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Precipitation-Runoff Simulations of Current 

and Natural Streamflow Conditions in the 

Methow River Basin, Washington

By D. Matthew Ely

Tacoma, Washington

2003

Water-Resources Investigations Report 03-4246

U.S. GEOLOGICAL SURVEY

Prepared in cooperation with

OKANOGAN COUNTY
U.S. DEPARTMENT OF THE INTERIOR

GALE A. NORTON, Secretary

U.S. GEOLOGICAL SURVEY

Charles G. Groat, Director

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does 

not imply endorsement by the U.S. Government.

For additional information write to:

Director, Washington Water Science Center

U.S. Geological Survey

1201 Pacific Avenue – Suite 600

Tacoma, Washington 98402

http://wa.water.usgs.gov

Copies of this report can be purchased from:

U.S. Geological Survey

Information Services

Building 810

Box 25286, Federal Center

Denver, CO 80225-0286

Suggested citation:

Ely, D.M., 2003, Precipitation-runoff simulations of current and natural streamflow conditions in the 

Methow River Basin, Washington: U.S. Geological Survey Water-Resources Investigations Report

03-4246, 35 p. 
Contents iii

CONTENTS

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

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

Purpose and Scope ...................................................................................................................................... 3

Description of Study Area........................................................................................................................... 3

Acknowledgments....................................................................................................................................... 6

Enhanced Precipitation-Runoff Model................................................................................................................. 6

Model Description....................................................................................................................................... 7

Delineation of Basin Physical Characteristics ................................................................................... 7

Time-Series Data................................................................................................................................ 9

Precipitation .............................................................................................................................. 9

Air Temperature........................................................................................................................ 11

Streamflow Discharge............................................................................................................... 11

Irrigation Canal Discharge and Seepage................................................................................... 12

Irrigation Application................................................................................................................ 12

Model Parameterization ..................................................................................................................... 12

Model Calibration .............................................................................................................................. 17

Parameter Sensitivities....................................................................................................................... 24

Long-Term Simulations .............................................................................................................................. 25

Analysis of Simulation Limitations ............................................................................................................ 29

Parameter Error .................................................................................................................................. 29

Data Error........................................................................................................................................... 29

Model Error........................................................................................................................................ 32

Summary and Conclusions................................................................................................................................... 32

References Cited .................................................................................................................................................. 34
iv Figures

FIGURES

Figure 1. Map showing location of the Methow River Basin study area, Washington..................................... 4

Figure 2. Map showing drainage network and Modeling Response Units delineated for the 

precipitation-runoff model for the Methow River Basin, Washington.............................................. 8

Figure 3. Map showing data-collection network used for the precipitation-runoff model for 

the Methow River Basin, Washington............................................................................................... 10

Figure 4. Map showing irrigation canals used to simulate surface-water diversions for the 

precipitation-runoff model for the Methow River Basin, Washington.............................................. 13

Figure 5. Map showing major subbasins delineated for the precipitation-runoff model and 

periods of records for streamflow-gaging stations, Methow River 

Basin, Washington ............................................................................................................................. 18

Figure 6. Hydrographs showing simulated and measured daily mean streamflow for 

selected streamflow-gaging stations in the Methow River Basin, 

water years 1995-2001....................................................................................................................... 20

Figure 7. Hydrographs showing simulated and measured mean monthly streamflow 

for selected streamflow-gaging stations in the Methow River Basin, 

Washington, water years 1992-2001.................................................................................................. 22

Figure 8. Composite scaled sensitivities for model parameters used in the precipitation-runoff 

model for the Twisp River Basin, the Chewuch River Basin, and the Methow River 

Basin, Washington ............................................................................................................................. 25

Figure 9. Graphs showing mean daily difference between contributions of irrigation-canal 

seepage to streamflow in the Chewuch and Twisp Rivers, May 2000-April 2001, 

simulated by the precipitation-runoff model using two scenarios: 50-percent returned 

seepage and zero-percent returned seepage ....................................................................................... 28
Tables v

TABLES

Table 1. Climate stations used in model simulations....................................................................................... 9

Table 2. Description of USGS streamflow-gaging stations, Methow River Basin, Washington .................... 11

Table 3. Simulated irrigation diversions.......................................................................................................... 14

Table 4. Source of parameter values for distributed and (selected) nondistributed parameters 

for the Methow River Basin, Washington ......................................................................................... 15

Table 5. Simulated minimum, maximum, mean monthly streamflows and standard deviations 

for the Methow River Basin, Washington, 

water years 1960-2001....................................................................................................................... 26

Table 6. Simulated and measured mean annual streamflows, relative error, in percent, and bias, 

in percent, for water years 1992-2001 ............................................................................................... 30

Table 7. Simulated and measured mean monthly streamflows, relative error, in percent, and bias, 

in percent, for low-flow periods, water years 1992-2001.................................................................. 31
vi Conversion Factors and Vertical Datum

CONVERSION FACTORS AND DATUMS

CONVERSION FACTORS

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 National Geodetic Vertical Datum of 1929 

(NGVD 29). Horizontal coordinate information is referenced to the North American Datum of 1983 

(NAD 83).

Multiply By To obtain

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

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

foot (ft) 0.3048 meter

inch (in.) 2.54 centimeter

inch (in.) 25.4 millimeter

inch per day (in/d) 25.4 millimeter per day

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

mile (mi) 1.609 kilometer

square mile (mi2) 2.590 square kilometer 
Abstract 1

Precipitation-Runoff Simulations of 

Current and Natural Streamflow Conditions 

in the Methow River Basin, Washington

By D. Matthew Ely

ABSTRACT

Management of the water resources of the 

Methow River Basin is changing in response to the 

listing of three species of fish under the 

Endangered Species Act and the Washington 

State-legislated watershed-planning process. This 

report describes the construction and calibration of 

an enhanced precipitation-runoff model for the 

Methow River Basin and evaluates the model as a 

predictive tool for assessing the current and natural 

streamflow conditions. 

This study builds upon a previous 

precipitation-runoff model for the Methow River 

Basin and validates the current model using a new, 

more extensive streamflow data network. The 

major enhancement was the simulation of current 

flow conditions with the addition of irrigation 

diversions, returns, and application. The 

Geographic Information System Weasel 

characterized the physical properties of the basin 

and the Modular Modeling System, using the 

Precipitation-Runoff Modeling System, simulated 

the hydrologic flow.

Streamflow was simulated for water years 

1992–2001 to calibrate the model to measured 

streamflows. A sensitivity analysis was completed 

using nonlinear regression to determine 

hydrologic parameters pertinent to the modeling 

results. Simulated and measured streamflow 

generally showed close agreement, especially 

during spring runoff from snowmelt. Low-flow or 

baseflow periods, most restrictive to fish 

habitation, were simulated reasonably well yet 

possessed the most uncertainty. Simulations of 

annual mean streamflow as a percentage of 

measured annual mean streamflow for the 10-year 

calibration period at six of the seven streamflowgaging stations ranged from -35.2 to +26.2 

percent, with 65 percent of the simulated values 

within 15 percent. One station was intentionally 

calibrated to over-simulate discharge (simulated 

discharge greater than measured discharge) in 

order to compensate for observed channel losses 

not simulated by the model. Simulation of water 

years 1960-2001 demonstrated great variability in 

monthly streamflow statistics. The simulated mean 

monthly flows for 11 streamflow-gaging stations 

were an average of 2.5 percent higher for water 

years 1992-2001 than for the entire simulation 

period. If water year 2001, an extreme drought 

year, is omitted, simulated mean monthly flows for 

the 11 streamflow-gaging stations were an average 

of 9.0 percent higher than for the entire simulation 

period. The calibrated model also examined the 

effects of irrigation-canal seepage on streamflow. 

Irrigation-canal seepage contributed to streamflow 

throughout the year, with the greatest effect during 

the irrigation season.
2 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

INTRODUCTION

Management of the water resources of the 

Methow River Basin in eastern Washington is changing 

in response to the listing of three species of fish under 

the Endangered Species Act (ESA) and the Washington 

State-legislated watershed-planning process 

(Engrossed Substitute House Bill 2514). Water 

resources are used in the basin for human consumption 

and irrigation. Management options must be 

considered that minimize adverse effects on people but 

meet instream flow needs for fish. Diversions of water 

from the Methow River above existing and historical 

streamflow-gaging stations make it difficult to assess 

the natural streamflow conditions in the basin. To better 

estimate the effects of different management strategies 

throughout the basin prior to their implementation, a 

watershed model can be used to estimate both natural 

streamflow conditions and flows that could result from 

different management options. At present, such a 

management tool does not exist.

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

cooperation with Okanogan County, began a study in 

October 2000 to evaluate natural streamflow for the 

Methow River and its tributaries. In the first phase of 

the study a basin-wide precipitation-runoff model for 

the watershed was constructed and calibrated to 

simulate daily natural streamflow at selected sites 

throughout the basin (Ely and Risley, 2001). The first 

phase discussed the limitations of accurately 

simulating natural streamflow, considering that 

locations where most data were measured (streamflowgaging stations) were located below diversions and 

therefore did not represent the natural flow system. In 

the second phase, discussed in this report, an improved 

precipitation-runoff model is constructed and evaluated 

for its accuracy as a predictive tool for assessing 

current and natural streamflow conditions. This model 

builds upon the previous watershed model. The current 

model is validated using a new, more extensive 

streamflow data network. The major enhancement was 

the simulation of current flow conditions with the 

addition of irrigation application, diversions, and 

returns. Further refinement was accomplished through 

the use of more detailed soil and land-use data. A 

calibrated watershed model could provide a tool to 

assess the available water resources throughout the 

basin and simulate long-term time-series of the natural 

streamflow conditions.

In certain years, moisture availability is limited 

by climatic conditions and streamflow become severely 

reduced, resulting in dewatered reaches, winter icing, 

and higher summertime water temperatures 

(Washington Conservation Commission, 2000). If 

conditions are severe enough, the extent of dewatered 

reaches can expand, restricting access to habitat by 

fish, dewatering redds (nests where females deposit 

eggs), and stranding juvenile fish. Furthermore, it has 

been suggested that human alteration of the basin 

exacerbates naturally limiting conditions (Washington 

Conservation Commission, 2000). These actions could 

include construction of roads and dikes, conversion of 

riparian habitat to agriculture and residential 

development, and water diversions. For these reasons, 

periods of low streamflow, typical in late-summer 

months, are of primary interest and are the focus of this 

study.

The term “natural” streamflow refers to 

streamflow conditions that would exist if irrigation 

diversion take-outs and returns were not present. 

Natural streamflow is stressed in this report because 

surface water within the Methow River Basin is used 

extensively for agricultural irrigation. Through a 

system of irrigation canals, ranging from unlined to 

completely constructed of pipe, surface water is 

diverted from streams and spread over fields during the 

period from mid-April to early October. This practice 

dates back to the late 1800s and predates any formal 

streamflow-gaging measurements. Therefore, 

streamflow conditions that would exist if irrigation 

diversion take-outs and returns were not present are 

unknown. 
Introduction 3

To protect habitat for naturally producing 

salmonid populations, the National Marine Fisheries 

Service (NMFS) set minimum instream flow 

requirements for three tributaries to the Methow River: 

the Chewuch River, Early Winters Creek, and Wolf 

Creek (National Marine Fisheries Service, 2000a, b, c). 

Instream flow requirements resulted from the NMFS 

statement that biologic requirements for endangered 

and threatened fish species are "best expressed in terms 

of environmental factors that define flow, habitat 

quantity, and passage condition attributes necessary for 

survival and recovery of the species" (National Marine 

Fisheries Service, 2000a, b, c). Upper Columbia River 

steelhead, including the Methow River run, were listed 

under the ESA as "endangered" on August 18, 1997. 

Upper Columbia River spring-run Chinook salmon, 

including the Methow River run, were listed under the 

ESA as "endangered" on March 24, 1999. Bull trout in 

the Methow River were listed under the ESA as 

“threatened” on June 10, 1998. 

Different methods were used to determine flow 

requirements for the three tributaries, including 

analysis of limited flow data and the Instream Flow 

Incremental Methodology (IFIM) (Caldwell and 

Catterson, 1992). The IFIM, described in detail by 

Bovee (1982), is a tool used to determine the relation 

between fish habitat and streamflow. The Chewuch 

River has flow requirements based on the IFIM that 

vary for each 15-day period between April 1 and 

October 16. The minimum instream flow requirement 

for Early Winters Creek is 35 cubic feet per second 

(ft3/s), based on the IFIM and limited streamflow data 

and agreed upon by the Early Winters Ditch Company 

and the U.S. Forest Service. Wolf Creek has a 

minimum of 8 ft3/s based on a report by Mullan and 

others (1992) and limited streamflow data. When flows 

in these streams decrease to the minimums set by the 

NMFS, selected irrigation canals are shut down.

Purpose and Scope

The purpose of this report is to document a tool 

that can be used to simulate streamflow conditions in 

the Methow River Basin. This report (1) describes the 

construction and calibration of the Methow River Basin 

watershed model, (2) evaluates the accuracy of the 

model as a predictive tool for assessing the current and 

natural streamflow conditions, and (3) discusses the 

limitations of the model. Time-series data for the study 

included streamflow data from 13 streamflow-gaging 

stations for water years 1960–2001 and climate data for 

water years 1959–2001. (A water year is the 12-month 

period beginning on October 1 of the previous year and 

ending on September 30.) The model was calibrated for 

water years 1992-2001 using streamflow data from 7 

streamflow-gaging stations. The calibrated model was 

used to simulate streamflow outside of the period of 

record at 11 gaging stations for water years 1960-2001, 

and to examine the contribution of irrigation-canal 

seepage to streamflow at 2 gaging stations.

Description of Study Area

The Methow River Basin occupies most of the 

western one-third of Okanogan County in north-central 

Washington State and covers an area of about 1,800 

square miles (fig. 1). The basin is bordered on the west 

by the Cascade Range, on the east by the Okanogan 

River Basin, on the north by the Canadian border, and 

on the south by the Columbia River down to latitude 

48°00′N. The Methow River originates in the Cascade 

Range and flows southeasterly for about 60 miles to the 

confluence with the Columbia River near Pateros. The 

Methow River is formed by the confluence of the West 

Fork Methow River and Robinson Creek and is joined a 

short distance downstream by Lost River. Principal 

tributaries are the Chewuch and Twisp Rivers. The 

Chewuch River originates near the Canadian border 

and flows south for about 36 miles, joining the Methow 

River near Winthrop. The Twisp River originates high 

in the Cascade Range and flows easterly for about 27 

miles to its confluence with the Methow River near the 

town of Twisp.

The population in the Methow River Basin was 

approximately 4,700 in 2000 (Washington State Office 

of Financial Management, 2002), with the majority 

concentrated within the Methow River valley between 

the towns of Mazama and Pateros. The largest towns 

are Twisp and Winthrop.

Topography in the Methow River Basin ranges 

from peaks reaching 8,950 feet above NGVD 29 along 

the Cascade crest down to 775 feet at the confluence of 

the Methow and Columbia Rivers near Pateros. Ridges 

rising to elevations of 7,000 feet above sea level and 

steep U-shaped canyons carved by glacial erosion 

dominate most of the study area, but in some areas, 

such as between Mazama and Carlton, the Methow 

River flows through broad valley bottoms with gentle 

relief. 
4 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

CHELAN COUNTY

DOUGLAS

COUNTY

SKAGIT COUNTY

OKANOGAN COUNTY

OKANOGAN COUNTY

OKANOGAN RIVER B

ASIN

CASCADE RANGE

Drake Creek

Lost

River

Monument Creek

Eu rek a

Cree

R

k

obinson

Creek

Goat Cre ek

Early

Winters Creek

South Creek

War Creek

Eagle

Creek

Buttermilk

Creek

South F or k

Gold Creek

Martin Creek Gold Creek

North

Fork

McFarland

Creek

Black C an y on Cr.

Twisp

River

Ca nyon

Creek

Cath edral Creek

Lake

Cre ek

Andrews Cree k

Horseshoe Creek

Farewell Creek

Twent ymile

Creek

Boulder C reek Bear

Creek

Beaver

Creek

Cub

Creek

North

Fork

South Fork

West

Fork Methow

Little Bridge

Creek

North Fork

Wolf Cree k

Libby Creek

Eightmile Cre ek

Chewuch River

Chew uc h

R vi er

Me thow

River

River

Methow

R iver

Benson Creek

Little Boulder Creek

Poorma n Creek

Alde

Newby Creek

LAKE CHELAN

COL U

ER

Mazama

Winthrop

Twisp

Carlton

Methow

Pateros

120°30' 120° 49°

48°30'

48°

Base from U.S. Geological Survey Digital Data, 1:100,000, 1985

Universal Transverse Mercator projection,

Zone 10, Datum NAD 83

METHOW STUDY BOUNDARY

WENATCHEE-OKANOGAN

NATIONAL FOREST

EXPLANATION

0

0

10 MILES

10 KILOMETERS

Study area

WASHINGTON

Figure 1. Location of the Methow River Basin study area, Washington.
Introduction 5

Geology of the Methow River Basin is described 

in many reports, including Pitard (1958), Waitt (1972), 

Walters and Nassar (1974), and Barksdale (1975). A 

recent USGS study investigated the hydrogeology of 

the unconsolidated sediments of the Methow River 

Basin, and the brief summary that follows is taken 

largely from that work (Konrad and others, 2003).

The Methow River Basin is underlain by 

bedrock, which is exposed at the surface or is thinly 

covered by sediments almost everywhere except 

beneath the floors of the major valleys. The bedrock is 

of many different rock types with a wide range of ages. 

These rocks have been folded and faulted into a 

complex pattern (Walters and Nassar, 1974). Starting 

just south of Twisp and extending up the Methow 

River, the bedrock consists of sedimentary and volcanic 

rocks that have been downfaulted between large blocks 

of igneous and metamorphic rocks. These rocks are 

exposed over a 15- to 20-mile-wide expanse that 

extends about 35-40 miles northeast to southwest. 

Downriver of the sedimentary and volcanic rocks, the 

bedrock underlying the river is primarily igneous and 

metamorphic. The basin has been described as a graben 

(Barksdale, 1975) or a rift-block valley (Waitt, 1972). 

Shales, siltstones, sandstones, conglomerates, breccias, 

and tuffs are the major sedimentary and volcanic rocks 

present in the basin. Most of the igneous and 

metamorphic rocks are granite, gneiss, marble, and 

schist. The unconsolidated sediments that overlie the 

bedrock are mostly sands and gravels of glaciofluvial 

origin. Glacial till and glaciolacustrine silts and clays 

are also present, but are much less extensive than the 

sands and gravels.

The Methow River Basin was almost entirely 

covered by ice several times during the Pleistocene 

Epoch. As a result, upland areas were eroded and 

ultimately mantled with relatively thin glacial deposits, 

whereas thick accumulations of sand and gravel, along 

with some tills, silts, and clays, were deposited along 

the lower slopes and bottoms of the major valleys 

(Walters and Nassar, 1974). Although the glacial 

deposits at the surface originated from the ice-sheet 

glaciation that covered most of the basin, there are 

clear indicators of significant prior erosion from alpine 

glaciation (Waitt, 1972). The alpine glaciation is 

responsible for the wide, U-shaped cross-valley 

profiles of the Methow River valley upriver of Carlton 

and the Twisp River valley upriver of Little Bridge 

Creek (Waitt, 1972). Beneath parts of these U-shaped 

valleys, alpine glaciation apparently eroded the 

bedrock many hundreds of feet below the level of the 

bedrock immediately downriver. Alluvial and alpine 

and ice-sheet glacial sediments later filled these deep 

sections.

Coarse-grained materials, mostly sand and 

gravel, dominate the unconsolidated sediments beneath 

the main Methow River valley. These coarse-grained 

materials are highly transmissive and, where saturated, 

are the most productive aquifers in the basin. These 

materials included Quaternary alluvium deposited 

recently (Holocene) by river or glaciofluvial sediments 

that were deposited earlier by glaciers and rivers.

Minor amounts of silts, clays, and till occur 

within the mass of coarse-grained unconsolidated 

deposits. The fine-grained deposits are relatively 

poorly transmissive and locally act as confining units. 

Beneath some parts of the main Methow River valley, 

these confining units are nearly non-existent.

The alluvial aquifer extends from above Lost 

River continuously to around Black Canyon Creek, 

where bedrock is exposed along the river channel. 

Alluvial deposits along Lost River, Chewuch River, 

Twisp River, Beaver Creek, Benson Creek, and Libby 

Creek are contiguous with deposits along the Methow 

River.

The Methow River Basin also is an area of 

diverse climate with wide variations in temperature and 

precipitation. The high mountainous regions generally 

have the coldest temperatures and receive the greatest 

precipitation. More than 80 inches of precipitation falls 

each year near the crest of the Cascade Range. At low 

elevations, the climate becomes semi-arid. The valley 

floor near Pateros receives about 10 inches of 

precipitation per year. The eastern side of the basin 

receives considerably less annual precipitation than 

equal elevations on the western side. Therefore most 

snow, and thus spring runoff from snowmelt, originates 

near the Cascade crest. Average annual precipitation 

for the entire basin is about 32 inches per year (Walters 

and Nassar, 1974). Temperatures range from about 

100°F (degrees Fahrenheit) to -20°F. Temperatures 

generally are highest in July and lowest in January.
6 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Only small glaciers exist in this part of the North 

Cascades. Post and others (1971) report that the 

Methow River Basin contains 15 glaciers, ranging in 

size from 0.03 to 0.07 square mile. Total surface area of 

the glaciers equals about 0.54 square mile, only 0.03 

percent of the total basin.

Seventy-five percent of the Methow River Basin 

is forested. Douglas fir (Psuedotsuga menziesii), 

lodgepole pine (Pinus contorta), and ponderosa pine 

(Pinus ponderosa) forests cover mid-elevation (2,000 

to 5,000 feet) areas of the basin. Shrub-steppe 

communities are common below 4,000 feet and 

subalpine fir (Abies lasiocarpa), Pacific silver fir 

(Abies amabilis), mountain hemlock (Tsuga 

mertensiana), whitebark pine (Pinus albicaulis), and 

subalpine larch (Larix lylli) are common above 3,000 

feet. Deciduous trees including black cottonwood 

(Populus trichocarpa) and aspen (Populus tremuloides) 

occupy valley bottoms and riparian areas. Vegetation is 

sparser and trees, shrubs, and grasses are smaller at 

high elevations. Most agricultural production occurs on 

the valley bottom and is limited to alfalfa and small 

orchards. Most of the Methow River Basin and all of its 

headwaters are in the Okanogan National Forest 

(fig. 1). Land use in the National Forest includes 

recreation, grazing, and timber harvesting. Historically, 

fire was the dominant landscape process influencing 

the structure, composition, and extent of vegetation 

communities in the Methow River Basin (Knott and 

others, 1998).

Acknowledgments

The author thanks the members of the Methow 

Basin Planning Unit for contributing knowledge of 

current and historical conditions in the basin, and 

Gregory H. Knott of the U.S. Bureau of Reclamation 

for his technical guidance.

ENHANCED PRECIPITATION-RUNOFF 

MODEL

The Modular Modeling System (MMS), 

developed by Leavesley and others (1996), is a 

framework for modeling that allows for the 

modularization of existing models and integration into 

MMS. Modularization allows the user to select 

appropriate algorithms or develop new algorithms to 

create an optimal model for the desired application. 

The Precipitation-Runoff Modeling System (PRMS; 

Leavesley and others, 1983) was the computersimulation model chosen for this study. PRMS is a 

physically based, distributed-parameter model 

designed to simulate precipitation and snowmelt 

runoff. Major advantages of this system include the 

ability to (1) simulate the moisture balance of each 

component of the hydrologic cycle, (2) account for 

heterogeneous physical characteristics of a basin, and 

(3) appropriately simulate both mountainous and flat 

areas.

PRMS and MMS have been used in numerous 

studies. PRMS was used to model watershed systems 

for Williams Draw and Bush Draw Basins, Jackson 

County, Colorado (Kuhn, 1989), the southern Yampa 

River Basin, Colorado (Parker and Norris, 1989), 11 

small drainage basins in the Oregon Coast Range 

(Risley, 1994), the Willamette River Basin, Oregon 

(Laenen and Risley, 1997), the Lake Tahoe Basin, 

California and Nevada (Jeton, 1999a), and the Truckee 

River Basin, California and Nevada (Jeton, 1999b). 

MMS was used to model the San Juan River Basin, 

Colorado and New Mexico (Kuhn and others, 1998) 

and the Yakima River Basin, Washington (Mastin and 

Vaccaro, 2002b).
Enhanced Precipitation-Runoff Model 7

Model Description

The MMS modules used in the current study 

were similar to those used in the previous Methow 

River Basin simulation. A more complete description 

of the model modules is given in Ely and Risley (2001). 

An improved ground-water flow module and new 

modules to simulate the diversion, return, and 

application of irrigation water were used in the current 

model. Those modules are documented by Mastin and 

Vaccaro (2002a).

Delineation of Basin Physical Characteristics

The physical attributes of the basin were 

characterized in a format that could be readily used in 

the modeling process. Digital data exist that describe 

the topographic features, soils, land use, and 

vegetation. These spatial features determine, in large 

part, the quantity and movement of water throughout a 

basin, and subsequent steps in the modeling process 

will build upon this initial characterization.

The drainage network in the Methow River Basin 

(fig. 2) was delineated with the Geographic 

Information System (GIS) Weasel (Viger and others, 

1998). The GIS Weasel is an interface for the treatment 

of spatial information used in watershed modeling and 

provides an accessible tool to delineate and 

characterize a watershed. The GIS Weasel uses 

standard ARC/INFO routines for delineation and 

therefore develops objective and reproducible results. 

Modeling Response Units (MRUs) are delineated to 

reflect spatially distributed attributes such as slope, 

aspect, elevation, soils, and vegetation, and to respond 

similarly to hydrologic inputs such as precipitation. 

Each MRU is a smaller polygon area of a subbasin on 

which these physical characteristics are assumed to be 

homogeneous. The GIS Weasel also delineates a 

drainage network and computes the connection 

between the MRU and possible stream locations. 

Accuracy of the characterization can be dependent 

upon the scale and quality of the digital input data, as 

well as on hydrologic judgment.

The initial input to the GIS Weasel to define 

topographic surfaces was a standard 100-foot (30-

meter) USGS 7.5-minute digital elevation model 

(DEM) of the Methow River Basin, in ARC/GRID 

format. The 100-foot DEM contains regularly spaced 

cells, 100 feet on center, with elevation reported to the 

nearest 1 foot at each cell. A more detailed 30-foot (10-

meter) DEM for the study area was not used because, 

given the large size of the basin, it would add additional 

computation time without significant gain in precision 

for the purpose of basin delineation. Because of the 

relatively large area of the study, the number of grid 

cells for the 100-foot DEM exceeded the maximum 

allowed by the GIS Weasel's parameterization process. 

The DEM grid was resampled to 150-foot (45-meter) 

intervals with no noticeable loss of precision. 

A flow-accumulation surface is determined using 

the flow direction from the DEM to compute the 

number of cells upslope from each cell. A drainage 

network is extracted from the flow-accumulation 

surface by selecting points on the surface that drain, 

according to the flow-accumulation surface, an area 

equal to or greater than a user-specified threshold 

(Viger and others, 1998). This threshold represents the 

minimum upslope area needed to initiate a first-order 

link in the drainage network (Jenson and Domingue, 

1988). In this study, a threshold of 4,500 cells, or 3.5 

square miles, was chosen. 

The GIS Weasel computed initial MRUs on the 

basis of the automatic two flow-plane process. With 

this feature, each side of the subbasin divided by the 

stream becomes a separate MRU. The MRUs were 

further delineated by using USGS streamflow-gaging 

station locations as the downstream outlet from which 

the drainage area was computed. To account for the 

effect of increasing precipitation in mountainous areas, 

elevation bands were incorporated at 1,000-foot 

intervals to subdivide any MRUs that may have 

spanned several of these intervals. Finally, all MRUs 

smaller than 1 square mile were dissolved into adjacent 

MRUs. This process resulted in 620 MRUs for the total 

Methow River Basin (fig. 2). 
8 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

CHELAN COUNTY

DOUGLAS COUNTY

SKAGIT COUNTY

OKANOGAN COUNTY

OKANOGAN COUNTY

OKANOGAN RIVER B

ASIN

CASCADE RANGE

Mazama

Winthrop

Twisp

Carlton

Methow

Pateros

LAKE CHELAN

COL U MB AI RIVER

SR

153

SR

20

US

97

SR

20

SR

20

120°30' 120° 49°

48°30'

48°

Base from U.S. Geological Survey Digital Data, 1:100,000, 1985

Universal Transverse Mercator projection,

Zone 10, Datum NAD83

0 10

10 20 30 KILOMETERS

20 MILES

0

BOUNDARY OF MODELING RESPONSE UNIT

EXPLANATION

METHOW RIVER DRAINAGE NETWORK

Figure 2. Drainage network and Modeling Response Units delineated for the precipitation-runoff model for the 

Methow River Basin, Washington.
Enhanced Precipitation-Runoff Model 9

Time-Series Data

Streamflow in the Methow River Basin was 

simulated using measured precipitation, airtemperature, and streamflow-discharge time-series 

data. The period of climate record used in model 

simulations was water years 1959-2001. Not all 

stations existed for the entire period of record. 

Precipitation and temperature modules employed a 

distance-weighted average approach. Periods of 

missing data from any station simply were not used in 

the calculation, so the missing data caused no problems 

in the simulation. Monthly mean precipitation ratios 

between climate stations and MRUs were calculated 

using estimates from the Parameter-estimation 

Regressions on Independent Slopes Model (PRISM) 

(Daly and others 1994; Daly and others 1997). 

Precipitation

Daily precipitation totals used in the MMS 

model simulations were measured at precipitation 

gages located throughout the Methow River Basin and 

surrounding basins. Precipitation gages operated by the 

U.S. National Weather Service (NWS) and Snowpack 

Telemetry (SNOTEL) sites operated by the Natural 

Resources Conservation Service (NRCS) provided data 

from a total of 18 gages (fig. 3, table 1) with varying 

periods of record. The rain module requires mean 

monthly estimates of precipitation for each MRU to 

compute ratios between rain gage locations and the 

MRU. For this purpose, the PRISM model estimates 

(Daly and others 1994; Daly and others 1997) were 

used. Data input to the PRISM model consisted of 

mean monthly precipitation for the period 1961-90 

from National Oceanic and Atmospheric 

Administration Cooperative sites, SNOTEL sites, and 

selected State network stations.

Table 1. Climate stations used in model simulations

[Station name: See figure 3 for locations; Agency: NWS, National Weather Service; NRCS, Natural Resources Conservation 

Service. Daily data used in model: ppt, precipitation; temp, temperature. Latitude and longitude: Degrees, minutes, seconds]

Station name Agency

Daily data 

used in 

model

Latitude Longitude

Elevation

(feet above

NGVD 29)

Period of record

Chelan NWS ppt, temp 47 50 00 120 02 00 1,120 July 1890 to present

Chief Joseph Dam NWS ppt, temp 48 00 00 119 39 00 820 Oct. 1949 to present

Conconully NWS ppt, temp 48 33 00 119 45 00 2,320 June 1948 to present

Mazama NWS ppt, temp 48 37 00 120 27 00 2,170 April 1950 to present

Mazama 6 NWS ppt 48 32 00 120 20 00 1,960 June 1948 to Oct. 1976

Methow 2 NWS ppt, temp 48 08 00 120 01 00 1,170 Aug. 1957 to June 1970

Methow 2S NWS ppt, temp 48 06 00 120 01 00 1,170 July 1970 to present

Omak NWS ppt, temp 48 25 00 119 32 00 851 Jan. 1931 to Dec. 1998

Omak 4 NWS ppt, temp 48 28 00 119 31 00 1,301 Nov. 1980 to July 1991

Ross Dam NWS ppt, temp 48 44 00 121 03 00 1,236 Sept. 1960 to present

Stehekin 4 NW NWS ppt, temp 48 21 00 120 43 00 1,270 Jan. 1931 to present

Tonasket 4 NNE NWS ppt, temp 48 46 00 119 25 00 960 July 1984 to present

Winthrop 1 WSW NWS ppt, temp 48 28 00 120 11 00 1,755 Jan. 1931 to present

Harts Pass NRCS ppt, temp 48 43 00 120 39 00 6,500 Oct. 1981 to Oct. 1982,

Oct. 1983 to present

Pope Ridge NRCS ppt, temp 47 59 00 120 34 00 3,580 Oct. 1981 to present

Rainy Pass NRCS ppt, temp 48 33 00 120 43 00 4,780 Oct. 1981 to present

Salmon Meadows NRCS ppt, temp 48 40 00 119 50 00 4,500 Oct. 1981 to Oct. 1982,

Oct. 1983 to present

Thunder Basin NRCS ppt, temp 48 31 00 120 59 00 4,200 Oct. 1989 to present
10 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

49

48

48

30'

121 120 30 120 119 30

0 5 10 15 20 MILES

0 5 10 15 20 KILOMETERS

EXPLANATION

METHOW RIVER DRAINAGE

BASIN BOUNDARY

USGS STREAMFLOW-GAGING STATION

AND NUMBER

NATIONAL WEATHER SERVICE STATION

Precipitation

Temperature

NATURAL RESOURCES CONSERVATION

SERVICES STATION (SNOTEL)

Precipitation

Temperature

12447390

Base from U.S. Geological Survey Digital Data, 1:100,000, 1985

Universal Transverse Mercator projection,

Zone 10, Datum NAD83

LOCATION OF FIGURE 4

OKANOGAN COUNTY

DOUGLAS COUNTY

CHELAN COUNTY

SKAGIT COUNTY

WHATCOM COUNTY

SNOHOMISH

COUNTY

OKANOGAN COUNTY

Pope Ridge

Chelan

Chief Joseph Dam

Stehekin 4NW

Thunder Basin

Rainy Pass

Mazama

Mazama 6

Ross Dam Harts Pass

Winthrop 1 WSW

Methow 2

Methow 2S

Omak

Omak 4

CANADA

UNITED STATES

BRITISH COLUMBIA

WASHINGTON

Tonasket 4NNE

Conconully

12447390

12447387 12448000

12448500

12447500

12448998

12449500

12448990

12449710

12449950

12447383

12447370

12447382

Salmon Meadows

SR

153

SR

153

SR

20

US

97

SR

20

SR

20

SR

155

SR

20

SR

20

SR

17

US

97

SR

172

US

97

SR

20

US

97

US

97

Figure 3. Data-collection network used for the precipitation-runoff model for the Methow River Basin, Washington.
Enhanced Precipitation-Runoff Model 11

Air Temperature

Measured, daily, minimum, and maximum airtemperature data were collected by the NWS and the 

NRCS at 17 locations (fig. 3, table 1). To account for 

differences in elevation between the stations (table 1) 

and the basins, MMS adjusts the temperature data 

using a calculated lapse rate for every 1,000-foot 

increase in elevation.

Streamflow Discharge

Daily mean streamflow data were collected at 13 

streamflow-gaging stations in the Methow River Basin 

(fig. 3, table 2), according to standardized techniques 

of the USGS (Rantz, 1982). The data obtained at a 

streamflow-gaging station include a continuous record 

of stage (water-surface elevation referenced to a gage 

datum), individual measurements of discharge, and 

observations of factors that may affect the relation 

between stage and discharge. The individual discharge 

measurements are then plotted against the stage and a 

stage-discharge relation curve is constructed. From 

these curves, discharges for any stage in the measured 

range can be approximated. The USGS rates the 

accuracy of its streamflow records on the basis of the 

stability of the stage-discharge relation and the quality 

of the measurements of stage and discharge. Accuracy 

levels of "good" indicate that 95 percent of the 

measurements are within 10 percent of the true values 

and "fair" indicate the measurements are within 15 

percent.

Records of daily mean streamflow for 

Washington are available, by water year, in USGS 

Water-Data Reports. Streamflow data collected at 

streamflow-gaging stations are referred to as 

"measured" data throughout this report.

Table 2. Description of USGS streamflow-gaging stations, Methow River Basin, Washington

[Station No.: U.S. Geological Survey streamflow-gaging station; See figure 5 for locations. Latitude and longitude: Degrees, minutes, seconds. 

Abbreviation: mi2, square mile]

Station No. Station name Latitude Longitude Period of record

Drainage 

area

(mi2)

Elevation 

(feet above 

NGVD 29)

12447370 Lost River near Mazama 48 39 19 120 30 18 Oct. 2000 to present 146 2,386

12447382 Early Winters Creek near Mazama 48 35 55 120 26 31 Oct. 2000 to present 80 2,180

12447383 Methow River above Goat Creek 

near Mazama

48 34 32 120 23 05 April 1991 to present 373 2,040

12447387 Wolf Creek below diversion near 

Winthrop

48 29 00 120 18 24 Oct. 2000 to present 32 2,660

12447390 Andrews Creek near Mazama 48 49 23 120 08 41 June 1968 to present 22 4,300

12447500 Chewuch River above Cub Creek 

near Winthrop

48 33 53 120 10 35 Oct. 2000 to present 466 1,980

12448000 Chewuch River near Winthrop 48 28 38 120 11 07 Oct. 1991 to present 525 1,736

12448500 Methow River at Winthrop 48 28 25 120 10 34 Aug. 1971 to June 1972,

Nov. 1989 to present

1,007 1,718

12448990 Twisp River above Newby Creek 

near Twisp

48 22 51 120 15 38 Oct. 2000 to present 207 2,040

12448998 Twisp River near Twisp 48 22 12 120 08 51 May 1975 to Sept. 1979,

Oct. 1989 to present

245 1,640

12449500 Methow River at Twisp 48 21 55 120 06 54 June 1919 to Sept. 1962

Apr. 1991 to present

1,301 1,580

12449710 Beaver Creek near Twisp 48 19 43 120 03 29 Oct. 2000 to Sept. 2001 110 1,540

12449950 Methow River near Pateros 48 04 39 119 59 02 Apr. 1959 to present 1,772 900
12 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Irrigation Canal Discharge and Seepage

Konrad and others (2003) calculated aquifer 

recharge from irrigation-canal seepage in the Methow 

River Basin using a surface-water discharge balance 

and discharge measurements made in 14 irrigation 

systems. Discharge measurements were made at the 

upstream and downstream ends of 45 canal reaches 

comprising 31.6 miles, or approximately half of the 

total length of unlined irrigation canals, not including 

lateral canals, operating in the Methow River Basin 

during water year 2001. Measurements made by Klohn 

Leonoff, Inc. (1990) along an additional 9 reaches 

comprising 23.6 miles were also included in the 

recharge calculations. Measurement locations excluded 

large water users and spills from the irrigation canals, 

however the calculated difference between inflows and 

outflows along some reaches may have neglected small 

users. As a result, there may be a small upward bias in 

the estimates of recharge for some reaches. The mean 

seepage rate for each canal was calculated as the sum 

of the measured losses for each reach divided by the 

sum of canal-reach lengths. Reach lengths were 

calculated from digital raster graphics of 7.5-minute 

quadrangle maps (1:24,000 scale) using a GIS.

The mean seepage rate during water year 2001 

from unlined irrigation canals was 2.0 (ft3/s)/mi from 

May through August. Seepage rates are estimated to 

decline by approximately 50 percent in September 

because of the combined effects of subsurface 

saturation, reduction in the infiltration capacity of the 

canal beds from the accumulation of fine-grained 

materials, and lower diversion rates. Seepage estimates 

for individual irrigation canals varied from 1.6 to 6.2 

(ft3/s)/mi and, with the exception of one canal that had 

been excavated shortly before the seepage run, the 

standard deviation of seepage rates was 0.9 (ft3/s)/mi. 

Surface-water diversions for 16 irrigation canals 

(fig. 4) were simulated by removing streamflow from 

associated model nodes. Daily diversion rates (table 3) 

were selected from a range of reported values or 

determined from examining streamflow data near a 

diversion point. In general, the irrigation season was 

simulated as May 1–October 7. There are many small 

irrigation canals in the Beaver Creek subbasin. Those 

canals were simulated as one lumped canal (fig. 4). 

Many more irrigation canals exist in the Methow River 

Basin, but there is limited information concerning their 

operations. Most of the excluded canals divert small 

amounts of surface water, generally 2 ft3/s or less.

Irrigation Application

Irrigation water was applied to MRUs at a 

constant rate of 0.2 inch per day for the period May 1 

through October 7. The total amount of water for the 

160-day period equaled 32.0 inches of water. This 

application rate was an average annual water 

requirement for alfalfa (Molenaar and others, 1952; 

James and others, 1982; Cline and Collins, 1992). 

MRUs were designated for irrigation application on the 

basis of an evaluation of land use from aerial 

photographs as part of the Washington State 

Department of Ecology’s Methow Air Photo 

Assessment (MAPA) project.

Model Parameterization

Mathematically, parameters are defined as 

numerical constants used to determine variables. 

Variables are computed by equations during the 

simulation. PRMS has distributed and non-distributed 

parameters. Distributed parameters are attributed to 

each MRU and describe (1) physiographic 

characteristics such as area, slope, and aspect, (2) 

hydrologic processes within the MRU such as 

subsurface or ground-water flow, and (3) climatic input 

to the MRU such as precipitation and temperature 

adjustments. Non-distributed parameters are 

parameters held constant throughout the watershed, 

such as the Julian date to force snowpack depletion or 

the temperature that determines the form of 

precipitation. All MMS parameters are defined and 

discussed in depth by Leavesley and others (1983 and 

1996). The distributed and non-distributed parameters 

for the precipitation-runoff model and their sources are 

listed in table 4.
Enhanced Precipitation-Runoff Model 13

Figure 4. Irrigation canals used to simulate surface-water diversions for the precipitation-runoff model for the Methow 

River Basin, Washington.

SR

153

SR

20

SR

20

120 25' 120

48 15'

48 35'

Goat Creek

Eagle

Creek

Buttermilk

Creek

Canyon

Creek

Boulder Cree k

Bear Creek

Beaver

Creek

Cub

Creek

North

Fork

South Fork

Little

Bridge

Creek

N orth

Fork

Wolf

Creek

Lib by Cree k

Chewuch River

Methow

River

Twisp

River

Mazama

Winthrop

Twisp

C

Carlton HELAN COUNTY

OKANOGAN COUNTY

IRRIGATION CANALS

EXPLANATION

Barkley

Chewuch Beaver

Early Winters

Foster Eightmile

Fulton

Kumm-Holloway and

McKinney Mtn

MVID East

MVID West

Rockview

Skyline

TVPI

Wolf Creek Foghorn

0246 8

246 8

10 MILES

0 10 KILOMETERS

Base from U.S. Geological Survey Digital Data, 1:100,000, 1985

Universal Transverse Mercator projection,

Zone 10, Datum NAD83
14 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Parameters for the discrete spatial features of the 

study area were generated using the GIS Weasel. In 

addition to elevation, slope, and aspect, ancillary 

information concerning soils, land use and land cover, 

and vegetation were incorporated to assign further 

characteristics to each MRU. Digital soil data were 

obtained from a modified version of general soil maps 

from the State Soil Geographic Database (STATSGO; 

U.S. Department of Agriculture, 1994) and the Soil 

Survey Geographic (SSURGO) database (U.S. 

Department of Agriculture, 2000). Parameters from the 

contiguous U.S. Forest Type Groups map and U.S. 

Forest Density map provided vegetation information 

(Zhu and Evans, 1992; Powell and others, 1998). The 

USGS, the University of Nebraska-Lincoln, and the 

European Commission's Joint Research Centre 

generated a 3,281-foot (1-kilometer) resolution 

database of global land-cover characteristics (Loveland 

and others, 1991; U.S. Geological Survey, 1992) for 

use in a wide range of environmental research and 

modeling applications. The land cover/land use grid 

used by the GIS Weasel is a composite of the global 

land cover, the MAPA land-use cover, and the Forest 

Type Group data listed above.

Table 3. Simulated irrigation diversions

[Abbreviations: MVID, Methow Valley Irrigation District; TVPI, Twisp Valley Power and Irrigation; ft3/s, cubic feet per second]

Month/

day

Daily diversion rate (ft3/s)

Barkley Beaver Chewuch Early 

Winters Eightmile Foghorn Foster Fulton

May 1–15 12 12 25 14 5 15 5 15

May 16–31 12 12 30 14 5 15 5 15

June 1–15 12 17 30 14 5 15 5 15

June 16–30 12 17 35 14 5 15 5 15

July 1–15 12 17 35 14 7 18 5 18

July 16–31 18 12 30 14 7 18 5 18

August 1–15

August 16–31

18 12 25 14 7 13 5 20

15 12 25 14 7 13 5 20

September 1–15 15 12 25 14 7 10 5 17

September 16–30 9 12 20 14 7 10 5 17

October 1-7 4.5 0 10 7 3.5 5 2.5 8.5

Month/

day

KummHollowayMcKinney 

Mountain MVID East MVID West Rockview Skyline TVPI Wolf Creek

May 1–15 4.5 5 39 24 9 20 10 8

May 16–31 4.5 5 39 24 9 20 12 8

June 1–15 4.5 5 41 24 9 22 11 8

June 16–30 4.5 3 41 24 9 22 11 8

July 1–15 4.5 3 42 26 10 17 11 8

July 16–31 4.5 3 42 26 10 17 11 8

August 1–15

August 16–31

4.5 3 37 26 10 20 10 8

4.5 3 37 26 10 20 9 8

September 1–15 4.5 3 39 25 9 15 7 8

September 16–30 4.5 3 39 25 9 15 7 8

October 1-7 2.3 1.5 19.5 10 4.5 7.5 2.5 4
Enhanced Precipitation-Runoff Model 15

Table 4. Source of parameter values for distributed and (selected) nondistributed parameters for the Methow River Basin, Washington

[GIS: Data computed in geographic information system from digital data. Computed: Results from climatological data or other measured data. Literature:

Obtained from Ely and Risley (2001) as estimated or empirical data. Default value: Parameters whose values are considered as provided by Leavesley and 

others (1983). Calibration: Parameters that have an initial value estimated from measured or published data and later adjusted during calibration. 

Abbreviations: MRU, Modeling Response Unit; ET, Evapotranspiration. –, no data available]

Model parameter Description of parameter

Source

GISderived Computed LiteratureDefault 

value

Calibration

Distributed (MRU-dependent) parameters

CAREA_MAX Maximum area contributing to surface runoff – X – – –

COV_TYPE Vegetation cover type X – – – –

COVDEN_SUM Vegetation cover density for summer X – – – –

COVDEN_WIN Vegetation cover density for winter X – – – –

GWFLOW_COEF Ground-water routing coefficient to obtain groundwater flow contribution to streamflow– – – – X

GWSINK_COEF Ground-water sink coefficient to compute seepage 

from each reservoir to ground-water sink

– – – – X

GWSTOR_INIT Storage in each ground-water reservoir at beginning 

of simulation, in inches

– – – – X

HRU_AREA MRU area, in acres X – – – –

HRU_DEPLCRV Index number for snowpack depletion curve X – – –

HRU_ELEV Mean MRU elevation, in feet X – – – –

HRU_GWRES Index number for ground-water reservoir X – – – –

HRU_PERCENT_IMPERV MRU impervious area as decimal percent of total 

MRU area

X – – – –

HRU_RADPL Index number of solar radiation plane – X – – –

HRU_SLOPE MRU slope in decimal percent, vertical 

feet/horizontal feet

X – – – –

HRU_SSRES Index number of subsurface reservoir receiving 

excess water from soil zone

X – – – –

IMPERV_STOR_MAX Maximum impervious retention storage for MRU, in 

inches

X – – – –

JH_COEF_HRU Air-temperature coefficient used in Jensen Haise 

potential ET computations for each MRU, in 

degrees Fahrenheit

– X – – –

LOSS_DIV Percentage of canal flow routed to specified MRU – X – – –

PSTA_MON Monthly factor to adjust measured precipitation from 

each climate station to each MRU

– – – – X

RAD_TRNCF Transmission coefficient for short-wave radiation 

through winter canopy

– X – – –

RAIN_MON Monthly factor to adjust measured precipitation (rain) 

to each MRU

– – – – X

SMIDX_COEF Coefficient in nonlinear contributing area algorithm X – – – –

SMIDX_EXP Exponent in nonlinear contributing area algorithm X – – – –

SNAREA_THRESH Maximum snow-water equivalent below which snowcovered area depletion curve is appliedX – – – –

SNOW_INTCP Snow-interception storage capacity for major 

vegetation type on MRU

X – – – –

SNOW_MON Monthly factor to adjust measured precipitation 

(snow) to each MRU

– – – – X
16 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Distributed (MRU-dependent) parameters—Continued

SNOWINFIL_MAX Maximum infiltration rate for snowmelt, in inches per 

day

– – – – X

SOIL_MOIST_INIT Initial value of available water in soil profile, in 

inches

X – – – –

SOIL_MOIST_MAX Maximum available water-holding capacity of soil 

profile, in inches

– – – – X

SOIL_RECHR_INIT Initial value for available water in soil recharge zone, 

in inches

X – – – –

SOIL_RECHR_MAX Maximum value for available water in soil recharge 

zone, in inches

– – – – X

SOIL_TYPE MRU soil type X – – – –

SOIL2GW_MAX Amount of soilwater excess for MRU that is routed 

directly to associated ground-water reservoir, in 

inches per day

– – – – X

SRAIN_INTCP Summer interception storage capacity for major 

vegetation type on MRU, in inches

X – – – –

SSR2GW_RATE Coefficient to route water from subsurface to groundwater reservoir– – – – X

SSRCOEF_LIN Linear subsurface routing coefficient to route 

subsurface storage to streamflow

– – – – X

SSRCOEF_SQ Nonlinear subsurface routing coefficient to route 

subsurface storage to streamflow

– – – – X

TMAX_ADJ MRU maximum temperature adjustment to MRU 

temperature based on slope and aspect of MRU, in 

degrees Fahrenheit

– – – – X

TMIN_ADJ MRU minimum temperature adjustment to MRU 

temperature based on slope and aspect of MRU, in 

degrees Fahrenheit

– – – – X

TRANSP_BEG Month to begin summing maximum temperature for 

each MRU; when sum is greater than or equal to 

TRANSP_TMAX, transpiration begins

– – X – –

TRANSP_END Last month for transpiration computations – – X – –

TRANSP_TMAX Temperature index to determine specific date of start 

of transpiration period

– – – X –

WRAIN_INTCP Winter rain-interception storage capacity for the 

major vegetation type on MRU, in inches

X – – – –

Table 4. Source of parameter values for distributed and (selected) nondistributed parameters for the Methow River Basin, Washington (Continued)

[GIS: Data computed in geographic information system from digital data. Computed: Results from climatological data or other measured data. Literature:

Obtained from Ely and Risley (2001) as estimated or empirical data. Default value: Parameters whose values are considered as provided by Leavesley and 

others (1983). Calibration: Parameters that have an initial value estimated from measured or published data and later adjusted during calibration. 

Abbreviations: MRU, Modeling Response Unit; ET, Evapotranspiration. –, no data available]

Model parameter Description of parameter

Source

GISderived Computed LiteratureDefault 

value

Calibration
Enhanced Precipitation-Runoff Model 17

Selected non-distributed (basinwide) parameters

(Temperature/precipitation-dependent)

ADJMIX_RAIN Monthly factor to adjust rain proportion in mixed 

rain/snow event

– – – – X

JH_COEF Monthly air-temperature coefficient used in JensenHaise potential ET computations– – – – X

MELT_FORCE Julian date to force snowpack to spring snowmelt – – – – X

MELT_LOOK Julian date to start looking for spring snowmelt – – – – X

TMAX_ALLRAIN Maximum temperature above which all precipitation 

is simulated as rain

– – – – X

TMAX_ALLSNOW Maximum temperature below which all precipitation 

is simulated as snow

– – – – X

TMAX_LAPSE Monthly maximum temperature lapse rate 

representing change in maximum temperature per 

1,000 feet of elevation change for each month

– – X – –

TMIN_LAPSE Monthly minimum temperature lapse rate 

representing change in minimum temperature per 

1,000 feet of elevation change for each month

– – X – –

Table 4. Source of parameter values for distributed and (selected) nondistributed parameters for the Methow River Basin, Washington (Continued)

[GIS: Data computed in geographic information system from digital data. Computed: Results from climatological data or other measured data. Literature:

Obtained from Ely and Risley (2001) as estimated or empirical data. Default value: Parameters whose values are considered as provided by Leavesley and 

others (1983). Calibration: Parameters that have an initial value estimated from measured or published data and later adjusted during calibration. 

Abbreviations: MRU, Modeling Response Unit; ET, Evapotranspiration. –, no data available]

Model parameter Description of parameter

Source

GISderived Computed LiteratureDefault 

value

Calibration

Model Calibration

The calibration phase of the modeling effort 

consisted of matching simulated and measured 

variables, such as streamflow and snow water 

equivalent. Calibration was accomplished using an 

ordered approach of manual trial and error. 

Streamflow was simulated for water years 1992–

2001 to calibrate the model to measured streamflows at 

the seven USGS streamflow-gaging stations operating 

for that period (fig. 5, table 2). Additional streamflow 

data were used for water years 2000–2001 from six 

other USGS streamflow-gaging stations established for 

this study. Initial estimates of the parameters were 

taken from values determined by the GIS Weasel, 

values calculated from the MMS algorithms, MMS 

algorithm default values, and parameter estimates used 

in the previous Methow River Basin watershed model 

(Ely and Risley, 2001) (table 4). The model was 

calibrated by adjusting several sensitive parameters 

within the acceptable range of known values. Most 

parameter adjustments were made to precipitation 

(rain_mon and snow_mon), temperature (tmax_adj), 

and the ground-water flow coefficient (gwflow_coef). 

These parameters were adjusted uniformly throughout 

a subbasin. Other parameters, including 

snowinfil_max, soil2gw_max, melt_force, melt_look, 

tmax_allrain, tmax_allsnow, ssr2gw_rate, and the 

monthly jh_coef, were altered to an acceptable estimate 

and applied throughout the entire Methow River Basin. 

Many more parameters could have been adjusted 

during calibration, but there were inadequate data to 

justify that approach and additional assumptions about 

the physical processes would be required.
18 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Figure 5. Major subbasins delineated for the precipitation-runoff model and periods of records for streamflow-gaging 

stations, Methow River Basin, Washington. 

CHELAN COUNTY

DOUGLAS COUNTY

SKAGIT COUNTY

OKANOGAN COUNTY

OKANOGAN COUNTY

OKANOGAN RIVER B

ASIN

CASCADE RANGE

SR

153

SR

20

US

97

SR

20

SR

20

120°30' 120° 49°

48°30'

48°

Base from U.S. Geological Survey Digital Data, 1:100,000, 1985 Universal Transverse Mercator projection,

Zone 10, Datum NAD83

0 10

10 20 30 KILOMETERS

20 MILES

0

EXPLANATION

Chewuch River

USGS STREAMFLOW-GAGING STATION AND NUMBER

MAJOR SUBBASINS

Andrews Creek

Methow River near Winthrop

Twisp River

Beaver Creek

Water years 1992 - 2001

Water years 2000 - 2001

METHOW RIVER NEAR PATEROS

12447370

12447382

12447383

12447500

12447390

12447387

12448000

12448500

12448990

12448998

12449500

12449710

12449950

Andrews

Creek

Che

wuch

River C he w uc h

River

Met how

River

Methow

River

River

Twisp

Beaver

Creek

Methow

R

vi er

Mazama

Winthrop

Twisp

Carlton

Methow

Pateros
Enhanced Precipitation-Runoff Model 19

Simulated and measured streamflow have many 

similarities and generally show close agreement, 

especially during the spring runoff from snowmelt, as 

demonstrated by hydrographs of simulated and 

measured daily mean streamflows for four streamflowgaging stations for water years 1995–2001 (fig. 6). 

Measured streamflow for April through June at the 

most downstream streamflow-gaging station 

(12449950, fig. 6D) averaged 66.2 percent of the total 

flow. Simulated streamflow for that period averaged 

64.0 percent of the total flow. Because of the 

hydrologic complexities of the subbasins, the model 

simulations performed less well in capturing the 

magnitude and timing of short-term (1- to 3-day) peak 

flows during the spring and summer runoff. The model 

also tended to over-simulate (simulated discharge 

greater than measured discharge) fall and winter peak 

flows. The model did represent the baseflow periods of 

autumn through winter well. Measured streamflow for 

October through February at the most downstream 

streamflow-gaging station (12449950) averaged 

12.4 percent of the total flow; simulated streamflow 

averaged 14.2 percent.

Precipitation in the Methow River Basin 

generally occurs as snowfall, and the amount of 

snowfall varies drastically throughout the basin. 

Increasing or decreasing the MRU PRISM value 

appropriately adjusted the ratio between the 

precipitation gage and the MRU. To match snowfall 

totals at the high elevations, monthly values used in the 

calculation of snowfall generally were increased from 

initial PRISM-estimated values. Monthly rainfall and 

snow amounts for low elevations were decreased from 

those values determined from PRISM data. 

Precipitation on MRUs located below Beaver Creek 

was lowered from initial PRISM-estimated values by as 

much as 50 percent. This procedure resulted in a 

simulated average annual precipitation for the basin of 

32.6 inches per year for the calibration period.

After the annual streamflow totals were adjusted, 

the timing of flows was matched. Hydrographs of 

simulated and measured mean monthly streamflow for 

four streamflow-gaging stations, which include spring 

runoff of snowmelt and autumn/winter baseflows, 

demonstrate good agreement between the two (fig. 7). 

The timing of the spring runoff and shape of the 

hydrograph during high flow was controlled largely by 

adjusting rain- and snow-rate adjustments and 

maximum-temperature. Temperature adjustments also 

affected the form of precipitation (snow versus rain) 

and evapotranspiration (ET). Potential and actual ET 

were calculated using the Jensen-Haise equation 

(Jensen and Haise, 1963). Potential and actual ET for 

the calibration period were 51.5 and 19.0 inches per 

year, respectively.

The recessionary limb of the hydrograph was 

affected by precipitation and temperature (as it affects 

snowmelt rates) and the subsurface and ground-water 

flow parameters. Flow algorithms in MMS move water 

to a ground-water reservoir from both a soil zone and a 

subsurface reservoir. Detailed explanations of these 

processes are given in Leavesley and others (1983). 

The ground-water flow coefficient proved to be an 

important parameter to estimate, having a great effect 

on the shape of the hydrograph during low-flow 

periods. The ground-water reservoir for each MRU was 

assigned a flow coefficient based on reasonable ranges 

and measured streamflow. MRUs located largely in the 

broader alluvial valley near the rivers were given high 

ground-water flow coefficients within an acceptable 

range and were adjusted to correctly shape the 

simulated hydrograph. The higher coefficient resulted 

in more ground-water discharge per area. The values 

given to the ground-water flow coefficients decreased 

with distance from the streams. 

The irrigation diversion module required a 

diversion-loss parameter (loss_div), the percentage of 

the canal flow routed to a specified MRU. Field studies 

found that seepage rates vary throughout the irrigation 

season. Also, each irrigation canal loses water at 

different rates and rates vary along the length of an 

individual canal. Measured seepage rates were 

averaged for initial parameter values and a final 

seepage rate of 50 percent for each canal was chosen. 

Several irrigation canals have been lined or converted 

to pipes in recent years, eliminating any seepage loss. 

These changes were not incorporated in the simulations 

because the module does not allow time-varying values 

for loss_div. The changes mostly would affect water 

years 2000 and 2001. In response to NMFS biological 

opinions (National Marine Fisheries Service, 2000a 

and b), Early Winters and Wolf Creek ditches shut 

down early in the irrigation season during water years 

2000 and 2001.
20 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Figure 6. Simulated and measured daily mean streamflow for selected streamflow-gaging stations in the Methow River Basin, water 

years 1995-2001.

0

2,000

4,000

6,000

8,000

10,000

12,000

DAILY MEAN STREAMFLOW, IN CUBIC FEET PER SECOND

A. Chewuch River near Winthrop (12448000)

B. Methow River at Winthrop (12448500)

5,000

10,000

15,000

20,000

25,000

0

1995 1996 1997 1998 1999 2000 2001

WATER YEAR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

Simulated

Measured

DAILY MEAN STREAMFLOW

1995 1996 1997 1998 1999 2000 2001

WATER YEAR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR
Enhanced Precipitation-Runoff Model 21

Figure 6. — Continued.

0

1,000

500

1,500

2,000

2,500

3,000

3,500

C. Twisp River near Twisp (12448998)

D. Methow River near Pateros (12449950)

5,000

10,000

15,000

20,000

25,000

0

Simulated

Measured

DAILY MEAN STREAMFLOW

1995 1996 1997 1998 1999 2000 2001

WATER YEAR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

1995 1996 1997 1998 1999 2000 2001

WATER YEAR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

OCT

APR

DAILY MEAN STREAMFLOW, IN CUBIC FEET PER SECOND
22 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Figure 7. Simulated and measured mean monthly streamflow for selected streamflow-gaging stations in the 

Methow River Basin, Washington, water years 1992-2001.

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

MONTH

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

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

MONTH

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

MEAN MONTHLY STREAMFLOW, IN CUBIC FEET PER SECOND

A. Chewuch River near Winthrop (12448000)

B. Methow River at Winthrop (12448500)

Simulated

Measured

MEAN MONTHLY STREAMFLOW
Enhanced Precipitation-Runoff Model 23

Figure 7. — Continued.

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

MONTH

0

200

100

300

400

500

600

700

800

900

1,000

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

MONTH

0

1,000

2,000

3,000

4,000

5,000

6,000

MEAN MONTHLY STREAMFLOW, IN CUBIC FEET PER SECOND

C.

D.

Simulated

Measured

MEAN MONTHLY STREAMFLOW Twisp River near Twisp (12448998)

Methow River near Pateros (12449950)
24 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Parameter Sensitivities

A sensitivity analysis determines the extent to 

which a parameter value affects the simulated value, or, 

in this case, streamflow. This analysis provides a good 

indication of how parameter uncertainty will adversely 

affect predicted streamflow.

The computer program UCODE (Poeter and 

Hill, 1998), a universal inverse model using nonlinear 

regression, was applied with the MMS model to 

measure the amount of information provided by the 

streamflow data. A sensitivity analysis was completed 

to determine hydrologic parameters pertinent to the 

modeling results. The diagnostic statistics generated by 

UCODE used in this sensitivity analysis were the 

dimensionless scaled sensitivities and the composite 

scaled sensitivities. A complete discussion of these 

statistics is given by Hill (1998) and Poeter and Hill 

(1998). Dimensionless scaled sensitivities indicate the 

sensitivity of the simulated equivalent of each 

measurement (mean daily streamflow) to the 

parameter. The dimensionless scaled sensitivity, ssij, is 

calculated as (Hill, 1998):

(1)

where

Composite scaled sensitivities (CSS) summarize 

all the sensitivities for one parameter. CSS is calculated 

for each parameter using the scaled sensitivities for all 

observations (here, the daily mean streamflow). 

Because they are dimensionless, CSS can be used to 

compare the amount of information provided by 

different types of parameters. Model results will be 

more sensitive to parameters with large CSS relative to 

those for other parameters. The CSS for the jth 

parameter, cssj, is calculated as (Hill, 1998):

(2)

where ND is the number of observations being used in 

the regression, b is a vector that contains the parameter 

values at which the sensitivities are evaluated, and the 

quantity in parentheses equals the scaled sensitivities of 

equation 1.

CSS for 15 parameters were calculated for the 

final model, using daily mean streamflow for water 

years 1991-94 from streamflow-gaging stations 

12448000 (Chewuch River near Winthrop), 12448998 

(Twisp River near Twisp), and 124499500 (Methow 

River near Pateros) as the observations (fig. 8). The 

monthly air-temperature coefficient used in the 

Jensen-Haise potential ET computation was divided 

into six parameters: jh_coef1 represented January and 

February; jh_coef2 represented March; jh_coef 3 

represented April and May; jh_coef 4 represented 

June – August; jh_coef 5 represented September and 

October; and jh_coef 6 represented November and 

December. CSS for tmax_allsnow, the maximum 

temperature below which all precipitation is simulated 

as snow, was the highest, jh_coef 3 and 4 the next 

highest. This jh_coef 3 and 4 result is expected because 

those parameters control ET during the spring and 

summer months. During periods when streamflow is 

dominated by ground-water flow (typically late 

summer through winter), the ground-water and 

subsurface flow parameters have a great effect on 

simulated streamflow.

i identifies one of the observations;

j identifies one of the parameters;

is the simulated value associated with 

ith observations;

bj is the jth estimated parameter;

is the sensitivity of the simulated value 

associated with the ith observation with 

respect to the jth parameter and is 

evaluated at the final parameter values; 

and

is the weight for the ith observation.

ssij

∂y'

i

∂bj

-------- ⎝ ⎠ ⎛ ⎞ bj

ωii

1/2 =

y'

i

∂y'

i

∂bj

--------

ωii

CSSj

ssij

2 ( ) b

i = 1

ND

∑

ND ----------------------------

1

2

--

=
Enhanced Precipitation-Runoff Model 25

Figure 8. Composite scaled sensitivities for model parameters used in the precipitation-runoff model for the Twisp River Basin, the Chewuch 

River Basin, and the Methow River Basin, Washington.

0

2,000

4,000

6,000

8,000

12,000

14,000

gwflow_coef1

gwflow_coef2

soil2gw_max

ssr2gw_rate

ssrcoef_sq

ssrcoef_lin

loss_div

tmax_allsnow

snowinfil_max

jh_coef1

jh_coef2

jh_coef3

jh_coef4

jh_coef5

jh_coef6

PARAMETER

COMPOSITE SCALED SENSITIVITY

RIVER BASIN

Twisp

Chewuch

Methow

Long-Term Simulations

The calibrated model can be used as a tool to 

simulate natural streamflows, streamflows at gaged 

streams for periods outside the measured streamflow 

record, and ungaged streams. This ability allows the 

examination of the system's hydrologic response to 

climatic conditions. Also, long-term means could differ 

significantly from those means produced from the 

shorter-term record of the streamflow-gaging stations. 

Annual, or even decadal, trends in streamflows would 

be more evident. To demonstrate the capability of 

simulating streamflow outside the streamflow-gaging 

record, water years 1960–2001 were simulated and 

monthly streamflow statistics generated for 11 

streamflow-gaging stations (table 5). Standard 

deviations for the period of simulation were large, 

suggesting great variability in monthly streamflows. 

Mean monthly streamflows for water years 1992–2001 

were compared with mean monthly streamflows for the 

simulation period 1960-2001. The simulated mean 

monthly streamflows for the 11 streamflow-gaging 

stations were an average of 2.5 percent higher for 

1992–2001 than for the entire simulation period. If 

water year 2001, an extreme drought year, is omitted, 

simulated mean monthly streamflows for the 11 

streamflow-gaging stations were an average of 9.0 

percent higher than for the entire simulation period.
26 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Table 5. Simulated minimum, maximum, mean monthly streamflows and standard deviations for the Methow River Basin, Washington, 

water years 1960-2001

[Station No.: U.S. Geological Survey streamflow gaging station; 

Standard deviation: . Abbreviatons: ft3/s, cubic feet per second]

Simulated monthly streamflow (ft3/s)

Station No. OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

12447370 Minimum 15.6 22.8 19.3 16.0 16.3 16.4 61.3 278.0 53.4 23.1 18.0 16.6

Maximum 529.0 502.5 103.5 66.2 92.5 231.1 760.2 2,061.1 2,603.4 964.5 3,15.3 175.1

Mean 78.5 92.2 41.5 36.9 42.8 61.4 326.1 1,025.4 888.2 288.1 78.1 50.1

Standard 

deviation

105.8 98.4 19.9 13.5 18.3 36.9 181.7 399.6 539.1 250.7 68.1 28.3

12447382 Minimum 5.2 9.8 7.5 5.8 4.5 4.4 11.3 210.7 39.0 18.8 11.6 7.8

Maximum 389.2 264.9 55.6 35.4 40.3 57.7 343.6 1,181.1 1,471.5 685.0 508.7 249.8

Mean 58.4 55.3 23.2 18.0 18.4 20.4 140.8 627.0 589.2 295.9 133.5 65.3

Standard 

deviation

71.7 57.4 13.0 8.4 9.0 12.7 93.6 205.0 278.0 177.7 124.5 61.6

12447383 Minimum 38.2 62.2 46.4 40.8 46.1 38.0 119.8 858.9 175.1 79.8 57.4 45.4

Maximum 1,420.6 1,430.0 344.0 191.8 238.6 433.1 1,558.0 4,443.3 5,889.1 2,491.1 1,528.1 634.6

Mean 228.6 273.6 123.5 101.9 114.1 141.5 670.0 2,391.6 2,173.7 913.4 349.7 192.6

Standard 

deviation

274.8 278.5 63.4 40.8 55.6 76.9 363.6 829.3 1,133.1 636.8 326.7 135.7

12447387 Minimum 1.7 3.1 2.9 2.2 2.1 3.2 9.9 10.8 4.6 3.5 2.7 2.2

Maximum 94.4 80.1 15.4 12.6 14.0 41.8 162.1 431.9 367.8 71.9 26.3 19.7

Mean 14.5 14.8 7.0 5.9 6.4 8.5 79.7 179.3 78.8 22.1 12.9 10.4

Standard 

deviation

17.5 14.8 3.3 2.6 2.9 6.9 39.5 86.9 74.0 14.9 5.1 3.7

12447390 Minimum 2.6 2.3 2.2 2.1 2.0 2.1 2.8 28.5 7.1 4.4 3.6 3.1

Maximum 33.8 13.8 7.3 7.1 7.0 6.4 95.2 282.3 4,27.4 105.6 30.9 11.2

Mean 5.6 5.1 4.0 3.9 4.0 4.0 27.6 154.8 116.0 32.1 9.7 5.8

Standard 

deviation

4.7 2.7 1.1 1.1 1.2 1.3 24.9 60.5 90.6 28.1 5.1 1.8

12448000 Minimum 27.4 41.6 34.5 33.7 38.5 48.3 102.4 135.0 50.4 37.7 33.0 29.8

Maximum 459.8 536.9 293.9 284.1 305.9 367.0 1,419.6 4,711.2 5,243.7 1,302.3 329.7 198.5

Mean 104.8 131.0 97.3 92.9 124.5 154.7 528.4 1,711.1 1,233.8 357.2 162.5 110.3

Standard 

deviation

75.4 115.4 63.1 57.3 71.0 77.4 307.7 966.7 1,095.2 270.1 71.3 42.8

12448500 Minimum 69.9 125.5 96.1 95.5 114.2 142.9 430.7 1,032.1 240.6 127.4 98.0 81.2

Maximum 2,192.3 2,310.8 757.2 567.6 628.8 891.5 3,420.2 9,508.9 12,495.3 3,714.2 1,897.8 868.7

Mean 380.2 473.1 271.6 243.7 311.0 398.1 1,454.2 4,601.5 3,691.2 1,362.4 561.0 337.1

Standard 

deviation

409.7 447.5 156.6 123.3 154.7 175.2 721.8 1,930.3 2,427.2 925.8 403.0 177.9

n x2

∑ ∑x ⎝ ⎠ ⎛ ⎞ 2

–

n n( ) – 1 ---------------------------------------------

·
Enhanced Precipitation-Runoff Model 27

12448998 Minimum 18.7 22.4 17.3 16.0 21.3 36.1 131.7 287.5 95.3 42.4 27.8 20.8

Maximum 499.0 752.2 219.5 199.2 192.1 278.4 946.6 2,252.0 2,137.1 801.5 365.2 169.4

Mean 78.6 105.6 62.7 64.3 78.3 110.5 450.1 1,079.8 867.3 313.2 114.4 64.7

Standard 

deviation

85.2 126.6 47.5 44.4 42.7 59.1 178.2 384.9 475.2 226.3 69.3 32.3

12449500 Minimum 88.7 150.6 117.4 125.3 154.9 250.8 575.2 1,329.4 338.7 170.6 126.5 102.5

Maximum 2,718.6 3,138.7 1,030.1 858.2 926.0 1,196.3 4,437.3 11,931.6 14,766.9 4,439.5 2,151.0 1,015.6

Mean 464.1 597.9 360.9 340.5 446.9 580.1 1,968.9 5,726.9 4,601.8 1,699.3 687.3 409.0

Standard 

deviation

493.8 582.0 224.0 192.1 218.1 249.2 899.0 2,324.5 2,892.4 1,153.3 468.5 210.6

12449710 Minimum 0.7 3.5 2.7 3.4 3.4 2.6 2.1 2.0 1.4 1.1 1.0 0.8

Maximum 35.3 132.9 70.5 89.2 131.2 170.9 371.8 579.9 203.4 92.1 35.0 26.5

Mean 14.4 20.0 19.1 24.0 42.6 65.6 140.1 130.5 50.0 26.8 18.4 14.0

Standard 

deviation

8.1 24.0 16.8 21.0 28.7 37.4 101.4 117.4 45.6 16.2 9.1 6.9

12449950 Minimum 90.5 170.6 130.8 148.9 169.7 290.4 598.5 1,381.5 353.9 177.1 131.5 106.4

Maximum 2,843.1 3,324.4 1,429.4 1,274.1 1,400.6 1,956.4 5,006.7 12,738.9 15,380.3 4,678.2 2,267.8 1,094.3

Mean 506.6 676.4 442.4 453.0 629.1 832.0 2,280.8 6,002.3 4,847.2 1,826.6 757.9 455.9

Standard 

deviation

508.4 640.2 294.1 287.1 324.9 386.4 1,006.0 2,497.6 3,018.6 1,220.4 499.5 228.8

Table 5. Simulated minimum, maximum, mean monthly streamflows and standard deviations for the Methow River Basin, Washington, 

water years 1960-2001 (Continued)

[Station No.: U.S. Geological Survey streamflow gaging station; 

Standard deviation: . Abbreviatons: ft3/s, cubic feet per second]

Simulated monthly streamflow (ft3/s)

Station No. OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP

n x2

∑ ∑x ⎝ ⎠ ⎛ ⎞ 2

–

n n( ) – 1 ---------------------------------------------

·

The calibrated model can also be used to 

examine the effects of irrigation-canal seepage on 

streamflow. Konrad and others (2003) report that 

ground-water discharge contributes a substantial 

portion (33 to 96 percent) of baseflow in the Methow 

and Twisp Rivers. Field studies have shown that 

50 percent or more of canal discharge can be returned 

to the ground-water system through canal seepage. To 

examine the contribution of irrigation-canal seepage to 

streamflow, two scenarios were simulated for irrigation 

canals diverting water from the Chewuch and Twisp 

Rivers. The first scenario returned 50 percent of the 

diverted water to the ground-water system. The second 

scenario returned none of the diverted water to the 

ground-water system, representing a system of lined 

irrigation canals. The difference in daily mean 

streamflow from the two scenarios (fig. 9) shows an 

increasing gain in streamflow throughout the irrigation 

season (May 1–October 7). When the canals are shut 

down after October 7, the net gain begins to decrease, 

but remains throughout the year. The effect on 

Chewuch River streamflow is greater because of the 

greater amount of diverted flow (fig. 9A).
28 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Figure 9. Mean daily difference between contributions of irrigation-canal seepage to streamflow in the Chewuch and 

Twisp Rivers, May 2000-April 2001, simulated by the precipitation-runoff model using two scenarios: 50-percent returned 

seepage and zero-percent returned seepage. 

A. Chewuch River near Winthrop (12448000)

0

2

4

6

8

10

12

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

2000

IRRIGATION PERIOD, MAY 1 - SEPT 30

2001

0

2

4

6

8

10

12

14

16

18

DAILY MEAN STREAMFLOW, IN CUBIC FEET PER SECOND

B. Twisp River near Twisp (12448998)
Enhanced Precipitation-Runoff Model 29

Analysis of Simulation Limitations

All modeling studies contain errors. Certain 

approximations and simplifications must be made to 

simulate the actual flow systems. Errors in 

precipitation-runoff modeling typically are caused by a 

combination of inadequate input data, inadequate 

representation of the physical processes by the 

algorithms of the model, and inadequate parameter 

estimation during the calibration procedure (Troutman, 

1985). A lack of understanding about some aspects of 

the natural flow system accounts for much of the error. 

As a result, model-predicted values must be used with 

caution, in consideration of simulation error.

Mean annual streamflow totals for simulated and 

measured streamflow for 1992-2001 varied 

considerably during many of the water years, and there 

was no well-defined pattern or bias in the results 

(table 6). Simulated and measured mean annual 

streamflow for the 10-year period matched closely for 

all calibration points with the exception of station 

12447383, Methow River above Goat Creek, for which 

streamflow sometimes was over-simulated, mostly for 

the baseflow period of autumn and winter. A large 

amount of Methow River flow goes into the ground 

water above Goat Creek and then returns to the surface 

upstream of Winthrop (Caldwell and Catterson, 1992). 

The current model does not simulate channel losses 

and, therefore it does not correctly represent this 

ground water-surface water interaction. During the 

calibration process, streamflow was purposely 

simulated to exceed measured streamflow at the 

Methow River above Goat Creek. At the next 

downstream station on the Methow River (12448500), 

the cumulative annual streamflow is under-estimated 

by 5.5 percent. Simulations of the mean annual 

streamflow as a percentage of measured mean annual 

streamflow for the 10-year calibration period at six 

streamflow-gaging stations (12447383 was omitted for 

the reasons discussed) ranged from -35.2 to +26.2 

percent, with 65 percent of the simulated values within 

15 percent (table 6). For the 10-year simulation period, 

measured annual streamflow totals were highest for 

water years 1995, 1996, 1997, and 1999. Aboveaverage measured annual flows during water years 

1996 and 1999 were under-simulated at all streamflowgaging stations, indicating that the current model is less 

effective at simulating high-flow conditions. The 

model performed reasonably well during low-flow 

years (table 6) and during typically low-flow months 

(table 7). These results were deemed acceptable, 

because the low-flow period is of primary interest in 

issues concerning fish habitat quality.

Parameter Error

Parameter error occurs when improper values are 

chosen during the calibration process. Various 

combinations of parameter values can achieve the 

desired reduction in residual error yet improperly 

represent the actual system. For example, an increase in 

ET or a decrease in precipitation would both result in 

lowered discharge at a node. Model fit was 

accomplished primarily with manual calibration, but 

nonlinear regression was used to measure the 

sensitivities for model parameters. With most 

watershed models, the most sensitive parameters will 

be those directly related to precipitation and 

temperature, as found in this study. Ground-water flow 

parameters had a large effect on the shape of the 

simulated hydrograph during low-flow periods. The 

uncertainty associated with the estimation of these 

parameter values propagates through to the model 

predictions.

Data Error

The MMS watershed model requires measured 

precipitation, temperature, and streamflow time-series 

data and physical characteristics of the basin. 

Precipitation volume is often the most important 

driving factor of the simulation, and it is often the most 

difficult to estimate. Precipitation records are point 

measurements, whereas the model requires input 

distributed throughout the study area. This study used 

18 precipitation sites within and adjacent to the study 

area, and the measurements were extrapolated to 

estimate precipitation throughout the entire basin. 

Precipitation in the Methow River Basin varies widely. 

Mean elevation of an MRU can differ significantly 

from that of the closest rain gage, and the MRU can 

include a wide range of average precipitation. In 

addition to the problems with spatial distribution, much 

of the precipitation comes in the form of snowfall, 

which can be underestimated if the collection device is 

not protected from the wind. Catchment losses also 

occur for rain, but they are believed to be smaller than 

for snow. 
30 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Station No. Water 

year

Mean streamflow 

(ft3/s)/d)

Relative 

error 

(percent)

Bias

(percent) Simulated Measured

112447383 1992 447.7 405.5 10.4

1993 459.2 309.2 48.5

1994 387.0 286.4 35.1

1995 767.2 590.7 29.9

1996 776.6 690.3 12.5

1997 953.3 725.4 31.4

1998 706.5 589.9 19.8

1999 661.6 798.3 -17.1

2000 541.0 499.7 8.3

2001 242.1 147.4 64.2

Total 5,942.3 5,042.9 17.8 24.3

12447390 1992 22.1 22.1 0.1

1993 31.8 25.3 25.8

1994 20.2 28.4 -29.0

1995 41.7 40.7 2.5

1996 34.0 41.4 -17.8

1997 54.9 43.5 26.2

1998 46.3 37.2 24.7

1999 33.1 45.0 -26.5

2000 25.2 28.2 -10.7

2001 13.3 10.7 23.6

Total 322.6 322.4 0.0 1.9

12448000 1992 216.6 223.3 -3.0

1993 338.1 275.4 22.7

1994 220.1 281.6 -21.8

1995 565.9 527.6 7.3

1996 461.1 529.3 -12.9

1997 724.9 580.7 24.8

1998 627.3 511.1 22.7

1999 408.0 629.7 -35.2

2000 277.7 333.2 -16.7

2001 93.8 101.4 -7.5

Total 3,933.6 3,993.3 -1.5 -2.0

12448500 1992 741.2 869.3 -14.7

1993 870.8 814.5 6.9

1994 668.5 758.1 -11.8

1995 1,529.8 1,444.1 5.9

1996 1,402.3 1,570.4 -10.7

1997 1,878.3 1,639.6 14.6

1Station 12447383 was intentionally oversimulated to compensate 

for channel losses not simulated by the model.

1998 1,490.4 1,423.6 4.7

1999 1,217.1 1,728.9 -29.6

2000 927.9 1,067.0 -13.0

2001 369.4 427.2 -13.5

Total 11,095.6 11,742.7 -5.5 -6.1

12448998 1992 179.7 179.8 -0.1

1993 171.0 170.4 0.4

1994 156.5 151.9 3.1

1995 393.3 341.4 15.2

1996 346.4 409.5 -15.4

1997 395.4 384.9 2.7

1998 310.9 294.9 5.4

1999 324.0 344.0 -5.8

2000 262.3 248.2 5.7

2001 101.5 98.7 2.8

Total 2,641.2 2,623.7 0.7 1.4

12449500 1992 934.7 1033.8 -9.6

1993 1055.3 947.7 11.4

1994 834.2 901.3 -7.4

1995 1,985.1 1,752.4 13.3

1996 1,790.0 1,915.3 -6.5

1997 2,322.3 1,922.8 20.8

1998 1,840.4 1,670.9 10.1

1999 1,573.8 1,968.6 -20.1

2000 1,211.6 1,305.1 -7.2

2001 472.4 503.7 -6.2

Total 14,019.9 13,921.6 0.7 -0.1

12449950 1992 1,045.2 1,084.0 -3.6

1993 1,161.7 1,022.9 13.6

1994 924.2 963.0 -4.0

1995 2,312.6 1,854.1 24.7

1996 2,010.9 2,053.6 -2.1

1997 2,532.9 2,128.1 19.0

1998 2,074.0 1914.2 8.3

1999 1,775.4 2,251.0 -21.1

2000 1,361.2 1,439.3 -5.4

2001 512.2 572.5 -10.5

Total 1,5710.3 15,282.8 2.8 1.9

Station No. Water 

year

Mean streamflow 

(ft3/s)/d)

Relative 

error 

(percent)

Bias

(percent) Simulated Measured

Table 6. Simulated and measured mean annual streamflows, relative error, in percent, and bias, in percent, for water years 1992-2001

[Station No.: U.S. Geological Survey streamflow gaging station. See figure 5 for location. Water year: a water year begins on October 1 of the previous year 

and ends on September 30; Abbreviation: (ft3/s)/d, cubic feet per second per day; 

Bias: . Relative error: (s – m)/m × 100, where s is simulated daily mean streamflow, in cubic feet per second; m is measured daily mean 

streamflow, in cubic feet per second; and N is number of measured values]

∑[ ] ( ) s m– /m

N ------------------------------------- × 100
Enhanced Precipitation-Runoff Model 31

Table 7. Simulated and measured mean monthly streamflows, relative error, in percent, and bias, in 

percent, for low-flow periods, water years 1992-2001

[Station No.: U.S. Geological Survey streamflow gaging station. See figure 5 for location. Water year: a 

water year begins on October 1 of the previous year and ends on September 30; Abbreviation: (ft3/s)/d, 

cubic feet per second per day;

Bias: . Relative error (s – m)/m × 100, where s is simulated daily mean streamflow, 

in cubic feet per second; m is measured daily mean streamflow, in cubic feet per second; and N is number 

of measured values]

Station No. Month

Mean streamflow (ft3/s)/d) Relative error 

(percent)

Bias

(percent) Simulated Measured

112447383 September 198.5 37.7 427.2

October 175.6 30.7 471.0

November 277.1 107.1 158.8

December 130.7 79.1 65.2

Total 781.8 254.6 207.1 112.2

12447390 September 6.6 7.6 -12.5

October 4.6 6.4 -28.6

November 4.0 7.2 -44.8

December 3.3 5.7 -41.0

Total 18.5 26.9 -31.0 -12.7

12448000 September 82.5 75.6 9.1

October 102.6 96.3 6.6

November 115.9 101.6 14.1

December 100.2 84.6 18.5

Total 401.2 358.0 12.1 4.8

12448500 September 292.6 268.0 9.2

October 312.8 288.2 8.5

November 461.4 388.0 18.9

December 280.8 337.7 -16.9

Total 1,347.5 1,281.9 5.1 2.0

12448998 September 52.1 39.7 31.2

October 62.5 62.9 -0.7

November 85.8 99.7 -13.9

December 64.0 86.3 -25.9

Total 264.4 288.6 -8.4 -0.9

12449500 September 327.3 300.1 9.1

October 389.2 362.7 7.3

November 575.9 482.7 19.3

December 376.0 414.1 -9.2

Total 1,668.5 1,559.7 7.0 2.6

12449950 September 399.4 400.4 -0.2

October 434.3 438.1 -0.9

November 635.3 558.1 13.8

December 482.3 502.7 -4.0

Total 1,951.4 1,899.2 2.7 0.9

1Station 12447383 was intentionally oversimulated to compensate for channel losses not 

simulated by the model.

∑[ ] ( ) s m– /m

N ------------------------------------- × 100
32 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Temperature data can be the source of as much 

potential error as the rainfall data. Again, temperature 

is recorded as a point measurement and basin-wide 

distributed values must be estimated for each MRU. 

Differences of a few degrees can determine if 

precipitation is simulated as snow or rain or if an 

accumulated snowpack melts. Precipitation, combined 

with air temperature, determines both the cumulative 

annual streamflow and the basic shape of the simulated 

hydrograph.

In general, the DEM and the GIS Weasel 

represented the physical characteristics of the basin 

well. Even though the basin was delineated into 620 

MRUs, however, approximations of slope and aspect 

were necessary. Coarse coverages of forest density, 

land use, and soils introduced error in sensitive 

parameters that determine ET, infiltration, and groundwater recharge.

Model Error

The precipitation-runoff algorithm cannot 

completely represent all physical processes of a basin. 

Determining whether a weakness in a simulation is 

attributable to input data error or model weakness is 

almost impossible in some cases. The nonlinear 

regression used in the sensitivity analysis helped to 

understand the possible effects of this uncertainty.

Ground-water flow is a dominant component of 

streamflow during autumn and winter months. These 

low-flow periods can limit accessible habitat to 

resident fish species, and therefore are a critical period 

to understand and accurately simulate. MMS is 

designed to simulate surface and shallow subsurface 

flow and simplifies ground-water flow much more than 

would a ground-water flow model. The current groundwater flow equations use few parameters and are not 

physically based. Separating the baseflow component 

from the total streamflow produced complex baseflow 

hydrographs that could not be reproduced by the 

standard parameter algorithm for ground-water flow 

used by PRMS.

SUMMARY AND CONCLUSIONS

The U.S. Geological Survey, in cooperation with 

Okanogan County, constructed and calibrated an 

enhanced precipitation-runoff model for the Methow 

River Basin in eastern Washington, and evaluated the 

model as a predictive tool for assessing the current and 

natural streamflow conditions. This effort was part of a 

larger study to evaluate streamflow conditions for the 

Methow River and its tributaries in eastern Washington, 

prompted in part by the listing of Upper Columbia 

River steelhead, including the Methow River run, and 

Upper Columbia River spring-run Chinook salmon, 

including the Methow River run, as "endangered" 

under the Endangered Species Act. Also, Bull trout in 

the Methow River were listed under the Endangered 

Species Act as "threatened." This new model builds 

upon the previous watershed model and the current 

model is validated using a new, more extensive 

streamflow data network. The major enhancement was 

the simulation of current streamflow conditions with 

the addition of irrigation diversions, returns, and 

application. Further refinement was accomplished 

through the use of more-detailed soil and land-use data. 

A calibrated watershed model provides a tool to assess 

the available water resources throughout the basin and 

simulate long-term time-series of natural streamflow 

conditions.

The Geographic Information System (GIS) 

Weasel was used to delineate subbasins and the 

drainage network. A 150-foot (45-meter) USGS 7.5-

minute digital elevation model of the Methow River 

Basin was used to define topographic surfaces. The 

GIS Weasel delineated 620 modeling response units 

(MRU) based on slope, aspect, elevation, and flow 

planes. The parameterization component of the 

program then generated parameters for the MRUs, 

incorporating ancillary information concerning soils, 

land use and land cover, and vegetation.
Summary and Conclusions 33

Information from the GIS Weasel was used to 

construct and calibrate a computer-simulation model of 

current and natural streamflow conditions for the 

Methow River Basin. The Modular Modeling System 

(MMS), using the Precipitation-Runoff Modeling 

System, was the simulation model chosen for this 

study. Major advantages of this system include the 

ability to (1) simulate the moisture balance of each 

component of the hydrologic cycle, (2) account for 

heterogeneous physical characteristics of a basin, and 

(3) appropriately simulate both mountainous and flat 

areas. Model calibration was accomplished using 

measured precipitation, air-temperature, and 

streamflow time-series data.

Streamflow was simulated for water years 1992–

2001 to calibrate the model to measured streamflows at 

seven USGS streamflow-gaging stations. Additional 

data for water years 2000-2001 from six other USGS 

streamflow-gaging stations were also used. Initial 

estimates of parameters were taken from values 

determined by the GIS Weasel, values calculated from 

the MMS algorithms, MMS algorithm default values, 

and parameter estimates used in the previous Methow 

River Basin watershed model. Most parameter 

adjustments were made to precipitation, temperature, 

and the ground-water flow coefficient.

Simulated and measured streamflow generally 

showed close agreement, especially for spring runoff 

and baseflows. Because of the hydrologic complexities 

of the subbasins, the model simulations were less 

accurate at capturing the magnitude and timing of 

short-term (1- to 3-day) peak flows. The model also 

tended to over-simulate (simulated discharge greater 

than measured discharge) fall and winter peak flows. 

One streamflow-gaging station location was 

intentionally calibrated to over-simulate discharge in 

order to compensate for observed channel losses not 

simulated by the model.

A sensitivity analysis determines the extent to 

which a parameter value affects the simulated value, or, 

in this case, streamflow. Composite scaled sensitivities 

summarize all the sensitivities for one parameter and 

can be used to compare the amount of information 

provided by different types of parameters. Parameters 

that control the form of precipitation and monthly airtemperature coefficients used to compute potential 

evapotranspiration were most important. During 

periods when streamflow is dominated by groundwater flow (typically late summer through winter), the 

ground-water and subsurface flow parameters had a 

great effect on simulated streamflow.

The calibrated model can be used as a tool to 

simulate natural streamflows, streamflows at gaged 

streams for periods outside the measured streamflow 

record, and ungaged streams. To demonstrate this 

ability, streamflow for water years 1960–2001 was 

simulated and monthly streamflow statistics generated. 

The simulated mean monthly flows for 11 streamflowgaging stations were an average of 2.5 percent higher 

for water years 1992–2001 than for the entire 

simulation period. If water year 2001, an extreme 

drought year, is omitted, simulated mean monthly 

flows for the 11 streamflow-gaging stations were an 

average of 9.0 percent higher. The calibrated model 

also examined the effects of irrigation canal seepage on 

streamflow. Irrigation-canal seepage contributed to 

streamflow throughout the year, with the greatest effect 

during the irrigation season.

Mean annual streamflow for simulated and 

measured streamflow for 1992–2001 varied 

considerably during many water years. Simulated and 

measured mean annual streamflow for the 10-year 

period matched closely for all calibration points with 

the exception of station 12447383, Methow River 

above Goat Creek. Station 12447383 was intentionally 

over-simulated to compensate for channel losses not 

simulated by the model. Simulations of the mean 

annual streamflow as a percentage of measured mean 

annual streamflow for the 10-year calibration period at 

six streamflow-gaging stations ranged from -35.2 to 

+26.2 percent, with 65 percent of the simulated values 

within 15 percent. 
34 Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

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