U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2015–5122

Prepared in cooperation with the San Gorgonio Pass Water Agency

Estimating Natural Recharge in San Gorgonio Pass 

Watersheds, California, 1913–2012
Cover. Simulated average recharge for the San Gorgonio Pass watersheds draped over a three-dimensional image created using U.S. 

Geological Survey National Elevation Dataset, U.S. Department of Agriculture National Agriculture Imagery Program (2010) and ESRI 

ArcScene.
Estimating Natural Recharge in San Gorgonio 

Pass Watersheds, California, 1913–2012

By Joseph A. Hevesi and Allen H. Christensen

Prepared in cooperation with the San Gorgonio Pass Water Agency

Scientific Investigations Report 2015–5122

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment—visit 

http://www.usgs.gov or call 1–888–ASK–USGS (1–888–275–8747).

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod/.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

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

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

Suggested citation:

Hevesi, J.A., and Christensen, A.H., 2015, Estimating natural recharge in San Gorgonio Pass watersheds, California, 

1913–2012: U.S. Geological Survey Scientific Investigations Report 2015–5122, 74 p. http://dx.doi.org/10.3133/

SIR20155122.

ISSN 2328-0328 (online)
iii

Acknowledgments

The authors would like to acknowledge the support of the San Gorgonio Pass Water Agency 

who cooperatively funded this study. 

The authors would like to also acknowledge several individuals that contributed to the technical 

content of this report, including technical reviewers Claudia C. Faunt (California Water Science 

Center, CAWSC) and Christopher Kunkel (Oklahoma Water Science Center), surface water 

specialist reviewer Charles Berenbrock (CAWSC), program chief reviewer Debra S. Curry 

(CAWSC), editorial reviewer Robin Miller (Sacramento Publishing Science Center, SacPSC), and 

technical illustrator Donna Knifong (SacPSC).
iv
v

Contents

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

Introduction.....................................................................................................................................................1

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

Study Area.......................................................................................................................................................5

Model Description .........................................................................................................................................6

Root-Zone Water Balance...................................................................................................................6

Shallow Perched Groundwater Zone................................................................................................8

Model Development ......................................................................................................................................8

Simulation Period..................................................................................................................................8

Initial Conditions....................................................................................................................................8

Model Layout and Discretization .......................................................................................................8

Grid-Cell Parameters............................................................................................................................9

Topography ...................................................................................................................................9

Land Cover..................................................................................................................................11

Soil .........................................................................................................................................15

Geology........................................................................................................................................16

Soil and Root-Zone Layer Thicknesses..................................................................................17

Climate Inputs......................................................................................................................................20

Daily Climate Records...............................................................................................................20

Average Monthly Precipitation and Air Temperature..........................................................20

Atmospheric Parameters .........................................................................................................20

Model Coefficients..............................................................................................................................25

Modeled Climate, Snowfall, and Potential Evapotranspiration (PET).................................................26

Precipitation................................................................................................................................26

Air Temperature and Snowfall.................................................................................................26

Potential Evapotranspiration (PET).........................................................................................26

Annual Variability in Climate ....................................................................................................26

Average Monthly Variability in Climate..................................................................................31

Model Calibration.........................................................................................................................................33

Streamflow Records...........................................................................................................................33

Calibration Results..............................................................................................................................33

Little San Gorgonio Creek (LSGC) ...........................................................................................33

San Timoteo Creek near Redlands (STCR) ............................................................................37

Snow Creek near White Water (SCWW)...............................................................................37

Chino Canyon Creek below Tramway (CCCT)........................................................................37

Tahquitz Creek near Palm Springs (TCNP) ............................................................................44

Summary of Model Calibration Results..................................................................................44

Model Sensitivity.................................................................................................................................44
vi

Model Results...............................................................................................................................................47

Average Annual Results ....................................................................................................................47

Evapotranspiration ....................................................................................................................48

Runoff .........................................................................................................................................49

Recharge.....................................................................................................................................50

Annual Results.....................................................................................................................................51

Average Monthly Results ..................................................................................................................54

Mountain Front Recharge .................................................................................................................60

Effects of Imperviousness.................................................................................................................61

Comparison of Models.......................................................................................................................61

Model Limitations.........................................................................................................................................67

Summary and Conclusions.........................................................................................................................70

References Cited..........................................................................................................................................72

Figures

1. Map showing study area for the San Gorgonio Pass watershed model, southern

California region, California: A, hydrographic areas and land-surface altitude;

and B, groundwater subbasins ..................................................................................................3

2. Diagrams showing representations of the soil zones by the INFILv3 code used 

for the San Gorgonio Pass watershed model, California, of A, the multi-layered 

root zone; and B, the perched zone...........................................................................................7

3. Map showing number of upstream cells indicating the cascade flow-routing

network for the San Gorgonio Pass watershed model, California.....................................10

4. Map showing calculated land surface slope used in the San Gorgonio Pass

watershed model, California.....................................................................................................11

5. Map showing percent impervious area, defined as the percentage of the cell

area that is covered by an artificial surface such as roadways, rooftops, and

parking lots, used in the San Gorgonio Pass watershed model, California......................12

6. Map showing land-cover type, San Gorgonio Pass watershed model, California..........13

7. Map showing State Soil Geographic Database (STATSGO) soil map units used

in the San Gorgonio Pass watershed model, California.......................................................15

8. Map showing surficial geology used in the San Gorgonio Pass watershed

model, California .........................................................................................................................17

9. Map showing estimated soil thickness used in the San Gorgonio Pass

watershed model, California.....................................................................................................18

10. Map showing estimated layer 6 thickness used in the San Gorgonio Pass

watershed model, California.....................................................................................................19

11. Map showing spatially distributed average annual precipitation estimated for

water years 1913–2012 by using the San Gorgonio Pass watershed model,

California ......................................................................................................................................27

12. Map showing spatially distributed average air temperature and snowfall

estimated for water years 1913–2012 using the San Gorgonio Pass watershed

model, California .........................................................................................................................28

Contents
vii

13. Map showing spatially distributed average annual potential evapotranspiration

simulated for water years 1913–2012 using the San Gorgonio Pass watershed

model, California .........................................................................................................................29

14. Graphs showing annual basin-wide averages for water years 1913–2012 using

the San Gorgonio Pass watershed model, California, for A, estimated

precipitation; B, estimated air temperature; C, simulated snowfall; and D,

simulated potential evapotranspiration ..................................................................................30

15. Graphs showing basin-wide averages for water years 1913–2012, using the 

San Gorgonio Pass watershed model, California, for A, estimated monthly 

precipitation; B, estimated air temperature; C, simulated snowfall; and 

D, simulated potential evapotranspiration .............................................................................32

16. Map showing location of five streamgages used for model calibration, 

San Gorgonio Pass watershed model, California..................................................................34

17. Graphs showing comparison of simulated and measured streamflow at the

Little San Gorgonio Creek streamgage using the San Gorgonio Pass watershed

model, California: A, monthly mean discharge; B, water year mean discharge; 

C, average monthly mean discharge; D, scatterplot of simulated to measured

monthly mean discharge; and E, scatterplot of simulated to measured water

year mean discharge .................................................................................................................35

18. Graphs showing comparison of simulated and measured streamflow at the 

San Timoteo Creek near Redlands streamgage using the San Gorgonio Pass

watershed model, California: A, monthly mean discharge; B, water year mean

discharge; C, average monthly mean discharge; D, scatterplot of simulated

against measured monthly mean discharge; and E, scatterplot of simulated

against measured water year mean discharge.....................................................................38

19. Graphs showing comparison of simulated and measured streamflow at the

Snow Creek near White Water streamgage using the San Gorgonio Pass

watershed model, California: A, monthly mean discharge; B, water year mean

discharge; C, average monthly mean discharge; D, scatterplot of simulated to

measured monthly mean discharge; and E, scatterplot of simulated to

measured water year mean discharge...................................................................................40

20. Graphs showing comparison of simulated and measured streamflow at the

Chino Canyon Creek below Tramway streamgage using the San Gorgonio Pass

watershed model, California; A, monthly mean discharge; B, water year mean

discharge; C, average monthly mean discharge; D, scatterplot of simulated to

measured monthly mean discharge; and E, scatterplot of simulated to

measured water year mean discharge...................................................................................42

21. Graphs showing comparison of simulated to measured streamflow at the

Tahquitz Creek near Palm Springs streamgage using the San Gorgonio Pass

watershed model, California: A, monthly mean discharge; B, water year mean

discharge; C, average monthly mean discharge; D, scatterplot of simulated

versus measured monthly mean discharge; and E, scatterplot of simulated

versus measured water year mean discharge......................................................................45

22. Map showing average annual evapotranspiration simulated for water years

1913–2012 using the San Gorgonio Pass watershed model, California.............................48

23. Map showing average annual runoff simulated for water years 1913–2012 

using the San Gorgonio Pass watershed model, California................................................49

Figures
viii

24. Map showing average annual recharge simulated for water years 1913–2012

using the San Gorgonio Pass watershed model, California................................................50

25. Graph showing simulated annual water budgets for water years 1913–2012

showing inflows, outflows, and changes in storage, using the San Gorgonio

Pass watershed model, California ...........................................................................................52

26. Graphs showing annual simulation results for water years 1913–2012, using the

San Gorgonio Pass watershed model: A, evapotranspiration; B, recharge; and

C, runoff........................................................................................................................................53

27. Graphs showing cumulative departure from the mean using the San Gorgonio 

Pass watershed model, California, for A, estimated precipitation and simulated 

evapotranspiration; and B, simulated recharge and runoff ................................................55

28. Map showing simulated recharge using the San Gorgonio Pass watershed

model, California, for A, water year 1993; and B, water year 2004.....................................56

29. Graphs showing monthly simulation results for selected components of the

water budget using the San Gorgonio Pass watershed model, California: 

A, average monthly results for water years 1913–2012; B, monthly results for 

water year 1993; and C, monthly results for water year 2004..............................................59

30. Map showing san Bernardino and San Jacinto mountain block areas used to

simulate mountain-front recharge for the Banning and Cabazon hydrographic

areas of the San Gorgonio Pass groundwater study area, San Gorgonio Pass

watershed model, California.....................................................................................................60

31. Graphs showing annual simulation results for the San Gorgornio Pass

groundwater study area and the San Bernardino and San Jacinto mountain

blocks using the San Gorgonio Pass watershed model, California: A, recharge;

and B, runoff................................................................................................................................62

32. Graphs showing average monthly simulation results for water years 1913 

to 2012 for the San Gorgornio Pass groundwater study area and the 

San Bernardino and San Jacinto mountain blocks using the San Gorgonio 

Pass watershed model, California: A, recharge; and B, runoff...........................................63

33. Map showing difference in simulated runoff for imperviousness-included minus

imperviousness-excluded simulations using the San Gorgonio Pass watershed

model, California .........................................................................................................................64

34. Map showing difference in simulated recharge for imperviousness-included

minus imperviousness-excluded simulations using the San Gorgonio Pass

watershed model, California.....................................................................................................65

35. Map showing difference in simulated recharge results from the updated model

minus results from the previous model using the San Gorgonio Pass watershed

model, California .........................................................................................................................66

36. Graphs showing comparison of annual results for updated and previous

models, water years 1930–2001, using the San Gorgonio Pass watershed model,

California: A, estimated precipitation; B, estimated air temperature; C, simulated

snowfall; D, simulated potential evapotranspiration; E, simulated

evapotranspiration; F, simulated recharge; and G, simulated runoff.................................68

Figures
ix

Tables

1. Vegetation parameters used in the San Gorgonio Pass watershed model, 

California ......................................................................................................................................14

2. Soil parameters developed from State Soil Geographic Database (STATSGO)

soil map units, root-zone layers 1–5, San Gorgonio Pass watershed model,

California ......................................................................................................................................16

3. Geologic parameters assigned to root-zone layers 6 and 7, San Gorgonio Pass

watershed model, California.....................................................................................................16

4. Climate stations having records of daily precipitation or records both of daily

precipitation and daily maximum and minimum air temperatures, 1909–2012,

used in the San Gorgonio Pass watershed model, California.............................................21

5. Parameters for regression models used to spatially interpolate maximum and

minimum daily air temperature for the San Gorgonio Pass watershed model,

California ......................................................................................................................................25

6. Monthly values of atmospheric parameters used to simulate solar radiation 

and potential evapotranspiration with the San Gorgonio Pass watershed 

model, California .........................................................................................................................25

7. Streamflow records used for model testing, San Gorgonio Pass watershed

model, California .........................................................................................................................34

8. Simulated 100-year water budget using the San Gorgonio Pass watershed

model, California: long-term averages for water years 1913–2012 ....................................47

9. Simulated 100-year water budget using the San Gorgonio Pass watershed 

model, California: results for water year 1993 .......................................................................58

10. Simulated 100-year water budget using the San Gorgonio Pass watershed

model, California: results for water year 2004 .......................................................................58
x

International System of Units to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

meter (m) 1.094 yard (yd) 

Area

hectare (ha) 2.471 acre

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 264.2 gallon (gal) 

cubic meter (m3) 35.31 cubic foot (ft3)

cubic meter (m3) 0.0008107 acre-foot (acre-ft) 

Flow rate

cubic meter per second (m3/s) 70.07 acre-foot per day (acre-ft/d) 

meter per year (m/yr) 3.281 foot per year (ft/yr) 

cubic meter per day (m3/d) 35.31 cubic foot per day (ft3/d) 

cubic meter per day (m3/d) 264.2 gallon per day (gal/d) 

milligrams per day (mm/d) 0.0016 inch per hour (in/h)

millimeter per year (mm/yr) 0.03937 inch per year (in/yr)

Supplemental Information

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

°F = (1.8 × °C) + 32.

Datum

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

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

Conversion Factors
xi

Abbreviations

CCCT Chino Canyon Creek below Tramway

CIMIS California Irrigation Management Information System

CIWMC California Interagency Watershed Mapping Committee

D-8 eight direction

DWR California Department of Water Resources

ET Evapotranspiration

ETo Reference Evapotranspiration

GIS Geographic Information System

GSFLOW Groundwater and Surface water Flow model

HA hydrographic area

HSA hydrographic subarea

INFILv3 INFILtration code version 3.0

LCMMP California Land Cover Mapping and Monitoring Program

LOWESS Locally Weighted Scatterplot Smoothing

LSGC Little San Gorgonio Creek

NED National Elevation Dataset

NLCD National Land Cover Data

NSME Nash-Sutcliffe Model Efficiency

PAEE percent average estimation error

PET potential evapotranspiration

PRISM Parameter Regression on Independent Slopes Model

PTC Potrero Creek

SCWW Snow Creek near White Water

SGPGSA San Gorgonio Pass groundwater study area

SGPWA San Gorgonio Pass Water Agency

SGPWM San Gorgonio Pass watershed model

SGR San Gorgonio River

STATSGO State Soil Geographic Database

STC San Timoteo Creek

STCR San Timoteo Creek near Redlands

USGS U.S. Geological Survey
xii
Abstract

A daily precipitation-runoff model was developed to 

estimate spatially and temporally distributed recharge for 

groundwater basins in the San Gorgonio Pass area, southern 

California. The recharge estimates are needed to define transient boundary conditions for a groundwater-flow model being 

developed to evaluate the effects of pumping and climate on 

the long-term availability of groundwater. The area defined 

for estimating recharge is referred to as the San Gorgonio Pass 

watershed model (SGPWM) and includes three watersheds: 

San Timoteo Creek, Potrero Creek, and San Gorgonio River. 

The SGPWM was developed by using the U.S. Geological 

Survey INFILtration version 3.0 (INFILv3) model code used 

in previous studies of recharge in the southern California 

region, including the San Gorgonio Pass area. The SGPWM 

uses a 150-meter gridded discretization of the area of interest 

in order to account for spatial variability in climate and watershed characteristics. The high degree of spatial variability in 

climate and watershed characteristics in the San Gorgonio 

Pass area is caused, in part, by the high relief and rugged 

topography of the area.

Daily climate data developed from a network of monitoring sites and published average monthly precipitation maps 

were used to develop the climate inputs for the SGPWM. 

Geographic Information System (GIS) data defining land 

surface altitude, vegetation, soils, surficial geology, and land 

cover were used to define input parameters representing the 

physical characteristics of the land surface, root zone, and 

shallow subsurface underlying the root zone. Model parameterization was based on a previous INFILv3 model developed 

for an area including the upper parts of the San Timoteo Creek 

and Potrero Creek drainages and the western part of the San 

Gorgonio River watershed. The previous INFILv3 model was 

calibrated by using available streamflow records from the 

model area. The SGPWM uses an updated INFILv3 version 

to represent shallow groundwater flow better beneath the root 

zone that contributes to lateral, downslope seepage rather than 

deep recharge. The SGPWM calibration was tested by using 

available streamflow records in the San Gorgonio Pass region.

The SGPWM was used to simulate a 100-year water 

budget, including recharge and runoff, for water years 1913 

through 2012. Results indicated that most recharge came 

from episodic infiltration of surface-water runoff in the larger 

stream channels. Results also indicated periods of great variability in recharge and runoff in response to variability in 

precipitation. More recharge was simulated for the area of the 

groundwater basin underlying the more permeable alluvial fill 

of the valley floor compared to recharge in the neighboring 

upland areas of the less permeable mountain blocks. The 

greater recharge was in response to the episodic streamflow 

that discharged from the mountain block areas and quickly 

infiltrated the permeable alluvial fill of the groundwater basin. 

Although precipitation at the higher altitudes of the mountain 

block was more than double precipitation at the lower altitudes 

of the valley floor, recharge for inter-channel areas of the 

mountain block was limited by the lower permeability bedrock 

underlying the thin soil cover, and most of the recharge in 

the mountain block was limited to the main stream channels 

underlain by alluvial fill.

Introduction

The San Gorgonio Pass Water Agency (SGPWA) is 

a regional water agency, established in 1961, that helps 

protect local groundwater basins in the San Gorgonio Pass 

region of southern California (http://www.sgpwa.com). The 

SGPWA is concerned with meeting water demands from 

Calimesa to Cabazon, California. The SGPWA service area 

is about 554 square kilometers (km2) of semi-arid badlands, 

alluvial plains, benches, and canyon watersheds that are in 

the San Gorgonio Pass region of southern California, about 

137 kilometers (km) east of Los Angeles (fig. 1A). The 

SGPWA (fig. 1B) includes areas in the San Gorgonio Pass 

and the San Timoteo groundwater subbasins (California 

Department of Water Resources, 2003). The SGPWA also 

includes areas in the Noble Creek, Cherry Valley, Beaumont, 

Banning, Cabazon, and Gilman Hot Springs Hydrographic 

Subareas (HSAs) defined by the California Interagency 

Estimating Natural Recharge in San Gorgonio Pass 

Watersheds, California, 1913–2012

By Joseph A. Hevesi and Allen H. Christensen
2 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Watershed Mapping Committee (CIWMC; 2004). This 

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

with the SGPWA, is a component of studies to evaluate the 

groundwater resources in the areas of the San Timoteo and 

San Gorgonio Pass groundwater subbasins, including a small 

part of the westernmost edge of the Indio groundwater subbasin (fig. 1B). This area includes the groundwater storage 

units identified by Bloyd (1971). The area of interest for 

these groundwater studies is referred to as the San Gorgonio 

Pass groundwater study area (SGPGSA) in this report. The 

SGPGSA is bounded by the San Bernardino Mountains to 

the north, the Whitewater River drainage to the east, the San 

Jacinto Mountains to the south, the San Timoteo Badlands 

and the Potrero Creek drainage to the southwest, and the San 

Timoteo Creek drainage to the west (fig. 1A). The primary 

directions of surface-water and groundwater outflow from 

the SGPGSA are eastward toward the downstream parts of 

the San Gorgonio and Whitewater River drainages on the 

east and westward towards the San Timoteo drainage on the 

west. A minor amount of outflow also goes southward toward 

the downstream part of the Potrero Creek drainage along the 

southwestern boundary. The drainage divide between the San 

Timoteo Creek drainage and the San Gorgonio River drainage is defined by the boundary between the Noble Creek and 

Cabazon HSAs. The drainage divide between the San Timoteo 

Creek drainage and the Potrero Creek drainage is defined by 

the boundary between the Beaumont and Gilman Hot Springs 

HSAs. The drainage divide between the San Gorgonio River 

drainage and the Potrero Creek drainage is defined by the 

boundary between the Banning and Gilman Hot Springs 

HSAs.

Groundwater has been the only source of potable water 

supply for residential, industrial, and agricultural users in the 

Beaumont and Banning areas of the San Gorgonio Pass region 

(Rewis and others, 2006). The SGPWA has developed an 

extensive groundwater database to help manage and protect 

the local groundwater-storage units identified by Bloyd 

(1971). Groundwater levels near Beaumont declined by as 

much as 100 feet (ft) between the early 1920s and early 2000s, 

and numerous natural springs have stopped flowing in the San 

Timoteo subbasin (Bloyd, 1971). Because of the ongoing concerns regarding future availability of groundwater resources, 

the SGPWA is evaluating groundwater storage in the San 

Gorgonio Pass area, including the San Gorgonio Pass and San 

Timoteo subbasins.

Much of the area served by the SGPWA is experiencing 

groundwater overdraft, where more water is pumped out of 

the ground each year than is replaced by nature through rain 

and snowmelt (http://www.sgpwa.com/faq/). Since 2003, the 

SGPWA has been importing water through the California 

Aqueduct to recharge local groundwater basins used by retail 

water companies, by private companies, and by individuals 

for domestic and other purposes. According to the SGPWA, 

without the imported water and with continued groundwater 

usage at rates similar to past rates, groundwater levels would 

likely continue to decline (in some areas, water levels have 

decreased nearly 90 feet in the past 70 years). A continued 

decline in groundwater levels would likely cause some wells 

to dry up, and the power required to pump the water to the 

surface would also likely continue to increase each year. The 

overall objective of the groundwater studies is to evaluate the 

groundwater resources of the SGPGSA and, additionally, to 

determine the hydrologic response of the groundwater system 

to future climatic conditions and projected land use. The future 

climatic conditions could change the amount of available 

recharge, while the projected land uses could require more 

water.

The availability of groundwater in the SGPWA service 

area has been evaluated by using groundwater-flow models 

(Bloyd, 1971; Rewis and others, 2006). An important 

component of groundwater models is the inflow to the 

groundwater system from natural recharge (Reichard and 

others, 2003; Nishikawa and others, 2004; Rewis and others, 

2006; Belcher and Sweetkind, 2010. Natural recharge includes 

recharge in direct response to precipitation, snowmelt, and 

infiltration of surface runoff and streamflow.

Estimates of natural recharge have been made in the 

southern California region by using precipitation-runoff 

models, including in the Death Valley region (Hevesi and 

others, 2003), the Joshua Tree area (Nishikawa and others, 

2004), Big Bear Valley (Flint and Martin, 2012), and the 

western part of the SGPGSA (Rewis and others, 2006). The 

precipitation-runoff models in these studies were developed by using the INFIL version 3.0 (INFILv3) model code 

(U.S. Geological Survey, 2008), and recharge was simulated 

as a component of the water budget. Results for the previous 

INFILv3 model developed for the western SGPGSA (Rewis 

and others, 2006), referred to here as the Rewis model, 

consisted of the spatially and temporally distributed recharge 

and runoff for water years 1930–2001, and included natural 

recharge simulated in the area of a groundwater model 

developed for the western SGPSA as well as natural recharge 

simulated for drainages in the mountain block areas upstream 

of and bordering the boundary of the groundwater model. 

A portion of the total recharge simulated by the INFILv3 

model for the upstream drainages was included as a mountainfront recharge boundary condition for the groundwater model 

(Rewis and others, 2006).

Purpose and Scope

The purpose of this report is to document a study done 

to provide updated estimates of transient areal and mountainfront recharge for the SGPGSA by using a deterministic 

precipitation-runoff modeling approach. The study presented 

in this report is an enhancement of the Rewis model developed by Rewis and others (2006). The model enhancement 

was done (1) to cover the total area of the SGPGSA, including 

the eastern extent of the Cabazon Hydrographic Area (HA); 

(2) to include more recent climate records to enable a longer 
Purpose and Scope 3

79

243

60

10

10

86

62

38

San Bernardino County

Riverside County

San Timoteo WashSan Timoteo Wash

Yucaipa CreekYucaipa Creek

Oak

Glen Creek

Oak

Glen Creek

Noble CreekNoble Creek

Wilson CreekWilson Creek Birch Creek

Birch Creek San Jacinto River San Jacinto River

Potrero CreekPotrero Creek

Poppet Creek Poppet Creek

Montgomer y CreekMontgomer y Creek

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Hathaway CreekHathaway Creek

Potrero Creek Potrero Creek

Stone CreekStone Creek

Indian CreekIndian Creek

Tahquitz CreekTahqui

Wil

tz Creek

low Creek

Willow Creek

Long Valley CreekLong Valley Creek

Whitewater River Whitewater River

Whitewater RiverWhitewater River

Gorgonio

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Jenson Creek Jenson Creek

Snow CreekSnow Creek

Falls Creek Falls Creek

One Horse

San

Creek

River

Little San Gorgonio CreekLittle San Gorgonio Creek

Cherry Valley

HSA

Beaumont HSA

Noble

Creek

HSA Cabazon HSA

Banning

HSA

Gilman Hot Springs HSA

Indio HSA

San

Timoteo

Badlands

San

Jacinto

Basin

San Bernardino

Mountains San Gorgonio Mountain

Salton

Sea

Basin

San Jacinto

Mountains

San

Jacinto

Peak

Little San Gorgonio

Peak

Santa Ana

River Watershed

Hemet

Banning

Yucaipa

Cabazon

Beaumont

Redlands

White Water

San Jacinto

Palm

Springs

Moreno Valley

Cherry

Valley

Morongo

Valley

Gilman Hot Springs

Calimesa

EXPLANATION

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

San Gorgonio Pass Water Agency

Hydrographic subareas (HSA) defined by the California 

 Interagency Watershed Mapping Comittee

Land surface altitude, in meters

0–250

251–500

501–750

751–1,000

1,001–1,250

1,501–1,750

1,751–2,000

2,001–2,250

2,251–2,500

2,501–2,750

1,251–1,500 2,751–3,500

County boundary

Mountain peak

117°10' 117° 116°50' 116°40'

34°

33°

50'

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Base modified from U.S. Geological Survey and 

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

A

"

"

"

San Diego

Los

Angeles

San

Francisco

CALIFORNIA

San Gorgonio

Pass study

area

sac15-0556_fig 1a

Figure 1. Study area for the San Gorgonio Pass watershed model, southern California region, California: A, hydrographic areas and 

land-surface altitude; and B, groundwater subbasins.
4 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

79

243

60

10

10

86

62

38

San Bernardino County

Riverside County

San Timoteo CreekSan Timoteo Creek

Yucaipa CreekYucaipa Creek

Oak

Glen Creek

Oak

Glen Creek

Noble CreekNoble Creek

Wilson CreekWilson Creek Birch Creek

Birch Creek San Jacinto River San Jacinto River

Potrero CreekPotrero Creek

Poppet Creek Poppet Creek

Montgomer y CreekMontgomer y Creek

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Hathaway CreekHathaway Creek

Potrero Creek Potrero Creek

Stone CreekStone Creek

Indian CreekIndian Creek

Tahquitz CreekTahqui

Wil

tz Creek

low Creek

Willow Creek

Long Valley CreekLong Valley Creek

Whitewater River Whitewater River

Whitewater RiverWhitewater River

Gorgonio

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Jenson Creek Jenson Creek

Snow CreekSnow Creek

Falls Creek Falls Creek

One Horse

San

Creek

River

Little San Gorgonio CreekLittle San Gorgonio Creek

Cherry Valley

HSA

Beaumont HSA

Noble

Creek

HSA Cabazon HSA

Banning

HSA

Gilman Hot Springs HSA

Indio HSA

San

Timoteo

Badlands

San

Jacinto

Basin

San Bernardino

Mountains San Gorgonio Mountain

Salton

Sea

Basin

San Jacinto

Mountains

San

Jacinto

Peak

Little San Gorgonio

Peak

Hemet

Banning

Yucaipa

Cabazon

Beaumont

Redlands

White Water

San Jacinto

Palm

Springs

Moreno Valley

Cherry

Valley

Morongo

Valley

Gilman Hot Springs

Calimesa

EXPLANATION

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

San Gorgonio Pass Water Agency

Hydrographic subareas (HSA) defined by the California 

 Interagency Watershed Mapping Comittee

Previous model boundary from

 Rewis and others, 2006

County boundary

Mountain peak

Groundwater subbasins

Indio

San Gorgonio Pass

San Timoteo 

San Gorgonio Pass groundwater-

 study area

117°10' 117° 116°50' 116°40'

34°

33°

50'

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Base modified from U.S. Geological Survey and 

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

B

"

"

"

San Diego

Los

Angeles

San

Francisco

CALIFORNIA

San Gorgonio

Pass study

area

sac15-0556_fig 1b

Figure 1. —Continued

simulation period; (3) to include the INFILv3 seepage-flow 

modification developed in Flint and Martin (2012); and (4) to 

use a model grid matched to the grid being considered for 

groundwater modeling studies. The model was developed 

primarily as an expansion and update of the Rewis model 

(Rewis and others, 2006). A limited calibration was performed 

on the model for selected parameters by comparing simulated 

and measured streamflow at streamgage locations.

In contrast to transient recharge estimated by using 

empirical precipitation-scaling methods (Reichard and 

others, 2003; Farrar and others, 2006), the precipitationrunoff modeling approach provides a more physically based 

method for estimating the spatial and temporal distribution of 

recharge. The advantage of the precipitation-runoff method is 

that it provides a process-based representation of variations in 

recharge caused by the spatially varying physical characteristics of the watersheds being modeled. The precipitation-runoff 

modeling method also provides a process-based representation 

of variations in all components of the water budget, including 

recharge and runoff, caused by the effects of temporal and 

spatial differences in temperature and precipitation. In general, 

deterministic precipitation-runoff models are more applicable 

to evaluating hydrologic responses to variations in climate 

and watershed characteristics compared to empirical methods 

and provide a more appropriate tool for evaluating the effects 

of potential management scenarios. Model results can be 

mapped and subsequently used to evaluate the integrated 

effect of spatially distributed climate, terrain, and watershed 
Study Area 5

characteristics (for example, land cover, soils, and geology) 

on the spatial and temporal distribution of runoff and potential 

recharge.

The INFILv3 application presented in this report included 

an expanded model domain that contains all surface-water 

drainages potentially contributing runoff and recharge inflows 

to the SGPGSA. The model used a simulation period of 

100 years, starting with water year 1913 and ending with 

water year 2012. The 100-year simulation period was used to 

evaluate temporal and spatial variability in natural recharge 

as well as long-term average recharge rates. To provide a 

more efficient coupling between the INFILv3 model and a 

groundwater-flow model being developed for the SGPGSA, 

the model grid size was increased from 30 to 150 meters (m), 

and the grid orientation was matched to the grid size and 

orientation used for the groundwater-modeling studies of the 

SGPGSA. This updated model was used to develop monthly 

and daily recharge results in the SGPGSA as well as for the 

surrounding drainages that had the potential to contribute 

inflows to the SGPGSA. To represent shallow subsurfaceflow paths beneath the root zone that can result in a seepage 

inflows to downslope areas, the updated model used a version 

of INFILv3 that included a seepage-flow component (Flint 

and Martin, 2012). The seepage-flow component allows for 

the lateral redistribution of potential recharge (where potential 

recharge is infiltration through the root zone). To more directly 

represent the effect of impervious areas on simulated recharge 

and runoff, the INFILv3 code was modified to allow for 

the partitioning of grid cells into pervious and impervious 

components.

The precipitation-runoff model does not account for 

induced recharge from retention basins, spreading grounds, 

and injection wells. To help evaluate potential effects of 

urbanization on the hydrologic system, however, the model 

was applied to evaluate changes in runoff and recharge in 

response to impervious areas associated with urban-land uses. 

Model results were also compared to results obtained by using 

the Rewis model (Rewis and others, 2006).

Study Area

The study area for this report is defined as the San 

Gorgonio Pass watershed model (SGPWM), and covers 

688 km2 (fig. 1). The SGPWM is in the western parts of San 

Bernardino and Riverside counties. The SGPWM consists of 

three separate model domains that were simulated separately: 

(1) The San Timoteo Creek drainage, (2) the Potrero Creek 

drainage, and (3) the San Gorgonio River drainage. The 

SGPWM area was defined on the basis of the need to include 

all surface-water drainages that could potentially affect 

the natural recharge boundary condition for the SGPGSA, 

including spatially distributed recharge in the SGPGSA and 

mountain-front recharge from the drainages bordering the 

SGPGSA.

The SGPWM is bounded by Little San Gorgonio Peak 

and the San Bernardino Mountains to the north and San 

Jacinto Peak and the San Jacinto Mountains to the south 

(fig. 1A). The northern part of the SGPWM includes the rugged terrain of the San Bernardino Mountains. Land-surface 

altitudes range from a low of 200 m in the valley of the Indio 

subbasin along the eastern boundary of the SGPWM to a 

high of 2,900 m at the summit of Little San Gorgonio Peak. 

The southern part of the SGPWM includes the very steep and 

rugged terrain of the San Jacinto Mountains, which reach an 

altitude of 3,300 m at the summit of San Jacinto Peak, the 

highest location in the SGPWM. The upland terrain of the 

mountain block areas to the north and south of the SGPGSA 

has land cover consisting primarily of natural vegetation. The 

soil cover is generally thin in the upland areas, and the underlying bedrock consists primarily of low-permeability crystalline rocks. The lower altitudes of the valley floor are underlain 

by thicker soils and alluvial deposits. Urban development is 

limited primarily to the lower altitudes of the valley floor.

The SGPWM includes three main surface-water drainages: San Timoteo Creek draining to the west, San Gorgonio 

River draining toward the east, and Potrero Creek draining 

toward the south (fig. 1A). The San Timoteo Creek and Potrero 

Creek drainages are tributaries of the Santa Ana River watershed, which drains westward into the Pacific Ocean, and the 

San Gorgonio River drainage is a tributary of the Whitewater 

River watershed, which drains southeastward into the Salton 

Sea basin. The three surface-water drainages were modeled 

separately by using the three SGPWM model domains. 

Streamflow in the SGPWM is generally ephemeral to 

intermittent; however, low flows are maintained by spring 

discharge for some of the smaller tributaries in the mountain 

blocks. Prolonged streamflow of several weeks and longer can 

also be maintained in some channels by snowmelt. The San 

Timoteo Creek drainage includes three HSAs defined by the 

California Interagency Watershed Mapping Committee (2004): 

Noble Creek, Cherry Valley, and Beaumont (fig. 1). The Noble 

Creek and Cherry Valley HSAs are tributaries to the Beaumont 

HSA, which includes areas downstream and outside of the San 

Timoteo Creek drainages. The San Gorgonio River drainage 

also includes three HSAs defined by the CIWMC: Banning, 

Cabazon, and Indio. The Banning HSA is upstream of the 

Cabazon HSA, the Cabazon HSA is upstream of the Indio 

HSA, and the Indio HSA includes areas downstream of the 

San Gorgonio River drainage.

Climate is a critical characteristic of the hydrologic 

system represented by the SGPWM. Variations in the timing, 

frequency, amount, and spatial distributions of precipitation 

are important factors affecting the hydrologic system. Spatial 

and temporal variations in air temperature, including diurnal 

differences between maximum and minimum daily temperature, are critical factors affecting the energy available for 

evapotranspiration (ET) and the form of precipitation (rain or 

snow). In the southern California region, precipitation falls 

primarily during the cooler months of October through May. 
6 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

The months of June through September are generally hot and 

dry for most locations. Notable spatial variability in climate 

is based on distance from the coastline and topography; these 

both affect precipitation and air temperature. Inland locations 

are generally hotter during the summer and colder during the 

winter compared to locations closer to the coastline. Higher 

elevations are generally colder and wetter compared to the 

low-lying coastal plane and interior valleys. Temperature 

inversions, where colder, denser air sinks to the valley floor, 

are common in the interior basins of southern California.

The San Gorgonio Pass area is in a transition zone of 

higher precipitation and lower potential evapotranspiration 

(PET) toward the west and lower precipitation and higher PET 

to the east. Potential evapotranspiration, also referred to as 

reference evapotranspiration (ETo), is estimated throughout 

California by the California Irrigation Management and 

Information System (CIMIS) using a network of field 

monitoring stations (California Irrigation Management and 

Information System, (CIMIS), 2005). The ETo estimate represents the maximum amount or rate of evapotranspiration under 

fully saturated conditions (California Irrigation Management 

and Information System, (CIMIS), 2005). According to 

CIMIS, ETo for the general area of the SGPWM is about 

1,600 millimeters per year (mm/yr). The SGPWM is mostly 

in the CIMIS ETo zone number 16, with a minimum average 

monthly PET of about 40 millimeters (mm) for December 

and January and a maximum average monthly PET of about 

240 mm for July (California Irrigation Management and 

Information System,(CIMIS), 2005). The easternmost part of 

the SGPWM is in CIMIS ETo zone 18, with a higher annual 

ETo of about 1,800 mm/yr. The westernmost part of the 

SGPWM is within CIMIS ETo zone 9, with a lower annual 

PET of about 1,400 mm/yr.

Model Description

The SGPWM was developed by using the INFILv3 code 

(U.S. Geological Survey, 2008). INFILv3 is a deterministic, 

grid-based, distributed-parameter, precipitation-runoff 

modeling application that calculates the temporal and spatial 

distribution of daily net infiltration at the lower boundary of 

the root zone. The bottom of the root zone is the estimated 

maximum depth below ground surface affected by ET. In 

many field applications, net infiltration can be assumed to 

equal recharge to an underlying water-table aquifer and 

can be used to define the recharge boundary condition 

for groundwater-flow models (Hevesi and others, 2003; 

Nishikawa and others, 2004; Rewis and others, 2006). A more 

detailed description of INFILv3 modeling code is provided 

in Hevesi and others (2003) and USGS (2008). The INFILv3 

code and documentation are available at http://water.usgs.gov/

nrp/gwsoftware/Infil/Infil.html.

The INFILv3 code requires the discretization of the 

watershed being simulated into a horizontal grid-based 

network of model cells (fig. 2). The grid-based discretization 

is used to spatially distribute daily climate inputs and model 

parameters representing the physical characteristics of the 

watersheds in the model domain. The model cells are connected into a drainage network, and runoff generated by a cell 

is routed across the grid by using a simple cascading flowrouting process (U.S. Geological Survey, 2008).

The INFILv3 code provides an estimate of recharge 

based on simulated daily net infiltration, where net infiltration 

is defined as the percolation of water below the maximum 

depth of the root zone (or the zone of ET) in response to infiltration from rain, snowmelt, and surface-water inflows (Hevesi 

and others, 2003; Nishikawa and others, 2004; Rewis and 

others, 2006; U.S. Geological Survey, 2008; Flint and Martin, 

2012). Daily net infiltration and evapotranspiration are simulated by INFILv3 using a multi-layered representation of the 

root zone, and simulated daily runoff is allowed to infiltrate 

back into the root zone during the process of surface-water 

flow routing, thereby accounting for the effects of streamflow 

on recharge (fig. 2A).

Root-Zone Water Balance

INFILv3 uses six layers to simulate the root-zone 

water balance, including net infiltration through the root 

zone. Five upper layers are used to represent the soil component of the root zone, and a lower, sixth layer is used to 

represent the geologic unit (either bedrock or unconsolidated 

deposits) underlying the soil zone (fig. 2A). All root-zone 

layers can have either uniform or variable thicknesses and are 

parameterized by using maps of geology, soils, and vegetation. 

The bottom of the root zone is the estimated maximum depthbelow-ground surface affected by ET.

INFILv3 uses a daily time step for simulating the water 

balance of the root zone and an hourly time step for simulating solar radiation and PET. The simulated daily water 

balance of the root zone includes precipitation (as either 

rain or snow), snow accumulation, sublimation, snowmelt, 

infiltration into the root zone, ET, percolation through the root 

zone, water-content changes for each root-zone layer, surfacewater runoff, and net infiltration from the root zone (defined 

as drainage from the bottom root-zone layer; fig. 2). PET is 

simulated by using an hourly time step to better represent 

the shading effects of rugged terrain relative to changes in 

solar position throughout the year (Flint and Childs, 1987). 

By using a modified form of the Priestley-Taylor equation 

(equations 10–14, U.S. Geological Survey, 2008), daily ET is 

simulated as a combined function of daily PET, the vertical 

distribution of available water in the root-zone layers, and the 

root-zone density, where the root-zone density represents the 

characteristics of vegetation.
Model Description 7

Maximum rootzone thickness

Net infiltration

Recharge

Net

infiltration

Soil zone

Bedrock

Bedrock layer (6)

Bedrock layer (6)

Perched zone (7)

Inflow

Runoff

Outflow

Snowmelt

Infiltrated

runoff

Seepage

Runoff

Evapotranspiration

Rain

Evapotranspiration

Root

zone

Root

zone

Runoff

Soil layer (1)

Soil layer

Soil layer (2)

Soil layer (3)

Soil layer (4)

Soil layer (5)

A

B

Figure 2. sac15-0556_fig 02 Representations of the soil zones by the INFILv3 code used for the San Gorgonio Pass watershed model, California, of A, the 

multi-layered root zone; and B, the perched zone.
8 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Shallow Perched Groundwater Zone

Following the approach used in Flint and Martin (2012), 

a modified version of the INFILv3 code was applied in this 

study in order to include a shallow perched-groundwater zone 

(layer 7) beneath the bottom layer of the root zone (fig. 2B). 

The shallow perched zone is used to partition net infiltration 

through the root zone into two separate flow components: 

(1) lateral flux (seepage) and (2) downward flux (recharge), 

with the lateral-flux component contributing to the inflows of 

the downstream cell (Flint and Martin, 2012). The amount of 

seepage flow is dependent on the water content of layer 7, the 

land-surface slope, and the effective lateral hydraulic conductivity assigned to layer 7. The amount of recharge is dependent 

on the water content of layer 7 after the calculation of seepage 

and on the effective vertical hydraulic conductivity assigned to 

layer 7.

Seepage flow is considered to be potentially important in 

rugged mountainous areas with steep slopes (Flint and Martin, 

2012). The inclusion of seepage flow allows for a portion of 

simulated net infiltration to flow laterally to the active root 

zone of the downstream grid cell. In general, the net effect 

of seepage flow is to reduce recharge for the upstream grid 

cell, while increasing ET for downstream cells. Recharge for 

downstream cells can also be increased in response to seepage 

flow. The increased seepage flow generally causes an increase 

in runoff during dry periods, which represents the baseflow 

component of streamflow. Lateral subsurface flow tends to 

cause more rapid drainage and, thus, drier antecedent conditions in the root zone of upstream cells, and this, in-turn, can 

reduce runoff.

Model Development

Development of the SGPWM by using the INFILv3 code 

was done by defining (1) the simulation period; (2) the initial 

conditions; (3) the model layout and spatial discretization; 

(4) the grid-cell parameters representing the physical 

characteristics of the model domain; (5) the climate inputs, 

consisting of daily climate records and model parameters for 

spatially interpolating daily precipitation and air temperature 

over the model domain; and (6) model coefficients.

Simulation Period

The target simulation period for the SGPWM was a 

continuous 100-year period, starting with water year 1913 and 

ending with water year 2012. The target simulation period 

was used to develop and analyze simulation results, including 

recharge and all components of the water budget. Simulations were started on January 1, 1909, to allow for a 45-month 

(3.75 year) model initialization period prior to the start of the 

target period. Precipitation-runoff models, including INFILv3, 

generally require at least some initialization period in order to 

help minimize uncertainties associated with the assumed or 

estimated initial conditions. A 1-year initialization period is 

considered adequate for modeled storage components (rootzone layers and snowpack storage) for study areas where 

measurable snow cover does not persist through the summer 

months (Markstrom and others, 2008). Previous INFILv3 

applications for the San Gorgonio Pass study area indicated 

that a 3-year initialization period for the root-zone water content was sufficient to generate results independent of the initial 

conditions assumed for most locations (Rewis and others, 

2006). The 45-month initialization period used in the SGPWM 

was assumed to be sufficient for reducing uncertainty associated with the initial conditions (see following section). The 

initialization period was excluded in the calculation of simulation results, such as the long-term average recharge and runoff 

rates. 

Initial Conditions

Initial conditions consisted of the starting water contents 

of the root-zone layers (layers 1 through 6), of the perched 

zone (layer 7), and of the snowpack. Initial conditions for 

the SGPWM were made equivalent to initial conditions used 

in previous INFILv3 studies (Flint and Martin, 2012). The 

initial conditions consisted of an initial water content for soil 

layers in the root zone (layers 1 through 5) equal to 1.5 times 

the wilting-point water content. An initial water content of 

zero was assumed for root-zone layer 6, for the perched zone 

(layer 7), and for the snowpack.

Model Layout and Discretization

Spatial discretization is used to represent the 

heterogeneity of the model domain and includes both 

horizontal and vertical discretization. With the INFILv3 code, 

horizontal discretization is defined by the grid-cell spacing, 

orientation, and extent. Vertical discretization is defined by 

the thickness of the seven model layers (six layers for the root 

zone and one layer for the perched zone), which can vary from 

cell to cell. 

The INFILv3 code requires that the area modeled is discretized into a horizontal two-dimensional grid of equal-area 

(square) cells that are linked to create a cascading-flow routing 

network used to represent the mapped drainages and streamlines. The gridded discretization is similar to the approach 

used in other models, such as the Precipitation Runoff 

Modeling System (PRMS; Jeton and Maurer, 2011), Topmodel 

(Beven and Kirkbys, 1979), the Basin Characterization Model 

(BCM; Flint and Flint, 2007), and the Groundwater and 

Surface-water Flow model (GSFLOW; Markstrom and others, 

2008; Hevesi and others, 2011; Woolfenden and Nishikawa, 

2014). In addition to defining the streamlines, the gridded discretization is used to represent heterogeneities in climate and 

the physical characteristics of the watershed. Each cell in the 

two-dimensional grid represents a homogeneous hydrologic 
Model Development 9

response unit. For the SGPWM, a grid-cell geometry of 

150 by 150 m was used, aligned according to the Universal 

Transverse Mercator, zone 11 projection, North American 

Datum of 1983. The grid-cell size and geometry was selected 

to match the grid used for groundwater modeling of the 

SGPGSA.

The SGPWM model domain and layout are defined by 

the flow-routing connections, the watershed boundaries, and 

the model subdomains. The SGPWM model domain includes 

30,595 active model cells covering an area of 68,832 hectares 

(688.3 km2) and consists of three separate subdomains. Gridcell connections defining the downstream cell for surfacewater and seepage-flow routing were developed by using a 

combination of grid-cell altitudes and mapped hydrographic 

features. The average grid-cell altitudes were defined in a 

geographic information system (GIS) by using a 10-m digital 

elevation model compiled from the National Elevation Dataset 

(NED, Gesch and others, 2009) obtained from http://nationalmap.gov/elevation.html. Initial flow directions defining the 

downstream cell were defined in GIS by using the standard 

eight-direction (D-8) routing method (Maidment, 2002). The 

D-8 “many-to-one” routing method specifies a single downstream cell for each grid cell, but a given cell can receive 

inflows from multiple upstream cells (to a maximum of eight). 

For some parts of the SGPWM, the flow directions were 

modified to provide a better match of the simulated drainage 

network to the mapped hydrography.

The total number of cells upstream of each cell, calculated in GIS by using the D-8 flow directions, was used to 

provide a visual representation of the SGPWM cascadingflow routing network (fig. 3). Locations with more than 

200-upstream cells indicate the larger channels represented by 

the model, such as Noble Creek, San Timoteo Creek, Potrero 

Creek, Smith Creek, San Gorgonio River, and Snow Creek. 

Locations with 11 to 100 upstream cells indicate the smaller, 

first-order streams.

The three SGPWM subdomains represent the three 

topographically defined surface-water drainages, each with a 

single cell at the lowest altitude of the subdomain defining the 

location of all surface-water and seepage discharge. The San 

Gorgonio River (SGR) subdomain has 23,291 model cells and 

an area of 52,405 hectares (524 square kilometers), the San 

Timoteo Creek (STC) subdomain has 6,362 model cells and 

an area of 14,315 hectares (143 square kilometers), and the 

Potrero Creek (PTC) subdomain has 939 model cells and an 

area of 2,113 hectares (21 square kilometers; figs. 1A, 3).

The INFILv3 code requires vertical discretization of the 

root zone into six layers, which are used to account for differences as a function of depth in root density, storage capacity, 

and vertical hydraulic conductivity. In addition to the six rootzone layers, the INFILv3 code used in the SGPWM includes a 

seventh layer that represents a perched zone beneath the root 

zone (Flint and Martin, 2012; fig. 2B). The seventh layer is 

used to simulate lateral flow in the unsaturated zone, referred 

to in this study as seepage flow. The model version used in the 

previous INFILv3 recharge study (Rewis and others, 2006) 

did not include a seventh layer or a seepage-flow component. 

In the INFILv3 version used in the SGPWM, seepage flow is 

routed from upstream to downstream cells by using the same 

D-8 cascade-routing network used for surface-water runoff 

(Flint and Martin, 2012).

The vertical discretization enables the simulation of differences in the root-zone water content and ET as a function 

of depth and allows for the representation of differences in 

the characteristics of the root zone between soil and bedrock. 

Model layers 1 through 5 represent the soil-zone component 

of the root zone, whereas the sixth model layer represents consolidated bedrock for locations with thin soils. For locations 

with thick soils, layer 6 can have a thickness of zero; however, 

net infiltration through the root zone is limited by the vertical 

hydraulic conductivity defined for layer 6. The layer 6 vertical 

hydraulic conductivity is used to represent the permeability of 

unconsolidated geologic units underlying the soil component 

of the root zone.

The model layers are assigned a storage capacity and 

a vertical hydraulic conductivity. The storage capacity is 

calculated by using an estimated porosity and layer thickness 

for each cell (Rewis and others, 2006; U.S. Geological Survey, 

2008, Flint and Martin, 2012). For a given cell, a layer can 

be assigned a thickness of zero, in which case the storage 

capacity of the layer is also zero. The layer thicknesses are 

determined on the basis of the total estimated root-zone and 

soil thicknesses, which, in turn, are estimated by using a combination of vegetation, soil, and geology maps. For this study, 

root-zone layer thicknesses were based on the Rewis model 

in the Beaumont and Banning subbasins (Rewis and others, 

2006) and were extended to the Cabazon and Indio subbasins. 

A more detailed discussion of layer thicknesses defined for the 

SGPWM is given in the following section.

Grid-Cell Parameters

Grid-cell parameters defining the physical characteristics 

of watersheds, including the land surface, the underlying root 

zone, and the shallow perched zone beneath the root zone, 

can be grouped into parameters representing (1) topography, 

(2) land cover, (3) soils, (4) geology, and (5) soil and rootzone layer thicknesses. These parameters were estimated for 

all model grid cells by using GIS and preprocessing routines.

Topography

The 10-m resolution USGS NED data (Gesch and others, 

2009) used in the development of the flow-routing network 

also was used to define topographic parameters used to 

simulate PET. The parameters used to simulate PET include 

cell altitude, slope, aspect, the sky view parameter (used to 

simulate incoming solar radiation), and a set of 36 blocking 

ridge angles (used to simulate the effects of shading on PET in 

rugged areas; U.S. Geological Survey, 2008).
10 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Number of upstream cells

0–10

11–20

21–50

51–100

101–200

201–500

501–1000

1001–2000

2001–5000

5001–23300

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 03

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 3. Number of upstream cells indicating the cascade flow-routing network for the San Gorgonio Pass watershed model, 

California.

Grid-cell altitudes for the SGPWM range from a 

minimum of 332 m along the eastern boundary, in the Indio 

subbasin, to a maximum of 3,293 m at the summit of San 

Jacinto Peak (fig. 1A). The average altitude for the SGPWM is 

1,059 m. The SGR subdomain has the highest average altitude 

at 1,108 m, and the PTC subdomain has the lowest average 

altitude at 784 m. The SGPGSA includes the lowest altitudes 

in the SGPWM, having an average altitude of 740 m, a maximum altitude of 960 m, and a minimum altitude of 332 m.

Average grid-cell slope was defined by calculating the 

slope in degrees of the10-m NED grid and, then, calculating 

the average 10-m NED slope in the area of each cell. The 

calculated average slope for the SGPWM is 12 degrees in the 

SGPGSA, with a maximum slope of 50 degrees for a cell on 

the upper northern slope of San Jacinto Peak and a minimum 

slope of zero degrees for a cell on the valley floor, (fig. 4). The 

SGR subdomain has the highest average slope at 14 degrees, 

and the PTC subdomain has the lowest average slope at 

3 degrees.

Grid-cell aspect is the direction of maximum slope and 

was defined as the majority aspect value of the 10-m digital 

elevation model in the area of each cell. The northern part of 

the SGPWM includes the southern part of the San Bernardino 

Mountains and is dominated by southeast, south, and southwest facing aspects, whereas, the southern part of the SGPWM 

includes the northern part of Mount San Jacinto and is dominated by north, northeast, and northwest facing aspects. In the 

SGPGSA, southeast, south, and southwest aspects are the most 

prevalent.
Model Development 11

10

10

60

79

243

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Average land-surface slope, in degrees

0–5

6–10

11–15

16–20

21–25

26–30

31–35

36–40

41–45

46–50

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

San Bernardino County

Riverside County

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 04

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 4. Calculated land surface slope used in the San Gorgonio Pass watershed model, California.

In addition to slope and aspect, shading and reflection 

of sunlight from surrounding terrain can have a big effect on 

calculated PET (Flint and Childs, 1987). To account for the 

shading effect of surrounding terrain, model input includes 36 

blocking-ridge angles that were calculated at each 10-degree 

azimuth direction by using methods described in Hevesi and 

others (2003). In general, simulated PET is lowest for steep, 

north-facing valleys and gullies that have persistent shading 

from higher ridges and is highest for south-facing slopes 

and ridges not affected much by shading from surrounding 

topography.

Land Cover

Three land-cover parameters are used in the SGPWM: 

(1) percent imperviousness, (2) percent canopy cover, and 

(3) land cover type. The percentage of imperviousness and 

of canopy cover were estimated by using the 2001 National 

Land Cover Data (NLCD), which has a grid resolution of 

30 m (Homer and others, 2007). Land-cover type was defined 

by using the “whrtyp” attribute of the California Land Cover 

Mapping and Monitoring Program (LCMMP; California 

Department of Forestry and Fire Protection, 2002).
12 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Average imperviousness, in percent

0–5

6–10

11–15

16–20

21–25

26–30

31–35

36–40

41–45

46–71

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 5. Percent impervious area, defined as the percentage of the cell area that is covered by an artificial surface such as 

roadways, rooftops, and parking lots, used in the San Gorgonio Pass watershed model, California.

The NLCD percent imperviousness is defined as the percentage of the cell area that is covered by an artificial, impervious surface such as roadways, rooftops, and parking lots. As 

used in the SGPWM, imperviousness does not represent locations that have relatively low permeability caused by exposed 

bedrock or by thin soils underlain by bedrock. The average 

imperviousness for each model cell was calculated in GIS. The 

average imperviousness for the SGPWM was about 2 percent. 

About 85 percent of the cells in the SGPWM had an imperviousness value of 0 to 1 percent (fig. 5). Only about 1 percent 

of the cells had imperviousness of 50 percent or greater. The 

maximum imperviousness value was 71 percent per cell and 

was in the valley of the Cabazon subbasin. In general, the 

areas with greater imperviousness (20 percent and more) were 

along the main roadways and were indicative of areas characterized by urbanization and land development.

Estimates of the average percentage of canopy cover 

for the SGPWM were also calculated in GIS. The percentage 

of canopy cover represented by NLCD is defined as the 

percentage of the cell area covered by natural forest canopy. 

The NLCD forest canopy map does not represent vegetation 

cover for urban or agricultural landscapes. A maximum forest 

canopy cover of 83 percent was calculated for a model cell on 

an upper north-facing slope of Little San Gorgonio Peak, and 
Model Development 13

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Agricultural crops

Annual grass

Barren

Chamise-redshank chaparral

Coastal oak woodland

Coastal scrub

Desert riparian

Desert scrub

Desert wash

Eucalyptus

Dominant vegetation type

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

Jeffrey pine

Juniper

Mixed chaparral

Montane chaparral

Montane hardwood

Montane hardwood conifer

Montane riparian

Perennial grass

Ponderosa pine

Sagebrush

Sierran mixed conifer

Subalpine conifer

Urban

Unidentified

Valley foothill riparian

Water

Wet meadow

White fir

117° 116°50' 116°40'

34°

33°

50'

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 6. Land-cover type, San Gorgonio Pass watershed model, California.

an average value of 9.4 percent was calculated for the entire 

SGPWM domain. Forest canopy values of 0 percent were 

obtained for most of the developed and urbanized lowland 

areas and throughout the SGPGSA.

A total of 28 different land-cover types were identified for 

the SGPWM by using the LCMMP (fig. 6; table 1). Estimated 

root densities for soil layer 2, which had a maximum estimated 

thickness of 0.4 m, ranged from high values of 97 percent for 

Ponderosa Pine and for White Fir, and 82 percent for Sierran 

Mixed Conifer, to low values of 8 percent for barren ground, 

28 percent for annual grass, and 44 percent for sagebrush. 

Estimated root densities for soil layer 5, which had a maximum estimated thickness of 4 m, varied from maximum 

values of 68 percent for Ponderosa Pine and White Fir to 

minimum values of 6 percent for desert scrub, barren ground, 

and annual grass.
14 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Table 1. Vegetation parameters used in the San Gorgonio Pass watershed model, California.

Land 

cover 

type1

Estimated root densities 

(volume percent), 

from Flint and Martin (2012)

Layer 6

estimated

maximum

thickness

(meters) Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6

Undefined 1 50 50 50 50 50 2

Mixed chaparral 12 53 47 42 37 37 3

Desert scrub 0 31 19 12 6 6 2

Barren 3 8 7 6 6 6 2

Montane hardwoods conifer 37 54 48 43 37 37 3

Montane hardwood 39 75 67 60 52 52 4

Montane chaparral 20 49 44 39 34 34 3

Sagebrush 5 44 26 18 9 9 3

Desert wash 1 71 43 29 14 14 3

Valley foothill riparian 12 50 45 40 35 35 4

Juniper 1 61 43 31 18 18 3

Urban 1 71 43 29 14 14 3

Chamise-redshank chapar 8 71 50 36 21 21 3

Sierran mixed conifer 39 82 74 65 57 57 4

Annual grass 1 28 17 11 6 6 2

Jeffrey pine 27 62 56 50 44 44 4

Coastal scrub 1 70 42 28 14 14 2

Wet meadow 18 73 44 29 15 15 3

Agriculture crops 2 53 32 21 11 11 2

Desert riparian 0 31 19 12 6 6 2

Ponderosa pine 45 97 87 77 68 68 4

Montane riparian 16 35 32 28 25 25 4

Perennial grass 2 53 32 21 11 11 2

Water 4 50 100 100 100 100 3

White fir 47 97 87 77 68 68 4

Subalpine conifer 19 51 46 41 36 36 3

Eucalyptus 8 71 50 36 21 21 3

Coastal oak woodland 18 35 32 28 25 25 4

1

From California Land Cover Mapping and Monitoring Program (LCMMP) Geographic Information System data (http://frap.cdf.ca.gov/projects/land_cover/

index.html).
Model Development 15

Soil

Soil parameters were estimated for each model cell by 

using the State Soil Geographic Database (STATSGO) digital 

map and associated attribute tables (U.S. Department of 

Agriculture,1994). A total of 10 different soil types, or soil 

map units (MUIDs) as defined by STATSGO, are included 

in the SGPWM (fig. 7; table 2). Soil parameters calculated 

by using the STATSGO data include soil porosity, residual 

water content, a drainage function coefficient, and an upper 

and lower vertical saturated hydraulic conductivity (Hevesi 

and others, 2003). Porosity and residual water content is used 

with the estimated soil depth to define the root-zone storage 

capacities for the five soil layers. For the SGPWM, soil 

porosities varied from a minimum of 0.35 to a maximum of 

0.43. The soil drainage function coefficients and the vertical 

saturated hydraulic conductivities are used in the drainage and 

ET calculations for the soil component of the root zone. For 

the SGPWM, the saturated hydraulic conductivities ranged 

from a minimum of 179 millimeters per day (mm/day) for soil 

CA624 to a maximum of 7,376 mm/day for soil CA601 on the 

valley floor of the Indio subbasin, on the eastern side of the 

SGPWM (fig. 7; table 2).

10

10

60

79

243

Smith Creek Smith Creek

San Bernardino County

Riverside County

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and other 1 3

Federal and State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

STATSGO map unit identifier (MUID)

CA601

CA609

CA614

CA624

CA625

CA632

CA639

CA648

CA667

CA671

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 7. State Soil Geographic Database (STATSGO) soil map units used in the San Gorgonio Pass watershed model, California.
16 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Table 2. Soil parameters developed from State Soil Geographic Database (STATSGO) soil map units, root-zone layers 1–5, 

San Gorgonio Pass watershed model, California.

STATSGO 

map-unit 

identifier1

(MUID)2

Estimated parameter values

Soil

Porosity

Residual

water

content

Drainage 

function

coefficient

Layer 6 lower 

hydraulic conductivity

(millimeters/day)

Layer 6 upper 

hydraulic conductivity

(millimeters/day)

CA667 0.35 0.030 4.21 752 4,655

3

CA639 0.36 0.026 3.71 919 5,026

CA671 0.43 0.014 3.54 977 4,780

3

CA614 0.36 0.053 4.98 517 2,392

3

CA648 0.37 0.067 5.33 385 1,196

3

CA609 0.36 0.070 5.73 363 925

3

CA625 0.37 0.031 3.89 936 2,728

3

CA624 0.38 0.103 7.18 179 923

CA632 0.36 0.034 4.29 686 880

CA601 0.35 0.004 2.22 1,934 7,376

1

State Soil Geographic database, U.S. Department of Agriculture, 1994, State Soil Geographic (STATSGO) Data Base–Data use information: Natural 

Resource Conservation Service, Miscellaneous Publication no. 1492: Soil Survey Staff, Natural Resources Conservation Service, United States Department of 

Agriculture U.S. General Soil Map (STATSGO) for California.

2

MUID is the mapped area and identifier used by STATSGO.

3

Gray shading indicates values for porosity, residual water content, and drainage function coefficient that are the same as in Rewis and others (2006). Layer 6 

residual water content is wilting point in Rewis and others (2006). Lower hydraulic conductivity is approximately one tenth of the values in Rewis and others 

(2006).

Geology

Parameters for layers 6 and 7 are used to represent the 

properties of geologic materials (consolidated and unconsolidated rock types) underlying the soil zone and were estimated 

for each of the five generalized geologic types defined for the 

SGPWM (table 3; fig. 8). The generalized geologic types were 

based on the surficial-geology map units defined by Jennings 

(1977). The parameters for layer 6 are the effective porosity 

and upper and lower saturated hydraulic conductivity (table 3). 

The parameters for layer 7 are the thickness (table 3) and 

two drainage function coefficients. In this study, the drainage 

function coefficients were set to one for all geologic types. 

Estimates of effective porosity and upper and lower saturated 

hydraulic conductivity for layer 6 were based on a general 

knowledge of the characteristics of the different geologic 

units (Rewis and others, 2006). For example, unconsolidated 

deposits were assumed to have greater effective porosity and 

saturated hydraulic conductivity compared to consolidated 

rocks, and sedimentary rocks were assumed to have greater 

saturated hydraulic conductivity relative to igneous and 

metamorphic rocks. Following a previous application of the 

modified INFILv3 code (Flint and Martin, 2012), the thickness 

of layer 7 was defined by assuming a water-storage capacity 

of 0.6096 m (24 inches) and by using the estimated effective 

porosity defined for layer 6 to calculate a thickness for layer 7. 

For example, if the effective porosity for layer 6 is 0.1, the 

thickness of layer 7 needs to be 240 inches in order to have a 

storage capacity of 24 inches.

Table 3. Geologic parameters assigned to root-zone layers 6 and 

7, San Gorgonio Pass watershed model, California.

Surficial 

geology 

(fig. 8)

1

Layer 6 

effective 

porosity2

Estimated model parameters

Layer 6 upper and 

lower saturated 

hydraulic conductivity

(millimeters/day)

Layer 7 

thickness

(meters)

Mixed sediments 0.35 100 3.4

Alluvium 0.35 50 3.4

Continental 

sediments

0.25 25 4.8

Cretaceous granite 0.05 0.5 24.0

Precambrian 

mixed rock, 

metamorphic 

rocks

0.05 0.25 24.0

1

Jennings, C.W., 1977, Geologic map of California: California Division of 

Mines and Geology Geologic Data Map no. 2, scale 1:750,000.

2

Layer 6 porosity from Rewis and others (2006).
Model Development 17

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and other 1 3

Federal and State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Surficial geology (Jennings, 1977) 

Alluvium

Continental sediments

Cretaceous granite

Mixed sediments

Precambrian mixed rock and

metamorphic rocks

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 08

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 8. Surficial geology used in the San Gorgonio Pass watershed model, California.

Soil and Root-Zone Layer Thicknesses

Soil thickness (fig. 9) was estimated by using surficial 

geology (fig. 8) and the STATSGO soils data (fig. 7). 

Locations with thick soils (greater than 3 m) were defined 

by using the unconsolidated alluvium geology type. Soil 

thickness was increased for all grid cells representing 

important channels. Cells having a minimum of 100 upstream 

cells (fig. 3) were assumed to represent important channels and 

were assigned a maximum soil thickness of 7.5 m for channels 

in alluvium. The increased soil thickness for stream channels 

was done to represent deeper root zones that were assumed for 

locations receiving runoff from upstream areas. Additionally, 

the increased soil thickness assigned for channels in upland 

areas that had surficial geology consisting of consolidated rock 

types was done to represent narrow bands of alluvial fill in 

upland channels.

Root-zone layer 6 is a layer that extends below the soil 

zone and is used mostly to represent the extension of the root 

zone into weathered and fractured consolidated-rock types 

(fig. 10). Layer 6 can also be used to represent the extension 

of the root zone into unconsolidated deposits in areas that 

have thick soils; however, the thickness of layer 6 is generally 

set to zero for these locations. The thickness for layer 6 

was estimated on the basis of soil thickness and vegetation 

type (fig. 6). If the soils are thin and vegetation is dense (for 

example, mountain forested areas), layer 6 is thick (less than 

or equal to 7.5 m). For areas underlain with thick alluvium 
18 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

10

10

60

79

243

San Bernardino County

Riverside County

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Estimated soil thickness, in meters

0.07–0.25

0.26–0.5

0.51–0.75

0.76–1.0

1.1–2.0

2.1–3.0

3.1–4.0

4.1–5.0

5.1–6.0

6.1–7.5

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 09

San

Jacinto

Peak

Little San Gorgonio

Peak

Figure 9. Estimated soil thickness used in the San Gorgonio Pass watershed model, California.
Model Development 19

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Estimated layer 6 thickness, in meters

0.00–0.40

0.41–0.80

0.81–1.20

1.21–1.60

1.61–2.00

2.01–2.40

2.41–2.80

2.81–3.20

3.21–3.60

3.61–3.93

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 10

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 10. Estimated layer 6 thickness used in the San Gorgonio Pass watershed model, California.

(fig. 8), a maximum soil thickness of 7.5 m was used, and 

the thickness of layer 6 was set to zero (Rewis and others, 

2006). Transpiration was simulated in only the five top-most 

soil layers, and layer 6 was used only to define the saturated 

hydraulic conductivity at the bottom of the root zone for the 

areas with thick alluvium.

The total root-zone thickness is the combined thickness 

of layers 1 through 6, and it was assumed to vary from 

a minimum of 2.0 m to a maximum of 7.5 m for grid 

cells representing the main channels. The total root-zone 

thickness ranged from 3.2 to 3.5 m for the intra-channel 

locations throughout the lowlands and valley floor mapped 

as unconsolidated alluvium. These locations had five soil 

layers representing the root zone, and layer 6 was assigned a 

thickness of zero m. For upland areas underlain by consolidated rock types, the total root-zone thickness ranged from 

2 m throughout the lower altitudes of the mountain blocks, 

where vegetation cover is sparse, to more than 3.8 m at locations mapped as having forest cover (generally the higher 

altitudes of the mountain blocks). The root-zone thickness for 

the non-forested locations of the upland areas varied from 2.9 

to 3.2 m.

The total root-zone storage capacity is calculated by 

using the porosity and thickness of the six root-zone layers, 

and it defines the maximum water-storage capacity of the root 

zone. The total root-zone storage capacity is an important 

component of the SGPWM; more runoff tends to be simulated 

for grid cells that have less storage capacity, whereas more 

ET tends to be simulated for grid cells with more storage 

capacity. Recharge can either increase or decrease, depending 

on the hydraulic conductivity of layer 6. The highest root-zone 

storage capacities of 2,510 to 3,080 mm were estimated for 
20 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

the main channels, where the thickest root zones and highest 

porosities were defined. High storage capacities of 1,000 to 

2,000 mm also were estimated for the intra-channel areas 

mapped as having thick soils underlain by unconsolidated 

alluvium. Storage capacities were much less for the upland 

areas underlain by metamorphic rocks and granite, ranging 

from a minimum of 125 mm to a maximum of 600 mm.

Climate Inputs

Climate inputs used for the SGPWM consist of three 

components: (1) daily climate records for precipitation, 

maximum air temperature, and minimum air temperature from 

a network of climate stations centered over the SGPWM; 

(2) estimates of average monthly precipitation and maximum 

and minimum air temperature for all model cells and all 

climate stations; and (3) atmospheric parameters used in 

simulating daily PET.

Daily Climate Records

Daily climate inputs were developed by using available 

climate records. The SGPWM requires that the daily climate 

records consist of a minimum of two records of precipitation 

and one record each for maximum and minimum air temperature for all days of the simulation period. Daily climate 

records (precipitation and air temperature) were available from 

a network of 134 climate stations in the southern California 

region centered on the SGPWM (table 4). Of these, records 

from 112 stations are collected and stored by the National 

Climatic Data Center (NCDC), which were acquired from two 

different sources: (1) Earth-Info Summary of the Day (EISD) 

data files (Earth Info, 2011) and (2) data files downloaded by 

using the Downsizer application (Ward-Garrison and others, 

2009). Records from 22 stations were collected and stored by 

the National Interagency Fire Center’s Remote Automated 

Weather Stations (RAWS). The RAWS data were accessed 

in January 2008 from http://www.raws.dri.edu/wraws/scaF.

html. The 134 climate records were compiled to develop the 

daily climate inputs required for a simulation period beginning 

January 1, 1909, and ending December 31, 2012.

Average Monthly Precipitation and Air 

Temperature

The SGPWM spatially interpolates daily precipitation 

and daily maximum and minimum air temperature for each 

grid cell by using a modified inverse-distance-squared 

interpolation (Hevesi and others, 2003). The interpolation 

method requires an estimate of average monthly precipitation 

and maximum and minimum air temperature at all grid 

cells and at all climate stations with records used in the 

interpolation. Following the methods used in Flint and 

Martin (2012), average monthly precipitation estimated by 

the Parameter-elevation Regressions on Independent Slopes 

Model (PRISM) data (Daly and others, 1994, 2004) was used 

to define the estimates of average monthly precipitation both 

for grid cells and climate stations. The monthly PRISM data 

used in the SGPWM consist of average monthly precipitation maps developed for the area of the United States on an 

approximate 800-m grid spacing for the 30-year period from 

1971 to 2000 (Daly and others, 1994, 2004). The monthly 

PRISM estimates incorporate multiple variables in order to 

account for complex orographic effects on precipitation, such 

as rain shadows, adiabatic cooling, average storm trajectory, 

and distance from moisture sources (Daly and others, 1994, 

2004).

Following the approach used in Hevesi and others (2003) 

and Rewis and others (2006), estimates of average monthly 

maximum and minimum air temperature were calculated 

by using a linear regression model, where altitude was the 

dependent variable. The monthly regression models were 

developed by using daily air-temperature records from 14 

stations (table 4) that ranged in altitude from 130 m (station 

identifier 46635, Palm Springs) to 2,077 m (station identifier 

40742, Big Bear Lake dam). The months of November 

through February showed the strongest correlation with altitude for monthly maximum air temperature, with R-squared 

values of 0.95 or greater (table 5). The months of May and 

June showed the weakest correlation between monthly 

maximum air temperature and altitude, with R-squared values 

of 0.77 for May and 0.71 for June. The overall correlation 

between average monthly minimum air temperature and 

altitude was weaker than the correlation with maximum air 

temperature. March and April showed the strongest correlation, with R-squared values of 0.92 for both those months. 

July and December showed the weakest correlation, with 

R-squared values of 0.64 for July and 0.68 for December. The 

weaker correlation for minimum air temperature was likely 

caused by temperature inversions, frequently during night and 

early morning, the time when the minimum daily air temperature is usually recorded.

Atmospheric Parameters

Monthly atmospheric parameters are used by the 

SGPWM to simulate solar radiation on an hourly basis. 

Hourly solar radiation is used to simulate daily PET on the 

basis of an energy balance calculation that also incorporates 

the daily air temperature inputs. The atmospheric parameters 

used for the SGPWM are from the USGS (2008) and are the 

same as values used in the previous INFILv3 application for 

San Gorgonio Pass (Rewis and others, 2006), as well as the 

INFILv3 application in the nearby Big Bear Lake and Baldwin 

Lake watersheds, north of the SGPWM (Flint and Martin, 

2012). The monthly atmospheric parameters include (1) the 

ozone-layer thickness (ozone), in centimeters; (2) precipitable 

water in the atmosphere (wp), in centimeters; (3) the dimensionless mean atmospheric turbidity (beta); and (4) the 

dimensionless circumsolar radiation (csr). The monthly ozone 

parameter varied from 0.27 to 0.33 cm, the wp parameter 
Model Development 21

Table 4. Climate stations having records of daily precipitation or records both of daily precipitation and daily maximum and minimum 

air temperatures, 1909–2012, used in the San Gorgonio Pass watershed model, California.

[Station identifications (IDs) for Earth-Info Summary of the Day (EISD) and Downsizer data sources are National Weather Service-National Climatic Data 

Center (NWS-NCDC) station identifiers; Station IDs for Remote Automated Weather Service Stations (RAWS) are National Environmental Satellite Service 

(NESS) station identifiers. Data Sources: EISD: EarthInfo, Inc., 2011, NCDC Summary of the Day, West: Boulder, Colorado, Earth Info, Inc., CD ROM; 

Downsizer: Ward-Garrison, C., Markstrom, S.L., and Hay, L.E., 2009; Downsizer—A graphical user interface-based application for browsing, acquiring, and 

formatting time-series data for hydrologic modeling: U.S. Geological Survey Open-File Report 2009–1166, 27 p.; RAWS: Remote Automated Weather Service, 

http://www.raws.dri.edu/wraws/scaF.html. Gray shading indicates stations used for developing the altitude-air temperature regression model. 

Abbreviations: °, degree; ', minutes; ", seconds]

Station

name

NWS and NESS 

station identifier

Data

source

Station coordinates 

(North American Datum of 1983)

Station

altitude

(meters) Longitude (North) Latitude (West)

ACTON ESCONDIDO FC261 40014 EISD 118° 16' 20" 34° 29' 41" 905

ADELANTO 40024 EISD 117° 25' 03" 34° 34' 60" 869

ALTADENA 40144 EISD 118° 08' 21" 34° 10' 55" 344

ANAHEIM 40192 EISD 117° 50' 36" 33° 51' 53" 102

ANZA 40235 EISD 116° 40' 29" 33° 33' 21" 1,193

APPLE VALLEY 40244 EISD 117° 13' 03" 34° 31' 00" 895

BARSTOW 40519 EISD 117° 02' 03" 34° 53' 60" 659

BARSTOW 40521 EISD 117° 01' 22" 34° 53' 34" 677

BEAUMONT 40606 Downsizer 116° 58' 33" 33° 55' 45" 796

BEAUMONT #2 40609 Downsizer 116° 58' 04" 33° 55' 31" 793

BEAUMONT PUMPING PLT 40607 Downsizer 116° 58' 03" 33° 58' 60" 930

BENNETT RCH 40678 EISD 117° 27' 03" 34° 10' 00" 564

BIG BEAR LAKE 40741 EISD 116° 54' 17" 34° 14' 39" 2,060

BIG BEAR LAKE DAM 40742 EISD 116° 58' 31" 34° 14' 29" 2,077

BIG TUJUNGA DAM FC46 40798 EISD 118° 11' 17" 34° 17' 39" 709

BLYTHE 40924 EISD 114° 35' 53" 33° 36' 47" 82

BLYTHE ASOS 40927 EISD 114° 42' 54" 33° 37' 07" 120

BORREGO DESERT PARK 40983 EISD 116° 24' 55" 33° 13' 53" 245

BRAWLEY 2 SW 41048 EISD 115° 33' 32" 32° 57' 16" –31

BURBANK VLY PUMP PLT 41194 EISD 118° 20' 56" 34° 11' 12" 200

CABAZON 41250 Downsizer 116° 47' 03" 33° 55' 00" 549

CANOGA PARK PIERCE CLG 41484 EISD 118° 34' 31" 34° 10' 55" 241

CORONA 42031 EISD 117° 33' 03" 33° 52' 60" 186

COVINA CITY YRD FC387B 42090 EISD 117° 52' 53" 34° 05' 31" 178

COYOTE CANYON 42103 EISD 116° 30' 03" 33° 25' 60" 695

CRESTLINE 42162 EISD 117° 18' 03" 34° 15' 00" 1,485

CULVER CITY 42214 EISD 118° 24' 49" 34° 00' 18" 17

DAGGETT AP 42257 EISD 116° 47' 12" 34° 51' 13" 584

DEEP CANYON LAB 42327 EISD 116° 22' 38" 33° 39' 05" 366

DOWNEY FIRE DEPT FC107C 42494 EISD 118° 08' 47" 33° 55' 47" 34

EAGLE MTN 42598 EISD 115° 27' 06" 33° 48' 32" 297

ELSINORE 42805 EISD 117° 19' 58" 33° 40' 09" 392

FONTANA 5 N 43118 EISD 117° 27' 03" 34° 10' 60" 601

FONTANA KAISER 43120 EISD 117° 31' 03" 34° 04' 60" 336

GLENDORA FC 287B 43452 EISD 117° 50' 53" 34° 08' 47" 280

HAYFIELD PUMPING PLT 43855 EISD 115° 37' 47" 33° 42' 16" 418
22 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Station

name

NWS and NESS 

station identifier

Data

source

Station coordinates 

(North American Datum of 1983)

Station

altitude

(meters) Longitude (North) Latitude (West)

HEMET 43896 Downsizer 116° 53' 41" 33° 44' 17" 552

HENSHAW DAM 43914 EISD 116° 45' 44" 33° 14' 14" 823

HESPERIA 43935 EISD 117° 18' 03" 34° 25' 00" 976

IDYLLWILD FIRE DEPT 44211 Downsizer 116° 42' 27" 33° 45' 26" 1,640

INDIO FIRE STN 44259 EISD 116° 12' 58" 33° 42' 31" –6

IRON MTN 44297 EISD 115° 07' 22" 34° 8' 50" 281

JOSHUA TREE 44405 EISD 116° 16' 32" 34° 8' 49" 781

JOSHUA TREE 3 S 44407 EISD 116° 19' 03" 34° 06' 00" 1,064

KEE RCH 44467 EISD 116° 32' 03" 34° 10' 00" 1,321

LAGUNA BEACH 44647 EISD 117° 46' 56" 33° 32' 43" 13

LAKE ARROWHEAD 44671 EISD 117° 11' 21" 34° 14' 48" 1,587

LONG BCH DAUGHERTY AP 45085 EISD 118° 08' 50" 33° 48' 42" 9

LONG BEACH PUB SVC 45082 EISD 118° 12' 03" 33° 46' 00" 9

LOS ANGELES DWTN USC 45115 EISD 118° 17' 32" 34° 01' 18" 52

LOS ANGELES INTL AP 45114 EISD 118° 23' 23" 33° 56' 17" 30

LUCERNE VALLEY 45182 EISD 116° 57' 03" 34° 26' 60" 903

LYTLE CREEK PH 45215 EISD 117° 27' 03" 34° 12' 00" 686

LYTLE CREEK RS 45218 EISD 117° 28' 18" 34° 14' 18" 832

MARCH FLD 45326 EISD 117° 15' 03" 33° 52' 00" 454

MECCA FIRE STN 45502 EISD 116° 4' 39" 33° 34' 17" –55

MILL CREEK 2 45629 Downsizer 117° 02' 03" 34° 04' 60" 897

MITCHELL CAVERNS 45721 EISD 115° 32' 52" 34° 56' 37" 1,326

MORONGO VALLEY 45863 Downsizer 116° 35' 03" 34° 01' 60" 781

MT SAN JACINTO WSP 45978 Downsizer 116° 38' 03" 33° 48' 00" 2,568

MT WILSON CBS 46006 EISD 118° 04' 19" 34° 13' 51" 1,740

MURCELL RCH 46035 EISD 116° 46' 03" 33° 31' 60" 1,132

NEWPORT BEACH HARBOR 46175 EISD 117° 52' 52" 33° 36' 09" 3

NIGHTINGALE 46196 EISD 116° 26' 03" 33° 34' 60" 1,229

NILAND 46197 EISD 115° 31' 29" 33° 16' 39" –18

OAK GROVE RS 46319 EISD 116° 47' 33" 33° 23' 10" 838

OCEANSIDE MARINA 46377 EISD 117° 23' 45" 33° 12' 35" 3

OCOTILLO WELLS 46383 EISD 116° 08' 03" 33° 09' 00" 55

PACOIMA DAM FC 33 A-E 46602 EISD 118° 24' 01" 34° 19' 57" 457

PALM SPRINGS 46635 Downsizer 116° 30' 38" 33° 49' 39" 130

PALMDALE 46624 EISD 118° 05' 42" 34° 35' 16" 791

PALOMAR MTN OBSVTRY 46657 EISD 116° 50' 27" 33° 22' 41" 1,692

Table 4. Climate stations having records of daily precipitation or records both of daily precipitation and daily maximum and minimum 

air temperatures, 1909–2012, used in the San Gorgonio Pass watershed model, California.—Continued

[Station identifications (IDs) for Earth-Info Summary of the Day (EISD) and Downsizer data sources are National Weather Service-National Climatic Data 

Center (NWS-NCDC) station identifiers; Station IDs for Remote Automated Weather Service Stations (RAWS) are National Environmental Satellite Service 

(NESS) station identifiers. Data Sources: EISD: EarthInfo, Inc., 2011, NCDC Summary of the Day, West: Boulder, Colorado, Earth Info, Inc., CD ROM; 

Downsizer: Ward-Garrison, C., Markstrom, S.L., and Hay, L.E., 2009; Downsizer—A graphical user interface-based application for browsing, acquiring, and 

formatting time-series data for hydrologic modeling: U.S. Geological Survey Open-File Report 2009–1166, 27 p.; RAWS: Remote Automated Weather Service, 

http://www.raws.dri.edu/wraws/scaF.html. Gray shading indicates stations used for developing the altitude-air temperature regression model. 

Abbreviations: °, degree; ', minutes; ", seconds]
Model Development 23

Table 4. Climate stations having records of daily precipitation or records both of daily precipitation and daily maximum and minimum 

air temperatures, 1909–2012, used in the San Gorgonio Pass watershed model, California.—Continued

[Station identifications (IDs) for Earth-Info Summary of the Day (EISD) and Downsizer data sources are National Weather Service-National Climatic Data 

Center (NWS-NCDC) station identifiers; Station IDs for Remote Automated Weather Service Stations (RAWS) are National Environmental Satellite Service 

(NESS) station identifiers. Data Sources: EISD: EarthInfo, Inc., 2011, NCDC Summary of the Day, West: Boulder, Colorado, Earth Info, Inc., CD ROM; 

Downsizer: Ward-Garrison, C., Markstrom, S.L., and Hay, L.E., 2009; Downsizer—A graphical user interface-based application for browsing, acquiring, and 

formatting time-series data for hydrologic modeling: U.S. Geological Survey Open-File Report 2009–1166, 27 p.; RAWS: Remote Automated Weather Service,

http://www.raws.dri.edu/wraws/scaF.html. Gray shading indicates stations used for developing the altitude-air temperature regression model. 

Abbreviations: °, degree; ', minutes; ", seconds]

Station

name

NWS and NESS 

station identifier

Data

source

Station coordinates 

(North American Datum of 1983)

Station

altitude

(meters) Longitude (North) Latitude (West)

PALOS VERDES ES FC43D 46663 EISD 118° 23' 31" 33° 47' 59" 66

PASADENA 46719 EISD 118° 08' 44" 34° 08' 54" 263

PERRIS 46816 EISD 117° 14' 03" 33° 46' 60" 448

PERRIS 1 WSW 46818 EISD 117° 15' 03" 33° 46' 60" 488

POMONA FAIRPLEX 47050 EISD 117° 45' 59" 34° 04' 52" 317

RAYWOOD FLATS 47279 EISD 116° 49' 03" 34° 03' 00" 2,156

REDLANDS 47306 EISD 117° 11' 25" 34° 03' 10" 402

RIVERSIDE CITRUS EXP 47473 EISD 117° 21' 44" 33° 58' 01" 301

RIVERSIDE FIRE STN 3 47470 EISD 117° 23' 20" 33° 57' 04" 256

SAN BERNARDINO F S 226 47723 EISD 117° 15' 17" 34° 08' 04" 348

SAN DIMAS FIRE FC 95 47749 EISD 117° 48' 10" 34° 06' 19" 292

SAN FERNANDO 47759 EISD 118° 28' 03" 34° 16' 60" 296

SAN GABRIEL CANYON PH 47776 EISD 117° 54' 31" 34° 09' 19" 227

SAN GABRIEL DAM FC425B- 47779 EISD 117° 51' 42" 34° 12' 19" 451

SAN GABRIEL FIRE DEPT 47785 EISD 118° 06' 03" 34° 06' 22" 137

SAN JACINTO 47810 Downsizer 116° 58' 03" 33° 46' 60" 470

SAN JACINTO RS 47813 Downsizer 116° 57' 33" 33° 47' 13" 476

SAN PEDRO 47876 EISD 118° 16' 03" 33° 43' 00" 3

SANTA ANA FIRE STN 47888 EISD 117° 52' 03" 33° 44' 39" 41

SANTA ANA RIVER PH 1 47894 EISD 117° 04' 03" 34° 07' 60" 845

SANTA MONICA PIER 47953 EISD 118° 29' 59" 34° 00' 29" 4

SEVEN OAKS 48105 EISD 116° 57' 03" 34° 10' 60" 1,549

SNOW CREEK UPPER 48317 Downsizer 116° 40' 50" 33° 52' 21" 591

SOUTH FORK CABIN 48390 Downsizer 116° 49' 03" 34° 04' 00" 2,172

SQUIRREL INN 1 48476 EISD 117° 15' 03" 34° 13' 60" 1,598

SQUIRREL INN 2 48479 EISD 117° 14' 03" 34° 13' 60" 1,732

SUN CITY 48655 EISD 117° 11' 27" 33° 42' 56" 433

THERMAL FCWOS 48892 EISD 116° 09' 39" 33° 37' 40" –34

TOPANGA PATROL FC-6 48967 EISD 118° 35' 59" 34° 05' 03" 227

TORRANCE AP 48973 EISD 118° 20' 31" 33° 48' 06" 34

TUSTIN IRVINE RCH 49087 EISD 117° 45' 17" 33° 42' 09" 72

TWENTYNINE PALMS 49099 EISD 116° 02' 16" 34° 07' 41" 602

U C L A 49152 EISD 118° 26' 37" 34° 04' 11" 131

VICTORVILLE PUMP PT 49325 EISD 117° 18' 24" 34° 32' 06" 871

VINCENT FS FC 120 49345 EISD 118° 08' 34" 34° 29' 16" 956

VISTA 2 NNE 49378 EISD 117° 13' 40" 33° 13' 46" 155
24 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Table 4. Climate stations having records of daily precipitation and/or maximum and minimum air temperature used as input for the 

San Gorgonio Pass watershed model, California.—Continued

[Station identifications (IDs) for Earth-Info Summary of the Day (EISD) and Downsizer data sources are National Weather Service-National Climatic Data 

Center (NWS-NCDC) station identifiers; Station IDs for Remote Automated Weather Service Stations (RAWS) are National Environmental Satellite Service 

(NESS) station identifiers. Data Sources: EISD: EarthInfo, Inc., 2011, NCDC Summary of the Day, West: Boulder, Colorado, Earth Info, Inc., CD ROM; 

Downsizer: Ward-Garrison, C., Markstrom, S.L., and Hay, L.E., 2009; Downsizer—A graphical user interface-based application for browsing, acquiring, and 

formatting time-series data for hydrologic modeling: U.S. Geological Survey Open-File Report 2009–1166, 27 p.; RAWS: Remote Automated Weather Service, 

http://www.raws.dri.edu/wraws/scaF.html. Gray shading indicates stations used for developing the altitude-air temperature regression model. 

Abbreviations: °, degree; ', minutes; ", seconds]

Station

name

NWS and NESS 

station identifier

Data

source

Station coordinates 

(North American Datum of 1983)

Station

altitude

(meters) Longitude (North) Latitude (West)

WALNUT NI FC102C 49431 EISD 117° 52' 00" 34° 00' 06" 149

WARNER SPRINGS 49447 EISD 116° 38' 03" 33° 17' 00" 970

WHITTIER CY YD FC106C 49660 EISD 118° 01' 23" 33° 58' 34" 128

WOODLAND HLS PIERCE CL 49785 EISD 118° 34' 31" 34° 10' 55" 241

Anza CA44C7A2 RAWS 116° 40' 14" 33° 33' 11" 1,195

Beaumont CA264670 RAWS 116° 56' 14" 33° 55' 30" 817

Big_Pine_Flat 3275C268 RAWS 117° 00' 28" 34° 19' 06" 2,091

Burns_Canyon 325775FC RAWS 116° 38' 01" 34° 12' 22" 1,829

Clark CA44E14E RAWS 117° 18' 19" 33° 52' 22" 524

Converse 3237D736 RAWS 116° 54' 28" 34° 11' 23" 1,712

Corona 327F9346 RAWS 117° 32' 34" 33° 52' 17" 195

Covington FA4166C4 RAWS 116° 20' 02" 34° 03' 07" 1,416

Cranston 3275D11E RAWS 116° 50' 17" 33° 44' 15" 594

Devore CA44F238 RAWS 117° 24' 07" 34° 13' 10" 634

El_Cariso 3230A392 RAWS 117° 24' 14" 33° 39' 04" 926

Fawnskin 3230F3EE RAWS 116° 53' 34" 34° 15' 35" 2,103

Keenwild 32675560 RAWS 116° 46' 00" 33° 40' 00" 1,500

Lost_Horse FA6065D0 RAWS 116° 11' 10" 34° 01' 02" 1,280

Lytle_Creek 3275E484 RAWS 117° 28' 29" 34° 14' 01" 851

Means_Lake 325333CC RAWS 116° 31' 01" 34° 23' 16" 884

Mill_Creek_BDF 3230B0E4 RAWS 117° 02' 03" 34° 05' 01" 899

Mormon Rock 32310190 RAWS 117° 30' 04" 34° 19' 02" 1,006

Oak_Grove 32309608 RAWS 116° 47' 25" 33° 23' 22" 839

Santa_Rosa_Plateau CA4A6332 RAWS 117° 13' 30" 33° 31' 26" 604

Temescal_CNF 3242E0C0 RAWS 117° 28' 35" 33° 45' 27" 343

Yucca_Valley CA450046 RAWS 116° 24' 17" 34° 07' 14" 994
Model Development 25

Table 5. Parameters for regression models used to spatially interpolate maximum and minimum daily air temperature for the San 

Gorgonio Pass watershed model, California.

[Abbreviations: deg. C, temperature in degrees Celsius; m, meters]

Month

Average monthly maximum air temperature 

regression model coefficients and statistics

Average monthly minimum air temperature 

regression model coefficients and statistics

Slope

(deg. C/m)

Intercept

(deg. C)

R-squared

(dimension-less)

Slope

(deg. C/m)

Intercept

(deg. C)

R-squared

(dimension-less)

Jan –0.0075 21.5 0.95 –0.0064 6.9 0.75

Feb –0.0078 23.6 0.96 –0.0066 8.4 0.83

Mar –0.0082 25.8 0.93 –0.0068 9.7 0.92

Apr –0.0080 29.2 0.88 –0.0067 12.0 0.92

May –0.0075 32.5 0.77 –0.0064 14.7 0.88

Jun –0.0072 37.1 0.71 –0.0057 17.6 0.76

Jul –0.0076 41.4 0.81 –0.0055 21.3 0.64

Aug –0.0077 41.0 0.85 –0.0057 21.3 0.70

Sep –0.0077 38.4 0.90 –0.0062 19.2 0.72

Oct –0.0076 32.8 0.93 –0.0063 14.8 0.73

Nov –0.0077 26.6 0.96 –0.0061 9.9 0.70

Dec –0.0074 22.2 0.95 –0.0058 6.9 0.68

Table 6. Monthly values of atmospheric parameters used to 

simulate solar radiation and potential evapotranspiration with the 

San Gorgonio Pass watershed model, California.

Month

Monthly atmospheric parameter values1

Ozone2

(centimeters)

WP3

(centimeters)

Beta4

(dimensionless)

CSR5

(dimensionless)

Jan 0.29 1.00 0.075 0.85

Feb 0.31 1.00 0.075 0.85

Mar 0.32 1.05 0.075 0.85

Apr 0.33 1.10 0.085 0.85

May 0.33 1.50 0.085 0.74

Jun 0.32 1.80 0.090 0.74

Jul 0.30 2.20 0.090 0.57

Aug 0.29 2.44 0.084 0.57

Sep 0.28 2.00 0.077 0.66

Oct 0.27 1.40 0.075 0.74

Nov 0.27 1.05 0.075 0.90

Dec 0.28 0.95 0.075 0.90

1

Parameter values from U.S. Geological Survey, 2008, Documentation of 

computer program INFIL3.0—A distributed-parameter watershed model to 

estimate net infiltration below the root zone: U.S. Geological Survey Scientific 

Investigations Report 2008–5006, 98 p.

2

Ozone: ozone-layer thickness.

3

WP: precipitable water in the atmosphere.

4

Beta: mean atmospheric turbidity.

5

CSR: circumsolar radiation.

varied from 1.0 to 2.44 cm, the beta parameter varied from 

0.075 to 0.09, and csr parameter varied from 0.57 to 0.9 

(table 6). A more complete description of these parameters 

is provided in Rewis and others (2006), Hevesi and others 

(2003), and U.S. Gelogical Survey (2008).

Model Coefficients

Model coefficients include parameters used to model 

snowmelt and sublimation, to define stream-channel characteristics, and to define precipitation intensity by using specified 

winter and summer storm durations. For the SGPWM, model 

coefficients were based on previous applications of the 

INFILv3 code in the Southern California region (Rewis and 

others, 2006; Flint and Martin, 2012). Model coefficients for 

simulating snowmelt and sublimation were from Hevesi and 

others (2003) for the Death Valley region and were assumed to 

be applicable for the San Gorgonio Pass region because of the 

similar climate characteristics. 

Model coefficients used to represent stream-channel 

characteristics included the minimum number of upstream 

cells used to define the main-stream channels and the saturated 

hydraulic-conductivity multiplier for soils in the main stream 

channels. The minimum number of upstream cells was set to 

100 (approximately 9 hectares), and the saturated hydraulicconductivity multiplier was set to 10. This configuration 

assumed coarser soils in active channels that had upstream 

areas of 9 hectares or greater as well as a 10-fold increase in 

the saturated hydraulic conductivity of the channel bed relative 

to the surrounding inter-channel areas.
26 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Modeled Climate, Snowfall, and 

Potential Evapotranspiration (PET)

The spatial and temporal distributions of the interpolated 

climate inputs (precipitation and air temperature), as well 

as the spatial and temporal distributions of simulated snowfall and PET, were analyzed as part of model development. 

Snowfall and PET are included as climate components in the 

SGPWM simulated water budget because snowfall is dependent only on spatially interpolated precipitation and air temperature and PET is dependent only on the interpolated climate 

inputs (precipitation and air temperature), latitude, topographic 

parameters (slope, aspect, and blocking ridges), and the atmospheric parameters (U.S. Geological Survey, 2008). Clear sky 

PET is simulated by the INFILv3 code using maximum and 

minimum daily air-temperature input and an energy-balance 

approach that includes hourly simulated solar radiation (Flint 

and Childs, 1987; Flint and Childs, 1991; U.S. Geological 

Survey, 2008). To account for cloud cover, PET is reduced 

on the basis of the amount of precipitation. Precipitation, air 

temperature, snowfall, and PET were analyzed by using the 

mapped spatial distribution of the long-term 100-year averages 

for the target simulation period (1913–2012), the annual 

(water year) time series, and the 100-year monthly averages.

Precipitation

Spatially distributed precipitation, averaged for 

water years 1913–2012, ranged from a minimum of about 

230 millimeters per year (mm/yr), on the valley floor at the 

Indio subbasin along the eastern boundary, to a maximum of 

about 970 mm/yr, at the summit of Little San Gorgonio Peak 

(fig. 11). Average precipitation in the western and central parts 

of the SGPGSA was 421 and 480 mm/yr, respectively. Basinwide average precipitation for the SGPWM was 496 mm/yr. 

In general, the distribution of precipitation was similar to the 

average annual PRISM map (Daly and others, 2004). Unlike 

PRISM, however, the precipitation—interpolated by the 

modified inverse distance squared method—was more closely 

matched to the daily records of the climate stations in and 

around the SGPWM because the daily data are preserved in 

the spatial interpolation algorithm (Hevesi and others, 2003; 

Flint and Martin, 2012).

Air Temperature and Snowfall

The spatially distributed average daily air temperature, 

calculated as the average of the maximum and minimum daily 

air temperature for water years 1913–2012 and interpolated 

by the SGPWM, varied from a maximum of 20 oC in the Indio 

subbasin to a minimum of about 0 oC for the summit area of 

Little San Gorgonio Peak (fig. 12). The 100-year average air 

temperature in the central part of the SGPGSA varied from 

about 16 to 18 oC. The basin-wide average air temperature for 

the SGPWM was 14.5 oC. In contrast to the spatial distribution 

of precipitation, the spatial distribution of interpolated air 

temperature is largely determined by the air temperature and 

altitude regression model and, therefore, closely follows the 

topography.

The spatially distributed average snowfall for water years 

1913–2012, calculated by the SGPWM using the spatially 

interpolated air temperature and precipitation inputs, ranged 

from a minimum of zero throughout the valley floor to a maximum of about 880 mm for the summit of Little San Gorgonio 

Peak (fig. 12). Snowfall amounts of about 51 mm/yr and more, 

or amounts greater than the basin-wide average snowfall of 

50 mm/yr for the SGPWM, only fell in the higher-altitudes 

of Little San Gorgonio Peak and San Jacinto Peak, where the 

average air temperature was about 12 oC or less. The 100-year 

average snowfall in the central part of the SGPGSA was 

negligible (less than 2 mm/yr).

Potential Evapotranspiration (PET)

Average annual PET for water years 1913–2012 ranged 

from a maximum of 2,080 mm/yr for a cell on the southfacing slope of the San Gorgonio River valley floor, close to 

the eastern boundary of the model domain, to a minimum of 

485 mm/yr for a cell in an upper ravine on the north-facing 

slope of Mount Jan Jacinto (fig. 13). The basin-wide average 

simulated PET for the SGPWM was about 1,620 mm/yr, and 

was in good agreement with the average value of 1,590 mm/yr 

for CIMIS ETo zone 16 that included most of the SGPWM. 

Simulated PET for most of the north-facing slope of San 

Jacinto Peak was less than 1,500 mm/yr because of shading 

effects from the rugged topography. In contrast, simulated 

PET for most of the south-facing slope of Little San Gorgonio 

Peak was greater than 1,500 mm/yr, with the exception of the 

highest altitudes and north-facing slopes of smaller canyons 

and ravines in the mountain block.

Annual Variability in Climate

The temporal variability in the basin-wide climate components of the SGPWM was evaluated by using the time series 

of spatially averaged annual (water year) precipitation, air 

temperature, snowfall, and PET for water years 1913–2012. 

The spatially averaged annual values were calculated by 

summing the annual results for all 30,595 model cells and 

dividing the sum by the number of model cells. Annual precipitation during the century showed high year-to-year variability 

relative to the 100-year average precipitation of 496 mm/yr, 

with a maximum annual precipitation of 1,100 mm for water 

year 1993 and a minimum annual precipitation of 98 mm 

for water year 2007 (fig. 14A). Water years with relatively 

high precipitation, 800 mm and greater, were often followed 

by water years with relatively low precipitation, 400 mm 

and less. The annual results indicated a pattern of increasing 

year-to-year variability, with the 4 wettest and the 3 driest 

years all after water year 1960. The 5 driest years (1959, 1961, 
Modeled Climate, Snowfall, and Potential Evapotranspiration (PET) 27

10

10

60

79

243

San Bernardino County

Riverside County

40607

40606

40609 50002

41250

48317

47279

48390

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Interpolated (1913–2012) average

precipitation, in millimeters per year

233–300

301–360

361–420

421–480

481–540

541–600

601–660

661–720

721–780

781–973

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

40607 Climate station

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 11

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 11. Spatially distributed average annual precipitation estimated for water years 1913–2012 by using the San Gorgonio Pass 

watershed model, California.
28 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

10

10

60

79

243

San Bernardino County

Riverside County

18

16

14

12

10

8

6

4

2

16

14

12

16

16

14

12

10

8

6

4

16

14

12

10

8

6

4

18

40607

40606

40609

50002

41250

48317

47279

48390

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Interpolated (1913–2012) average

snow fall, in millimeters per year

0–1

2–25

26–50

51–75

76–100

101–200

201–300

301–400

401–500

501–878

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

Interpolated average air-temperature

countour, in degrees Celsius

40607 Climate station

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 12

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 12. Spatially distributed average air temperature and snowfall estimated for water years 1913–2012 using the San Gorgonio 

Pass watershed model, California.
Modeled Climate, Snowfall, and Potential Evapotranspiration (PET) 29

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

Little San Gorgonio

Peak

San

Jacinto

Peak

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983 EXPLANATION

Simulated (1913–2012) average potential 

evapotranspiration, in millimeters per year

485–820

821–960

961–1,100

1,101–1,240

1,241–1,380

1,381–1,520

1,521–1,660

1,661–1,800

1,801–1,940

1,941–2,080

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 13

Figure 13. Spatially distributed average annual potential evapotranspiration simulated for water years 1913–2012 using the San 

Gorgonio Pass watershed model, California.
30 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

0

100

200

300

400

500

600

700

800

900

1,000

1,100

Precipitation, in millimeters

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

Water year

A

B

C

D

1,500

1,525

1,550

1,575

1,600

1,625

1,650

1,675

1,700

1,725

1,750

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

LOWESS

Statewide drought periods

Annual

11-year moving average

100-year average

EXPLANATION

Potential evapotranspiration, 

in millimeters

0

20

40

60

80

100

120

140

160

Snowfall, in millimeters

11.5

12.0

12.5

13.0

13.5

14.0

14.5

15.0

15.5

16.0

16.5

17.0

Air temperature, 

in degrees Celsius

sac15-0556_fig 14

Figure 14. Annual basin-wide averages for water years 1913–2012 using the San Gorgonio Pass watershed model, California, for A,

estimated precipitation; B, estimated air temperature; C, simulated snowfall; and D, simulated potential evapotranspiration (PET).
Modeled Climate, Snowfall, and Potential Evapotranspiration (PET) 31

2002, 2007, and 2009) were all during state-wide drought 

periods (fig. 14) identified by the California Department of 

Water Resources (2012). Average precipitation for the 37 

drought years in the 100-year period was 396 mm/yr, or about 

80 percent of the 100-year period average of 496 mm/yr.

To help identify multi-year trends and cycles in the 

annual time series, two smoothing functions were applied: 

(1) the 11-year moving mean and (2) the locally weightedscatterplot-smoothing (LOWESS) method (Maidment, 2002). 

The California Department of Water Resources (DWR) 

uses the 11-year moving average in the analysis of annual 

statewide precipitation because precipitation and other 

climatic variables have been associated with 11-year periods (California Department of Water Resources, 2012). The 

locally weighted-scatterplot-smoothing (LOWESS) method 

was applied because it spans the entire domain and is not sensitive to outliers as is the moving average (Maidment, 2002). 

The smoothed time series of annual precipitation based on the 

11-year moving average indicated relatively wetter than average periods for water years 1915–44, 1973–85, and 1990–97, 

whereas extended dry periods were indicated for water years 

1945–72, and 1998–2008. The LOWESS curve indicated 

wetter than average periods for water years 1912–44, 1966–

68, 1976–82, and 1992–94, and drier than average periods for 

water years 1945–65, 1983–91, and 1996–2011.

The annual and smoothed time series indicated dry 

periods that were in good agreement with the DWR state-wide 

drought periods of 1947–50, 1959–62, 1987–92, 2000–02, and 

2007–09. The results showed poor agreement to the state-wide 

drought periods of 1928–35 and 1976–77, however, and only 

partial agreement to the drought periods of 1918–20, 1923–26, 

and 1987–92. In addition, many of the drier years that had less 

than 300 mm precipitation (for example, water years 1951, 

1970, 1972, 1996, 1999, and 2000) were not during the DWR 

drought periods. The comparison with the state-wide drought 

periods indicated that the SGPWM climate might not always 

follow state-wide climate conditions, and local-scale climate 

variability is likely to be important. Average precipitation 

for the 37 DWR drought years was 396 mm/yr, and annual 

precipitation was usually less than the long-term average of 

496 mm/yr for most years identified as drought years. Exceptions included water years 1926, 1932, and 1935; all these had 

annual precipitation greater than 600 mm.

The annual time series for average air temperature indicated a well-defined trend of increasing air temperature from 

water year 1913 to 2012 (fig. 14B). Water years with average 

air temperature less than 13 oC were all prior to water year 

1933, and water years with average air temperature greater 

than 16.5 oC were all after water year 1995. Both the 11-year 

centered moving-average air temperature and the LOWESS 

average air temperature were about 13.5 oC during the beginning of the period and both increased to almost 16 o

C by the 

end of the period. 

Variability in estimated annual snowfall indicated the 

combined effects of estimated precipitation and air temperature (fig. 14C). Similar to precipitation, annual snowfall for 

the SGPWM showed a high degree of year-to-year variation; 

however, snowfall also was sensitive to the annual variations in air temperature and to the intra-annual timing of 

precipitation in a given water year. For example, water year 

1932 had the highest basin-wide average snowfall of 150 mm. 

The high snowfall resulted from above average annual 

precipitation, with a basin-wide average precipitation of about 

725 mm for water year 1932, at the same time the average air 

temperature was the second coldest (12.4 oC) in the 100-year 

period. Following the trend of increasing air temperature, a 

slight trend of decreasing snowfall was indicated by both the 

11-year moving mean and the LOWESS curve for water year 

1933–2012 (fig. 14C). With the exception of water year 1979, 

all years that had high annual snowfall amounts of 100 mm 

or greater were prior to water year 1950, whereas most of the 

years that had low annual snowfall amounts of 20 mm and less 

were after water year 1950.

The annual variability in basin-wide average PET 

(fig. 14D) was closely correlated to the annual variability in 

basin-wide average air temperature; however, there was also 

some dependency on the variability in annual precipitation. 

The SGPWM accounts for the effects of cloud cover on PET 

by using an empirical function that reduces PET as a function 

of the daily precipitation (U.S. Geological Survey, 2008). 

Water year 1921 had the lowest PET at about 1,510 mm 

and also was the water year with the lowest average air 

temperature at 12 oC. Water years 1983 and 1998 also had the 

second and fifth lowest annual PET values at about 1,527 mm 

and 1,546 mm, respectively, because annual temperatures 

were near average and annual precipitation was relatively 

high, second and fourth highest and in water years 1983 and 

1998, respectively. Overall, annual PET, the 11-year moving 

average, and LOWESS showed a trend toward increasing PET. 

This PET trend was caused primarily by higher air temperatures in the later part of the simulation period. 

Average Monthly Variability in Climate

The basin-wide monthly averages, maximums, and minimums for spatially interpolated precipitation, air temperature, 

snowfall, and PET indicated the average seasonal variability 

in the climate components for the SGPWM (fig. 15). The 

monthly averages were calculated for each month by summing the monthly totals for water years 1913 to 2012 for all 

30,595 model cells and dividing by the number of model 

cells and the number of months in the 100-year period 

(1,200). On average, monthly precipitation and snowfall were 

greatest during December through March and least during 

May through September (figs. 15A, C). All months included 

a minimum monthly precipitation and snowfall of zero, and 

maximum monthly values more than three times greater than 

the mean, indicating a high degree of temporal variability. The 

seasonal variation in average monthly air temperature and PET 

was also pronounced; however, in contrast to precipitation 

and snowfall, air temperature and PET were greatest during 

summer months and least during winter months. In addition, 
32 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

0

100

200

300

400

500

600

Monthly precipitation, in millimeters

0

5

10

15

20

25

30

Monthly air temperature,

in degrees Celsius

A

B

C

D

0

20

30

40

50

60

70

Monthly snow fall, in millimeters

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Water year

10

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

0

25

75

175

200

225

250

Monthly potential evapotranspiration, 

in millimeters 50

150

125

100

Maximum monthly precipitation

Average monthly precipitation

Minimum monthly precipitation

EXPLANATION

Maximum monthly air temperature

Average monthly air temperature

Minimum monthly air temperature

EXPLANATION

Maximum monthly snowfall

Average monthly snowfall

Minimum monthly snowfall

EXPLANATION

Maximum monthly potential

 evapotranspiration

Average monthly potential

 evapotranspiration

Minimum monthly potential

 evapotranspiration

EXPLANATION

sac15-0556_fig 15

Figure 15. Basin-wide averages for water years 1913–2012, using the San Gorgonio Pass watershed model, California, for A, estimated 

monthly precipitation; B, estimated air temperature; C, simulated snowfall; and D, simulated potential evapotranspiration.
Model Calibration 33

the temporal variability in air temperature and PET for a given 

month was much lower compared to precipitation and snowfall, as indicated by the smaller relative difference between 

maximum and minimum monthly values (figs. 15B, D). For 

the winter months of December through February, precipitation exceeded PET, increasing the potential for groundwater 

recharge and runoff for these months.

Model Calibration

Precipitation-runoff models typically are calibrated and 

tested by comparing simulated streamflow to available records 

of measured streamflow, preferably using continuous records 

that span multi-year periods (Hevesi and others, 2003; Rewis 

and others, 2006; Markstrom and others, 2008; Jeton and 

Maurer, 2011). In the model-calibration procedure, parameters 

affecting streamflow that have a high degree of uncertainty are 

adjusted iteratively until an acceptable fit between simulated 

and measured streamflow is achieved. The model developed 

for this study is an expanded and updated version of the 

calibrated INFILv3 model from Rewis and others (2006). 

Calibration was limited for this study to adjusting parameters 

that only control seepage flow because this process was not 

simulated by the Rewis model. Parameters affecting seepage 

flow include the hydraulic conductivities for layers 6 and 7. 

In addition, calibration did not include the use of automated 

parameter estimation algorithms, such as the parameterestimation (PEST) application (Doherty, 2008).

The model was calibrated both qualitatively and quantitatively by comparing simulated and measured streamflow 

at five USGS stream streamgages (Little San Gorgonio 

Creek, LSGC; San Timoteo Creek near Redlands, STCR; 

Snow Creek near White Water, SCWW; Chino Canyon 

Creek below Tramway, CCCT; and Tahquitz Creek near 

Palm Springs, TCNP) in and next to the SGPWM (fig. 16; 

table 7). The five streamgages were selected on the basis of 

(1) the location of drainage area either in or directly next to 

the SGPWM and (2) a length of record of 25 years or longer. 

The goodness-of-fit between simulated and measured streamflow was evaluated by using monthly mean discharge, annual 

(water year) mean discharge, and average monthly mean 

discharge. The qualitative analysis also included scatterplots 

of simulated against measured monthly and annual streamflow 

to evaluate residuals. The comparison of average monthly 

streamflow was done to evaluate model performance in terms 

of representing the seasonal distribution of streamflow.

A quantitative analysis of the goodness-of-fit between 

simulated and measured streamflow was done by using the 

percent-average estimation error (PAEE) and the NashSutcliffe model efficiency (NSME) statistics (Nash and 

Sutcliffe, 1970; Woolfenden and Nishikawa, 2014). The 

PAEE statistic provides a measure of model bias, where a 

value of 0.0 percent indicates a purely unbiased model fit. 

Values of PAEE in the range of plus or minus 10 percent are 

usually considered to indicated a good fit. For this study, 

values of plus or minus 30 percent were considered satisfactory because streamflow in the study area tends to be a small 

component of the water balance relative to precipitation and 

evapotranspiration. The NSME statistic is a standardized mean 

squared error statistic that is often used to compare results 

between different models (Nash and Sutcliffe, 1970; Markstrom and others, 2008). An NSME value greater than 0.0 

indicates the model provides a better match to the measured 

values than the mean of the measured values. The closer the 

NSME is to 1.0, the better the match between simulated and 

measured values. The sample mean provides an NSME of 

0.0, and negative NSME values indicate a model that does 

not perform as well as the sample mean in terms of predicting 

observed values. For this study, an NSME of 0.3 was considered a satisfactory fit.

Streamflow Records

Daily streamflow records for the five streamgages were 

obtained from NWIS (http://waterdata.usgs.gov/ca/nwis/dv). 

Monthly and annual mean discharge for each streamgage was 

calculated by using the daily records; in some cases, months 

that had missing daily records were still used for the comparison of simulated against measured monthly and annual 

streamflow. Lengths of record for the streamgages ranged 

from a maximum of 63 years for streamgage Tahquitz Creek 

near Palm Springs (TCNP) to a minimum of 25 years for 

streamgage Chino Canyon Creek below Tramway (CCCT). 

The drainage areas upstream of the streamgages range from 

5 square-km for streamgage Little San Gorgonio Creek 

(LSGC) to 306 square-km for streamgage San Timoteo Creek 

near Redlands (STCR). Only two of the five streamgages, 

LSGC and Snow Creek near White Water (SCWW), have 

drainage areas completely within the SGPWM. About half of 

the STCR drainage area is outside of the SGPWM. The STCR 

streamgage was included in model calibration because of its 

proximity to the SGPWM and because it includes the entire 

area of the STC subdomain. Two of the streamgages, TCNP 

and CCCT, have drainage areas completely outside of the 

SGPWM, but were included because of their proximity to the 

SGPWM.

Calibration Results

Little San Gorgonio Creek (LSGC)

Comparison of simulated to measured streamflow at 

streamgage LSGC was done by using results from this model 

and the Rewis model. The LSGC streamgage was the only 

streamgage where a comparison of both models could be done.

 Comparison of simulated and measured monthly streamflow at streamgage LSGC indicated that the fit-to-measured 

streamflow was very similar, but not identical, to the Rewis 

model (fig. 17). The reason for the differences in results was 
34 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Table 7. Streamflow records used for model testing, San Gorgonio Pass watershed model, California.

[Abbreviations: mm/dd/yyyy, month/day/year; NAD29, North American Datum of 1929; SGPWM, San Gorgonio Pass Watershed Model; sq-km, square 

kilometers; USGS, U.S. Geological Survey; °, degree; ', minutes; ", seconds]

SGPWM

station

code

USGS

station 

identifier

USGS

station 

name

Station coordinates 

(NAD29) Drainage

area

(sq-km)

Records use in this study

Latitude

(North)

Longitude

(West)

Begin date

(mm/dd/yyyy)

End date

(mm/dd/yyyy)

Years of 

record

SCWW 10256500 Snow Creek near White Water Calif. 33° 52' 14" 116° 40' 49" 28 07/21/1921 07/17/2011 52

CCCT 10257720 Chino Canyon Creek below tramway 

near Palm Springs Calif.

33° 50' 39" 116° 36' 16" 12 10/01/1986 12/31/2012 25

TCNP 10258000 Tahquitz Creek near Palm Spring Calif. 33° 48' 18" 116° 33' 30" 44 10/01/1947 11/04/2012 63

LSGC 11056500 Little San Gorgonio Creek near 

Beaumont Calif.

34° 01' 59" 116° 56' 43" 5 10/01/1948 09/30/1985 37

STCR 11057000 San Timoteo Creek near Redland Calif. 34° 01' 59" 117° 12' 29" 306 10/01/1926 04/07/1979 48

San Bernardino County

Riverside County

!

!

!

! !

SCWW

TCNP

LSGC

CCCT

STCR

Hidden Fork

Creek

San Timoteo CreekSan Timoteo Creek

Yucaipa CreekYucaipa Creek

Oak Glen CreekOak Glen Creek

Noble CreekNoble Creek

Wilson CreekWilson Creek Birch Creek

Birch Creek San Jacinto River San Jacinto River

Potrero CreekPotrero Creek

Poppet Creek Poppet Creek

Montgomer y CreekMontgomer y Creek

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Hathaway CreekHathaway Creek

Potrero Creek Potrero Creek

Stone CreekStone Creek

Indian CreekIndian Creek

Tahquitz CreekTahqui

Wil

tz Creek

low Creek

Willow Creek

Long Valley CreekLong Valley Creek

Whitewater River Whitewater River

Whitewater RiverWhitewater River

Gorgonio

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Jenson Creek Jenson Creek

Snow CreekSnow Creek

Falls Creek Falls Creek

One Horse

San

Creek

River

Little San Gorgonio CreekLittle San Gorgonio Creek

Little San Gorgonio

Peak

San

Jacinto

Peak

EXPLANATION

!

Calibration watersheds San Gorgonio Pass

 groundwater-study area

San Gorgonio Pass 

 watershed model

San Gorgonio Pass 

 watershed-model subareas

SCWW-Snow Creek near 

 White Water, Calif.

TCNP-Tahquitz Creek near 

 Palm Springs, Calif.

LSGC-Little San Gorgonio Creek 

 near Beaumont, Calif.

CCCT-Chino Canyon Creek below 

 tramway near Palm Springs, Calif.

STCR-San Timoteo Creek near 

 Redlands, Calif.

County boundary

U.S. Geological Survey

 streamgage

117°10' 117° 116°50' 116°40'

34°

33°

50'

0 2 4 6 8 10 MILES

0 2 4 6 8 10 KILOMETERS

Base modified from U.S. Geological Survey and 

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

sac15-0556_fig 16

Figure 16. Location of five streamgages used for model calibration, San Gorgonio Pass watershed model, California.
Model Calibration 35

Monthly mean discharge,

in cubic meters per second

Annual mean discharge,

in cubic meters per second

Average monthly mean discharge,

in cubic meters per second

1947 1949 1953 1955 1959 1963 1965 1967 1971 1973 1977 1979 1983 1987

A

B

C

Water year

Month

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

1947 1949 1953 1955 1959 1963 1965 1967 1971 1975 1977 1979 1983 1987

Water year

1951 1957 1961 1969 1973 1981 1985

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

1951 1957 1961 1969 1975 1981 1985

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

1.40

1.50

Statewide drought periods

Measured

Simulated

Simulated (Rewis and others, 2006)

EXPLANATION

Measured

Simulated

Simulated (Rewis and others, 2006)

EXPLANATION

EXPLANATION

Statewide drought periods

Measured

Simulated

Simulated (Rewis and others, 2006)

sac15-0556_fig 17abc

Figure 17. Comparison of simulated and measured streamflow at the Little San Gorgonio Creek (LSGC) streamgage using the 

San Gorgonio Pass watershed model, California: A, monthly mean discharge; B, water year mean discharge; C, average monthly mean 

discharge; D, scatterplot of simulated to measured monthly mean discharge; and E, scatterplot of simulated to measured water year 

mean discharge.
36 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

D

E

Measured monthly mean discharge, in cubic meters per second

Simulated monthly mean discharge, in cubic meters per second Simulated annual mean discharge, in cubic meters per second

Measured annual mean discharge, in cubic meters per second

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

LCGC simulated versus measured

 (this study)

LCGC simulated versus measured 

 (Rewis and others, 2006)

1 to 1 line

EXPLANATION

LCGC simulated versus measured

 (this study)

LCGC simulated versus measured 

 (Rewis and others, 2006)

1 to 1 line

EXPLANATION

sac15-0556_fig 17de

Figure 17. —Continued
Model Calibration 37

that the SGPWM model developed in this study includes the 

seepage flow modification to INFILv3, a coarser grid-cell 

discretization (150-m grid for current study compared to the 

30-m grid used in the Rewis model), differences in the daily 

climate inputs, and modifications to the spatial-interpolation 

model for the climate inputs (the previous model did not incorporate PRISM maps of average monthly precipitation to interpolate daily precipitation). The overall fit of the SGPWM to 

the LSGC streamgage was considered adequate on the basis of 

similarity to results for the Rewis model, which was calibrated 

in the previous study (Rewis and others, 2006). In terms of 

estimation bias, the PAEE values of −25 percent for monthly 

and annual mean discharge indicated a tendency of the model 

to underestimate streamflow at this site (figs. 17A, B), whereas 

the Rewis model indicated less estimation bias, having a 

PAEE of only −6 percent for mean monthly discharge and 

−5 percent for mean annual discharge.

The NSME statistics for both models were about zero for 

monthly flows, indicating a marginal calibration caused mostly 

by the underestimation of the very high-flow event in 1969, 

combined with an overestimation of the much smaller peak 

monthly flows prior to 1969. The NSME values for annual 

mean discharge indicated a better match between simulated 

and measured flows, with a slightly improved fit for the Rewis 

model (0.43 for the Rewis model compared to 0.39 for the 

SGPWM). The model used in this study provides a better fit 

to the average monthly mean discharge, however (fig. 17C). 

The comparison of streamflow at the LSGC streamgage 

included the very high daily mean discharge value of 33 cubic 

meters per second (m3/s) measured on February 25, 1969. This 

high daily mean discharge resulted in a peak monthly mean 

discharge of about 1.4 m3/s, as well as a high annual mean 

discharge of about 0.24 m3/s. Simulated daily mean discharges 

for February 25, 1969, were much lower, with about 4 m3/s 

simulated by the Rewis model and about 5 m3/s simulated by 

this model.

The scatterplots of simulated to measured monthly and 

annual mean discharge indicated the highest estimation error 

for monthly flows was about 1.0 m3/s for both models, and the 

highest estimation error for annual flows was about 0.16 m3/s 

for both models (figs. 17D, E). The scatterplots indicated a 

slightly improved fit to measured annual mean discharge by 

using the previous model, as confirmed by the greater NSME 

value for the previous model.

San Timoteo Creek near Redlands (STCR)

Comparison of simulated to measured monthly and 

annual mean discharge at streamgage STCR indicated an 

adequate overall fit between simulated and measured streamflow in terms of PAEE, but overestimation of the higher 

flows during wet years and underestimation of many of the 

low and intermediate flows during average and dry periods, 

resulting in poor NSME values of approximately zero for 

both monthly and annual streamflow (fig. 18A, B). Although 

the model tended to overestimate the peak monthly flows, 

resulting in PAEE values of 18 and 19 percent for monthly 

and annual streamflow, the overall distribution of the average monthly mean discharge was generally represented by 

the model (fig. 18C). The distribution of annual streamflow 

for the period of record was also generally represented by the 

model (fig. 18B). Although there was some overestimation of 

the largest annual flows and underestimation of the smallest 

annual flows, the timing and frequency of wet and dry years 

were sufficiently represented.

The general fit to average monthly streamflow (fig. 18C) 

showed some deviation from the monthly distribution for 

January, August, and September. The scatterplots of simulated 

to measured monthly and annual mean discharge indicated 

the tendency of the model to overestimate flows at this 

streamgage, but the overall correlation between simulated and 

measured streamflow was good, and the model fit was considered sufficient for the purposes of this study (figs. 18D, E).

Snow Creek near White Water (SCWW)

Comparison of simulated and measured monthly mean 

discharge at streamgage SCWW indicated an adequate general 

fit to the wet and dry periods in the monthly streamflow time 

series (fig. 19A); however, low flows during the spring and 

summer months was underestimated by the model, as indicated by the comparison of average monthly mean discharge 

(fig. 19C). In addition, considerable estimation bias was 

caused by underestimation of streamflow for dry periods, 

as indicated by the annual mean discharge hydrographs 

(fig. 19B). The low flows at this streamgage could be springfed baseflows that are difficult for the SGPWM to simulate 

because there is no deep groundwater discharge component 

represented by the model. Despite these issues, the monthly 

and annual streamflow hydrograph (fig. 19A) showed that 

the model captured the timing and variation in the monthly 

flows, with a fair match to most, but not all, of the peak flows. 

The simulation provided acceptable NSME values of 0.42 for 

monthly flows and of 0.45 for annual flows. The PAEE values 

of about −40 percent indicated considerable underestimation 

of streamflow, however.

The scatterplots of monthly and annual streamflow indicated a fair correlation between simulated and measured flows. 

Comparison of the annual flows indicated a strong model 

bias in terms of underestimating most of the annual flows, 

however (figs. 19D, E). The largest absolute estimation errors 

for annual flows were more than 0.4 m3/s, for the two greatest 

measured mean annual discharges of about 0.92 and 0.93 m3/s.

Chino Canyon Creek below Tramway (CCCT)

In contrast to the results obtained at SCWW, comparison of simulated to measured monthly and annual mean 

discharge at streamgage CCCT indicated a tendency of the 

model to overestimate streamflow at this location, especially 

for wetter-than-average periods (figs. 20A, B). Although 
38 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

0

1

2

3

4

5

6

Monthly mean discharge,

in cubic meters per second

0.00

0.05

0.10

0.30

0.35

0.40

0.45

Annual mean discharge,

in cubic meters per second

Average monthly mean discharge,

in cubic meters per second

1926 1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978

0.15

0.20

0.25

A

B

C

Water year

Month

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

1926 1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978

Water year

EXPLANATION

Measured

Simulated

EXPLANATION

Statewide drought periods

Measured

Simulated

EXPLANATION

Statewide drought periods

Measured

Simulated

sac15-0556_fig 18abc

Figure 18. Comparison of simulated and measured streamflow at the San Timoteo Creek near Redlands (STCR) streamgage using the 

San Gorgonio Pass watershed model, California: A, monthly mean discharge; B, water year mean discharge; C, average monthly mean 

discharge; D, scatterplot of simulated against measured monthly mean discharge; and E, scatterplot of simulated against measured 

water year mean discharge.
Model Calibration 39

D

E

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

Simulated annual mean discharge, in cubic meters per second

Measured annual mean discharge, in cubic meters per second

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Simulated monthly mean discharge, in cubic meters per second

Measured monthly mean discharge, in cubic meters per second

Simulated versus measured

1 to 1 line

EXPLANATION

Simulated versus measured

1 to 1 line

EXPLANATION

sac15-0556_fig 18de

Figure 18. —Continued
40 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012 Monthly mean discharge, in cubic meters per second Annual mean discharge, in cubic meters per second Average monthly mean discharge, in cubic meters per second

1961 1965 1969 1973 1981 1985 1993 2001 2013

A

B

C

Water year

Month

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

1957 1965 1969 1973 1981 1985 1997 2001 2013

Water year

1961 1977 1989 1993 2005 2009

1957 1977 1989 1997 2005 2009

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50

EXPLANATION

Measured

Simulated

Statewide drought periods

Measured

Simulated

EXPLANATION

Statewide drought periods

Measured

Simulated

EXPLANATION

sac15-0556_fig 19abc

Figure 19. Comparison of simulated and measured streamflow at the Snow Creek near White Water (SCWW) streamgage using the 

San Gorgonio Pass watershed model, California: A, monthly mean discharge; B, water year mean discharge; C, average monthly mean 

discharge; D, scatterplot of simulated to measured monthly mean discharge; and E, scatterplot of simulated to measured water year 

mean discharge.
Model Calibration 41

D

E

Measured monthly mean discharge, in cubic meters per second

Simulated monthly mean discharge, in cubic meters per second Simulated annual mean discharge, in cubic meters per second

Measured annual mean discharge, in cubic meters per second

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Simulated versus measured

1 to 1 line

EXPLANATION

Simulated versus measured

1 to 1 line

EXPLANATION

sac15-0556_fig 19de

Figure 19. —Continued
42 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012 Monthly mean discharge, in cubic meters per second Annual mean discharge, in cubic meters per second Average monthly mean discharge, in cubic meters per second

1988 1990 1992 1994 1998 2000 2002 2006 2012

A

B

C

Water year

Month

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

1986 1990 1992 1994 1998 2004 2006 2012

Water year

1988 1996 2000 2002 2008 2010

1986 1996 2004 2008 2010

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

EXPLANATION

Measured

Simulated

Statewide drought periods

Measured

Simulated

EXPLANATION

Statewide drought periods

Measured

Simulated

EXPLANATION

sac15-0556_fig 20abc

Figure 20. Comparison of simulated and measured streamflow at the Chino Canyon Creek below Tramway (CCCT) streamgage using 

the San Gorgonio Pass watershed model, California; A, monthly mean discharge; B, water year mean discharge; C, average monthly 

mean discharge; D, scatterplot of simulated to measured monthly mean discharge; and E, scatterplot of simulated to measured water 

year mean discharge.
Model Calibration 43

D

E

Measured monthly mean discharge, in cubic meters per second

Simulated monthly mean discharge, in cubic meters per second Simulated annual mean discharge, in cubic meters per second

Measured annual mean discharge, in cubic meters per second

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.00 0.05 0.10 0.15 0.20 0.25 0.30

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00

Simulated versus measured

1 to 1 line

EXPLANATION

Simulated versus measured

1 to 1 line

EXPLANATION

sac15-0556_fig 20de

Figure 20. —Continued
44 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

the timing of periods with significant streamflow as well as 

periods of no streamflow was represented well by the model, 

the magnitude of peak streamflow was overestimated, which 

led to a high estimation bias and poor model fit, with PAEE 

values greater than 100 percent and negative NSME values. 

The overestimation of flows during the wet winter months 

was very apparent in the comparison of simulated to measured average monthly mean discharge (fig. 20C). The model 

provided a fair representation of the monthly distribution of 

streamflow, however, with January having the highest average monthly streamflow. The model provided a fairly good 

match to the low average monthly discharge for May through 

November.

The monthly and annual scatterplots indicated a fair correlation between simulated and measured streamflow, especially for the annual flows; however, the strong bias in terms 

of overestimating the measured flows was also very apparent. 

The largest absolute estimation error tended to be for the largest monthly and annual flows.

Tahquitz Creek near Palm Springs (TCNP)

Comparison of simulated to measured monthly streamflow at streamgage TCNP indicated a good match to monthly 

mean discharge (fig. 21A). The model indicated a good general 

fit to annual mean discharge, particularly to most water years 

that had higher streamflow, such as 1952, 1958, 1978, 1993, 

1995, 1998, and 2005, (fig. 21B). The average monthly mean 

discharge simulated at streamgage TCNP indicated a good 

fit to measured streamflow for the months of August through 

January and March, but a poor fit for February and April 

through July (fig. 21C). February had the highest average 

monthly mean discharge for simulated streamflow, whereas 

measured streamflow indicated May had the highest average 

monthly mean discharge. The difference in peak average 

monthly streamflow could be caused by the SGPWM underestimating snowfall for the drainage upstream of the TCNP 

streamgage.

The scatterplots of monthly and annual mean discharge 

indicated a favorable match between simulated and measured 

streamflow at this streamgage (figs. 21D, E). Although the 

PAEE values of −24 percent for monthly flows and −25 

percent for annual flows indicated a tendency of the model 

to underestimate flows at this streamgage, the scatterplots 

showed a strong correlation between simulated and measured 

flows; the NSME of 0.49 for monthly mean discharge and 0.83 

for annual mean discharge indicated the best overall model fit 

of the five streamgages used to evaluate model performance.

Summary of Model Calibration Results

The model provided a satisfactory fit to measured 

monthly mean discharge, in terms of the NSME statistic, 

at two of the five streamgages (SCWW and TCNP), and a 

satisfactory fit to annual mean discharge at three of the five 

streamgages analyzed (LSGC, SCWW, and TCNP). In terms 

of the PAEE statistic, the model provided a satisfactory fit 

to three of the five streamgages (LSGC, STCR, and TCNP) 

both for monthly and annual mean discharge. The model 

provided a good fit for two of the five streamgages (LSGC 

and STCR) on the basis of results for average monthly mean 

discharge. At streamgages LSGC, SCWW, and TCNP, the 

model underestimated streamflow, whereas streamflow was 

overestimated at streamgage STCR and CCCT. The strongest 

bias in terms of underestimating flow is at streamgage SCWW. 

There was a fair match to the peak monthly and annual flows 

at this streamgage, however, resulting in satisfactory NSME 

values. The strongest bias in terms of overestimating flow was 

for streamgage CCCT. Out of the five streamgages, model fit 

was poorest at this streamgage, resulting in negative NSME 

values. The best overall model fit was obtained at streamgage 

TCNP, as indicated by the highest NSME results compared to 

the other streamgages. Although there was a tendency for the 

model to underestimate flow at this streamgage, as indicated 

by PAEE values of −24 and −25 for monthly and annual flows, 

the PAEE values were considered satisfactory. In general, the 

model calibration results indicated that the application of the 

SGPWM, which was based on an extension and modification 

of a model calibrated previously to observed streamflow at 

streamgage LSGC, was appropriate for the intended use of 

simulating natural recharge for the SGPGSA.

The NSME statistic is similar to the R-squared statistic 

in that it provides a standardized indication of model performance, such that the model fit can be compared to results 

obtained in other studies. For this study area, however, it 

was difficult to obtain NSME values close to 1.0 simultaneously for all gages because of model limitations regarding 

the simulation of intermittent streamflow and the sensitivity 

of streamflow to variability in precipitation and snowmelt 

in conjunction with highly variability channel properties. 

Changes in model parameters that would result in an improvement in NSME for one gage would simultaneously cause a 

deterioration in NSME for another gage. For these reasons, the 

calibration criteria used for identifying a calibrated model in 

this study were much less stringent than the criteria commonly 

used for precipitation-runoff models.

Model Sensitivity

A qualitative analysis of model sensitivity was done 

as part of this study. Simulated recharge was found to be 

most sensitive to soil and root zone thickness, and to the 

estimated hydraulic conductivity for layers 6 and 7. In most 

cases, changes in parameter values that caused an increase 

in recharge also resulted in a decrease in runoff along with a 

decrease in ET. Runoff, recharge, and ET were all very sensitive to spatially interpolated precipitation as well as the timing 

and frequency of precipitation. The effect of air temperature 

on the simulation of precipitation as rain or snow was also 

found to be a critical input for the higher elevation cells in 

the San Bernardino and San Jacinto Mountains. Specifically, 

precipitation as rain tended to cause an increase in streamflow 

and, subsequently, an increase in recharge resulting from the 
Model Calibration 45

Monthly mean discharge,

in cubic meters per second

Annual mean discharge,

in cubic meters per second

Average monthly mean discharge,

in cubic meters per second

1947 1951 1955 1959 1963 1967 1975 1983 1987 1991 1995 1999 2007 2011

A

B

C

Water year

Month

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

1951 1955 1959 1963 1971 1975 1983 1987 1991 1995 1999 2007 2011

Water year

1947 1967 1979 2003

1971 1979 2003

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.4

0.8

1.2

1.6

2.0

2.4

2.8

3.2

3.6

EXPLANATION

Measured

Simulated

Statewide drought periods

Measured

Simulated

EXPLANATION

Statewide drought periods

Measured

Simulated

EXPLANATION

sac15-0556_fig 21abc

Figure 21. Comparison of simulated to measured streamflow at the Tahquitz Creek near Palm Springs (TCNP) streamgage using the 

San Gorgonio Pass watershed model, California: A, monthly mean discharge; B, water year mean discharge; C, average monthly mean 

discharge; D, scatterplot of simulated versus measured monthly mean discharge; and E, scatterplot of simulated versus measured water 

year mean discharge.
46 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

D

E

Simulated annual mean discharge, in cubic meters per second

Measured annual mean discharge, in cubic meters per second

Simulated monthly mean discharge, in cubic meters per second

Measured monthly mean discharge, in cubic meters per second

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Simulated versus measured

1 to 1 line

EXPLANATION

Simulated versus measured

1 to 1 line

EXPLANATION

sac15-0556_fig 21de

Figure 21. —Continued
Model Results 47

infiltration of streamflow. Precipitation as snow tended to 

cause a delayed response in runoff and recharge, and this, in 

turn, usually caused a decrease in runoff and recharge along 

with an increase in ET. The increased ET occurs because the 

infiltration of snowmelt into the root zone causes the rootzone water content to be greater in the spring months (March 

through May) than if the precipitation had been rain. With 

increased ET, less water is available for recharge and runoff. 

These observations were generally consistent with findings in 

previous studies (Flint and Martin, 2012).

On average, areas with thinner soil cover (generally the 

undeveloped mountainous areas), lower soil hydraulic conductivity, and a higher percentage of impervious areas generated 

greater runoff. In addition, the frequency and magnitude of 

runoff was found to be sensitive to the estimated hydraulic 

conductivity for layer 6. Low values of hydraulic conductivity 

for layer 6 resulted in a greater potential for the generation 

of saturation-excess runoff, or Dunnian runoff (Markstrom 

and others, 2008), which is runoff that is generated when the 

storage capacity of the soil has been exceeded. The magnitude 

and timing of runoff was sensitive to air temperature and 

precipitation as snow, especially for the higher altitudes, where 

the percentage of precipitation that is snow was high, and to 

the layer 7 drainage function coefficients. 

The seepage-flow component of simulated streamflow, 

used in this study to represent laterally redistributed net 

infiltration below the root zone, was found to be sensitive to 

the estimated horizontal hydraulic conductivity for layer 7 

(defined by the estimated hydraulic conductivity and drainage 

function coefficients for layer 7) as well as slope. Higher 

horizontal conductivity and slope resulted in more seepage 

(and less overland flow). Parameter values that caused 

increased seepage also tended to cause an increase in ET and a 

decrease in runoff and recharge.

Model Results

The simulation period for the SGPWM was from 

January 1, 1909, through September 30, 2012. The 45-month 

period from January 1, 1909, through September 30, 1912, 

was used as the model initialization period. Model results were 

developed and analyzed for the 100-year target simulation 

period, from water years 1913 through 2012 (October 1, 1912, 

through September 30, 2012). Results that were analyzed 

included (1) the long-term average annual water budget, 

including the spatial variability of average ET, runoff, and 

recharge rates; (2) the annual water budgets; (3) the average 

monthly water budgets; (4) mountain front recharge; and 

(5) the effects of imperviousness on recharge and runoff. In 

addition, model results developed in this study were compared 

with results from the Rewis model.

Average Annual Results

The average annual results for water years 1913–2012 

were calculated for all model components to develop an 

estimate of the long-term historic climate water budget for the 

SGPWM (table 8). Inflow to the SGPWM from precipitation 

was 34,171 hectare-meters per year (ha-m/yr). The ET was the 

largest outflow, averaging 26,632 ha-m/yr. The next largest 

outflow was recharge at 5,483 ha-m/yr, followed by runoff at 

1,684 ha-m/yr. Sublimation was the smallest outflow component, averaging 343 ha-m/yr. The long-term change in storage 

(root zone and perched zones combined) was only 28 ha-m/yr 

for the 100-year simulation period. Maps of average annual 

ET, runoff, and recharge were used to analyze the spatial 

variability and distribution of these model components and to 

identify locations of maximum and minimum values.

Table 8. Simulated 100-year water budget using the San Gorgonio Pass watershed model, California: long-term averages for water 

years 1913–2012.

[Abbreviations: ha-m/yr, hectare-meters per year; mm/yr, millimeters per year; SGPWM, San Gorgonio Pass Watershed Model]

Average annual 

results for the 

period 1913–2012

Average depths Average volumes

Model subdomains

SGPWM

(mm/yr)

Model subdomains

SGPWM

(ha-m/yr) Waterbudget

component

Potrero 

Creek

(mm/yr)

San Timoteo 

Creek

(mm/yr)

San Gorgonio 

River

(mm/yr)

Potrero 

Creek

(ha-m/yr)

San Timoteo 

Creek

(ha-m/yr)

San Gorgonio 

River

(ha-m/yr)

Inflows

Precipitation 449 493 499 496 950 7,060 26,161 34,171

Outflows

Evapotranspiration 363 399 385 387 768 5,708 20,156 26,632

Recharge 85 86 78 80 180 1,238 4,066 5,483

Runoff 1.2 5.2 31 24 2.6 75 1,607 1,684

Sublimation 0.0 2.8 5.8 5.0 0.1 40 303 343

Sub-total 449 493 499 496 950 7,061 26,132 34,143

Change in storage

Root zone –0.4 –0.5 –0.2 –0.3 –0.9 –6.9 –11 –18

Perched zone –0.0 0.5 0.8 0.7 –0.0 6.6 40 46

Sub-total –0.5 –0.0 0.6 0.4 –1.0 –0.3 29 28
48 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Evapotranspiration

ET is the primary outflow of water from the SGPWM, 

with a basin-wide average of 387 mm/yr for the 100-year 

simulation period, about 78 percent of the basin-wide average 

precipitation of 496 mm/yr (fig. 22; table 8). The modeled 

average ET values for the three model subdomains were 

similar: the San Timoteo Creek subdomain had the highest 

basin-wide average ET at 399 mm/yr, and the Potrero 

Creek subdomain had the lowest basin-wide average ET 

at 367 mm/yr. The mapped results for all cells indicated a 

minimum average ET of about 100 mm/yr in the southeastern 

part of the Indio HSA and a maximum average ET of about 

1,200 mm/yr for a stream channel on a south-facing slope 

near the summit of Little San Gorgonio Peak (fig. 22). The 

simulated average ET was least in the Indio HSA, where the 

soil was the thinnest, the vegetation cover was sparse, and 

precipitation was the least. In the San Bernardino Mountains, 

average ET was much less for north-facing slopes compared 

to that of south-facing slopes. For many areas of the SGPWM, 

the greatest average ET was simulated for stream channels, 

where the root zone extends the deepest and the availability of 

water in the root zone is maximized in response to infiltrating 

streamflow.

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Simulated (1913–2012) average

evapotranspiration, in millimeters per year

102–200

201–260

261–320

321–380

381–440

441–500

501–560

561–620

621–680

681–1,196

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 22. Average annual evapotranspiration simulated for water years 1913–2012 using the San Gorgonio Pass watershed model, 

California.
Model Results 49

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Simulated (1913–2012) average

runoff, in millimeters per year

0–5

6–10

11–50

51–100

101–500

501–1,000

1,001–5,000

5,001–10,000

10,001–50,000

50,001–720,000

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

Little San Gorgonio

Peak

San

Jacinto

Peak

Runoff

The 100-year basin-wide average simulated runoff 

for the SGPWM for water years 1913–2012 was 24 mm/yr 

(table 8), indicating that about 5 percent of precipitation falling in the SGPWM was discharged as surface-water runoff 

to areas downstream of the SGPWM. Basin-wide average 

runoff for the San Gorgonio River subdomain was much 

greater than the basin-wide average runoff for the Potrero 

Creek and San Timoteo subbasins because of the thinner 

soils, more precipitation at the higher altitudes of Little San 

Gorgonio Peak and San Jacinto Peak, and steeper slopes 

of the San Gorgonio River subbasin. The map of simulated 

average runoff depth indicated the average depth of surfacewater runoff for the 100-year simulation period over the 

2.25 ha (150 m × 150 m = 2.25 ha) area of each model cell 

(fig. 23). The average annual runoff depth for each cell can 

be represented as daily flow volumes of cubic meters per 

day by multiplying the runoff depth (in meters) by the cell 

area (2.25 ha equals 22,500 cubic meters) and dividing by 

365.25 days. Results indicated that maximum average runoff 

was simulated for the cells representing the main channels of 

the San Timoteo and San Gorgonio Rivers. Relatively high 

average annual runoff depths of 50,001 to 720,000 mm/yr 

(3,100 to 44,000 cubic meters per day; m3/d) were simulated 

for the lower sections of the San Gorgonio River, and high 

average annual runoff depths of 10,001 to 50,000 mm/yr (616 

to 3,100 m3/d) were also simulated for the lower section of 

San Timoteo Creek. Simulated average annual runoff in the 

main channels of the subdrainages in the upland areas of Little 

San Gorgonio Peak and San Jacinto Peak varied from 1,001 to 

50,000 mm/yr (62 to 3,100 m3/d).

Figure 23. Average annual runoff simulated for water years 1913–2012 using the San Gorgonio Pass watershed model, California.
50 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Recharge

The basin-wide average annual recharge simulated for 

the SGPWM for water years 1913–2012 was 80 mm/yr for the 

SGPWM and was about 16 percent of the basin-wide average 

annual precipitation for the SGPWM (table 8). High average 

recharge rates of more than 200 mm/yr were simulated for 

larger stream channels underlain by alluvium, such as Smith 

Creek, Noble Creek, San Timoteo Creek, Potrero Creek, and 

San Gorgonio River (fig. 24). High average recharge of about 

200 mm/yr and greater was also simulated for the intrachannel areas of high-altitude alluvial valleys such as Potrero 

Creek, San Gorgonio River, Smith Creek, and Noble Creek, 

and for locations at the base of side slopes, at the transition 

from areas with low permeability bedrock to areas with 

more permeable alluvium. Low average recharge of less than 

21 mm/yr was simulated for much of the south-facing slope 

of Little San Gorgonio Peak, where a greater percentage of 

precipitation became runoff or was lost to ET. In the area of 

the SGPGSA, most of the intra-channel locations had average 

recharge rates ranging from 51 to 200 mm/yr. The greatest 

average recharge in the SGPGSA was along the channel of 

Noble Creek and San Gorgonio River. Isolated areas that had 

low average recharge (less than 21 mm/yr) were in locations 

having a high percentage of impervious areas.

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Simulated (1913–2012) average

recharge, in millimeters per year

0–10

11–20

21–30

31–40

41–50

51–100

101–150

151–200

201–250

251–5,771

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 24. Average annual recharge simulated for water years 1913–2012 using the San Gorgonio Pass watershed model, California.
Model Results 51

Annual Results

The time series of spatially averaged annual water 

budgets simulated for water years 1913 through 2012 

indicated high year-to-year variability in all water-budget 

components (fig. 25). The spatial averages were calculated 

by summing the annual results (based on water years) for 

all 30,595 model cells and dividing by the number of cells. 

The annual water budget includes inflows from precipitation; 

changes in storage (root zone and perched zone combined); 

and outflows from ET, recharge, surface water discharge 

(runoff), and sublimation. In order to illustrate a balanced 

water budget for the annual results shown in figure 25, where 

inflows equal outflows for each water year, decreases in 

storage were shown as inflows from storage, and increases 

in storage were shown as outflows to storage (an increase in 

storage is an outflow in the water budget, and a decrease in 

storage is an inflow in the water budget). Overall, the annual 

water budgets indicated a trend of increasing variability in 

response to increasing variability in precipitation. The four 

wettest years (1978, 1983, 1993, and 2005) and three out of 

the four driest years (2002, 2007, and 2009) were all after 

water year 1978. The wettest year (1993) and the three driest 

years (2002, 2007, and 2009) were after water year 1992.

Water years with high precipitation are balanced by high 

ET, recharge, and runoff. ET was the largest outflow in the 

water budget for all water years. The highest ET was 650 mm 

(shown as −650 mm to indicate that this was an outflow) 

and was in water year 1983, which was the second wettest 

year in the simulation period, with an annual precipitation 

of 1,010 mm. Simulated ET exceeded precipitation for 17 of 

100 water years, and all of the 17 years had less than average 

precipitation.

The magnitude of annual recharge and runoff tended 

to be directly correlated with the magnitude of annual 

precipitation. The maximum annual recharge of about 400 mm 

and the maximum annual runoff of about 250 mm were both 

simulated for water year 1993, the wettest year in the period 

at about 1,100 mm of precipitation. ET was not strongly 

correlated to precipitation, but was weakly dependent on 

air temperature. In contrast to the greatest simulated ET of 

642 mm for 1983, simulated ET for 1993, the wettest year in 

the period, was only 436 mm. The differences in the temporal 

distribution of ET relative to recharge and runoff result from 

differences in the timing, frequency, and magnitude of daily 

precipitation, combined with year-to-year variations in air 

temperature affecting PET.

Changes in storage (the combined change in storage of 

the root zone and perched zones) and sublimation were the 

smallest components of the annual water budgets, and the 

amount of sublimation was much smaller than changes in 

storage, averaging just 5 mm/yr for the SGPWM (table 8). 

With the exception of water year 2008, no water years with 

less than 400 mm precipitation indicated a gain in water 

storage (outflow to storage). The increase in storage (outflow 

to storage) for water year 2008, a somewhat drier than average 

year with about 363 mm precipitation (compared to 496 mm 

for an average year), resulted because it followed the driest 

water year in the simulation period, water year 2007, with 

only 94 mm precipitation. The greatest increases in annual 

storage were for wetter than average years that followed drier 

than average years, and the largest decreases in storage were 

for drier than average water years that followed wetter than 

average water years.

To evaluate annual variability as well as trends and 

deviations from the averages for the 100-year (yr) period, the 

annual time series for ET, recharge, and runoff were analyzed 

by using the 11-year centered moving-average and also the 

LOWESS method (fig. 26). Results indicated periods of 

greater than average ET for water years 1917–45, water years 

1976–86, and water years 1992–95 (fig. 26A). Very low values 

of annual ET less than 150 mm were simulated for water years 

2002 and 2007, the 2 driest years of the simulation period. 

Year-to-year variations in ET for water years 1913–46 were 

less than for the rest of the period because average precipitation was greater for this period than other periods. In addition, 

average air temperatures were lower during this earlier period, 

which resulted in greater amounts of snowfall than during 

other periods (figs. 14B, C). ET was greater during this earlier 

period compared to the long-term average, even though PET 

was lower, mostly because of the lower air temperatures 

(fig. 14D), because of the greater water availability and 

because snowmelt contributes more to ET than precipitation 

falling as rain. Winter seasonal rainfall promotes runoff and 

recharge, whereas snowfall during the winter causes a delay 

in water availability (as snowmelt) into spring, when PET is 

higher, promoting higher ET and, correspondingly, decreased 

runoff and recharge.

Annual recharge indicated greater year-to-year variability 

than did ET, with relatively high annual recharge of 250 

mm and greater simulated for water years 1922, 1937, 1969, 

1978, 1980, and 1993, compared to very low recharge of 

50 mm and less simulated for 54 of the 100 (a little more than 

half) water years (fig. 26B). Periods of greater than average 

recharge tended to be short (1 to 3 years), whereas periods 

of below-average recharge tended to be longer (3–7 years in 

most cases). Water year 1993, the wettest year in the simulation period, also had the most annual recharge at 380 mm, and 

it was followed by a year that had below-average recharge of 

45 mm.

Annual runoff indicated year-to-year variability similar 

to that of recharge (fig. 26C). Unlike recharge, however, very 

small annual runoff amounts of less than 1 mm (0.02 m3/s) 

were simulated for many years, causing the year-to-year variability in the annual time series of runoff to be greater than the 

variability for recharge. Periods of 6 years or longer that had 

less than average annual runoff were water years 1944–57, 

1959–65, 1970–75, 1984–92, and 1999–2004. Similar to 

recharge, periods with greater than average runoff were very 

short (1 to 3 years), and only two 3-year periods had greater 

than average runoff: 1914–16 and 1978–80. In general, the 

overall variability in annual runoff was the greater than that of 

the other model components, and the maximum annual runoff 

was about 250 mm (5.45 m3/s) for water year 1993.
52 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Storage inflows and outflowsRecharge (outflow)

Sublimation (outflow)Evapotranspiration (outflow)

Runoff (outflow)

Precipitation (inflow)

−1,100−1,000

−900−800−700−600−500−400−300−200−10

00

100200300400500600700800900

1,0001,100

, in millimeters s wolft uo dna s wolf ni ,l at ot r aey r et a W

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

Water year

Outflows

Inflows

EXPLANATION

sac15-0556_fig 25 Figure 25. Simulated annual water budgets for water years 1913–2012 showing inflows, outflows, and changes in storage, using the San Gorgonio Pass watershed model, 

California.
Model Results 53

100

150

200

250

300

350

400

450

500

550

600

650

Evapotranspiration, in millimeters

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

Water year

A

B

C

0

50

100

150

200

250

300

350

400

Recharge, in millimeters

0

40

80

100

140

160

200

240

260

Runoff, in millimeters

220

180

120

60

20

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

Statewide drought periods

LOWESS

Annual

11-year moving average

100-year average

EXPLANATION

sac15-0556_fig 26

Figure 26. Annual simulation results for water years 1913–2012, using the San Gorgonio Pass watershed model: A, evapotranspiration; 

B, recharge; and C, runoff.
54 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

The 11-year moving average for ET indicated wet and dry 

periods very similar to those indicated by LOWESS (fig. 26A). 

Prior to water year 1936, both curves indicated a poor correlation to state-wide dry periods identified by the DWR drought 

periods. After water year 1936, there was a better match 

between periods of low ET and the DWR drought periods. 

In contrast to ET, results for recharge and runoff indicated a 

greater difference between the 11-year moving average and 

LOWESS relative to the 100-year period average (figs. 26). 

Although the wet and dry periods indicated by the two curves 

were similar both for recharge and runoff, the LOWESS curve 

for recharge had values above the 100-year average for some 

water years, whereas the LOWESS values for runoff were all 

below the 100-year mean for runoff. The 11-year moving average indicated wetter than average periods (values greater than 

the 100-year mean), both for recharge and runoff, for water 

years 1932–43, 1973–84, and 1988–98. The 11-year moving 

average and LOWESS curves differed because the 11-year 

moving average is sensitive to outliers (very wet years or very 

dry years), which tend to be episodic.

The 11-year moving average and LOWESS curves are 

useful for identifying patterns in the annual time series of the 

selected water-budget components. The wet and dry periods 

indicated by both curves are dependent on the parameters used 

to define the curves, however, and are therefore subjective. 

The cumulative departure from the mean is often used to identify long-term trends and deviations in the annual time series 

of selected water-budget components, where a positive slope 

in the cumulative departure indicates wetter than average periods, and a negative slope indicates drier than average periods.

The cumulative departure from the mean was analyzed 

for precipitation, ET, recharge, and runoff (fig. 27). Precipitation was wetter than average periods for water years 1913–45, 

1977–83, and 1990–98 and drier than average for water years 

1946–77, 1984–90, and 1998–2012 (fig. 27A). Annual ET 

deviations from mean ET were strongly correlated to the wet 

and dry precipitation periods (figs. 27A, B). Unlike ET, annual 

recharge and runoff did not correlate as well to the initial 

period of greater than average precipitation, although there 

was a distinct period of greater than average recharge from 

water years 1936 to 1944 (fig. 27B). After 1944, the periods 

of more and less recharge and runoff were well matched to the 

wet and dry periods for precipitation (fig. 27). The difference 

in the cumulative departure curves in the earlier part of the 

simulation period was caused, in part, by the trend of decreasing snowfall discussed previously and the effect of snowfall as 

opposed to rain on ET, recharge, and runoff.

The spatial distribution of simulated annual recharge for 

a wet year (water year 1993) and a dry year (water year 2004) 

were compared (fig. 28). Water year 1993, the wettest year at 

1,095 mm annual precipitation, also had the highest simulated 

annual recharge at 383 mm (table 9). Water year 2004 was a 

drier than average year, at 330 mm annual precipitation, and 

also had the eighth lowest annual recharge amount at 21 mm 

(table 10). Water year 2004 was selected for analysis because 

it was the last year in a relatively long (6-year) period of little

recharge and also had the lowest combined recharge indicated 

by the 11-year moving average, at 45 mm, and by the LOWESS curve value of 25 mm (fig. 26B).

For water year 1993, recharge in the SGPGSA ranged 

from 201 to more than 501 mm for inter-channel locations 

(fig. 28A), whereas it ranged from 0 to 50 mm for water year 

2004 (fig. 28B). For mountain block locations with thin soil, 

water year 1993 recharge was limited by the permeability 

of the underlying bedrock and, therefore, was similar to the 

long-term average recharge (fig. 24). Most of the increased 

recharge for water year 1993 was in areas with thick soils and 

alluvium, where recharge was not limited by the permeability 

at the bottom of the root zone. In contrast to water year 1993, 

recharge for water year 2004 was mostly in the mountainous 

areas, especially the north-facing slope of San Jacinto Peak. 

Recharge was less than 2 mm for widespread areas having 

thick soils, including areas with thick soils at the higher altitudes of the San Bernardino Mountains.

Average Monthly Results

Average monthly simulation results for precipitation, ET, 

recharge, runoff, and change in storage were used to analyze 

the monthly and seasonal variability in the simulated water 

budget (fig. 29A). The average monthly results were calculated by summing the monthly results for a given month for 

all 30,595 model cells for water years 1913 through 2012 

and dividing by the number of model cells and the number of 

months in the 100-year period (1,200). March had the greatest 

average monthly recharge and ET rates were in March, about 

15 and 75 mm, respectively. The average monthly precipitation and runoff rates were greatest in February, at about 100 

and 10 mm, respectively. Average monthly recharge and ET 

were highest during March because the average monthly water 

content of the root zone also was at a maximum during March. 

The root zone tended to have the largest increase (outflow) 

in water content during December and the largest decrease 

(inflow) in water content during April. The maximum increase 

in the perched-zone water content, about 8 mm, was during 

February, and the largest decrease (inflow) in the perched-zone 

water content, about 3 mm, was during May.

For a given water year, the monthly distribution of simulated water budget components can be much different than 

the long-term average distribution indicated in figure 29A. 

Monthly simulation results for the wetter than average water 

year 1993 (1,095 mm precipitation) indicated a much wetter 

than average January, with 521 mm precipitation compared 

to the 88 mm long-term average (figs. 29A, B). January 1993 

precipitation was much greater than precipitation for the 

next 2 wettest months, February and December, of that water 

year. Although June was typically the driest month, with only 

3.8 mm precipitation (fig. 29A), June precipitation for water 

year 1993 (45 mm) was about 12 times greater than average. Runoff during water year 1993 was greatest in January 

(160 mm), in contrast to the long-term average, for which 

the greatest average monthly runoff, only 9.8 mm, was in 

February.
Model Results 55

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

Water year

A

BCumulative departure from mean, in millimeters

−400

−300

−200

−100

0

100

200

300

400

500

Cumulative departure from

mean, in millimeters

1912 1916 1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

Statewide drought periods

Simulated recharge

Simulated runoff

EXPLANATION

−200

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

2,200

Statewide drought periods

Precipitation

Simulated evapotranspiration

EXPLANATION

sac15-0556_fig 27 Figure 27. Cumulative departure from the mean using the San Gorgonio Pass watershed model, California, for A, estimated 

precipitation and simulated evapotranspiration; and B, simulated recharge and runoff.
56 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Simulated 1993 recharge,

in millimeters

0–10

11–20

21–30

31–40

41–50

51–100

101–150

151–200

201–250

251–10,324

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 28a

Little San Gorgonio

Peak

San

Jacinto

Peak

A

Figure 28. Simulated recharge using the San Gorgonio Pass watershed model, California, for A, water year 1993; and B, water year 

2004.
Model Results 57

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Simulated 2004 recharge,

in millimeters

0–10

11–20

21–30

31–40

41–50

51–100

101–150

151–200

201–250

251–10,324

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

B 117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 28b

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 28. —Continued
58 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Table 9. Simulated 100-year water budget using the San Gorgonio Pass watershed model, California: results for water year 1993.

[Abbreviations: ha-m, hectare-meters; mm, millimeter; SGPWM, San Gorgonio Pass watershed model]

Results for water 

year 1993 

(a wet water year)

Average depths Total volumes

Model subdomains

SGPWM

(mm)

Model subdomains

SGPWM

Water budget (ha-m)

component

Potrero 

Creek

(mm)

San Timoteo 

Creek

(mm)

San Gorgonio 

River

(mm)

Potrero 

Creek

(ha-m)

San Timoteo 

Creek

(ha-m)

San Gorgonio 

River

(ha-m)

Inflows

Precipitation 1,040 1,077 1,103 1,095 2,200 15,422 57,779 75,401

Outflows

Evapotranspiration 420 461 429 436 888 6,606 22,486 29,979

Recharge 582 532 334 383 1,231 7,620 17,490 26,341

Runoff 19 60 313 251 41 862 16,397 17,301

Sublimation 0.0 4.1 9.6 8.2 0.0 59 503 562

Sub-total 1,021 1,058 1,085 1,078 2,159 15,147 56,877 74,183

Change in storage

Root zone 14 16 7.6 9.4 30 222 397 649

Perched zone 4.9 3.7 9.7 8.3 10 53 506 569

Sub-total 19 19 17 18 41 275 903 1,218

Table 10. Simulated 100-year water budget using the San Gorgonio Pass watershed model, California: results for water year 2004.

[Abbreviations: ha-m, hectare-meters; mm, millimeter; SGPWM, San Gorgonio Pass watershed model]

Results for 

water year 2004 

(a dry water year)

Average depths Total volumes

Model subdomains

SGPWM

(mm)

Model subdomains

SGPWM

(ha-m) Water 

budget 

component

Potrero 

Creek

(mm)

San Timoteo 

Creek

(mm)

San Gorgonio 

River

(mm)

Potrero 

Creek

(ha-m)

San Timoteo 

Creek

(ha-m)

San Gorgonio 

River

(ha-m)

Inflows

Precipitation 367 343 325 330 776 4,916 17,037 22,729

Outflows

Evapotranspiration 352 344 315 323 745 4,925 16,534 22,203

Recharge 12 14 24 21 26 203 1,239 1,468

Runoff 16 4.4 0.0 1.4 34 64 0.0 98

Sublimation 0.0 1.1 2.4 2.0 0.0 16 125 141

Sub-total 381 364 342 347 805 5,207 17,898 23,910

Change in storage

Root zone –11 –18 –11 –12 –23 –251 –551 –825

Perched zone –2.6 –2.8 –5.9 –5.2 –5.5 –40 –310 –356

Sub-total –14 –20 –16 –17 –29 –291 –861 –1,181
Model Results 59

A

B

C

Outflows

Inflows

Outflows

Inflows

Outflows

Inflows

−140

−120

−100

−80

−60

−40

−20

0

20

40

60

80

100

120

140

Water year 2004 monthly inflows (positive values) 

and outflows (negative values), in millimeters −550

−500

−450

−400

−350

−300

−250

−200

−150

−100

−50

0

50

100

150

200

250

300

350

400

450

500

550

Water year 1993 monthly inflows (positive values) 

and outflows (negative values), in millimeters −110

−100

−90

−80

−70

−60

−50

−40

−30

−20

−10

0

10

20

30

40

50

60

70

80

90

100

110

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

1913–2012 average monthly inflows (positive values)

 and outflows (negative values), in millimeters 

Month

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Storage inflows and outflows

Runoff (outflow)

Recharge (outflow)

Evapotranspiration (outflow)

Precipitation (inflow)

EXPLANATION

sac15-0556_fig 29

Figure 29. Monthly simulation results for selected components of the water budget using the San Gorgonio Pass watershed model, 

California: A, average monthly results for water years 1913–2012; B, monthly results for water year 1993; and C, monthly results for 

water year 2004.
60 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Monthly simulation results for the drier than average 

water year 2004, with 330 mm precipitation, indicated 

February was the wettest month, with 135 mm precipitation, 

which was consistent with the long-term average monthly 

distribution (figs. 29A, C). November and April, however, 

were wetter than December, January, and March during 2004, 

in contrast to the long-term average, where November and 

April were drier than these months. About 98 percent of the 

precipitation for water year 2004 was lost to ET. In general, 

the results for selected water years indicated high variability in 

monthly recharge and runoff compared to the long-term average monthly values; recharge varied by a factor of about 4 to 

5, and runoff varied by a factor of about 15 to 17, for wet and 

dry years compared to the long-term average.

Mountain Front Recharge

The SGR subdomain of the SGPWM was used to 

evaluate differences in recharge and runoff between the area of 

the SGPGSA in the Banning and Cabazon hydrographic areas 

and in the surrounding surface-water subdrainages upstream 

of the SGPGSA, including the San Bernardino mountain 

block to the north and San Jacinto Peak mountain block to the 

south (fig. 30). A total of 52 surface-water subdrainage areas 

were represented by using the SGPWM flow-routing network 

and the location of major stream channels intersecting the 

SGPGSA boundary. Simulated runoff and recharge for the two 

mountain block areas are inflows to the SGPGSA and can be 

used to help define boundary conditions for groundwater-flow 

models of the SGPGSA.

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

San Gorgonio Pass watershed-model subregions

in the Banning–Cabazon Hydrographic Area 

San Gorgonio Pass groundwater-study 

area (SGPGSA), Banning–Cabazon section

San Bernardino mountain block

San Jacinto mountain block

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

San Gorgonio Pass watershed-model subdrainage areas

County boundary

117° 116°50' 116°40'

34°

33°

50'

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 30. San Bernardino and San Jacinto mountain block areas used to simulate mountain-front recharge for the Banning and 

Cabazon hydrographic areas of the San Gorgonio Pass groundwater study area, San Gorgonio Pass watershed model, California.
Model Results 61

Comparison of the annual (water year) time series of 

simulated recharge for the SGPGSA and the two mountain 

block areas indicated similar annual recharge volumes for the 

SGPGSA and the San Bernardino mountain block (fig. 31A). 

The annual results for the SGPGSA and the two mountain 

block areas were calculated by summing the annual results 

for all model cells in each area and dividing the sum by the 

number of cells in each area. Recharge for the SGPGSA 

and the San Bernardino mountain block had a high degree 

of annual variability directly correlated to the annual variability in precipitation. Annual recharge varied between 100 

and 7,400 hectare-meters for the SGPGSA and between 300 

and 6,050 hectare-meters for the San Bernardino mountain 

block. In contrast, recharge for the San Jacinto mountain 

block had much less year-to-year variability, and recharge 

varied between 300 and 700 hectare-meters throughout the 

100-year simulation period. For the wettest years, such as 

1993, recharge was greatest for the SGPGSA. In contrast, 

during dry periods such as water years 2000–04, recharge in 

both of the mountain block areas was greater than recharge in 

the SGPGSA. The distribution of recharge changed because 

during wet years the greater recharge in the SGPGSA and 

the upper valleys of the San Bernardino mountain block 

was caused mostly by the infiltration of streamflow, whereas 

during the dry years recharge was mostly the result of direct 

infiltration of rainfall and snowmelt in the higher altitudes of 

the mountains, rather than the infiltration of streamflow.

Similar to the results for recharge, the time series of 

simulated annual runoff showed very similar results for the 

SGPGSA and the San Bernardino mountain block areas 

(fig. 31B). Unlike the results for annual recharge, simulated 

annual runoff for the San Jacinto Peak mountain block showed 

much more year-to-year variability. For most water years, the 

runoff volume for the San Jacinto Peak mountain block tended 

to be much less compared to runoff from the other areas.

Average monthly recharge and runoff was calculated 

for the SGPGSA and the two mountain block areas by summing the monthly results for each month for all cells in each 

area and dividing by the number of cells in each area and the 

number of months (1,200). The average monthly results for 

recharge and runoff indicated very similar seasonal variations 

for all three areas (fig. 32). Average monthly recharge was 

greatest during March for all areas (fig. 32A), and average 

monthly runoff was greatest during February for all areas 

(fig. 32B). Similar to the annual results, the average monthly 

recharge results indicated much less variability in seasonal 

recharge for the San Jacinto Peak mountain block compared 

to the SGPGSA and the San Bernardino mountain block 

(figs. 31A, 32A). In contrast to the results for recharge, average 

monthly runoff indicated much more seasonal variability for 

the San Jacinto Peak mountain block, similar to the variability 

in runoff for the SGPGSA and the San Bernardino mountain 

block (fig. 32B).

Effects of Imperviousness

The effects of imperviousness on runoff and recharge 

were evaluated by mapping the difference in results between 

simulations where imperviousness was included (fig. 5) 

and simulations where imperviousness was set to zero for 

all cells. The difference in simulated runoff, calculated as 

the imperviousness-excluded result subtracted from the 

imperviousness-included result, indicated the increase in runoff expected when impervious surfaces were included in the 

model (fig. 33). Runoff in the more developed areas increased 

by as much as 101 to 1,000 mm/yr (7.1E-5 to 7.1E-4 m3/s). 

The increased runoff was routed to the main channels, increasing streamflow in the San Gorgonio River and San Timoteo 

Creek by 1,001 to 2,830 mm/yr (7.1E-4 m3/s to 2.0E-3 m3/s). 

Imperviousness associated with roads in the mountain block 

areas, upstream of the developed areas, also caused minor 

increases in average annual runoff of 2.1 to 50 mm/yr (1.5E-6 

to 3.6E-5 m3/s).

Unlike runoff, the difference in simulated recharge 

indicated both decreases and increases in recharge in response 

to the inclusion of impervious areas in the SGPWM (fig. 34). 

Locations with a high percentage of imperviousness had 

decreases in recharge as a result of the decreased infiltration. 

Decreases in recharge were as much as −126 to −50 mm/yr 

for cells in the urban centers and along the main transportation corridor. Adjacent to and directly downstream of these 

cells, recharge was increased by as much as 51 to 1,178 mm/yr 

because of the increased inflow of runoff from the impervious 

areas. The basin-wide average recharge was 78 mm/yr when 

imperviousness was excluded and was 80 mm/yr when 

imperviousness was included. The slight increase indicated the 

effect of increased runoff on recharge, but the difference was 

not considered important on a basin-wide scale.

Comparison of Models

The simulated long-term average recharge was compared 

to results from the Rewis model (fig. 35). Comparisons were 

made by using GIS to upscale the 30-m grid from the Rewis 

model to the coarser 150-m grid that was used in the SGPWM 

and, then, subtracting the results from both models. The comparisons were limited to the smaller area of the Rewis model, 

which covers the western part of the SGPWM.

Simulated recharge for the two models were similar in 

terms of the basin-wide average, however, as indicated in 

figure 35, large positive and negative differences (greater 

than 25 mm and less than −25 mm) were calculated for many 

locations. The average long-term recharge for the previous 

model, simulated for water years 1930–2001 by using a 30-m 

grid, was about 86 mm/yr (Rewis and others, 2006). Average 

recharge for the up-scaled Rewis model was 82 mm/yr. The 

result for the SGPWM was 90 mm/yr, which was less than a 

10-percent increase. The comparison indicated that upscaling 

the Rewis model results did affect model results, but not by 

much.
62 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012

Water year

A

B

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

5,500

6,000

6,500

7,000

7,500

Annual recharge, in hectare-meters

1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012

Water year

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

12,000

13,000

14,000

Annual runoff, in hectare-meters

Statewide drought periods 

San Gorgonio Pass groundwater-study area,

 Banning-Cabazon Hydrographic Area

San Jacinto mountain block

San Bernardino mountain block

EXPLANATION

Statewide drought periods

San Gorgonio Pass groundwater-

 study area, Banning-Cabazon

 Hydrographic Area

EXPLANATION

San Jacinto mountain block

San Bernardino mountain block

sac15-0556_fig 31

Figure 31. Annual simulation results for the San Gorgornio Pass groundwater study area (SGPGSA) and the San Bernardino and 

San Jacinto mountain blocks using the San Gorgonio Pass watershed model, California: A, recharge; and B, runoff.
Model Results 63

A

B

0

50

100

150

200

250

300

350

400

450

500

550

600

Average monthly runoff, in hectare-meters

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Month

Month

0

20

40

60

80

100

120

140

160

180

200

220

240

260

280

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Average monthly recharge, in hectare-meters

San Gorgonio Pass groundwater-study area,

 Banning-Cabazon Hydrographic Area

San Jacinto mountain block

San Bernardino mountain block

EXPLANATION

San Gorgonio Pass groundwater-study area,

 Banning-Cabazon Hydrographic Area

San Jacinto mountain block

San Bernardino mountain block

EXPLANATION

sac15-0556_fig 32

Figure 32. Average monthly simulation results for water years 1913 to 2012 for the San Gorgornio Pass groundwater study area 

(SGPGSA) and the San Bernardino and San Jacinto mountain blocks using the San Gorgonio Pass watershed model, California: 

A, recharge; and B, runoff.
64 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Difference in simulated runoff (imperviousness 

included minus imperviousness excluded) water 

years 1913–2012, in millimeters per year

0–2

2.1–5

5.1–10

11–20

21–50

51–100

101–200

201–500

501–1,000

1,001–2,830

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 33

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 33. Difference in simulated runoff for imperviousness-included minus imperviousness-excluded simulations using the 

San Gorgonio Pass watershed model, California. 
Model Results 65

10

10

60

79

243

San Bernardino County

Riverside County

Smith Creek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

San Gorgonio RiverSan Gorgonio River

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Difference in simulated recharge (imperviouseness 

included minus imperviousness excluded) water 

years 1913–2012, in millimeters per year

−126 to −50

−49 to −20

−19 to −10

−9 to −1

0

1 to 10

11 to 20

21 to 50

51 to 1,178

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

San Gorgonio Pass watershed-model subareas

County boundary

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 34

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 34. Difference in simulated recharge for imperviousness-included minus imperviousness-excluded simulations using the 

San Gorgonio Pass watershed model, California.
66 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

10

10

60

79

243

San Bernardino County

Riverside County

San Gorgonio RiverSan Gorg

Smith C

onio River

reek Smith Creek

San Gorgonio RiverSan Gorgonio River

Smith Creek Smith Creek

Potrero Creek Potrero Creek

Azalea CreekAzalea Creek

Twin Pines CreekTwin Pines Creek

Brown CreekBrown Creek

Potrero CreekPotrero Creek

San Timoteo CreekSan Timoteo Creek

Noble CreekNoble Creek

Little San Gorgonio CreekLittle San Gorgonio Creek

Montgomery CreekMontgomery Creek

Hathaway CreekHathaway Creek

Creek

One Horse

Snow CreekSnow Creek

0 2 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Base modified from U.S. Geological Survey and 1 3

other State digital data, various scales

Universal Transverse Mercator, zone 11

North American Datum of 1983

EXPLANATION

Difference in simulated recharge [1913–2012 average 

recharge minus 1930–2001 average recharge (Rewis

and others, 2006)], in millimeters per year

−27,000 to −200

−199 to −100

−99 to −50

−49 to −25

−24 to 25

26 to 50

51 to 100

101 to 200

201 to 4,200

San Gorgonio Pass groundwater-study area

San Gorgonio Pass watershed model

Model boundary (Rewis and others, 2006)

County boundary

Model boundary (Rewis and others, 2006)

117° 116°50' 116°40'

34°

33°

50'

sac15-0556_fig 35

Little San Gorgonio

Peak

San

Jacinto

Peak

Figure 35. Difference in simulated recharge results from the updated model minus results from the previous model using the San 

Gorgonio Pass watershed model, California.

On a cell-by-cell basis, there were large differences in 

recharge between the two models. The greatest differences 

were in areas along rivers and creeks because the locations 

of streamlines were not identical for the two models. The 

differences in the streamline locations resulted from the combined effects of upscaling and differences in the flow-routing 

layout for the two models. Other factors causing differences 

in simulated recharge between the two models were (1) the 

effect of seepage flow by using the updated model, (2) the 

differences in the climate inputs between the two models, and 

(3) the differences in the simulation periods used to calculate 

the long-term average recharge rates. The models were most 

similar in the upland areas of the San Bernardino Mountains, 

as indicated by the large area where differences in recharge 

were between −24 and 25 mm/yr. Areas with large negative 

differences (less than −24 mm/yr) were in the main drainages 

of Noble Creek and the upper San Gorgonio River drainage. These reductions in recharge, where recharge simulated 

by the SGPWM was less than recharge simulated by the 

Rewis model, were caused by the effect of the seepage-flow 

component included in the SGPWM.

Additional comparisons were made by using annual 

results for water years 1930–2001, the period simulated by the 

Rewis model. Seven model components were compared: precipitation, air temperature, snowfall, PET, ET, recharge, and 

runoff. The t-test was used to identify significant differences 

in annual results between the models at a 95-percent level of 

confidence, and the kensen test (an R program application 
Model Limitations 67

of the Mann-Kendall test for trend significance) was used to 

identify trends in the annual time series (http://www.inside-r.

org/node/96907; Slack and others, 2003).

The average annual precipitation was about the same in 

both models, with no significant difference indicated by the 

t-test and no significant trend indicated by the kensen test of 

the annual time series (fig. 36A). Differences in annual precipitation were slight for most water years, with larger differences 

for water years 1932, 1937, 1941, 1943, 1944, and 1946. Similar to precipitation, differences in annual air temperature were 

not found to be significant at the 95-percent confidence level, 

with a long-term mean temperature of 14.5 oC for the SGPWM 

and of 14.7 oC for the Rewis model (fig. 36B). The k test indicated no significant trend in air temperature for either model 

for the 72-year period. Results for annual snowfall and PET 

indicated greater relative differences between the two models 

(figs. 36C–D). Annual average snowfall for the SGPWM and 

the Rewis model was 51 mm/yr and 30 mm/yr, respectively, 

and annual PET was 1,620 and 1,440 mm/yr, respectively. The 

t-tests both for snowfall and PET indicated a significant difference between the two models, and the kensen test indicated 

significant trends for decreasing snowfall and increasing PET 

in both models for the 72-year period. In general, the yearto-year variability for all components was similar for both 

models, with a good match in the high and low annual values.

Results for model components ET and recharge were 

similar for the two models, with a close match in the year-toyear variability in the annual time series (figs. 36E, F). The 

long-term average ET for the SGPWM was 390 mm/yr, compared to 391 mm/yr for the Rewis model, and the long-term 

average recharge for the SGPWM was 83 mm/yr, compared 

to 86 mm/yr for the Rewis model. Both models indicated an 

increase in the year-to-year variability in recharge in the second half of the simulation period, with the three highest annual 

recharge amounts after water year 1977. The t-test results for 

ET and recharge indicated no significant difference between 

the models at the 95-percent confidence level, and the kensen 

test indicated no significant trends in the annual time series for 

either ET or recharge.

Results for simulated annual runoff indicated a difference 

between the models (fig. 36G). The 72-year average runoff for 

the SGPWM was 25 mm/yr, compared to 16 mm/yr for the 

Rewis model. Peak annual runoff was more for the SGPWM 

model than for the Rewis model. Maximum runoff for both 

models was during water year 1993; however, the 250 mm 

runoff for the SGPWM was much greater than the 140 mm for 

the Rewis model. Although the relative differences in runoff 

were greater than the relative differences in ET and recharge, 

the t-test did not indicate a significant difference between the 

two models, and the kensen test did not indicate a significant 

trend in the annual time series.

Generally, results for the two models were quite consistent in terms of matching wet and dry periods. Average 

precipitation for DWR drought years was 408 mm/yr for the 

SGPWM, compared to 404 mm/yr for the Rewis model. For 

both models, average snowfall and PET for the DWR drought 

periods were similar to the 72-year average PET and snowfall. Recharge and runoff, however, showed large differences 

between averages for DWR drought years and the 72-year 

averages. For the SGPWM, the average recharge rate for the 

DWR drought years was 47 mm/yr, or about 57 percent of the 

72-year average recharge rate. For the Rewis model, the average recharge rate for the DWR drought years was 33 mm/yr, 

or about 39 percent of the 72-year average rate. Runoff was 

most sensitive to the drought years: for the SGPWM, average 

runoff during the drought years was 5.8 mm/yr (about 23 

percent of the 72-year average), and for the Rewis model, 

the average runoff for drought years was 3.5 mm/yr (about 

22 percent of the 72-year average).

Model Limitations

The SGPWM presented in this study is an expanded and 

updated version of a previous model calibrated to streamflow 

records at a single streamgage site (LSGC). With the updated 

model, comparisons of simulated to measured streamflow 

were made by using daily mean discharge records from five 

USGS streamgages. The comparison between simulated 

and observed streamflow indicated that the fit to measured 

streamflow at streamgage LSGC was generally consistent with 

the previous calibrated model presented in Rewis and others 

(2006). The comparison at the four additional streamgages 

indicated a satisfactory result at streamgages STCR, SCWW 

and TCNP. A poor fit was obtained at streamgage CCCT, 

however.

Model calibration, the process of adjusting model parameters to improve the match between simulated and observed 

streamflow at the five streamgages, was only done on a 

limited basis. As discussed in the “Model Calibration” section, 

changes in model parameters that would result in an improvement in the NSME statistic for one gage would simultaneously 

cause deterioration in the NSME for another gage because 

the parameter adjustments were made consistently across the 

entire model domain by using the mapped basin properties 

(topography, land-cover, soils, and geology). It was concluded 

that in order to improve NSME values for all gages, a more 

rigorous simulation of streamflow (for example, by using an 

hourly time step and numerically simulating streamflow, rather 

than by using simple routing), as well as a more rigorous 

simulation of snow accumulation and snowmelt, would likely 

be needed.

A major limitation in the SGPWM simulated streamflow 

is that daily streamflow is based on a daily routing algorithm 

that assumed episodic streamflows with durations less than 

24 hours. All simulated streamflow either discharged from the 

drainage basin or infiltrated to the root zone at the end of each 

day. The effect of the simplified 1-day routing for streamflow 

is that there was a tendency to overestimate the hydrograph 

peak and underestimate the trailing limb of the hydrograph, 

which generally represents a combination of delayed runoff, 

interflow, and baseflow contributions to total streamflow.
68 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012 Annual precipitation, in millimeters

Water year

A

B

C

DAverage air temperature, 

in degrees Celsius

Annual snowfall, in millimeters Annual potential evapotranspiration, 

in millimeters

1,300

1,350

1,400

1,450

1,500

1,550

1,600

1,650

1,700

1,750

1,800

1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998

1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998

1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998

1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998

0

20

40

60

80

100

120

140

160

11.5

12.0

12.5

13.0

13.5

14.0

14.5

15.0

15.5

16.0

16.5

17.0

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

Statewide drought

 periods

EXPLANATION

Annual, this study

Average, this study

Annual (Rewis and others, 2006)

Average (Rewis and others, 2006)

sac15-0556_fig 36abcd

Figure 36. Comparison of annual results for updated and previous models, water years 1930–2001, using the San Gorgonio Pass 

watershed model, California: A, estimated precipitation; B, estimated air temperature; C, simulated snowfall; D, simulated potential 

evapotranspiration (PET); E, simulated evapotranspiration (ET); F, simulated recharge; and G, simulated runoff.
Model Limitations 69

Annual evapotranspiration, 

in millimeters

E

F

GAnnual recharge, in millimeters Annual runoff, in millimeters

0

20

40

60

80

100

120

140

160

180

200

220

240

260

1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998

Water year

1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998

1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998

0

50

100

150

200

250

300

350

400

450

500

200

250

300

350

400

450

500

550

600

650

700

150

100

EXPLANATION

Statewide drought periods Annual, this study

Average, this study

Annual (Rewis and others, 2006)

Average (Rewis and others, 2006)

sac15-0556_fig 36efg

Figure 36. —Continued
70 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

An additional limitation of the SGPWM is that dispersive 

or divergent streamflow, which can be an important characteristic of streamflow and overland flow across alluvial fans and 

basins with braided channels, is not represented in the surfacewater flow-routing algorithm. With INFILv3, all surface-water 

flow is simulated as convergent streamflow, and this could 

result in an overestimation of runoff in some parts of the study 

area, especially along the larger channels on alluvial fans and 

across the transition from steeper channels in the more rugged 

upland terrain to the more gently sloping, open area of alluvial 

fans.

Groundwater baseflow is not directly simulated by 

the SGPWM. Baseflow is only partially represented by the 

seepage-flow component of the model. The simulation of 

seepage flow is intended for representing springs and seeps 

originating from shallow flow paths in rugged mountainous 

terrain. Seepage flow is not intended to represent spring discharge from deep groundwater aquifers.

Storm runoff is modeled as a cascading flow process in 

the SGPWM. For a given model cell, surface-water runoff is 

distributed to the area of the downstream cell as a uniform 

inflow depth. All runoff generated for a daily time step is 

routed to the point of surface-water discharge for the three 

model domains (SGR, STC, PTC). This method does not 

account for delays in outflow caused by channel storage.

The physical characteristics of the watersheds defined in 

the SGPWM are static; they do not change with time. Changes 

in land use and land cover caused by factors such as urbanization and wildfires can cause noticeable changes through time 

in imperviousness, vegetation, land cover characteristics, and 

stream channel characteristics that are not represented by the 

model. 

 Snow is an important component of the hydrologic 

system at higher altitudes in the San Bernardino and San 

Jacinto Mountains. Testing results indicated high uncertainty 

in the simulation of snowfall and snowmelt in the current 

SGPWM configuration because it is sensitive to small changes 

in air temperature. In the SGPWM, snow was simulated only 

when the average daily air temperature was 0 oC or less. In 

actuality, meaningful snow amounts can accumulate during 

storms when the average recorded air temperature is greater 

than 0 oC. Also uncertainty in the spatial distribution of 

precipitation as a result of a sparsity of climate stations could 

limit the calibration of precipitation-runoff models for this 

study area. Additional model calibration based on a comparison of simulated snow cover and satellite data could be used 

to improve the simulation of recharge and runoff for the higher 

altitudes, where snowfall and snowmelt become important.

The resolution of the state-wide geology map (Jennings, 

1977) available to estimate soil depth was considered poor for 

the scale of the study area (the resolution of the geology map 

is 1:700,000 scale). Higher resolution maps were available 

for the SGPGSA, but these did not cover the higher altitudes 

of the mountain blocks. The state-wide map was used for 

consistency. This caused some discrepancy in the boundary 

between locations in valleys with thick soils (as defined by 

alluvium) and thin soils in rugged upland areas (as defined by 

consolidated bedrock), however, especially along the northern 

boundary of the SGPGSA. The resolution of the STATSGO 

soils map used to develop many of the parameters for the rootzone layers also was low. In general, the STATSGO data are 

applied to more regional-scale state-wide or national studies. 

Unfortunately, higher resolution soils data (for example, Soil 

Survey Geographic data, or SSURGO) for the complete area 

of the SGPWM were not available at the time of this study.

Summary and Conclusions

A daily rainfall-runoff model for the San Gorgonio Pass 

watershed, referred to in this study as the San Gorgonio Pass 

watershed model (SGPWM), was used to estimate transient 

natural recharge for the San Gorgonio Pass groundwater 

study area (SGPGSA). The SGPWM includes the area of 

the SGPGSA as well as surrounding watersheds adjacent to 

and outside of the SGPGSA that have the potential to contribute surface-water inflows and mountain-front recharge 

(fig. 1). The SGPWM was developed by using the INFILv3 

code, which was used in a previous study (Rewis and others, 

2006) to develop recharge estimates in the western part of the 

SGPGSA that included the Beaumont and Banning Hydrographic Subareas (HSAs). Natural recharge simulated by 

the SGPWM includes aerially distributed recharge resulting 

from the in-place infiltration of rainfall and snowmelt and 

also recharge from the infiltration of runoff and seepage from 

upstream areas.

A primary advantage of the application of the SGPWM 

for recharge estimation is that the effect of climate on recharge 

is simulated deterministically. Variations in recharge and 

runoff in response to the spatial and temporal variability of 

climate and the spatial variability of watershed characteristics 

are accounted for by using a daily time step and a 150-meter 

(m) gridded discretization of the model domain. Daily climate 

data obtained from a regional network of monitoring sites, 

along with a Parameter-elevation Regressions on Independent 

Slopes Model (PRISM) average monthly precipitation, were 

used to develop the climate inputs for the SGPWM. Geographic Information System (GIS) data defining land use, land 

cover, soils, vegetation, and surficial geology were used as 

input for defining grid-cell parameters representing the physical characteristics of the SGPWM.

Streamflow simulated by using the SGPWM was 

compared to available records of streamflow at two U.S. 

Geological Survey (USGS) streamgages in the SGPWM and 

three USGS streamgages next to the SGPWM (fig. 16). In 

terms of the Nash Sutcliffe model efficiency (NSME) index, 

simulated streamflow provided an acceptable match to three 

of the annual streamflow records and two of the monthly 

streamflow records. In terms of the percent average estimation 

error (PAEE), simulated streamflow provided an acceptable fit 
Summary and Conclusions 71

to three of the five streamflow records. A major challenge in 

simulating streamflow in this study area is that streamflow was 

intermittent and was sensitive to hourly precipitation rates, in 

terms of the hourly precipitation intensity exceeding the infiltration capacity of the ground surface. The hourly precipitation 

rates are not directly accounted for by using the daily time step 

applied with the SGPWM. In addition, considerable model 

uncertainty exists in the simulation of precipitation as snow 

and subsequent snowmelt, because of uncertainty in spatially 

interpolated air temperature. The uncertainty in snowfall 

and snowmelt contributed to limitations in model calibration 

because small differences in air temperature can have a large 

effect on snowfall and snowmelt, and this can a have a large 

effect on the magnitude and timing of runoff.

The SGPWM was used to analyze the spatial and 

temporal distribution of simulated water-budget components 

for water years 1913–2012. The 100-year time-averaged 

results indicate that 78 percent of the basin-wide average 

precipitation of 496 millimeters per year (mm/yr) was lost to 

evapotranspiration (ET; table 8). About 5 percent (24 mm/

yr) of the precipitation falling in the SGPWM was discharged 

as surface-water runoff (table 8). Maximum runoff was 

simulated for the cells representing the main channels of the 

San Timoteo and San Gorgonio Rivers (fig. 23). The average 

100-year simulated recharge rate was 80 mm/yr, 16 percent 

of the average precipitation rate (table 8). Comparatively 

high recharge rates (greater than about 200 mm/yr) were 

simulated for larger stream channels underlain by alluvium, 

for the intra-channel areas of high-altitude alluvial valleys, as 

well as for locations at the base of side slopes where upslope 

areas with low permeability bedrock and thin soils transition 

to downslope areas with more permeable alluvium (fig. 24). 

The most recharge in the SGPGSA was along the channels of 

Noble Creek and San Gorgonio River (fig. 12).

Analysis of the temporal distribution of simulated 

water-budget components indicated great year-to-year 

variability compared to the long-term average, and runoff had 

the highest year-to-year variability (figs. 25, 26). Results for 

selected wet and dry water years indicated large differences 

in spatial distributions of annual recharge (fig. 28) and great 

variability in monthly recharge and runoff compared to the 

100-year-average monthly values (fig. 29), with recharge 

varying by a factor of about 4 to 5 and runoff varying by a 

factor of about 15 to 17 for wet and dry years compared to the 

long-term average.

The SGPWM was used to evaluate differences in 

recharge between the area of the SGPGSA in the Banning 

and Cabazon HSAs and the surrounding San Bernardino 

and San Jacinto Peak mountain block areas upstream of the 

SGPGSA. Results (figs. 31, 32) showed that the SGPGSA 

had more recharge than the San Jacinto Peak mountain block 

because of the greater permeability of the alluvium and valley fill material in the SGPGSA, which allows for greater 

amounts of infiltration and percolation of runoff from the 

mountain block areas. In contrast to the San Jacinto Peak 

mountain block, the San Bernardino mountain block had more 

recharge than the SGPGSA because of major alluvium-filled 

valleys upstream of the SGPGSA that allow for large volumes 

of infiltration of runoff from the headwater areas of the San 

Bernardino Mountain block.

The upper San Gorgonio River subdrainage had the 

highest 100-year average recharge in the mountain block area 

outside of the SGPGSA because of the high-permeability 

of the alluvium underlying the main channel of the San 

Gorgonio River. Runoff from upstream subdrainages, including the summit areas such as Little San Gorgonio Peak in 

the San Bernardino mountain block, which have the most 

precipitation, readily infiltrates along the larger, alluviumfilled channel of the San Gorgonio River. The Smith Creek 

and the Potrero Creek tributaries of the San Gorgonio River 

also had relatively high recharge because of high-permeability 

alluvium underlying the main channels and the relatively highprecipitation in the headwater areas.

The effects of imperviousness on recharge and runoff 

were evaluated by mapping the difference between simulation results from simulations that included and that excluded 

imperviousness (figs. 33, 34). Runoff in the more developed 

areas increased by as much as 101 to 1,000 mm/yr when 

imperviousness was included. The increased runoff for simulations that had imperviousness included was routed to the main 

channels of the San Gorgonio River, San Timoteo Creek, 

and Potrero Creek, increasing runoff by as much as 1,001 to 

2,830 mm/yr (7.1E-4 m3/s to 2.0E-3 m3/s) for some locations. 

Impervious areas resulted in increases in ET of as much as 51 

to 305 mm/yr for many locations downstream of the impervious areas in response to the infiltration of increased runoff 

from the impervious areas. For locations with significant 

(greater than 10 percent) imperviousness, recharge was found 

to decrease with increased imperviousness because the fraction of the cell area that can take on water and, thus, contribute 

to recharge decreases with increasing imperviousness. Similar 

to ET, recharge increased for locations downstream of the 

impervious areas in response to infiltration of the increased 

runoff from the impervious areas. The 100-year basin-wide 

average recharge for the impervious-excluded simulation was 

78 mm/yr, compared to 80 mm/yr for the impervious-included 

simulation.

The simulated long-term average recharge was compared 

to results from the previous model (Rewis and others, 2006). 

The average recharge simulated by the updated model for 

the smaller area of the previous model was 90 mm/yr (about 

8 mm/yr more than the previous recharge estimate), with a 

maximum of 4,170 mm/yr, a minimum of 0.0 mm/yr, and a 

standard deviation of 190 mm/yr. The greatest increase in 

recharge of the updated model relative to the previous model 

was 4,160 mm/yr, and the greatest decrease relative to the 

previous model was 27,400 mm/yr, in the main channels, such 

as San Gorgonio River and Noble Creek (fig. 35). These large 

differences in recharge between the two models were mostly 

the result of differences in the locations of stream channels 

represented by the 30-m grid of the previous model compared 

to the 150-m grid of the updated model.
72 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

For both models, runoff was the most sensitive waterbudget component, with large year-to-year variability primarily caused by temporal variability in precipitation. Most years 

had runoff well below the long-term average, and periodic 

wetter than average years caused runoff to be many times 

greater than the long-term average. Runoff was also most 

sensitive to drought periods, and the average runoff rate during 

the California Department of Water Resource (DWR) drought 

periods was about 22 to 23 percent of the long-term average 

for water years 1930–2001. Recharge was also greatly reduced 

relative to the long-term mean during drought periods. For 

the updated model, recharge during the DWR drought years 

was 57 percent of the average rate for water years 1930–2001. 

For the previous model, recharge during the DWR drought 

years was 39 percent of the recharge rate for water years 

1930–2001. In general, the long-term average rates from the 

updated model for water years 1930–2001 were similar to the 

100-year average rates for water years 1913–2012. By using 

the updated model, average recharge for the DWR drought 

years was 43 mm/yr, or about 54 percent of the 100-year average recharge rate of 80 mm/yr. Compared to recharge, runoff 

indicated more sensitivity to drought years. Average runoff for 

the DWR drought years was 5 mm/yr, or about 20 percent of 

the 100-year average rate of 24 mm/yr. For most years of the 

100-year simulation period, both recharge and runoff were less 

than the long-term average rate.

Results from the SGPWM indicated that simulated runoff 

and recharge were most sensitive to the climate inputs, which, 

in turn, are interpolated, and therefore associated with some 

degree of uncertainty. Although a large number of climate stations were used to develop the daily climate inputs, the density 

of climate stations in the study area was low. The comparison 

with the state-wide drought periods indicated that the SGPWM 

climate did not always follow state-wide climate conditions 

and that more local-scale climate variability is likely to be 

important.

Results for both models indicated that infiltration of 

runoff was an important source of recharge for the SGPGSA, 

especially for the area of the SGPGSA and for mountain front 

recharge from the San Bernardino Mountains. The greatest 

localized recharge came from infiltrated runoff along the main 

stream channels. Both models also indicated that the yearto-year variability in runoff was very large, which, in-turn, 

resulted in large year-to-year variability in recharge. Intrachannel recharge in direct response to precipitation and snowmelt was less than recharge along channels; however, intrachannel recharge affected a much larger area. The updated 

model indicated slightly more recharge and runoff compared 

to the previous model, with the differences caused by a combination of different grid sizes used to develop the models, 

differences in the climate inputs and simulation periods, and 

the inclusion of seepage flow in the updated model.

The updated model provides estimates of natural recharge 

for a larger area than the previous model and includes all 

mountain front drainages upstream of the SGPGSA and having a potential to contribute runoff and recharge inflows to the 

SGPGSA. For calculating long-term average water-budget 

components for the SGPGSA, the updated model provides 

a longer simulation period of 100 years, compared to the 

72-year period for the previous model. The updated model 

incorporated a seepage-flow component not simulated by the 

previous model, which allows for simulation of lateral redistribution of net infiltration below the root zone and can greatly 

change the spatial distribution of estimated recharge.

References Cited

Beven, K.J., Kirkby, M.J., 1979, A physically based, variable 

contributing area model of basin hydrology: Hydrological 

Sciences Bulletin, v. 24, p. 43–69.

Belcher, W.R., and Sweetkind, D.S., eds., 2010, Death Valley 

regional groundwater flow system, Nevada and California—

Hydrogeologic framework and transient groundwater flow 

model: U.S. Geological Survey Professional Paper 1711, 

398 p.

Bloyd, R.M., 1971, Underground storage of imported water 

in the San Gorgonio Pass area, Southern California: U.S. 

Geological Survey Water-Supply Paper 1999–D, 37 p.

California Department of Forestry and Fire Protection, 2002, 

Monitoring land cover changes in California, California 

Land Cover Mapping and Monitoring Program, South Coast 

Project Area, July 2002, Fire and Resource Assessment Program, 161 p., accessed January 20, 2015, at http://frap.cdf.

ca.gov/projects/land_cover/monitoring/pdfs/socdp_final2.

pdf.

California Department of Water Resources, 2012, Drought in 

California: State of California, Natural Resources Agency, 

accessed November 2014 at http://www.water.ca.gov/waterconditions/docs/Drought2012.pdf.

California Department of Water Resources, 2003, California’s 

groundwater: Bulletin 118, update 2003: Sacramento, Calif., 

State of California, The Resources Agency, Department 

of Water Resources, accessed July 1, 2004, at http://water.

ca.gov/groundwater/bulletin118/report2003.cfm.

California Interagency Watershed Mapping Committee, 

2004, The California Interagency Watershed Map of 1999 

(updated May 2004): California Department of Fish and 

Game, available at http://www.atlas.ca.gov/download.

html#/casil/inlandWaters.

California Irrigation Management Information System 

(CIMIS), 2005, Meteorological data: California Department 

of Water Resources and University of California at Davis, 

available at http://www.cimis.water.ca.gov/.
References Cited 73

Daly, C., Neilson, R.P., and Phillips, D.L., 1994, A statisticaltopographic model for mapping climatological precipitation 

over mountain terrain: Journal of Applied Meteorology, 

v. 33, no. 2, p. 140–158.

Daly, C., Gibson, W.P., Doggett, M., Smith, J., and Taylor, 

G., 2004, Up-to-date monthly climate maps for the conterminous United States: Procedures of the 14th American 

Meteorological Society Conference on Applied Climatology, 84th American Meteorological Society Annual Meeting Combined Preprints, American Meteorological Society, 

Seattle, Washington, January 13–16, 2004, Paper P5.1, 

CD-ROM.

Doherty, John, 2008, PEST: Model-independent parameter 

estimation, user manual (5th ed.): Brisbane, Australia, 

Watermark Numerical Computing, 336 p.

EarthInfo, Inc., 2011, NCDC Summary of the Day, West: 

Boulder, Colo., Earth Info, Inc., CD ROM.

Farrar, C.D., Metzger, L.F., Nishikawa, Tracy, Koczot, K.M., 

and Reichard, E.G., 2006, Geohydrologic characterization, 

water-chemistry, and groundwater flow simulation model of 

the Sonoma Valley area, Sonoma County, California, with 

a section on basement rock configuration interpreted from 

gravity data by Victoria E. Langenheim: U.S. Geological 

Survey Scientific Investigations Report 2006–5092, 167 p.

Flint, A.L., and Childs, S.W., 1987, Calculation of solar radiation in mountainous terrain: Journal of Agricultural and 

Forest Meteorology, v. 40, p. 233–249.

Flint, A.L., and Childs, S.W., 1991, Use of the Priestley-Taylor 

evaporation equation for soil water limited conditions in a 

small forest clearcut: Agricultural and Forest Meteorology, 

v. 56, p. 247–260.

Flint, A.L., and Flint, L.E., 2007, Application of the basin 

characterization model to estimate in-place recharge and 

runoff potential in the Basin and Range carbonate-rock 

aquifer system, White Pine County, Nevada, and adjacent 

areas in Nevada and Utah: U.S. Geological Survey Scientific Investigations Report 2007–5099, 20 p.

Flint, L.E., and Martin, Peter, eds., 2012, with contributions 

by Brandt, Justin, Christensen, A.H., Flint, A.L., Flint, L.E., 

Hevesi, J.A., Jachens, Robert, Kulongoski, J.T., Martin, 

Peter, and Sneed, Michelle, 2012, Geohydrology of Big 

Bear Valley, California: Phase 1—geologic framework, 

recharge, and preliminary assessment of the source and age 

of groundwater: U.S. Geological Survey Scientific Investigations Report 2012–5100, 112 p., available at http://pubs.

usgs.gov/sir/2012/5100/.

Gesch, D., Evans, G., Mauck, J., Hutchinson, J., Carswell, 

W.J., Jr., 2009, The National Map—Elevation: U.S. 

Geological Survey Fact Sheet 2009–3053, 4 p., accessed 

September 30, 2010, at http://pubs.usgs.gov/fs/2009/3053/.

Hevesi, J.A., Flint, A.L., and Flint, L.E., 2003, Simulation 

of net infiltration using a distributed parameter watershed 

model for the Death Valley regional flow system, Nevada 

and California: U.S. Geological Survey Water-Resources 

Investigation Report 03–4090, 104 p., available at http://

pubs.usgs.gov/wri/wri034090/.

Hevesi, J.A., Woolfenden, L.R., Niswonger, R.G., Regan, 

R.S., and Nishikawa, Tracy, 2011, Decoupled application 

of the integrated hydrologic model, GSFLOW, to estimate 

agricultural irrigation in the Santa Rosa Plain, California: 

Golden, Colorado, MODFLOW and More 2011 Conference 

Proceedings, p. 115–119.

Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., 

Larson, C., Herold, N., McKerrow, A., VanDriel, J.N., 

and Wickham, J., 2007, Completion of the 2001 National 

Land Cover Database for the Conterminous United States: 

Photogrammetric Engineering and Remote Sensing, v. 73, 

no. 4, p. 337–341, accessed March 2010, at http://www.

asprs.org/a/publications/pers/2007journal/april/highlight.

pdf.

Jennings, C.W., 1977, Geologic map of California: California 

Division of Mines and Geology, Geologic Data Map no. 2, 

scale 1:750,000.

Jeton, A.E., and Maurer, D.K., 2011, Precipitation and runoff 

simulations of select perennial and ephemeral watersheds 

in the middle Carson River basin, Eagle, Dayton, and 

Churchill Valleys, west-central Nevada: U.S. Geological 

Survey Scientific Investigations Report 2011–5066, 44 p.

Maidment, D.R., ed., 2002, Arc Hydro: GIS for Water 

Resources: Redlands, California, ESRI Press.

Markstrom, S.L., Niswonger, R.G., Regan, R.S., Prudic, D.E., 

and Barlow, P.M., 2008, GSFLOW—Coupled ground-water 

and surface-water flow model based on the integration of 

the Precipitation-Runoff Modeling System (PRMS) and the 

Modular Ground-Water Flow Model (MODFLOW–2005): 

U.S. Geological Survey Techniques and Methods 6–D1, 

240 p.

Nash, J.E., and Sutcliffe, J.V., 1970, River flow forecasting 

through conceptual models part I—A discussion of principals: Journal of Hydrology, v. 10, no. 3, p. 282–290.

Nishikawa, Tracy, Izbicki, J.A., Hevesi, J.A., Stamos, C.L., 

and Martin, Peter, 2004, Evaluation of geohydrologic 

framework, recharge estimates, and groundwater flow of 

the Joshua Tree area, San Bernardino County, California: 

U.S. Geological Survey Scientific Investigations Report 

2004–5267, 127 p.
74 Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012

Reichard, E.G., Land, Michael, Crawford, S.M., Johnson, 

Tyler, Everett, R.R., Kulshan, T.V., Ponti, D.J., Halford, 

K.L., Johnson, T.A., Paybins, K.S., Nishikawa, Tracy, 2003, 

Geohydrology, geochemistry, and ground-water simulationoptimization of the Central and West Coast Basins, Los 

Angeles County, California: U.S. Geological Survey Water 

Resources Investigations Report: 2003–4065, 184 p.

Rewis, D.L., Christensen, A.H., Matti, J., Hevesi, J.A., 

Nishikawa, T., and Martin, P., 2006, Geology, groundwater 

hydrology, geochemistry, and groundwater simulation of 

the Beaumont and Banning storage units, San Gorgonio 

Pass Area, Riverside County, California: U. S. Geological 

Survey Scientific Investigations Report 2006–5026, 191 p.

Slack, J.R., Lorenz, D.L., and others, 2003, USGS library 

for S-PLUS for Windows—Release 2.1: U.S. Geological 

Survey Open-File Report 03–357.

U.S. Department of Agriculture, 1994, State soil geographic 

(STATSGO) Data base–Data use information: Natural 

Resource Conservation Service, Miscellaneous Publication 

no. 1492: Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. U.S. 

General Soil Map (STATSGO) for California, accessed June 

21, 2009, at http://sdmdataaccess.nrcs.usda.gov/. 

U.S. Geological Survey, 2008, Documentation of computer 

program INFIL3.0—A distributed-parameter watershed 

model to estimate net infiltration below the root zone: 

U. S. Geological Survey Scientific Investigation Report 

2008–5006, 98 p., available at http://pubs.usgs.gov/

sir/2008/5006/.

Ward-Garrison, C., Markstrom, S.L., and Hay, L.E., 2009, 

Downsizer—A graphical user interface-based application 

for browsing, acquiring, and formatting time-series data for 

hydrologic modeling: U.S. Geological Survey Open-File 

Report 2009–1166, 27 p.

Woolfenden, L.R., and Nishikawa, Tracy, eds., 2014, Simulation of groundwater and surface-water resources of the 

Santa Rosa Plain watershed, Sonoma County, California: 

U.S. Geological Survey Scientific Investigations Report 

2014–5052, 258 p., available at http://pubs.usgs.gov/

sir/2014/5052/.
Prepared by the Sacramento Publishing Service Center.

For more information concerning this report, contact:

Director

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:

http://ca.water.usgs.gov
Hevesi and Christensen—Estimating Natural Recharge in San Gorgonio Pass Watersheds, California, 1913–2012—Scientific Investigations Report 2015–5122

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/SIR20155122