Prepared in cooperation with the Bureau of Reclamation

Development of a Precipitation-Runoff Model to Simulate 

Unregulated Streamflow in the Salmon Creek Basin, 

Okanogan County, Washington

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2006–5274

Construction of Conconully Dam in 

1908 and completed outlet tunnel, 

Salmon Creek, Okanogan County, 

Washington. USRS is United States 

Reclamation Service, renamed 

Bureau of Reclamation in 1923.
Cover: Photographs courtesy of the Bureau of Reclamation.
Development of a Precipitation-Runoff Model to 

Simulate Unregulated Streamflow in the Salmon 

Creek Basin, Okanogan County, Washington

By Marijke van Heeswijk

Prepared in cooperation with the Bureau of Reclamation

Scientific Investigations Report 2006–5274

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2006

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

van Heeswijk, Marijke, 2006, Development of a precipitation-runoff model to simulate unregulated streamflow 

in the Salmon Creek Basin, Okanogan County, Washington: U.S. Geological Survey Scientific Investigations Report 

2006-5274, 36 p.
iii

Contents

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

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

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

Description of Study Area ...................................................................................................................4

Development of Precipitation-Runoff Model ............................................................................................7

Description of Simulation Model........................................................................................................7

Time-Series Data.................................................................................................................................10

Precipitation and Air Temperature..........................................................................................10

Streamflow..................................................................................................................................12

Corrected Unregulated Streamflow ..............................................................................13

Corrections for Ground-Water Losses .................................................................13

Corrections for Evaporative Losses......................................................................14

Delineation of Basin Physical Characteristics .......................................................................................15

Model Parameterization .............................................................................................................................15

Model Calibration and Testing...................................................................................................................20

Model Limitations................................................................................................................................29

Suggestions for Data Collection................................................................................................................30

Summary........................................................................................................................................................30

Acknowledgments.......................................................................................................................................33

References Cited..........................................................................................................................................33

Appendix 1. Monthly Values of Total Water in Storage in Conconully and Salmon Lake 

Reservoirs, Releases from Conconully Reservoir, and Computed Unregulated 

Precipitation Runoff From Upper Salmon Creek Basin, January 2003–March 2006...........35
iv

Figures

Figure 1. Map showing location of the Salmon Creek Basin study area and locations of

climate stations, Okanogan County, Washington ....................................... 2

Figure 2. Map showing locations of dams, diversions, and stream gages in the Salmon

Creek Basin, Okanogan County, Washington .......................................... 3

Figure 3. Map showing mean annual precipitation for the Salmon Creek Basin,

Okanogan County, Washington, 1971–2000 ............................................. 6

Figure 4. Graphs showing mean monthly precipitation for the Salmon Creek Basin,

Okanogan County, Washington, 1971–2000, and annual precipitation at 

Conconully, water years 1971-2000 ...................................................... 7

Figure 5. Graph showing mean minimum and maximum air temperatures for the

Conconully climate station, Okanogan County, Washington, 1971–2000 ............ 7

Figure 6. Diagram showing modified version of the Precipitation-Runoff 

Modeling System ........................................................................... 8

Figure 7. Graph showing mean monthly reference evapotranspiration as a percentage

of mean annual reference evapotranspiration for alfalfa (1982 Kimberly-Penman 

equation) at the Omak OMAW AgriMet station, water years 1990–2004 ............ 14

Figure 8. Map showing Modeling Response Units and drainage network delineated for

the precipitation-runoff model for the Salmon Creek Basin, Okanogan County,

Washington ................................................................................. 16

Figure 9. Map showing generalized slopes in the Salmon Creek Basin, Okanogan County, 

Washington ................................................................................. 17

Figure 10. Map showing land cover in the Salmon Creek Basin, Okanogan County,

Washington ................................................................................. 18

Figure 11. Map showing major subbasins and locations of model nodes in the Salmon

Creek Basin, Okanogan County, Washington .......................................... 19

Figure 12. Graphs showing estimated and simulated mean monthly unregulated

streamflows for Salmon Creek at Conconully Dam, Okanogan County, 

Washington ................................................................................. 22

Figure 13. Graphs showing time series of estimated and simulated monthly mean

unregulated streamflows for Salmon Creek at Conconully Dam, Okanogan

County, Washington ........................................................................ 23

Figure 14. Graph showing time series of estimated and simulated annual mean

unregulated streamflows for Salmon Creek at Conconully Dam, Okanogan

County, Washington. ........................................................................ 27

Figure 15. Graphs showing measured and simulated mean monthly snowpack

water-equivalent for the Salmon Meadows SNOTEL station, Okanogan County, 

Washington ................................................................................. 29

Figure 16. Graphs showing time series of measured and simulated monthly mean

snowpack water-equivalent for the Salmon Meadows SNOTEL station,

Okanogan County, Washington ............................................................ 29
Conversion Factors, Datums, and Abbreviations or 

Acronyms

Conversion Factors

Multiply By To obtain

acre 4,047 square meter

acre 0.004047 square kilometer

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

acre-foot per month (acre-ft/mo) 1,233 cubic meter per month

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

cubic foot (ft3) 0.02832 cubic meter

cubic foot per second (ft3/s) 0.02832 cubic meter per second

cubic foot per day (ft3/d) 0.02832 cubic meter per day

foot (ft) 0.3048 meter

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

inch (in.) 25.4 millimeter

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

mile (mi) 1.609 kilometer

square foot (ft2) 0.09290 square meter

square mile (mi2) 2.590 square kilometer 

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

°C=(°F-32)/1.8

Datums

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

(NAVD 88). 

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

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

Tables

Table 1. Modules used in the precipitation-runoff model ....................................... 9

Table 2. Climate stations used in model simulations or to estimate missing data ............ 11

Table 3. Estimated evaporation from Conconully Reservoir, assuming a mean annual

free-water-surface evaporation of 30 inches .......................................... 14

Table 4. Periods of simulation and model input time series for each type of 

model simulation ........................................................................... 21

Table 5. Mean monthly, annual, and April-July estimated and simulated unregulated

streamflows for Salmon Creek at Conconully Dam and the percentage of

error for the CALIBRATION, TESTING 1, TESTING 2, TESTING 3, TESTING 4,

and COMPOSITE model simulations ...................................................... 24

Table 6. Annual mean and April-July mean estimated and unregulated streamflows

for Salmon Creek at Conconully Dam for the driest 4 water years and

wettest 4 water years, 1950-89, and the percentage of error and the

absolute error for the model simulations ................................................ 28
vi

Conversion Factors, Datums, and Abbreviations or 

Acronyms—Continued

Abbreviations or Acronyms

Abbreviation or Acronym Meaning

CUS corrected unregulated streamflow

DEM digital elevation model

DOE Washington State Department of Ecology

DSS Decision Support System

ESP Extended Streamflow Prediction

HDB Hydrologic Database

MMS Modular Modeling System

MRU Modeling Response Unit

NRCS Natural Resources Conservation Service

NWS National Weather Service

OID Okanogan Irrigation District

OUI Object User Interface

PRMS Precipitation-Runoff Modeling System

Reclamation Bureau of Reclamation

USGS U.S. Geological Survey

UUS uncorrected unregulated streamflow

WARSMP Watershed and River Systems Management Program
Development of a Precipitation-Runoff Model to Simulate 

Unregulated Streamflow in the Salmon Creek Basin, 

Okanogan County, Washington

By Marijke van Heeswijk

Abstract

Surface water has been diverted from the Salmon Creek 

Basin for irrigation purposes since the early 1900s, when the 

Bureau of Reclamation built the Okanogan Project. Spring 

snowmelt runoff is stored in two reservoirs, Conconully 

Reservoir and Salmon Lake Reservoir, and gradually released 

during the growing season. As a result of the out-of-basin 

streamflow diversions, the lower 4.3 miles of Salmon Creek 

typically has been a dry creek bed for almost 100 years, except 

during the spring snowmelt season during years of high runoff. 

To continue meeting the water needs of irrigators but also 

leave water in lower Salmon Creek for fish passage and to help 

restore the natural ecosystem, changes are being considered in 

how the Okanogan Project is operated.

This report documents development of a precipitationrunoff model for the Salmon Creek Basin that can be used to 

simulate daily unregulated streamflows. The precipitationrunoff model is a component of a Decision Support System 

(DSS) that includes a water-operations model the Bureau of 

Reclamation plans to develop to study the water resources 

of the Salmon Creek Basin. The DSS will be similar to the 

DSS that the Bureau of Reclamation and the U.S. Geological 

Survey developed previously for the Yakima River Basin in 

central southern Washington.

The precipitation-runoff model was calibrated for water 

years 1950–89 and tested for water years 1990–96. The model 

was used to simulate daily streamflows that were aggregated 

on a monthly basis and calibrated against historical monthly 

streamflows for Salmon Creek at Conconully Dam. Additional 

calibration data were provided by the snowpack waterequivalent record for a SNOTEL station in the basin. Model 

input time series of daily precipitation and minimum and 

maximum air temperatures were based on data from climate 

stations in the study area. Historical records of unregulated 

streamflow for Salmon Creek at Conconully Dam do not 

exist for water years 1950–96. Instead, estimates of historical 

monthly mean unregulated streamflow based on reservoir 

outflows and storage changes were used as a surrogate for the 

missing data and to calibrate and test the model. The estimated 

unregulated streamflows were corrected for evaporative 

losses from Conconully Reservoir (about 1 ft3/s) and groundwater losses from the basin (about 2 ft3

/s). The total of the 

corrections was about 9 percent of the mean uncorrected 

streamflow of 32.2 ft3/s (23,300 acre-ft/yr) for water years 

1949–96. For the calibration period, the basinwide mean 

annual evapotranspiration was simulated to be 19.1 inches, 

or about 83 percent of the mean annual precipitation of 

23.1 inches.

Model calibration and testing indicated that the daily 

streamflows simulated using the precipitation-runoff model 

should be used only to analyze historical and forecasted 

annual mean and April–July mean streamflows for Salmon 

Creek at Conconully Dam. Because of the paucity of model 

input data and uncertainty in the estimated unregulated 

streamflows, the model is not adequately calibrated and tested 

to estimate monthly mean streamflows for individual months, 

such as during low-flow periods, or for shorter periods such as 

during peak flows. No data were available to test the accuracy 

of simulated streamflows for lower Salmon Creek. As a result, 

simulated streamflows for lower Salmon Creek should be used 

with caution.

For the calibration period (water years 1950–89), both 

the simulated mean annual streamflow and the simulated mean 

April–July streamflow compared well with the estimated 

uncorrected unregulated streamflow (UUS) and corrected 

unregulated streamflow (CUS). The simulated mean annual 

streamflow exceeded UUS by 5.9 percent and was less than 

CUS by 2.7 percent. Similarly, the simulated mean April–July 

streamflow exceeded UUS by 1.8 percent and was less than 

CUS by 3.1 percent. However, streamflow was significantly 

undersimulated during the low-flow, baseflow-dominated 

months of November through February when simulated 

monthly streamflows were as much as 57.2 percent less 

than UUS and significantly oversimulated during August 

and September when simulated monthly streamflows were 

as much as 193.6 percent more than CUS. Only a small 

percentage of the estimated mean annual streamflow occurs 

during baseflow-dominated months. About 84 percent of 

the estimated mean annual UUS occurs from April through 

July. For the testing period (water years 1990–96), the model 

simulated a close fit for the mean annual streamflow and a 
good fit for the mean April–July streamflow. The simulated 

mean annual streamflow exceeded UUS by 10.7 percent 

and was the same as CUS. The simulated mean April–July 

streamflow exceeded UUS by 5.1 percent and was less than 

CUS by 0.8 percent. Testing of the precipitation-runoff model 

using different model input time series demonstrated that the 

model simulates the best fit between estimated and simulated 

streamflows for Salmon Creek at Conconully Dam, if the input 

time series were based on daily minimum and maximum air 

temperatures for the Omak OMAW AgriMet station and daily 

precipitation and minimum and maximum air temperatures 

for the Conconully climate station or the Conconully CCR 

Hydromet station.

The precipitation-runoff model could be improved if 

additional data were collected. Specifically, it is suggested 

that year-round collection of climate data be resumed at 

Conconully climate station and that a stream gage be installed 

in Salmon Creek downstream of the spillway of Conconully 

Dam to measure all runoff from the upper Salmon Creek 

Basin.

Introduction

Salmon Creek, a tributary to the Okanogan River (fig. 1), 

contains two reservoirs that supply irrigation water for out-ofbasin use. The irrigation project, called the Okanogan Project, 

was authorized by the U.S. Department of the Interior in 1905 

and has been in operation since the early 1900s. Conconully 

Reservoir is formed by Conconully Dam (fig. 2), which was 

completed in 1910, and Salmon Lake Reservoir (also referred 

to as Conconully Lake in other publications) is formed by 

Figure 1. Location of the Salmon Creek Basin study area and locations of climate stations, Okanogan County, 

Washington.

Okanogan

Chewuch

Twisp

Salmon Cr

West Fork

South Fk

Lake

Chelan

Palmer

Lake

Omak

Lake

Osoyoos

Lake

Methow

River

River

River

River

COLUMBIA

RIVER

Stehekin

River

Cr

Johnson

Salmon

Meadows

EXPLANATION

Climate station and

 name

County boundary

Okanogan National

 Forest boundary

Base map modified from USGS digital data 1:100,000, 1995

Hillshade modified from National Elevation Dataset, 2000, 10-meter resolution

Universal Transverse Mercator projection, zone 11, 

North American Datum of 1983, North American Vertical Datum of 1988

Omak

OMAW

Conconully

Conconully

CCR

Omak

4 N

Winthrop 1 WSW

Salmon

Meadows

CHELAN

WHATCOM

SKAGIT

O K A N O G A N

DOUGLAS

FERRY

CANADA

WASHINGTON

Salmon

Lake

Dam

Conconully

Dam

20

20

97

97

20

WASHINGTON

Salmon

Creek

Basin

Okanogan

River Basin

Methow

River

Basin

C A S C A D E R A N G

E

48°20'

48°40'

48°00'

49°00'

120°40' 120°20' 120°00' 119°40' 119°20' 119°00' 118°40'

0 10 20 30 MILES

0 10 20 30 40 KILOMETERS

Pateros

Omak

Okanogan

Winthrop

Twisp

Conconully

 Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Figure 2. Locations of dams, diversions, and stream gages in the Salmon Creek Basin, Okanogan County, 

Washington.

WA19-0043_fig02

Creek

Creek

Scotch

Johnson

20

20

Conconully

Dam

Salmon Lake

Dam

North Fork

Diversion

Okanogan

Irrigation

District

Diversion

Conconully

Okanogan

Graveyard

Flat

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS 97

119°50' 119°40'

48°30'

48°40'

Conconully

Reservoir

SALMON CREEK

South Fork

Creek

West

Salmon

Salmon

Creek

Fork

North

Fork

Salmon

Creek

Salmon Lake

Reservoir

Base map modified from USGS digital data 1:100,000, 1995

Hillshade modified from National Elevation Dataset, 2000, 10-meter resolution

Universal Transverse Mercator projection, zone 11, 

North American Datum of 1983, North American Vertical Datum of 1988

Salmon

Creek

Basin

boundary

DOE

49M100

USGS

12446500

USGS

12447000

Okanogan Irrigation

District gage

EXPLANATION

DAM

STREAM GAGE

DIVERSION

Introduction 
Salmon Lake Dam, which was completed in 1921. The 

Bureau of Reclamation (Reclamation) owns the dams and the 

project is operated by the Okanogan Irrigation District (OID). 

As a result of streamflow diversions, the lower 4.3 miles of 

Salmon Creek typically is a dry creek bed except during the 

spring snowmelt season during years of high runoff (Dames 

and Moore, 1999). To continue meeting the water needs of 

irrigators but also to leave water in lower Salmon Creek for 

fish passage and to help restore the natural ecosystem, changes 

in how the Okanogan Project is operated currently (2006) are 

being considered. In preparation for the potential changes, a 

draft environmental impact statement that considers different 

management alternatives was issued in August 2004 (U.S. 

Department of Energy, 2004).

Reclamation plans to develop a water-operations model 

to study the water resources of the Salmon Creek Basin 

and asked the U.S. Geological Survey (USGS) to develop a 

precipitation-runoff model that will provide input data for 

the water-operations model. Both models are components 

of a Decision Support System (DSS) for the Salmon Creek 

Basin that will be similar to the DSS that Reclamation and 

the USGS developed previously for the Yakima River Basin 

in central southern Washington (Mastin and Vaccaro, 2002b; 

U.S. Geological Survey, 1998). In addition to the two models, 

the DSS consists of a Hydrologic Database (HDB) that is 

an interface between the models. The HDB provides daily 

time-series input to the precipitation-runoff model (described 

in this report) and receives daily streamflows simulated by 

the model. The streamflows then are provided as input to 

the planned RiverWare model (Bureau of Reclamation and 

CADSWES, 2000), a water-operations planning model that 

will be used to simulate different water-operations options 

for the available streamflow. The DSS for the Yakima River 

Basin was developed as part of the Watershed and River 

Systems Management Program (WARSMP), a federallyfunded program under which Reclamation and the USGS have 

collaborated since 1995 (U.S. Geological Survey and Bureau 

of Reclamation, 2006). WARSMP’s purpose is to develop, test, 

and implement a framework for water-resources management 

in Reclamation Act states.

The objective of this study was to develop the 

precipitation-runoff-model component of the DSS. The 

precipitation-runoff-model component can be used to simulate 

historical daily unregulated streamflows for different locations 

in the Salmon Creek Basin and to forecast daily unregulated 

streamflows for the runoff season (April–July) or longer 

periods as far as 1 year in the future. Forecasting is based 

on near-real-time hydrologic conditions in the basin and an 

assumption that historical climate records will recur with the 

same probability in the future as in the past. Reclamation plans 

to use the simulated historical daily unregulated streamflows 

for long-term-planning studies and may use forecasted 

daily streamflows to assist with seasonal planning of water 

operations.

Purpose and Scope

This report documents development of a precipitationrunoff model for the Salmon Creek Basin in Okanogan 

County, Washington, that can be used to simulate daily 

unregulated streamflows in the basin. Unregulated streamflows 

are defined as streamflows unaltered by human activities such 

as streamflow diversions and impoundments in reservoirs. The 

precipitation-runoff model that was developed is a modified 

version of the Precipitation-Runoff Modeling System (PRMS; 

Leavesley and others, 1983) and was run within the Modular 

Modeling System (MMS; Leavesley and others, 1996). The 

modified model version that was used is identical to that 

used previously to simulate streamflow in the neighboring 

Methow River Basin (Ely, 2003; Ely and Risley, 2001). Input 

data used to run the model are based on daily precipitation 

and daily minimum and maximum air temperatures for three 

National Weather Service stations (Conconully, Omak 4 N, 

and Winthrop 1 WSW), a Natural Resources Conservation 

Service SNOTEL station (Salmon Meadows), a Bureau of 

Reclamation AgriMet station (Omak OMAW), and a Bureau 

of Reclamation Hydromet station (Conconully CCR). The 

model was used to simulate daily streamflows that were 

aggregated on a monthly basis and calibrated against historical 

monthly streamflows for Salmon Creek at Conconully Dam. 

Historical monthly streamflows were estimated by Dames and 

Moore (1999) and the U.S. Department of Energy (2004) and 

as part of this study from monthly outflows from Conconully 

Reservoir and from storage changes in Conconully Reservoir 

and Salmon Lake Reservoir. Additional calibration data were 

provided by the snowpack water-equivalent record for the 

Salmon Meadows SNOTEL station. The precipitation-runoff 

model was calibrated for water years 1950–89 (a water year 

starts October 1 and ends September 30) and tested for water 

years 1990–96.

Description of Study Area

The Salmon Creek Basin is located in the northeastern 

part of the Cascade Range (fig. 1) and encompasses an area of 

152 mi2. Elevations in the basin range from 820 to 8,250 ft and 

have a mean of 4,050 ft. Upper Salmon Creek Basin, defined 

 Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
as the area upstream of Conconully Dam, encompasses 78 

percent of the basin. Elevations in the upper basin range from 

2,270 to 8,250 ft and have a mean of 4,450 ft. Much of the 

upper basin is located in the Okanogan National Forest and 

is largely vegetated with evergreen forests of Ponderosa pine 

(Pinus ponderosa) and Interior Douglas-fir (Pseudotsuga 

menziesii) (Cassidy, 1997). The study area is sparsely 

populated. Conconully, the only town in the basin, had a 

population of 185 in 2000 (U.S. Census Bureau, 2006).

The two reservoirs in the basin, Conconully Reservoir 

and Salmon Lake Reservoir (fig. 2), have a maximum 

active storage capacity of about 13,000 and 10,500 acre-ft, 

respectively (U.S. Department of Energy, 2004). (Active 

storage capacity is defined as the usable reservoir capacity 

available for seasonal or cyclic water storage.) Most of the 

water stored in the reservoirs is spring snowmelt runoff that 

is released later in the season or in subsequent years for 

irrigation purposes. The Okanogan Project currently supplies 

irrigation water to about 5,000 acres of agricultural land 

outside Salmon Creek Basin along the Okanogan River near 

the town of Okanogan (Bureau of Reclamation, 2006b).

Salmon Lake Dam allows the level of the natural Salmon 

Lake to be raised. Water in the reservoir behind the dam 

consists of runoff from the Salmon Lake subbasin and water 

that is diverted from North Fork Salmon Creek at the North 

Fork diversion (fig. 2). Releases from Salmon Lake Reservoir 

flow into Conconully Reservoir, which also receives runoff 

from West Fork Salmon Creek and any runoff from North Fork 

Salmon Creek that is not diverted to Salmon Lake Reservoir. 

South Fork Salmon Creek joins West Fork Salmon Creek 

about 0.6 mi west of Conconully Reservoir. Seepage, releases, 

and uncontrolled spills from Conconully Reservoir flow about 

12 mi downstream along the main stem of Salmon Creek to 

the OID diversion (fig. 2) from where water is diverted outside 

the basin through a series of canals. Any water not diverted 

flows another 4.3 mi and then discharges to the Okanogan 

River. Since the early days of the Okanogan Project, the 

streambed between the OID diversion and the mouth of 

Salmon Creek has been dry except during those spring-runoff 

events when uncontrolled spills occurred at Conconully Dam 

(Dames and Moore, 1999).

Mean annual precipitation in the Salmon Creek Basin 

based on PRISM data from the Spatial Climate Analysis 

Service-Oregon State University (2006) during the most 

recent climate-normal period (1971–2000) was about 21 in. 

and ranged from about 12 in. near the mouth of the basin to 

almost 33 in. near the crest (fig. 3). In the upper basin, mean 

annual precipitation was about 23 in. and ranged from 15.4 in. 

to almost 33 in. (Spatial Climate Analysis Service-Oregon 

State University, 2006). About 58 percent of mean annual 

precipitation occurs from October through March. Minimum 

precipitation in the basin occurs in September, and maximum 

precipitation occurs in November (fig. 4A). A secondary 

precipitation maximum occurs in May. The snowpack that 

accumulates in the basin during the winter melts off by early 

summer. The interannual variability in precipitation in the 

basin is large. For example, based on the National Weather 

Service climate record for the Conconully station, for which 

some missing data were estimated in this study, the mean 

annual precipitation for 1971–2000 was about 15 in. The 

minimum was 7.8 in. in water year 1979, and the maximum 

was 26.3 in. in water year 1983 (fig. 4B). The mean minimum 

air temperature for the Conconully station ranged from about 

15 to 53°F, and the mean maximum ranged from about 31 

to 82°F (fig. 5). On average, the coldest month of the year is 

January and the warmest month is August.

The large amount of interannual variability in 

precipitation in the basin results in a large amount of 

interannual variability in runoff. For example, based on 

information provided by Dames and Moore (1999), the U.S. 

Department of Energy (2004), and T. Sullivan (Okanogan 

Irrigation District, written commun., 2006), the mean annual 

runoff from upper Salmon Creek Basin for water years 

1949–2004 is estimated to be about 23,300 acre-ft (about 

32 ft3/s). The minimum annual mean was 4,400 acre-ft (6 ft3/s) 

in water year 1966, and the maximum annual mean was 

65,800 acre-ft (91 ft3/s) in water year 1983. The mean annual 

runoff from upper Salmon Creek Basin is about equal to the 

total maximum active storage capacity of Conconully and 

Salmon Lake Reservoirs. During most years, the majority of 

annual runoff from the upper basin occurs from April through 

July because of melting of the snowpack that accumulated 

during the previous autumn and winter.

Most of the Salmon Creek Basin is in steep terrain 

that consists predominantly of Mesozoic intrusive and 

metamorphic rocks and Mesozoic-Paleozoic metamorphic 

rocks (Schuster, 2005). In parts of the lower basin and near 

the center, including the areas surrounding Conconully and 

Conconully Reservoir, sedimentary deposits of Pleistocene 

continental glacial drift comprise the surface. Most of lower 

Salmon Creek runs along the trace of a fault (Schuster, 2005) 

that has formed a narrow valley. Based on the topography and 

surficial geology of the area, at some time in the geologic past, 

lower Salmon Creek may have drained to the southeast of 

Conconully Reservoir into what are now Scotch and Johnson 

Creeks instead of into the present channel (fig. 2).

Introduction 
WA19-0043_fig03

Conconully Dam

Salmon Lake Dam

North Fork

Diversion

Conconully

Okanogan

Graveyard Flat

Okanogan

Irrigation

District

Diversion

97

Conconully Reservoir

SALMON CREEK

South Fork

Creek

West

Salmon

Salmon

Creek

Fork

North Fork

Salmon

Creek

Salmon Lake Reservoir

Base map modified from USGS digital data 1:100,000, 1995

Hillshade modified from National Elevation Dataset, 2000, 10-meter resolution

Universal Transverse Mercator projection, zone 11, 

North American Datum of 1983, North American Vertical Datum of 1988

32

18

18

16

20

22

24

26

28

30

14

EXPLANATION

Line of equal mean annual 

precipitation—Interval is 

2 inches

22

16

24

16

Salmon

Creek

Basin

boundary

119°50' 119°40'

48°30'

48°40'

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Figure 3. Mean annual precipitation for the Salmon Creek Basin, Okanogan County, Washington, 1971–2000. 

(Based on PRISM data from the Spatial Climate Analysis Service-Oregon State University, 2006.)

 Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Development of Precipitation-Runoff 

Model

A modified version of the distributed-parameter, 

physically-based PRMS (Leavesley and others, 1983) was 

used to numerically simulate the hydrologic processes that 

occur in the Salmon Creek Basin. The model was run inside 

the MMS (Leavesley and others, 1996), which is a modeling 

system that allows users to develop application-specific 

models by selecting or creating a set of modules that each 

represent specific hydrologic processes. Examples of such 

processes include interception of precipitation by vegetation, 

evapotranspiration, and snow accumulation and melt.

In addition to the Salmon Creek Basin, MMS and/

or PRMS have been used for many other basins in the 

western United States, including the Yakima River Basin in 

Washington (Mastin and Vaccaro, 2002b), small basins in 

the Oregon Coast Range (Risley, 1994), the Willamette River 

Basin in Oregon (Laenen and Risley, 1997), the San Juan 

River Basin in Colorado and New Mexico (Kuhn and others, 

1998), the Truckee River Basin in California and Nevada 

(Jeton, 1999), and the upper Rio Grande Basin in Colorado 

and New Mexico (Boyle and others, 2004).

Description of Simulation Model

The modified version of the PRMS (Leavesley and 

others, 1983) used in this study simulates hydrologic processes 

that occur in Salmon Creek Basin (fig. 6). The model was 

developed from a set of 15 modules given in table 1 that are 

identical to those used in precipitation-runoff simulations 

for the neighboring Methow River Basin (Ely and Risley, 

2001; Ely, 2003). Each module represents either a hydrologic 

process or reads model input parameters or time series. Of 

the 15 modules, 5 are standard PRMS modules (Leavesley 

and others, 1983 and 1996) and 10 are modified versions 

developed for either the Yakima River Basin study (Mastin 

and Vaccaro, 2002a and 2002b) or the Methow River Basin 

study (Ely and Risley, 2001; Ely, 2003). The functionality of 

the module that reads model input time series of irrigation 

diversions, irrigation returns, and irrigation applications 

(divrt_apply_prms.f, table 1) is not used in this study because 

the precipitation-runoff model is used to simulate unregulated 

streamflow. A detailed description of the combination of 

modules used in this study is provided in the documentation 

of the precipitation-runoff simulations for the Methow River 

Basin (Ely and Risley, 2001).

The Methow River Basin model was selected so that 

calibrated parameters from that model could be used as initial 

estimates for model parameters in this study. Justifications for 

this approach are that the Methow River and Salmon Creek 

Basins are adjacent and share some hydrologic characteristics 

and that multiple long-term records of measured daily 

streamflows for the Methow River Basin were available for 

WA19_0043_fig04

A

B

0

0.5

1.0

1.5

2.0

2.5

3.0

JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT NOV DEC

PRECIPITATION, IN INCHES

0

5

10

15

20

25

30

1970 1975 1980 1985 1990 1995 2000

WATER YEAR

PRECIPITATION, IN INCHES

Figure 4. Mean monthly precipitation for the Salmon 

Creek Basin, Okanogan County, Washington, 1971–2000 

(based on PRISM data from the Spatial Climate Analysis 

Service-Oregon State University, 2006), and annual 

precipitation at Conconully, water years 1971–2000.

Minimum temperature

Maximum temperature

0

10

20

30

40

50

60

70

80

90

JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT NOV DEC

AIR TEMPERATURE, IN DEGREES FAHRENHEIT

Figure 5. Mean minimum and maximum air 

temperatures for the Conconully climate station, 

Okanogan County, Washington, 1971–2000.

Development of Precipitation-Runoff Model 
model calibration while none are available for the Salmon 

Creek Basin. Both the Methow River and Salmon Creek 

Basins drain mountainous subbasins on the east side of the 

Cascade Range (fig. 1) that have similar types of vegetation. 

The elevations in the basins also are similar and range from 

775–8,950 ft in the Methow River Basin and from 

820–8,250 ft in the Salmon Creek Basin. Some differences 

between the basins are that the area of the Methow River Basin 

is significantly greater than that for the Salmon Creek Basin 

(1,800 mi2 versus 152 mi2) and that mean annual precipitation 

for the Methow River Basin is greater than that for the Salmon 

Creek Basin (32 in. versus 21 in.).

Spatial diversity in the model area is represented by 

simulating the basin as a set of sub-areas, called Modeling 

Response Units (MRUs), that have similar hydrologic 

characteristics. Characteristics assigned to each MRU 

included such parameters as slope and aspect, elevation, 

vegetation type and summer and winter density, soil type, and 

percent pervious. In addition to the spatial characteristics, 

model inputs included time series of measured or estimated 

precipitation and air temperatures. The model distributes the 

data from the locations of the climate stations to each MRU 

by considering differences in elevations and distances between 

the MRUs and the stations.

At each MRU, the model simulates a sequence of 

hydrologic processes (fig. 6) at a user-selected time step, 

which was 24 hours for this study. At the end of each 

time step, several model outputs, including surface runoff, 

subsurface flow, and ground-water flow, are available at 

each MRU. Those model outputs form the components 

of streamflow for unregulated conditions, when irrigation 

WA19_0043_fig06 diversions and returns do not occur (fig. 6). The time series 

INPUTS

Air temperature Precipitation

Interception Evaporation

Evaporation

Evapotranspiration

Evaporation

Sublimation

Sublimation

Transpiration

Transpiration

Throughfall

Snowpack

Snowmelt

Recharge zone

Lower zone

Streamflow

Surface

runoff

Surface runoff

Impervious zone

reservoir

Soil zone reservoir

Soil zone excess

Ground-water

recharge

Ground-water

reservoir

Ground-water

sink

Ground-water

flow

Subsurface

recharge

Subsurface

flow

Solar radiation Irrigation application

Ground-water

recharge

Irrigation

return

Irrigation

diversion

Subsurface

reservoir

INPUTS

Figure 6. Modified version of the Precipitation-Runoff Modeling System.

 Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Name of module used Module function Version description

basin_prms.f Declares basin and MRU physical parameters. Standard PRMS

soltab_prms.f Computes potential solar radiation. Standard PRMS

obs_sno.f Reads measured data. YRBS

divrt_apply_prms.f1 Reads daily values of diversions, returns, and irrigation 

applications.

YRBS

temp_2sta_prms_dist2.f Distributes temperatures to MRUs. YRBS

precip_prms_dist2.f Determines precipitation type and distributes precipitation 

to MRUs.

YRBS

ccsolrad_prms.f Computes daily solar radiation from temperature/cloudcover relation.Standard PRMS

potet_jh_prms.f Determines whether transpiration is occurring and computes 

potential evapotranspiration using the Jensen-Haise (1963) 

approach.

Standard PRMS

intcp_prms_apply.f Computes amount of intercepted precipitation and evaporation 

from interception.

YRBS

snowcomp_prms_gmelt.f Computes snowpack accumulation and depletion using an 

energy-budget approach.

YRBS

srunoff_smidx_prms_glacr_apply.f Computes surface runoff and infiltration. YRBS

smbal_prms_wtrgmelt.f Computes soil-moisture mass balance. Computes infiltration, 

actual evapotranspiration, and seepage to subsurface and 

ground-water reservoirs.

YRBS

ssflow_prms.f Computes sum of inflows to subsurface reservoirs and calculates 

outflow to ground-water reservoirs and streams.

Standard PRMS

gwflow_loss_min_darcy.f Computes sum of inflows to ground-water reservoirs and outflow 

to streams and ground-water sinks.

MRBS

musroute_prms_divretrn.f Routes water between stream nodes. YRBS

1

Functionality not used in this study.

Table 1. Modules used in the precipitation-runoff model.

[MRU, Modeling Response Unit; PRMS, Precipitation-Runoff Modeling System; YRBS, Yakima River Basin study (Mastin and Vaccaro, 

2002a and 2002b); MRBS, Methow River Basin study (Ely and Risley, 2001; Ely, 2003)]

of simulated streamflow components for each MRU are 

assigned to user-selected stream nodes that represent different 

locations of interest in the stream network. At each node, the 

contributions from the assigned MRUs are accumulated and 

routed downstream to simulate the daily streamflow at the 

various stream locations represented by the nodes. Simulated 

streamflows are compared to estimated streamflows and the 

model is calibrated by adjusting the model parameters until the 

fit between the simulated and estimated values is reasonable. 

For this study, measurements of daily streamflows were not 

available. Therefore, the model was calibrated by comparing 

simulated and estimated monthly mean streamflows.

After calibration, the precipitation-runoff model can 

be used to forecast daily unregulated streamflows based 

on near-real-time known or simulated initial hydrologic 

conditions in the basin and an assumption of future climate 

conditions. Streamflows can be forecast using the Extended 

Streamflow Prediction (ESP) technique in MMS, which is 

based on a modified version of the National Weather Service 

ESP program (Day, 1985; Leavesley and others, 1996). In 

the precipitation-runoff model for this study, ESP can be 

used to forecast the probability of streamflows for as far as 

1 year in the future by assuming that historical records of 

daily precipitation and air temperatures will recur with the 

same probability as in the past. For example, if near-realtime initial conditions have been simulated for March 31 of a 

particular year and streamflows need to be predicted for the 

following April, the model will be run multiple times in the 

ESP mode for March 31 through April 30. Each run will have 

a different set of historical climate data for April but the same 

initial conditions for March 31. The resulting simulated daily 

streamflows for April can be analyzed further to determine 

Development of Precipitation-Runoff Model 
probabilistic forecasts of variables of interest, such as the 

probability distribution of peak flow and total flow volume. 

An alternative use of ESP includes constraining forecasts 

by using only historical climate data that represent specific 

conditions, such as El Niño conditions. Use of the calibrated 

model is facilitated by a user-friendly Object User Interface 

(OUI) similar to that described for the Yakima River Basin 

precipitation-runoff model by Mastin and Vaccaro (2002b).

Time-Series Data

The precipitation-runoff model requires input time 

series of measured or estimated daily precipitation and daily 

minimum and maximum air temperatures. In addition, time 

series of measured or estimated streamflow are needed to 

calibrate the model.

Precipitation and Air Temperature

Historical records of daily precipitation and minimum 

and maximum air temperatures were obtained from different 

sources, including the National Weather Service (Hydrosphere 

Data Products, 2005), Natural Resources Conservation Service 

(U.S. Department of Agriculture, 2006), and Reclamation 

(Bureau of Reclamation, 2006a and 2006c). The climate 

stations used in this study are listed in table 2 and shown in 

figure 1.

Real-time inputs of precipitation and air temperature are 

required by the precipitation-runoff model to simulate realtime initial conditions for use in the ESP mode. Of the stations 

used in this study, only the Conconully CCR Hydromet, 

Omak OMAW AgriMet, and Salmon Meadows SNOTEL 

stations provide real-time data. The first water year for which 

complete sets of daily air temperature and precipitation data 

are available for two of the real-time stations, Omak OMAW 

AgriMet and Salmon Meadows SNOTEL, is 1990 (table 2). 

The first water year for which complete sets of daily air 

temperature and precipitation data are available for all three 

of the real-time stations is 2000. For the Conconully CCR 

Hydromet station, real-time daily precipitation and minimum 

and maximum air temperatures for water years 2000–04 were 

used as input to the model. For the Omak OMAW AgriMet 

station, real-time daily precipitation for water years 1990–96 

and minimum and maximum air temperatures for water years 

1990–2004 were used as input to the model. For the Salmon 

Meadows SNOTEL station, real-time daily precipitation data 

for water years 1990–96 were used as input to the model. 

The daily minimum and maximum air temperatures were not 

used in the model because those data were questionable for 

April 21, 1999, through August 16, 2005 (S. Strachan, Natural 

Resources Conservation Service, written commun., 2006). 

The February 1989 through September 1998 data were used to 

compute minimum and maximum air temperature lapse rates 

between the Omak OMAW AgriMet and Salmon Meadows 

SNOTEL stations.

Model simulations of historical conditions used input 

time series based on climate data from non-real-time stations 

for water years 1949–89, from non-real-time and realtime stations for water years 1990–99, and from real-time 

stations for water years 2000–04. For the Conconully station, 

daily precipitation and daily minimum and maximum air 

temperatures for water years 1949–99 were used as input 

to the model. The daily precipitation record was missing 8 

percent of the data for water years 1949–96 and 23 percent 

of the data for water years 1997–99. The daily minimum and 

maximum air-temperature records were missing 8 percent of 

the data for water years 1949–96 and 21 percent of the data for 

water years 1997–99. Water years 1997 through at least 2003 

were missing data for most of the winter months (generally for 

November through February). The missing daily precipitation 

and minimum and maximum air temperatures were estimated 

for water years 1949–99 on the basis of all possible 

combinations of single and multiple linear regressions (with 

intercept zero for regressions of daily precipitation) between 

the Conconully station and either one or both of the other 

long-term stations, the Omak 4 N and the Winthrop 1 WSW 

stations (fig. 1). The regression that had the highest coefficient 

of determination (r2) for which all required independent 

variables were available was used to estimate the missing data.

Synthetically generated temperatures for the Omak 

OMAW AgriMet station were used as inputs to simulate 

historical conditions for water years 1949–89. Synthesized 

temperatures for the Omak OMAW AgriMet station were 

used in addition to measured temperatures for the Conconully 

station to assure continuous daily minimum and maximum 

air-temperature time series at one location for water years 

1949–2004. The synthetic daily minimum and maximum air 

temperatures for the Omak OMAW Agrimet station were 

generated on the basis of all possible combinations of single 

and multiple linear regressions between that station and one 

to three long-term climate stations, the Conconully, Omak 

4 N, and Winthrop 1 WSW stations, in the study area (fig. 1). 

Separate sets of regressions were performed for each month 

to account for seasonal shifts in temperature relations. For 

each month, the regression that had the highest coefficient of 

determination for which all required independent variables 

were available was used to estimate the air temperatures for 

the Omak OMAW AgriMet station.

Daily precipitation and minimum and maximum air 

temperatures for the real-time Conconully CCR Hydromet 

station were used as inputs to simulate historical conditions 

for water years 2000–04. For the purposes of this study, it 

was assumed that the daily precipitation and air temperatures 

measured for the Conconully CCR Hydromet station were 

equivalent to the daily precipitation and air temperatures 

measured for the Conconully station. This assumption was 

based on a comparison of monthly mean minimum and 

maximum air temperatures and monthly total precipitation 

when measurements were available for both stations, August 

1999 through September 2003. During this period, however, 

10 Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Station name 

(real-time station) Agency

Latitude Longitude

Altitude 

(feet) Period of record Record used in study

(degrees, minutes, and 

seconds)

Conconully NWS 48 33 00 119 45 00 2,319 June 1948 to present precipitation and temperature, 

Oct. 1948–Sept. 1999, used as 

model input

temperature, Oct. 1948–Sept. 

1989, used to calculate synthetic 

daily minimum and maximum air 

temperatures for Omak OMAW 

AgriMet station

Conconully CCR Reclamation 48 32 17 119 44 50 2,290 Aug. 1999 to present precipitation and temperature, 

Oct. 1999–Sept. 2004, used as 

model input

Omak 4 N NWS 48 27 00 119 31 00 1,301 Jan. 1931 to present precipitation and temperature, 

Oct. 1948–Sept. 1999, used to 

estimate missing record for Conconully station 

temperature, Oct. 1948–Sept. 

1989, used to calculate synthetic 

daily minimum and maximum air 

temperatures for Omak OMAW 

AgriMet station

Omak OMAW Reclamation 48 24 09 119 34 34 1,235 Jan. 1989 to present precipitation, Oct. 1989–Sept. 1996, 

used as model input 

temperature, Oct. 1989–Sept. 2004, 

used as model input

temperature, Feb. 1989–Sept. 1998, 

used to compute lapse rates 

between this station and Salmon 

Meadows SNOTEL station

Salmon Meadows NRCS 48 40 00 119 50 00 4,500 precipitation: 

Oct. 1981 to Oct. 1982

Oct. 1983 to present

temperature: 

Feb. 1989 to present

precipitation, Oct. 1989–Sept. 1996, 

used as model input

temperature, Feb. 1989–Sept. 1998, 

used to compute lapse rates 

between this station and Omak 

OMAW AgriMet station

Winthrop 1 WSW NWS 48 27 00 120 12 00 1,755 Jan. 1931 to present precipitation and temperature, 

Oct. 1948–Sept. 1999, used to 

estimate missing record for Conconully station 

temperature, Oct. 1948–Sept. 

1989, used to calculate synthetic 

daily minimum and maximum air 

temperatures for Omak OMAW 

AgriMet station

Table 2. Climate stations used in model simulations or to estimate missing data.

[NWS, National Weather Service; NRCS, Natural Resources Conservation Service; Reclamation, Bureau of Reclamation. All stations record daily precipitation 

and daily minimum and maximum air temperature]

Development of Precipitation-Runoff Model 11
measurements were missing for the Conconully station from 

November through February. In 2003, the missing record 

extended through April. Linear regressions and plots of the 

data demonstrated that the monthly mean air temperatures 

and total precipitation compared well between the stations. 

The coefficient of determination was 0.99 for monthly mean 

minimum temperatures, 0.99 for monthly mean maximum 

temperatures, and 0.84 for monthly total precipitation for 

a linear regression with intercept zero. Because future 

measurements for the Conconully CCR Hydromet station 

will be used for simulating near-real-time initial hydrologic 

conditions for ESP forecasting, the precipitation-runoff model 

could be more reliable if the assumption of equivalency of 

air temperatures and precipitation between Conconully CCR 

Hydromet and Conconully climate stations could be confirmed 

using future, year-round data for a multiyear period for both 

stations.

Streamflow

Measured historical streamflows for Salmon Creek are 

limited to records for two USGS gages that were discontinued 

in the early 1900s, records for a Washington State Department 

of Ecology (DOE) gage that has been in operation since 2002, 

and records for a continuously recording gage operated by the 

OID since 2003 (T. Sullivan, Okanogan Irrigation District, 

oral commun., 2006). Prior to becoming a continuously 

recording gage in 2003, the OID gage was a staff gage that 

was read daily. The discontinued USGS gages are located just 

downstream of Conconully Reservoir (fig. 2; USGS station 

no. 12446500) and 6 mi upstream of the mouth of Salmon 

Creek (USGS station no. 12447000). The periods of record 

for the gages are water years 1912 through 1922 and 1904 

through part of water year 1910, respectively. The DOE gage 

(DOE station no. 49M100) is located in North Fork Salmon 

Creek, about 2 mi upstream of the North Fork Diversion 

(fig. 2). The first complete April–July streamflow record was 

measured in water year 2004 and data for water year 2005 is 

currently (2006) provisional (Washington State Department 

of Ecology, 2006a; J. Shedd, Washington State Department of 

Ecology, written commun., 2006). The OID gage is located 

just upstream of the OID diversion in lower Salmon Creek 

(fig. 2). During periods of high snowmelt runoff in the spring, 

the OID gage is submerged and does not accurately measure 

streamflow for Salmon Creek. The OID also has measured 

diversions from Salmon Creek at the OID diversion since 1996 

(Dames and Moore, 1999).

To expand the limited historical data set, Dames and 

Moore (1999) estimated a monthly time series of unregulated 

streamflow for Salmon Creek at Conconully Dam for 1904 

through 1998 based on reservoir outflows and storage changes. 

The U.S. Department of Energy (2004) extended the estimates 

through 2002, and in this study, the estimates were extended 

through March 2006 (appendix 1) using storage and outflow 

data provided by the OID (T. Sullivan, Okanogan Irrigation 

District, written commun., 2006). The estimates represent 

unregulated streamflows for the upper Salmon Creek Basin 

(defined as the drainage area upstream of Conconully Dam), 

which encompasses 78 percent of the total drainage area of the 

basin.

The method used to estimate unregulated streamflow 

from reservoir outflows and storage changes has several 

potential sources of error. The first source of error is that the 

estimate does not include a correction for evaporative losses 

from Conconully Reservoir and the larger surface area of 

Salmon Lake Reservoir compared to the natural lake that 

was present prior to the construction of Salmon Lake Dam. 

Dames and Moore (1999) estimated that those evaporative 

losses are about 2.2 ft3/s or about 1,600 acre-ft/yr. A second 

source of error is measurement error in the estimation of 

reservoir storage. Reservoir storage is estimated by measuring 

the stage in a reservoir and then converting the stage to a 

storage volume by using a stage-storage relation. Even small 

errors in measuring the stage can lead to significant errors in 

estimating the storage volume and, thus, to significant errors in 

estimating the monthly storage changes. Such errors may help 

explain why some estimates of monthly runoff are negative 

(Dames and Moore, 1999). For example, Dames and Moore 

(1999) suggested that stage-reading errors that resulted from 

wave run-up caused by wind may amount to storage-volume 

estimate errors of several hundred acre-ft. During a period 

of months, however, the errors in the estimates of monthly 

storage change are expected to cancel each other.

A third source of error in the estimation of unregulated 

streamflow is measurement error in reservoir outflow. Dames 

and Moore (1999) reported that prior to 1997, reservoir 

outflow was measured periodically by a weir located in 

Salmon Creek a few hundred feet downstream of Conconully 

Dam. This measurement included water that seeped below the 

dam, scheduled releases from the reservoir, and uncontrolled 

spills during periods of high runoff. Measurements of 

uncontrolled spills, however, are considered approximate 

(Dames and Moore, 1999; U.S. Department of Energy, 2004). 

Starting in 1997, the weir no longer was used and reservoir 

releases were measured only through the outlet tunnel. As a 

result, the measurements no longer include seepage below the 

dam [estimated to be about 1.6 ft3/s or 100 acre-ft per month 

by Dames and Moore (1999)] and uncontrolled spills.

Dames and Moore (1999) indicated significant, 

unexplained discrepancies existed between the measured 

outflow from Conconully Reservoir and streamflow measured 

at the OID diversion. They attributed the discrepancies to 

probable errors in the measured outflows from Conconully 

Reservoir. Starting in 1997, the reliability of the estimates of 

runoff from upper Salmon Creek Basin becomes less certain 

than in previous years. Therefore, only data through water 

year 1996 were used in this study to calibrate and test the 

precipitation-runoff model.

A fourth source of error in the estimation of unregulated 

streamflow is that Conconully Reservoir overlies relatively 

permeable sediments through which ground-water recharge 

may occur. Some of the recharged ground water may leave 

12  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Salmon Creek Basin through subsurface flow because 

the surface-water drainage boundary to the southeast of 

Conconully Reservoir in the Graveyard Flat area (fig. 2) is 

unlikely to be a ground-water divide during current (2006) 

hydrologic conditions. Therefore, some ground water is 

expected to flow southeasterly towards Scotch Creek. This 

interpretation is based on water-level altitude data for surfacewater features adjacent to the Graveyard Flat area and wells 

in the Graveyard Flat area. The water-level altitude data may 

indicate the raised water level of Conconully Reservoir above 

the natural creek bed may have created a hydraulic connection 

between the reservoir, the water-table aquifer below Graveyard 

Flat, and Scotch Creek. Too few data currently are available 

to determine whether a similar hydraulic connection may 

have existed prior to the creation of Conconully Reservoir. 

However, even if a hydraulic connection did exist, groundwater losses would have been significantly smaller during 

unregulated conditions than during current conditions.

In summary, the estimated streamflows for the upper 

Salmon Creek Basin, including estimates prior to 1997, have 

several potential sources of error. However, the estimates are 

the best available and, therefore, were used in this study as a 

surrogate for the missing long-term unregulated streamflows 

for Salmon Creek at the current location of Conconully Dam. 

The time series of unregulated streamflows is referred to as 

uncorrected unregulated streamflow (UUS) in this report.

Corrected Unregulated Streamflow

A second time series of unregulated streamflow for 

Salmon Creek at Conconully Dam was generated by adding 

estimates of ground-water losses and evaporative losses to the 

uncorrected time series. The corrected time series is expressed 

as

CUS = UUS + GW + RES_EVAP, (1)

where

CUS is corrected unregulated streamflow, in cubic 

feet per second;

UUS is uncorrected unregulated streamflow, in 

cubic feet per second;

GW is the ground-water flux, in cubic feet per 

second, from Salmon Creek Basin; and

RES_EVAP is reservoir evaporation, in cubic feet per 

second.

As described in the sections “Corrections for GroundWater Losses” and “Corrections for Evaporative Losses,” 

the ground-water flux from the basin (GW) is estimated to 

be about 2 ft3/s and reservoir evaporation (RES_EVAP) is 

estimated to be about 1 ft3/s. The total of the corrections 

is about 9 percent of the long-term mean UUS, which is 

32.2 ft3/s (23,300 acre-ft/yr) for water years 1949–96. On an 

annual basis, the sum of the estimated ground-water flux and 

reservoir evaporation also represents about 9 percent of the 

total maximum active storage capacity of 23,500 acre-ft for 

Conconully and Salmon Lake Reservoirs.

Corrections for Ground-Water Losses

Conconully Reservoir possibly recharges the underlying 

ground-water system and part of the recharged water may 

leave Salmon Creek Basin through subsurface flow in the 

general direction of Scotch Creek (fig. 2). The surficial 

geology between Conconully Reservoir and the headwaters 

of Scotch Creek is mapped as Continental Glacial Drift 

deposits (Stoffel, 1990). Driller’s logs obtained from the 

Washington State Department of Ecology (2006b) for two 

wells in the Graveyard Flat area show a relatively thick 

sequence of sedimentary deposits that is likely underlain by 

a former bedrock valley (field-verification of well locations 

was beyond the scope of this study). The saturated sediments, 

which start at about 115 to 130 ft below land surface, consist 

of sand, gravels, and some clay. Based on the lithologic and 

water-level information from these logs, it is estimated that the 

Graveyard Flat area is underlain by at least 70 ft of saturated 

sediments. Assuming horizontal flow, Darcy’s law states that 

ground-water flux can be estimated as follows:

Q = K A i, (2)

where

Q is ground-water discharge, in cubic feet per day;

K is horizontal hydraulic conductivity of the sediments, in 

feet per day;

A is cross-sectional area perpendicular to the direction of 

flow, in square feet; and

i is hydraulic gradient (dimensionless).

The hydraulic conductivity (K) of the saturated 

glacial-drift materials is estimated to be 40 ft/d, which is a 

conservatively low estimate based on hydraulic conductivities 

calculated for permeable glacial sediments elsewhere in 

Washington State (for example, Kahle, 1998; and Kahle 

and others, 2003). [In a summary of hydraulic-conductivity 

estimates for glacial sediments from multiple sources, 

Fetter (1994) reported that glacial-outwash deposits can 

have hydraulic conductivities that range from about 3 ft/d to 

about 3,000 ft/d depending on the degree of sorting of the 

sediments.] Assuming the cross-sectional area through which 

the southeasterly-flowing ground water discharges is 2,000 ft 

wide and 70 ft thick, the cross-sectional area perpendicular 

to the direction of flow (A) is 140,000 ft2. A mean hydraulic 

gradient (i) of 0.03 was computed by assuming a water level of 

2,282 ft for Conconully Reservoir and by assuming the water 

table intersects the land surface at 2,110 ft in the headwaters 

of Scotch Creek, where the creek first becomes a perennial 

stream according to the 1:24,000-scale topographic map for 

the area. Using these estimates in equation 2, about 2 ft3/s 

(about 1,400 acre-ft/yr) of ground water discharges from upper 

Salmon Creek Basin in a southeasterly direction. Some of 

this ground water likely discharges to Scotch Creek while the 

remainder is likely to travel along longer and deeper flowpaths 

and become incorporated in the regional ground-water flow 

system.

Development of Precipitation-Runoff Model 13
Currently available data are insufficient to determine 

whether ground-water losses occurred prior to the creation 

of Conconully Reservoir or, if losses did occur, to determine 

the rate of the losses. If ground-water losses did occur, 

however, they would have been significantly smaller than the 

losses estimated for current conditions. Therefore, for this 

study, out-of-basin ground-water losses were assumed to be 

negligible during unregulated conditions.

Corrections for Evaporative Losses

The construction of Conconully and Salmon Lake 

Reservoirs introduced evaporative losses from the reservoir 

surfaces as an additional source of water loss from the basin. 

For the purpose of this study, the increase in evaporative 

losses from Salmon Lake Reservoir was ignored because 

Salmon Lake Reservoir is located in a narrow canyon and 

the conversion of Salmon Lake to Salmon Lake Reservoir 

was assumed to result in a relatively small increase in watersurface area. The construction of Conconully Reservoir, 

however, created a relatively shallow water body that has a 

maximum size of about 400 acres where previously only a 

stream and natural vegetation were present.

Farnsworth and others (1982) estimated, on the basis 

of regional data collected from 1956 through 1970, an 

annual free-water-surface evaporation of about 30 in. at the 

location of Conconully Reservoir. They also estimated that 

about 23 in., or 77 percent of the annual total, evaporates 

from May through October. Monthly estimates of reference 

evapotranspiration for alfalfa at the Omak OMAW AgriMet 

station (Bureau of Reclamation, 2006a) for water years 

1990–2004 range from 0.6 percent of the mean annual total in 

December to 19.9 percent in July (fig. 7). From May through 

October, the reference evapotranspiration for alfalfa is 36.5 in., 

or 81.5 percent of the mean annual total of 44.8 in.

Assuming that the reservoir is at a maximum size of 

400 acres during May through July, 200 acres during April and 

August, and 100 acres during the remainder of the year, and 

that the annual evaporation is 30 in. distributed according to 

the monthly reference evapotranspiration at the Omak OMAW 

AgriMet station, the annual evaporative loss from Conconully 

Reservoir is estimated to be about 1 ft3/s (about 700 acre-ft) 

(table 3). Different assumptions for the values of annual freewater-surface evaporation and monthly reservoir surface area 

could alter the estimate significantly.

Figure 7. Mean monthly reference evapotranspiration 

as a percentage of mean annual reference 

evapotranspiration for alfalfa (1982 Kimberly-Penman 

equation) at the Omak OMAW AgriMet station, water 

years 1990–2004.

WA19_0043_fig07

OCT NOV DEC JAN FEB MAR APR MAY JUNE JULY AUG SEP

0

2

4

6

8

10

12

14

16

18

20

PERCENTAGE OF MEAN ANNUAL REFERENCE

 EVAPOTRANSPIRATION 

Month

Estimated 

surface area 

of Conconully 

Reservoir 

(acres)

Percentage 

of annual 

evaporation

Evaporation 

(inches) (ft3/s) (acre-ft)

October 100 4.9 1.5 0.20 12

November 100 1.3 0.39 0.054 3.3

December 100 0.6 0.18 0.025 1.5

January 100 0.7 0.21 0.028 1.7

February 100 1.8 0.55 0.076 4.6

March 100 5.1 1.5 0.21 13

April 200 9.0 2.7 0.75 45

May 400 13.5 4.0 2.2 1.4 × 102

June 400 16.5 5.0 2.7 1.7 × 102

July 400 19.9 6.0 3.3 2.0 × 102

August 200 16.3 4.9 1.4 82

September 100 10.4 3.1 0.43 26

 Annual mean 30 1.0 6.9 × 102

Table 3. Estimated evaporation from Conconully Reservoir, 

assuming a mean annual free-water-surface evaporation of 

30 inches.

[ft3/s, cubic feet per second; acre-ft, acre-feet]

14  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Delineation of Basin Physical 

Characteristics

Salmon Creek Basin was subdivided into 179 MRUs 

(fig. 8) using a tool called the GIS (Geographic Information 

System) Weasel (Viger and others, 1998) and manually adding 

separate MRUs for Conconully and Salmon Lake Reservoirs. 

The GIS Weasel is a computer program that allows easy 

generation of MRUs and a drainage network (fig. 8) on the 

basis of a digital elevation model (DEM). The computer 

program also creates initial model input parameters for each 

MRU on the basis of a variety of digital geographic data, 

including soils, land use, and vegetation type and density.

The MRUs and drainage network were delineated 

according to the same procedures used by Ely and Risley 

(2001) and Ely (2003) in the precipitation-runoff study of the 

Methow River Basin. The differences are that, in the current 

study, a higher-resolution DEM was used for the delineation of 

the MRUs and drainage network and the selection of different 

delineation thresholds resulted in generally smaller MRUs and 

a more detailed drainage network.

The MRUs and drainage network were delineated on the 

basis of a DEM that had a cell size of 33 ft (10 m) by 33 ft. An 

initial set of MRUs was generated by considering the range 

of slopes (fig. 9) and aspects in the basin. This set of MRUs 

then was subdivided by applying the GIS Weasel two-plane 

option, which creates separate MRUs on each side of a stream 

segment. A first-order stream was assumed to drain at least 

35,000 DEM cells or an area of 1.35 mi2 (865 acres). The set 

of MRUs then was subdivided a second time by partitioning 

the basin into eight equal-elevation bands of 930 ft to account 

for the large elevation range in the basin. This process 

created some very small MRUs that then were merged with 

neighboring, larger MRUs. The merging of MRUs recombined 

a few MRUs on opposite sides of the streams. The final 

179 MRUs range in size from 141 to 1,971 acres and have 

a mean size of 544 acres. For comparison, the 620 MRUs 

in the precipitation-runoff study of the Methow River Basin 

range in size from 596 to 7,370 acres and have a mean size of 

1,870 acres.

Model Parameterization

Initial model parameters were assigned by applying 

the GIS Weasel (Viger and others, 1998), computing values 

from measured data, and using parameters from the Methow 

River Basin precipitation-runoff model (Ely and Risley, 2001; 

Ely, 2003). A subset of the parameters was adjusted during 

calibration to achieve a good fit between the simulated and 

measured or estimated variables, as described in the “Model 

Calibration and Testing” section of this report.

Using the GIS Weasel, mean slope, aspect, and elevation 

were assigned to each MRU on the basis of the DEM, soil 

characteristics were assigned on the basis of the State Soil 

Geographic (STATSGO) database (U.S. Department of 

Agriculture, 1994), and the dominant vegetation type and 

vegetation characteristics were assigned on the basis of 

National Land Cover Data (NLCD 92) (U.S. Geological 

Survey, 1992; Vogelmann and others, 2001; fig. 10) and digital 

maps of United States forest types and density (Powell and 

others, 1993; Zhu and Evans, 1992). Monthly mean minimum 

and maximum air-temperature lapse rates were computed 

from air temperatures for the Omak OMAW AgriMet and 

Salmon Meadows SNOTEL stations for February 1989 

through September 1998. Parameters that represent the mean 

monthly rainfall or snowfall on each MRU were obtained from 

values computed with the Parameter-elevation Regressions on 

Independent Slopes Model (PRISM; Daly and others, 1994 

and 2002; Daly and Johnson, 1999) for the climate-normal 

period of 1971–2000 (Spatial Climate Analysis ServiceOregon State University, 2006). The precipitation-runoff 

model for this study distributes daily precipitation measured at 

climate stations across the model area according to the ratios 

between monthly PRISM values and monthly precipitation at 

the climate stations and weighted by the inverse of the square 

of the distance to the climate stations.

The model simulates streamflow for 11 locations (fig. 11) 

distributed at the mouths of subbasins (nodes 2 through 6), at 

the mouth of the entire basin (node 11), at points of diversion 

(nodes 1 and 10), and at three additional locations in lower 

Salmon Creek (nodes 7 through 9). Node 6, which represents 

Salmon Creek at the location of Conconully Dam, captures 

streamflow generated in upper Salmon Creek Basin.

Model Parameterization 15
Figure 8. Modeling Response Units and drainage network delineated for the precipitation-runoff model for the 

Salmon Creek Basin, Okanogan County, Washington.

WA19-0043_fig08

97

119°50' 119°40'

48°30'

48°40'

Base map modified from USGS digital data 1:100,000, 1995

Hillshade modified from National Elevation Dataset, 2000, 10-meter resolution

Universal Transverse Mercator projection, zone 11, 

North American Datum of 1983, North American Vertical Datum of 1988

Boundary of Modeling Response Unit 

Drainage network defined by GIS Weasel

EXPLANATION

North Fork

Diversion

Conconully

Okanogan

Okanogan

Irrigation

District

Diversion

Conconully Dam

Salmon Lake Dam

Graveyard Flat

Conconully Reservoir

Salmon Lake Reservoir

Salmon

Creek

Basin

boundary

SALMON CREEK

South Fork

Creek

West

Salmon

Salmon

Creek

Fork

North Fork

Salmon

Creek

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

16  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
WA19-0043_fig09

Conconully Dam

Salmon Lake Dam

North Fork

Diversion

Conconully

Okanogan

Graveyard Flat

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS 97

119°50' 119°40'

48°30'

48°40'

Conconully Reservoir

SALMON CREEK

South Fork

Creek

West

Salmon

Salmon

Creek

Fork

North

Fork

Salmon

Creek

Salmon Lake Reservoir

Okanogan

Irrigation

District

Diversion

Base map modified from USGS digital data 1:100,000, 1995

Hillshade modified from National Elevation Dataset, 2000, 10-meter resolution

Universal Transverse Mercator projection, zone 11, 

North American Datum of 1983, North American Vertical Datum of 1988

EXPLANATION

Slope—As rate of maximum change 

with elevation, in degrees

0-10

10.1-20

20.1-40

40.1-70

Salmon

Creek

Basin

boundary

Figure 9. Generalized slopes in the Salmon Creek Basin, Okanogan County, Washington.

Model Parameterization 17
WA19-0043_fig10

North Fork

Diversion

Conconully

Okanogan

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS 97

119°50' 119°40'

48°30'

48°40'

SALMON CREEK

South Fork

Creek

West

Salmon

Salmon

Creek

Fork

North

Fork

Salmon

Creek

Conconully Dam

Salmon Lake Dam

Graveyard Flat

Conconully Reservoir

Salmon Lake Reservoir

Okanogan

Irrigation

District

Diversion

Base map modified from USGS digital data 1:100,000, 1995

Hillshade modified from National Elevation Dataset, 2000, 10-meter resolution

Universal Transverse Mercator projection, zone 11, 

North American Datum of 1983, North American Vertical Datum of 1988

Land cover modified from National Land Cover Dataset,

1992, 30-meter resolution,

Albers Equal-Area Conic projection, 

North American Datum of 1983

EXPLANATION

Land cover

Evergreen forest

Deciduous shrubland

Orchards or vineyards

Grassland

Water

Developed

Barren

Mixed deciduous

 and evergreen

 forest

Salmon

Creek

Basin

boundary

Figure 10. Land cover in the Salmon Creek Basin, Okanogan County, Washington.

18  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Figure 11. Major subbasins and locations of model nodes in the Salmon Creek Basin, Okanogan County, 

Washington.

WA19-0043_fig11

97

119°50' 119°40'

48°30'

48°40'

Base map modified from USGS digital data 1:100,000, 1995

Hillshade modified from National Elevation Dataset, 2000, 10-meter resolution

Universal Transverse Mercator projection, zone 11, 

North American Datum of 1983, North American Vertical Datum of 1988

Salmon

Creek

Basin

boundary

West Fork Subbasin

North Fork Subbasin

Lower Salmon 

Creek Subbasin

South Fork 

Subbasin

Salmon Lake

Reservoir

Subbasin

Conconully

Reservoir Subbasin

11

10

6 4

5

2 3 1

9

8

7

EXPLANATION

Boundary of Modeling Response Unit

Drainage network defined by GIS Weasel

Downstream boundary of drainage area 

upstream of node 7, 8, 9, or 10

Description of selected nodes

 1 - Diversion North Fork Salmon Creek

 2 - Mouth of North Fork Salmon Creek

 3 - Outlet of Salmon Lake Reservoir

 4 - Mouth of West Fork Salmon Creek

 5 - Mouth of South Fork Salmon Creek

 6 - Outlet of Conconully Reservoir

10 - Diversion Okanogan Irrigation District

11 - Mouth of Salmon Creek

1

0 1 2 3 4 5 MILES

0 1 2 3 4 5 KILOMETERS

Model Parameterization 19
Model Calibration and Testing

The precipitation-runoff model was calibrated for a 

40-year period, water years 1950–89, and tested for a 7-year 

period, water years 1990–96 (table 4). The actual model 

simulations used data for water years 1949–89, but results of 

the first year were not included in the calibration analysis to 

allow the model to properly initialize basinwide hydrologic 

conditions for that year. The goal of the calibration effort was 

to adjust the initial model parameters so that the simulated 

monthly unregulated streamflows for Salmon Creek at the 

location of Conconully Dam (node 6; fig. 11) were more than 

the estimated UUS and less than the estimated CUS. The 

model was tested by using the calibrated parameter values to 

simulate streamflows for a period not used for calibration and 

then comparing the results for node 6 with the UUS and CUS.

As explained in the “Corrected Unregulated Streamflow” 

section, CUS includes a correction for ground-water losses 

from the Salmon Creek Basin and evaporative losses from 

Conconully Reservoir. The precipitation-runoff model as 

applied in this study does not simulate ground-water losses 

but does simulate a fraction of reservoir evaporation up to 

the amount of precipitation that falls on the reservoir. For 

example, a mean annual evaporation of 18.9 in. was simulated 

for Conconully Reservoir for the calibration period, and the 

long-term mean annual reservoir evaporation is estimated to be 

30 in. (table 3). As a result, the model is considered calibrated 

if simulated streamflows are larger than UUS but smaller than 

CUS.

During model calibration, model parameters were 

adjusted from initial values as follows: Parameter jh_coef, 

which helps determine the rate of potential evapotranspiration 

as computed with the Jensen-Haise approach (Jensen and 

Haise, 1963; Mastin and Vaccaro, 2002a), was increased by 

25 percent for each month compared to the values used in the 

Methow River Basin model. This increased the basinwide 

simulated mean annual actual evapotranspiration for water 

years 1950–89 by 1.7 in. and generated a simulated mean 

annual water budget that matched the estimated mean annual 

water budget. For water years 1950-89, the basinwide 

simulated mean annual precipitation and evapotranspiration 

were 23.1 and 19.1 in., respectively. The high 

evapotranspiration rates are reasonable given the extensive 

forests in the Salmon Creek Basin.

The minimum and maximum air-temperature time 

series for each MRU were lowered by increasing parameters 

tmin_adj and tmax_adj from 0 to 7°F each month. The lower 

temperatures resulted in a simulated mean hydrograph that 

best matched the shape of the UUS/CUS hydrograph for the 

calibration period. The date when the model starts looking 

for spring snowmelt was delayed by 20 days to April 20 

by changing parameter melt_look from a Julian date of 

90 to 110. However, simulated snowmelt still started too 

early in the runoff season. Therefore, the initial monthly 

mean minimum and maximum air-temperature lapse rates 

computed from measured air temperatures for the Omak 

OMAW Agrimet and Salmon Meadows SNOTEL stations 

were reduced by 2.5°F per 1,000 ft in March (to -5.58 and 

-7.32°F per 1,000 ft, respectively) and 1.5°F per 1,000 ft 

in April (to -4.47 and -6.61°F per 1,000 ft, respectively) to 

further delay the onset of melting, and the lapse rates were 

increased by 0.5°F per 1,000 ft in May (to -2.76 and -4.15°F 

per 1,000 ft, respectively) to increase the rate of snowmelt. 

The monthly lapse rates were kept constant throughout the 

model simulations. Finally, parameter freeh2o_cap, the 

free-water-holding capacity of the snowpack, expressed as 

a decimal fraction of the snowpack water-equivalent, was 

reduced 50 percent to 0.025 to help improve the shape of the 

hydrograph from May through July.

The results of the model calibration are shown in 

figures 12A and 13A through 13D and are given in table 5. 

For the calibration period, water years 1950–89, both the 

simulated mean annual streamflow and the simulated mean 

April–July streamflow compare well with the estimated 

values for UUS and CUS. The simulated mean annual 

streamflow exceeds UUS by 5.9 percent and is less than 

CUS by 2.7 percent (table 5). Similarly, the simulated mean 

April–July streamflow exceeds UUS by 1.8 percent and is 

less than CUS by 3.1 percent. A comparison of the estimated 

and simulated mean monthly streamflows, however, shows 

that streamflow is significantly undersimulated during the 

low-flow, baseflow-dominated months of November through 

February when simulated monthly streamflows are as much 

as 57.2 percent less than UUS (table 5) and significantly 

oversimulated during August and September when simulated 

monthly streamflows are as much as 193.6 percent more 

than CUS (table 5). During the low-flow months, however, 

estimated mean monthly streamflow is only a small percentage 

of the estimated mean annual streamflow, about 2 percent in 

November through February and about 1 percent in August 

and September (fig. 12A). Therefore, the absolute errors 

during the low-flow months are relatively small even though 

the percentages of error are large. For April through July, 

the estimated mean streamflow is about 84 percent of the 

estimated mean annual streamflow (fig. 12A). Because the 

simulated spring snowmelt season starts too early in April and 

extends too far into July (fig. 12A), the simulated streamflows 

exceed the estimated streamflows in April and July and are 

less than the estimated streamflows in May and June (table 5). 

Because the precipitation-runoff model will be used primarily 

as a tool to manage spring runoff, model calibration focused 

on matching estimated and simulated mean April–July 

streamflow rather than streamflows for individual months.

20 Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Type of model 

simulation Period of simulation Model input time series Remarks

CALIBRATION Oct. 1948 - Sept. 1989 Daily precipitation and minimum and 

maximum air temperatures for Conconully 

climate station

Oct. 1948 - Sept. 1949 omitted from analysis

Synthetic daily minimum and 

maximum air temperatures for 

Omak OMAW AgriMet station

TESTING 1 Oct. 1989 - Sept. 1996 Daily precipitation and minimum and maximum air temperatures for Conconully 

climate station 

Differs from CALIBRATION in that measured, realtime air temperatures are used instead of synthetic 

air temperatures for Omak OMAW AgriMet 

Daily minimum and maximum air tempera- station

tures for Omak OMAW AgriMet station

TESTING 2 Oct. 1989 - Sept. 1996 Daily precipitation and minimum and 

maximum air temperatures for Conconully 

climate station

Differs from TESTING 1 in that measured, real-time 

precipitation data for Salmon Meadows SNOTEL 

station are added

Daily minimum and maximum air temperatures for Omak OMAW AgriMet station

Daily precipitation for Salmon Meadows 

SNOTEL station

TESTING 3 Oct. 1989 - Sept. 1996 Daily precipitation and minimum and 

maximum air temperatures for Conconully 

climate station and Omak OMAW AgriMet 

station

Differs from TESTING 1 in that measured, real-time 

precipitation data for Omak OMAW AgriMet station are added

TESTING 4 Oct. 1989 - Sept. 1996 Daily precipitation and minimum and 

maximum air temperatures for Conconully 

climate station and Omak OMAW AgriMet 

station

Differs from TESTING 1 in that measured, real-time 

precipitation data for Salmon Meadows SNOTEL 

and Omak OMAW AgriMet stations are added

Daily precipitation for Salmon Meadows 

SNOTEL station

COMPOSITE Oct. 1948 - Sept. 2004 Oct. 1948 - Sept. 1989: 

Daily precipitation and minimum and maximum air temperatures for Conconully 

climate station

Identical to CALIBRATION Oct. 1948 - Sept. 1989; 

identical to TESTING 1 Oct. 1989 - Sept. 1996;

model input identical to TESTING 1 Oct. 1996 -

Sept. 1999; model input differs from TESTING 1

Oct. 1999 - Sept. 2004 in that daily precipitation 

and minimum and maximum air temperatures 

are used for Conconully CCR Hydromet station 

instead of Conconully climate station

Synthetic daily minimum and 

maximum air temperatures for 

Omak OMAW AgriMet station

Oct. 1989 - Sept. 1999: 

Daily precipitation and minimum and 

maximum air temperatures for Conconully 

climate station

Daily minimum and maximum air temperatures for Omak OMAW AgriMet station

Oct. 1999 - Sept. 2004:

Daily precipitation and minimum and maximum air temperatures for Conconully CCR 

Hydromet station

Daily minimum and maximum air temperatures for Omak OMAW AgriMet station

Table 4. Periods of simulation and model input time series for each type of model simulation.

Model Calibration and Testing  21
WA19_0043_fig12

A

B

MEAN MONTHLY STREAMFLOW, IN CUBIC FEET PER SECOND MEAN MONTHLY STREAMFLOW, IN CUBIC FEET PER SECOND 

Estimated (UUS)

Simulated

Estimated (CUS)

CALIBRATION, WATER YEARS

1950-89

-20

0

20

40

60

80

100

120

140

160

-20

0

20

40

60

80

100

120

140

160

TESTING 1, WATER YEARS 1990-96

C

-20

0

20

40

60

80

100

120

140

160

OCT NOV DEC JAN FEB MAR APR MAY JUNE JULY AUG SEPT

TESTING 2, WATER YEARS 1990-96 F

-20

0

20

40

60

80

100

120

140

160

COMPOSITE, WATER YEARS

1950-2004

-5

0

5

10

15

20

25

30

35

40

-5

0

5

10

15

20

25

30

35

40

-20

0

OCT NOV DEC JAN FEB MAR APR MAY JUNE JULY AUG SEPT

D

-20

0

20

40

60

80

100

120

140

160

TESTING 3, WATER YEARS 1990-96

E

20

40

60

80

100

120

140

160

TESTING 4, WATER YEARS 1990-96

-5

0

5

10

15

20

25

30

35

40

45

-5

0

5

10

15

20

25

30

35

40

45

PERCENTAGE OF MEAN ANNUAL ESTIMATED STREAMFLOW (UUS) FOR THE SIMULATED PERIOD PERCENTAGE OF MEAN ANNUAL ESTIMATED STREAMFLOW (UUS) FOR THE SIMULATED PERIOD 

-5

0

5

10

15

20

25

30

35

40

45

-5

0

5

10

15

20

25

30

35

40

45

Figure 12. Estimated and simulated mean monthly unregulated streamflows for Salmon Creek at Conconully Dam, 

Okanogan County, Washington. (UUS is uncorrected unregulated streamflow; CUS is corrected unregulated streamflow.)

22  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
WA19_0043_fig13

1990 1991 1992 1993 1994 1995 1996

Estimated (UUS)

Simulated, TESTING 1

Simulated, TESTING 2

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979

WATER YEAR

1960 1961 1962 1963 1964 1965 1966 1967 1968 1969

A

B

C

D

E -50

0

100

200

300

400

500

600

700

800

-50

0

100

200

300

400

500

600

-50

0

100

200

300

400

500

600

700

800

-50

0

100

200

300

400

500

600

-50

0

100

200

300

400

500

600

1950 1952 1953 1954 1955 1956 1957 1958 1959

MONTHLY MEAN STREAMFLOW, IN CUBIC FEET PER SECOND 

MONTHLY MEAN STREAMFLOW, IN CUBIC FEET PER SECOND 

Estimated (UUS)

Simulated, CALIBRATION

Estimated (UUS)

Simulated, CALIBRATION

Estimated (UUS)

Simulated, CALIBRATION

Estimated (UUS)

Simulated, CALIBRATION

1990 1991 1992 1993 1994 1995 1996

Estimated (UUS)

Simulated, TESTING 3

Simulated, TESTING 4

F

-50

0

100

200

300

400

500

600

1990 1992 1994 1996 1998 2000 2002 2004

WATER YEAR

G

-50

0

100

200

300

400

500

600

Estimated (UUS)

Simulated, partial COMPOSITE 

water years 1990-2004

1951

Figure 13. Time series of estimated and simulated monthly mean unregulated streamflows for Salmon Creek at Conconully 

Dam, Okanogan County, Washington. (UUS is uncorrected unregulated streamflow.)

Model Calibration and Testing  23
Table 5. Mean monthly, annual, and April-July estimated and simulated unregulated streamflows for Salmon Creek at Conconully Dam 

and the percentage of error for the CALIBRATION, TESTING 1, TESTING 2, TESTING 3, TESTING 4, and COMPOSITE model simulations.

[UUS, uncorrected unregulated streamflow; CUS, corrected unregulated streamflow; Percentage of error with respect to UUS or CUS: Values may not match 

presented values because of rounding; CALIBRATION: Model simulations are based on input time series of daily precipitation and minimum and maximum 

air temperatures for Conconully climate station and synthetic daily minimum and maximum air temperatures for Omak OMAW AgriMet station; TESTING 1: 

Model simulations are based on input time series of daily precipitation and minimum and maximum air temperatures for Conconully climate station and daily 

minimum and maximum air temperatures for Omak OMAW AgriMet station; TESTING 2: Model simulations are based on input time series of daily precipitation and minimum and maximum air temperatures for Conconully climate station, daily minimum and maximum air temperatures for Omak OMAW AgriMet 

station, and daily precipitation for Salmon Meadows SNOTEL station; TESTING 3: Model simulations are based on input time series of daily precipitation 

and minimum and maximum air temperatures for Conconully climate station and Omak OMAW AgriMet station; TESTING 4: Model simulations are based 

on input time series of daily precipitation and minimum and maximum air temperatures for Conconully climate station and Omak OMAW AgriMet station and 

daily precipitation for Salmon Meadows SNOTEL station; COMPOSITE: Model simulations are based on input time series of daily precipitation and minimum 

and maximum air temperatures for Conconully climate station and synthetic daily minimum and maximum air temperatures for Omak OMAW AgriMet station 

for water years 1950-89, daily precipitation and minimum and maximum air temperatures for Conconully climate station and daily minimum and maximum air 

temperatures for Omak OMAW AgriMet station for water years 1990-99, and daily precipitation and minimum and maximum air temperatures for Conconully 

CCR Hydromet station and daily minimum and maximum air temperatures for Omak OMAW AgriMet station for water years 2000-04; percentage of error = 

100 × (simulated – estimated)/estimated. Unit abbreviation: ft3/s, cubic foot per second]

Time period Mean estimated

Mean 

simulated 

unregulated 

streamflow

(ft3/s)

Percentage of 

error with 

respect to 

UUS

(ft3/s)

CUS 

(ft3/s) UUS CUS

CALIBRATION, water years 1950–89

October 9.5 11.7 8.6 -9.2 -26.0

November 8.8 10.9 5.6 -36.1 -48.2

December 8.2 10.2 3.6 -56.2 -64.7

January 7.3 9.3 3.1 -57.2 -66.3

February 9.0 11.2 5.6 -37.6 -50.0

March 13.4 15.6 21.7 61.9 39.5

April 36.6 39.3 50.4 37.7 28.0

May 149.9 154.0 138.5 -7.6 -10.1

June 122.1 126.8 107.3 -12.1 -15.4

July 24.4 29.6 42.5 74.4 43.8

August 3.8 7.1 20.9 445.1 193.6

September 5.2 7.6 13.0 151.9 70.7

 Annual1 33.3 36.2 35.2 5.9 -2.7

 April–July1 83.3 87.5 84.8 1.8 -3.1

TESTING 1, water years 1990–96

October 8.9 11.0 8.0 -9.8 -27.4

November 7.4 9.4 4.8 -35.5 -49.7

December 7.5 9.5 3.1 -58.2 -66.9

January 8.3 10.3 2.4 -71.6 -77.1

February 8.1 10.4 3.4 -58.6 -67.5

March 10.6 12.7 17.8 68.5 39.9

April 40.5 43.3 33.3 -17.9 -23.1

May 104.2 108.3 110.5 6.0 2.0

June 107.3 112.1 102.4 -4.5 -8.6

July 28.6 33.8 48.3 68.5 42.7

August 0.9 4.2 18.4 1,866.6 337.0

September -2.9 0.5 11.2 -481.0 2,380.6

 Annual1 27.5 30.4 30.4 10.7 0.0

 April–July1 70.1 74.3 73.7 5.1 -0.8

Time period Mean estimated

Mean 

simulated 

unregulated 

streamflow

(ft3/s)

Percentage of 

error with 

respect to 

UUS

(ft3/s)

CUS 

(ft3/s) UUS CUS

TESTING 2, water years 1990–96

October 8.9 11.0 8.4 -5.3 -23.8

November 7.4 9.4 5.2 -29.1 -44.6

December 7.5 9.5 3.4 -54.8 -64.2

January 8.3 10.3 2.6 -68.7 -74.7

February 8.1 10.4 3.8 -53.7 -63.6

March 10.6 12.7 18.4 74.1 44.5

April 40.5 43.3 32.3 -20.4 -25.5

May 104.2 108.3 146.5 40.6 35.3

June 107.3 112.1 116.8 8.9 4.2

July 28.6 33.8 55.8 94.8 64.9

August 0.9 4.2 21.8 2,226.1 416.9

September -2.9 -0.5 12.8 -535.2 -2,704.7

 Annual1 27.5 30.4 35.8 30.4 17.8

 April–July1 70.1 74.3 88.1 25.6 18.5

TESTING 3, water years 1990–96

October 8.9 11.0 8.0 -10.0 -27.5

November 7.4 9.4 4.9 -34.1 -48.5

December 7.5 9.5 3.2 -56.6 -65.7

January 8.3 10.3 2.7 -67.4 -73.6

February 8.1 10.4 5.0 -39.1 -52.2

March 10.6 12.7 18.5 74.9 45.2

April 40.5 43.3 34.1 -15.8 -21.2

May 104.2 108.3 120.9 16.1 11.6

June 107.3 112.1 109.6 2.1 -2.2

July 28.6 33.8 47.9 67.3 41.6

August 0.9 4.2 19.2 1,954.1 356.5

September -2.9 -0.5 11.6 -494.1 -2,459.0

 Annual1 27.5 30.4 32.3 17.4 6.0

 April–July1 70.1 74.3 78.2 11.6 5.3

24  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Table 5. Mean monthly, annual, and April-July estimated and simulated unregulated streamflows for Salmon Creek at Conconully 

Dam and the percentage of error for the CALIBRATION, TESTING 1, TESTING 2, TESTING 3, TESTING 4, and COMPOSITE model 

simulations.—Continued

[UUS, uncorrected unregulated streamflow; CUS, corrected unregulated streamflow; Percentage of error with respect to UUS or CUS: Values may not match 

presented values because of rounding; CALIBRATION: Model simulations are based on input time series of daily precipitation and minimum and maximum 

air temperatures for Conconully climate station and synthetic daily minimum and maximum air temperatures for Omak OMAW AgriMet station; TESTING 1: 

Model simulations are based on input time series of daily precipitation and minimum and maximum air temperatures for Conconully climate station and daily 

minimum and maximum air temperatures for Omak OMAW AgriMet station; TESTING 2: Model simulations are based on input time series of daily precipitation and minimum and maximum air temperatures for Conconully climate station, daily minimum and maximum air temperatures for Omak OMAW AgriMet 

station, and daily precipitation for Salmon Meadows SNOTEL station; TESTING 3: Model simulations are based on input time series of daily precipitation 

and minimum and maximum air temperatures for Conconully climate station and Omak OMAW AgriMet station; TESTING 4: Model simulations are based 

on input time series of daily precipitation and minimum and maximum air temperatures for Conconully climate station and Omak OMAW AgriMet station and 

daily precipitation for Salmon Meadows SNOTEL station; COMPOSITE: Model simulations are based on input time series of daily precipitation and minimum 

and maximum air temperatures for Conconully climate station and synthetic daily minimum and maximum air temperatures for Omak OMAW AgriMet station 

for water years 1950-89, daily precipitation and minimum and maximum air temperatures for Conconully climate station and daily minimum and maximum air 

temperatures for Omak OMAW AgriMet station for water years 1990-99, and daily precipitation and minimum and maximum air temperatures for Conconully 

CCR Hydromet station and daily minimum and maximum air temperatures for Omak OMAW AgriMet station for water years 2000-04; percentage of error = 

100 × (simulated – estimated)/estimated. Unit abbreviation: ft3/s, cubic foot per second]

Time period Mean estimated

Mean 

simulated 

unregulated 

streamflow

(ft3/s)

Percentage of 

error with 

respect to 

UUS

(ft3/s)

CUS

(ft3/s) UUS CUS

TESTING 4, water years 1990–96

October 8.9 11.0 8.5 -4.7 -23.3

November 7.4 9.4 5.3 -28.7 -44.3

December 7.5 9.5 3.4 -54.5 -64.0

January 8.3 10.3 2.8 -66.8 -73.2

February 8.1 10.4 5.1 -37.5 -50.9

March 10.6 12.7 18.8 77.9 47.7

April 40.5 43.3 34.0 -16.0 -21.4

May 104.2 108.3 150.2 44.2 38.7

June 107.3 112.1 119.5 11.4 6.7

July 28.6 33.8 55.9 95.1 65.2

August 0.9 4.2 22.1 2,260.3 424.5

September -2.9 0.5 13.0 -541.5 -2,742.3

 Annual1 27.5 30.4 36.7 33.6 20.7

 April–July1 70.1 74.3 90.1 28.6 21.3

Time period Mean estimated

Mean 

simulated 

unregulated 

streamflow

(ft3/s)

Percentage of 

error with 

respect to 

UUS

(ft3/s)

CUS

(ft3/s) UUS CUS

COMPOSITE, water years 1950–2004

October 9.6 11.8 8.2 -14.7 -30.3

November 9.1 11.2 5.3 -42.0 -52.7

December 8.5 10.5 3.4 -60.0 -67.6

January 7.7 9.6 2.9 -62.1 -69.9

February 9.0 11.2 4.9 -45.8 -56.6

March 12.8 15.0 21.5 67.8 43.6

April 39.4 42.2 46.4 17.8 10.1

May 140.1 144.2 128.5 -8.3 -10.9

June 117.1 121.9 103.8 -11.4 -14.9

July 26.4 31.6 41.9 58.9 32.8

August 3.8 7.1 19.4 406.0 172.9

September 4.3 6.8 12.0 177.2 77.1

 Annual1 32.4 35.3 33.3 2.8 -5.7

 April–July1 80.8 85.0 80.2 -0.7 -5.6

The precipitation-runoff model was tested for water years 

1990–96 using measured daily precipitation and minimum 

and maximum air temperatures for the Conconully station and 

measured daily minimum and maximum air temperatures for 

the Omak OMAW AgriMet station. The data set used for this 

testing is referred to as “TESTING 1” (figs. 12B and 13E; 

tables 4 and 5). The difference between the input time series 

for TESTING 1 and the input time series for model calibration 

is that measured temperatures are used instead of synthetic 

temperatures.

For TESTING 1, the model simulated a close fit for 

the mean annual streamflow and a good fit for the mean 

April–July streamflow. The simulated mean annual streamflow 

exceeds UUS by 10.7 percent and is the same as CUS 

(table 5). The simulated mean April–July streamflow exceeds 

UUS by 5.1 percent and is less than CUS by 0.8 percent 

(table 5). The testing results indicate that the precipitationrunoff model is adequately calibrated for the purpose of 

simulating annual mean and April–July mean streamflows.

The precipitation-runoff model was tested three more 

times to determine if adding different combinations of daily 

precipitation to the input time series for TESTING 1 for 

two of the real-time stations, Omak OMAW AgriMet and 

Salmon Meadows SNOTEL, would improve the fit between 

estimated and simulated streamflows for water years 1990–96. 

For TESTING 2, daily precipitation for Salmon Meadows 

1

Values may not match presented values because of rounding.

Model Calibration and Testing  25
SNOTEL station was added to the input time series for 

TESTING 1; for TESTING 3, daily precipitation for Omak 

OMAW AgriMet station was added to the input time series 

for TESTING 1; and for TESTING 4, daily precipitation for 

Salmon Meadows SNOTEL and Omak OMAW AgriMET 

stations was added to the input time series for TESTING 1 

(table 4). Results of TESTING 2, TESTING 3, and TESTING 

4 are shown in figures 12C through 12E, 13E, and 13F and are 

given in table 5.

For TESTING 2, TESTING 3, and TESTING 4, the 

simulated mean annual and mean April–July streamflows 

were significantly larger than the estimated streamflows. 

The oversimulation was largest for TESTING 4 and smallest 

for TESTING 3. The simulated mean annual streamflow 

for TESTING 4 exceeds CUS by 20.7 percent (compared to 

17.8 percent for TESTING 2 and 6.0 percent for TESTING 3), 

and the simulated mean April-July streamflow for TESTING 

4 exceeds CUS by 21.3 percent (compared to 18.5 percent 

for TESTING 2 and 5.3 percent for TESTING 3). The testing 

results indicate that adding precipitation data for two of 

the real-time stations, Omak OMAW AgriMet and Salmon 

Meadows SNOTEL, to the input time series of the calibrated 

precipitation-runoff model does not improve simulated annual 

mean and April–July mean streamflows.

A final comparison between estimated and simulated 

streamflows was made for the entire simulation period, 

water years 1950–2004. The input time series used for this 

simulation is referred to as “COMPOSITE” and is identical 

to the input time series for model calibration (water years 

1950–89) and for TESTING 1 (water years 1990–96). For 

water years 1997–2004, the input time series consists of 

data for the same climate stations as CALIBRATION and 

TESTING 1, except that for water years 2000–04 daily 

precipitation and minimum and maximum air temperatures 

are used for the real-time Conconully CCR Hydromet station 

instead of Conconully climate station. When using the 

precipitation-runoff model for ESP forecasting, Reclamation 

will expand the COMPOSITE data set to include the most 

recent real-time input time-series data for Conconully CCR 

Hydromet station (daily precipitation and air temperatures) 

and Omak OMAW AgriMet station (daily air temperatures 

only) for simulating near-real-time hydrologic conditions 

in the Salmon Creek Basin. For COMPOSITE, the model 

simulated a good fit for the mean annual streamflow and a 

close fit for the mean April–July streamflow (figs. 12F and 

13G; table 5). The simulated mean annual streamflow exceeds 

UUS by 2.8 percent and is less than CUS by 5.7 percent 

(table 5). The simulated mean April–July streamflow is less 

than UUS by 0.7 percent and less than CUS by 5.6 percent 

(table 5). The model simulated a better fit for mean April, 

June, and July streamflow for COMPOSITE than for 

CALIBRATION. Simulated mean April streamflow exceeds 

CUS by 10.1 percent for COMPOSITE and 28.0 percent for 

CALIBRATION. Simulated mean June streamflow is less than 

UUS by 11.4 percent for COMPOSITE and 12.1 percent for 

CALIBRATION. Simulated mean July streamflow exceeds 

CUS by 32.8 percent for COMPOSITE and 43.8 percent 

for CALIBRATION. A comparison of the estimated and 

simulated annual mean streamflows for CALIBRATION, 

TESTING 1, and COMPOSITE is shown in figure 14.

The simulated mean annual basinwide precipitation 

amount during the 40-year calibration period, 1950–89, 

was 23.1 in. and ranged from 11.8 in. for water year 1979 

to 41.3 in. for water year 1983. During the driest 4 water 

years (1964, 1977, 1979, and 1985), the model simulated 

poor fits for the annual mean streamflows and April–July 

mean streamflows. Simulated annual mean streamflow is 

less than UUS by 81.3 percent in water year 1977, 46.8 

percent in water year 1964, and 42.9 percent in water year 

1979 (fig. 14; table 6). In water year 1985, simulated annual 

mean streamflow exceeds CUS by 31.2 percent. Simulated 

April-July mean streamflow is less than UUS by 74.8 percent 

in water year 1977, 74.2 percent in water year 1979, and 

39.7 percent in water year 1964 (table 6). In water year 1985, 

simulated April–July mean streamflow exceeds CUS by 

24.0 percent. The large percentages of error are with respect 

to relatively small estimated annual mean and April–July 

mean streamflows for Salmon Creek at Conconully Dam. 

When comparing undersimulated streamflow with respect to 

UUS and oversimulated streamflow with respect to CUS, the 

absolute errors range from an undersimulation of annual mean 

streamflow of 8.0 ft3/s in water year 1964 to an oversimulation 

of annual mean streamflow of 3.4 ft3/s in water year 1985 

(table 6). Similarly, the absolute errors range from an 

undersimulation of April–July mean streamflow with respect 

to UUS of 13.6 ft3/s in water year 1964 to an oversimulation of 

April–July mean streamflow with respect to CUS of 4.2 ft3/s in 

water year 1985 (table 6).

During the wettest 4 water years (1951, 1971, 1982, 

and 1983), the model also simulated poor fits for the annual 

mean streamflows and April–July mean streamflows. 

Simulated annual mean streamflow exceeds CUS by 

73.3 percent in water year 1971, 17.4 percent in water year 

1983, and 15.0 percent in water year 1982 (table 6). In water 

year 1951, simulated annual mean streamflow is less than 

UUS by 4.7 percent (fig. 14). Simulated April–July mean 

26  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
WA19_0043_fig14

0

20

40

60

80

100

120

140

WATER YEAR

ANNUAL MEAN STREAMFLOW, IN CUBIC FEET PER SECOND

Estimated (UUS)

Simulated,

CALIBRATION TESTING 1

COMPOSITE

1950196019701980199020002004

COMPOSITE, water years 1950-2004

Figure 14. Time series of estimated and simulated annual mean unregulated streamflows for Salmon Creek at Conconully 

Dam, Okanogan County, Washington. (UUS is uncorrected unregulated streamflow.)

streamflow exceeds CUS by 75.6 percent in water year 1971, 

13.7 percent in water year 1983, and 6.7 percent in water 

year 1982 (table 6). In water year 1951, simulated April–July 

mean streamflow is less than UUS by 13.1 percent. When 

comparing undersimulated streamflow with respect to UUS 

and oversimulated streamflow with respect to CUS, the 

absolute errors range from an undersimulation of annual mean 

streamflow of 2.8 ft3/s in water year 1951 to an oversimulation 

of annual mean streamflow of 26.6 ft3/s in water year 1971. 

Similarly, the absolute errors range from an undersimulation 

of April–July mean streamflow with respect to UUS of 19.8 

ft3/s in water year 1951 to an oversimulation of April–July 

mean streamflow with respect to CUS of 74.1 ft3/s in water 

year 1971 (table 6).

In addition to the estimated streamflow data for Salmon 

Creek at Conconully Dam that were available for calibration 

and testing, snowpack water-equivalent data are available for 

the Salmon Meadows SNOTEL station for water year 1982 

and for water years 1984 to the present (2006). A comparison 

of measured and simulated mean monthly snowpack waterequivalents for TESTING 1, TESTING 2, TESTING 3, and 

TESTING 4 (fig. 15A and 15B) and the partial COMPOSITE 

for water years 1990–2004 (fig. 15C) indicates the model 

oversimulates the overall snowpack water-equivalent for the 

Salmon Meadows SNOTEL station and delays the simulated 

timing of spring snowmelt by 1 month. If snowpack melting 

throughout upper Salmon Creek Basin is simulated to occur 

according to the pattern for the Salmon Meadows SNOTEL 

Model Calibration and Testing  27
Time period

Mean simulated 

basinwide precipitation Mean estimated Mean simulated 

unregulated 

streamflow

(ft3/s)

Percentage of error 

with respect to

Absolute error 

with respect to

(in/yr) (ft3/s) UUS

(ft3/s)

CUS

(ft3/s) UUS CUS UUS(ft3/s)

CUS

(ft3/s)

Driest 4 water years, 1950-89

Water year 1979 11.8 132.1 7.7 10.7 4.4 -42.9 -58.9 -3.3 -6.3

April-July, 1979 12.4 16.6 3.2 -74.2 -80.7 -9.2 -13.4

Water year 1977 12.0 134.4 6.4 9.3 1.2 -81.3 -87.1 -5.2 -8.1

April-July, 1977 10.3 14.6 2.6 -74.8 -82.2 -7.7 -12.0

Water year 1964 14.6 163.5 17.1 20.0 9.1 -46.8 -54.5 -8.0 -10.9

April-July, 1964 34.3 38.5 20.7 -39.7 -46.2 -13.6 -17.8

Water year 1985 15.0 168.0 7.9 10.9 14.3 81.0 31.2 6.4 3.4

April-July, 1985 13.2 17.5 21.7 64.4 24.0 8.5 4.2

Wettest 4 water years, 1950-89

Water year 1971 31.7 355.0 33.4 36.4 63.0 88.6 73.1 29.6 26.6

April-July, 1971 93.8 98.0 172.1 83.5 75.6 78.3 74.1

Water year 1951 32.5 364.0 59.3 62.2 56.5 -4.7 -9.2 -2.8 -5.7

April-July, 1951 151.7 155.9 131.9 -13.1 -15.4 -19.8 -24.0

Water year 1982 34.9 390.8 66.6 69.5 79.9 20.0 15.0 13.3 10.4

April-July, 1982 165.3 169.5 180.8 9.4 6.7 15.5 11.3

Water year 1983 41.3 462.5 90.9 93.8 110.1 21.1 17.4 19.2 16.3

April-July, 1983 233.1 237.3 269.7 15.7 13.7 36.6 32.4

Table 6. Annual mean and April-July mean estimated and unregulated streamflows for Salmon Creek at Conconully Dam for the driest 

4 water years and wettest 4 water years, 1950-89, and the percentage of error and the absolute error for the model simulations.

[UUS, uncorrected unregulated streamflow; CUS, corrected unregulated streamflow; percentage of error = 100 x (simulated – estimated)/estimated; ft3/s, cubic 

foot per second; in/yr, inch per year]

station, however, peak runoff for Salmon Creek at Conconully 

Dam would occur in April and May rather than May and June. 

Reasons for this discrepancy may be that conditions at the 

Salmon Meadows SNOTEL station may be sunnier or windier 

than the average for all MRUs in upper Salmon Creek Basin. 

A comparison of measured and simulated monthly mean 

snowpack water-equivalent at the Salmon Meadows SNOTEL 

station for TESTING 1 and a partial COMPOSITE for water 

years 1990–2004 is shown in figure 16. The extent to which 

the fit between the measured and simulated snowpack waterequivalent is representative of the entire basin is unknown 

because the snowpack measurement for the Salmon Meadows 

SNOTEL station represents only one point.

28  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
WA19_0043_fig15

A

B

MEAN MONTHLY SNOWPACK WATER-EQUIVALENT, IN INCHES

C

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

2

4

6

8

10

12

14

0

2

4

6

8

10

14

12

0

2

4

6

8

10

14

12

Measured,

 water years 1990-96

Simulated, TESTING 2

 water years 1990-96

Simulated, TESTING 1

 water years 1990-96

Measured,

 water years 1990-96

Simulated, TESTING 4

 water years 1990-96

Simulated, TESTING 3

 water years 1990-96

Measured,

 water years 1990-2004

Simulated, partial

 COMPOSITE water

 years 1990-2004

Figure 15. Measured and simulated mean monthly 

snowpack water-equivalent for the Salmon Meadows 

SNOTEL station, Okanogan County, Washington.

Model Limitations

The precipitation-runoff model is a mathematical 

representation of the physical processes that occur in the 

Salmon Creek Basin. As a result, the quality of the model 

results depends on the accuracy of the mathematical 

representation of the physical processes (model error), the 

quality and accuracy of the precipitation and air-temperature 

input time series and the streamflow calibration/testing 

time series (data error), and the accuracy of the calibrated 

model parameters (parameter error). Model calibration and 

testing indicate that daily streamflows simulated using the 

precipitation-runoff model described in this report should 

be used only to analyze historical and forecasted annual 

mean and April–July mean streamflows for Salmon Creek at 

Conconully Dam. Because of the paucity of model input data 

and uncertainty in the estimated unregulated streamflows, 

the model is not adequately calibrated and tested to estimate 

monthly mean streamflows for individual months, such as 

during low-flow periods, or for shorter periods such as during 

peak flows. In addition, no data were available to test the 

accuracy of simulated streamflows for lower Salmon Creek. 

Thus, although the simulated streamflows appear reasonable, 

the model should not be relied on to analyze historical and 

forecasted streamflows for Salmon Creek downstream of 

Conconully Dam (nodes 7 through 11; fig. 11). Instead, 

simulated streamflows for Salmon Creek at Conconully 

Dam should be considered a base estimate of streamflows 

that can be expected in lower Salmon Creek. The estimated 

WA19_0043_fig16

Measured

Simulated,

 TESTING 1

 water years 1990-96

partial COMPOSITE

 water years 1990-2004

0

4

8

12

16

20

24

28

28

1990 1992 1994 1996 1998 2000 2002 2004

WATER YEAR

MONTHLY MEAN SNOWPACK

W

ATER-EQUIVALENT, IN INCHES

Figure 16. Time series of measured and simulated 

monthly mean snowpack water-equivalent for the Salmon 

Meadows SNOTEL station, Okanogan County, Washington.

Model Calibration and Testing  29
streamflows may be reduced in reaches of lower Salmon 

Creek that are losing water and increased in reaches that are 

gaining water or that receive runoff from tributaries in lower 

Salmon Creek subbasin.

The two principal reasons for the less-than-optimal 

model calibration are as follows. First, historical records 

of precipitation and air-temperature data, particularly 

daily minimum and maximum air temperatures at high 

altitudes in upper Salmon Creek Basin, are limited. Second, 

the number of measurements for streamflow in Salmon 

Creek Basin is extremely limited and only an estimated 

time series of monthly mean runoff is available for upper 

Salmon Creek Basin. This estimated time series may contain 

several questionable values (Dames and Moore, 1999; U.S. 

Department of Energy, 2004; and this study). However, 

the estimated time series was used to calibrate and test the 

precipitation-runoff model because the time series represents 

the best available data for the basin. Errors in the precipitation 

and air-temperature model input time series and in the 

estimated streamflow data likely affected the accuracy of the 

calibrated model parameters.

The precipitation-runoff model described in this report 

is expected to be used for simulating historical streamflows 

and ESP forecasting of streamflows. ESP forecasting requires 

accurate simulation of initial hydrologic conditions on the 

basis of real-time precipitation and air-temperature data, and 

forecasting the probability of streamflows for as far as 1 year 

in the future by assuming that historical records of daily 

precipitation and air temperature will recur with the same 

probability as in the past. The historical record that can be 

used for ESP forecasts includes water years 1950–2004 (the 

COMPOSITE data set) and can be extended to the present 

by adding the most recent real-time precipitation and airtemperature data for the Conconully CCR Hydromet station 

and the most recent real-time air-temperature data for the 

Omak OMAW AgriMet station. The COMPOSITE data 

set includes both wet and dry years but does not include an 

extended, multiyear dry period known to have occurred in the 

Salmon Creek Basin from the late 1920s through the early 

1930s. During that period, the record low annual mean runoff 

from upper Salmon Creek Basin was 2.1 ft3/s (1,513 acreft) for water year 1931 (Dames and Moore, 1999; U.S. 

Department of Energy, 2004). The lowest annual mean runoff 

during water years 1950–2004 is 6.1 ft3/s (4,400 acre-ft) for 

water year 1966. The simulation of historical streamflows, 

initial hydrologic conditions, and the ESP forecast rely on the 

calibrated precipitation-runoff model described in this report 

and are subject to the model limitations discussed.

Suggestions for Data Collection

Starting in water year 1997 and continuing through 

at least water year 2003, data collection at the Conconully 

climate station stopped during the winter months, generally 

for November through February. As a result, the available 

records of overlapping measurements of daily precipitation 

and daily minimum and maximum air temperatures for the 

Conconully climate and Conconully CCR Hydromet stations 

is generally limited to non-winter months between August 

1999 and the present (2006). If year-round data collection 

for the Conconully station were resumed, the resulting 

overlapping records would allow more thorough testing of 

the assumption made in this study that the daily precipitation 

and air temperatures measured for the Conconully CCR 

Hydromet station are equivalent to the daily precipitation 

and air temperatures measured for the Conconully station. 

Confirmation of this assumption is important because the 

use of the precipitation-runoff model developed in this study 

for ESP forecasts depends on real-time measurements for 

the Conconully CCR Hydromet station to be equivalent to 

the non-real-time measurements for the Conconully climate 

station.

Dames and Moore (1999) reported that starting in 1997, 

releases from Conconully Reservoir were measured only 

through the outlet tunnel. This means that the measurements 

no longer include seepage below the dam and uncontrolled 

spills. It would be helpful if a stream gage were installed in 

Salmon Creek downstream of the spillway of Conconully Dam 

to measure all runoff from the upper Salmon Creek Basin.

Improved availability of climate and streamflow data for 

Salmon Creek Basin should enable improved calibration of 

the precipitation-runoff model described in this report. The 

improved model would increase the reliability of simulated 

historical streamflows and ESP forecasts of streamflows 

in the Salmon Creek Basin. This in turn would make the 

precipitation-runoff model a more reliable component of 

the DSS that Reclamation plans to use to study the water 

resources of the Salmon Creek Basin.

Summary

The U.S. Geological Survey (USGS), in cooperation 

with the Bureau of Reclamation (Reclamation), developed 

a precipitation-runoff model for the Salmon Creek Basin 

that can be used to simulate daily unregulated streamflows 

30 Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
in the basin. The precipitation-runoff model is a component 

of a Decision Support System (DSS) that includes a wateroperations model Reclamation plans to develop to study 

the water resources of the Salmon Creek Basin. The DSS 

will be similar to the DSS that Reclamation and the USGS 

developed previously for the Yakima River Basin in central 

southern Washington. The precipitation-runoff model that 

was developed is a modified version of the PrecipitationRunoff Modeling System (PRMS; Leavesley and others, 

1983) and was run within the Modular Modeling System 

(MMS; Leavesley and others, 1996). The model can be used to 

simulate historical streamflows and streamflows for as far as 

1 year in the future using the Extended Streamflow Prediction 

(ESP) technique in MMS.

Model input time series were based on historical records 

of daily precipitation and daily minimum and maximum air 

temperatures for three National Weather Service stations, a 

Natural Resources Conservation Service SNOTEL station, 

a Bureau of Reclamation AgriMet station, and a Bureau 

of Reclamation Hydromet station. Model-calibration and 

testing time series were estimated records of monthly mean 

unregulated streamflow for Salmon Creek at Conconully Dam 

[referred to as uncorrected unregulated streamflow (UUS) in 

this study] based on reservoir outflows and storage changes 

(Dames and Moore,1999; U.S. Department of Energy, 2004; 

this study) and records of daily snowpack water-equivalent 

for the SNOTEL station. A second estimated time series of 

monthly mean unregulated streamflow for Salmon Creek at 

Conconully Dam was generated by adding estimates of groundwater losses and evaporative losses to the uncorrected time 

series [referred to as corrected unregulated streamflow (CUS) 

in this study]. The total of the corrections was about 9 percent 

of the long-term mean uncorrected unregulated streamflow. The 

time series of estimated monthly mean uncorrected unregulated 

streamflow has several potential sources of error (Dames and 

Moore, 1999; U.S. Department of Energy, 2004). However, the 

estimates were the best available and were used in this study as 

a surrogate for the missing long-term unregulated streamflows 

for Salmon Creek at Conconully Dam.

Salmon Creek Basin was subdivided into 179 Modeling 

Response Units (MRUs), each with similar physical, soil, 

and vegetation characteristics. Initial model parameters 

were assigned by applying the GIS (Geographic Information 

System) Weasel program (Viger and others, 1998), computing 

values from measured data, and using parameters from 

the Methow River precipitation-runoff model (Ely and 

Risley, 2001; Ely, 2003). The precipitation-runoff model 

was calibrated for water years 1950–89 (a water year starts 

October 1 and ends September 30) and tested for water 

years 1990–96. A subset of the initial model parameters 

was adjusted during the calibration process with the goal 

of simulating monthly unregulated streamflows for Salmon 

Creek at Conconully Dam that were more than the estimated 

UUS and less than the estimated CUS.

Model calibration and testing indicate that daily 

streamflows simulated using the precipitation-runoff model 

described in this report should be used only to analyze 

historical and forecasted annual mean and April–July 

mean streamflows for Salmon Creek at Conconully Dam. 

Because of the paucity of model input data and uncertainty 

in the estimated unregulated streamflows, the model is not 

adequately calibrated and tested to estimate monthly mean 

streamflows for individual months, such as during low-flow 

periods, or for shorter periods such as during peak flows.

For the calibration period, water years 1950–89, both 

the simulated mean annual streamflow and the simulated 

mean April–July streamflow compare well with the estimated 

values for UUS and CUS. The simulated mean annual 

streamflow exceeds UUS by 5.9 percent and is less than CUS 

by 2.7 percent. Similarly, the simulated mean April–July 

streamflow exceeds UUS by 1.8 percent and is less than CUS 

by 3.1 percent. A comparison of the estimated and simulated 

mean monthly streamflows, however, shows that streamflow 

is significantly undersimulated during the low-flow, baseflowdominated months of November through February when 

simulated monthly streamflows are as much as 57.2 percent 

less than UUS and significantly oversimulated during August 

and September when simulated monthly streamflows are as 

much as 193.6 percent more than CUS. During the low-flow 

months, however, estimated mean monthly streamflow is only 

a small percentage of the estimated mean annual streamflow 

and absolute errors are relatively small even though the 

percentages of error are large.

The precipitation-runoff model was tested for water 

years 1990–96 using model input time series for the same 

climate stations as for model calibration, except that measured 

temperatures were used for the AgriMet station instead of 

synthetic temperatures. The data set used for this testing is 

referred to as “TESTING 1.” For TESTING 1, the model 

simulated a close fit for the mean annual streamflow and a 

good fit for the mean April–July streamflow. The simulated 

mean annual streamflow exceeds UUS by 10.7 percent 

and is the same as CUS. The simulated mean April–July 

streamflow exceeds UUS by 5.1 percent and is less than 

CUS by 0.8 percent. The precipitation-runoff model was 

tested three more times to determine if adding different 

combinations of daily precipitation for the real-time AgriMet 

and SNOTEL stations to the input time series for TESTING 1 

Summary  31
would improve the fit between estimated and simulated 

streamflows for water years 1990–96. For all three tests, the 

simulated mean annual and mean April–July streamflows 

were significantly larger than the estimated streamflows. 

The oversimulation was largest when precipitation for the 

AgriMet and SNOTEL stations was added (the simulated 

mean annual and mean April–July streamflows exceeded CUS 

by 20.7 percent and 21.3 percent, respectively) and smallest 

when precipitation for the Agrimet station was added (the 

simulated mean annual and mean April–July streamflows 

exceeded CUS by 6.0 percent and 5.3 percent, respectively). 

The testing results indicate that the precipitation-runoff model 

is adequately calibrated for the purpose of simulating annual 

mean and April–July mean streamflows using the input time 

series used for model calibration and testing. The addition of 

precipitation data for the AgriMet and/or SNOTEL stations to 

the input time series results in oversimulated annual mean and 

April–July mean streamflows.

A final comparison between estimated and simulated 

streamflows was made for the entire simulation period, 

water years 1950–2004. The input time series used for this 

simulation is referred to as “COMPOSITE” and is identical 

to the input time series for model calibration (water years 

1950-89) and for TESTING 1 (water years 1990–96). For 

water years 1997–2004, the input time series consists of 

data for the same climate stations as CALIBRATION and 

TESTING 1, except that for water years 2000–04 daily 

precipitation and minimum and maximum air temperatures for 

one of the stations were replaced with data for the nearby realtime Hydromet station that were assumed to be equivalent. For 

COMPOSITE, the model simulated a good fit for the mean 

annual streamflow and a close fit for the mean April–July 

streamflow. The simulated mean annual streamflow exceeds 

UUS by 2.8 percent and is less than CUS by 5.7 percent. The 

simulated mean April–July streamflow is less than UUS by 

0.7 percent and less than CUS by 5.6 percent. For forecasting 

purposes, Reclamation will expand the COMPOSITE data 

set to include the most recent real-time precipitation and airtemperature data for the Hydromet station and the most recent 

real-time air-temperature data for the AgriMet station.

During the driest 4 water years (1964, 1977, 1979, 

and 1985) and the wettest 4 water years (1951, 1971, 1982, 

and 1983) of the 40-year calibration period, the model 

simulated poor fits for the annual mean streamflows and 

April–July mean streamflows based on the percentages of 

errors with respect to UUS and CUS. However, during the 

driest 4 water years, the large percentages of error are with 

respect to relatively small streamflows for Salmon Creek 

at Conconully Dam. During the driest 4 water years, the 

absolute errors ranged from an undersimulation of annual 

mean streamflow with respect to UUS of 8.0 ft3/s (46.8 percent 

error) in water year 1964 to an oversimulation of annual mean 

streamflow with respect to CUS of 3.4 ft3/s (31.2 percent 

error) in water year 1985. The absolute errors ranged from an 

undersimulation of April–July mean streamflow with respect 

to UUS of 13.6 ft3/s (39.7 percent error) in water year 1964 to 

an oversimulation of April–July mean streamflow with respect 

to CUS of 4.2 ft3/s (24.0 percent error) in water year 1985. 

During the wettest 4 water years, the absolute errors ranged 

from an undersimulation of annual mean streamflow with 

respect to UUS of 2.8 ft3/s (4.7 percent error) in water year 

1951 to an oversimulation of annual mean streamflow with 

respect to CUS of 26.6 ft3/s (73.1 percent error) in water year 

1971. The absolute errors ranged from an undersimulation of 

April–July mean streamflow with respect to UUS of 19.8 ft3/s 

(13.1 percent error) in water year 1951 to an oversimulation of 

April–July mean streamflow with respect to CUS of 74.1 ft3/s 

(75.6 percent error) in water year 1971.

No data were available to test the accuracy of simulated 

streamflows for lower Salmon Creek. Thus, although 

the simulated streamflows appear reasonable, the model 

should not be relied on to analyze historical and forecasted 

streamflows for Salmon Creek downstream of Conconully 

Dam. Instead, simulated streamflows for Salmon Creek at 

Conconully Dam should be considered a base estimate of 

streamflows that can be expected in lower Salmon Creek. The 

estimated streamflows may be reduced in reaches of lower 

Salmon Creek that are losing water and increased in reaches 

that are gaining water or that receive runoff from tributaries in 

lower Salmon Creek subbasin.

The precipitation-runoff model described in this report 

is expected to be used for simulating historical streamflows 

and ESP forecasting of streamflows. ESP forecasting requires 

accurate simulation of initial hydrologic conditions on the 

basis of real-time precipitation and air-temperature data, 

and forecasting the probability of streamflows for as far as 

1 year in the future by assuming that historical records of 

daily precipitation and air temperature will recur with the 

same probability as in the past. The historical record that can 

be used for ESP forecasts includes water years 1950–2004 

and can be extended to the present by adding the most recent 

real-time data. The simulation of historical streamflows, 

initial hydrologic conditions, and the ESP forecast rely on the 

calibrated precipitation-runoff model described in this report 

and are subject to the model limitations that result from model 

error, data error, and parameter error.

The precipitation-runoff model described in this 

report could be improved in the future if additional data 

were collected. Specifically, it is suggested that yearround collection of climate data be resumed at Conconully 

station and that a stream gage be installed in Salmon Creek 

downstream of the spillway of Conconully Dam to measure 

all runoff from the upper Salmon Creek Basin. Improved 

availability of climate and streamflow data for the Salmon 

Creek Basin should enable improved calibration of the 

precipitation-runoff model, which would make the model a 

more reliable component of the DSS that Reclamation plans to 

use to study the water resources of the Salmon Creek Basin.

32  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Acknowledgments

The author thanks Tom Sullivan, Okanogan Irrigation 

District, for supplying measurements of monthly storage 

in Conconully and Salmon Lake Reservoirs and releases 

from Conconully Reservoir from January 2003 through 

March 2006. The author also thanks Mark C. Mastin 

and John J. Vaccaro, U.S. Geological Survey, for helpful 

discussions.

References Cited

Boyle, D.P., Fritchel, P.E., Lamorey, G., and Markstrom, 

S., 2004, Evaluation of a hydrologic model applied to a 

headwater basin in the Rio Grande (USA) using observed 

and modelled land surface fluxes and states, in PahlWostl, C., Schmidt, S., Rizzoli, A.E., and Jakeman, A.J., 

eds., Complexity and Integrated Resources Management, 

Transactions of the 2nd Biennial Meeting of the 

International Environmental Modelling and Software 

Society, iEMSs: Manno, Switzerland, 2004, ISBN 88-

900787-1-5.

Bureau of Reclamation, 2006a, AgriMet historical archive 

weather data access: accessed April 30, 2006, at http://

www.usbr.gov/pn/agrimet/webarcread.html

Bureau of Reclamation, 2006b, Description of Okanogan 

Project: accessed May 23, 2006, at http://www.usbr.gov/

dataweb/html/okanogan.html

Bureau of Reclamation, 2006c, Hydromet historical data 

access: accessed September 7, 2006, at http://www.usbr.

gov/pn/hydromet/arcread.html

Bureau of Reclamation and Center for Advanced Decision 

Support for Water and Environmental Systems 

(CADSWES), 2000, RiverWare; accessed May 26, 2006, 

Fact Sheet at http://www.usbr.gov/pmts/rivers/warsmp/

riverware/index.html

Cassidy, K.M., 1997, Land cover of Washington State: 

Description and management, Gap Analysis of Washington 

State—Final Report, v. 1: Washington Cooperative Fish and 

Wildlife Research Unit, University of Washington, 260 p.

Daly, C., Gibson, W.P., Taylor, G.H., Johnson, G.L., and 

Pasteris, P., 2002, A knowledge-based approach to the 

statistical mapping of climate: Climate Research, v. 22, 

p. 99-113.

Daly, C., and Johnson, G.L., 1999, PRISM spatial climate 

layers—their development and use: Short Course on Topics 

in Applied Climatology, 79th Annual Meeting of the 

American Meteorological Society, January 10-15, Dallas, 

Texas, 49 p.

Daly, C., Neilson, R.P., and Phillips, D.L., 1994, A statisticaltopographic model for mapping climatological precipitation 

over mountainous terrain: Journal of Applied Meteorology, 

v. 33, p. 140-158.

Dames and Moore, 1999, Joint study on Salmon Creek—final 

report prepared for Colville Confederated Tribes and 

Okanogan Irrigation District, [about 250] p.

Day, G.N., 1985, Extended streamflow forecasting using 

NWSRFS: Journal of Water Resources Planning and 

Management, ASCE, v. 111, no. 2, p. 157-170.

Ely, D.M., 2003, Precipitation-runoff simulations of current 

and natural streamflow conditions in the Methow River 

Basin, Washington: U.S. Geological Survey WaterResources Investigations Report 03-4246, 35 p.

Ely, D.M., and Risley, J.C., 2001, Use of a precipitation-runoff 

model to simulate natural streamflow conditions in the 

Methow River Basin, Washington: U.S. Geological Survey 

Water-Resources Investigations Report 01-4198, 36 p.

Farnsworth, R.K., Thompson, E.S., and Peck, E.L., 1982, 

Evaporation atlas for the contiguous 48 United States: 

NOAA Technical Report NWS 33, Washington, D.C., 

4 plates, 26 p.

Fetter, C.W., 1994, Applied hydrogeology (3d ed.): Upper 

Saddle River, N.J., Prentice Hall, 691 p.

Hydrosphere Data Products, 2005, CD-ROM National 

Climatic Data Center summary of the day – west 2, v. 16.3.

Jensen, M.E., and Haise, H.R., 1963, Estimating 

evapotranspiration from solar radiation: Proceedings of the 

American Society of Civil Engineers, Journal of Irrigation 

and Drainage, v. 89, no. IR4, p. 15-41.

Jeton, A.E., 1999, Precipitation-runoff simulations for 

the upper part of the Truckee River Basin, California 

and Nevada: U.S. Geological Survey Water-Resources 

Investigations Report 99-4282, 41 p.

Kahle, S.C., 1998, Hydrogeology of Naval Submarine Base 

Bangor and vicinity, Kitsap County, Washington: U.S. 

Geological Survey Water-Resources Investigations 

Report 97-4060, 107 p.

Kahle, S.C., Longpré, C.I., Smith, R.R., Sumioka, S.S., 

Watkins, A.M., and Kresch, D.L., 2003, Water resources in 

the ground-water system in unconsolidated deposits of the 

Colville River watershed, Stevens County, Washington: 

U.S. Geological Survey Water-Resources Investigations 

Report 03-4128, 76 p.

References Cited  33
Kuhn, G., Parker, R.S., Hay, L.E., and Leavesley, G.H., 

1998, Precipitation distribution alternatives in applying the 

Modular Modeling System in the San Juan River Basin, 

Colorado and New Mexico, in Proceedings of the First 

Federal Interagency Hydrologic Modeling Conference, 

April 19-23, 1998: Las Vegas, Nevada, Interagency 

Advisory Committee on Water Data, v. 2, chap. 5, p. 85-92.

Laenen, A., and Risley, J.C., 1997, Precipitation-runoff 

and streamflow-routing models for the Willamette River 

Basin, Oregon: U.S. Geological Survey Water-Resources 

Investigations Report 95-4284, 197 p.

Leavesley, G.H., Lichty, R.W., Troutman, B.M., and Saindon, 

L.G., 1983, Precipitation-runoff modeling system—User’s 

manual: U.S. Geological Survey Water-Resources 

Investigations Report 83-4238, 207 p.

Leavesley, G.H., Restrepo, P.J., Markstrom, S.L., Dixon, M., 

and Stannard, L.G., 1996, The modular modeling system 

(MMS)—User’s manual: U.S. Geological Survey Open-File 

Report 96-151, 200 p.

Mastin, M.C., and Vaccaro, J.J., 2002a, Documentation of 

precipitation runoff modeling system modules for the 

Modular Modeling System modified for the Watershed 

and River Systems Management Program: U.S. Geological 

Survey Open-File Report 02-362, 5 p.

Mastin, M.C., and Vaccaro, J.J., 2002b, Watershed models for 

decision support in the Yakima River Basin, Washington: 

U.S. Geological Survey Open-File Report 02-404, 46 p.

Powell, D.S., Faulkner, J.L., Darr, D.R., Zhu, Z., and 

MacCleery, D.W., 1993, Forest resources of the United 

States, 1992: U.S. Forest Service General Technical Report 

RM-234, Rocky Mountain Forest and Range Experiment 

Station, Fort Collins, Colo., 132 p.

Risley, J.C., 1994, Use of a precipitation-runoff model for 

simulating effects of forest management on streamflow in 

11 small drainage basins, Oregon Coast Range: 

U.S. Geological Survey Water-Resources Investigations 

Report 93-4181, 61 p.

Schuster, J.E., 2005, Geologic map of Washington State: 

Washington Division of Geology and Earth Resources 

Geologic Map GM-53, 44 p., 1 plate.

Stoffel, K.L., compiler, 1990, Geologic map of the Oroville 

1:100,000 quadrangle, Washington: Washington Division 

of Geology and Earth Resources Open File Report 90-11, 

58 p., 1 plate.

Spatial Climate Analysis Service-Oregon State University, 

2006: accessed January 19, 2006, at http://www.ocs.orst.

edu/prism/

U.S. Census Bureau, 2006: accessed May 23, 2006, at http://

www.ofm.wa.gov/census2000/profiles/place/1605314310.

pdf

U.S. Department of Agriculture, 1994, State Soil Geographic 

(STATSGO) Database—Data use information: Fort 

Worth Texas, U.S. Soil Conservation Service, National 

Cartography and GIS Center.

U.S. Department of Agriculture, 2006, Natural Resources 

Conservation Service, Historic data — daily SNOTEL 

data: accessed April 30, 2006, at http://www.wa.nrcs.usda.

gov/snow/data/historic.html

U.S. Department of Energy, 2004, Salmon Creek Project—

Draft Environmental Impact Statement DOE/EIS-0346: 

[about 800] p.

U.S. Geological Survey, 1992, 1990 conterminous U.S. land 

cover characteristics data set CD-ROM: Sioux Falls, South 

Dakota, EROS Data Center, National Mapping Division.

U.S. Geological Survey, 1998, Watershed and river systems 

management program—Application to the Yakima River 

Basin, Washington: U.S. Geological Survey Fact Sheet 

FS-037-98, 4 p.

U.S. Geological Survey and Bureau of Reclamation, 2006, 

Watershed and River Systems Management System: 

accessed May 26, 2006, at http://wwwbrr.cr.usgs.gov/

warsmp

Viger, R.J., Markstrom, S.L., and Leavesley, G.H., 1998, 

The GIS Weasel—An interface for the treatment of spatial 

information used in watershed modeling and water resource 

management, in Proceedings of First Federal Interagency 

Hydrologic Modeling Conference, April 19-23, 1998: Las 

Vegas, Nevada, Interagency Advisory Committee on Water 

Data, v. 2, chap. 7, p. 73-80.

Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., 

Wylie, B.K., and Van Driel, N., 2001, Completion of the 

1990s national land cover data set for the conterminous 

United States from Lansat thematic mapper data and 

ancillary data sources: Photogrammetric Engineering and 

Remote Sensing, v. 67, p. 650-652.

Washington State Department of Ecology, 2006a, River and 

stream flow monitoring: accessed September 24, 2006 

at https://fortress.wa.gov/ecy/wrx/wrx/flows/station.

asp?sta=49M100

Washington State Department of Ecology, 2006b, Well logs: 

accessed May 2006 at http://apps.ecy.wa.gov/welllog

Zhu, Z., and Evans, L.D., 1992, Mapping midsouth forest 

distributions: Journal of Forestry, v. 90, no. 12, p. 27-30.

34  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Appendix 1. Monthly Values of Total Water in Storage in Conconully and 

Salmon Lake Reservoirs, Releases from Conconully Reservoir, and Computed 

Unregulated Precipitation Runoff From Upper Salmon Creek Basin, 

January 2003–March 2006

Year Month

Total water in storage in 

Conconully and Salmon Lake 

Reservoirs at the end of the 

month1 

(acre-feet)

Releases from 

Conconully 

Reservoir 1

(acre-feet)

Computed unregulated 

watershed runoff from upper 

Salmon Creek Basin 

(acre-feet)

2002 12 6,108

2003 1 6,476 0 368

2003 2 6,837 0 361

2003 3 7,350 216 729

2003 4 9,404 1,080 3,134

2003 5 12,845 1,475 4,916

2003 6 15,642 2,060 4,857

2003 7 13,321 2,540 219

2003 8 10,482 2,590 -249

2003 9 8,532 1,840 -110

2003 10 9,008 0 476

2003 11 9,354 0 346

2003 12 9,778 0 424

2004 1 10,156 0 378

2004 2 10,442 0 286

2004 3 10,738 0 296

2004 4 12,530 255 2,047

2004 5 14,103 1,945 3,518

2004 6 14,072 2,030 1,999

2004 7 12,602 2,400 930

2004 8 10,859 2,310 567

2004 9 9,033 1,810 -16

2004 10 9,601 0 568

2004 11 10,029 0 428

2004 12 10,474 0 445

2005 1 10,900 0 426

2005 2 11,149 0 249

2005 3 11,659 66 576

2005 4 12,632 140 1,113

2005 5 14,941 1,480 3,789

2005 6 15,684 1,850 2,593

2005 7 13,816 2,490 622

2005 8 11,154 2,370 -292

2005 9 9,480 1,710 36

2005 10 10,106 180 806

2005 11 10,646 0 540

2005 12 11,065 0 419

2006 1 11,553 0 488

2006 2 12,018 0 465

2006 3 12,559 0 541

1

Data from T. Sullivan, Okanogan Irrigation District, written commun., 2006.

Appendix 1.  35
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36  Precipitation-Runoff Model to Simulate Unregulated Streamflow, Salmon Creek Basin, Okanogan County, Washington
Manuscript approved for publication, October 17, 2006

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Publishing Service Center, Tacoma, Washington

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For more information concerning the research in this report, contact the 

Director, Washington Water Science Center 

U.S. Geological Survey, 934 Broadway — Suite 300

Tacoma, Washington 98402

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Development of a Precipitation-Runoff Model to Simulate Unregulated Streamflow 

in the Salmon Creek Basin, Okanogan County, Washington