Prepared in cooperation with the U.S. Environmental Protection Agency and the Oregon 

Climate Change Research Institute

Potential Climate-Induced Runoff Changes and Associated 

Uncertainty in Four Pacific Northwest Estuaries 

Open-File Report 2012–1274

U.S. Department of the Interior

U.S. Geological Survey
Cover. National Estuarine Research Reserve in the South Slough of Coos Bay 

(Wikimedia Commons, 2010)
Potential Climate-Induced Runoff Changes and Associated 

Uncertainty in Four Pacific Northwest Estuaries 

By Madeline O. Steele1, Heejun Chang1, Deborah A. Reusser2, Cheryl A. Brown3, and Il-Won Jung1

1. Portland State University, Portland, OR. 2. U.S. Geological Survey, Western Fisheries Research Center, Newport 

Duty Station, Newport, OR. 3. U. S. EPA, Western Ecology Division, Newport, OR.

Prepared in cooperation with the U.S. Environmental Protection Agency and the Oregon Climate Change Research 

Institute

Open-File Report 2012–1274

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Steele, M.O., Chang, Heejun, Reusser, D.A., Brown, C.A., and Jung, I.-W., 2012, 

Potential climate-induced runoff changes and associated uncertainty in four Pacific Northwest estuaries: 

U.S. Geological Survey Open-File Report 2012-1274, 63 p.

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

endorsement by the U.S. Government.

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

copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

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

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

Study Area ................................................................................................................................................................. 3

Coos Bay South Slough Estuary and Watershed................................................................................................... 3

Coquille River Estuary and Watershed................................................................................................................... 5

Willapa Bay Estuary and Watershed ...................................................................................................................... 6

Yaquina River Estuary and Watershed .................................................................................................................. 7

Data............................................................................................................................................................................... 7

Geographic Information System (GIS) Data............................................................................................................... 7

Flow Data................................................................................................................................................................... 7

Historical Climate Data............................................................................................................................................... 8

Future Climate Scenarios........................................................................................................................................... 8

Methods........................................................................................................................................................................12

Precipitation Runoff Modeling System (PRMS).........................................................................................................12

Calibration Flow Gage Selection ...........................................................................................................................14

HRU Delineation....................................................................................................................................................16

HRU Parameterization ..........................................................................................................................................20

Model Calibration ..................................................................................................................................................21

Use of Climate Scenarios..........................................................................................................................................25

Uncertainty Analysis .................................................................................................................................................25

Results..........................................................................................................................................................................26

Model Performance...................................................................................................................................................26

PRMS Outputs with Climate Scenarios.....................................................................................................................30

Percent Change in Monthly Mean Flow Rate from Reference Period to Future Period.........................................30

Changes in Monthly Coefficient of Variation (CV) from Reference to Future Period .............................................34

Changes in Other Indices from Reference Period to Future Period.......................................................................34

Uncertainty Analysis Results.....................................................................................................................................37

PRMS Parameter Uncertainty ...............................................................................................................................37

NARCCAP Uncertainty..........................................................................................................................................37

Discussion ....................................................................................................................................................................45

Study Limitations.......................................................................................................................................................45

Key Findings .............................................................................................................................................................45

Conclusions..................................................................................................................................................................45

Acknowledgments ........................................................................................................................................................46

References Cited..........................................................................................................................................................46

Appendix A. Presentations and Posters Based on this Research.................................................................................52
iv

Figures

Figure 1. Location of four study-area watersheds, Washington and Oregon...................................................4

Figure 2. U.S. Geological Survey National Land Cover Dataset Land Use-Land Cover composition in each of the 

four study-area watersheds, Oregon and Washington, 2011......................................................................................... 5

Figure 3. Terrine or geologic group composition in each of the four study-area watersheds, based on Oregon 

Geologic Data Compilation (Ma and others, 2009)........................................................................................................ 6

Figure 4. Examples of North American Regional Climate Change Assessment Program scenario outputs. ............ 9

Figure 5. Absolute change in mean maximum daily temperatures from reference period (1971–95) to future period 

(2041–65) by month, in the Yaquina Bay watershed, Oregon. .................................................................................... 11

Figure 6. Percent change in mean total daily precipitation from reference period (1971–95) to future period (2041–

65) by month, Yaquina Bay watershed, Oregon. ......................................................................................................... 11

Figure 7. Processes modeled by Precipitation Runoff Modeling System................................................................ 13

Figure 8. Calibration data for Coquille River watershed, Oregon............................................................................ 14

Figure 9. Calibration data for Yaquina Bay watershed, Oregon.............................................................................. 15

Figure 10. Calibration data for Willapa Bay watershed, Washington. ................................................................... 15

Figure 11. Calibration data for Coos Bay South Slough watershed, Oregon. ....................................................... 16

Figure 12. Hydrologic Response Units (HRUs) for Coos Bay South Slough watershed, Oregon. ........................ 17

Figure 13. Hydrologic Response Units (HRUs) for Coquille River watershed, Oregon......................................... 18

Figure 14. Hydrologic Response Units (HRUs) for Willapa Bay watershed, Washington. .................................... 19

Figure 15. Hydrologic Response Units (HRUs) for Yaquina Bay watershed, Oregon........................................... 20

Figure 16. Mean monthly performance of calibrated models. .............................................................................. 27

Figure 17. Monthly hydrograph of Coos Bay calibration sub-watershed, Oregon, Oct. 1982–Sept. 1991. ........... 28

Figure 18. Monthly hydrograph of Coquille River calibration sub-watershed, Oregon, Oct. 1982–Sept. 1991...... 28

Figure 19. Monthly hydrograph of Willapa Bay calibration sub-watershed, Washington, Oct. 1982– Sept. 1991. 29
v

Figure 20. Monthly hydrograph of Yaquina Bay calibration sub-watershed, Oregon, Oct. 1982–Sept. 1991........ 29

Figure 21. Percent change in mean monthly percent flow from reference period (1971–1995) to future period 

(2041–2065), Coos Bay South Slough watershed, Oregon. ........................................................................................ 31

Figure 22. Percent change in mean monthly percent flow from reference period (1971–1995) to future period 

(2041–2065), Coquille River watershed, Oregon......................................................................................................... 32

Figure 23. Percent change in mean monthly percent flow from reference period (1971–1995) to future period 

(2041–2065), Willapa Bay watershed, Washington. .................................................................................................... 32

Figure 24. Percent change in mean monthly percent flow from reference period (1971–1995) to future period 

(2041–2065), Yaquina Bay watershed, Oregon........................................................................................................... 33

Figure 25. Percent change in mean monthly percent flow from reference period (1971–1995) to future period 

(2041–2065), averaged across all four study-area watersheds. .................................................................................. 33

Figure 26. Climate scenario ensemble mean change in mean monthly percent flow from reference point (1971–

1995) to future period (2041–2065), by study-area watershed. ................................................................................... 34

Figure 27. Graph showing percent change in monthly coefficient of variation (CV) from reference period (1971–

1995) to future period (2041–2065), averaged across all four study-area watersheds................................................ 35

Figure 28. Graph showing climate scenario ensemble mean change in monthly coefficient of variation (CV) from 

reference period (1971–1995) to future period (2041–2065), by study-area watershed.............................................. 35

Figure 29. Graph showing percent change in flow in top 5 percent of days from reference period (1971–1995) to 

future period (2041–2065). .......................................................................................................................................... 36

Figure 30. Graph showing percent change in 7-day low flow from reference period (1971–1995) to future period 

(2041–2065). ............................................................................................................................................................. 36

Figure 31. Precipitation Runoff Modeling System parameter uncertainty for Coos Bay calibration sub-watershed, 

Oregon. (a) is a linear scale, and (b) is a logarithmic scale. ........................................................................................ 38
vi

Figure 32. Precipitation Runoff Modeling System parameter uncertainty for Coquille River calibration subwatershed, Oregon. (a) is a linear scale, and (b) is a logarithmic scale....................................................................... 39

Figure 33. Precipitation Runoff Modeling System parameter uncertainty for Willapa Bay calibration sub-watershed, 

Washington. (a) is a linear scale, and (b) is a logarithmic scale. ................................................................................. 40

Figure 34. Precipitation Runoff Modeling System parameter uncertainty for Yaquina Bay calibration subwatershed, Oregon. (a) is a linear scale, and (b) is a logarithmic scale....................................................................... 41

Figure 35. Box-and-whisker plots showing changes in top 5 percent high flow, 7-day low flow, and coefficient of 

variation for four study areas. The lines in the middle of boxes denote the median values, and the upper and lower 

boundaries of the boxes show the 25th and 75th percentiles, respectively. The red diamond symbol indicates outliers. 

A is for RCM3-CGCM, B is for HRM3-HADCM3, C is for CRCM-CGCM3, and D is for CRCM-CCSM. ...................... 42

Figure 36. Box-and-whisker plots showing changes in monthly runoff for four study areas. The lines in the middle 

of boxes denote the median values, and the upper and lower boundaries of the boxes show the 25th and 75th 

percentiles, respectively. The red diamond symbol indicates outliers. A is for RCM3-CGCM, B is for HRM3-HADCM3, 

C is for CRCM-CGCM3, and D is for CRCM-CCSM.................................................................................................... 44
vii

Tables

Table 1. Watershed area, estuary type, elevation, and slope for the four study-area watersheds........................... 6

Table 2. Geographic Information System data sources........................................................................................... 7

Table 3. Streamflow-gaging stations used in model calibration. .............................................................................. 8

Table 4. North American Regional Climate Change Assessment Program climate scenarios used to force 

Precipitation Runoff Modeling System models............................................................................................................. 10

Table 5. Number of Hydrologic Response Units (HRUs) in each watershed and calibration sub-watershed......... 20

Table 6. Hydrologic Response Unit parameters derived from Geographic Information System data. ................... 21

Table 7. Parameters used in model calibration...................................................................................................... 22

Table 8. Runoff ratios in all calibration sub-watersheds, and new runoff ratios in the Willapa calibration subwatershed after Climate Impacts Group adjustments. ................................................................................................. 24

Table 9. Precipitation Runoff Modeling System parameters used in uncertainty analysis. .................................... 26

Table 10. Model goodness of fit metrics. ............................................................................................................. 30
viii

Conversion Factors, Datums, Abbreviations and Acronyms

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd)

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 0.000811 acre-foot (acre-ft)

Flow rate

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

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

cubic meter per second (m3/s) 22.83 million gallons per day (Mgal/d)

Inch/Pound to SI

Multiply By To obtain

Volume

acre-foot (acre-ft) 1,233.00 cubic meter (m3)

acre-foot (acre-ft) 0.00123 cubic hectometer (hm3)

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or micrograms per 

liter (μg/L).

Datums

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88).

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

Elevation, as used in this report, refers to distance above sea level based on NAVD 88.
ix

Acronyms and Abbreviations

AOGCM Atmosphere-Ocean General Circulation Model

CCSM Climate Change Simulation Model

CIG Climate Impacts Group

COOP Cooperative Observer Program

CRCM-CCSM Canadian Regional Climate Model-NCAR Community Climate Model version 3

CRCM-CGCM3 Canadian Regional Climate Model-Canadian Global Climate Model version 3

CV Coefficient of Variation

DEM Digital Elevation Model

DO Dissolved Oxygen

DOGAMI Oregon Department of Geology and Mineral Industries

GCM Global Climate Models

GIS Geographic Information System

HRM3-HADCM3 Hadley Regional Model version 3-Hadley Centre Climate Model version 3

HRU Hydrologic Response Unit

LULC Land Use-Land Cover

MATLAB Matrix Laboratory

NARCCAP North American Regional Climate Change Assessment Program

NLCD National Land Cover Dataset

NRCS National Resources Conservation Service

NSE Nash Sutcliffe Efficiency

OCCRI Oregon Climate Change Research Institute

OWRD Oregon Water Resources Department

PNW Pacific Northwest

PRECIP Total Precipitation

PRISM Parameter-elevation Regressions on Independent Slopes Model

PRMS Precipitation Runoff Modeling System

R2 Coefficient of determination

RCM Regional Climate Model

RCM3-CGCM Regional Climate Model-(National Center for Atmospheric Research) Canadian 

Global Climate System Model version 3

SSURGO Soil Survey Geographic (SSURGO) Database

TASMAX Maximum daily surface air temperature

TASMIN Minimum daily surface air temperature

TMAX Maximum temperature

TMIN Minimum temperature

USGS U. S. Geological Survey
1

Potential Climate-Induced Runoff Changes and 

Associated Uncertainty in Four Pacific Northwest 

Estuaries

By Madeline O. Steele, Heejun Chang, Deborah A. Reusser, Cheryl A. Brown, and Il-Won Jung

Abstract

As part of a larger investigation into potential effects of climate change on estuarine habitats in 

the Pacific Northwest, we estimated changes in freshwater inputs into four estuaries: Coquille River 

estuary, South Slough of Coos Bay, and Yaquina Bay in Oregon, and Willapa Bay in Washington. We 

used the U.S. Geological Survey’s Precipitation Runoff Modeling System (PRMS) to model watershed 

hydrological processes under current and future climatic conditions. This model allowed us to explore 

possible shifts in coastal hydrologic regimes at a range of spatial scales. All modeled watersheds are 

located in rainfall-dominated coastal areas with relatively insignificant base flow inputs, and their areas

vary from 74.3 to 2,747.6 square kilometers. The watersheds also vary in mean elevation, ranging from 

147 meters in the Willapa to 1,179 meters in the Coquille. The latitudes of watershed centroids range 

from 43.037 degrees north latitude in the Coquille River estuary to 46.629 degrees north latitude in 

Willapa Bay. We calibrated model parameters using historical climate grid data downscaled to onesixteenth of a degree by the Climate Impacts Group, and historical runoff from sub-watersheds or 

neighboring watersheds. Nash Sutcliffe efficiency values for daily flows in calibration sub-watersheds 

ranged from 0.71 to 0.89. After calibration, we forced the PRMS models with four North American 

Regional Climate Change Assessment Program climate models: Canadian Regional Climate Model-

(National Center for Atmospheric Research) Community Climate System Model version 3, Canadian 

Regional Climate Model-Canadian Global Climate Model version 3, Hadley Regional Model version 3-

Hadley Centre Climate Model version 3, and Regional Climate Model-Canadian Global Climate Model 

version 3. These are global climate models (GCMs) downscaled with regional climate models that are 

embedded within the GCMs, and all use the A2 carbon emission scenario developed by the 

Intergovernmental Panel on Climate Change. With these climate-forcing outputs, we derived the mean 

change in flow from the period encompassing the 1980s (1971–1995) to the period encompassing the 

2050s (2041–2065). Specifically, we calculated percent change in mean monthly flow rate, coefficient 

of variation, top 5 percent of flow, and 7-day low flow. The trends with the most agreement among 

climate models and among watersheds were increases in autumn mean monthly flows, especially in 

October and November, decreases in summer monthly mean flow, and increases in the top 5 percent of 

flow. We also estimated variance in PRMS outputs owing to parameter uncertainty and the selection of 

climate model using Latin hypercube sampling. This analysis showed that PRMS low-flow simulations

are more uncertain than medium or high flow simulations, and that variation among climate models was 

a larger source of uncertainty than the hydrological model parameters. These results improve our 

understanding of how climate change may affect the saltwater-freshwater balance in Pacific Northwest

estuaries, with implications for their sensitive ecosystems.
2

Introduction

Estuaries of the Pacific Northwest (PNW) provide critical habitat for fish, shellfish, and fowl

that may be affected negatively as anthropogenic climate change leads to increasing air temperatures, 

sea level rise, shifts in the intensity and frequency of storms, changes in oceanic temperature and pH, 

and changes in the timing and volume of freshwater inputs (Ruggiero and others, 2010). To better 

understand and help partners prepare for these potential changes, the U.S. Geological Survey (USGS), 

through the National Climate Change and Wildlife Science Center, lead a multi-agency and interdisciplinary investigation in collaboration with the U.S. Environmental Protection Agency (EPA) to 

model the range of ecological and physical effects of climate shifts in PNW estuaries. The primary 

objectives of the research project are to predict the effect of climate-related alterations on the 

distribution and abundance of two species of oysters, two species of seagrass, and tidal marsh habitat. In 

many PNW estuaries (including Coos, Yaquina, and Willapa Bays), oysters provide commercially

important shellfish production and important ecosystem function, such as filtration of particulates from 

the water. Seagrasses provide critical habitats that serve as nurseries for estuarine fishes and crabs. 

Additionally, seagrasses and tidal marshes are important bird and juvenile salmon habitats. The 

interaction between freshwater flow into an estuary and seawater from the ocean creates a salinity 

regime that is vital to the existence of these critical habitats. 

As a part of the overall research effort, hydrological models were commissioned for four 

estuaries in Oregon and Washington coastal areas: South Slough of Coos Bay, Coquille River estuary, 

Willapa Bay, and Yaquina Bay (fig. 1). These estuaries were selected because they represent a range of 

estuary types, from a marine-dominated estuary (South Slough of Coos Bay) to a highly riverdominated estuary with a large watershed (Coquille River estuary), providing insight into how climatic 

drivers may alter different estuarine environments. Although the larger project is focused on estuaries, 

the models were developed for the entire area that drains to each estuary (that is, its watershed). Note 

that in two cases (Willapa Bay and Coos Bay South Slough), several small streams or rivers drain into a 

single estuary. In the models, we treated all such multi-tributary watersheds as single watersheds, and 

refer to them as such in this report. 

These hydrological models were developed to improve our understanding of how freshwater

inflows to these estuaries may shift under climate change. Such shifts could be important, as they may 

cause changes in estuarine salinity, water quality (Scavia and others, 2002), the distribution of habitat 

and health aquatic species (Roessing and others, 2004; Levinton and others, 2011), and lead to increases 

in the spread of invasive species (Stachowicz and others, 2002; Winder and others, 2011).

Different types of estuaries (marine- versus river-dominated) will have differing responses to 

changes in freshwater inflow. Salinity is an important factor in estuaries that influences the distribution 

of organisms as well as physical properties, such as stratification and mixing. Studies in other regions 

have demonstrated that climate change has the potential to influence estuarine salinity (Gibson and 

Najjar, 2000; Hilton and others, 2008). Climate change may influence the salinity distribution of 

estuaries, primarily through changes in freshwater inflow and rising sea level. The importance of each 

of these drivers on estuarine salinity will vary depending on season and location within an estuary.

Declines in the freshwater inflow during the low-flow period may result in increased intrusion of 

saltwater up the estuary and increased water residence time, which can affect water quality. Such 

changes can influence the distribution of estuarine organisms. For example, Levinton and others (2011)

show that changes in precipitation and resulting freshwater associated with climate change may result in 

increased mortality of oysters.

Freshwater inflow to estuaries can influence other key water-quality conditions. For instance, 

dissolved oxygen (DO) is an important water-quality metric because of its effects on the well-being of 
3

resident and transitory estuarine organisms, particularly salmonids. The State of Oregon DO criterion 

for estuaries (6.5 mg/L) is based on a review of physiological requirements of biota, and is high 

compared to DO criteria for other estuaries (U.S. Environmental Protection Agency, 2003). A review of 

the DO criterion found that 6.5 mg/L may be difficult to achieve in Oregon estuaries during the summer 

because of natural conditions (Oregon Department of Environmental Quality, 1995). There have been 

occurrences of severe hypoxia on the inner continental shelf of Oregon (Grantham and others, 2004; 

Chan and others, 2008). This low-oxygen water is periodically advected into PNW estuaries (Brown and 

Power, 2011; Roegner and others, 2011). Declines in freshwater inflow during the low-flow period 

combined with sea level rise may result in increased intrusion of low-oxygen water into the estuaries,

whereas in upriver parts of the estuaries, dissolved oxygen levels also may be influenced by climate 

change through increased stratification, reduced flushing, as well as temperature effects.

Another important factor for water quality in PNW estuaries is bacterial impairment, which can 

affect commercial aquaculture and can be influenced by climate change. As an example, high 

concentrations of fecal coliform in the Tillamook estuary (indicator of fecal contamination) in autumn

were preceded by dry conditions and high intensity rainfall (Sullivan and others, 2005). Climate 

projections for the PNW suggest that there may be decreases in summer precipitation and increases in 

winter precipitation. With this projected change in precipitation pattern, it is possible that there may be 

higher fecal coliform levels during autumn and early winter if there are sources in the watershed.

Study Area

Figure 1 shows locations of the watersheds draining to each of the four study estuaries, and table 

1 gives basic facts about each watershed. Estuary type information is from Lee and Brown (2009), and 

other facts were derived from a 30-m USGS DEM (Digital Elevation Model). In the PNW region, 

rainfall is highly seasonal, with more than 75 percent of precipitation falling between October and 

March. Temperatures are mild in these coastal watersheds because they are near the ocean, so most

precipitation falls as rain, even in winter.

Figure 2 shows the Land Use-Land Cover (LULC) in each watershed according to the 2001 

USGS National Land Cover Dataset (NLCD); evergreen forests are the predominant land cover class in 

all four watersheds, ranging from 36 to 60 percent of total area. All watersheds include some developed 

land, ranging from 5 to 10 percent of total area. Figure 3 shows the basic geological profile of each 

watershed. Throughout most of this region, marine sedimentary rocks, which have limited permeability, 

are the dominant type.

Coos Bay South Slough Estuary and Watershed

The Coos Bay South Slough watershed, the smallest of the four study watersheds with an area of

74.3 km2

, is located on the southern Oregon Coast (fig. 1). The study area is the watershed draining only 

into the South Slough of Coos Bay; most of Coos Bay is not modeled. Coos County, which contains the 

bay, has an economy primarily based on fish, lumber, and shellfish harvests. Although there are no 

towns or cities within the South Slough watershed, the cities of Coos Bay (population 15,967) and 

North Bend (population 9,695) (U.S. Census Bureau, 2011), are located within 5 km of the study area 

on the northern shores of Coos Bay. In 1974, the Coos Bay South Slough was declared the nation’s first 

National Estuarine Research Reserve, and research on estuaries and how to restore them has been 

conducted there ever since (Oregon Coastal Management Program, 2011b). As shown in figure 3, this 

watershed has a greater percentage of sedimentary rocks than any of the other watersheds. There are no 

dams or diversions within the watershed (Falk and Robison, 2010).
4

Figure 1. Location of four study-area watersheds, Washington and Oregon, with National Land Cover Dataset, 

2001. Watershed boundaries are from the USGS National Hydrography Dataset. Projection is WGS 1984 Web 

Mercator. Locator inset data is from Natural Earth, with NAD 1983 North America Albers Equal Area Conic.
5

Figure 2. U.S. Geological Survey National Land Cover Dataset Land Use-Land Cover composition in each of the 

four study-area watersheds, Oregon and Washington, 2001.

Coquille River Estuary and Watershed

The Coquille River estuary is at the mouth of the Coquille River, which drains a large watershed 

in southwest Oregon (fig. 1). As in Coos Bay, the primary economic foundations of the area are fishing 

and lumber, but dairy farming and other forms of agriculture also are common (Coquille Watershed 

Association, 2011). The 2,747.6 km2 watershed primarily is in Coos County, but small parts also are in 

Douglas and Curry Counties. The watershed contains three cities: Coquille (population 3,866), Myrtle 

Point (population 2,514), and Powers (population 689). It also includes parts of Bandon (population

3,066) near the Coquille River estuary (U.S. Census Bureau, 2011). 

Of the four study estuaries, the Coquille River estuary has the largest watershed and the 

strongest riverine influence. Nevertheless, saltwater penetrates the Coquille River, and tidal head has 

been observed as far as 41 mi upstream of the river’s mouth (Oregon Coastal Management Program, 

2011a). The watershed contains two major dams. Major dams have a height greater than or equal to 50 

ft (15 m), a normal storage capacity greater than or equal to 5,000 acre-feet (6,200,000 m3), or a 

maximum storage capacity greater than or equal to 25,000 acre-feet (31,000,000 m3). Combined, the 

two major dams have a normal storage capacity of 330 acre-feet (410,000 m3

) (National Atlas of the 

United States, 2006).

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coos Coquille Willapa Yaquina

Land use/land cover, percent

Open Water

Developed, Open Space

Developed, Low Intensity

Developed, Medium Intensity

Developed, High Intensity

Barren Land

Deciduous Forest

Evergreen Forest

Mixed Forest

Shrub/Scrub

Grassland/Herbaceous

Pasture/Hay

Cultivated Crops

Woody Wetlands

Emergent Herbaceous Wetlands
6

Figure 3. Terrine or geologic group composition in each of the four study-area watersheds, based on Oregon 

Geologic Data Compilation (Ma and others, 2009).

Table 1. Watershed area, estuary type, elevation, and slope for the four study-area watersheds.

Watershed Name Area

(km2) Estuary type

Mean elevation

(m)

Mean slope

(percent)

Coos Bay South

Slough 74.3 Marine-dominated 194 17.7

Coquille River 2,747.6 River-dominated 1,179 35.0

Willapa Bay 2,484.8 Marine-dominated 147 24.2

Yaquina Bay 650.7 Marine-dominated 482 31.3

Willapa Bay Estuary and Watershed

Willapa Bay is the second largest estuary on the Pacific Coast of the United States, and is home 

to the Willapa National Wildlife Refuge, which hosts more than 100,000 shorebirds during the spring 

migration (U.S. Fish and Wildlife Service, 2011). Willapa Bay’s 2,484.8 km2 watershed contains three 

cities: Raymond (population 7,892), South Bend (population 1,637), and Long Beach (population 1,392) 

(U.S. Census Bureau, 2011). The watershed also contains parts of the cities of Westport (population

6,661) and Ilwaco (population 936), as well as many unincorporated communities. There is one major 

dam in the watershed, with a normal storage of 846 acre-feet (1,000,000 m3

) (National Atlas of the 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coos Coquille Yaquina Willapa

Geologic group composition, percent

Marine Sedimentary Rocks

Sediments

Intrusive Rocks

Volcanic Rocks

Metamorphic Rocks

Melange Rocks

Other

Water

100

90

80

70

60

50

40

30

20

10

0
7

United States, 2006). As in all PNW coastal watersheds, timber plantations are a major part of the 

economy. Oyster production is especially important here (Wolf, 1993).

Yaquina River Estuary and Watershed

The 676.6-km2 Yaquina watershed is drained by the Yaquina River and its major tributary, Big 

Elk Creek, which enter the Pacific Ocean at Yaquina Bay. The watershed contains parts of Newport 

(population 9,989), all of Toledo (population 3,465) (Oregon Department of Transportation, 2009; U.S. 

Census Bureau, 2011), and several other communities. Major economic activities include lumber, 

fishing, tourism, and agriculture (Ame, 2007). At the mouth of the river, Yaquina Bay supports an 

estuary with an area of 17.5 km2, which provides critical spawning, breeding, and nesting habitat for 

many species, including endangered and threatened salmonids (Bauer and others, 2011). The Yaquina 

estuary is a drowned river mouth estuary (Oregon Coastal Management Program, 2011c) and is tidedominated (Lee and Brown, 2009). There are two major dams in the watershed (National Atlas of the 

United States, 2006).

Data

Geographic Information System (GIS) Data

Our analysis involved the use of data from numerous GIS sources, as listed in table 2. We used 

several GIS layers to create hydrologic response units (HRUs), or areas where the hydrologic response 

is assumed to be the same, for use in the Precipitation Runoff Modeling System (PRMS). We also used 

GIS to estimate parameter values for each HRU as explained in the “Methods” section.

Table 2. Geographic Information System data sources. 

Data source Resolution

USGS 30 m DEM1 30 m pixels

USGS 2001 LULC1 30 m pixels

USGS 2001 Percent Impervious 30 m pixels

DOGAMI Oregon Geology1 Varies from 1:12,000 to 1:500,000

Washington State Surface Geology1 1:100,000

NRCS SSURGO soil data1 Varies from 1:12,000 to 1:63,360

CIG (Climate Impacts Group)

historical climate data grid

1/8 degree grid (lat-long)

1 Indicates that the layer was used in Hydrologic Response Unit delineation.

Flow Data

To calibrate and test a PRMS model, a daily flow record is required. A flow record of 20–30 

years is ideal for model calibration and validation, and the minimum data requirement is about 6–7 

years. Flow records were not available for the mouths of any of the four watersheds of interest. 

Therefore, for each watershed, we identified a suitable gaged calibration sub-watershed or similar 

neighboring watershed, and used its flow record to develop the model. This process will be described in 

more detail in “Methods.” The four streamflow-gaging stations used are listed in table 3.
8

Table 3. Streamflow-gaging stations used in model calibration.

Watershed Gaging station

No.

Record length

(years)

Calibration period Verification period

Coos Bay South Slough OWRD 14324590 14 1982–1991 1991–1996

Coquille River USGS 14325000 28 1970–1984 1984–1998

Yaquina Bay USGS 14306030 19 1972–1982 1982–1991

Willapa Bay USGS 12010000 30 1970–1985 1985–2000

Historical Climate Data

Other required data for hydrologic modeling included historical minimum temperature (TMIN),

maximum temperature (TMAX), and total precipitation (PRECIP) at a daily time step. For these inputs, 

we used the synthetic weather grid data prepared by the Climate Impacts Group (CIG), University of 

Washington, as a part of the Columbia Basin Climate Change Scenarios Project (CBCCSP) (Hamlet and 

others, 2010). The gridded data, hereafter referred to as CIG data, are based on the Parameter-elevation 

Regressions on Independent Slopes Model (PRISM), which is an interpolation of historical station data

with consideration of orographic effects, temperature inversions, and coastal effects (Daly and others, 

2002; Salathe and others, 2007; PRISM Climate Group, 2012). CIG data points inside of or within 5 km 

of each calibration sub-watershed were averaged into a single data file. 

This approach has several advantages. Most importantly, climate inputs are representative of the 

entire sub-watershed, avoiding errors associated with generalization from a few weather stations to a 

large area. Orographic effects, which are considered in the CIG data, are especially important in 

mountainous and complex terrain such as that of the Coast Range. CIG data also have no gaps, so 

patching was not required, and the CIG period of record is long (1915–2006). Finally, using CIG 

historical climate data to calibrate all four coastal watershed models improved model comparability, 

because the smallest watersheds had no good candidates for historical weather stations, and had to be 

calibrated with CIG data. To confirm that the CIG data accurately reflected the historical record in our 

study areas, we selected nine weather COOP (COoperative Observer Program) stations that were inside 

of or within 10 km of the four calibration sub-watersheds (COOP ID 350471, 354133, 356820, 358182, 

358494, 358833, 453333, 455774, and 456914). We then compared PRECIP, TMAX, and TMIN values 

from each station to the values from its closest CIG grid point. Across the nine stations, the mean 

coefficients of determination (R2

) from simple linear regressions for PRECIP, TAMX, and TMIN were 

0.92, 0.97, and 0.99, respectively. This high level of fit supported the decision to use the CIG data.

Future Climate Scenarios

The other key datasets in this analysis were the future climate scenario outputs. We selected 

scenarios from the North American Regional Climate Change Assessment Program (NARCCAP) to 

increase compatibility with other aspects of the larger project on estuaries and climate change, and 

because they are state-of the-art scenarios. These climate change simulations are produced by 

embedding Regional Climate Models (RCMs) within global-scale Atmosphere-Ocean General 

Circulation Models (AOGCMs) (North American Regional Climate Change Assessment Program, 

2011a). By combining the models, the resolution of the outputs is improved greatly over those of an 
9

AOGCM alone. Outputs from an AOGCM and an AOGCM-RCM pair are shown in figure 4. The 

enhanced resolution is especially apparent over the Rocky Mountains.

Figure 4. Examples of North American Regional Climate Change Assessment Program scenario outputs. Outputs 

from an Atmosphere-Ocean General Circulation Model (AOGCM) are shown on the left, and outputs from an 

AOGCM-Regional Climate Model (RCM) pair are shown on the right (North American Regional Climate 

Change Assessment Program, 2011b).

Because of limited resources, the NARCCAP focuses on developing as many AOGCM-RCM

pairs as possible, rather than using multiple carbon emissions scenarios. The NARCCAP selected a 

single emission scenario, A2, which is at the higher end of the emissions scenarios, though it is not the 

highest. Like the other carbon emission scenarios commissioned by the Intergovernmental Panel on 

Climate Change, it is described in Nakicenvoic and others (2000). NARCCAP researchers argue that 

selecting a single high-emission scenario is reasonable because if we plan for extreme circumstances, 

we will also be prepared for moderate changes. The recent trajectory of carbon emissions (1990 to 

2011) also best fits a fairly high emission scenario (North American Regional Climate Change 

Assessment Program, 2011c). Although the NARCCAP eventually will release many different 

AOGCM-RCM simulations, at the time of this research (2012) only four were completed for our 

reference period (1971–1995) and a future period (2041–2065). These simulations are listed in table 4. 

We did not force the models with the ensemble mean, because this masks the variability of each 

scenario, and we were interested in seeing a range of possible future outcomes. Daily data for each 
10

watershed were extracted from the North American Regional Climate Change Assessment Program web

site by Darrin Sharp at the Oregon Climate Change Research Institute (OCCRI), Oregon State 

University. All the data points within a bounding box for each watershed were averaged to produce 

mean values for that watershed. Parameters used were TASMAX/TASMIN (maximum and minimum 

daily surface air temperature) and PRECIP (precipitation). The precipitation value was reported every 3 

hours, and had to be aggregated and averaged to compute a daily value.

Table 4. North American Regional Climate Change Assessment Program climate scenarios used to force 

Precipitation Runoff Modeling System models.

Acronym Regional Climate Model (RCM) Atmosphere-Ocean General 

Circulation Model (AOGCM)

CRCM_CCSM Canadian Regional Climate Model (Canada) NCAR Community Climate Model 

version 3 (United. States.)

CRCM_CGCM3 Canadian Regional Climate Model (Canada) Canadian Global Climate Model 

version 3 (Canada)

HRM3_HADCM3 Hadley Regional Model version 3 (United Kingdom) Hadley Centre Climate Model 

version 3 (United Kingdom)

RCM_CGCM3 Regional Climate Model produced by University of California at

Santa Cruz, version 3 (United. States.)

Canadian Global Climate Model 

version 3 (Canada)

Figures 5 and 6 show the changes in maximum daily temperature and total daily precipitation 

from the reference period to the future period in an example watershed (Yaquina Bay watershed). All 

four scenarios show increasing mean temperatures from the reference period to the future period (fig. 5), 

with CRCM-CGCM3 and HRM3-HADCM3 showing larger increases in summer. However, there is 

much less consistency in modeled changes in precipitation (fig. 6). In most months, some models show 

decreases in precipitation, and some show increases. Only in August and November is there agreement 

as to the direction of change. Changes in input data from the reference period to the future period vary 

among the watersheds, but this pattern of greater consistency in temperature than precipitation is present 

in all four study areas. Because the relation between precipitation and flow is direct in these lowpermeability, rain-dominated watersheds, such discrepancies in predicated precipitation make it difficult 

to model future runoff confidently.
11

Figure 5. Absolute change in mean maximum daily temperatures from reference period (1971–1995) to future 

period (2041–2065) by month, in the Yaquina Bay watershed, Oregon.

Figure 6. Percent change in mean total daily precipitation from reference period (1971–1995) to future period 

(2041–2065) by month, Yaquina Bay watershed, Oregon.

0

0.5

1

1.5

2

2.5

3

3.5

4

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Temperature (C)

Change in Mean Maximum

Temperature, Yaquina

CRCM_ccsm

CRCM_cgcm3

HRM3_hadcm3

RCM3_cgcm3

Ensemble mean

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Percent Change in Precipitation, Yaquina

CRCM_ccsm

CRCM_cgcm3

HRM3_hadcm3

RCM3_cgcm3

Ensemble mean
12

Methods

Precipitation Runoff Modeling System (PRMS)

We selected the Precipitation Runoff Modeling System (PRMS) because it has been used in 

many investigations and regularly improved (Markstrom and others, 2008). PRMS also has been used 

successfully in climate change impact assessments around the world (for example, Burlando and Rosso, 

2002; Legesse and others, 2003; Bae and others, 2007; Qi and others, 2009; Chang and Jung, 2010; 

Jung and Chang, 2011). 

PRMS is a semi-distributed, physically based surface runoff model developed by the USGS 

(Leavesley and others, 1983; Leavesley and Stannard, 1995). The model is composed of Hydrologic 

Response Units (HRUs) that are assumed to have a homogeneous hydrological response. PRMS 

computes a daily water balance for each HRU, and the sums of water and energy balances, weighted by 

each HRU’s relative area, yield daily watershed output values for flow, soil moisture, 

evapotranspiration, and other variables (Hay and others, 2009). The processes and water storage zones 

represented by equations in PRMS are shown in figure 7.
13

Figure 7. Processes modeled by Precipitation Runoff Modeling System.
14

Calibration Flow Gage Selection

Because only a part of each estuary’s watershed was gaged, we used calibration sub-watersheds

or neighboring similar watersheds in which the model outputs could be fitted to observed data. The 

calibrated parameter values then could be transferred to the ungaged watershed of interest. Several 

previous studies demonstrated the robustness of this approach (for example, Merz and Bloschl, 2004; 

Kay and others, 2006; Chang and Jung, 2010). When selecting calibration gages, we looked first for 

gaged sub-watersheds with the largest possible spatial and temporal coverage, and then for nearby 

watersheds with similar characteristics. We also avoided gages that were downstream of dams or major 

withdrawals. Calibration sub-watersheds for the Coquille River, Willapa Bay, and Yaquina Bay

watersheds are shown in figures 8, 9, and 10. The Coos Bay South Slough watershed had no gaged subwatershed, so we looked for a neighboring gaged watershed that was comparable in land use, elevation, 

area and other factors, and that had adequate temporal coverage. Selected calibration watersheds are 

shown in figure 11. 

In the remainder of this report, these calibration regions are referred to as “sub-watersheds” to 

avoid confusion with the estuary watersheds of interest (though note that the Coos Bay South Slough 

calibration watershed is not a true sub-watershed). The best available flow station for Coos Bay South 

Slough had a shorter-than-ideal period of record for PRMS (table 3). Additionally, given its location, 

Coos Bay South Slough may be influenced by marine tides. These two factors, along with the small size 

of the sub-watershed, make it more difficult to simulate its flow accurately. After identifying the flow 

gages, we delineated sub-watersheds for each gage and for the entire watershed of interest using a 30-m 

USGS DEM.

Figure 8. Calibration data for Coquille River watershed, Oregon.
15

Figure 9. Calibration data for Yaquina Bay watershed, Oregon.

Figure 10. Calibration data for Willapa Bay watershed, Washington.
16

Figure 11. Calibration data for Coos Bay South Slough watershed, Oregon.

HRU Delineation

With our watershed boundaries identified, the next step was to delineate HRUs. To do so, we 

made minor modifications to a method described by Leanen and Risley (1997). For their PRMS model 

of the Willamette River Basin in Oregon, they delineated HRUs based on geology, hydrologic soil 

group, land use-land cover, and slope and aspect. Slopes were classified as 0–5 percent, 5–30 percent, 

and greater than 30 percent. When slopes were greater than 30 percent, they were further subdivided by 

aspect (north, south, east, or west). 

Simplification steps were added to the Leanen and Risley (1997) method because the GIS layers 

currently available have much higher resolution than those available when the method was developed. 

Without simplification, tens of thousands of HRUs would be created for each watershed. To a point, 

increasing the number of HRUs improves accuracy, but above a threshold, model computation (or run) 

time increases, making it inefficient. We smoothed all input rasters, including slope, aspect, and LULC, 

with a majority filter. This filter replaces raster cell values based on the majority value of neighboring 

cells, which results in rasters with larger areas of homogeneity. When rasters are smoothed before they 

are used to define HRUs, fewer HRUs are ultimately produced. We also merged all HRUs with an area 

of less than 0.5 km2 into a neighboring HRU. Resulting HRUs are shown in figures 12–15; the number 

of HRUs in each watershed and calibration sub-watershed are given in table 5.
17

Figure 12. Hydrologic Response Units (HRUs) for Coos Bay South Slough watershed, Oregon.
18

Figure 13. Hydrologic Response Units (HRUs) for Coquille River watershed, Oregon.
19

Figure 14. Hydrologic Response Units (HRUs) for Willapa Bay watershed, Washington.
20

Figure 15. Hydrologic Response Units (HRUs) for Yaquina Bay watershed, Oregon.

Table 5. Number of Hydrologic Response Units (HRUs) in each watershed and calibration sub-watershed. [km2

, square kilometer]

Watershed Area

(km2)

Area of calibration

sub-watershed (km2)

Number of HRUs

in target watershed

Number of HRUs in

calibration sub-watershed

Coos 74.30 14.05 119 23

Coquille 2,747.61 446.40 542 90

Willapa 2,484.83 141.86 1,953 135

Yaquina 650.65 183.26 360 98

HRU Parameterization

For many PRMS parameters, such as land cover type and winter rain interception, we derived 

HRU values using GIS. We followed a detailed procedure on HRU parameterization prepared by the 

USGS (Lamorey, 2009). This method involves using zonal statistics to derive mean values for each 

parameter from various layers, many of which were functions of the USGS 2001 LULC layer, Soil 
21

Survey Geographic (SSURGO) soil data, or a DEM. For example, summer rain interception is 

calculated for each HRU based on the vegetation density, which is generated from the LULC map. The 

20 HRU parameters derived directly or indirectly from GIS layers are listed in table 6. For a full 

description of the equations and GIS techniques involved in their derivation, see Lamorey (2009). We 

used static values for the geospatial parameters in reference and future periods, although land cover has 

changed during the study period, and likely will vary in the future periods. We did not have an LULC 

layer from the start of the reference period, nor were future LULC scenarios available. It was beyond 

the scope of the study to prepare such LULC layers, so static parameter values were used.

Model Calibration

To calibrate the models, we used an iterative approach, and focused on adjusting parameters 

known to be sensitive in rain-dominated watersheds (U.S. Geological Survey, 2011). After each change, 

we assessed goodness of fit scores for a calibration period (table 3), reserving data from a verification 

period for a final evaluation. First, we focused on fitting the overall monthly water balance. Next, we

attempted to fit high-flow periods. Finally, we attempted to fit low-flow periods. The parameters used in 

calibration, and their final value for each sub-watershed, are listed in table 7. Any parameters not listed 

here or in table 6 were assigned default values or were borrowed from USGS PRMS example models. 

We used default values for all parameters relating to snow accumulation and melt processes because

these parameters are not sensitive in the rain-dominated coastal region. For more information about our 

calibration methods, see Chang and Jung (2010).

For one calibration sub-watershed, we also made adjustments to the historical CIG precipitation 

data. Direct manipulation of data inputs is generally not reasonable in hydrological modeling, but in this 

case, we had evidence that precipitation had been underestimated. A similar approach was used in 

assessing the effects of climate change on the hydrology of the Columbia River basin (Elsner and 

others, 2010). Specifically, in the calibration watershed for Willapa Bay, the annual ratio of CIG input 

precipitation to runoff (the runoff ratio) was 90 percent (table 8). This value is unrealistically high for a

watershed with almost no impervious surfaces (Jung and others, 2011).

Table 6. Hydrologic Response Unit parameters derived from Geographic Information System data.

Name Description GIS data source

cov_type Vegetation cover type for each HRU (bare, grass, shrubs, trees) 2001 USGS National LULC

covden_sum Summer vegetation cover density for each HRU 2001 USGS National LULC

covden_win Winter vegetation cover density for each HRU 2001 USGS National LULC

hru_aspect Mean aspect of each HRU USGS 30 m DEM

hru_elev Mean elevation of each HRU USGS 30 m DEM

hru_lat Latitude of each HRU Calculated with ArcMap®

hru_percent_imperv Proportion of each HRU area that is impervious 2001 USGS National 

Impervious Surface

hru_slope Mean slope of each HRU USGS 30 m DEM

hru_type Type of each HRU (land, water, swale) 2001 USGS National LULC
22

jh_coef_hru Air temperature coefficient used in Jensen-Haise PET 

computations for each HRU.

2001 USGS National LULC

rad_trncf Transmission coefficient for short-wave radiation through the 

winter vegetation canopy

2001 USGS National LULC

snow_intcp Snow interception storage capacity for each HRU 2001 USGS National LULC

snow_intcp Snow interception storage capacity for the major vegetation type 

in each HRU

2001 USGS National LULC

soil_moist_max Maximum available water holding capacity of soil profile for each 

HRU

USDA SSURGO and USGS 

LULC

soil_rechr_max Maximum value for soil recharge zone for each HRU USDA SSURGO and USGS 

LULC

soil_type HRU soil type (sand, clay, or loam) USDS SSURGO Soil Maps

srain_intcp Summer rain interception storage capacity each HRU 2001 USGS National LULC

tmax_adj Adjustment to max. temp. for each HRU based on slope and 

aspect

USGS 30 m DEM

tmin_adj Adjustment to min. temp. for each HRU based on slope and aspect USGS 30 m DEM

wrain_intcp Winter rain interception storage capacity each HRU 2001 USGS National LULC

John Risley of the USGS recommended that we check these findings by estimating annual water 

budgets for this watershed using PRISM (Parameter-elevation Regressions on Independent Slopes 

Model) data, mean annual runoff depth from USGS annual data reports, and evapotranspiration 

estimates from the National Oceanic and Atmospheric Administration evaporation atlas (Farnsworth 

and others, 1982). We did so, and determined that the runoff ratio was also too high in this estimated 

water budget. Because the CIG data are derived from PRISM data, this is not surprising; both datasets 

share the same probable underestimation. The origin of the error may be under-catch in the weather 

stations used to derive the PRISM dataset; Legates and Deliberty (1993) estimate precipitation undercatch due to wind at more than 18 mm per winter month in this region. Fog drip, or occult precipitation, 

also has been estimated at 30 percent of total precipitation in a Douglas-fir forest in the northern Oregon 

Cascades (Harr, 1982), and it likely is important in the Willapa calibration sub-watershed as well. 

Therefore, we were justified in manipulating the precipitation inputs. We did this by multiplying all 

daily precipitation totals by 1.1 for September–April, but because of greater flow underestimation in 

summer, we used 1.3 for May, June, and August, and 1.4 for July. Improved runoff ratios are shown in 

table 8. Final model performance and calibration scores are presented in the results section.

After adjusting the CIG data and calibrating the sub-watersheds, we created models of the 

ungaged study watersheds using the same parameter values as were used in their respective calibration 

sub-watersheds.

Table 7. Parameters used in model calibration. Some GIS-derived values were multipled by a coefficient during 

calibration; this is indicated by GIS Value * (some number).

Name Description Units Possible 

range

Default

Calibrated value

Coos

Bay

Coquille

River

Willapa

Bay

Yaquina

Bay

carea_max Maximum possible area 

contributing to surface

runoff expressed as a 

portion of the HRU area

Decimal 

fraction

0 to 1 0.6 0.6 0.6 0.3 0.3

crad_coef Coefficient (B) in 

Thompson (1976) equation

None 0.1 to 0.7 0.4 0.7 0.7 0.7 0.7
23

Name Description Units Possible 

range

Default

Calibrated value

Coos

Bay

Coquille

River

Willapa

Bay

Yaquina

Bay

fastcoef_lin Coefficient to route 

preferential-flow storage 

down slope

1 per day 0 to 1 0.1 0.1 0.5 0.7 0.7

fastcoef_sq Coefficient to route 

preferential-flow storage 

down slope

None 0 to 1 0.8 1 1 1 1

gwflow_coef Groundwater routing 

coefficient

1/day 0 to 1 0.015 0.025 0.017 0.017 0.017

gwstor_init Storage in each groundwater 

reservoir at the beginning of 

a simulation

Inches 0 to 20 0.1 0.1 0.1 3 3

imperv_stor_max Maximum impervious area 

retention storage for each 

HRU

Inches 0 to 10 0 0 0.1 0 0.1

jh_coef Monthly air temperature 

coefficient used in JensenHaise PET computations

Per 

degrees

0.005 to 

0.06

0.014 0.025 

to 0.05

0.006 

to 0.02

.009 to 

.013

0.007 

to 

0.036

jh_coef_hru Air temperature coefficient 

used in Jensen-Haise 

potential evapotranspiration 

computations for each 

HRU.

Per 

degrees

5 to 20 13 GIS 

value * 

0.8

GIS 

value

GIS 

value

GIS 

value

melt_look Julian date to start looking 

for spring snowmelt stage

Julian 

day

1 to 366 90 1 1 1 1

pref_flow_den Preferential-flow pore 

density

decimal 

fraction

0 to 1 0.2 0.2 0.2 0.2 0.1

sat_threshold Soil saturation threshold, 

above field-capacity 

threshold

inches 1 to 999 999 999 999 999 15

slowcoef_lin Coefficient to route gravityflow storage down slope1 per day 0 to 1 0.1 0.015 0.1 0.003 0.003

smidx_coef Coefficient in non-linear 

contributing area algorithm

Decimal 

fraction

0.0001 to 

1

0.01 0.001 0.001 0.001 0.001

snarea_curve Snow area depletion curve 

values, 11 values for each 

curve (0.0 to 1.0 in 0.1 

increments)

Decimal 

fraction

0 to 1 1 0.05 to 

1

0.05 to 

1

0.05 to 

1

0.05 to 

1

soil_moist_init Initial value of available 

water in soil profile

Inches 0 to 20 3 3 3 0.1 0.1

soil_moist_max Maximum available water 

holding capacity of soil 

profile for each HRU

Inches 0 to 20 6 GIS 

value * 

0.8

GIS 

value

GIS 

value

GIS 

value *

1.5

soil_rechr_max Maximum value for soil 

recharge zone (upper 

portion of soil_moist where 

losses occur as both 

evaporation and 

transpiration).

Inches 0 to 10 2 GIS 

value * 

0.8

GIS 

value

GIS 

value

GIS 

value

soil2gw_max The maximum amount of 

the soil water excess for an 

HRU that is routed directly 

to the associated 

Inches 0 to 5 0 0.05 0.6 0.1 0.1
24

Name Description Units Possible 

range

Default

Calibrated value

Coos

Bay

Coquille

River

Willapa

Bay

Yaquina

Bay

groundwater reservoir each 

day

ssr2gw_exp Coefficient in equation used 

to route water from the 

subsurface reservoirs to the 

groundwater reservoirs

None 0 to 3 1 1 0.5 0.5 0.5

ssr2gw_rate Coefficient in equation used 

to route water from the 

subsurface reservoirs to the 

groundwater reservoirs

1 per day 0 to 1 0.1 0.15 0.001 0.001 0.001

tmax_allrain If maximum temperature of 

an HRU is greater than or 

equal to this value (for each 

month, January to 

December), precipitation is 

assumed to be rain, in deg C 

or F, depending on units of 

data

Degrees 0 to 90 40 41 to 

74

41 to 

74

41 to 

74

41 to 

74

tmax_lapse Change in min. temp. per 

1000 elev_units of elevation 

change for each month

Degrees -10 to 10 3 -2.0 to 

-0.6

-9.2 to 

10

-2.0 to 

-0.6

-4.0 to 

-1.3

tmin_lapse Change in min. temp. per 

1000 elev_units of elevation 

change for each month

Degrees -10 to 10 3 -1.3 to 

-0.2

-4.7 to 

0

-1.3 to 

-0.2

-2.57 to 

-0.47

transp_beg Month to begin summing 

tmaxf for each HRU; when 

sum is >= to transp_tmax, 

transpiration begins

Month 1 to 12 4 4 4 1 1

transp_end Month to stop transpiration 

computations; transpiration 

is computed thru end of 

previous month

Month 1 to 12 10 10 10 12 12

Table 8. Runoff ratios in all calibration watersheds, and new runoff ratios in the Willapa calibration watershed after 

Climate Impacts Group adjustments.

[Qmeas- measured flow; Qsim- simulated flow; PCIG- CIG precipitation]

Calibration subwatershed Qmeas/PCIG

Qmeas/PCIG after CIG 

adjustments Qsim/PCIG

Qsim/PCIG after CIG 

adjustments Reference period

Coos 0.47 N/A 0.43 N/A 1982–1996

Coquille 0.69 N/A 0.68 N/A 1970–1996

Willapa 0.90 0.80 0.74 0.78 1970–2000

Yaquina 0.62 N/A 0.62 N/A 1972–1991
25

Use of Climate Scenarios

As described in “Data”, we obtained four NARCCAP simulations with complete daily records 

for a reference period (1971–1995) and a future period (2041–2065) (table 4). We used these data to 

force the calibrated models of the watersheds of interest (not the calibration sub-watersheds), and then 

compared reference and future outputs within models to calculate potential climate-induced runoff 

changes. Although we adjusted the precipitation inputs used to calibrate for Willapa Bay, we did not 

make any changes to the NARCCAP precipitation data because these different climate models would 

not necessarily share the same biases. The final outputs are summarized in “Results”.

Uncertainty Analysis

All physical modeling of future conditions involves uncertainty. In hydrologic modeling, the 

primary sources of uncertainty are future climate and emissions scenarios. Precipitation is especially 

uncertain, and becomes more uncertain under high carbon emissions scenarios. Hydrological model 

parameter values also contribute to uncertainty (Praskievicz and Chang, 2009). Because PRMS uses 

many parameters, there is a strong likelihood of equifinality (Beven and Freer, 2001; Kirchner, 2006; 

Beven and others, 2007), which means that many different combinations of parameter values would all 

give rise to a statistically acceptable outcome. That is, one set of parameter values can score as well as 

another, even if it poorly reflects the physical reality. This results in uncertainty in the accuracy of 

future projections. Future climate models also introduce uncertainty to the simulations, especially in 

their precipitation estimates (fig. 6). To estimate the degree of uncertainty owing to PRMS and climate 

models, we conducted an uncertainty analysis.

After Chang and Jung (2010), we used a modified version of a method first suggested by Wilby 

(2005) to estimate the amount of uncertainty associated with each source. We selected 13 sensitive 

calibration parameters (table 9) and generated random values within the acceptable range for each 

parameter. A total of 20,000 random sets of parameters were generated for each calibration subwatershed using Latin hypercube sampling in Matrix Laboratory (MATLAB®) (MathWorks, Natick, 

Massachusetts). Wilby (2005) used Monte Carlo sampling to generate his parameter sets, but we used

Latin hypercube sampling instead because it has been shown to be more effective and efficient than 

Monte Carlo sampling (Davey, 2008). Each parameter set then was used in PRMS to simulate runoff, 

and the performance of each set was evaluated. 

We then selected parameter sets that performed well for each calibration sub-watershed. Our 

criteria were: a Nash Sutcliffe Efficiency (NSE) coefficient of 0.8 or more, an index of agreement of 

greater than or equal to 0.85, and an annual percent bias of no more than plus-or-minus 10 percent. All 

performance scores were calculated using daily data. The NSE is a commonly used measure of 

goodness of fit in hydrologic models; as a model improves, its NSE approaches 1 (Nash and Sutcliffe, 

1970). We changed the criteria slightly for the Coos Bay calibration sub-watershed because none of its 

20,000 randomly generated parameter sets had an NSE of 0.8 or more. We lowered the cutoff for this 

sub-watershed to an NSE of 0.7, but kept the other criteria the same. After identifying the best 

parameter sets for each calibration sub-watershed, we used them to find the uncertainty associated with 

PRMS parameters. 

To estimate uncertainty owing to climate models, we translated the best selected parameter sets 

to the ungaged study watersheds, and then forced these models with the four climate scenarios (table 4). 

We used these outputs to find uncertainty in estimated changes in monthly flow, coefficient of variation 

(CV), top 5 percent of flow, and 7-day low flow from the reference period to the future period. The CV 

is the ratio of the standard deviation to the mean, and frequently is used as a metric of variability in 

flow. The top 5 percent, or Q5, is the mean discharge rate of the 5 percent of days with the highest flow 
26

for each year. Seven-day low flow is the lowest 7-day running mean flow for each year (Chang and 

Jung, 2010). This metric is similar to 7Q10, a standard hydrologic metric, but is averaged over 30 years 

instead of 10 years. All findings are presented in “Results”. Because all four climate scenarios use one 

emission scenario (as discussed in “Data”), uncertainty in climate projections is greater than the results 

indicate.

Table 9. Precipitation Runoff Modeling System parameters used in uncertainty analysis.

Name Description Possible range

carea_max Maximum possible area contributing to surface runoff expressed as a portion 

of the HRU area

0 to 1

crad_coef Coefficient (B) in Thompson (1976) equation 0.1 to 0.7

fastcoef_lin Coefficient to route preferential-flow storage down slope 0 to 1

fastcoef_sq Coefficient to route preferential-flow storage down slope 0 to 1

gwflow_coef Groundwater routing coefficient 0 to 1

pref_flow_den Preferential-flow pore density 0 to 1

slowcoef_lin Coefficient to route gravity-flow storage down slope 0 to 1

slowcoef_sq Coefficient to route gravity-flow storage down slope 0 to 1

smidx_coef Coefficient in non-linear contributing area algorithm 0.0001 to 1

smidx_exp Coefficient in non-linear contributing area algorithm 0.2 to 0.8

soil2gw_max The maximum amount of the soil water excess for an HRU that is routed 

directly to the associated groundwater reservoir each day

0 to 5

ssr2gw_exp Coefficient in equation used to route water from the subsurface reservoirs to 

the groundwater reservoirs

0 to 3

ssr2gw_rate Coefficient in equation used to route water from the subsurface reservoirs to 

the groundwater reservoirs

0 to 1

Results

Model Performance

Four modeled goodness-of fit metrics: NSE, correlation coefficient, index of agreement, and 

annual percent bias are shown in table 10. All scores are based on daily data. Mean simulated and 

measured monthly flow rates are shown in figure 16, and a monthly hydrograph for each sub-watershed 

for October 1982–September 1991 is shown in figures 17–20.

Together, the figures and table show that the calibration sub-watersheds performed well. For 

example, annual percent bias was less than plus-or-minus 5 percent in all cases, and the NSE was 0.8 or 

greater for all watersheds except for the Coos Bay South Slough calibration sub-watershed. The Coos 

Bay South Slough calibration sub-watershed performed more poorly than the other watersheds by all 

measures, which is not surprising, given its short period of record, geologic composition, small area, 

and the likely influence of tides close to the ocean. Peak flows were underestimated in this subwatershed (fig. 17), in the Willapa Bay sub-watershed (fig. 19), and, to a lesser extent, in the Yaquina 
27

Bay sub-watershed (fig. 20). This systematic underestimation bias in peak flows could be due to an 

underlying weakness in the PRMS model, precipitation under-catch, or some combination thereof. 

Overall, however, performance was satisfactory, because the NSE scores were above the 0.6 cutoff 

suggested in Choi and Beven (2007).

Figure 16. Mean monthly performance of calibrated models. 

0

5

10

15

20

25

30

35

Oct

NovDec

Jan

Feb

Mar

Apr

May

Jun

Jul

AugSep

Flow (m3/s)

Coos

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

Oct

NovDec

Jan

Feb

Mar

Apr

May

Jun

Jul

AugSep

Flow (m3/s)

Coquille

0

200

400

600

800

1,000

1,200

Oct

NovDec

Jan

Feb

Mar

Apr

May

Jun

Jul

AugSep

Flow (m3/s)

Willapa

0

100

200

300

400

500

600

700

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Flow (m3/s)

Yaquina
28

Figure 17. Monthly hydrograph of Coos Bay calibration sub-watershed, Oregon, October 1982–September 1991.

Figure 18. Monthly hydrograph of Coquille River calibration sub-watershed, Oregon, October 1982–September 1991.

0

0.5

1

1.5

2

2.5

Oct-82

Jan-83

Apr-83

Jul-83

Oct-83

Jan-84

Apr-84

Jul-84

Oct-84

Jan-85

Apr-85

Jul-85

Oct-85

Jan-86

Apr-86

Jul-86

Oct-86

Jan-87

Apr-87

Jul-87

Oct-87

Jan-88

Apr-88

Jul-88

Oct-88

Jan-89

Apr-89

Jul-89

Oct-89

Jan-90

Apr-90

Jul-90

Oct-90

Jan-91

Apr-91

Jul-91

Avg Flow (m3/s)

Coos Bay

Measured

Simulated

0

20

40

60

80

100

120

Oct-82

Jan-83

Apr-83

Jul-83

Oct-83

Jan-84

Apr-84

Jul-84

Oct-84

Jan-85

Apr-85

Jul-85

Oct-85

Jan-86

Apr-86

Jul-86

Oct-86

Jan-87

Apr-87

Jul-87

Oct-87

Jan-88

Apr-88

Jul-88

Oct-88

Jan-89

Apr-89

Jul-89

Oct-89

Jan-90

Apr-90

Jul-90

Oct-90

Jan-91

Apr-91

Jul-91

Avg Flow (m3/s)

Coquille

Measured

Simulated
29

Figure 19. Monthly hydrograph of Willapa Bay calibration sub-watershed, Washington, October 1982– September 1991.

Figure 20. Monthly hydrograph of Yaquina Bay calibration sub-watershed, Oregon, October 1982–September 1991.

0

10

20

30

40

50

Oct-82

Jan-83

Apr-83

Jul-83

Oct-83

Jan-84

Apr-84

Jul-84

Oct-84

Jan-85

Apr-85

Jul-85

Oct-85

Jan-86

Apr-86

Jul-86

Oct-86

Jan-87

Apr-87

Jul-87

Oct-87

Jan-88

Apr-88

Jul-88

Oct-88

Jan-89

Apr-89

Jul-89

Oct-89

Jan-90

Apr-90

Jul-90

Oct-90

Jan-91

Apr-91

Jul-91

Avg Flow (m3/s)

Willapa Measured

Simulated

0

5

10

15

20

25

30

35

Oct-82

Jan-83

Apr-83

Jul-83

Oct-83

Jan-84

Apr-84

Jul-84

Oct-84

Jan-85

Apr-85

Jul-85

Oct-85

Jan-86

Apr-86

Jul-86

Oct-86

Jan-87

Apr-87

Jul-87

Oct-87

Jan-88

Apr-88

Jul-88

Oct-88

Jan-89

Apr-89

Jul-89

Oct-89

Jan-90

Apr-90

Jul-90

Oct-90

Jan-91

Apr-91

Jul-91

Avg Flow (m3/s)

Yaquina Measured

Simulated
30

Table 10. Model goodness of fit metrics.

Calibration

Sub-watershed

Nash-Sutcliffe 

Efficiency

(NSE)

Correlation 

coefficient

(r)

Index of 

agreement

(d)

Annual bias

(percent)

Coos Bay

Calibration period 0.73 0.85 0.91 4.92

(1982–1991)

Verification Period 0.69 0.84 0.91 -3.43

(1991–1996)

Total period 0.71 0.85 0.91 1.73

(1982–1996)

Coquille River

Calibration period 0.87 0.93 0.96 -3.38

(1970–1984)

Verification period 0.81 0.91 0.95 0.52%

(1984–1996)

Total Period 0.84 0.92 0.96 -1.90

(1970–1996)

Willapa Bay

Calibration period 0.88 0.94 0.97 -1.10

(1970–1985)

Verificationperiod 0.90 0.95 0.97 -4.30

(1985–2000)

Total period 0.89 0.94 0.97 -2.69

(1970–2000)

Yaquina Bay

Calibration period 0.87 0.94 0.97 2.55

(1972–1982)

Verification period 0.83 0.92 0.96 -3.53

(1982–1991)

Total period 0.85 0.93 0.96 -0.37

(1972–1991)

PRMS Outputs with Climate Scenarios

Percent Change in Monthly Mean Flow Rate from Reference Period to Future Period

The outputs produced by forcing the ungaged study watershed models with NARCCAP 

scenarios are summarized in figures 21–30. Figures 21–24 show the percent change in mean 
31

monthly flow from the reference period to the future period. Each watershed has its own chart, 

and the different NARCCAP scenarios are symbolized by contrasting colors. The inconsistency 

in the direction of change among the scenarios is striking. In most months, CRCM-CGCM3 and 

RCM3-CGCM3 (which share an AOGCM; see table 4) show increasing trends, whereas the 

other two scenarios show decreasing trends throughout much of the year. Only in October and 

November is there more agreement among the models; in these months, almost all models show 

increasing mean flow. This is clear in figure 25, which shows the inter-watershed mean percent 

change for each month. However, the September values for the Coquille River watershed do not 

agree with the general pattern (fig. 22). This may be because the Coquille River watershed is the 

southernmost in this study, and is likely to have differing future precipitation and temperature. In 

figure 26, the ensemble mean of the four climate scenarios is displayed, and the different colored 

bars represent watersheds rather than scenarios. Here, the mean increases in October and 

November flow are even more apparent. Decreases in summer flow (July and August especially) 

also become evident in figure 26, because although the direction of the trend varies among the 

scenarios for most watersheds in this period, the magnitude of the decreasing trends is much 

greater, and so the ensemble mean shows a decrease.

Figure 21. Percent change in mean monthly percent flow from reference period (1971–1995) to future 

period (2041–2065), Coos Bay South Slough watershed, Oregon.

-40%

-20%

0%

20%

40%

60%

80%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Coos Bay South Slough Mean Flow Change

CRCM_ccsm CRCM_cgcm3 HRM3_hadcm3 RCM3_cgcm3
32

Figure 22. Percent change in mean monthly percent flow from reference period (1971–1995) to future 

period (2041–2065), Coquille River watershed, Oregon.

Figure 23. Percent change in mean monthly percent flow from reference period (1971–1995) to future 

period (2041–2065), Willapa Bay watershed, Washington.

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Coquille River Mean Flow Change

CRCM_ccsm CRCM_cgcm3 HRM3_hadcm3 RCM3_cgcm3

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Willapa Bay Mean Flow Change

CRCM_ccsm CRCM_cgcm3 HRM3_hadcm3 RCM3_cgcm3
33

Figure 24. Percent change in mean monthly percent flow from reference period (1971–1995) to future 

period (2041–2065), Yaquina Bay watershed, Oregon.

Figure 25. Percent change in mean monthly percent flow from reference period (1971–1995) to future 

period (2041–2065), averaged across all four study-area watersheds.

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Yaquina Bay Mean Flow Change

CRCM_ccsm CRCM_cgcm3 HRM3_hadcm3 RCM3_cgcm3

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Inter-watershed Mean Flow Change

CRCM_ccsm CRCM_cgcm3 HRM3_hadcm3 RCM3_cgcm3
34

Figure 26. Climate scenario ensemble mean change in mean monthly percent flow from reference point

(1971–1995) to future period (2041–2065), by study-area watershed.

Changes in Monthly Coefficient of Variation (CV) from Reference to Future Period

As shown in figures 27 and 28, CV shows a small increase in most months (September–

May) and for most scenarios. In July and August, however, CV decreases, probably because of 

fewer precipitation events and, thus, lower and steadier flow rates. The most dramatic change, 

however, is a spike in September CV in the HRM3-HADCM3 and RCM3-CGCM3 scenarios 

(fig. 27). Even when all scenarios are averaged (fig. 28), this increase is the most notable result. 

The increase could be owing to the fact that more rainstorms and associated variability in flow 

will be more likely in September with climate change.

Changes in Other Indices from Reference Period to Future Period

We also investigated percent change in the top 5 percent of flow (fig. 29) and in the 7-day 

low flow (fig. 30). The Willapa Bay and Yaquina Bay watersheds show likely increases in top 5

percent of flow because all four models agree as to sign, and three of four scenarios show 

increases of more than 5 percent from the reference period to the future period. The percent 

change in 7-day low flow shows less agreement among climate scenarios, although there is an 

ensemble mean decrease for all watersheds except the Yaquina Bay watershed. The differing 

value in the Yaquina Bay watershed may arise from local variability in the RCMs. In an 

investigation of GCM downscaling techniques for hydrologic modeling, Wood and others (2004) 

determined that data downscaled with an RCM gave hydrologically probable results only after 

bias correction. Poor resolution of realism of the RCMs also may be a factor here. Given the 

higher degree of uncertainty associated with these low flows, and the lack of a strong signal, it is 

better not to rely on the ensemble mean in this case.

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ensemble Mean Flow Change

Coos Coquille Yaquina Willapa
35

Figure 27. Graph showing percent change in monthly coefficient of variation (CV) from reference period 

(1971–1995) to future period (2041–2065), averaged across all four study-area watersheds.

Figure 28. Graph showing climate scenario ensemble mean change in monthly coefficient of variation (CV)

from reference period (1971–1995) to future period (2041–2065), by study-area watershed.

-60%

-40%

-20%

0%

20%

40%

60%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Inter-watershed Mean Change in CV

CRCM_ccsm CRCM_cgcm3 HRM3_hadcm3 RCM3_cgcm3

-40%

-20%

0%

20%

40%

60%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ensemble Mean Change in CV

Coos Coquille Yaquina Willapa
36

Figure 29. Graph showing percent change in flow in top 5 percent of days from reference period (1971–

1995) to future period (2041–2065).

Figure 30. Graph showing percent change in 7-day low flow from reference period (1971–1995) to future 

period (2041–2065).

-10%

0%

10%

20%

30%

40%

50%

60%

Coos Coquille Willapa Yaquina

Change in Top 5 Percent of Flow Days

CRCM_ccsm

CRCM_cgcm3

HRM3_hadcm3

RCM3_cgcm3

Ensemble Mean

-30%

-20%

-10%

0%

10%

20%

30%

Coos Coquille Willapa Yaquina

Change in 7-day Low Flow

CRCM_ccsm

CRCM_cgcm3

HRM3_hadcm3

RCM3_cgcm3

Ensemble Mean
37

Uncertainty Analysis Results

PRMS Parameter Uncertainty

Figures 31, 32, 33, and 34 show the uncertainty owing to model parameters in the four 

calibration sub-watersheds. In each figure, the red points represent observed flow values, and the 

gray region shows the range of values generated by the randomly generated parameter sets that 

met performance criteria described in ”Methods”. For each model, results are displayed on a 

linear scale (a in figures) and logarithmic scale (b in figures), so that high- and low-flow 

uncertainty can be readily seen. In each model, the same 6-year period (October 1984–

September 1990) is shown, and the X- axis represents number of days. The logarithmic charts 

show that the uncertainty of low flow is high, which agrees well with previous research (for 

example, Chang and Jung, 2010). The smallest calibration sub-watershed, for the Coos Bay 

South Slough, shows consistent under-prediction of the high flow, regardless of the parameter set 

used. The range of possible values does not include the observed data point in several cases. This 

may suggest a systematic bias in PRMS that becomes more obvious in smaller watersheds, and it 

also may be related to a possible underestimation of precipitation data.

NARCCAP Uncertainty

To assess uncertainty associated with climatic data, we used the random parameter sets 

that performed well in the calibration sub-watershed to create multiple models of the ungaged 

watersheds, which we then forced with the NARCCAP climate scenario data. Statistics from the 

resulting outputs are shown in figures 35 and 36. In figure 35, the percent change in each statistic 

(top 5 percent of flow, 7-day low flow, and CV), from the reference period to the future period,

is shown for each watershed and each climate scenario. The lines in the middle of the boxes 

denote the median values and the upper and lower boundaries of the boxes show the 25th and 

75th percentiles, respectively. The red diamond symbol indicates outliers. There are considerable 

differences among the scenarios for each statistic and each watershed. Percent change in CV for

the Yaquina Bay watershed is an extreme example of such discrepancies; the ranges of values for 

each scenario do not overlap. 

Figure 36 shows the percent change in flow for each month, by watershed, with a 

separate box plot for each scenario. As in figure 35, the lines in the middle of the boxes denote 

the median values, the upper and lower boundaries of the boxes show the 25th and 75th

percentiles, and red diamonds represent outliers. In many cases, there is little overlap in the range 

of changes among the four scenarios; this is especially evident in March and April, when RCM3-

CGCM and CRCM-CGCM3 show increases, and HRM3-HADCM3 and CRCM-CCSM show 

decreases. The HRM3-HADCM3 model, which shows the greatest increases in autumn flow, 

also has the greatest range of results.

These boxplots, with their lack of agreement among models, confirm that the selection of 

climate models, rather than hydrologic parameter values, is the greatest source of uncertainty. 

Many previous studies have reached the same conclusion (for example, Wilby and Harris, 2006; 

Graham and others, 2007; Maurer, 2007; Prudhomme and Davies, 2009; and Chang and Jung,

2010).
38

Figure 31. Precipitation Runoff Modeling System parameter uncertainty for Coos Bay calibration subwatershed, Oregon. (a) is a linear scale, and (b) is a logarithmic scale.
39

Figure 32. Precipitation Runoff Modeling System parameter uncertainty for Coquille River calibration subwatershed, Oregon. (a) is a linear scale, and (b) is a logarithmic scale.
40

Figure 33. Precipitation Runoff Modeling System parameter uncertainty for Willapa Bay calibration subwatershed, Washington. (a) is a linear scale, and (b) is a logarithmic scale.
41

Figure 34. Precipitation Runoff Modeling System parameter uncertainty for Yaquina Bay calibration subwatershed, Oregon. (a) is a linear scale, and (b) is a logarithmic scale.
42

Figure 35. Box-and-whisker plots showing changes in top 5 percent high flow, 7-day low flow, and 

coefficient of variation for four study areas. The lines in the middle of boxes denote the median values,

and the upper and lower boundaries of the boxes show the 25th and 75th percentiles, respectively. The 

red diamond symbol indicates outliers. A is for RCM3-CGCM, B is for HRM3-HADCM3, C is for CRCMCGCM3, and D is for CRCM-CCSM.
43
44

Figure 36. Box-and-whisker plots showing changes in monthly runoff for four study areas. The lines in the 

middle of boxes denote the median values, and the upper and lower boundaries of the boxes show the 

25th and 75th percentiles, respectively. The red diamond symbol indicates outliers. A is for RCM3-

CGCM, B is for HRM3-HADCM3, C is for CRCM-CGCM3, and D is for CRCM-CCSM.
45

Discussion

Study Limitations

This study has several important limitations. First, only four climate scenarios were used, 

and only one greenhouse gas emission scenario was used. Thus, the full range of possible climate 

change effects on watershed hydrology is greater than that reported here. Second, constant land 

cover parameters were used in model calibration and in future simulations, although some 

changes in land cover type occurred during the base period, and more will likely occur in the 

future. Modeling the effects of logging, which is common in these coastal areas, on hydrology 

would be an excellent extension of this study. Third, a full sensitivity analysis, which could 

calculate the influence of the various parameters over time and space, was beyond the scope of 

this study. Fourth, PRMS does not consider sub-daily flow variability, which can be considerable 

in these steep, narrow watersheds, nor can it model geomorphology and in-stream processes. 

Fifth, the groundwater component of PRMS is quite simple compared to other hydrologic models

such as Ground water and Surface-water FLOW (GSFLOW). It would have been difficult, 

though, to calibrate a more complex groundwater model in the study watersheds as groundwater 

head data points are extremely scarce in the coastal PNW (Marshall Gannet, U.S. Geological 

Survey, personal commun.). Sixth, PRMS does not consider wind as a climate input, although it 

can strongly affect evapotranspiration. Finally, climatic data were averaged across the 

watersheds, which obscures local variation due to coastal effects, orographic effects, and 

topographic complexity. 

Key Findings

Despite the limitations of this study, the research has yielded several findings. First, the 

random parameter set generated in the uncertainty analysis for these watersheds systematically 

underestimated high flows. This finding could be attributed to limitations in the modeling of

extreme flows by PRMS (fig. 31), which suggests that further research into PRMS’s ability to 

simulate accurately extreme flow events would be helpful. The high flow underestimation bias 

also may be due to underestimation of precipitation in the CIG data in the study watersheds.

Second, by comparing future outputs to reference outputs for each scenario, in each 

watershed, we determined that increases in autumn flow are probable in most watersheds. This 

change is driven directly by increases in precipitation in the same period in the NARCCAP 

climate data. Third, increases in the top 5 percent of flow days are likely, as are increases in late 

summer variability in flow and decreases in summer flow. Fourth, our uncertainty analysis 

demonstrated that there is more uncertainty associated with low flows. Therefore, findings

regarding summer flows should be interpreted with caution. Finally, the uncertainty analysis also 

confirmed that the primary source of uncertainty in future flow projections is the climate models, 

rather than PRMS parameter values. Given that all NARCCAP climate simulations are based on 

a single emission scenario that does not represent the full range of possible emission levels, the 

uncertainty due to climate change is even greater than is indicated by these findings. 

Conclusions

We modeled four coastal watersheds that drain to important estuaries in the Pacific 

Northwest as part of a larger interagency investigation into the possible effects of anthropogenic 

climate change on estuaries and ecosystem services they provide. Our findings show that change 
46

in the freshwater inflow regimes of these watersheds is likely. In particular, increases in autumn 

flow and increases in the top 5 percent of flow are probable. As in previous studies, the results 

also show that most of the considerable uncertainty in these projections is owing to the climate 

models used. Not only were there large discrepancies in the degree of the changes of many 

indices, but there also were differences in direction as well. In these rainwater dominated coastal 

watersheds, the primary driver of flow changes will be shifts in precipitation, which are difficult 

to simulate accurately. Nevertheless, our outputs allow coastal managers to see a range of 

possible changes, which may help them prepare for climate change. Our simulated flow output 

also can be used as input to models of estuarine ecosystems and water dynamics that can be used

to better understand possible climate-driven shifts in the location, quality, and extent of critical 

estuarine habitat.

Although this study modeled only freshwater flow changes, these flow changes will 

likely lead to shifts in numerous other important estuarine variables, especially salinity. Altered

salinity could affect the distribution and abundance of native species, and encourage the spread 

of invasive species. Freshwater flow declines in summer also could lead to reduced levels of

dissolved oxygen, which will have negative implications for salmonids and could lead to 

hypoxia. Bacterial impairments also may increase, because drier summers may be followed by 

more extreme heavy rains in autumn. In the past, such weather patterns have contributed to high 

concentrations of fecal coliform levels in the Tillamook Bay. Therefore, the potential changes in 

freshwater flow highlighted by this study have many implications for water-quality conditions 

and distribution of aquatic organisms in Pacific Northwest estuaries, but further research is 

needed to determine the degree and likelihood of these possible shifts.

Acknowledgments 

Thanks very much to Dr. Phil Mote, Oregon Climate Change Research Institute (OCCRI)

Director, and Darrin Sharp, OCCRI Faculty Research Assistant, for extracting and resampling 

the NARCCAP data used in this research. We are also grateful to Marshall Gannett and John 

Risley of the USGS for their advice and feedback during initial planning, and to reviewers at the 

U.S. Environmental Protection Agency (EPA) and the USGS, whose comments greatly 

strengthened the report. This publication was subjected to review by the National Health and 

Environmental Effects Research Laboratory's Western Ecology Division of the EPA (EPA 

Report Number EPA/600/R/12/634), and the USGS Western Fisheries Research Center, and is 

approved for publication. However, approval does not signify that the contents reflect the views 

of the EPA. 

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Appendix A. Presentations and Posters Based on this Research

Chang, H., Steele, M., and Jung, I-W., Basin responses to climate change in heterogeneous 

hydrologic landscape regions: Poster presented at the American Geophysical Union Meeting of 

the Americas, Iguassu Falls, Brazil, August 10, 2010.

Chang, H., Steele, M., Jung, I.-W., and Salathe, E., Potential impacts of climate change on runoff 

generation processes across different hydrologic landscape regions: Poster presented at 

Hydrology Conference 2010, San Diego, California, October 11–13, 2010.

Steele, M., Chang, H., and Jung, I.-W., Potential climate-induced runoff changes in two Oregon 

coastal watersheds: Paper presented at the annual meeting of Oregon Academy of Sciences, 

Portland, Oregon, February 26, 2011.

Steele, M., Chang, H., and Jung, I.-W., Potential climate-induced runoff changes in Pacific 

Northwest coastal watersheds: Paper presented at the annual meeting of the Association of 

American Geographers, Seattle, Washington, April 14, 2011.

Steele, M., Chang, H., and Jung, I.-W., Possible climate-induced runoff changes and associated 

uncertainty in five Pacific Northwest coastal watersheds: Paper presented at the fall meeting of 

the American Geophysical Union, San Francisco, California, December 6, 2011.