunt and others, 2009a) show that the 

time frame for the stabilization is typically less than several 

months to years of the simulation.

Model Calibration and Sensitivity 

The CUHM was calibrated through a combination of 

trial-and-error and an automated process of minimizing 

differences between “real-world” observations and model 

output. The hydrologic framework and definitions of 

water balance zones were modified as part of this process. 

Simulation with the CUVHM requires specification of several 

hundred parameters that vary spatially and temporally, making 

it a challenge to develop an optimized set of calibrated 

parameter values. As a result, a parameterization procedure 

was employed to allow a limited number of parameter values 

to control the temporal and spatial variability of a much larger 

number of model inputs. The parameterization procedure 

followed that of Hill and Tiedeman (2007) in defining the 

term “parameters” to mean model inputs of hydraulic and 

hydrologic properties but also included landscape and croprelated properties from FMP and fractions of BCM-simulated 

surface inflow from runoff and recharge of surrounding 

watersheds. As mentioned earlier, all inflow to the model 

domain is combined in the SFR2 inflows. Calibration 

consisted of a systematic application of the parameter 

estimation method to limit the range of possible solutions. 

Even though some parameters demonstrated significant 

correlations, those parameters selected for model calibration 

were assumed to be independent. Parameter estimation 

software packages (UCODE, Poeter and others, 2005; PEST, 

Doherty, 2010a, b, c; Doherty and Hunt, 2010) were used 

directly for all sensitivity analyses and parameter estimation. 

Initially, UCODE was used to estimate parameters. In order to 

use some of the extended capabilities in PEST, a combination 

of PEST and manual adjustments were used to conduct the 

final parameter estimation and sensitivity analyses.

Calibration of transient-state conditions was dependent 

on the components of the use and movement of water across 

the landscape and their interplay with the streamflow network 

and groundwater flow system. Calibration started with 

adjustments of all parameters from the landscape, such as 

fractions of transpiration, irrigation efficiencies, stress factors 

for Kcs, and fractions of runoff, as well as aquifer properties, 

including fault conductances across fault planes or zones. 

Then, adjustments were made to other factors related to 

movement of water on the land surface, such as the hydraulic 

conductivity of the streambeds in the upstream portions 

of the streamflow network, and the recharge areas of the 

groundwater flow system. The calibration of the groundwater 

flow simulations involved adjustment of parameters that 

control the inflows and outflows to the groundwater flow 

system. The dominant sources of inflow to the groundwater 

system are streamflow infiltration and recharge from 

landscape processes. Therefore, parameters controlling inflow 

included vertical hydraulic conductivity of the streambed and 

runoff parameters. Parameters controlling outflow include 

pumpage and evapotranspiration. Some of the water-budget 

components are specified values of inflows and outflows that 

were not adjusted during calibration; these include the runoff 

component from the BCM model of stream inflows, urban 

and domestic pumpage, monthly precipitation and reference 

evapotranspiration, and water-balance area and crop properties 

(table 14). The remaining water-budget components that are 

calculated by the model include streamflow gains/losses, 

outflow through the stream network, actual evaporation 

and transpiration, groundwater pumpage from agricultural 

uses, runoff from irrigation and from precipitation, farm-net 

recharge, wellbore flow through MNW wells, and changes in 

groundwater storage. The implementation of the multi-node 

well package maintained the net pumpage but redistributed 

groundwater flow vertically and related vertical head 

differences between model layers, by intra-wellbore flow. This 

wellbore flow occurs not only during periods of pumpage and 

for undestroyed and unused wells but also in wells that are 

only used periodically for water supply or during the irrigation 

season. A total of 200 parameters were initially created to 

facilitate model calibration, but this number was reduced to 

65 parameters after initial global sensitivity and calibration 

analysis (table 14).
24 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Observations Used In Model Calibration

The ability of the transient hydrologic flow model to 

simulate the hydrologic system was evaluated on the basis of 

comparisons with select hydrologic observations, hydrologic 

time series, and groundwater-level maps. These comparisons 

were used to assess the capacity of the model to predict the 

effects of changing inflows and outflows on the hydrologic 

system, based on reasonable estimates of hydraulic, river, 

and landscape properties used to estimate pumpage, recharge, 

and changes in groundwater storage. Model calibration was 

based primarily on comparisons of spatially and temporally 

distributed groundwater and subsidence components. 

Simulated changes in water levels and water-level differences 

were compared to those measured in long-term, longscreened supply wells used as part of the new valley-wide 

monitoring network and from recent depth-specific multiplewell monitoring sites (Everett and others, 2013). Recent land 

subsidence observations from GPS and InSAR satellite images 

(Everett and others, 2013) were also used for calibration. 

Some limited estimates of hydraulic properties from aquifer 

tests and slug tests were also used (Everett and others, 2013) 

to help constrain parameters adjusted during calibration. 

Calibration adjustments were based on the combined fit of 

simulated values to these observations (figs. 26 and 27). The 

simulated values were compared to all observed values and 

provided a measure of model performance through various 

historical time intervals and subregions of the valley. The 

resulting error distributions constrain the model parameters, 

and the comparison between simulated and observed 

values provided a basis for sensitivity analysis of selected 

parameters. In addition, groundwater-level maps were used 

for qualitative comparisons. However, these maps were 

considered less reliable than time-series data because the 

composite water-level measurements and manually drawn 

contour lines represent averaged conditions. In many areas 

there are vertical-head differences within some parts of the 

aquifer systems. These differences are not well represented by 

composite water-level measurements and the manual contour 

lines. An overall estimate of model fit was made using all 

available groundwater level data.

Although the CUVHM was calibrated to available 

observations, model uncertainty exists because of the 

large number of variables that were adjusted as part of 

the calibration procedure. In addition, limitations are 

inherent in the necessary simplifications and assumptions 

needed to represent a complex hydrologic system with a 

numerical model. These uncertainties and model limitations 

are discussed later in this report in the section “Model 

Uncertainty, Limitations, and Potential Improvements.”

Groundwater Observations

The largest set of observed values used for calibration 

consisted of the groundwater levels and changes in 

groundwater levels over time. SBDPWWA maintains a 

database of key wells in the Cuyama Valley that are regularly 

measured as part of their monitoring network for their 

annual summary of the valley. These data were combined 

to form a database of available water levels throughout the 

Cuyama Valley from 1949 to 2010. About 4,465 water-level 

measurements (herein referred to as observations) from 258 

single and multiple-aquifer wells and the recently installed 

multi-well monitoring sites were used for model calibration 

(fig. 25). Despite the number of wells, the lack of wells in 

southern and southeastern part of the basin means that the 

model calibration has greater uncertainty in these areas. The 

well data included 258 initial head observations and 4,207 

drawdown observations. Hydrographs for 36 observation 

wells were developed and used to represent the Main-zone, 

Ventucopa Upland, and Sierra Madre Foothills subregions of 

the Cuyama Valley (fig. 26). 

In order to represent the overall trends in heads 

throughout the region and to minimize the potential effects of 

initial conditions, a set of observations were made for each 

well based on the overall change in head relative to the first 

observation for the time span of measurements from each 

well. In addition to changes in water levels, 45 water-level 

differences were estimated between 17 pairs of observation 

wells completed in vertically adjacent aquifers (fig. 25). These 

observations were used to help with the calibration of vertical 

hydraulic conductivity and distribution of pumpage during 

parameter estimation. 

Hydrographs that show both simulated and measured 

heads for select wells help to illustrate the match of water 

levels throughout the upper and lower parts of the system 

(fig. 26). The minimum period over which model simulations 

can accurately reproduce fluctuations in the groundwater flow 

system (the response time of the model) varies with the depth 

to water, hydrologic setting, hydraulic properties, climate, and 

land use. The amplitude of monthly fluctuations in simulated 

heads are generally less than fluctuations in measured heads, 

are smallest at the water table, and increase with depth below 

the land surface, because of the varying pumping rates during 

monthly stress periods, applications of irrigation water, 

transition between unconfined and confined conditions, and 

depth of unsaturated zone.

The overall model fit for water-level comparisons is 

generally good when the simulated head values are compared 

with the measured water levels over the combined 1,200 ft 

range of measured levels. About 37 percent of the residuals 

were between −20 and +20 ft, and 49 percent were between 

−30 and +30 ft. (fig. 27A). Simulated water levels generally 

match measured water levels, as indicated by an average 
Model Calibration and Sensitivity 25

residual of 15.6 ft and a sum of squared weighted residual 

(SOSWR) of 1.02 ft; the residuals ranged from –198 to 371 ft 

and the standard deviation was 52 ft. The residuals calculated 

from results of the CUVHM simulations are generally 

within 30 ft of the measured values, which represents about 

2.5 percent of the total elevation range of the aquifers. The 

simulated water levels tend to underestimate water levels 

(positive residuals), which is considered a conservative bias. 

The total change in measured water levels in wells ranged 

from –345 ft (rise) to 142 ft (decline) and the total simulated 

change in water levels at these well locations range from –475 

to 136 feet. The larger range in observed changes may reflect 

some values that are instantaneous water-levels affected by 

nearby pumping. The crossplot of simulated versus measured 

water levels (herein referred to as correlation diagrams) 

also indicate a generally good fit across the wide range of 

altitudes in the valley (fig. 27B). Most of the outliers are a 

result of underestimation of measured water-level changes in 

the southern Ventucopa subregion, where large interannual 

fluctuations in wells near the Cuyama River are related to 

climate cycles. Overall, the time series of simulated and 

measured water levels across the valley indicates the model 

is fairly accurate in the Main-zone and Ventucopa subregions, 

but does not replicate the elevated water levels of wells in 

parts of the Sierra Madre Foothill subregions (fig. 26C). The 

water levels fit better in recent times (the last 10 years of the 

simulation, 2000–10), when the land use and related crop 

information is better defined.

The hydrographs for the Main-zone subregions generally 

indicate a reasonable fit of rates of water-level decline, and in 

some regions show similar water-levels altitudes. For example, 

in the northern Main zone, water levels match for the early 

years but are underestimated for some wells in the more recent 

decades, which could reflect incomplete land-use data for the 

1980s through 2010. Similarly, for the Western Main-zone 

subregion, the rates of decline are similar to those in historical 

records but some of the temporal changes are missing, which 

also could be a function of incomplete land-use data. In 

the Southern Main-zone subregion, the simulated rates of 

decline show variable matches with observed rates and may 

overestimate some declines for select subregions, which also 

could be a result of incomplete land-use data. The hydrographs 

in the Ventucopa subregions are similar to those constructed 

from historical records but do not capture the interannual 

fluctuations for some of the wells near the Cuyama River. This 

could be a function of the delay in runoff from surrounding 

watersheds as has been observed in similar settings, such as 

the Santa Clara–Calleguas basin, where there is multi-year 

recession of wet-year baseflow (Hanson and others, 2003). 

Although the simulated trend is similar to that of the historical 

record for most wells, some of the annual fluctuations were 

not captured by the simulated water levels. This again is 

probably a function of incomplete land-use data used to drive 

the demand for irrigation and related groundwater pumpage. 

The hydrographs in the Sierra Madre Foothills are more 

variable, matching trends in parts of the northern and central 

subregions for some wells, and over- or underestimating 

trends for other wells. The number of water-level observations 

and the land-use data precluded a better match in this region. 

Additional refinements of the model combined with more 

detailed land-use and well data likely will allow for a better 

match with subsequent updates of the model.

Variations in matches of individual hydrographs indicate 

that simulation results generally provide a reasonable fit, given 

the general lack of information on the use and movement of 

water in the valley. The monthly to interannual fluctuations 

indicate the influence of climate, streamflow infiltration, and 

annual changes in land use. The goal of the model calibration 

was to try to match individual groups of hydrographs, and to 

minimize the sum of squared weighted residuals (SOSWR) for 

all simulated heads. As mentioned previously, there are large 

areas for which no water-level data are available (fig. 25). 

The use of WBSs that represent multiple farms, estimated 

pumpage rates, spatially and temporally coarse (multi-year) 

land-use and crop distributions for the periods prior to the last 

two decades, and assumptions made in spatially distributing 

pumpage may limit the ability of the model to accurately 

simulate water levels for the periods before detailed land-use 

data became available (about 1999). The spatial distribution 

of the residuals and water-level matches is discussed in more 

detail in the “Groundwater Levels Map” section. Much of 

the error, and the primary source of the average error, could 

be associated with the lack of spatial and temporal detail in 

land-use estimates in the valley that ultimately drives ET 

consumption through irrigation and pumping. 

Vertical water-level differences range between –54 ft 

(upward gradient) and 49 ft (downward gradient; fig. 27C). 

Residuals between observed and simulated vertical water-level 

differences generally ranged from –14.1 to 1.1 ft and were 

largest between the upper and lower alluvium in the Mainzone subregions of the model. The water-level differences 

have a median residual of –0.8 ft, and the model fit is best for 

the shallower layers, such as in the 10N26W region of the 

Southern Main-zone subregion (fig. 26). About 58 percent 

of the simulated vertical head differences are within 5 ft 

of the measured head differences. Overall, the simulated 

and observed vertical water-level differences are similar 

in magnitude and sign and for many sites improve in later 

(more recent) years with improved information on land use 

that drives agricultural consumption and related pumpage. 

Despite the matches, there are areas in which agreement 

between observed and simulated values could be improved. 

For example, measured vertical head differences range 

about 100 ft, whereas simulated differences range only 15 ft, 

indicating that the simulated vertical hydraulic conductivity 

may be too large in some areas.
26 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Water-Level Maps

The spatial comparison of the CUVHM-simulated data to 

observed data allow compilation of water-level maps for the 

Cuyama Valley aquifer system for September 1966 (fig. 28A), 

and Spring and Fall of 2010 (figs. 28B, C). The simulated 

groundwater levels (fig. 28) are in general agreement with the 

water-level maps for these periods. The thematic pixels from 

the simulated water levels are a thickness weighted average of 

composite water levels. The thickness weighted average was 

used because this is consistent with the observation process in 

MF-OWHM and is more consistent with the composite water 

levels derived from wells that were used to create the handcontoured water level maps and the composite simulated water 

levels derived from the HOB Package (Hill and others, 2000; 

Harbaugh, 2005). The water-level maps were useful during 

the model calibration by providing additional information 

on the effects of internal flow boundaries along faults and 

the adjustments to select model hydraulic properties such as 

vertical hydraulic conductivities. 

The sequence of simulated and measured water-level 

maps both indicate regions in the center of Cuyama Valley 

where water levels continue to decline and that the declines 

are concentrated in the Main-zone subregions (fig. 28). 

Changes in measured and simulated groundwater levels 

from spring to fall in 2010 range between –3 (rise) and 90 ft 

(fig. 28B, C). By the fall of 2010, water levels below 1,900 ft 

persisted in the Main-zone subregions, a pattern replicated by 

output of the CUVHM (fig. 28C). However, simulated water 

levels underestimate the hand-drawn contours in northeastern 

parts of the model (northeast of Ventucopa) where additional 

refinement of aquifer properties, land use, or recharge may be 

required (fig. 28).

Land-Subsidence Observations

Measurements of land subsidence were made at two 

continuous GPS sites and five reference point InSAR sites 

(fig. 29A). A total of 308 monthly observations were derived 

from the GPS and InSAR data. These observations show 

from little to no subsidence to a maximum of about 0.2 feet 

between 2000 and 2010 when measurements were available. 

The CUVHM model matches the relative deformation in the 

Main zone and Ventucopa Upland subregions based on both 

observed data types. Overall, the CUVHM underestimates the 

relative vertical displacements but the rates are comparable 

in the Southern Main-zone subregion at the Cuyama High 

School Plate Boundary Observation (PBO) site (fig. 29A). 

The simulated subsidence is generally restricted to the 

fault-bounded regions of the Main-zone subregions for the 

period 1950–2010 with 0.1–1.6 ft of simulated subsidence 

in this region (fig. 29B). Although these magnitudes and 

rates are currently relatively small compared to the 30 feet 

of subsidence at rates approaching 1 foot per year at times 

in the Central Valley (Faunt and others, 2009a), if waterlevels in the Cuyama Valley continue to decline there will be 

more subsidence. Despite the small magnitude, much of this 

subsidence is inelastic, resulting in a small permanent loss 

of storage in the aquifer system. The simulated subsidence 

indicates the initiation of inelastic subsidence in the late 

1970s. Because the amounts of subsidence were so small and 

the number of observations is sparse, no estimation of residual 

errors was calculated on this limited set of data.

Pumpage Observations

Observations of agricultural pumpage included previous 

estimates based on land-use and power records reported for 

the period 1947–66 by Singer and Swarzenski (table 1, 1970). 

These annual pumpage estimates were used to guide the final 

adjustments of landscape properties and irrigation efficiencies 

but were not used in the formal parameter estimation. The 

CUVHM model matches the annual agricultural pumpage 

within 14 percent of the reported values for any particular 

year and underestimates average annual agricultural pumpages 

by 1.6 percent for the early dry years (1949–58) and by 

2.0 percent for early wet years (such as 1959, 1964, and 

1969). Reported pumpage for the early years can vary by as 

much as 20 percent and is aligned with climate variability to 

some degree, but indicates persistent pumpage even during 

the wet years. Overall, the agricultural pumpage increases 

with changes in land use in more recent years and shows 

considerable variability that is aligned with changes in climate 

conditions, with simulated pumpage from 1967 to 2010 

ranging from about 42,000 to 88,000 acre-ft/yr (fig. 30). For 

example, simulated pumpage increases rapidly in 1977, which 

is coincident with increased agricultural land use, such as 

alfalfa production, with pumpage for irrigation estimated to 

have increased to about 76,000 acre-ft/yr. (fig. 7A).

Model Parameters

Although many parameters were originally defined in 

the model (tables 14 and 15), only about 69 parameters were 

determined to be relatively sensitive and were subsequently 

considered and included in the automated calibration process. 

These parameters included landscape and crop-related 

properties, hydraulic parameters of the aquifers and multinode wells, fault conductances (table 14), streamflow vertical 

conductivities, and fractions of BCM-simulated combined 

runoff and recharge inflowing along the Cuyama River and 

Santa Barbara Creek into the SFR2 Package (table 15). 

Hydraulic properties were initially assigned values based on 

published values and earlier modeling studies, then adjusted 

during model calibration. Model parameters were adjusted 

within ranges of reasonable values to best-fit historical 

hydrologic conditions measured in the aquifer, the stream 

network, and the landscape. 

Calibration started with the landscape processes, followed 

by adjustment of hydraulic properties, streambed properties, 

multi-aquifer well properties, general-head boundary 

conductances, and fault conductances. Because many of these 

properties are head dependent or were correlated through 
Model Calibration and Sensitivity 27

their exchange of water, these properties were adjusted 

recursively through automated and trial-and-error analysis. 

The calibration process also required modifications to the 

parameter framework. For example, calibration also required 

additional partitions of hydraulic-property-zone parameters 

and observations for the alluvium. By using the sensitivities 

calculated as a function of the observations, only those 

parameters that were determined to be sensitive were adjusted 

during automated calibration.

Farm Process Parameters

Farm Process parameters that were adjusted during 

calibration included selected crop properties. Some parameters 

were fixed, some were adjusted manually, and some were 

adjusted using PEST. Tables 13–15 indicate which parameters 

were estimated at some point during the calibration. These 

included scale factors for seasonal Kcs, percent runoff from 

inefficient losses from precipitation and irrigation for select 

crops and natural vegetation, and seasonal scale factors for 

irrigation efficiencies. The scale factors for seasonal Kcs are 

used to represent the stress factors (Allen and others, 1998) 

that amplify or reduce the Kcs, which were estimated under 

unstressed conditions. Because published Kcs are estimated 

under unstressed conditions (Kc less than or equal to 1), the 

Kcs used in this study required reductions for wet winters, 

summers, and falls of 19–44 percent for the early years and 

increases of 2–15 percent for later years. Stress factors for 

Kcs for wet springs were reduced for early and late years by 

20 and 13 percent, respectively. Similarly, stress factors for 

dry-year seasons were increased by 65 percent for dry, lateyear summers and 20–24 percent for early- and late-year dry 

winters (table 14) to align estimated agricultural pumpage 

with water-level declines. Part of this adjustment could be 

related to antecedent soil moisture not being accounted for by 

the FMP, but this would represent a relatively small amount 

of water. The scale factors for Kcs were adjusted to reach 

somewhat subjective matches to observations of ET and 

agricultural pumpage estimates for 1950 and 1966 (Singer 

and Swarzenski, 1970). Irrigation efficiencies for the early 

decades were adjusted for dry-year seasons, with spring values 

increased by 10 percent, fall values reduced by 7 percent, 

and summer values reduced by 2 percent during calibration. 

For the greater efficiencies of the recent decades, irrigation 

efficiencies for all seasons were increased by 10 percent 

except for dry springs. Irrigation efficiencies for early-year 

wet-year seasons were increased by 8–11 percent. Irrigation 

efficiencies for later years were increased relative to the 

initial estimates by 10–20 percent for winters, springs, and 

summers (table 14) and reduced relative to initial estimates 

by 20 percent for wet-year springs. This could indicate that 

irrigation is less efficient during wetter periods or could 

include pre-wetting of soils for vegetable crops. Runoff from 

selected crops and native vegetation is a direct control on the 

water available for deep percolation or for overland runoff to 

the streamflow network. The fractions of inefficient losses to 

runoff were initially adjusted for truck and vegetable crops, 

orchards, field, pasture, and grain-and-hay crops, but were 

finally held constant at 97 percent excess water to runoff 

after ET consumption of irrigation and precipitation for final 

calibration. Similarly, fractions of runoff from precipitation 

were increased to about 92 percent to control the deep 

percolation and additional runoff from the native vegetation, 

which is the largest component of the land use in Cuyama 

Valley. The multiple caliche layers that are common in many 

parts of the Main-zone subregions of the valley may also 

enhance runoff and further impede deep percolation from 

precipitation and irrigation.

Hydraulic Parameters

The model was used to determine the values of 

15 hydraulic properties within each model layer during 

calibration. The values of Kc and Kf for each model layer 

were adjusted to produce simulated heads representing the 

long-term trends in the aquifer and to produce heads that best 

matched the measured heads and estimated streamflow losses. 

Because of the differences in depositional environments 

within the various zones of each layer, the hydraulic properties 

were also adjusted subregionally by using 72 related parameter 

scale factors for the parameter subregions that are multipliers 

for horizontal and vertical hydraulic conductivity and storage 

properties (fig. 24B–D). The hydraulic properties that were 

adjusted included coarse- and fine-grained values for hydraulic 

conductivity, porosities, specific yields, and skeletal specific 

storage for coarse- and fine-grained end-members, and the 

exponent of the vertical hydraulic conductivity (eqn. 4). 

Specifying a single exponent value of p of –0.9 for the vertical 

hydraulic conductivity was replaced with individual values for 

each model layer of –0.9, –0.5, and –0.7 for model layers 1–3, 

respectively. This resulted in values of Kc (coarse-grained) and 

Kf (fine-grained) which are relatively close to the harmonic 

mean of vertical hydraulic conductivity (eqns. 2 and 4) for 

the Recent Alluvium and Morales Formation aquifer layers 

and closer to the geometric mean for the Older Alluvium 

model layer. The compressibility of water was specified as a 

component of the storage properties proportional to the coarseand fine-grained porosities and was held constant.

The calibration of hydraulic properties required the 

adjustment and rescaling of these intrinsic properties based 

on water-level hydrographs (fig. 26). The most sensitive 

parameters were vertical hydraulic conductivities (represented 

by the hydraulic conductivity of the fine-grained fraction) that, 

in part, controlled the seasonal amplitudes and vertical waterlevel differences between aquifer layers. Scaled reductions in 

vertical hydraulic conductivity and storage properties were 

required for select confined zones and scaled increases in these 

properties were required for unconfined zones. Horizontal 

hydraulic conductivities (represented by the hydraulic 

conductivity of the coarse-grained fraction) were increased 

during model calibration in many of the aquifer layers 

(table 13). Because the model was relatively less sensitive to 

values of porosity and specific yield, these were not included 

in automated parameter estimation.
28 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Streamflow Properties

The model also required calibration of the streambed 

vertical hydraulic conductivity parameters. Groups of stream 

segments where stream channels are similar were represented 

by 30 parameters of streambed vertical hydraulic conductivity 

(figs. 8; table 15). The groupings and calibrated vertical 

hydraulic conductivity values range from 0.2 ft/d in tributaries 

crossing the alluvium of the western Main-zone subregion to 

as much as 49 ft/d in the Cuyama River and select tributaries 

crossing the Recent Alluvium of the southern Ventucopa 

Uplands (table 15). Because there are no downstream gages, 

no downstream streamflows or differences between gages 

as gains and losses on rivers or tributaries were available for 

calibration of streambed conductivities. The final distribution 

of streambed vertical hydraulic conductivities for each of the 

577 segments is summarized in figure 8 and table 15.

Multi-Aquifer Well Parameters

The skin factors in the MNW Package define the friction 

losses to water flowing from the aquifer into the well due to 

the screen and to formation damage. They affect the interlayer 

flow and related water-level difference between model layers. 

Three skin factors were used as parameters to control the 

retardation of wellbore flow within each layer for the multiaquifer wells (table 14). Skin factors were relatively high to 

maintain the observed vertical head differences and to control 

wellbore flow between layers. The final calibrated skin factors 

ranged between 395 square feet per day (ft2/d) for the Recent 

Alluvium layer and 1,625 ft2/d for the Morales Formation 

(table 14).

General-Head Boundary Parameters

The conductance factors in the GHB Package for the 

groundwater underflow were constant model values. These 

conductances controlled the small inflows beneath Reyes 

Creek and the Cuyama River and outflows beneath the 

Cuyama River at its western groundwater outflow from the 

valley. The final conductances that controlled lateral outflow 

were set relatively large in comparison to typical hydraulic 

conductances to promote underflow from the western 

boundaries, ranging from 5.4x106 to 8.6x106 ft2/d. The 

conductances for the inflows were held small, ranging from 

1.1 to 0.1 ft2/d for the alluvium and Morales Formation layers, 

respectively (table 14). This small conductance restricts flow 

from the adjacent watersheds through the very narrow alluvial 

channel in the upper layer. The majority of the inflow from the 

adjacent watersheds is relatively small and is incorporated into 

the inflows of the SFR2 Package based on the BCM data.

Horizontal Flow Barrier Parameters

The conductance factors in the HFB Package affected the 

subsurface flow of water between the groundwater subregions 

that collectively represent Cuyama Valley. In turn, these flows 

not only affected water levels but also indirectly affect the 

propagation of storage depletion and subsurface recharge 

from underflow. Six parameters were used for the interior 

faults—Morales, Graveyard, Turkey Track Hill, Rehoboth, 

and Santa Barbara Faults—as delineated by Sweetkind and 

others (2013). The Santa Barbara Fault was split into two 

modeled flow barriers with one representing the fault within 

the Older Alluvium and Morales Formation and a second 

representing the flow barrier in the Recent Alluvium. Fault 

conductances were initially model-estimated parameters but 

ultimately specified at low values and were held constant for 

final calibration. These low conductances are consistent with 

the discontinuities in the water levels mapped by Singer and 

Swarzenski (1970) and the concept of subregions with limited 

groundwater flow between them. For example, the Santa 

Barbara Fault appears to separate the southern Ventucopa 

Uplands subregion from the Southern Main-zone subregion. 

The Morales, Graveyard and Turkey Track Hill Faults separate 

the Southern Main zone from the Western and Northern Mainzone subregions, and the Rehoboth Fault impedes underflow 

along the Sierra Madre Foothills region in the Older Alluvium 

and Morales Formation that would potentially replenish 

the Southern and Western Main-zone subregions. The final 

calibrated conductances for the faults are summarized in 

table 14.

Subsidence Parameters

The simulation of land subsidence and related changes 

in groundwater storage were controlled by nine model 

parameters that scaled the critical head, elastic skeletal 

storage, and inelastic skeletal storage for each model layer 

(table 14). Specified critical heads represent initial conditions 

in 1949 of overconsolidation and were estimated with PEST 

during calibration using scaling factors. The majority of the 

aquifer and interbed confined storage resides in the skeletal 

elastic storage values used with the SUB Package. These 

values were estimated using PEST during model calibration 

with scale factors of initial estimates; the final values were 

reduced to 42 percent for the Older Alluvium and increased 

to 34 and 50 percent for the Recent Alluvium and Morales 

Formation, respectively.

Sensitivity Analysis

The simulated equivalents of the suite of observations 

in CUVHM were most sensitive to scaling factors for initial 

heads in layers 1 and 2, secondarily to changes in select 

climate and landscape properties, and to a lesser extent 

to selected scaling factors for hydraulic properties for the 
Model Uncertainty, Limitations, and Potential Improvements 29

aquifers, streambed vertical hydraulic conductivities, and 

horizontal hydraulic conductivities. Systematic parameterestimation techniques were primarily used to estimate select 

model parameters and related sensitivities that are based on 

perturbation approaches with limited guidance from trial-anderror analysis. Although the sensitivity to initial conditions 

might be partially solved by simulating a longer initialization 

period, because groundwater levels adjust relatively slowly, it 

would take many decades with little information on stresses to 

arrive at a potentially more uncertain set of initial conditions. 

The sensitivity process in PEST identifies the sensitivity 

of computed values at the locations of measurements to 

changes in model parameters, and was used to identify 

which parameters to include and to adjust during calibration 

(Hill and others, 2000; Doherty and Hunt, 2010). Results 

of the sensitivity analysis indicated that the model was 

most sensitive to scaling factors for adjusting the initial 

heads within the Recent and Older Alluvium aquifer layers. 

Parameter sensitivities for an additional 29 parameters related 

to hydraulic properties, stress coefficients of Kcs, and scale 

factors for the runoff from precipitation for native vegetation 

are shown in declining order of sensitivity on figure 31. The 

model is next most sensitive to runoff from precipitation 

over native vegetation, which not only controls a major 

contribution to recharge through deep percolation but also 

intermittent ungaged runoff contributions to streamflow 

and stream channel infiltration. Included within the 20 most 

sensitive parameters are spring and summer scale factors for 

Kcs and irrigation efficiencies. The most sensitive streamflow 

parameters were the vertical hydraulic conductivity related 

to sections of Cuyama River channel (wvc_qyacc), various 

creeks in the unconfined Recent Alluvium in the Ventucopa 

Uplands subregion (vc_qyauc), the Western Main-zone 

subregion Cuyama River channel (wmz_qyacc), and northern 

tributary reaches of the Sierra Madre Foothills (wsmfh_qoan) 

subregion.

Model Uncertainty, Limitations, and 

Potential Improvements 

The CUVHM is a simplification of the real flow system, 

and, as such, has some inherent limitations. The accuracy 

of simulation results is related strongly to the quality and 

resolution (both spatial and temporal) of input data and of 

measurements of the system (such as precipitation, water 

levels, streamflow, and pumpage) used to constrain the 

calibration. The inflows and outflows in the model were a 

combination of measured values, adjustments to parameters to 

represent conceptualizations of the system, estimated inflows 

provided by the BCM model, and values specified through 

the use of the model code, MF-OWHM. Differences between 

simulated and actual hydrologic conditions arise from a 

number of sources and are collectively known as model error. 

While the CUVHM was designed with the capability 

to be accurate everywhere, the conceptual and numerical 

models were developed on the basis of assumptions and 

simplifications that may restrict the use of the model to 

regional and subregional levels of spatial analysis within 

seasonal to interannual temporal scales. Potential future 

refinements and enhancements will continue to improve the 

level of resolution and model accuracy. In general, proper 

design and calibration of flow models, along with better 

estimates of inflows and outflows and changing spatial data 

such as climate and land use, can minimize some of the 

inherent model limitations. Limitations of the modeling 

software, assumptions made during model development, 

and results of model calibration and sensitivity analysis all 

are factors that may constrain the appropriate use of this 

model and can be used to identify where potential future 

improvements in the simulation of specific processes are 

needed or where new data are needed to constrain simulations. 

Model discretization in space and time can be a potential 

source of error and uncertainty. Models represent a hydrologic 

system as a series of discrete spatial units, through which 

intrinsic properties and flows are assumed to be uniform. The 

use of a discretized model to represent a hydrologic system 

introduces limitations for features that occur at scales smaller 

than the discretization. Transient models are further discretized 

into a series of discrete units of time, during which specified 

hydrologic inflows and outflows are held constant. The use 

of monthly stress periods and two time steps per month in the 

CUVHM assumes that the variations of inflows and outflows 

and changes in water levels are piecewise linear changes. 

Changes at smaller time scales are not simulated, and are 

not discernable in the model results, which may contribute 

to some additional temporal uncertainty. For example, the 

distribution of daily precipitation and soil moisture within 

each monthly period used by the BCM and CUVHM can 

result in large variations in simulated recharge and runoff (for 

example, precipitation occurring as a large one-day storm 

rather than as a series of smaller storms), and this cannot be 

accounted for with the existing model. The temporal scale 

used in the CUVHM was expressly designed to separate the 

supply and demand components of water use and movement 

for agriculture. 

Differences between simulated and measured hydrologic 

features also can arise from the numerical solution that 

attempts to provide a cell-by-cell mass balance of inflows and 

outflows. Mass-balance errors are minimized by ensuring the 

model solution reaches a reasonable state of mass balance 

within each biweekly period. The twice a month time steps 

were used to remain consistent with the assumptions of the 

current version of the FMP process. The cumulative mass 

balance of the model was within 1 percent of the total flow 

over the 61 years of simulation.
30 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

An additional component of model error arises as a 

consequence of how well model-input values represent the 

actual hydrologic system. The accuracy of the calibrated 

model is contingent on the accuracy of the specified 

inflows and of the specified observed flows used for model 

comparison. Model calibration provides a means to use 

comparisons to indirectly constrain the differences between 

the real-world and simulated mass flows. Thus, the degree 

to which a simulated condition provides a reasonable 

representation of the hydrologic system can be evaluated 

through comparing simulated hydrologic conditions with 

those observed in the field, which in turn provides a massconstrained calibration. The performance and accuracy of 

CUVHM are constrained primarily by groundwater levels, 

and to a lesser degree by recent land subsidence and vertical 

groundwater-level differences. The model is used for 

developing a conceptual understanding of the flow system by 

quantifying the regional inflows and outflows and their relative 

proportions. Because the Cuyama Valley flow system is 

inherently complex, like all models, simplifying assumptions 

were made in developing and applying the numerical code, 

MF-OWHM. The model solves for average conditions 

within each 15-acre cell for each two-week period, with the 

parameters interpolated or extrapolated from measurements, 

and (or) estimated during calibration. Modeling the regional 

aquifer system without the delayed recharge of unconfined 

conditions also may affect the timing and magnitude of 

groundwater recharge and can be a potential source of error 

and uncertainty. Thus, results from the model should be 

interpreted at the sub-regional to regional scale and multi-year 

periods for comparative analysis and generalized estimates of 

flows.

Several elements of the revised model remain uncertain 

and will require additional investigation to help further 

improve the accuracy of the simulation of the groundwater 

and surface-water flow, the simulation of regional storage 

changes, and the simulation of the use and movement of water 

across the landscape. For example, some of the crop, soil, 

and landscape features that are inputs to the Farm Process 

and are used to calculate water use remain uncertain. Thus, 

model features such as pumpage and recharge are sensitive to 

some of these parameters such as Kcs, irrigation efficiencies, 

multiple cropping, or monthly land use. In particular, the 

distribution and change in land-use patterns needs to be 

improved to annual or even monthly scales to significantly 

increase accuracy of the simulation. Many of the stresses that 

are driven by these land uses varied throughout the simulation 

period at higher frequencies than the multi-year estimates of 

most of the historical land use. This is evident by the improved 

simulation since 2000, when land use estimates used in the 

model were more frequent. These variations also are driven 

by climatic conditions as well as growing periods. Hence, 

the changes appear seasonally and by climate-driven events 

that can be yearly or multi-year in length. Because the land 

use was based on generalized classification for the early 

years and select crop categories, some of the agricultural 

composite crop classes were replaced with the composite crop 

of identical extent from the 2000 land-use map. For example, 

where only generalized categories of land use were specified, 

the composite 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 time frame of 

the hydrologic simulation when that land-use map was used 

(figs. 18–22). In some cases, such as orchards, this is generally 

a good assumption; in other cases, the crops being grown 

may have changed several times during the years represented 

by the land-use map. Estimates of ETo and growing periods 

are uncertain and should be better delineated, especially in 

terms of their relation to climate changes. Finally, the natural 

vegetation represents between about 87 percent (historically) 

to 65 percent (recently) of the land use and, as such, is an 

important control for runoff and recharge in the upland regions 

of Cuyama Valley. Another potential future refinement to the 

model could include separation of the natural vegetation into 

several separate land-use subregions in different parts of the 

valley. This may improve simulated recharge and runoff in 

these areas. Though some additional uncertainties may be 

associated with estimating runoff as a fraction of precipitation 

and irrigation by crop type that does not consider the effect 

of soil properties on runoff, improving this feature would 

require prohibitively small time steps and longer simulation 

run times. Because the desert caliche layers and not the soils 

probably control much of the runoff and deep percolation, 

additional mapping of these layers may be needed to improve 

the simulation of runoff for these biweekly time intervals.

Some inflows and outflows, such as outflow along the 

Cuyama River, remain relatively uncertain, and the accuracy 

of the model could benefit from additional observations of 

streamflow from other major ungaged drainages such as Reyes 

Creek, especially if more constraints are needed to improve 

the overall hydrologic budget and estimates of local recharge 

and runoff. Continued monitoring of the inflows from the 

Cuyama River and Santa Barbara Creek will also be useful in 

maintaining an inventory of the major components of runoff 

from surrounding mountains.

The CUVHM may benefit from refinement of the location 

of the trace of the Santa Barbara and Rehoboth Faults, which 

may change the locations and extents of the flow barriers 

and potentially segregate the subregions of the valley into 

subbasins. The accuracy of the model could also be improved 

if the input values of selected hydraulic properties, such as 

horizontal and vertical hydraulic conductivities and storages, 

could be adjusted on the basis of additional field estimates. 

Additional estimates of horizontal hydraulic conductivity 

to further constrain the model properties could be obtained 

from aquifer tests at select supply well sites or well specificcapacity tests at single-aquifer supply wells. In addition, 

uncertainty in the data used to distribute the textural data 

is both more sparse and larger with increasing depth. The 

difference between simulated and measured heads generally 
Hydrologic Budget and Flow Analysis 31

increases with depth below the land surface. This may reflect 

decreasing accuracy with depth within the texture distribution 

used to estimate the hydraulic conductivities. Uncertainty 

in the values for the hydraulic properties of the Morales 

Formation may be especially large even though many wells 

currently produce water from this unit in the Cuyama Valley. 

Thus, textural-data uncertainty is smaller for the younger and 

Older Alluvium and larger for parts of the Morales Formation. 

Several of the processes within the model could also 

potentially allow for refined simulation of selected flow 

features. Improved simulation of multi-aquifer wells to 

account for partial penetration and better estimates of actual 

pumping capacities of all wells could increase the accuracy of 

simulated pumpage. Some WBS required virtual wells, so the 

additional location of wells or water conveyances that are used 

to service these properties requires additional investigation. 

Similarly, the simulation of runoff within the Farm Process 

could be enhanced to better simulate the intensity of wetyear winter precipitation events that would facilitate better 

estimates of runoff within the valley. Also, the simulation of 

unconfined conditions and the lowering of the water table in 

the Main-zone subregions could be improved by the use of the 

Unsaturated Zone Package (Niswonger and others, 2006) and 

related Newton-Raphson Solver (Niswonger and others, 2011). 

However, this upgrade could result in significant increases in 

total run time of the historical and future simulations.

In summary, some potential components that could 

improve the accuracy and reduce uncertainty of the simulation 

could include but are not limited to the following:

1. Improved temporal estimates of land use from annual to 

seasonal or monthly.

2. Improved estimation and application of crop and 

irrigation properties. 

3. Improved segregation of natural vegetation into multiple 

classes in different parts of the valley.

4. Improved estimates of ungaged stream inflows and 

outflows through additional streamflow gaging (either 

used directly or to improve the calibration of BCM).

5. Refined location and extents of the trace of the Santa 

Barbara and Rehoboth Faults.

6. Improved estimates of hydraulic properties through 

additional field tests. 

7. Improved texture estimates at depth and refined zonation 

of the Morales Formation.

8. Improved simulation of multi-aquifer wells to account for 

partial penetration and farm well pumping capacities and 

additional location of potential wells.

9. Improved groundwater, streamflow, land subsidence, and 

land cover observations for better model evaluation and 

calibration.

Despite all of these potential limitations, the CUVHM 

represents a realistic, reasonably accurate, and reliable 

means for understanding many aspects of the Cuyama Valley 

groundwater basin that are needed for planning and evaluating 

alternatives for managing water resources. Additional 

observed hydrologic and land-use data could also be used to 

improve the model calibration. When used correctly, CUVHM 

can help to continue developing understanding as more 

data and more capabilities are added. Additional observed 

hydrologic and land-use data could also be used to improve 

the model calibration

Hydrologic Budget and Flow Analysis

The CUVHM simulation of the conjunctive use and 

movement of water in Cuyama Valley indicates that, overall, 

the storage depletion and onset of land subsidence are driven 

by sustained and increased agriculture and related demand 

for water, thus resulting in a condition of overdraft. While 

periodic events of recharge occur from natural climate cycles, 

the current and historical sustained demand for water exceeds 

the long-term replenishment rate from these quasi-periodic 

events. The CUVHM confirms that the overdraft conditions 

have persisted since the onset of increased development in 

the 1970s up through 2010. The CUVHM indicates a level 

of pumpage that is consistent with estimates from the early 

years of reported pumpage and an increase in water demand 

with increased agricultural development. The overdraft is 

predominantly the result of cycles of storage depletion in the 

Main-zone subregions, which are also climatically driven over 

seasonal to interdecadal periods. 

As with groundwater storage depletion and 

replenishment, the temporal distribution of inflows and 

outflows to the landscape and surface-water systems also 

indicates a strong climatic influence. The total inflows to the 

landscape range from about 100,000 to more than 250,000 

acre-ft/yr (fig. 32A), which includes inflows, that are, on 

average, 53 percent precipitation, 44 percent irrigation 

from groundwater pumpage, and 3 percent direct uptake of 

groundwater through ET (fig. 32B). Similarly, the average 

total outflow from the landscape consists of 25 percent 

runoff, 8 percent deep percolation to groundwater recharge, 

36 percent ET from precipitation, 29 percent ET from 

irrigation, and less than 3 percent ET directly from uptake 

of groundwater (fig. 32B). Thus, about half the inflow of 

water to the landscape comes from precipitation and half 

from irrigation, and about a third of the outflows occur as 

each of ET from irrigation, runoff and recharge, and ET 

from precipitation and groundwater. ET from groundwater 

is a minor component of inflow to the landscape, and runoff, 

predominantly from precipitation, is a major outflow from the 

landscape. Deep percolation from precipitation and irrigation 

persists for all years but generally is larger during wet years. 
32 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

In 50 of the 60 years (fig. 33A), the total demand for water 

on the landscape for agriculture is much greater than the 

natural inflows in this high desert valley, with irrigation from 

groundwater supplementing the water needed for agriculture. 

Groundwater recharge from precipitation and irrigation is 

about half the groundwater pumpage in the wettest years (for 

example, in 1998 and 2005) and typically is 10 to 25 percent 

of groundwater pumpage for most years, resulting in sustained 

groundwater storage depletion and overdraft. Estimated 

agricultural pumpage during wetter years is sometimes 

comparable to that in dry years, which may reflect lower 

irrigation efficiencies during wet years.

The components of the net annual groundwater budget 

(fig. 33) are similar to inflows and outflows for the landscape, 

and vary with climate and changes in land use (fig. 33A). The 

average hydrologic budget overall and for the most recent 

decade (2000–10) indicates that streamflow infiltration and 

recharge from precipitation and irrigation are the largest 

inflows, and pumpage is the largest component of outflow, 

as summarized for select periods (fig. 33B; table 16). 

The percentages of these groundwater inflow and outflow 

components are summarized in a pie diagram (fig. 33C). 

Except for the occasional wet years, the major outflow is 

agricultural pumpage, and most of this outflow is supplied 

by a decrease in groundwater storage. The net groundwater 

budget averages about 70,000 acre-ft/yr (in and out of the 

groundwater system) but changes in groundwater storage can 

vary widely as shown by the large replenishment of about 

80,900 acre-ft that can occur in wet years such as 2005 or 

additional depletion of about 45,000 acre-ft in dry years such 

as 2009 (table 16). On average and valley wide, water released 

from storage contributes 68 percent to the total groundwater 

outflow, along with a contribution of 25 percent stream 

leakage and 6 percent farm-net recharge. About 38 percent of 

the groundwater outflow flows back into aquifer storage, for 

a net storage depletion of about 30 percent of groundwater 

flow (fig. 33C). The largest component (44 percent) of 

groundwater outflow is pumpage, (wells, farm wells, and 

multi-node wells), which is combined with 9 percent outflow 

to streams, 5 percent farm-net recharge (groundwater recharge 

minus ET from groundwater), and 4 percent as groundwater 

underflow (general head boundary) and spring discharges 

(drains; fig. 33C). Though some storage replenishment 

occurs during wet years, which offsets some of the storage 

depletion in dry years, the overall temporal distribution of 

net flows shows an increase in storage depletion (inflow of 

water from net storage; fig. 33D). This is largely focused in 

the Main-zone subregions, with small amounts of depletion 

in the Ventucopa Uplands subregions and a small accretion 

in storage in the Sierra Madre Foothills subregions. The total 

simulated storage depletion is about 2.1 million acre-ft for the 

period 1950–2010. The average storage depletion represents 

about half of the average agricultural pumpage (65,400 acre-ft/

yr) per year. The estimates of recharge and underflow are 

about 30 percent larger than the flows estimated for the earliest 

years of development (1947–66) by Singer and Swarzenski 

(1970). About 72 percent of the average storage depletion and 

87 percent of the agricultural pumpage occurs in the Mainzone subregion and the remaining average depletion and 

11 percent of the agricultural pumpage occurs in the Sierra 

Madre Foothills region for the historical period. Conversely, 

about 57 percent of the total average recharge and 64 percent 

of the average net streamflow infiltration occurs in the 

Ventucopa Uplands subregion.

The temporal distribution of groundwater pumpage 

is dominated by agricultural pumpage. Most of the flow of 

groundwater to wells is from the Recent Alluvium and ranges 

from 40 to 93 percent (fig. 34). Additional water is derived 

from the Morales Formation in the early years (1950–77) and 

from the Older Alluvium in the subsequent years (1978–2010) 

with shifts in development and related land use in the valley. 

The relative reductions in pumpage during the intervening 

wet periods show the sensitivity of the climate built into FMP 

calculations. For example, agriculture and related irrigation 

is very sensitive to climate as irrigation is supplemented by 

precipitation, a portion of which is consumptively used by 

crops, as demonstrated by the comparison with estimated 

pumpage for the early years (fig. 30). 

More than 70 percent of the recharge occurs within 

the Recent Alluvium layer during the years of greatest 

land-use development prior to the 1980s (fig. 35A). An 

additional 20 percent enters the groundwater flow system 

through the Older Alluvium. The fractions of recharge to the 

Morales Formation could be an artifact of the model’s initial 

conditions. There is some variation with climate in the relative 

proportions of recharge from year to year (deep percolation, 

fig. 32), but the overall percentages remain relatively constant 

valley wide. This exchange of water between aquifers largely 

occurs across layer boundaries and in relatively small amounts 

by intraborehole flow through long-screened supply wells that 

are open to both formations. As indicated from the depthspecific water level histories of the multiple-well monitoring 

sites (fig. 26A), the vertical head gradients can be downward 

during the growing season and upward during the nongrowing season. Most of the vertical interlayer flow occurs 

across layer boundaries and is focused in the regions where 

coarse-grained sediments are more prevalent, such as along 

the stream channels in the Recent Alluvium. The majority 

of the vertical flow is to the Older Alluvium or the Morales 

Formation from the Recent Alluvium, where the majority of 

the recharge is occurring (fig. 35B).

Projection of Potential Water 

Availability

Three 61-year projections were made to begin the 

assessment of the sustainability of the water resources 

in Cuyama Valley. This assessment included a base-case 

projection of “business-as usual” land use, a reduced-supply 

projection, and a reduced-demand projection with cessation 

of agriculture in the Main-zone subregion. Since there is no 

basin management plan, these three hypothetical scenarios 
Projection of Potential Water Availability 33

are used to assess potential water availability beyond 2010 

using existing land- and water-use conditions from 2010. All 

three projections use information on historical hydrologic 

conditions (streamflows and related climate data) and 2010 

land use, wells, and land ownership distributions, with 2010 

crop and irrigation properties, to simulate 61 years of potential 

future hydrologic conditions under current supply-and-demand 

conditions. This assumes that there is no additional growth 

or reductions in demand for water resources or additional 

development of land for agriculture, urban, or domestic 

uses. Though downscaled and bias-corrected climate-model 

projections could have been used to create future climate 

conditions (Hanson and others, 2012), historical climate 

data were used for these initial projections to ensure that 

climate variability and climate cycles were similar to recent 

historical conditions. This assures that the historical variability 

in inflows and demand from ET is maintained from the 

historical wet and dry periods. The monthly climate data 

and streamflows for each water year are used starting with 

the most recent year (2010) and working backward in years 

(but forward in the months of each year) to 1950 to make 

a projection for the period 2011–71 so that year 2071 uses 

conditions from 1950.

The second scenario is called a reduced-supply scenario 

that represents a potential management scenario through 

reduction in supply in which a target yield of the basin would 

be determined and some acreage-based reduction would have 

to be made to bring demand in line with the long-term average 

recharge of the valley. The long-term average recharge was 

determined from data for the historical period 1950–2010 

because conditions in this period are fairly similar and 

aligned with the PDO climate cycles (1947–77 dry period, 

1978–99 wet period, and 2000–10 partial dry period) that 

control runoff and resulting recharge. The long-term (61-year) 

average recharge rate from this historical period is about 

33,000 acre-ft/yr and the recharge for 2010 was estimated at 

about 35,000 acre-ft/yr. The distribution of this recharge is 

57 percent in the Ventucopa Uplands, 36 percent in the Mainzone, and 7 percent in the Sierra Madre Foothill subregions. 

The fraction of total recharge within each of the three groups 

of subregions was prorated by percentage of area in 2010 over 

the various WBS that are designated as agriculture within 

each group of subregions. In addition, the distribution of the 

portions of long-term recharge were distributed for each WBS 

based on the monthly fractions of simulated monthly usage for 

27 agricultural WBS that are part of the total 83 WBS in 2010.

The third scenario is called a reduced-demand scenario 

that represents the alternative to a reduced supply, a reduced 

demand projection, by retiring all agriculture in the Main 

zone. The difference is a reduced supply or a reduction in the 

amount of water that would be supplied from groundwater 

pumpage, while a reduced demand could represent a reduction 

in the acreages or types of crops grown that drives irrigation 

demand. This reduced demand scenario was implemented 

by simulating the return of all WBS agricultural land back 

to native vegetation without irrigation within the Main-zone 

subregions.

A reduction in supply may have a different effect in the 

three major subregions of Cuyama Valley. For example, in the 

Ventucopa region, where there is a lesser long-term storage 

depletion, less storage depletion may occur, which may 

increase stream baseflows during dry years. In wetter years, 

streamflow could pass farther west past the Santa Barbara 

Fault and into the Main-zone subregion and flow farther 

for more days of the year. In contrast, reduced supply in the 

Main-zone subregion will result in less artificial recharge from 

irrigation, which results in a reduced replenishment, as well 

as reduced demand from deficit irrigation or reduced acres of 

agriculture that can be sustained with reduced supply. 

For the first base-case scenario, the projected supplyand-demand indicates the potential for additional water-level 

declines of more than 350 ft in the Main-zone subregion, with 

a sustained agricultural pumpage of about 65,000 acre-ft/yr 

(fig. 36). The projected change in cumulative storage indicates 

that an additional 2.5 million acre-ft of water would be 

removed above and beyond any potential recharge (fig. 37A). 

The projected groundwater levels for the end of the projection 

period show sustained declines in areas of substantial 

agricultural demand in the Main zone and Sierra Madre 

Foothills subregions (figs. 2A, 36, 37B). With these sustained 

declines also comes additional potential land subsidence of 

almost two feet near Cuyama (fig. 37C) that is mainly focused 

in the areas of sustained agricultural demand in the Main-zone 

subregions. Conversely, the Ventucopa Uplands subregion 

appears to retain conditions similar to current conditions and 

there is only modest storage depletion in the Sierra Madre 

Foothills subregion. The combination of storage depletion 

with continued drawdowns in the aquifers within the younger 

and Older Alluvium combined with additional potential land 

subsidence is probably not a sustainable scenario in the Mainzone subregions.

The second scenario represents reduced supply with 

the use of groundwater allotments within FMP to limit the 

pumpage within each WBS to a proportional fraction of 

long-term recharge. This scenario still shows some small 

amount of long-term storage depletion of about 500 acre-ft 

(fig. 37A). There is about a 30- to 70-foot recovery of water 

levels in parts of the Main-zone; levels in other regions such 

as the Sierra Madre Foothills region show continued declines 

with additional storage depletion that may be the result of 

continued pumping from wells that actually serve the Main 

zone (fig. 37B). While there is a general cessation of additional 

land subsidence and some elastic rebound for this scenario 

(fig. 37C), there is still some potential land subsidence 

occurring in parts of the Main zone. The overall average 

rate of storage depletion is greatly reduced with pumpage 

held close to the average long-term recharge rate (table 16). 

This scenario indicates that while the storage depletion is 

largely arrested in the Main zone, there still may not be a 

sustainable resource after the projection of 61 years because 

of reduced artificial recharge from irrigation in the Main zone 

and lack of overall long-term storage recovery. This scenario 

is comparable to the scenarios that describe the relation 

between safe yield, sustainability, and the water-budget myth 
34 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

(Bredeheoft and others, 1982; Bredeheoft, 1997, 2002; Alley 

and Leake, 2004). The reduced pumpage may not allow the 

current intensity and extent of agriculture to be sustainable even 

if irrigation was more efficient, for example, by transition from 

sprinkler to drip or soaker irrigation, or if mulching, canopy, 

deficit irrigation, and other practices were used to reduce 

demand from irrigation (ET).

The third scenario that represents reduced demand with 

cessation of agriculture in the Main-zone subregions results in 

widespread recovery of water levels except within the Sierra 

Madre Foothills subregions. This scenario results in storage 

accretion averaging about 11,900 acre-ft/yr (fig. 37A), that 

results in about a 170-ft to a more than 200-ft of recovery in 

groundwater levels in parts of the Main zone but continued 

declines in parts of the Sierra Madre Foothills (fig. 37B) and 

cessation of potential land subsidence with some minor elastic 

recovery (relative uplift) (fig. 37C). Average recharge is again 

reduced by reduction in direct infiltration from irrigation 

supplied by pumpage that is about half the average recharge 

rate (table 16). This scenario represents a radical change in land 

use that is probably not realistic, but serves to demonstrate the 

changes that would need to made and long time frames needed 

to not only arrest storage depletion but to only partially recover 

the basin’s aquifers. This scenario may also not be feasible 

with respect to sustainable agriculture in the valley. The three 

scenarios indicate that other sources of water, combined with 

managed aquifer recharge, possibly through redistribution of 

streamflows further into the Main zone, and a comprehensive 

basin management plan could be needed to augment the current 

levels of water demand and reduce the disparity between supply 

and demand. Wet years alone cannot overcome the sustained 

deficit between supply and demand based on recent climate, 

land-use conditions and demand for water for irrigation at twice 

the long-term average recharge rate.

Additional projection scenarios with CUVHM could 

be simulated with alternative future climate conditions and 

adaptation of land-use and agricultural practices such as 

improved irrigation efficiencies to further assess the mitigation 

of potential overdraft conditions. Alternatively, simulations 

could be made to assess potential projects such as new land 

ordinances or reductions in agricultural acreage, groundwater 

management projects such as managed aquifer recharge that 

could redistribute streamflow from the Ventucopa Uplands 

corridor into the Main-zone subregions, climate-change 

adaptation that would facilitate capturing and replenishing 

water through managed aquifer recharge, or new policies 

regarding water use and reuse. These types of scenarios and 

analysis require a management structure that could develop 

and evaluate the feasibility of social, political, and engineering 

solutions and their costs, before a given management strategy 

and related policies and projects could be evaluated using the 

CUVHM model. This analysis could help to form the basis 

for evaluating a potential water-resource management plan by 

using alternative policies and projects. Though not simulated 

in this version of CUVHM, as the water table is lowered with a 

growing unsaturated zone, recharge can be delayed or reduced 

when the streamflow becomes hydraulically disconnected from 

the groundwater water table. 

Suggestions for Future Work

Future work could include refinement and temporal 

updates of the Cuyama Valley Hydrologic Model, through 

additional calibration, with additional model observations, 

and development of alternative projection scenarios based 

on a comprehensive basin management plan. An expanded 

monitoring network would allow a better understanding of 

changes in groundwater flow, streamflow, and streamflow 

infiltration, which are the main sources of recharge in the 

valley. In particular, the additional monitoring of stream inflow, 

groundwater pumpage, land subsidence, and groundwater 

levels throughout the valley would help to better quantify the 

state of the resources as well as provide valuable comparison 

to model performance. However, monitoring Cuyama River 

outflows or inflows from other major tributaries and continued 

monitoring of the inflows on Cuyama River and Santa Barbara 

Creek are also needed refine the hydrologic budget as well as 

to maintain and improve the accuracy of the CUVHM. The 

calibration of the model, based predominantly on groundwater 

levels, could be supplemented with additional calibration That 

include observations from remote-sensing estimates of ET 

and with additional streamflow values to help improve model 

accuracy. Additional verification of the numbers and conditions 

of wells used for irrigation and cropping practices would also 

potentially improve the accuracy of the model. Projections of 

water availability and sustainability of supply could include 

the analysis of alternative scenarios of land use, crops, and 

irrigation practices, as well as additional capture of intermittent 

runoff from wet years for managed aquifer recharge.

Summary and Conclusions

Cuyama Valley is north of Sierra Madre Mountains 

in south-central California (fig. 1A) and is one of the most 

productive agricultural regions in Santa Barbara County. 

However, increases in population and transitions to crops 

that consume additional water have increased the demand 

for water within the Cuyama Valley groundwater basin 

(CUVGB). While a small amount of urban supply is pumped 

from groundwater, irrigated agriculture is supplied solely 

by groundwater pumpage. This study provided a refined 

conceptual model, geohydrologic framework, and an 

integrated hydrologic model, the Cuyama Valley Hydrologic 

Model, or CUVHM. The goal of this study was to produce a 

model capable of being accurate at scales relevant to watersupply analysis needed for the evaluation of water availability 

and sustainability. The CUVHM is the first hydrologic 

model of this high desert basin. The Basin Characteristics 

Model (BCM) and the CUVHM were calibrated to historical 

conditions of water and land use and were used with the 

new geohydrologic and conceptual models to assess the use 

and movement of water throughout the Valley. These tools 

provide a means to understand the evolution of water use, its 

availability, and the limits of sustainability.
Summary and Conclusions 35

The conceptual model identified inflows and outflows, 

which include the movement and use of water from natural 

and human components. The groundwater flow system is 

characterized by a layered geologic sedimentary system that 

results in vertical hydraulic gradients due to the combined 

effects of the application of irrigation water and natural 

recharge from streamflow infiltration and direct infiltration 

at the land surface combined with groundwater pumpage, 

evapotranspiration (ET), and underflow as outflows. Overall, 

groundwater supplies most of the agricultural demand in the 

initial part of the growing season, which is augmented by 

precipitation during wet winter and spring seasons. In addition, 

the amount of groundwater used for irrigation varies from 

year to year in response to climate variation and can increase 

dramatically in dry years, but the model also indicates that 

irrigation may have been less efficient during wet years. While 

agricultural irrigation is not measured, it is the largest demand 

for water along with transpiration by native vegetation. The 

integrated hydrologic model, CUVHM, includes new waterbalance subregions, delineation of natural, municipal, and 

agricultural land use, streamflow networks, and groundwater 

flow systems. The redefinition of the geohydrologic framework 

(including the internal architecture of the deposits) and 

incorporation of these units into the simulation of the regional 

groundwater flow system indicate the importance of faults in 

compartmentalizing the alluvial deposits into subregions that 

have responded differently with respect to regional groundwater 

flow, locations of recharge, and the effects of development. 

The Cuyama Valley comprises nine subregions that are fault 

bounded, represent different proportions of the three layers of 

the valley’s aquifer system, and show differences in generally 

poor-quality water (Everett and others, 2013).

The BCM was used to estimate the monthly runoff and 

recharge in the 144 subbasin watershed that surround the 

alluvial basin of Cuyama Valley. The BCM of the surrounding 

watershed indicates that about 65 percent of water leaving the 

landscape after ET becomes runoff that flows into Cuyama 

Valley. Some additional recharge within these surrounding 

watersheds may also become rejected recharge and contribute 

to runoff into the valley. The BCM generally fits the limited 

streamflow data that were available from the region and 

provides a systematic estimate of runoff and recharge for 

the largely ungaged watersheds surrounding Cuyama Valley. 

Average annual streamflow applied to streamflow network 

boundaries is approximately 1,500 acre-ft/yr (acre-feet per year) 

and ranges from 0 to120,000 acre-ft/yr. Only 14 of 144 subbasin 

watersheds exceed 10 . for any of the last 40 years, and with 

the exception of the two largest subbasins in the southeastern 

conglomerates, all are present on the southern side of the valley, 

an area dominated by sandstones. These 14 subbasins contribute 

more than 60 percent of the total streamflow.

The Cuyama Valley Hydrologic Model was designed to 

reproduce the most important natural and human components 

of the hydrologic system, including components dependent 

on variations in climate, permitting an accurate assessment of 

groundwater conditions and processes that can inform water 

users, and help to improve planning for future conditions. 

Model development included a revised conceptual model of 

the flow system, construction of a precipitation-runoff model 

using the Basin Characterization Model, and construction 

of an integrated hydrologic flow model with MODFLOWOne-Water Hydrologic Flow Model (MF-OWHM). The new 

geohydrologic, conceptual, and hydrologic models were 

developed, and the hydrologic models were calibrated to 

historical conditions of water and land use, and then used to 

assess the use and movement of water throughout the Valley. 

These tools provide a means to understand the evolution of 

water use, its availability, and the limits of sustainability.

The CUVHM uses MF-OWHM to simulate and assess 

the use and movement of water, which includes the evolution 

of changing land use and related water-balance regions. The 

model is capable of being accurate at annual to interannual time 

frames and subregional to valley-wide spatial scales that allow 

for analysis of the assessment of the groundwater hydrologic 

budget for water years 1949–2010, as well as potential 

assessment of the sustainability of groundwater use. Overall, 

the model provides a good representation of the regional flow 

system and the movement and use of all water. 

Simulated changes in storage over time show that 

significant withdrawals from storage generally occurred not 

only during drought years (1976–77 and 1988–92) but also 

during the early stages of industrial agriculture that was initially 

dominated by alfalfa production. Since the 1990s, growers 

in the Cuyama Valley have shifted to more water-intensive 

organic vegetable crops such as carrots, broccoli, and potatoes 

that are rotated with field crops such as onions and grains. 

Combined with an extended growing season and increased 

irrigated acres, the shift in land use has increased demand 

on limited groundwater resources in excess of natural and 

artificial recharge. Measured and simulated groundwater levels 

indicate substantial declines in selected subregions, mining of 

groundwater that is thousands to tens of thousands of year old, 

increased storage depletion, and land subsidence. Simulated 

groundwater flow indicates that vertical gradients between 

aquifer layers fluctuate and even reverse in several parts of 

the basin as recharge and pumpage rates change seasonally 

and annually. The majority of recharge to the Cuyama Valley 

occurs from stream loss in the upland regions of Ventucopa and 

Sierra Madre Foothills, and the largest fractions of pumpage 

and storage depletion occur in the Main-zone subregions. The 

long-term imbalance between inflows and outflows results in 

modeled overdraft of the groundwater basin over the 61-year 

period 1949–2010. Changes in storage vary considerably from 

year to year, depending on land use, pumpage, and climate 

conditions. Climate-driven factors can greatly affect inflows, 

outflows, and water use by as much as a factor of two between 

wet and dry years. While inflows during inter-decadal wet 

years partly replenish water in the basin, the longer-term water 

use and storage depletion from pumping have restricted the 

effects of these major recharge events. Maps of simulated and 

measured water-level altitudes indicate large regions where 

depressed water levels have resulted in large desaturated zones 

in the recent and Older Alluvium layers in the Main-zone 

subregions. The projections of the base-case scenario and 2010 

land use 61 years into the future indicates that current supplyand-demand are not sustainable (assuming that the past 61 years 

are representative of future climate) and will result in the 

potential for additional groundwater-level declines and related 

storage depletion and land subsidence.
36 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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Figures
42 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

41

1

1

166

166

154

150

119

126

46

43

33

33

58

58

65

99

101

101

5

5

Limited access highway

Highway

Major road

City

EXPLANATION

Fault

Boundary of Cuyama groundwater basin

Maximum extent of Hydrologic Flow Model

Composite boundary of subwatersheds for

 Basin Characterization Model

0 10 20 MILES

0 10 20 KILOMETERS

Pacific Ocean Cuyama Valley

San

Joaquin

Valley Area shown

 on figure 1B

Transverse Ranges

C o a s t R a n g e s

Santa Barbara

Bakersfield

Santa 

Maria

sac13-0490 Figure 1a location map

Shaded relief base from ESRI ArcGIS Online Map Service 

http://services.arcgisonline.com/arcgis/services: ESRI_ShadedRelief_World_2D. 

Roads from Cal-Atlas Geospatial Clearinghouse http://atlas.ca.gov/download.html 

Place names sourced from USGS Geographic Names Information System, 

1974-2009. San Andreas fault from Bryant (2005). Albers Projection, NAD83

121° 120°30’ 120° 119°30’ 119°

35°

30’

35°

34°

30’

5

33

154

San Andreas fault

Twitchell

Reservoir 

AlamoNipono

AlamoSantaM

Aliso

CuyamaBuck

CuyamaSanta

CuyamaVentu

HuasnaArroyo

HuasnaSanta

Reyes

Wagon

Basin Characterization Model basins 

CALIFORNIA

Location of

map area

Bakersfield

Santa

 Barbara

San Francsico

Los Angeles

Pacific Ocean

A

Figure 1. A, Cuyama Valley watershed and groundwater basin, and B, detailed location map with the active hydrologic model grid, 

groundwater basin, and major rivers, Cuyama Valley, California.
Figures 43

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 1b_model grid

Reyes

Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active CUVHM

 model-grid

 boundary

Active model-grid

B

Maximum extent of

 Hydrologic Flow Model

 from figure 1A

Major river or stream

Santa Barbara

Creek

Figure 1. —Continued
44 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Morales fault

South Cuyama fault

Ozena fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Cuyama

syncline

166 166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 02a_zones&faults

Reyes

Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Caliente/Northern-Main (CNMZ)

Cuyuma groundwater basin subregions (table 1)

Central Sierra Madre Foothills (CSMFH)

Northeast Ventucopa Uplands (NEVU)

Northwestern Sierra Madre Foothills (NSMFH)

Northern Ventucopa Uplands (NVU)

Southern Sierra Madre Foothills (SSMFH)

Southern Ventucopa Uplands (SVU)

Southern-Main (SMZ)

Western-Main (WMZ)

(

Normal fault

Thrust fault

Thrust fault,

 concealed

M M Syncline

Syncline, concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

A

See table 1 for subregion designation

Figure 2. A, Groundwater hydrologic subregions and related geologic structures; B, simplified Cuyama major groundwater regions; 

and C, groups of landscape water-balance subregions for 1943–2010 in Cuyama Valley, California.
Figures 45

Morales fault

South Cuyama fault

Ozena fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Cuyama

syncline

166 166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 02b_simplified zones&faults

Reyes

Creek

Santa Barbara

Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Simplified Cuyuma groundwater basin zones

Sierra Madre Foothills

Ventucopa Uplands

Main

(

Normal fault

Thrust fault

Thrust fault,

 concealed

M M Syncline

Syncline, concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

B

Figure 2. —Continued
46 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166 166

33

33

166 166

33

33

166 166

33

33

166 166

33

33

166 166

33

33

166 166

33

33

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

1970-79

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 02c-1_Farms-time

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Caliente RangeCaliente Range

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS National Elevation 

Dataset (NED); North America Vertical Datum 1983 (NAVD83). Hydrology sourced from 

1:24,000-scale National Hydrography Dataset, 1974-2009. Place names sourced from USGS 

Geographic Names Information System, 1974-2009. Albers Projection, NAD83.

1. 1943–50 2. 1951–59

3. 1960–69 4. 1970–79

5. 1980–85 6. 1986–92

C

Figure 2. —Continued
Figures 47

166 166

33

33

166 166

33

33

166 166

33

33

166 166

33

33

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 02c-2_Farms-time

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Caliente RangeCaliente Range

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Mts

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

7. 1993–95 8. 1996–2000

9. 2001–09 10. 2010

CNMZ_Ag

CNMZ_Dom

CNMZ_Ind

CNMZ_Nat

CNMZ_Urb

CSMFH_Ag

CSMFH_Nat

NEVU_Nat

NSMFH_Urb

NVU_Ag

NVU_Dom

NVU_Ind

NVU_Nat

NVU_Urb

Native

SMZ_Ag

SMZ_Dom

SMZ_Nat

SMZ_Rch

SMZ_Urb

SSMFH_Nat

SVU_Ag

SVU_Dom

SVU_Ind

SVU_Nat

SVU_Rch

WMZ_Ag

WMZ_Nat

WMZ_Rch

NSMFH_Ag

Native NSMZ_Nat

SMZ_Ag

CNMZ_Ag

SMZ_Dom

SMZ_Ind

SSMFH_Rch

SVU_Ag

SVU_Ind

WMZ_Ag

EXPLANATION

Farm groundwater ID for 1943 to 1969 Farm groundwater ID for 1970 to 2010

Zone_primary use Zone_primary use Zone_primary use Zone_primary use

See table 1 and figure 2A

 for zone designation

C (Continued)

Figure 2. —Continued
48 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Ozena fault

M

M

M

M

A

A

A’

A’

B’

B

166

166

33

33

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North American Vertical Datum 1983 (NAVD83).

Hydrology sourced from 1:24,000-scale National Hydrography Dataset, 1974-2009.

Place names sourced from USGS Geographic Names Information System, 1974-2009.

San Andreas fault from Bryanr (2005). Albers Projection, NAD83.

Modified from Sweetkind and others, 2013

sac13-0490 Figure 3a Generalized geologic map of Cuyama Valley study area.

New

Cuyama Cuyama

Ventucopa

Maricopa

Carrizo

Plain

San Joaquin Valley

Cuyama

Badlands

Caliente Range

Cuyama River

KERN COUNTY

SAN LUIS 

OBISPO COUNTY

SANTA BARBARACOUNTY

VENTURA COUNTY

M

EXPLANATION

Generalized stratigraphic units

Recent alluvial aquifer

Older alluvial aquifer

Morales Formation aquifer

Bedrock

Normal fault

Thrust fault

Strike-slip fault

Thrust fault,

 concealed

Syncline, showing plunge direction

Syncline, concealed

Composite subbasin boundary for

 Basin Characterization Model

GRF, Graveyard Ridge fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

Oil field

Line of section

119°50’ 119°40’ 119°30’ 119°20’ 119°10’

35°

34°

50’

34°

40’

0 5 10 MILES

0 5 10 KILOMETERS

South Cuyama fault

fault

Russell

Morales

fault

Cuyama

syncline

South

Cuyama

field

Russell

Ranch

field

Whiterock fault

GRF

TTRF

SBCF

Rehoboth

fault

San Andreas fault

Santa Barbara

Canyon

Ballinger

Canyon

Salisbury

Canyon

A

Active model-grid boundary

 for CUVHM model

Figure 3. Generalized A, outcrops of geologic units and major faults within model grid; B, axial hydrogeologic cross-section (A–A’); and 

C, transverse hydrogeologic cross-section (B–B’) of Cuyama Valley, California.
Figures 49

Modified from Singer and Swarzenski (1970)

Water level - 1966Water level - 2008

B-B’ Section 

Turkey Trap Ridge fault

Russell fault

Morales

fault

00 1 2 3 4 Kilometers

1 2 3 4 Miles

E

A' A

W

3,0002,6002,2001,8001,4001,000

600200

Feet

Altitude, in feet above mean sea level

EXPLANATION

Faults

sac13-0490 Figure 3b. Xsec AA' 

B

Vertical exaggeration 26X

Section subparallel to Whiterock and Turkey Trap Ridge faults

Generalized stratigraphic units

Recent alluvial aquiferOlder alluvial aquiferMorales Formation aquifer

Figure 3. —Continued
50 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Modified from Singer and

 Swarzenski (1970)

sac13-0490_ Figure 03b.

EXPLANATION

Faults

Vertical exaggeration 26X

Generalized stratigraphic units

Recent alluvial aquifer

Older alluvial aquifer

Morales Formation aquifer

Bedrock

Rehoboth fault

Water level – 1966

Water level – 2008

0

0 1 2 3 4 Kilometers

1 2 3 4 Miles Section A-A’

3,000

2,600

2,200

1,800

1,400

1,000

600

200

Sea level

Feet

Altitude, in feet above mean sea level

S

B

C

N

B’

Morales fault

South Cuyama fault

Figure 3. —Continued
Figures 51

166

166

33

Carrizo 33

Plain San Joaquin Valley

Cuyama

Badlands

Caliente Range

Cuyama

River

119°50’ 119°40’ 119°30’ 119°20’ 119°10’

35°

34°

50’

34°

40’

0 5 10 MILES

0 5 10 KILOMETERS

Sierra

Madre

Mountains

EXPLANATION

sac13-4090 Figure 04a.

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North American Vertical Datum 1983 (NAVD83).

Hydrology sourced from 1:24,000-scale National Hydrography Dataset, 1974-2009.

Place names sourced from USGS Geographic Names Information System, 1974-2009.

Albers Projection, NAD83.

Percentage coarse-grained sediments

Estimated sedimentary texture

0 to 10

10.001 to 20

20.001 to 30

30.001 to 40

40.001 to 50

50.001 to 60

60.001 to 70

70.001 to 80

80.001 to 90

90.001 to 100

Cuyama Valley

groundwater

basin

A

Composite subbasin boundary for

 Basin Characterization Model

Recent alluvium aquifer

Active CUVHM

 model-grid

 boundary

Figure 4. Extent and percentage of coarse-grained deposits for the A, Recent Alluvial aquifer; B, Older Alluvial aquifer; and C, Morales 

Formation aquifer of Cuyama Valley, California.
52 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

Carrizo 33

Plain San Joaquin Valley

Cuyama

Badlands

Caliente Range

Cuyama

River

119°50’ 119°40’ 119°30’ 119°20’ 119°10’

35°

34°

50’

34°

40’

0 5 10 MILES

0 5 10 KILOMETERS

Sierra

Madre

Mountains

EXPLANATION

sac13-0490 Figure 04b.

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North American Vertical Datum 1983 (NAVD83).

Hydrology sourced from 1:24,000-scale National Hydrography Dataset, 1974-2009.

Place names sourced from USGS Geographic Names Information System, 1974-2009.

Albers Projection, NAD83.

0 to 10

10.001 to 20

20.001 to 30

30.001 to 40

40.001 to 50

50.001 to 60

60.001 to 70

70.001 to 80

80.001 to 90

90.001 to 100

Cuyama Valley

groundwater

basin

B

Composite subbasin boundary for

 Basin Characterization Model

Older alluvium aquifer

Percentage coarse-grained sediments

Estimated sedimentary texture

Active CUVHM

 model-grid

 boundary

Figure 4. —Continued
Figures 53

166

166

33

Carrizo 33

Plain San Joaquin Valley

Cuyama

Badlands

Caliente Range

Cuyama

River

119°50’ 119°40’ 119°30’ 119°20’ 119°10’

35°

34°

50’

34°

40’

0 5 10 MILES

0 5 10 KILOMETERS

Sierra

Madre

Mountains

Morales Formation aquifer

EXPLANATION

sac13-0490 Figure 04c.

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North American Vertical Datum 1983 (NAVD83).

Hydrology sourced from 1:24,000-scale National Hydrography Dataset, 1974-2009.

Place names sourced from USGS Geographic Names Information System, 1974-2009.

Albers Projection, NAD83.

0 to 10

10.001 to 20

20.001 to 30

30.001 to 40

40.001 to 50

50.001 to 60

60.001 to 70

70.001 to 80

80.001 to 90

90.001 to 100

Cuyama Valley

groundwater

basin

C

Composite subbasin boundary for

 Basin Characterization Model

Percentage coarse-grained sediments

Estimated sedimentary texture

Active CUVHM

 model-grid

 boundary

Figure 4. —Continued
54 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

W D W D W D W D W D W D W D W D

–40

–30

–20

–10

0

10

20

1940 1950 1960 1970 1980 1990 2000 2010

Cumulative departure of precipitation, in inches per year 

Water year 

Land-ownership periods

 and Farm-well periods

Land-use periods

Selected crop-attributes periods*

sac13-0490_Figure 05_precip departure

Station 436 (2,275) 

Station 402 (2,003) 

Station 221 (2,170)

EXPLANATION

Cumulative departure of precipitation

 and (Altitude, in feet) See figure 6A

 for location of station

Land-use periods by water year—

1 1943–50

2 1951–59

3 1960–69

4 1970–79

5 1980–85

6 1986–92

7 1993–95

1

1 2 3 4 5 6 7 8 9 101112 1314

2 3 4 5 6 7 8 9 10

8 1996–2000

9 2001–09

10 2010

1 1949–55 Figure 18A

2 1956–62 Figure 18B

3 1963–76 Figure 18C

4 1977–79 Figure 18D

5 Figure 19A

6 Figure 19B

9 Figure 20B

10 Figure 20C

11 Figure 21A

11 Figure 21B

1952

1959

1966

1977

1984 1980–84

1985–2000

2005

2006

2007

2000

7 2002 2001–02 Figure 19C

8 2004 2003–04 Figure 20A

2005

2006

2007

2008

2009

2008

13 2009 Figure 21C

14 2010 2010 Figure 22B

Recent precipitation cycles 

D Dry

W Wet

Land-ownership periods by

 water year (see figure 2B

 for location)—

*Crop coefficients and irrigation efficiencies

Figure 5. Cumulative departure of precipitation along with wet-dry periods, land-use map periods, periods of application for land-use, 

land-ownership (WBS) and related farm wells, and selected crop attributes for Cuyama Valley, California.
Figures 55

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 06a_PPT

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

Average precipitation, in inches per year

6.94 to 7.0

>7.0 to 7.5

>7.5 to 8.0

>8.0 to 8.5

>8.5 to 9.0

>9.0 to 9.5

>9.5 to 10.0

>11.0 to 11.5

>11.5 to 12.0

>12.0 to 12.5

>12.5 to 13.0

>13.0 to 13.5

>13.5 to 14.0

>14.0 to 14.5

>10.0 to 10.5

>10.5 to 11.0

>14.5 to 15.0

Station 436

Station 221

Station 402

Rainfall gages

Cuyama Ranch (1948-2008),

 Station 221

CalTrans New Cuyama (1955-2010),

 Station 402

Fire Station (1955-2010),

 Station 436

Figure 6. Average annual A, precipitation, and B, potential evapotranspiration for the simulation period (1949–2010) for Cuyama Valley, 

California.
56 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

 Station 88

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 06b_PET

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

Average potential evapotranspiration, in inches per year

44.6 to 45

>45 to 46

>46 to 47

>47 to 48

>48 to 49

>49 to 50

>51 to 52

>52 to 53

>53 to 54

>54 to 55

>55 to 56

>56 to 57

>50 to 51 >57 

California irrigation management

 information system (CIMIS)—

 Station 88

Figure 6. —Continued
Figures 57

1939 1948 1952 1960 1972 1985 1995 2008

1939—Cuyama 

Valley at 

“Steady-State” 

conditions, only 

one irrigated 

crop—

400 acres of 

potatoes 

1941–1945 

Irrigated acres, 

mostly potatoes 

(3,650 acres), 

irrigated from 

20 wells 

Irrigated farm 

acreage nearly 

doubles by 1952 

to 9,895 acres 

with total volume 

pumped 60,400 

ac-ft per year 

Irrigated farm acreage 

again doubles from 

1952–1972. Over 75 

percent of the acreage 

is in alfalfa (13,627 

acres), and total water 

use over 76,000 ac-ft 

per year (largest 

estimate).

By late 1950s 

Cuyama Valley 

was 4th most 

productive oil 

region in 

California

Another change in 

farming in the 1980s. 

Grain acreage doubles 

from what was once 

alfalfa fields, and 

carrots triple in 

acreage farmed 

By 2008, 

carrots and 

grains 

represent 

over half of 

all farming in 

the valley.

Discovery 

of oil on the 

Russell 

Ranch in 

1948.

Cuyama Valley development timeline

Timeline notes

(Singer and Swarzenski, 1970)

• From 1939–1946, rainfall was

 118% of average. (+PDO*)

• From 1947–1966, rainfall was

 90% of average. (–PDO*)

• From 1977–1999, rainfall was

 119% of average.

• Cuyama Valley produces 17%

 of the United States’ carrots.

1,119

1,844

2,709

4,821

7,324

8,275

8,614

1950

0

10,000

8,000

6,000

4,000

2,000

1960 1970 1980 1990 2000 2010

A

B

Years 1950 and 1960 were estimated using county data 

Total population

*Pacific Decadal Oscillation

sac13-0490 Figure 07ab.

Figure 7. Generalized A, history of water and land-use development through time, and B, population growth for Cuyama Valley, 

California.
58 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166 166

33

33

2

1

3

8 9 7 6 5 4

97

79

78

75 76

45 40

38

24

12

30

32

34

35

47

48 49

53

54 52

51

86

89

57

59 58

41

42

60

81 80

77

74 73

84

71 70

94

88

87

85

72

44

43

22

20 19

67

13

99

92 62

82

17

10

66

64

63

65

61

56 55

39

98 96

95

93

91

90 83

68

69

50

46

37

36

33

29

28

31

27 25 26 23

21

18

16 15

14

11

145 (WWTP)

132

128

124

121120

115

111

112

108

137

144

143142

141

101

130

104

122

127

126

136 135 134

129

113

110

114 109

117

123

107

140

139

138

133

131

125 116 118

119

106

105103

102

100

84

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 08_stream segments

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Stream channel parameter names USGS stream gage and number

Inflow points—Numbers represent

 streamflow identifiers for inflow points

 for runoff from BCM model and from the

 Waste-water Treatment Plant

 (WWTP-145)

Diversion location

QYA

Western zone

North Main zone

South Main zone

QYACC

QOA

QOA-North

QOA-Middle

QOA-South

QYAUC

QYACC

QOA

MOCU

QYA

QYACC

QYA

QYACC

QOA

QYA is Quaternary young alluvium

Sierra Madre Foothills

Ventucopa Uplands

QOA is Quaternary older alluvium

MOUC is Morales unconfined

CC is Cuyama Channel

UC is unconfined

11136501

11136480

11136400

11136500

11136600

11136650

Reyes Creek near Ventucopa—11136480

Wagon Road Creek near Stauffer—11136400

Aliso Canyon Creek near New Cuyama—11136650

Santa Barbara Canyon Creek

 near Ventucopa—11136600

Cuyama River near Ventucopa—11136501

Cuyama River near Ventucopa—11136500

Active CUVHM

 model-grid

 boundary

Figure 8. Distribution of streams with streamflow routing cells and segments, and location of inflows, Cuyama Valley, California.
Figures 59

166 166

33

33

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 09_wells

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Caliente Northern-Main

Cuyuma groundwater basin subregion Well and type

Central Sierra Madre Foothills

Northeast Ventucopa Uplands

Northwestern Sierra Madre Foothills

Northern Ventucopa Uplands

Southern Sierra Madre Foothills

Southern Ventucopa Uplands

Southern-Main

Western Basin

Agriculture

Other agriculture

Domestic

Municipal and industrial

Observation

Virtual

Cuyama

Ventucopa

New

Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Figure 9. Distribution of agricultural, urban-supply, and domestic wells, Cuyama Valley, California.
60 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

sac13-0490_Figure 10_pumpage

0

50

100

150

200

250

Total pumpage, in acre-feet per year

1950 1955 1960 1965 1970 1975 1980 

Calender year 

1985 1990 1995 2000 2005 2010 

EXPLANATION

Municipal and industrial pumpage

Domestic pumpage

Figure 10. Estimated groundwater pumpage from municipal and domestic wells, Cuyama Valley, California.
Figures 61

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

fault

Rehoboth fault

Ozena fault

166 166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 11_gen geol

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

(

Normal fault

Thrust fault

Thrust fault, concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

WWTP

Alluvium - valley fill, alluvium

Conglomerate, limestone

Conglomerate, sandstone

Granite, granite

Metasediments, metasediment

Sandstone - shale, limestone

Sandstone - shale, sandstone

Sandstone, limestone

Volcanics - lava flows, volcanics

Water, water

Generalized Cuyama geology—

 modified from Jennings, 1977

SFR_inflow points

Wastewater treament facility inflow

Watershed for Basin

 Characterization Model analysis

Subwatershed for Basin

 Characterization Model analysis

Minor stream

11136501

11136480

11136400

11136500

11136600

11136650

Reyes Creek near Ventucopa—11136480

Wagon Road Creek near Stauffer—11136400

Aliso Canyon Creek near New Cuyama—11136650

Santa Barbara Canyon Creek

 near Ventucopa—11136600

Cuyama River near Ventucopa—11136501

Cuyama River near Ventucopa—11136500

USGS stream gage and number

Figure 11. The generalized distribution of bedrock, subwatersheds and related inflow points used with the Basin Characterization 

Model (BCM) recharge-runoff estimates for Cuyama Valley.
62 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

0

2

4

6

8

10

0 2 4 6 8 10

CIMIS measured reference evapotranspiration (ETo), in inches per month 

BCM derived potential evapotranspiration, in inches per month 

1 to 1 correlation line

y = 1.1097x 

R2 = 0.96888 

Standard error of the

regression = 0.37

sac13-0490_Figure 12_PET/ETo

Figure 12. Comparison of Basic Characteristics Model (BCM) simulated potential evapotranspiration using the Priestley-Taylor 

approach with measured reference evapotranspiration (ETo) from California Irrigation Management Information System (CIMIS) station 

in Cuyama Valley, California.
Figures 63

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1972197319741975197619771978

Measured streamflow at gage

 (REYES CR NEAR VENTUCOPA , 11136480) 

Estimated streamflow from BCM 

0

1

2

3

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1972197319741975197619771978

Measured streamflow at gage

 (WAGON RD CR NEAR STAUFFER , 11136400) 

Estimated streamflow from BCM 

0

1

2

3

4

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Oct-2009 Nov-2009 Dec-2009 Jan-2010 Feb-2010 Mar-2010 Apr-2010 May-2010 Jun-2010 Jul-2010 Aug-2010 Sep-2010

Measured streamflow at gage

 (SANTA BARBARA CANYON CK NEAR VENTUCOPA , 11136600) 

Estimated streamflow from BCM 

0

0.1

0.2

0.3

0.4

0.5

0.6

0

0.2

0.4

0.6

0.8

1.0

1.2

196319641965196619671968196919701971

Measured streamflow at gage

 (ALISO CANYON CK NEAR NEW CUYAMA, 11136650) 

Estimated streamflow from BCM 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0

2

4

6

8

10

12

2

4

6

8

10

12

14

16

1945194619471948194919501951195219531954195519561957

Measured streamflow at gage

 (CUYAMA RIVER NEAR VENTUCOPA, 11136500) 

Estimated streamflow from BCM 

sac13-0490_Figure 13_streamflow graphs

Basin streamflow, in thousands of acre-feet per month

Basin streamflow, in millions of cubic meters per month

Year except Santa Barbara Canyon

Figure 13. Comparisons of basin discharge, estimated by using the Basin Characterization Model (BCM), with measured streamflow 

for gaged basins in the Cuyama Valley model domain.
64 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

0

20

40

60

80

100

120

140

0

0 5 10 15 20 25 30 35

100 200 300 400 500 600 700 800 900 1,000

Precipitation, in inches per year

Precipitation, in millimeters per year

Simulated streamflow, in thousands of acre-feet per year 

y = 1.6E–08x4.02

R2 = 0.70595 

W D W D W D W D W D W D W D W D

193919421945194819511954195719601963196619691972197519781981198419871990199319961999200220052008

0

20

40

60

80

100

120

Simulated streamflow, in thousands of acre-feet per year 

Year

sac13-0490_Figure 14ab_SF and precip

A

B

Recent

precipitation

cycles 

D

W

Figure 14. Annual streamflow for all Streamflow Routing (SFR) inflows in Cuyama Valley, California, A, for 1939–2009, and 

B, in comparison to precipitation.
Figures 65

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

0

0

40

80

120

160

0

40

0

0

40

80

40

80

Cuyama 120

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 15a_zones&faults

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Caliente Northern-Main

Cuyuma groundwater basin subregion

Central Sierra Madre Foothills

Northeast Ventucopa Uplands

Northwestern Sierra Madre Foothills

Northern Ventucopa Uplands

Southern Sierra Madre Foothills

Southern Ventucopa Uplands

Southern-Main

Western Basin

(

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

A

Estimated drawdown contour

 (1966–1947). 

 Interval is 40 feet

Modified from Singer and Swarzenski, 1970

160

0 5 10 MILES

0 5 10 KILOMETERS

Figure 15. A, drawdown contours for 1947–66; and groundwater-level contours for B, summer 1966, C, spring 2010, and D, summer 2010 

for Cuyama Valley, California.
66 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Caliente Northern-Main

Cuyuma groundwater basin subregion

Central Sierra Madre Foothills

Northeast Ventucopa Uplands

Northwestern Sierra Madre Foothills

Northern Ventucopa Uplands

Southern Sierra Madre Foothills

Southern Ventucopa Uplands

Southern-Main

Western Basin

(

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

B

Modified from Singer and Swarzenski, 1970

sac13-0490 Figure 15b—1966–Wls

Active model-grid boundary

Control point

Water-level altitude, summer 1966 ;

 interval is 50 feet 2,250

0 5 10 MILES

0 5 10 KILOMETERS

2,250

2,150

2,200

2,200

2,3002,350

2,400

2,450

2,500

2,100

2,600

2,550

2,950

2,650

3,000 3,050

2,050

3,100

2,900

3,150 3,200

2,850

3,250

2,750

2,800

2,000

2,700

3,950

4,000

3,850

3,800

3,700

3,750

3,600

3,350 3,300 3,400

3,650

3,550 3,500

3,450

4,100

1,900

1,950

1,850

4,150

4,050

2,050

2,000

2,050

3,900

2,450

2,700

2,000

2,800

2,000

3,400

3,350

3,250

1,900

2,100

3,150

3,100

2,950

2,250

2,900

2,150

3,000

2,850

2,950

3,200

2,850

3,600

2,750

3,050

Figure 15. —Continued
Figures 67

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

2,100

2,050

2,000

2,150

2,200

2,250

2,300

2,350

2,400

2,450

2,500 2,550

3,050

2,600

3,000

2,650

3,100 3,150

2,950

3,200

2,850

2,900

1,950

3,250

2,750

2,800

2,700

3,550

1,900

3,450

3,500

3,600

3,400

3,300

3,350

3,650

1,850

2,550

3,350

2,750

1,900

1,950

3,100

2,900

1,900

3,250

3,150

2,800

2,600

3,200

1,950

2,700

3,300

2,850

2,900

3,400

3,650

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Caliente Northern-Main

Cuyuma groundwater basin subregion

Central Sierra Madre Foothills

Northeast Ventucopa Uplands

Northwestern Sierra Madre Foothills

Northern Ventucopa Uplands

Southern Sierra Madre Foothills

Southern Ventucopa Uplands

Southern-Main

Western Basin

(

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

C

sac13-0490 Figure 15c—2010 spring WLs

Active model-grid boundary

Water-level altitude, spring 2010;

 interval is 50 feet

Control point

2,250

0 5 10 MILES

0 5 10 KILOMETERS

Figure 15. —Continued
68 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

2,100

2,050

2,000

2,000

2,150

2,200

2,250

2,300 2,350 2,400

1,950

2,450

2,550 2,500

3,050

3,000

2,600

31,00

2,950

2,650

3,150

2,900

3,200

3,250

2,850

2,750

2,800

3,600

2,700

3,550

3,500

3,4503,400

3,300

3,350

3,650

1,850

3,350

2,550

3,200

3,050

3,300

2,800

3,150

3,250

2,900

2,600

3,350

2,900

2,500

3,300

3,100

2,850

1,900

1,900

3,150

2,700

3,400

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Caliente Northern-Main

Cuyuma groundwater basin subregion

Central Sierra Madre Foothills

Northeast Ventucopa Uplands

Northwestern Sierra Madre Foothills

Northern Ventucopa Uplands

Southern Sierra Madre Foothills

Southern Ventucopa Uplands

Southern-Main

Western Basin

(

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

D

sac13-0490 Figure 15d—2010 summer WLs

Active model-grid boundary

Water-level altitude, summer 2010;

 interval is 50 feet

Control point

2,250

0 5 10 MILES

0 5 10 KILOMETERS

Figure 15. —Continued
Figures 69

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 16_sprs&seeps

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

Cu

basin

yama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83. Active model-grid

 boundary (No-flow

 boundary except for

 underflow at general-

 head boundary cells)

Upper-most active layer

Spring or seep

Hydraulic flow barrier

Fault

Springs as drains

Weir Seep

Layer 1—Younger alluvium

Layer 2—Older alluvium

Layer 3—Morales Formation

Graveyard Seep

Turkey Trap Fault Seeps

Turkey Trap Hills Seeps

Headquarters Spring

CuyamaRanch_Hiway/CalTransStation

No Name

North Caliente Ranch

St Barb Cyn-ReyesRch/QuatalRdSprBox/Quail

General-head boundary cells

Cuyama River below Greek Ranch

Reyes Creek above Ventucopa

Cuyama River above Ventucopa

0 5 10 MILES

0 5 10 KILOMETERS

Quail Spring

Bitter Spring

(092606P)

Boronville Spring

Quatal Road

Spring

Nettle

Spring

Yellow

Jacket

Spring

Corral Canyon

Tributary

Spring

Cox

Canyon

Spring

Santa Barbara Canyon/

ReyesRanch

Spring

Russell Spring

Caliente

Ranch Spring

NoName Spring

CalTrans

Station Spring

CuyamaRanch

Hiway Spring

Weir Spring

GraveyardNE2Seep

GraveyardNE1Seep

Headquarters

Spring1

Headquarters

Spring2

Figure 16. Distribution of model cells representing no-flow, groundwater underflow, springs, streams, and horizontal groundwater flow 

barrier boundaries in the Cuyama Valley, California.
70 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 17_soils

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

CUVHM model grid soil texture and (capillary fringe)

Active model-grid

 boundary Silty clay (4.9 feet)

Silt (3.3 feet)

Sandy loam (2.5 feet)

Sand (1.6 feet)

0 5 10 MILES

0 5 10 KILOMETERS

Figure 17. Agricultural soils for the Cuyama Valley simplified from Soil Survey Geographic Database (SSURGO) (U.S. Department of 

Agriculture Natural Resources Conservation Service, 2005).
Figures 71

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 18a_VCLU_1952

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

A

1% 

1% 

2

4

5

7

10

18

19

20

21

23

24

0 5 10 MILES

0 5 10 KILOMETERS

<1% (2) 

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

<1% (20) 

<1% (18) 

Land-use classification

and identifying number 3%

3%

4%

29%

45%

12%

Durum wheat

Alfalfa

Other hay

Potatoes

Pasture/grass

Developed/open space

Barren

Deciduous forest

Evergreen forest

Shrubland

Grassland herbaceous

(131)

(141)

(142)

(152)

(171)

(121)

(22)

(36)

(37)

(43)

(62)

Figure 18. Early periods of land-use (virtual crop) groups discretized to the model grid, and pie chart of percentages of total land use 

over the modeled area for A, 1952; B, 1959; C, 1966; and D, 1977 for Cuyama Valley, California.
72 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 18b_VCLU_1959

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

B

1% 

2

4

5

7

10

18

19

20

21

23

24

8 Onions

0 5 10 MILES

0 5 10 KILOMETERS

<1% (2) <1% (5) 

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

<1% 

<1% (20) 

<1% (15, 18) 

Land-use classification

and identifying number

6%

4%

29%

46%

12%

Durum wheat

Alfalfa

Other hay

Potatoes

Pasture/grass

Developed/open space

Barren

Deciduous forest

Evergreen forest

Shrubland

Grassland herbaceous

(131)

(141)

(142)

(152)

(171)

(121)

(22)

(36)

(37)

(43)

(62)

(49)

15 Other tree fruit (73)

Figure 18. —Continued
Figures 73

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 18c_VCLU_1966

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

C

2% 

2% 

<1% (20) 2

4

5

7

10

18

19

20

21

23

24

8 Onions

13 Apples

0 5 10 MILES

0 5 10 KILOMETERS

<1% (2)

<1% <1% 

<1% (13,15,18)

Land-use classification

and identifying number

5%

4%

29%

45%

12%

Durum wheat

Alfalfa

Other hay

Potatoes

Pasture/grass

Developed/open space

Barren

Deciduous forest

Evergreen forest

Shrubland

Grassland herbaceous

(131)

(141)

(142)

(152)

(171)

(121)

(22)

(36)

(37)

(43)

(62)

(49)

(68)

15 Other tree fruit (73)

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 18. —Continued
74 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 18d_VCLU_1977

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

D

0 5 10 MILES

0 5 10 KILOMETERS

<1% (1) 

3% 

<1% (3) 

12% 

13% 

<1% (18) 

4% 

<1% (20) 

12% 

30% 

24% 

1% 

(21)

(22)

(28)

(36)

(61)

(121)

(131)

(141)

(142)

(152)

(171)

(206)

Land-use classification

and identifying number

1 Barley

2 Durum wheat

3 Oats

4 Alfalfa

9 Fallow/idle cropland

18 Developed/open space

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

31 Carrots

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 18. —Continued
Figures 75

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 19a_VCLU_1984

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

A

0 5 10 MILES

0 5 10 KILOMETERS

<1% (1) 

3% 

<1% (3) 

12% 

13% 

4% 

12% 

24% 

29% 

1% 

Land-use classification

and identifying number

<1% (18) 

<1% (20) 

(21)

(22)

(28)

(36)

(61)

(121)

(131)

(141)

(142)

(152)

(171)

(206)

1 Barley

2 Durum wheat

3 Oats

4 Alfalfa

9 Fallow/idle cropland

18 Developed/open space

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

31 Carrots

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 19. Land-use (virtual crop) groups discretized to the model grid, and pie chart of percentages of total land use over the modeled 

area for A, 1984; B, 2000; and C, 2002 for Cuyama Valley, California.
76 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 19b_VCLU_2000

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

B

0 5 10 MILES

0 5 10 KILOMETERS

1 Barley

2 Durum wheat

3 Oats

4 Alfalfa

5 Other hay

6 Dry beans

8 Onions

9 Fallow/idle cropland

12 Peaches

13 Apples

14 Grapes, wine vinyards

16 Walnuts

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

40 Irrigated row vegetables crop

7 Potatoes

Land-use classification and identifying number

(131)

(141)

(142)

(152)

(171)

(204)

(206)

35 Nectarines (218)

(259)

(121)

(21)

(22)

(28)

(36)

(37)

(42)

(43)

(49)

(61)

(67)

(68)

(69)

(76)

15 Other tree fruit (73)

(111)

(258)

17 Open water

39 Irrigated field crops

4% 3% 

1% 

3% 

<1% (5,6,7,8) 

4% <1% (12) 

4% 

2% <1% (15,

 16,17,18) 

12% 

29% 

24% 

5% 

4% 

<1% (39) <1% (40) 

1% 

1% 

<1% (20) 

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 19. —Continued
Figures 77

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 19c_VCLU_2002

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

C

0 5 10 MILES

0 5 10 KILOMETERS

Land-use classification and identifying number

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(121)

(21)

(28)

(36)

(49)

(61)

(67)

(68)

(69)

(76)

(43)

(218)

(257)

(258)

(259)

(260)

35 Nectarines

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

1 Barley

3 Oats

4 Alfalfa

8 Onions

9 Fallow/idle cropland

12 Peaches

13 Apples

14 Grapes, wine vinyards

16 Walnuts

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

7 Potatoes

11 Cherries (66)

15 Other tree fruit (73)

17 Open water (111)

Values on pie charts are in list-order, clockwise 36 Apricots (223)

 from top center and may not equal 100 due to

 rounding of percentages.

2% 

2% 

<1%

<1% (41)

4% 

29% 

24% 

6% 

12% 

11% 

2% <1%

<1% (20)

<1% (12,13) 2% 

1%

1%

1%

<1% (35,36,38) <1% (15,16,17,18)

Figure 19. —Continued
78 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 20a_VCLU_2004

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

A

0 5 10 MILES

0 5 10 KILOMETERS

Land-use classification and identifying number

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(121)

(21)

(28)

(36)

(49)

(61)

(67)

(68)

(69)

(76)

(43)

(218)

(257)

(258)

(259)

(260)

35 Nectarines

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

1 Barley

3 Oats

4 Alfalfa

8 Onions

9 Fallow/idle cropland

12 Peaches

13 Apples

14 Grapes, wine vinyards

16 Walnuts

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

7 Potatoes

15 Other tree fruit (73)

17 Open water (111)

11 Cherries (66)

36 Apricots (223)

2% 1% 

4% 

12% 

29% 

24% 

6% 

11% 

2% 

<1% (35,36,38)

<1% (15,16,17,18)

<1% (41)

1% 

2% 2% 

1% 

<1% (11,12,13)

<1% (20)

<1% (8)

<1% (4)

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 20. 2004–06 periods of land-use (virtual crop) groups discretized to the model grid, and pie chart of percentages of total land 

use over the modeled area for A, 2004; B, 2005; and C, 2006 for Cuyama Valley, California.
Figures 79

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 20b_VCLU_2005

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

B

0 5 10 MILES

0 5 10 KILOMETERS

2% 

1% 

2% 2% 

1% 

4% 

12% 

29% 

24% 

1% 

6% 

11% 

2% 

<1% (41) 

<1% (35,36,38) 

Land-use classification and identifying number <1% (4) 

<1% (8) <1% (11,12) 

<1% (15,16,17,18) 

<1% (20) 

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(218)

(223)

(257)

(258)

(259)

(260)

(121)

(21)

(28)

(36)

(43)

(49)

(61)

(67)

(66)

(68)

(69)

(73)

(111)

(76)

15 Other tree fruit

1

3

4

7

8

9

12

11

13

14

17

16

19

20

21

23

24

18

30

31

35

36

38

39

40

41

Barley

Oats

Alfalfa

Potatoes

Onions

Fallow/idle cropland

Peaches

Cherries

Apples

Grapes, wine vinyards

Open water

Walnuts

Barren

Deciduous forest

Evergreen forest

Shrubland

Grassland herbaceous

Developed/open space

Pistachios

Carrots

Nectarines

Apricots

Forage hay/silage

Irrigated field crops

Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 20. —Continued
80 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 20c_VCLU_2006

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

C

0 5 10 MILES

0 5 10 KILOMETERS

1% 

1% 

4% 

1% 

4% 

12% 

30% 

24% 

1% 

11% 

8% 

1% 

<1% (41) <1%

Land-use classification and identifying number <1% (1)

<1% (8)

<1% <1% (15,17,18)

<1% (11,12)

<1% (20)

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(218)

(257)

(258)

(259)

(260)

(121)

(21)

(28)

(36)

(43)

(49)

(61)

(67)

(66)

(68)

(69)

(73)

(111)

1 Barley

3 Oats

4 Alfalfa

7 Potatoes

8 Onions

9 Fallow/idle cropland

12 Peaches

11 Cherries

13 Apples

14 Grapes, wine vinyards

15 Other tree fruit

17 Open water

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

35 Nectarines

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 20. —Continued
Figures 81

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 21a_VCLU_2007

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

A

0 5 10 MILES

0 5 10 KILOMETERS

1% 

4% 

2% 

1% 

<1% (15,17,18)

4% <1% (20)

<1% (11,12,13)

<1% (8)

<1% (3,4)

<1% (1)

<1% (40,41)

12% 

30% 

24% 

1% 

10% 

1% 

<1%(32,34)

8% 

Land-use classification and identifying number

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(209)

(214)

(218)

(257)

(258)

(259)

(260)

(121)

(21)

(28)

(22)

(36)

(43)

(49)

(61)

(67)

(66)

(68)

(69)

(73)

(111)

1 Barley

3 Oats

2 Durum wheat

4 Alfalfa

7 Potatoes

8 Onions

9 Fallow/idle cropland

12 Peaches

11 Cherries

13 Apples

14 Grapes, wine vinyards

15 Other tree fruit

17 Open water

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

32 Cantaloupes

34 Broccoli 

35 Nectarines

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 21. 2007–09 periods of land-use (virtual crop) groups discretized to the model grid, and pie chart of percentages of total land 

use over the modeled area for A, 2007; B, 2008; and C, 2009 for Cuyama Valley, California.
82 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 21b_VCLU_2008

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

B

0 5 10 MILES

0 5 10 KILOMETERS

1% 1% 

2% 

1% <1% (15,17,18)

<1% (11,12,13)

<1% (8)

<1% (2,4)

<1% (41)

<1% (35.38)

4% <1% (20) 

12% 

30% 24% 

1% 

6% 

11% 

5% 

Land-use classification and identifying number

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(218)

(257)

(258)

(259)

(260)

(121)

(21)

(22)

(36)

(43)

(49)

(61)

(67)

(66)

(68)

(69)

(73)

(111)

1 Barley

2 Durum wheat

4 Alfalfa

7 Potatoes

8 Onions

9 Fallow/idle cropland

12 Peaches

11 Cherries

13 Apples

14 Grapes, wine vinyards

15 Other tree fruit

17 Open water

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

35 Nectarines

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 21. —Continued
Figures 83

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 21c_VCLU_2009

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

C

0 5 10 MILES

0 5 10 KILOMETERS

5% 

1% 

3% 1% 

4% <1% (20)

<1% (15,17,18)

<1% (12,13)

12% 

30% 

24% 

1% 

10% 

1% 

2% 3% 

<1% (41)

<1% (35,37,38)

Land-use classification and identifying number

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(209)

(214)

(218)

(244)

(257)

(258)

(259)

(260)

(121)

(21)

(22)

(36)

(43)

(61)

(67)

(68)

(69)

(73)

(111)

1 Barley

2 Durum wheat

4 Alfalfa

7 Potatoes

9 Fallow/idle cropland

12 Peaches

13 Apples

14 Grapes, wine vinyards

15 Other tree fruit

17 Open water

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

32 Cantaloupes

34 Broccoli

35 Nectarines

37 Cauliflower

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 21. —Continued
84 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 22a_RCLU_2010

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

A

0 5 10 MILES

0 5 10 KILOMETERS

Land-use classification and identifying number

1% 

<1% (2,4,7,8) <1% (41) 

<1% (33,35,38) <1% (10,12,13,14,15) 

<1% (20) 

1% 2% 2% 

4% 

35% 

39% 

7% 

1% 1% 

4% 

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(218)

(257)

(258)

(259)

(260)

(121)

(21)

(22)

(36)

(43)

(49)

(61)

(67)

(62)

(68)

(69)

(73)

(111)

1 Barley

2 Durum wheat

4 Alfalfa

7 Potatoes

8 Onions

9 Fallow/idle cropland

12 Peaches

10 Pasture/grass

13 Apples

14 Grapes, wine vinyards

15 Other tree fruit

17 Open water

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

33 Olives (211)

35 Nectarines

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 22. A, actual major categories of land-use for 2010; B, equivalent land-use (virtual crop) groups discretized to the model grid, 

and pie chart of percentage of total land use over the entire model area; and C, changes in percentages of selected land use through 

time, Cuyama Valley, California.
Figures 85

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 22b_VCLU_2010

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 MILES

0 5 10 KILOMETERS

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

B

3% 2% 

1% 

2% 

1% 1% 

4% <1% (20)

<1% (15,17,18)

<1% (12,13)

<1% (2,4) <1% (41)

12% 

29% 

24% 

<1% 3% 

1% 

14% 

Land-use classification and identifying number

<1% (33,35,38)

(131)

(141)

(142)

(152)

(171)

(204)

(206)

(218)

(257)

(258)

(259)

(260)

(121)

(21)

(22)

(36)

(43)

(49)

(61)

(67)

(62)

(68)

(69)

(73)

(111)

1 Barley

2 Durum wheat

4 Alfalfa

7 Potatoes

8 Onions

9 Fallow/idle cropland

12 Peaches

10 Pasture/grass

13 Apples

14 Grapes, wine vinyards

15 Other tree fruit

17 Open water

19 Barren

20 Deciduous forest

21 Evergreen forest

23 Shrubland

24 Grassland herbaceous

18 Developed/open space

30 Pistachios

31 Carrots

33 Olives (211)

35 Nectarines

38 Forage hay/silage

39 Irrigated field crops

40 Irrigated row vegetables crop

Semiagricultural (livestock

feedlots,diaries, poultry farms) 

41

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 22. —Continued
86 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

sac13-0490_Figure 22c_LU_%_year

0

2

4

6

8

10

12

14

1952 1959 1966 1977 1984 2000 2002 2004 2005 2006 2007 2008 2009 2010 

Land-use percent 

Year 

Land-use percentages by year—See figure 5 and 18–22 

EXPLANATION 

Field crops

Alfalfa

Potatoes

Onions

Orchards

Grapes

Carrots

Barren

Walnuts, pistachios and olives

C

Figure 22. —Continued
Figures 87

Forest and Shrubland

 (20,21,22,23)

16

17 Grassland Herbaceous (24)

Crop Name

Irrigated Row and

 Field Crops (32,39,40)

13

Farmland and other

 local (25,26,27,28,29)

14

Open water and Semi-

 agricultural (livestock

 feedlots, dairies, 

 poultry farms) (17,41)

15

Crop Name

5 Dry Beans (6)

6 Potatoes and Onions (7,8)

8 Carrots (31)

Cauliflower and

 Broccoli (34,37)

10

Crop Name

Barley, Durum Wheat,

 and Oats (1,2,3)

3

4 Alfalfa and other hay (4,5)

7 Pasture/Grass (10)

12 Forage Hay/Silage (38)

Crop Name

Cherries, Apples, Walnuts,

 and other fruit trees

 (11,13,15,16)

1

Pistachios, Nectarines,

 Peaches, and Apricots

 (12,30,35,36)

2

9 Grapes, Wine Vinyards (14)

11 Olives (33)

Crop Name

EXPLANATION

sac13-0490_Figure 23abcde_crop Kc

0

0.2

0.4

0.6

0.8

1

1.2

0

0.2

0.4

0.6

0.8

1

1.2

0

0.2

0.4

0.6

0.8

1

1.2

0

0.2

0.4

0.6

0.8

1

1.2

0

0.2

0.4

0.6

0.8

1

1.2

Crop coefficient, dimensionless

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Calendar month

A

B

C

D

E

Figure 23. Monthly crop coefficients for A, orchards; B, grains and hay; C vegetables; D, general land use; and E, native vegetation in 

the Cuyama Valley, California.
88 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Caliente Northern-Main

Cuyuma groundwater basin subregion

Central Sierra Madre Foothills

Northeast Ventucopa Uplands

Northwestern Sierra Madre Foothills

Northern Ventucopa Uplands

Southern Sierra Madre Foothills

Southern Ventucopa Uplands

Southern-Main

Western Basin

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

Ref Pt. 2

Ref Pt. 3

Ref Pt. 4

Ref Pt. 1

Ref Pt. 5

VCST

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 24a_Aquifer tests

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

USGS-monitoring site

Pump-test site

InSAR reference point

Continuous GPS station

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Drains

Weir Seep

Graveyard Seep

Turkey Trap Fault Seeps

Turkey Trap Hills Seeps

Headquarters Spring

CuyamaRanch_Hiway/CalTransStation

No Name

North Caliente Ranch

St Barb Cyn-ReyesRch/QuatalRdSprBox/Quail

CUHS

0 5 10 MILES

0 5 10 KILOMETERS

CVKR 1-4

CVBR 1-4

CVFR 1-4

A

Figure 24. A, locations of wells with pumping tests, and the distribution of parameter zones used for model calibration of hydraulic 

properties for B, model layer 1, C, model layer 2, and D, model layer 3 in the Cuyama Valley, California.
Figures 89

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 24b_hyd_props_Ly1

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

B

Fault

NMZQYACC (2)

Layer 1 hydraulic property zone and (zone number)—

Northern Main Western Sierra Madre Foothills Ventucopa Uplands 

Southern Main

NMZ_QYA (1)

SMZ_QYACC (5)

SMZ_QYA (4)

WZ_QYACC (9)

VC_QYACC (6)

VC_QYAUC (7)

WZ_QYA (8) SMFHQYA (3)

(See table 13 for description of acronyms)

0 5 10 MILES

0 5 10 KILOMETERS

Figure 24. —Continued
90 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 24c_hyd_props_Ly2

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

C

Fault

NMZ_QOAC (18)

North Main Western Sierra Madre Foothills Ventucopa Uplands 

South Main

NMZ_QOA (10)

SMZ_QOAC (20)

WZ_QOA_S (16)

WZ_QOAC (22) SMFH_QOAC (19)

WZ_QOA_N (15)

SMFH_QOAM (12)

SMFH_QOAN (11)

SMFH_QOAS (13)

VC_QOAN (14)

VC_QOAC (21)

QOA_PHT (23)

Layer 2 hydraulic property zone and (zone number)—

(See table 13 for description of acronyms)

0 5 10 MILES

0 5 10 KILOMETERS

Figure 24. —Continued
Figures 91

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 24d_hyd_props_Ly3

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Active model-grid

 boundary

D

Fault

Ventucopa Uplands 

VC_MOUC (17)

Morales Formation 

MO_C (24)

Layer 3 hydraulic property zone and (zone number)—

(See table 13 for description of acronyms)

0 5 10 MILES

0 5 10 KILOMETERS

Figure 24. —Continued
92 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

R28W R27W R26W R25W R24W R23W R22W

T

11

N

T

10

N

T

9

N

T

8

N

T

7

N

166

166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

sac13-0490_Figure 25_WL sites

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama Valley

groundwater

basin

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Caliente Northern-Main

Cuyuma groundwater basin subregions Selected observation well shown on figure 26— All wells used for model

 observations shown on figure 27a

 Well used for vertical

 water-level difference

 as shown on figure 27c

Central Sierra Madre Foothills

Northeast Ventucopa Uplands

Northwestern Sierra Madre Foothills

Northern Ventucopa Uplands

Southern Sierra Madre Foothills

Southern Sierra Madre Foothills

Central Sierra Madre Foothills

Northern Sierra Madre Foothills

Southern Ventucopa Uplands

Southern Ventucopa Uplands

Southern-Main

Western Basin

Western Basin

Caliente Northern-Main

Southern-Main

0 5 10 MILES

0 5 10 KILOMETERS

Figure 25. Calibration data sites of wells for groundwater levels and water-level differences for the Cuyama Valley Hydrologic Model, 

Cuyama Valley, California.
Figures 93

1,850

1,930

1,910

1,830

1,850

1,870

1,890

1,875

1,900

1,925

1,950

2,160

2,180

2,220

2,240

2,200

2,280

2,260

2,300

2009 2010 2009 2010

2008 2009 2010

USGS observation wells

Water-level elevation, in feet above NAVD 88 Water-level elevation, in feet above NAVD 88

Year

Year Year

A

sac13-0490_Figure 26a1_USGS_wells

CVFR#1–4 (9N/25W-3B1) CVBR#1–4 (10N/26W-34N1)

CVKR#1–4 (10N/25W-19P2–5)

CVBR#1

CVBR#4

CVBR#3

CVBR#2

Measured water level Measured water level Simulated water level

CVKR#1

CVKR#4

CVKR#3

CVKR#2

Measured water level Simulated water level

Simulated water level

EXPLANATION EXPLANATION

EXPLANATION See figure 25 for location of wells

CVFR#4

CVFR#3

CVFR#2

CVFR#1

Figure 26. Simulated and measured hydrographs for selected wells in A, Main-zone, B, Ventucopa Upland, and C, Sierra Madre 

Foothills subregions, Cuyama Valley, California.
94 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

1,900

1,950

2,000

2,050

2,100

2,000

1,900

1,950

2,050

2,100

2,150

2,200

2,250

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

Northern-Main zone

Water-level elevation, in feet above NAVD 88

Year

Year

A (Continued)

sac13-0490_Figure 26a_Northern-Main Zone

10N/26W-9H1

10N/25W-17K1

10N/26W-4R1

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

1,900

1,950

2,000

1,750

1,800

1,850

2,050

2,150

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

Recent precipitation cycles 

Dry

Wet

Observed

Simulated

EXPLANATION

See figure 25 for location of wells

Figure 26. —Continued
Figures 95

1,850

1,900

1,950

2,000

1,800

1,850

1,750

1,900

1,950

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

Western Basin zone

Water-level elevation, in feet above NAVD 88

Year

Year

A

10N/27W-11C1

10N/27W-3L2

10N/27W-11A3

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

1,900

1,950

1,850

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

Recent precipitation cycles 

Dry

Wet

Observed

Simulated

EXPLANATION

See figure 25 for location of wells

A (Continued)

sac13-0490_Figure 26a_Western Basin Zone

Figure 26. —Continued
96 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

2,050

2,100

1,950

2,000

2,150

2,250

2,200

2,050

2,050

2,150

1,950

2,250

2,350

2,300

2,200

2,100

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

Southern-Main zone

Water-level elevation, in feet above NAVD 88

A (Continued)

sac13-0490_Figure 26a_Southern-Main Zone

10N/26W-18F1

10N/25W-24E1

10N/26W-22A1

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

1,800

1,850

1,900

2,200

2,000

2,100

2,150

2,050

1,950

1,800

1,850

1,900

2,200

2,000

2,100

2,150

Year

10N/26W-16Q1

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

1,900

1,950

1,800

1,850

2,050

2,150

2,250

2,200

2,100

2,000

2,300

10N/25W-21G1

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

Year

10N/26W-15N1

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

2,050

2,000

2,150

2,100

1,900

1,950

1,850

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

Recent precipitation cycles 

Dry Wet

Observed

Simulated EXPLANATION See figure 25 for location of wells

Figure 26. —Continued
Figures 97

2,050

1,950

2,050

2,150

2,250

1,800

1,850

1,900

2,000

2,300

2,200

2,100

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

Southern-Main zone

Water-level elevation, in feet above NAVD 88

A (Continued)

sac13-0490_Figure 26a_Southern-Main Zone

10N/25W-29P1

10N/25W-30F1

10N/25W-30P1

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

2,050

2,150

2,250

2,300

2,200

2,100

2,200

2,000

2,100

2,150

2,250

2,050

1,950

1,850

1,900

1,800

2,200

2,000

2,100

2,150

2,250

Year

10N/26W-27N1

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

1,800

1,900

1,950

1,850

2,050

2,150

2,250

2,200

2,100

2,000

10N/25W-23E1

1,900

1,950

2,050

2,150

2,250

2,200

2,100

2,000

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

Year

10N/25W-29K2

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

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

Recent precipitation cycles 

Dry Wet

Observed

Simulated EXPLANATION See figure 25 for location of wells

Figure 26. —Continued
98 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

2,050

1,950

1,850

2,150

1,900

1,800

1,700

1,750

2,000

2,100

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

Water-level elevation, in feet above NAVD 88

10N/25W-21Q2

10N/26W-17R1

10N/26W-24J4

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

2,050

Year

1,950

1,850

1,900

1,750

1,800

2,000

1,900

1,950

1,850

1,750

1,800

2,050

2,150

2,200

2,100

2,000

Year

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

10N/25W-20H1

2,050

2,000

2,150

2,250

2,300

2,100

2,200

1,900

1,950

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

Recent precipitation cycles 

Dry Wet

Observed

Simulated

EXPLANATION

See figure 25 for location of wells

Southern-Main zone

A (Continued)

sac13-0490_Figure 26a6_Southern-Main Zone

Figure 26. —Continued
Figures 99

2,750

2,700

2,650

2,800

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

Water-level elevation, in feet above NAVD 88

9N/24W-33M1

9N/24W-19Q1

8N/24W-8L1

2,650

2,600

2,550

2,700

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

2,950

2,850

2,900

Year

2,750

2,800

9N/25W-13B1

2,950

2,850

3,050

3,000

2,900

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

Year

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

Recent precipitation cycles 

Dry Wet

Observed

Simulated

EXPLANATION

See figure 25 for location of wells

sac13-0490_Figure 26a_Southern-Main Zone

Southern Ventucopa Uplands B

Figure 26. —Continued
100 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

2,150

2,050

2,250

2,000

2,100

2,200

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

Water-level elevation, in feet above NAVD 88

9N/26W-4J1

10N/26W-20M1

10N/27W-12R1

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

2,400

2,500

2,600

2,550

2,450

Year

1,900

1,950

2,050

2,100

2,000

2,300

2,200

2,250

2,250

10N/27W-11H1

1,900

1,950

2,050

2,150

2,200

2,100

2,000

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

Year

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

Recent precipitation cycles 

Dry Wet

Observed

Simulated

EXPLANATION

See figure 25 for location of wells

Northern Sierra Madre Foothills

C

sac13-0490_Figure 26c1_Northern Sierra Madre Foothills

Figure 26. —Continued
Figures 101

2,650

2,550

2,750

2,600

2,700

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

Water-level elevation, in feet above NAVD 88

9N/26W-1F2-3

9N/25W-11R2

9N/25W-2N1

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

2,400

2,500

2,600

2,550

2,650

2,450

Year

2,300

2,250

2,250

2,400

2,500

2,600

2,550

2,450

2,300

2,250

2,250

Year

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

Recent precipitation cycles 

Dry Wet

Observed

Simulated

EXPLANATION

See figure 25 for location of wells

Central Sierra Madre Foothills

Southern Sierra Madre Foothills

C (Continued)

sac13-0490_Figure 26c2_Central-Southern Sierra Madre Foothills

1F2 data 1F3 data

Figure 26. —Continued
102 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

0 

5

10

15

20

–100 –80 –60 –40 –30 –20 –15 –10 –5 0 5 10 15 20 30 40 60 80 100 150

Percent of observed values 

Distribution in residual groundwater levels, in feet

A

sac13-0490_Figure 27a Residual Groundwater Level Errors

Figure 27. A, histogram of distribution of water-level residuals (observed minus simulated) for the Cuyama Valley hydrologic model 

(CUVHM) model, B, correlation graph by subregions of measured versus simulated water levels, and C, correlation between simulated 

and measured vertical water-level differences for selected wells, Cuyama Valley, California.
Figures 103

B

1,800

1,900

2,000

2,100

2,200

2,300

2,400

2,500

2,600

2,700

2,800

2,900

3,000

3,100

3,200

1,800 1,900 2,000 2,100 2,200 2,300 2,400 2,500

Observed heads, in feet

Simulated heads, in feet

2,600 2,700 2,800 2,900 3,000 3,100 3,200

1 to 1 correlation line

sac13-0490_Figure 27b Obs vs Sim heads

Figure 27. —Continued
104 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

C

–60

–50

–40

–30

–20

–10

0

10

20

30

40

50

–60 –50 –40 –30 –20 –10 0 10 20 30 40 50

Measured water-level difference, in feet 

Simulated water-level difference, in feet 

1 to 1 correlation line

sac13-0490_Figure 27c Vertical WL differences

Figure 27. —Continued
Figures 105

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

2,250

2,150

2,200

2,200

2,3002,350

2,400

2,450

2,500

2,100

2,600

2,550

2,950

2,650

3,000 3,050

2,050

3,100

2,900

3,150 3,200

2,850

3,250

2,750

2,800

2,000

2,700

3,950

4,000

3,850

3,800

3,700

3,750

3,600

3,350 3,300 3,400

3,650

3,550 3,500

3,450

4,100

1,900

1,950

1,850

4,150

4,050

2,050

2,000

2,050

3,900

2,450

2,700

2,000

2,800

2,000

3,400

3,350

3,250

1,900

2,100

3,150

3,100

2,950

2,250

2,900

2,150

3,000

2,850

2,950

3,200

2,850

3,600

2,750

3,050

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

A

sac13-0490 Figure 28a—1966 summer WLs

Active model-grid boundary

Water-level altitude, summer 1966;

 interval is 50 feet

Simulated water-level altitude, summer 1966;

 interval is 50 feet

2,250

 <1,850

 >1,850 to 1,900

 >1,900 to 1,950

 >1,950 to 2,000

 >2,000 to 2,050

 >2,050 to 2,100

 >2,100 to 2,150

 >2,150 to 2,200

 >2,200 to 2,250

 >2,250 to 2,300

 >2,300 to 2,350

 >2,350 to 2,400

 >2,400 to 2,450

 >2,450 to 2,500

 >2,500 to 2,550

 >2,550 to 2,600

 >2,600 to 2,650

 >2,650 to 2,700

 >2,700 to 2,750

 >2,750 to 2,800

 >2,800 to 2,850

 >2,850 to 2,900

 >2,900 to 3,000

 >3,000 to 3,100

 >3,100

0 5 10 MILES

0 5 10 KILOMETERS

Figure 28. Comparison of the contoured measured water levels with simulated water levels A, for fall 1966, B, for spring 2010, and 

C, for fall 2010, Cuyama Valley, California.
106 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

2,100

2,050

2,000

2,150

2,200

2,250

2,300

2,350

2,400

2,450

2,500 2,550

3,050

2,600

3,000

2,650

3,100 3,150

2,950

3,200

2,850

2,900

1,950

3,250

2,750

2,800

2,700

3,550

1,900

3,450

3,500

3,600

3,400

3,300

3,350

3,650

1,850

2,550

3,350

2,750

1,900

1,950

3,100

2,900

1,900

3,250

3,150

2,800

2,600

3,200

1,950

2,700

3,300

2,850

2,900

3,400

3,650

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

B

sac13-0490 Figure 28b—2010 spring WLs

Active model-grid boundary

Water-level altitude, spring 2010;

 interval is 50 feet

Simulated water-level altitude, spring 2010;

 interval is 50 feet

2,250

>1,750 to 1,800

>1,850 to 1,850

 <1,750

 >1,850 to 1,900

 >1,900 to 1,950

 >1,950 to 2,000

 >2,000 to 2,050

 >2,050 to 2,100

 >2,100 to 2,150

 >2,150 to 2,200

 >2,200 to 2,250

 >2,250 to 2,300

 >2,300 to 2,350

 >2,350 to 2,400

 >2,400 to 2,450

 >2,450 to 2,500

 >2,500 to 2,550

 >2,550 to 2,600

 >2,600 to 2,650

 >2,650 to 2,700

 >2,700 to 2,750

 >2,750 to 2,800

 >2,800 to 2,850

 >2,850 to 2,900

 >2,900 to 3,000

 >3,000 to 3,100

 >3,100

0 5 10 MILES

0 5 10 KILOMETERS

Figure 28. —Continued
Figures 107

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

2,100

2,050

2,000

2,000

2,150

2,200

2,250

2,300 2,350 2,400

1,950

2,450

2,550 2,500

3,050

3,000

2,600

31,00

2,950

2,650

3,150

2,900

3,200

3,250

2,850

2,750

2,800

3,600

2,700

3,550

3,500

3,4503,400

3,300

3,350

3,650

1,850

3,350

2,550

3,200

3,050

3,300

2,800

3,150

3,250

2,900

2,600

3,350

2,900

2,500

3,300

3,100

2,850

1,900

1,900

3,150

2,700

3,400

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

C

sac13-0490 Figure 28c—2010 summer WLs

Active model-grid boundary

Water-level altitude, summer 2010;

 interval is 50 feet

Simulated water-level altitude, summer 2010;

 interval is 50 feet

2,250

0 5 10 MILES

0 5 10 KILOMETERS

>1,750 to 1,800

>1,850 to 1,850

 <1,750

 >1,850 to 1,900

 >1,900 to 1,950

 >1,950 to 2,000

 >2,000 to 2,050

 >2,050 to 2,100

 >2,100 to 2,150

 >2,150 to 2,200

 >2,200 to 2,250

 >2,250 to 2,300

 >2,300 to 2,350

 >2,350 to 2,400

 >2,400 to 2,450

 >2,450 to 2,500

 >2,500 to 2,550

 >2,550 to 2,600

 >2,600 to 2,650

 >2,650 to 2,700

 >2,700 to 2,750

 >2,750 to 2,800

 >2,800 to 2,850

 >2,850 to 2,900

 >2,900 to 3,000

 >3,000 to 3,100

 >3,100

Figure 28. —Continued
108 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

166

33

Ref Pt. 1 Ref Pt. 2 Ref Pt. 3

Ref Pt. 4

Ref Pt. 5

VCST

A

sac13-0490_Figure 29a_InSAR conventional

Relative 

Subsidence Uplift

+28 mm 0 −28mm

Fault

Reference point

Continuous GPS site

Cuyama groundwater

 basin boundary

EXPLANATION

mm is millimeter

0

0 1 2 3 4 5 MILES

1 2 3 4 5 KILOMETERS

CUHS

119°45’ 119°40’ 119°35’ 119°30’

35°0’

34°55’

34°50’

~25mm subsidence

February 7, 2008 to April 17, 2008

Measured

Simulated

Relative vertical displacement

2000 2002 2004 2006 2008 2010

Relative vertical

displacement, in feet

Relative vertical displacement, in feet Relative vertical displacement, in feet

2000 2002 2004 2006 2008 2010

Year

0

0.1

0.2

0.3

-0.1

–0.15

–0.10

–0.05

0

0.05

0.10

PInSAR-CUHS 

PBO- CUHS 

PBO- VCST 

2000 2002 2004 2006 2008 2010

PInSAR-Ref Pt. 5 

PInSAR-Ref Pt. 3

2000 2002 2004 2006 2008 2010

2000 2002 2004 2006 2008 2010

Year

Year

Relative vertical displacement, in feet

Year

–0.03

0

0.05

0.10

0.15

0.20

–0.03

0

0.05

0.10

0.15

0.20

–0.03

0

0.05

0.10

0.15

0.20

–0.03

0

0.05

0.10

0.15

0.20

–0.03

0

0.05

0.10

0.15

0.20

PInSAR-Ref Pt. 4

PInSAR-Ref Pt. 1 PInSAR-Ref Pt. 2

2000 2002 2004 2006 2008 2010 Year 2000 2002 2004 2006 2008 2010

Figure 29. Historical subsidence as A, map of seasonal InSAR with graphs of simulated and measured time series for selected 

locations of relative land-surface deformation from Plate-Boundary Observation (PBO) sites and Point InSAR targets, and B, simulated 

total subsidence 1950–2010 for the calibrated hydrologic flow model, Cuyama Valley, California.
Figures 109

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

Cuyama

Badlands

fault

Rehoboth fault

Ozena fault

166 166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama River

Caliente Range

Mts

EXPLANATION

0 5 10 Miles

0 5 10 Kilometers

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

B

sac13-0490 Figure 29b—subsidence

Active model-grid

 boundary

Simulated subsidence, in feet

 negative values are uplift

–0.2559 to –0.1

>–0.1 to 0

>0 to 0.10

>0.10 to 0.20

>0.20 to 0.30

>0.30 to 0.40

>0.40 to 0.50

>0.50 to 0.60

>0.60 to 0.70

>0.70 to 0.80

>0.80 to 0.90

>0.90 to 1.00

>1.00 to 1.10

>1.10 to 1.20

>1.20 to 1.59

Figure 29. —Continued
110 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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

Water year

Simulated pumpage, in thousands of acre-feet per year

0

10

20

30

40

50

60

70

80

100

90

sac13-0490_Figure 30 sim pumpage

EXPLANATION

Pumpage Recent precipitation cycles 

Dry

Wet

D W D W D W D W D W D W D W D

Simulated agricultural 

Reported total

Figure 30. Reported and simulated agricultural pumpage for Cuyama Valley, California.
Figures 111

0

500

1,000

1,500

2,000

2,500

3,500

3,000

4,000

Composite parameter sensitivities, dimensionless

Parameter name

Runoff

Parameter groups—

Irrigation

Storage property multiplier

Storage property

Streambed conductivity

EXPLANATION

Hydraulic conductivity

Hydraulic conductivity multipliers

Initial water-level multiplier

See tables 14 and 15 for names and explanations of all parameter identifiers.

fieswp_ntv 

kc_qya 

scl_hedly3 

hk_smz_qya 

sy1_fac 

phi_crs 

ss_smz_qya 

phi_fin_mo 

ss_vcqyacc 

hk_nmz_qya 

scl_effdsu 

hk_wz_qoan 

kc_qoa 

scl_effdsp 

hk_wz_qoac 

kc_mo 

vk_smfhqya 

qoa_ske 

hk_vc_qoac 

phi_fin_ao 

vksmfhqoas 

vk_vcqyauc 

qoa_skvb 

vc_qoas 

wvc_qyacc 

hk_mouc 

vc_qyacc 

ss_smfhqya 

qya_ske 

vk_nmzqoac 

vc_mouc 

hksmfhqoam 

ss_mo_c 

hksmzqyacc 

vk_wzqyacc 

hk_wz_qya 

vk_vc_qoan 

hk_smfhqya 

wwz_qyacc 

hk_nmz_qoa 

hk_smzqoac 

phi_crs_ao 

vksmfhqoac 

hk_vcqyacc 

flw84 

vksmfhqoan 

run84 

vc_qyauc 

vk_nmz_qya 

run113 

qya_skvb 

flw113 

sbc_flt1 

rch84 

rhf_flt 

rch113 

crt_hd_03 crt_hd_02 

sac13-0490_Figure 31_Composite sensitivities

Figure 31. Relative composite sensitivity of computed observations at calibration points to changes in selected parameters from 

analysis with PEST.
112 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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

Water year

Total landscape budget, in thousands of acre-feet per year

0

–50

–100

–150

–200

–250

–300

150

200

250

300

50

100

FLOW INTO LANDSCAPE SYSTEM FLOW OUT OF LANDSCAPE SYSTEM 

sac13-0490_Figure 32a total landscape budget

EXPLANATION

Pumpage 

Precipitation 

Evapotranspiration from groundwater 

Runoff 

Evapotranspiration from irrigation 

Evapotranspiration from precipitation 

Deep percolation 

A

Recent precipitation cycles 

Dry

Wet

D W D W D W D W D W D W D W D

Figure 32. Hydrologic budget for the landscape with A, the temporal distribution of total landscape inflows and outflows, and 

B, average annual components of farm budget of the simulated landscape flow system within the Cuyama Valley Hydrologic Model

(CUVHM), Cuyama Valley, California.
Figures 113

53% 

44% 

2% 1% 

Average landscape inflows,

 in percent

Average landscape outflows,

 in percent

Precipitation 

Groundwater well pumping (irrigation)

 deliveries 

Evaporation uptake directly 

 from groundwater 

Transpiration uptake directly 

 from groundwater 

B

2% 

18% 

2% 

27% 

18% 

25% 

8% 

Evaporation from irrigation 

Evaporation from precipitation 

Evaporation from groundwater 

Transpiration from irrigation 

Transpiration from precipitation 

Transpiration from groundwater 

Overland runoff 

Deep percolation 

<1%

(Transpiration

from groundwater ) 

sac13-00490_Figure 32b ins and outs_pie

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 32. —Continued
114 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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

Water year

Net groundwater budget, in thousands of acre-feet per year

0

–50

–100

–150

–200

50

100

Interbed storage Spring flow [drains] 

Rural-residential pumpage 

Multi-aquifer well 

Agricultural pumpage 

Farm net recharge 

Storage 

GW - INFLOW GW - OUTFLOW 

sac13-0490_Figure 33a net gw budget

EXPLANATION

Recent precipitation cycles 

Dry

Wet

D W D W D W D W D W D W D W D

A

Figure 33. A, the simulated net flow of groundwater in the hydrologic cycle, B, average annual components of simulated groundwater 

flow, and C, the cumulative change in storage and D, changes in groundwater storage, Cuyama Valley, California.
Figures 115

68% 

25% 

<1%

(Multi-node wells) 

<1%

(General-head

boundaries) 

1% 

6% 

Storage 

Groundwater underflow

 (general-head boundaries)

Stream leakage 

Multi-node wells

Interbed storage

Farm net recharge

38% 

1% 

3% 

9% 

13% 

31% 

5% 

Storage 

Wells 

Spring discharges (drains) 

Groundwater underflow

 (general-head boundaries)

Stream leakage 

Multi-node wells

Interbed storage

Farm wells 

Farm net recharge

Cumulative inflows,

 in percent

Cumulative outflows,

 in percent

<1% 

(Interbed storage) <1% (Wells) 

B

sac13-00490_Figure 33b ins and outs_pie

Values on pie charts are in list-order, clockwise

 from top center and may not equal 100 due to

 rounding of percentages.

Figure 33. —Continued
116 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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

Water year

–1,000

–1,500

–2,000

–2,500

500

0

–500

Net cumulative change in total storage, in thousands of acre-feet

C

Total change in storage 

Main zone

Sierra Madre Foothills

Ventucopa uplands

Cumulative storage by area

sac13-0490_Figure 33C storage change

EXPLANATION

Recent precipitation cycles 

Dry

Wet

D W D W D W D W D W D W D W D

Figure 33. —Continued
Figures 117

GROUNDWATER-FLOW BUDGET SUMMARY

Source

Time period (Water years) 1950–20104 2000–20105

Inflows:

Underflow (GU): 3,700 3,100

Direct infiltration (DI)7 5,600 3,100

Streamflow infiltration (SI) 27,500 30,300

Total recharge (DI+SI): 33,100 33,400

Total inflows: 67,200 68,200

Outflows:

Storage accretion:

Storage depletion:

0 0

34,100 34,800

Springs as drains: 1,000 600

Domestic pumpage: 20 10

Total outflows: 70,100 68,900

Valley wide

2005 (Wet)

700

16,600

93,600

110,200

164,210

54,010

0

700

10

123,000

2009 (Dry)

500

100

16,700

16,800

62,660

0

45,860

500

10

107,560

Water-supply pumpage: 90 190 180 1900

Agricultural pumpage: 65,300 68,100 68,100 61,000

Total pumpage: 65,400 68,300 68,290 61,200

Inflows - Outflows = –2,9008 –7008 41,2109 –44,9008

Springs as Pumpage

drains

Storage change* 

Groundwater

underflow (GU)

Groundwater

underflow (GU)

Recharge

(areal (DI), streams (SI))

sac13-0490_Figure 33b

B

(Average-net flows in acre-feet per year)

*Includes water derived from land subsidence

Base case1

2011–20716

2,900

2,400

29,500

31,900

64,600

0 0 11,900

32,700

400

7 7 7

190

63,700

63,900

67,200

–2,6008

Reduced

supply2

2011–2071

3,700

1,100

25,600

26,700

27,200

500

500

190

32,800

33,000

36,400

–9,2008

Reduced

demand3

2011–2071

3,000

1,300

29,500

30,800

30,800

0

600

190

15,800

16,000

31,500

–7009

Main zone

2000–2010

3,200

700

8,300

9,000

36,500

0

27,500

600

6

190

56,700

56,900

57,500

–21,0008

Ventucopa

Uplands

2000–2010

15

1,500

20,500

22,000

22,000

6,000

0

0

8

 0

10,000

10,000

16,000

6,0009

Sierra Madre

Foothills

2000–2010

0

900

1,600

2,500

16,300

0

13,800

0

2

 0

1,400

1,400

1,400

–14,9008

1 Base Case projection of current demand with historical climate

2 Base Case projection with supply limited to recharge

3 Base Case pojection with no agriculture in the Main-zone subregions

4 Historical period that represents two climate cycles

5 Historical period that represents recent climate and land use conditions

6 Projection of historical climate and 2010 land use

7 Includes water lost to evapotranspiration 

8 Demand greater than replenishment (overdraft) 

9 Replenishment is greater than demand

Figure 33. —Continued
118 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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

Water year

40

30

20

10

0

70

80

90

100

50

60

Percentage of pumpage by model layer

sac13-0490_Figure 34 pct pumpage by layer

EXPLANATION

Morales Formation 

Older alluvium

Recent alluvium 

Figure 34. Percentage of simulated groundwater pumpage for the water years 1950–2010 for all three model layers, Cuyama Valley, 

California.
Figures 119

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

Water year

40

30

20

10

0

70

80

90

100

50

60

Percentage of farm net recharge by model layer

A

sac13-0490_Figure 35a pct farm net recharge by layer

EXPLANATION

Morales Formation 

Older alluvial

Recent alluvium 

Figure 35. Stacked bar chart showing A, percentage of total recharge by aquifer model layers, and B, Net downward flow between 

model layers, Cuyama Valley, California.
120 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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

Water year

–10

–20

–30

–40

–50

200

30

40

50

0

10

B

GW - INFLOW GW - OUTFLOW 

sac13-0490_Figure 35b interlayer flow

EXPLANATION

Flow to Morales Formation 

Flow to older alluvium 

Flow to recent alluvium 

Net interlayer flow of groundwater, in thousands of acre-feet per year

Recent precipitation cycles 

Dry

Wet

D W D W D W D W D W D W D W D

Figure 35. —Continued
Figures 121

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

fault

Rehoboth fault

Ozena fault

166 166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Normal fault

Thrust fault

Thrust fault,

 concealed

GRF, Graveyard fault;

SBCF, Santa Barbara Canyon fault;

TTRF, Turkey Trap Ridge fault

sac13-0490 Figure 36a—2071 summer WLs

Simulated water-level altitude, in feet, Active model-grid boundary

 summer 2071; contour interval varies

 <1,400

 >1,400 to 1,450

 >1,450 to 1,500

 >1,500 to 1,550

 >1,550 to 1,600

 >1,600 to 1,650

 >1,650 to 1,700

 >1,700 to 1,750

 >1,750 to 1,800

 >1,800 to 1,850

 >1,850 to 1,900

 >1,900 to 1,950

 >1,950 to 2,000

 >2,000 to 2,050

 >2,050 to 2,100

 >2,100 to 2,150

 >2,150 to 2,200

 >2,200 to 2,300

 >2,300 to 2,400

 >2,400 to 2,500

 >2,500 to 2,600

 >2,600 to 2,800

 >2,800 to 3,000

 >3,000 to 3,400

 >3,400

A

0 5 10 MILES

0 5 10 KILOMETERS

Figure 36. A, projected simulated water levels and B, the difference in water levels between projection of simulated water levels in fall 

2071 and simulated water levels in fall 2010 for the hydrologic flow model of Cuyama Valley, California. 
122 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Morales fault

South Cuyama fault

Russell fault

TTRF GRF

Whiterock 

SBCF

fault

Rehoboth fault

166 166

33

33

Cuyama

Ventucopa

New Cuyama

Kern

County

Ventura

County

Santa Barbara

County

San Luis Obispo

County

119°50’ 119°40’ 119°30’ 119°20’

35°

34°

50’

34°

40’

Reyes Creek

Cuyama

Creek

Sierra Madre Mountains

Tehachapi

Cuyama River

Caliente Range

Mts

EXPLANATION

Shaded relief base created from 30-m digital elevation model from USGS 

National Elevation Dataset (NED); North America Vertical Datum 1983 

(NAVD83). Hydrology sourced from 1:24,000-scale National Hydrography 

Dataset, 1974-2009. Place names sourced from USGS Geographic 

Names Information System, 1974-2009. Albers Projection, NAD83.

Normal fault

Thrust fault

Thrust fault,

 concealed

sac13-0490 Figure 36b—2071 WLs diff

Simulated difference in water-level altitude, in feet, Active model-grid boundary

 summer 2010 minus summer 2071; interval varies

 <–150

>–150 to –100

>–100 to –50

 >–50 to 0

 >0 to 1

 >1 to 10

 >10 to 25

 >25 to 50

 >50 to 75

 >75 to 100

 >100 to 125

 >125 to 150

 >150 to 175

 >175 to 200

 >200 to 250

 >250 to 275

 >275 to 300

 >300 to 350

 >350 to 400

B

0 5 10 MILES

0 5 10 KILOMETERS

Figure 36. —Continued
Figures 123

2011 2016 2021 2026 2031 2036 2041 2046 2051 2056 2061 2066 2071

Water year

–1,000

–1,500

–2,000

–2,500

–3,000

500

0

–500

800

600

400

200

0

–200

–400

–600

800

600

400

200

0

–200

–400

–600

–800

Net total storage change and cumulative change, in thousands of acre-feet

A SCENARIO 1—Base case

SCENARIO 2—Reduced supply

SCENARIO 3—Reduced demand

sac13-0490_Figure 37a future net storage change

EXPLANATION

Total change in storage Cumulative storage by area Main zone Ventucopa uplands Sierra Madre Foothills

Figure 37. Three projected scenarios showing projected A, cumulative change in net groundwater storage, B, potential groundwater 

levels at CVKR and CVBR monitoring sites, and C, potential land subsidence near Cuyama, Cuyama Valley, California.
124 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

1,600

1,700

1,500

1,800

1,900

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070

Main Zone

Base case

Reduced supply

Reduced demand

Sierra Madre Foothills

Water-level elevation, in feet above NAVD 88

B

sac13-0490_Figure 37b_Future hydros

1,800

1,900

1,700

2,000

2,100

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070

Year Year

CVKR#1–3(10N/25W-19P2–4) CVBR#2,3 (10N/26W-34N2,3)

1,800

1,900

1,700

2,000

2,100

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070

2,100

2,000

2,200

2,300

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070

Base case

Reduced supply

Reduced demand

2,100

2,000

2,200

2,300

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070

2,100

2,000

2,200

2,300

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070

CVKR CVBR

Reversed historical precipitation cycles 

Dry Wet

EXPLANATION See figure 25 for

 location of wells

#1

#2

#3

#2

#3

Figure 37. —Continued
Figures 125

0

–0.2

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

–0.4

–0.6

–0.8

–1

2

2010 2020 2030 2040 2050

Year

2060 2070 2080

Vertical displacement, in feet 

Base case

Reduced demand

Reduced supply

C

EXPLANATION

Negative value is rebound

sac13-0490 Figure 37c—future subsidence

Figure 37. —Continued
126 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Tables
Tables 127

Table 1. Summary of groundwater regional zones and subregions for the Cuyama Valley Hydrologic Model (CUVHM), Cuyama Valley, 

California.

CUVHM 

hydrologic 

subregion 

zone number

Groundwater 

subregions 

group name 

(fig. 2A)

Regional zone 

groups

(fig. 2B)

Groundwater subregional zone description

1 Caliente northern

main zone (CNMZ) 

Main zone Tributaries to Cuyama River draining the Caliente

Foothills Badlands 

2 Central Sierra Madre

foothills (CSMFH) 

Sierra Madre foothills Central subregion of tributaries draining the Sierra

Madre foothills between Salsbury Canyon and Santa

Barbara Canyon 

3 Northeast Ventucopa

uplands (NEVU) 

Ventucopa uplands Northeastern Upper Cuyama Creek Drainage and

related tributaries and Reyes Creek 

4 Northwestern Sierra

Madre foothills

(NSMFH) 

Sierra Madre foothills Northwestern subregion of tributaries draining the

Sierra Madre foothills north of Salsbury Canyon 

5 Northern Ventucopa

uplands (NVU) 

Ventucopa uplands Region surrounding Berringer Canyon and draining

the Morales formation outcrop region 

6 Southern Sierra Madre

foothills (SSMFH) 

Sierra Madre foothills Southern subregion of tributaries draining the Sierra

Madre foothills south of Santa Barbara Canyon 

7 Southern Ventucopa

uplands (SVU) 

Ventucopa uplands Southern Ventucopa adjacent to Cuyama River

uplands corridor

8 Southern main zone

(SMZ)

Main zone South-Central Cuyama bounded by faults on north

and south 

9 Western main zone

(WMZ)

Main zone Western region surrounding Cuyama River at

outflow of Cuyama groundwater basin
128 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Table 2. Summary of climate periods for the Cuyama Valley 

Hydrologic Model, Cuyama Valley, California (as shown in 

figure 5).

Climate period1

(year) Climate

1939 19442 Wet

1945 19572 Dry

1958 1958 Wet

1959 1961 Dry

1962 1962 Wet

1963 1968 Dry

1969 1969 Wet

1970 1976 Dry

1977 1983 Wet

1984 1990 Dry

1991 1995 Wet

1996 1997 Dry

1998 2001 Wet

2002 2004 Dry

2005 2006 Wet

2007 2010 Dry

1

Calendar years.

2

Climate periods prior to model simulation period that begins in 

October, 1949.
Tables 129

Table 3. Scaling coefficients for estimation of streamflow for MODFLOW Streamflow 

Routing (SFR) from recharge and runoff maps developed by the Basin Characterization 

Model for ungaged basins in three geologic types in Cuyama Valley.

[Abbreviation: —, no estimate made]

Geologic type

Shallow subsurface 

flow from recharge

that becomes baseflow 

(SFR recharge)

Runoff that

becomes

streamflow 

(SFR runoff)

Runoff that 

becomes deep 

recharge 

(subsurface

recharge)

Alluvium 0.01 0.05 —

Sandstone 0.04 0.4 0.2

Conglomerate 0.01 0.2 0.3
130 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Table 4. Streamgages used for Basin Characterization Model (BCM) calibration with calibration statistics for Cuyama Valley, 

California.

[Abbreviations: BL, below; CA, California; CK, creek; CR, creek; CYN, Canyon; ID, identification; NR, near; NSE, Nash-Sutcliffe Efficiency statistic; 

R, river; RD, road] 

Gage1 Station ID

Period of

record

(year)

NSE

Calibration statistics Recharge 

and runoff 

returning as 

baseflow

(percent)

Recharge and 

runoff that is 

streamflow

(percent)

Total streamflow that is 

subsurface 

recharge to 

mountain block

(percent)

R2

monthly

R2

annual

WAGON RD CR NEAR

STAUFFER

11136400 1972–1978 0.81 0.81 0.9 0 15 36

REYES CR NEAR

VENTUCOPA

11136480 1972–1987 0.76 0.82 0.87 5 53 1

CUYAMA RIVER NEAR

VENTUCOPA

11136500 1945–1958 0.44 0.56 0.83 1 22 11

SANTA BARBARA

CANYON CK NEAR

VENTUCOPA

11136600 2009–2010 0.84 0.95 — 0 12 24

ALISO CANYON CK 

NEAR NEW CUYAMA

11136650 1963–1972 0.68 0.82 0 5 0

CUYAMA R BL 

BUCKHORN CYN NR

SANTA MARIA CA2

11136800 1963–2009 0.80 0.82 0 13 37

CUYAMA R NR SANTA

MARIA CA2

11137000 1939–1962 0.50 0.84 0 5 16

1

Locations are shown on figure 11.

2

Outside of basin area and downstream of study area.
Tables 131

Table 5. Summary of One-Water Hydrologic Flow Model (OWHM) Packages and processes used with the hydrologic flow model of 

Cuyama Valley, California.

[Abbreviations: FMP, farm process; MF-FMP, MODFLOW with the farm process; OBS, Observation Package]

Computer program

(packages, processes, 

parameter estimation)

Function References cited

Processes and solver

Groundwater flow (GWF) 

processes of MODFLOW-2005

Setup and solve equations simulating a basic groundwater flow 

model.

Harbaugh (2005), Harbaugh and others 

(2000), Hill and others (2000)

Preconditioned conjugategradient (PCG)Solves groundwater flow equations; requires convergence of 

heads and (or) flow rates.

Hill (1990); Harbaugh (2005)

Farm process (FMP) Setup and solve equations simulating use and movement of 

water on the landscape as irrigated agriculture, urban landscape, and natural vegetation.

Schmid and Hanson (2009), Schmid and 

others (2006a, b), Hanson and others 

(2014b)

Files

Name file (Name) Controls the capabilities of MF-FMP utilized during a simulation. Lists most of the files used by the OBS, and FMP 

processes.

Harbaugh (2005)

Output control option (OC) Used in conjunction with flags in other packages to output head, 

drawdown, and budget information for specified time periods 

into separate files.

Harbaugh (2005)

List file (LIST) Output file for allocation information, values used by the GWF 

process, and calculated results such as head, drawdown, and 

the water budget. 

Harbaugh (2005)

Discretization 

Basic package (BAS6) Defines the initial conditions and some of the boundary conditions of the model.Harbaugh (2005)

Discretization package (DIS) Space and time information. Harbaugh (2005)

Multiplier package (MULT) Defines multiplier arrays for calculation of model-layer characteristics from parameter values.Harbaugh (2005), Schmid and 

Hanson (2009)

Zones (ZONE) Defines arrays of different zones. Parameters may be composed 

of one or many zones.

Harbaugh (2005)

Aquifer parameters

Layer property flow package 

(LPF)

Calculates the hydraulic conductance between cell centers. Harbaugh (2005)

Hydrologic flow barriers 

(HFB6)

Simulates a groundwater barrier by defining a hydraulic conductance between two adjacent cells in the same layer.Hsieh and Freckelton (1993)

Boundary conditions

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.

Harbaugh (2005)

Recharge and discharge

Multi-node wells (MNW1) Simulates pumpage from wells with screens that span multiple 

layers.

Halford and Hanson (2002)

Streamflow routing (SFR2) Simulates the routed streamflow, infiltration, exfiltration, runoff, 

and returnflows from FMP.

Niswonger and Prudic (2005)

Output, observations and sensitivity

Headobservation (HOB) Defines the head observation and weight by layer(s), row, column, and time and generates simulated values for comparison 

with observed values.

Hill and others (2000), Harbaugh (2005)

Hydmod (HYD) Generates simulated values for specified locations at each timestep for groundwater levels and streamflow attributes.Hanson and Leake (1998)

Sensitivity (PVAL) Specifies parameter values used in other packages. Harbaugh (2005)
132 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Table 6. Coordinates of the hydrologic flow model of Cuyama Valley, California.

[Model grid is rotated 33 degrees west of north; coordinates below are calculated at the outer corner of the model grid using the North American Datum of 1983 

in the Universal Transverse Mercator (UTM) Projection of North America, Zone 11; each model cell is 250 meters by 250 meters. Abbreviation: DMS, degree, 

minute, second]

Corner of 

model grid

Model 

coordinates X 

(column)

Model 

coordinates Y

(row)

Latitude

(DMS)

Longitude

(DMS)

UTM 

coordinates X 

(easting)

(meters)

UTM

coordinates Y 

(northing)

(meters)

Northwest 1 1 34° 54' 57" 119° 56' 36" –231,090 3,867,673

Northeast 135 1 35° 10' 07" 119° 44' 23" –250,476 3,895,182

Southwest 1 300 34° 32' 54" 119° 15' 10" –293,276 3,825,260

Southeast 135 300 34° 48' 00" 119° 02' 53" –312,648 3,852,769
Tables 133

Table 7. Percentage of different virtual crop categories in Cuyama Valley Hydrologic Model for selected land-use periods.

[Abbreviations: FMP ID, farm process identification; no., number]

Description 

(FMP ID/cropsScape 

land-use no.)

Percentage of active model area

1952 1959 1966 1977 1984 2000 2002 2004 2005 2006 2007 2008 2009 2010

Field crops1 2.8 1.2 2.1 4 4 7.5 3.9 3.8 3.9 1.8 1 1 5.7 4.5

Alfalfa (4/36) 3.3 6.0 5.2 12.3 12.4 3.03 0.35 0.35 0.35 0.35 0.4 0.24 0.56 0.21

Dry beans (6/42) 0 0 0 0 0 0.32 0 0 0 0 0 0 0 0

Potatoes (7/43) 3.4 0.6 1.9 0 0 0.57 1.75 1.75 1.76 1.48 3.81 1.15 0.6 2.14

Onions (8/49) 0 0.7 0.5 0 0 0.35 0.09 0.09 0.09 0.31 0.31 0.22 0 0.66

Various orchards2 0 0.1 0.4 0 0 2.78 1.28 1.28 1.28 1.07 2.15 0.98 3.19 0.6

Grapes (14/69) 0 0 0 0 0 0.8 1.2 1.2 1.2 1.1 1 1.1 1.2 1

Walnuts (16/76) 0 0 0 0 0 0.1 0.1 0.1 0.1 0 0 0 0 0

Native trees3 12 11.9 11.9 11.8 11.8 11.8 11.8 11.8 11.8 11.8 11.8 11.8 11.8 11.9

Native shrubland 

(23/152) and grassland (24/171)

74.7 75.4 74.1 53.8 53.6 53.9 53.8 53.8 53.7 53.9 53.9 53.9 54 53.4

Various farmland 

categories4

4.1 0 3.8 4 4.2 4.1 4.2 4.2 4.1 4.1 4.1 4.1 4.1 4.1

Pistachios (30/204) 0 0 0 0 0 0.7 0.6 0.6 0.6 0.6 0.7 0.5 0.8 0.5

Carrots (31/206) 0 0 0 0.8 0.8 5.3 5.7 5.6 5.7 10.6 9.9 6 9.7 3.2

Cantaloupes

(32/209)

0 0 0 0 0 0 0 0 0 0 0.8 0 1.3 0

Broccoli (34/214) and 

cauliflower (37/244)

0 0 0 0 0 0 0 0 0 0 0.6 0 0.9 0

Irrigated row and 

vegetable crops 

(40/259)

0 0 0 0 0 0.5 1.7 1.7 1.7 1 0.2 5 2.7 14

Fallow/idle cropland 

(9/61)

0 0 0 12.9 12.9 4.2 2.3 2.4 2.4 3.5 2.3 2.1 2.6 2

1

Field crops were simulated separately as they occurred historically. Summaries of areas and percentages represent collective area for this group of crops that 

includes barley (1/21), durham wheat (2/22), oats (3/28), other hay (5/37), pasture/grass (10/62), forage hay/silage (38/257), and irrigated field crops (39/258).

2

Various fruit trees were simulated separately as they occurred historically. Summaries of areas and percentages represent collective area for this group of 

crops that includes cherries (11/66), peaches (12/67), apples (13/68), other fruit trees (15/73), nectarines (34/218), apricots (35/223), and olives (32/211).

3

Native trees were simulated separately as they occurred historically. Summaries of areas and percentages represent collective area for this group of crops that 

includes deciduous (20/141), evergreen (21/142), and mixed forest (22/143) vegetation.

4

Various farmland categories were simulated separately as they occurred historically. Summaries of areas and percentages represent collective area for this 

group of crops that includes prime farmland (25/183), statewide importance (26/184), unique farmland (27/185), local importance (28/186), and local potential 

(29/187), developed/open space (19/282).
134 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Table 8. Summary of Cuyama Valley Farm process (FMP) virtual-crop crop category, crop-index number, and select properties for the 

Cuyama Valley Hydrologic Model (CUVHM), Cuyama Valley, California.

Farm process (FMP) 

crop index number and

virtual-crop crop category1

Root 

depth 

(feet)

Root uptake pressure heads (feet) Fraction of 

surface-water runoff

(dimensionless) Anoxia

Lower 

optimal 

range

Upper 

optimal 

range

Wilting

Precipitation Irrigation

Field crops (1, 2, 3, 5, 10, 38) 4.4–12 ˗0.49 – ˗0.24 ˗0.98 – ˗0.66 ˗171 – ˗18 ˗525 – ˗262 0.8–0.99 0.24–0.97

Alfalfa (4) 12.0 ˗0.49 ˗0.98 ˗18 ˗262 0.9 0.6

Dry beans (6) 5.5 ˗0.43 ˗0.89 ˗23 ˗36 0.97 0.97

Potatoes (7) 4.7 ˗0.49 ˗0.98 ˗20 ˗262 0.97 0.97

Onions (8) 3.3 ˗0.49 ˗0.98 ˗24 ˗262 0.8 0.21

Various orchards (11, 12, 13, 15, 33, 35) 1.5–6.6 ˗0.49 – ˗0.43 ˗0.98– ˗0.89 ˗37 – ˗0.18 ˗377 – ˗262 0.95–0.97 0.05–0.97

Grapes (14) 5.0 ˗0.49 ˗0.98 ˗18 ˗262 0.97 0.25

Walnuts (16) 6.0 ˗0.49 ˗0.98 ˗18 ˗262 0.95 0.04

Native trees (20, 21, 22) 6.6–10.8 ˗0.49 ˗0.98 ˗18 ˗262 0.92 0.05

Native shrubland and grassland (23, 24) 5.3–15.4 ˗0.49 ˗0.98 ˗18 ˗262 0–0.9 0.05

Various farmland categories (19, 25, 26, 27, 28, 29) 0.3–12 ˗0.49–0 ˗0.98–0 ˗98 – ˗20 ˗406 – ˗262 0.8–0.97 0.21–0.97

Pistachios (30) 1.6 ˗0.49 ˗0.98 ˗171 ˗525 0.97 0.97

Carrots (31) 1.5 ˗0.43 ˗0.92 ˗37 ˗262 0.97 0.97

Cantaloupes (32) 1.5 ˗0.49 0 ˗27 ˗377 0.95 0.05

Broccoli (34) and cauliflower (37) 2.5–6.5 ˗0.49 – ˗0.43 ˗0.98–0.33 ˗37 – ˗1.0 ˗262 – ˗1.31 0.97 0.97

Irrigated row and vegetable crops (40) 1.5 ˗0.49 ˗0.98 ˗18 ˗262 0.97 0.97

Fallow/idle cropland (9) 5.3 ˗0.49 ˗0.98 ˗18 ˗262 0.97 0.97

1

Refer to table 7 for explanation of crop and vegetation groupings. For groups of crops, the root uptake pressure heads represent the range in values for this 

grouping of crops.
Tables 135

Table 9. Summary of fractions of transpiration and evaporation by month, for Cuyama Valley crop categories (virtual crops).[Abbreviations: FEI, fraction of evaporation from irrigation; FEP, fraction of evaporation from precipitation; FTR, fraction of transpiration] 

Crop January February March April FTR FEP FEI FTR FEP FEI FTR FEP FEI FTR FEP FEI

Field crops 0.05–0.58 0.43–0.95 0–0.5 0.05–0.78 0.23–0.95 0–0.5 0.05–0.88 0.13–0.95 0–0.5 0.15–0.97 0.03–0.85 0–0.1Alfalfa 0.5–0.5 0.5–0.5 0.1–0.1 0.6–0.6 0.4–0.4 0.1–0.1 0.78–0.78 0.22–0.22 0.15–0.15 0.97–0.97 0.03–0.03 0.03–0.03Dry beans 0.5–0.58 0.43–0.5 0.1–0.1 0.7–0.78 0.23–0.3 0.1–0.1 0.8–0.88 0.13–0.2 0.1–0.1 0.5–0.58 0.43–0.5 0.3–0.3Potatoes 0.2–0.28 0.73–0.8 0.1–0.1 0.3–0.38 0.63–0.7 0.1–0.1 0.5–0.58 0.43–0.5 0.1–0.1 0.6–0.68 0.33–0.4 0.2–0.2Onions 0.5–0.58 0.43–0.5 0.05–0.05 0.7–0.78 0.23–0.3 0.05–0.05 0.8–0.88 0.13–0.2 0.05–0.05 0.5–0.58 0.43–0.5 0.05–0.05Various orchards 0.03–0.58 0.43–0.97 0–0.1 0.03–0.78 0.23–0.97 0–0.1 0.1–0.88 0.13–0.9 0–0.1 0.5–0.68 0.33–0.5 0–0.1Grapes 0.05–0.13 0.88–0.95 0.1–0.1 0.05–0.13 0.88–0.95 0.1–0.1 0.28–0.36 0.65–0.72 0.1–0.1 0.4–0.48 0.53–0.6 0.1–0.1Walnuts 0.03–0.11 0.9–0.97 0.05–0.05 0.03–0.11 0.9–0.97 0.05–0.05 0.1–0.18 0.83–0.9 0.05–0.05 0.5–0.58 0.43–0.5 0.05–0.05Native trees 0.03–0.28 0.73–0.97 0–0.1 0.03–0.28 0.73–0.97 0–0.1 0.1–0.48 0.53–0.9 0–0.1 0.5–0.58 0.43–0.5 0–0.1Native shrubland and grassland 0.23–0.36 0.65–0.77 0–0.1 0.23–0.36 0.65–0.77 0–0.1 0.61–0.74 0.27–0.39 0–0.15 0.61–0.74 0.27–0.39 0–0.15Various farmland categories 0–0.5 0.5–1 0–0.1 0–0.5 0.5–1 0–0.1 0–0.5 0.5–1 0–0.2 0–0.97 0.03–1 0–0.03Pistachios 0.03–0.11 0.9–0.97 0.1–0.1 0.03–0.11 0.9–0.97 0.1–0.1 0.1–0.18 0.83–0.9 0.2–0.2 0.5–0.58 0.43–0.5 0.2–0.2Carrots 0.5–0.58 0.43–0.5 0–0 0.7–0.78 0.23–0.3 0–0 0.8–0.88 0.13–0.2 0–0 0.5–0.58 0.43–0.5 0–0Cantaloupes 0.2–0.28 0.73–0.8 0–0 0.3–0.38 0.63–0.7 0–0 0.5–0.58 0.43–0.5 0–0 0.6–0.68 0.33–0.4 0–0Broccoli and cauliflower 0.5–0.58 0.43–0.5 0.1–0.1 0.7–0.78 0.23–0.3 0.1–0.1 0.8–0.88 0.13–0.2 0.1–0.1 0.5–0.58 0.43–0.5 0.1–0.1Irrigated row and vegetable crops 0.2–0.28 0.73–0.8 0–0 0.3–0.38 0.63–0.7 0–0 0.5–0.58 0.43–0.5 0–0 0.6–0.68 0.33–0.4 0–0Fallow/idle cropland 0–0 1–1 0.1–0.1 0–0 1–1 0.1–0.1 0–0 1–1 0.1–0.1 0–0 1–1 0.03–0.03
136 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California Table 9. Summary of fractions of transpiration and evaporation by month for Cuyama Valley crop categories (virtual crops).—Continued

[Abbreviations: FEI, fraction of evaporation from irrigation; FEP, fraction of evaporation from precipitation; FTR, fraction of transpiration] 

Crop May June July August FTR FEP FEI FTR FEP FEI FTR FEP FEI FTR FEP FEI

Field crops 0.25–0.97 0.03–0.75 0–0.3 0.65–0.97 0.03–0.35 0.03–0.2 0.7–0.96 0.04–0.3 0.03–0.2 0.8–0.97 0.03–0.2 0.03–0.2Alfalfa 0.97–0.97 0.03–0.03 0.02–0.02 0.97–0.97 0.03–0.03 0.02–0.02 0.96–0.96 0.04–0.04 0.02–0.02 0.97–0.97 0.03–0.03 0.02–0.02Dry beans 0.6–0.68 0.33–0.4 0.3–0.3 0.65–0.73 0.28–0.35 0.03–0.03 0.7–0.78 0.23–0.3 0.03–0.03 0.8–0.88 0.13–0.2 0.03–0.03Potatoes 0.7–0.78 0.23–0.3 0.2–0.2 0.8–0.88 0.13–0.2 0.1–0.2 0.8–0.88 0.13–0.2 0.1–0.2 0.8–0.88 0.13–0.2 0.1–0.2Onions 0.6–0.68 0.33–0.4 0.05–0.05 0.65–0.73 0.28–0.35 0.05–0.05 0.7–0.78 0.23–0.3 0.05–0.05 0.8–0.88 0.13–0.2 0.05–0.05Various orchards 0.6–0.78 0.23–0.4 0–0.1 0.65–0.88 0.13–0.35 0–0.1 0–0.1 0.7–0.88 0.13–0.3 0–0.1 0.8–0.98 0.03–0.2Grapes 0.38–0.46 0.55–0.62 0.1–0.1 0.36–0.44 0.57–0.64 0.1–0.1 0.36–0.44 0.57–0.64 0.1–0.1 0.36–0.44 0.57–0.64 0.1–0.1Walnuts 0.6–0.68 0.33–0.4 0.05–0.05 0.7–0.78 0.23–0.3 0.05–0.05 0.8–0.88 0.13–0.2 0.05–0.05 0.9–0.98 0.03–0.1 0.02–0.05Native trees 0.6–0.68 0.33–0.4 0–0.3 0.7–0.78 0.23–0.3 0–0.2 0.8–0.88 0.13–0.2 0–0.2 0.9–0.98 0.03–0.1 0–0.1Native shrubland and grassland 0.61–0.74 0.27–0.39 0–0.15 0.61–0.74 0.27–0.39 0.1–0.15 0.61–0.74 0.27–0.39 0.1–0.15 0.61–0.74 0.27–0.39 0.1–0.15Various farmland categories 0–0.97 0.03–1 0–0.03 0–0.97 0.03–1 0–0.03 0–0.96 0.04–1 0–0.04 0–0.97 0.03–1 0–0.03Pistachios 0.6–0.68 0.33–0.4 0.3–0.3 0.7–0.78 0.23–0.3 0.2–0.3 0.8–0.88 0.13–0.2 0.1–0.2 0.9–0.98 0.03–0.1 0.02–0.1Carrots 0.6–0.68 0.33–0.4 0–0 0.65–0.73 0.28–0.35 0–0 0.7–0.78 0.23–0.3 0–0 0.8–0.88 0.13–0.2 0–0Cantaloupes 0.7–0.78 0.23–0.3 0–0 0.8–0.88 0.13–0.2 0–0 0.8–0.88 0.13–0.2 0–0 0.8–0.88 0.13–0.2 0–0Broccoli and cauliflower 0.6–0.68 0.33–0.4 0.1–0.3 0.65–0.73 0.28–0.35 0.1–0.2 0.1–0.2 0.7–0.78 0.23–0.3 0.1–0.2 0.8–0.88 0.13–0.2Irrigated row and vegetable crops 0.7–0.78 0.23–0.3 0–0 0.8–0.88 0.13–0.2 0–0 0.8–0.88 0.13–0.2 0–0 0.8–0.88 0.13–0.2 0–0Fallow/idle cropland 0–0 1–1 0.03–0.03 0–0 1–1 0.03–0.03 0–0 1–1 0.04–0.04 0–0 1–1 0.03–0.03
Tables 137

Table 9. Summary of fractions of transpiration and evaporation by month for Cuyama Valley crop categories (virtual crops).—Continued[Abbreviations: FEI, fraction of evaporation from irrigation; FEP, fraction of evaporation from precipitation; FTR, fraction of transpiration] 

Crop September October November December FTR FEP FEI FTR FEP FEI FTR FEP FEI FTR FEP FEI

Field crops 0.7–0.97 0.03–0.3 0.03–0.2 0.33–0.83 0.18–0.67 0.01–0.5 0.05–0.88 0.13–0.95 0.1–0.5 0.05–0.88 0.13–0.95 0.1–0.5Alfalfa 0.97–0.97 0.03–0.03 0.02–0.02 0.78–0.78 0.22–0.22 0.2–0.2 0.6–0.6 0.4–0.4 0.15–0.15 0.5–0.5 0.5–0.5 0.1–0.1Dry beans 0.8–0.88 0.13–0.2 0.03–0.03 0.75–0.83 0.18–0.25 0.1–0.25 0.8–0.88 0.13–0.2 0.1–0.2 0.8–0.88 0.13–0.2 0.1–0.1Potatoes 0.8–0.88 0.13–0.2 0.1–0.2 0.7–0.78 0.23–0.3 0.2–0.2 0.8–0.88 0.13–0.2 0.1–0.1 0.8–0.88 0.13–0.2 0.1–0.1Onions 0.8–0.88 0.13–0.2 0.05–0.05 0.75–0.83 0.18–0.25 0.05–0.05 0.8–0.88 0.13–0.2 0.05–0.05 0.8–0.88 0.13–0.2 0.05–0.05Various orchards 0–0.1 0.8–0.98 0.03–0.2 0–0.1 0.7–0.83 0.18–0.3 0–0.3 0.1–0.88 0.13–0.9 0–0.1 0.03–0.88 0.13–0.97Grapes 0.36–0.44 0.57–0.64 0.1–0.1 0.36–0.44 0.57–0.64 0.1–0.1 0.2–0.28 0.73–0.8 0.1–0.1 0.05–0.13 0.88–0.95 0.1–0.1Walnuts 0.9–0.98 0.03–0.1 0.02–0.05 0.7–0.78 0.23–0.3 0.05–0.05 0.1–0.18 0.83–0.9 0.05–0.05 0.03–0.11 0.9–0.97 0.05–0.05Native trees 0.9–0.98 0.03–0.1 0–0.1 0.7–0.78 0.23–0.3 0–0.3 0.1–0.28 0.73–0.9 0–0.5 0.03–0.28 0.73–0.97 0–0.1Native shrubland and grassland 0.61–0.74 0.27–0.39 0.1–0.2 0.61–0.74 0.27–0.39 0.1–0.2 0.61–0.74 0.27–0.39 0.1–0.15 0.23–0.36 0.65–0.77 0.1–0.1Various farmland categories 0–0.97 0.03–1 0–0.03 0–0.5 0.5–1 0–0.3 0–0.5 0.5–1 0–0.25 0–0.5 0.5–1 0–0.1Pistachios 0.9–0.98 0.03–0.1 0.02–0.1 0.7–0.78 0.23–0.3 0.2–0.2 0.1–0.18 0.83–0.9 0.1–0.1 0.03–0.11 0.9–0.97 0.1–0.1Carrots 0.8–0.88 0.13–0.2 0–0 0.75–0.83 0.18–0.25 0–0 0.8–0.88 0.13–0.2 0–0 0.8–0.88 0.13–0.2 0–0Cantaloupes 0.8–0.88 0.13–0.2 0–0 0.7–0.78 0.23–0.3 0–0 0.5–0.58 0.43–0.5 0–0 0.3–0.38 0.63–0.7 0–0Broccoli and cauliflower 0.1–0.2 0.8–0.88 0.13–0.2 0.1–0.2 0.75–0.83 0.18–0.25 0.01–0.2 0.8–0.88 0.13–0.2 0.1–0.2 0.8–0.88 0.13–0.2Irrigated row and vegetable crops 0.8–0.88 0.13–0.2 0–0 0.7–0.78 0.23–0.3 0–0 0.5–0.58 0.43–0.5 0–0 0.3–0.38 0.63–0.7 0–0Fallow/idle cropland 0–0 1–1 0.03–0.03 0–0 1–1 0.3–0.3 0–0 1–1 0.1–0.1 0–0 1–1 0.1–0.1

1Refer to table 7 for explanation of crop and vegetation groupings.
138 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California Table 10. Irrigation efficiency, by month, for each crop of the Cuyama Valley, California.1, 2 [Efficiencies are adjusted seasonally for wet and dry climatic periods with multipliers (see Model Calibration section). Refer to table 7 for explanation of crop and vegetation groupings.] Crop categories January February March April May June July August September October November December Field crops 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 Alfalfa 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 0.5–0.675 Dry beans 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 Potatoes 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 0.5–0.6 Onions 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 0.5–0.65 Various orchards 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 Grapes 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 0.5–0.875 Walnuts 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 Native trees 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 Native shrubland and grassland 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 0.5–0.5 Various farmland categories 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 Pistachios 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 Carrots 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 Cantaloupes 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 Broccoli and cauliflower 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 Irrigated row and vegetable crops 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 0.5–0.75 Fallow/idle cropland 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8 0.5–0.8

1Efficiencies are adjusted seasonally for wet and dry climatic periods with multipliers (see Model Calibration section)2Refer to table 7 for explanation of crop and vegetation groupings.
Tables 139

Table 11. Summary of reference evapotranspiration (ETh) comparisons between Pennman-Montieth from California Irrigation 

Management Information System (CIMIS) stations and Priestley-Taylor estimates from regional climate data, Cuyama Valley, California.

[Abbreviations: Apr., April; Aug., August; Dec., December; Feb., February; Jan., January; Mar., March; Nov., November; Oct., October; Sept., September]

ETh average

monthly value

(inches/month)

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

1

CIMIS station 88 (California Department of Water 

Resources, 2013)

2.10 2.43 3.97 5.33 7.07 8.06 8.55 7.84 6.01 4.39 2.58 1.94

Priestley-Taylor estimate 1.90 2.38 3.80 4.96 6.49 7.16 7.71 6.90 5.35 3.87 2.35 1.70

Adjusted fraction of CIMIS 

value of P-T estimate 

[dimensionless]

1.11 1.02 1.05 1.07 1.09 1.13 1.11 1.14 1.12 1.13 1.10 1.14

1

Average monthly values for 1989–2011.
140 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Table 12. Summary of hydraulic properties estimated from the Cuyama Valley hydrologic model (CUVHM) calibration.

Aquifer 

(model

layer)

Lateral 

hydraulic 

conductivity

(feet/day)

Specific

 storage 

[1/foot]

Specific yield

[dimensionless]

Vertical hydraulic 

conductivity

[feet/day]

(leakance, in

feet/day/feet)

Skeletal 

elastic storage, 

coarse and finegrained layers

[dimensionless]

Skeletal inelastic 

storage, finegrained layers 

[dimensionless]

Recent Alluvium (1) 5.2–85 2.2e-05–9.34e-03 0.02–0.14 0.0–12.3 5.9e-06–4.8e-04 6.37e-07–4.7e-03

Older Alluvium (2) 0.3–15.5 1.3e-06–8.0e-03 0.05–0.19 6.1e-04–0.34 7.4e-07–3.3e-04 1.5e-05–2.3e-02

Morales Formation (3) 0.02–0.4 1.3e-06–2.3e-02 0.06–0.25 3.4e-03–0.01 1.05e-05–4.5e-03 7.3e-05–9.2e-03
Tables 141

Table 13. Summary of parameter zones and related property parameter names used to calibrate horizontal hydraulic conductivity (KH), 

vertical hydraulic conductivity (KV), and aquifer specific storage and specific yield (SS) in the Cuyama Valley hydrologic model (CUVHM), 

Cuyama Valley, California. 

Feature/parameter zone Root subregion model

parameter names1

(zone number) Aquifer Description

(model layer)

Recent alluvial aquifer (layer 1) Unconfined Ventucopa

VC_QYAUC (7)

Unconfined main zones

NMZ_QYA (1)

SMZ_QYA (4)

WZ_QYA (8)

Sierra Madre foothills zones

SMFHQYA (3)

River channel

NMZQYACC (2)

SMZ_QYACC (5)

VC_QYACC (6)

WZ_QYACC (9)

Unconfined Ventucopa

Ventucopa

Unconfined Main zones 

Northern Main

Southern Main 

Western 

Sierra Madre foothills zones

Sierra Madre foothills 

River channel

Northern Main 

Southern Main 

Ventucopa 

Western (includes selected tributary channels)

Older alluvial aquifer (layer 2) Unconfined Ventucopa

VC_QOAN (14)

VC_QOAC (21)

Unconfined main zones 

NMZ_QOA (10)

WZ_QOA_N (15)

WZ_QOA_S (16)

Sierra Madre foothills zones

SMFH_QOAN (11)

SMFH_QOAM (12)

SMFH_QOAS (13)

River channel

None

Confined zone

NMZ_QOAC (18)

SMFH_QOAC (19)

SMZ_QOAC (20)

WZ_QOAC (22)

QOA_PHT (23)

Unconfined/confined Ventucopa

Northern Ventucopa foothills unconfined

Ventucopa confined

Unconfined Main Zones 

Northern Main 

Northern western—Badlands foothills

Southern western

Sierra Madre foothills zones

Northern 

Middle 

Southern 

River channel

None

Confined Ventucopa

None

Confined Main zones 

Northern Main 

Sierra Madre foothills

Southern Main

Western 

Phantom layer cells

Morales formation (layer 3) Unconfined Ventucopa

VC_MOUC (17)

Unconfined main zones

None

Sierra Madre foothills zones

None

River channel

None

Confined zone

MO_C (24)

Unconfined Ventucopa

Ventucopa foothills

Unconfined Main zones

None

Sierra Madre foothills zones

None

River channel

None

Confined Ventucopa and Main zones

Entire active model grid where Morales Formation is not 

uppermost model layer

1

Root names have HK, VK, and SS added to the front of these names for parameter names used in PVAL and LPF input files.
142 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Table 14. Summary of selected parameter values estimated for the Cuyama Valley hydrologic model (CUVHM), Cuyama Valley, 

California.

Parameter 

type

[model 

layers]

Parameter

 name

Parameter 

description

Final

values Units

Estimated 

using 

automated 

methods1

Rank and 

composite 

scaled 

sensitivity

Package/processparameter group

Crop properties

Early 

years

[1–3]

Dry seasons 

SCL_KCSDFL

SCL_KCSDWN

SCL_KCSDSP

SCL_KCSDSU

Wet seasons 

SCL_KCSWFL

SCL_KCSWWN

SCL_KCSWSP

SCL_KCSWSU

Stress coefficient

for early (1963–92)

agriculture crop

coefficients

0.85

1.10

1.10

0.82

====

0.67

0.71

0.80

0.80

Multiplier No

No

No

Yes

Yes

No

No

No

16/54.9

101/30.3

20/53.2

95/31.7

====

84/34.3

102/29.8

91/32.7

44/46.7

FMP—Kc-value

properties

Recent 

years 

[1–3]

Dry seasons 

SCL_KCSDFL2

SCL_KCSDWN2

SCL_KCSDSP2

SCL_KCSDSU2

Wet seasons 

SCL_KCSWFL2

SCL_KCSWWN2

SCL_KCSWSP2

SCL_KCSWSU2

Stress coefficient

for recent- 

(1993–2006) 

agriculture

crop coefficients

1.17

1.14

1.20

1.03

====

1.03

1.15

0.87

1.11

Multiplier No

No

Yes

Yes

No

No

No

No

117/25.4

111/27.0

71/38.3

126/18.4

====

115/26.2

112/26.7

94/31.8

99/30.7

FMP—Kc-value

properties

Runoff

[1–3] Fractions of inefficient losses

to runoff from precipitation

for truck-vegetable crops

FIESWP_TVR

field crops FIESWP_FLD

orchards FIESWP_ORC

pasture FIESWP_PAS

native FIESWP_NTV

Fraction runoff from

precipitation for 

selected land use 

class

0.97

0.97

0.97

0.97

0.92

Fraction No

No

No

No

Yes

7/7.40

66/40.4

70/38.5

59/41.6

48/45.4

FMP—runoff

[1–3] Fractions of inefficient losses

to runoff from irrigation for 

truck-vegetable crops 

FIESWI_TVR

field crops FIESWI_FLD

orchards FIESWI_ORC

pasture FIESWI_PAS

Fraction runoff from

irrigation for 

selected land-use 

class

0.97

0.97

0.97

0.97

Fraction No

No

No

No

127/17.2

5/65.1

86/33.9

28/49.9

FMP—runoff

Irrigation efficiency

Early 

years

[1–3]

Dry seasons 

SCL_EFFDFL

SCL_EFFDWN

SCL_EFFDSP

SCL_EFFDSU

Wet seasons 

SCL_EFFWFL

SCL_EFFWWN

SCL_EFFWSP

SCL_EFFWSU

Multiplier on 

irrigation

efficiency for wet 

and dry seasons

0.88

0.89

1.06

0.92

0.70

0.70

0.70

0.72

Multiplier No

No

Yes

Yes

No

No

No

No

166/<0.1 

167/<0.1 

168/<0.1 

169/<0.1 

158/<0.1 

159/<0.1 

160/<0.1 

161/<0.1

FMP—irrigation
Tables 143

Parameter 

type

[model 

layers]

Parameter

 name

Parameter 

description

Final

values Units

Estimated 

using 

automated 

methods1

Rank and 

composite 

scaled 

sensitivity

Package/processparameter group

Irrigation efficiency—Continued

Recent 

years

[1–3]

Dry seasons 

SCL_EFFDFL2

SCL_EFFDWN2

SCL_EFFDSP2

SCL_EFFDSU2

Wet seasons 

SCL_EFFWFL2

SCL_EFFWWN2

SCL_EFFWSP2

SCL_EFFWSU2

Multiplier on irrigation

efficiency for wet 

and dry seasons

1.1

1.1

1.0

1.1

1.1

1.1

0.8

1.1

Multiplier No

No

Yes

Yes

No

No

Yes

Yes

162/<0.1

163/<0.1

164/<0.1

165/<0.1

170/<0.1

171/<0.1

172/<0.1

173/<0.1

FMP—irrigation

Lateral hydraulic conductivity

[1–3] KC_QYA

KC_ QOA

KC_MO

Hydraulic conductivity 

of coarse-grained 

deposits for each 

model layer

20.3

12.6

0.76

Feet/day Yes

Yes

Yes

81/35.5

10/57.4

58/41.6

LPF/MULT—

hydraulic 

conductivity

[1–3] KF_ QYA

KF_ QOA

KF_MO

Hydraulic conductivity 

of fine-grained 

deposits for each 

model layer

0.004

0.004

0.003

Feet/day No

No

No

12/57.2

80/35.6

122/21.6

LPF/MULT—

hydraulic 

conductivity

[1] HK_NMZ_QYA

HKNMZQYACC

HK_SMFHQYA

HK_SMZ_QYA

HKSMZQYACC

HK_VCQYACC

HK_VCQYAUC

HK_WZ_QYA 

HK_WZQYACC

Hydraulic conductivity 

of the Recent 

Alluvium zones

1.95

2.61

1.3

4.87

4.20

4.20

1.58

2.55

2.62 

Multiplier Yes

No

Yes

Yes

Yes

Yes

No

Yes

No

109/27.1

113/26.6

69/39.6

33/49.2

9/57.9

98/30.8

55/42.3

18/53.4

93/32.5 

LPF/PVAL—

hydraulic 

conductivity

multipliers

[2] HK_NMZ_QOA

HKSMFHQOAN

HKSMFHQOAM

HKSMFHQOAS

HK_VC_QOAN

HK_WZ_QOAN

HK_WZ_QOAS

HK_NMZQOAC

HKSMFHQOAC

HK_SMZQOAC

HK_VC_QOAC

HK_WZ_QOAC

HK_QOA_PHT

Hydraulic conductivity

of the older alluvial 

zones

2.3

0.42

0.38

0.47

1.47

1.36

2.16

1.52

0.52

2.02

0.48

0.94

1.00

Multiplier Yes

No

Yes

No

No

Yes

No

No

No

Yes

Yes

Yes

No

34/49.1

50/44.1

82/35.4

25/50.7

83/34.7

85/34.1

21/52.9

4/71.5

32/49.3

49/44.3

110/27.1

65/40.6

====

LPF/PVAL—

hydraulic 

conductivity

multipliers

[3] HK_MO_C

HK_MOUC

Hydraulic conductivity 

of the Morales 

formation zones

0.73

0.10

Multiplier No

Yes

53/42.5

6/64.3

LPF/PVAL—

hydraulic 

conductivity

Table 14. Summary of parameter values estimated for the Cuyama Valley hydrologic model (CUVHM), Cuyama Valley, 

California.—Continued
144 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Parameter 

type

[model 

layers]

Parameter

 name

Parameter 

description

Final

values Units

Estimated 

using 

automated 

methods1

Rank and 

composite 

scaled 

sensitivity

Package/processparameter group

Vertical hydraulic conductivity

[1] VK_NMZ_QYA

VKNMZQYACC

VK_SMFHQYA

VK_SMZ_QYA

VKSMZQYACC

VK_VCQYACC

VK_VCQYAUC

VK_WZ_QYA 

VK_WZQYACC

Vertical hydraulic 

conductivity of the 

Recent Alluvium 

zones

0.03

2.52

1.00

0.09

4.75

3.56

2.00

2.64

1.72

Multiplier Yes

No

Yes

No

No

No

No

No

Yes

36/48.7

123/21.6

72/37.8

31/49.3

96/31.6

13/56.3

67/40.2

114/26.3

23/52.1

LPF/PVAL—

hydraulic 

conductivity

multipliers

[2] VK_NMZ_QOA

VKSMFHQOAN

VKSMFHQOAM

VKSMFHQOAS

VK_VC_QOAN

VK_WZ_QOAN

VK_WZ_QOAS

VK_NMZQOAC

VKSMFHQOAC

VK_SMZQOAC

VK_VC_QOAC

VK_WZ_QOAC

VK_QOA_PHT

Vertical hydraulic

conductivity of the 

older alluvial zones

0.94

0.50

1.10

0.18

0.80

0.80

1.02

0.11

0.45

2.84

2.16

2.25

1.00

Multiplier No

Yes

No

Yes

Yes

No

No

Yes

Yes

No

No

No

No

42/46.9

22/52.8 

119/25.1

78/36.3

90/33.5

37/48.7

76/36.4

3/77.8

108/28.1

61/41.2

17/54.8

2/80.6

====

LPF/PVAL—

hydraulic 

conductivity

multipliers

[3] VK_MO_C

VK_MOUC

Vertical hydraulic 

conductivity of the 

Morales formation 

zones

1.04

0.95

Multiplier No

No

38/48.6

68/40.1

LPF/PVAL—

hydraulic 

conductivity

multipliers

Storage properties

[1] SY1_FAC Specific yield of 

Recent

Alluvium

0.13 Fraction Yes 51/43.1 LPF/MULT—

storage properties

[2] SY2_FAC Specific yield of Older

Alluvium

0.10 Fraction No 63/40.7 LPF/MULT—

storage properties

[3] SY3_FAC Specific yield of 

Morales Formation

0.08 Fraction No 87/33.8 LPF/MULT—

storage properties

[1] PHI_CRS 

PHI_FIN

Porosity of Recent 

Alluvium 

20

37

Percentage Yes

No

57/42.1

46/46.3

LPF/MULT—

storage properties

Table 14. Summary of parameter values estimated for the Cuyama Valley hydrologic model (CUVHM), Cuyama Valley, 

California.—Continued
Tables 145

Parameter 

type

[model 

layers]

Parameter

 name

Parameter 

description

Final

values Units

Estimated 

using 

automated 

methods1

Rank and 

composite 

scaled 

sensitivity

Package/processparameter group

Storage properties—Continued

[2] PHI_CRS_AO

PHI_FIN_AO

Porosity of Older 

Alluvium

12

17

Percentage Yes

Yes

26/50.7

7/61.8

LPF/MULT—

storage properties

[3] PHI_CRS_MO

PHI_FIN_MO

Porosity of Morales

formation

10

29

Percentage No

Yes

104/29.1

45/46.7

LPF/MULT—

storage properties

[1] SS_NMZ_QYA

SSNMZQYACC

SS_SMFHQYA

SS_SMZ_QYA

SSSMZQYACC

SS_VCQYACC

SS_VCQYAUC

SS_WZ_QYA 

SS_WZQYACC

Specific storage of 

Recent Alluvium 

zones

1.21

1.15

2.00

0.21

1.12

1.0

1.00

0.66

1.24

Multiplier No

No

Yes

Yes

No

Yes

No

No

No

89/33.7

47/46.2

43/46.9

88/33.7

100/30.5

105/29.1

118/25.2

30/49.5

11/57.2

LPF/MULT—

storage properties

[2] SS_NMZ_QOA

SSSMFHQOAN

SSSMFHQOAM

SSSMFHQOAS

SS_VC_QOAN

SS_WZ_QOAN

SS_WZ_QOAS

SS_NMZQOAC

SSSMFHQOAC

SS_SMZQOAC

SS_VC_QOAC

SS_WZ_QOAC

SS_QOA_PHT

Specific storage of 

Older Alluvium 

zones

2.00

0.86

1.95

1.25

2.00

0.92

0.81

2.09

0.03

1.32

2.22

1.65

1.00

Multiplier No

No

No

No

No

No

No

No

No

No

No

No

No

39/47.9

56/42.1

120/23.9

15/54.9

74/37.1

41/47.2

92/32.7

106/28.51

24/52.1

77/36.4

14/56.3

60/41.4

====

LPF/PVAL—

storage properties

[3] SS_MO_C

SS_MOUC

Specific storage of 

Morales formation 

zones

0.37

1.06

Multiplier Yes

No

79/36.0

19/53.3

LPF/PVAL—

storage properties

Subsidence properties

[1–3] crt_hd_01

crt_hd_02

crt_hd_03

Critical heads for each 

layer

0.91

0.90

0.72

Multiplier as

fraction of

initial

groundwater

levels

No

Yes

No

1/97.7

64/40.6

52/42.9

SUB—storage 

properties

[1–3] QYA_SKE

QOA_SKE

MO_SKE

Skeletal elastic storage

coefficient for each 

layer

0.90

0.32

0.80

Multiplier Yes

Yes

No

54/42.5

124/21.3

73/37.1

SUB—storage 

properties

[1–3] QYA_SKVB

QOA_SKVB

MO_SKVBR

Skeletal inelastic 

storage coefficient 

for each layer

1.62e-05

2.30e-05

1.00e-05

1/Foot Yes

Yes

No

97/31.6

75/36.5

103/29.6

SUB—storage

properties

Table 14. Summary of parameter values estimated for the Cuyama Valley hydrologic model (CUVHM), Cuyama Valley, 

California.—Continued
146 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Parameter 

type

[model 

layers]

Parameter

 name

Parameter 

description

Final

values Units

Estimated 

using 

automated 

methods1

Rank and 

composite 

scaled 

sensitivity

Package/processparameter group

SKIN factor for multi-node wells

[1–3] SKIN_LY1

SKIN_LY2

SKIN_LY3

Skin factor for recent

and Older Alluvium, 

and Morales 

formation layers

395

1,536

1,622

ft2/day No

No

No

27/50.6

35/48.8

8/60.7

MNW1 hydraulic

property

Horizontal flow-barrier conductance2

[1–3] MO_FLT [2–3]

 GRV_FLT [1–3]

 TTHL_FLT [1–3]

 SBC_FLT [2–3]

 SBC_FLT1 [1]

 RHF_FLT [2–3]

Conductance of 

internal faults

7.5e˗12

1.4e˗10

7.2e˗10

1.9e˗13

1.9e˗13

4.0e˗04

ft2/day No

No

No

No

No

Yes

107/28.3

40/47.7

62/40.9

29/49.7

121/23.8

HFB—hydraulic

conductance factor

Initial groundwater levels3

[1–3] SCL_HEDLY1

SCL_HEDLY2

SCL_HEDLY3

Scale factor for 

adjusting initial 

groundwater levels

1.006

1.00

1.00

Multiplier Yes

Yes

Yes

==== Scale factor of initial

groundwater levels

1

Parameters used in calibration varies between calibration runs and indicators here reflect parameters that were generally estimated through the automated 

process. An additional 15 parameters for scaling precipitation and potential ET were included in the model but remained fixed at the standard values of units 

conversion.

2

MO_FLT is the Morales Fault, GRV_FLT is the Graveyard Fault, TTHL_FLT is the Turkey Track Hill Fault, SBC_FLT is the Santa Barbara Canyon Fault, 

and RHF_FLT is the Rehobith Farm Fault. Numbers within brackets are layers where flow barriers are present.

3

Scale factors for initial head not part of original sensitivity run. These parameters were added later and were then the most sensitive parameters.

Table 14. Summary of parameter values estimated for the Cuyama Valley hydrologic model (CUVHM), Cuyama Valley, 

California.—Continued
Tables 147

Table 15. Summary of streambed conductivity parameters and current values, Cuyama Valley, California.

Segment 

categories

Segment 

conductance 

group name1

Stream segment 

conductivity

(foot per day)

Estimated using 

automated

methods2

Rank and composite 

scaled sensitivity

(=== not estimated)

Tributary channels and

Cuyama River channel 

on Recent Alluvium

Ventucopa

VC_QYAUC 

WVC_QYAUC

Main zones

NMZ_QYA 

SMZ_QYA

WZ_QYA

WNMZ_QYA

WSMZ_QYA

WWZ_QYA

Sierra Madre Foothills zones

None 

Cuyama River channel

NMZ_QYACC

SMZ_QYACC

WZ_QYACC

VC_QYACC

WNMZ_QYACC

WSMZ_QYACC

WVC_QYACC

WWZ_QYACC

52.5

1.91

0.98

1.50

5.38

0.29

0.23

0.21

==

2.23

0.75

2.11

52.5

0.29

1.06

4.95

10.85

Yes

No

No

No

No

No

No

No

None

No

No

No

Yes

No

No

Yes

Yes

148/1.65

146/1.74

129/2.89

153/1.40

150/1.44

145/1.77

152/1.44

144/1.78

==

134/2.28

130/2.84

141/1.85

128/3.34

157/0.90

139/1.89

143/1.80

135/2.25

Tributary channels and

Cuyama River channel 

on Older Alluvium

Ventucopa

VC_QOAS

WVC_QOAS 

Main zones

NMZ_QOA 

WWZ_QOAS

WNMZ_QOA

WZ_QOAS

Sierra Madre Foothills zones

SMFH_QOAN

SMFH_QOAM

SMFH_QOAS 

WSMFH_QOAM

WSMFH_QOAN

WSMFH_QOAS

Cuyama River channel

None

2.16

2.99

0.40

0.65

1.73

0.11

0.18

0.71

0.27

0.27

6.87

0.34

Yes

No

No

No

No

No

No

No

No

No

No

No

None

156/1.09

140/1.88

138/1.94

151/1.44

147/1.72

155/1.39

132/2.49

154/1.40

131/2.75

133/2.42

137/1.94

136/2.07

Tributary channels and

Cuyama River channel 

on Morales formation

Ventucopa

VC_MOUC 

WVC_MOUC

Main zones

None

Sierra Madre Foothills zones

None

Cuyama River channel

None

3.15

3.40

Yes

No

None

None

None

149/1.60

142/1.81
148 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

Segment 

categories

Segment 

conductance 

group name1

Stream segment 

conductivity

(foot per day)

Estimated using 

automated

methods2

Rank and composite 

scaled sensitivity

(=== not estimated)

Fraction of inflows as recharge plus runoff from basin characterization model

Total inflow Cuyama River

Flw84

Total Inflow Santa Barbara Canyon

Flw113

1.00

1.00

No

No

====

Fraction of inflows as recharge or runoff from basin characterization model

Inflow Cuyama River as 

runoff or recharge

Run84

Rch84

Inflow Santa Barbara

as runoff or recharge Canyon

Run113

Rch113

1.00

0.78

1.00

0.76

No

Yes

No

Yes

====

1

Refer to figures 5 and 10 for distribution of stream segments and parameter distributions. 

Table 15. Summary of streambed conductivity parameters and current values, Cuyama Valley, California.—Continued
Tables 149

Table 16. Summary of groundwater-flow budgets for selected periods, Cuyama Valley, California.[Average flows in acre-feet per year. Water Year is October through September of the following year. The precision of the numbers do not reflect the variabe accuracy of the estimates and round off. Abbreviations: CUVHM, Cuyama Valley hydrologic model; SMFH, Sierra Madre Foothills; +, plus]

Region/subregion 

and time period

Average-net inflows Average-net outflows

Inflow –

outflow1 Storage 

depletion

Direct 

infiltration 

(DI)

Stream 

flow 

infiltration 

(SI)

Total 

recharge 

(DI + SI)

Total 

inflows

Storage 

accretion

Net 

under 

flow

Outflow 

as springs

Domestic 

pumpage

Watersupply 

pumpage

Agricultural 

pumpage

Total 

pumpage

Total 

discharge

CUVHM 1950–2010 234,500 5,600 27,500 33,100 67,600 0 3,700 1,000 16 90 65,300 65,400 70,100 –2,500CUVHM 2000–10 235,300 3,100 30,400 33,500 68,800 0 3,200 600 10 190 68,100 68,300 72,100 –3,300CUVHM 2005 (wet year) 28,100 16,600 93,200 109,800 190,800 80,900 3,200 700 17 180 68,500 68,700 153,600 37,200CUVHM 2009 (dry year) 45,200 100 16,700 16,800 62,000 0 3,000 600 16 190 59,800 60,000 63,600 –1,600Main zone 1950–2010 28,800 3,800 8,000 11,800 40,600 0 3,700 1,000 6 90 57,000 57,100 61,800 –21,200Ventucopa uplands 1950–2010 0 1,200 17,700 18,900 18,900 5,500 –15 0 8 0 7,400 7,400 12,900 5,900SMFH 1950–2010 11,200 600 1,800 2,400 13,600 0 0 0 2 0 900 900 900 12,800Main zone 2000–10 27,479 700 8,300 9,000 36,500 0 3,200 600 6 190 56,700 56,900 60,700 –24,200Ventucopa uplands 2000–10 0 1,500 20,500 22,000 22,000 6,000 –15 0 8 0 10,000 10,000 16,000 6,000SMFH 2000–10 13,800 900 1,600 2,400 16,300 0 0 0 2 0 1,400 1,400 1,400 14,900Base-case projection 

CUVHM 2011–71

32,700 2,400 29,500 31,900 64,600 0 2,900 400 7 190 63,700 63,900 67,200 –2,600

Reduced-supply projection 

2011–71

500 1,100 25,600 26,700 27,200 0 2,900 500 7 190 32,800 33,000 36,400 –9,200

Reduced-demand projection 

2011–71

0 1,300 29,500 30,800 30,800 11,900 3,000 600 7 190 15,800 16,000 31,500 –700

1Negative difference is net depletion and positive difference is net accretion of groundwater flow.2Valley wide storage depletion is net change in storage that also includes any storage accretion shown for individual zones.
150 Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California

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Prepared by the Sacramento Publishing Service Center.

For more information concerning this report, contact:

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U.S. Geological Survey

California Water Science Center

6000 J Street, Placer Hall

Sacramento, CA 95819

dc_ca@usgs.gov

or visit our Web site at:

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ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20145150

Hanson and others—Hydrologic Models and Analysis of Water Availability in Cuyama Valley, California—SIR 2014–5150, ver. 1.1