THESIS APPROVAL The abstract and thesis of Janice Anne Dougall for the Master of Science in

Geography were presented August 14, 2007, and accepted by the thesis committee

and the department.

COMMITTEE APPROVALS: ____________________________________ Andrew G. Fountain, Chair ____________________________________ Heejun Chang ____________________________________ Martin L. Lafrenz ____________________________________ Donald M. Truxillo Representative of the Office of Graduate Studies DEPARTMENT APPROVAL: ____________________________________ Martha A. Works, Chair Department of Geography

ABSTRACT

An abstract of the thesis of Janice Anne Dougall for the Master of Science in

Geography presented August 14, 2007.

Title: Downstream Effects of Glaciers on Stream Water Quality

I assess whether the rate of change of glacial water quality variables with

distance from the glacier scales with fraction of glacier cover relative to watershed

area. In particular, tests aim to determine whether the change in fractional glacier

cover with distance normalized by the square root of glacier area, called L*,

provides a useful model for water quality change with distance. Data were

collected from four glacial streams on Mt. Hood and two on Mt. Rainier, and from

three non-glacial streams. The data collected were temperature, turbidity,

suspended sediment, electrical conductivity, and nutrients. Nutrients include total

nitrogen, total phosphorus, ammonium, and soluble reactive phosphorus. In

addition, major anions were measured in all streams and macroinvertebrates were

sampled for two streams. Macroinvertebrates are the aquatic larval forms of some

insects.

Results indicate that change in some water quality characteristics,

temperature and conductivity, follow the proposed model, while others, turbidity

and suspended sediment concentration, do not. L* and fractional glacierization

correlate better and explain more of the variation in temperature and conductivity

than do distance and upstream basin area, other measures cited in the literature

when describing sample locations. Distance and upstream basin area perform

slightly better than L* and fraction glacierized for turbidity and suspended

sediment concentration. These results indicate that temperature and ionic

concentrations scale inversely with glacier size and L*, which must imply

diminishing glacial stream habitat with continued glacier shrinkage that results

from climate change. Loss of glacial stream habitat would result in loss of insects

toward the bottom of the alpine food web. The macroinvertebrates inhabiting

glacial streams are the aquatic larval forms of flying insects that become food

sources for birds, other insects, and fish. There may be downstream consequences

to fish in the form of reduced food supplies. Downstream populations of salmonids

may also suffer from warming water, since ideal habitat temperatures are 14°C or

less and the downstream extent of cold water will diminish as glaciers shrink.

Decreased salmon populations will affect the fishing industry, native culture, and

recreational fishing.

DOWNSTREAM EFFECTS OF GLACIERS

ON STREAM WATER QUALITY

by

JANICE ANNE DOUGALL

A thesis submitted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE in

GEOGRAPHY

Portland State University 2007

i

ACKNOWLEDGMENTS I heartily thank my advisor, Andrew Fountain, for all the time he spent

working with me, and for his patient and encouraging guidance. Committee

members Heejun Chang and Martin Lafrenz helped me make plans, refine my

ideas, improve analyses, and tighten the text. I would also like to thank them and

Donald Truxillo for serving on my committee and providing insightful comments

that helped improve this thesis. Although not on my committee, Keith Hadley also

directed me to important papers and gave solid advice.

I am eternally grateful to my many volunteer field assistants. First thanks

go to Michele Newell who sampled with me on four rivers, and then to Jon Norred

who sampled at two rivers and stuck with it when his drinking water filter broke

and there was nothing but glacial water to drink. Many others assisted me with

fieldwork, including Alex Levell, David Graves, Marius Dogar, Ben Bradley, April

Fong, my husband Norm Goldstein, and our dog Azda. Jean Jacoby, Seattle

University, and Joy Michaud also deserve special thanks for sampling

macroinvertebrates with bare hands in ice water. Thanks to Jean for identifying

them, too. Final thanks go to Ed Storto who taught me how to break into my car

when I left the keys inside at a trailhead; I still have the wire to use when I get the

chance to pass the lesson on.

There are many people and organizations deserving my thanks for the use

of equipment and facilities. Thanks for the loan of equipment to Briggy Thomas at

the City of Portland Water Bureau, Stephen Hinkle of the U.S. Geological Survey,

Barbara Samora of Mount Rainier National Park, and the Oregon Geographical

Alliance at Portland State University. Thanks for the use of laboratory facilities are

ii

owed to Ben Perkins of the Geology Department for the use of his ion

chromatograph. Thanks to Yangdong Pan and Mark Sytsma of the Environmental

Studies Department, for the use of their labs in nutrient analysis, with special

thanks to Christine Weilhofer for carefully helping me to do the analyses. Thanks

to Chris LeDoux, as well, for the use of her house during field work. Robert

Schlicting was my salvation for remembering Andrew’s stash of Hobo data loggers

and suggesting a method for encasing them.

What a relief not to have had to generate all my own data. Keith Jackson

and Thomas Nylen provided me with ArcMap shapefiles of glacier and permanent

ice and glacier area data for Mount Hood and Mount Rainier, respectively. I owe

Keith additional praise for passing this gauntlet ahead of me and providing his

work as an example to follow. Thanks and cheers to Sarah Fortner, Ohio State

University, for sending on chemistry data from snow pits on Eliot Glacier, and for

letting me be dirty hands for snow sampling of trace metals.

Support for this research has come from the following sources: the Rocky

Family Scholarship, the Portland State Geography Department for providing full

funding with a Teaching Assistantship with Geoffrey Duh, who was at times my

best personal motivator. Field expenses were paid by Andrew Fountain.

iii

TABLE OF CONTENTS

Acknowledgements………………………………………………………………...i

LIST OF TABLES……………………………………………...………………...vi

LIST OF FIGURES ……………………………………………………………viii

Chapter 1: Introduction………...………………………………………………..1

Chapter 2: Study Area……………………………………………………………6

2.1 Geology…………………………………………………………………...12

2.2 Glaciers and glaciation……………………………………………………15

2.3 Climate……………………………………………………………………15

Chapter 3: Previous Work………………………………………………………17

3.1 Physical water quality..................................................................................20

3.2 Nitrogen…………………………………………………………………...23

3.3 Glacial stream macroinvertebrates………………………………………..25

3.4 Climate change effects on glaciers and glacial rivers…………………..…25

Chapter 4: Methods ……………………………………………………………..27

4.1 Sampling locations………………………………………………………..27

4.2 Sampling methods………………………………………………………...29

4.3 Sampling chronology …………………………………………….……….30

4.4 Field and laboratory methods...…………………………………………...31

4.5 Macroinvertebrate sampling………………………………………………35

iv

4.6 Geographic Information System analysis………………...……………….35

4.7 Data analysis………………………………………………………………35

4.8 Assessment of Lagrangian method………………………………………..37

Chapter 5: Results and Analysis………………………………………………..40

5.1 Temperature……………………………………………………………….41

5.2 Turbidity ………………………………………………………………….51

5.3 Suspended sediment concentration………………………………………..59

5.4 Electrical conductivity…………………………………………………….66

5.5 Anion concentrations……………………………………………………...72

5.6 Macroinvertebrates……..…………………………………………………73

Chapter 6: Discussion……………………………………………………………75

Temperature……………………………………………………………….76

Electrical conductivity…………………………………………………….78

Anion concentrations……………………………………………………...80

Turbidity and suspended sediment………………………………………..80

Macroinvertebrates………………………………………………………..82

Climate change……………………………………………………………83

Chapter 7: Conclusions………………………………………………………….85

Suggestions for future research…………………………………………...87

References………………………………………………………………………..91

v

Appendix A: Assessment of Lagrangian sampling.……………………….…..99

Appendix B: Temperature……………………………………………………..100

Appendix C: Turbidity…………………………………………………………110

Appendix D: Suspended sediment concentration…………………………….117

Appendix E: Electrical conductivity…………………………………………..123

Appendix F: Anions………………………………………………….…………129

Appendix G: Nutrients…………………………………………………………130

vi

LIST OF TABLES Table 1. Glaciers and glacier areas draining to study rivers, listed by region…..….7 Table 2. Contributions to annual flow from glacier and non-glacial sources for three basins with different glacial cover (Verbunt et al., 2003)………….……….18 Table 3. Sampling locations and measures for each river, listed in order of decreasing contributing glacier area, given as a range. ..…………………...…….28 Table 4. Sampling dates, locations, duration listed by sampling method.………..30 Table 5. Number of hours Lagrangian sampling was conducted before or after estimated water travel times. …..…………………………………………………39 Table 6. Statistical summary of temperature data (°C) from data loggers.……….42 Table 7. Correlation coefficients from comparisons of location temperature averages to distance measures ……………………………………………………43 Table 8. Regression coefficients, R2, from comparisons of sample location average temperatures on the five distance measures. ………………………….………….44 Table 9. Statistical summary of turbidity data (NTU).………..……………….….51 Table 10. Correlation coefficients for correlation between turbidity values and distance measures……………………………..……………………………….….52 Table 11. Regression coefficients from regressions of average turbidity values per sample location on distance measures..…………………………………………...53 Table 12. Statistical summary of suspended sediment concentrations (mg/L)...…59 Table 13. Correlation coefficients for correlation between suspended sediment concentrations and distance measures.....................................................................60 Table 14. Regression coefficients from regressions of all suspended sediment concentrations on distance measures……………………………………………...61 Table 15. Downstream migration in SSC maxima observed during synoptic sampling on White River, Mount Rainier…………………………...……………63

Table 16. Statistical summary of specific conductivity by river (µS cm-1)…….....66

Table 17. Correlation coefficients for correlation between electrical conductivity and distance measures, excluding Sandy River..………………...………………..67

vii

Table 18. Regression coefficients, R2, from regressions of specific conductance on distance measures using all data but Sandy River (top) and Sandy River data alone (bottom).…………………………………………….…………………………….68 Table 19. Downstream migration in electrical conductivity maxima observed on White River, Mount Rainier……………………………….......………………….70

viii

LIST OF FIGURES

Figure 1. Decrease in fractional glacier cover with increasing stream distance normalized by glacier area (L*)……………………..…………………….……….4 Figure 2. Regional study area map with glacial watersheds on Mount Hood, Oregon, and Mount Rainier, Washington..…………………………………………6 Figure 3. Map of sample locations on White River and Nisqually River in the Mount Rainier study area. ……...………………………...………………………..8 Figure 4. Map of Mount Hood study area showing Eliot Creek, Sandy River, White River, Salmon River with non-glacial Multnomah Creek ……..……………...…...9 Figure 5. Map of sample locations on non-glacial Gales Creek and Tualatin River………………………………………………………………………………11 Figure 6. Map of sample locations on non-glacial Opal Creek and the Little North Santiam River, with one sample location on the glacial North Santiam River…...12 Figure 7. Graph of temperature data logger equilibration over time.……………..32 Figure 8. Estimated water travel time and actual sampling times are plotted against L* for September 4, 2005 Lagrangian sampling on White River, Mount Hood.....39 Figure 9. Average glacial stream temperatures are plotted against stream distance (left) and L* (right)..……………………………………...……………………….45 Figure 10. Twenty-four hour temperature variation plotted against time at locations along (a) White River, Mount Rainier, (b) Eliot Creek, (c) Sandy River, and (d) Salmon River...……………………………………………………………….…...46 Figure 11. Box plots of selected temperature logger data for all glacial streams are plotted in order of increasing L* (left) and stream distance in km (right). ………47 Figure 12. Daily temperature range plotted against the distance measures L*, stream distance (km), fraction glacierized, and upstream basin area (km2)………48 Figure 13. A comparison of temperature plotted against distance using actual and idealized Lagrangian sampling……………………………………………….…...48 Figure 14. Lagrangian temperature changes observed with distance on (a) White River, Mount Rainier, September 4, 2005, and (b) Nisqually River, Mount Rainier, September 5, 2005…………………………..………………………………...…..49 Figure 15. Glacial and non-glacial temperature averages plotted against distance measures…………………..………………...…………………….………………50

ix

Figure 16. Maximum turbidity (left) and normalized maximum values (right) are plotted against stream distance (km, top), L* (second row), fraction glacierized (third row), and upstream basin area (km2, bottom row)…………………….…...54 Figure 17. Lagrangian turbidity results plotted against L*.…………….……...…56 Figure 18. Turbidity changes over a 24-hour period at three locations on White River, Mount Rainier.…………………………………...……………………..….57 Figure 19. Glacial and non-glacial turbidity values plotted against distance measures..…............................................................................................................58 Figure 20. Average suspended sediment concentration plotted against L* for White and Salmon Rivers, Mount Hood (left), and remaining glacial rivers (right)…….60 Figure 21. Suspended sediment concentration plotted against time for three synoptically sampled locations on the White River, Mount Rainier………….…..62 Figure 22. Range of suspended sediment concentration plotted against L* for all synoptically sampled streams (left) and without White River, Mount Hood (right)..…………………………………………………..………………………...62 Figure 23. Lagrangian sampled suspended sediment concentrations are plotted against L* for all glacial streams.………………………………...……………….64 Figure 24. Glacial and non-glacial suspended sediment concentrations plotted against distance measures...……………………………………………………….65 Figure 25. Median glacial specific conductivity for each location plotted against L* contrasted with those along Sandy River (left), and without the Sandy River (right). ………………………………………….…………..……………………..67 Figure 26. Specific conductance plotted over time for White River, Mount Rainier (left), and Sandy River, Mount Hood (right)……………………………………...69 Figure 27. Specific conductance from Lagrangian sampling of White River, Mount Hood (left) and Sandy River (right) plotted against L*……………...…………...70 Figure 28. Glacial and non-glacial conductivity plotted together against distance measures………………………………….…………………………………….....71 Figure 29. Chloride concentrations are plotted against L* (left) for all rivers, and conductivity and chloride concentrations for Salmon River are plotted against L* (right).…………………………………………………………………………..…72 Figure 30. Sandy River sulfate concentrations (left), and Salmon and White River sulfate concentrations (right).……………………………......................................73

x

Figure 31. Average temperature per glacial sample location plotted against L* (left) and with lines connecting points on the same rivers (right)………………...77 Figure 32. Glaciers on the south flank of Mount Hood rest upon a wide debris fan formed during eruptive periods during the past 1,500 years (photo: Scott, et al., 1997)…....................................................................................................................84

1

Chapter 1: Introduction

Glaciers moderate flow and decrease year-to-year and month-to-month

variation in runoff (Fountain and Tangborn, 1985). In warm and sunny weather at

mid-latitudes, increased glacial melt compensates for lack of rain, whereas in cool,

rainy or snowy weather, glacial melt decreases. This moderating influence was

found to be significant in basins with more than about 10% glacier cover. In basins

with less than 10% glacier cover, there was no difference in runoff variation from

non-glacial basins. Due to storage processes in glaciers, annual peak flow from

glacial basins is delayed relative to nearby basins without glaciers (Fountain and

Walder, 1998). In the Pacific Northwest, delayed peak flow in glacial streams

occurs in July, August and September when temperatures are highest and

precipitation is lowest (Fountain and Tangborn, 1985). During these summer

months, headwater stream flow is dominated by glacier melt water, which is

diluted by non-glacial tributary stream flow with distance downstream.

Glacial melt water is characterized by low temperatures, low concentrations

of soluble ions, high suspended sediment concentrations, and high turbidity

(Milner and Petts, 1994). These water quality characteristics vary across all time

scales, as well as with distance from a glacial source (Milner and Petts, 1994).

Many studies have investigated spatial and temporal variation close to the glacial

source (Gurnell, 1982; Tockner et al., 2002; Uehlinger et al., 2003; Irvine-Flynn et

al., 2005).

Few (Arscott et al., 2001) have investigated the distal extent of glacial

water quality. Reports on near-glacier water quality variation characterize stream

location in terms of downstream distance from the glacier, regardless of whether

2

the glacier is large or small (Gurnell, 1982; Tockner et al., 2002; Hodson et al.,

2005). Close to the glacier, its size has little effect on water quality because all the

water is flowing from the glacier. With distance, glacier size should have an effect

because larger glaciers typically produce more melt water requiring longer travel

for dilution, chemical reactions, and temperature increases.

Climate warming is causing glaciers in western North America to recede

and shrink (Nylen, 2002; Granshaw and Fountain, 2006; Key et al., 2002). As

glaciers recede and disappear, it is reasonable to expect downstream water quality

characteristics to change until they resemble characteristics from other non-glacial

streams in periglacial environments. This process has not been addressed

previously. My study will help anticipate the change in water quality

characteristics due to glacier shrinkage. Sustained deglaciation will likely lead to

lower summer flows, shifts in peak flow, warmer water temperatures, and may

negatively affect anadromous fish species (Melack et al., 1997; Neitzal, et al.,

1991).

Glacial streams host biota that are adapted to glacial melt characteristics,

and may have done so since the beginning of the Pleistocene. Only in recent years

have we learned that glacial streams provide habitat for a few species adapted to

cold temperatures and high turbidity, such as macroinvertebrate Chironomid

species from the genus Diamesa (Füreder et al., 2005). Few glacial streams in the

Pacific Northwest have been sampled for macroinvertebrates or examined for the

physical conditions that support them. The term “kryal” has been used to describe

glacial stream habitat with unique biotic composition and maximum temperatures

of less than 4°C (Steffan, 1971). Macroinvertebrate population assemblages in

3

glacial reaches vary depending on glacial water quality characteristics, such as

temperature and suspended sediment concentration (Burgherr and Ward, 2001;

Robinson et al., 2001; Smith et al., 2001). Glacial stream macroinvertebrates are

aquatic larval stages of flying insects, and are an important part of the high alpine

food web. In adult form, these flying insects provide food to downstream fish and

to birds. Furthermore, low temperatures of glacier flow can influence fish

populations, such as salmonid species, further downstream (Burgherr and Ward,

2001; Neitzal et al., 1991). Cold glacial flow can be thought to have consequences

that are much farther reaching, such as to the salmon fishing industry, native

culture, and recreational uses by supporting salmon populations.

The intent of this study is to examine how water quality characteristics vary

as a function of distance from glaciers of varying size. I hypothesize that the water

quality characteristics of glacial streamflow will decrease rapidly with distance

from a glacier, according to the change in fractional glacier cover within the

upstream watershed. One way to examine downstream effects from glaciers is to

normalize stream distance relative to glacier area (Hoffman, M., and Fountain, A.

G., personal communication, May, 2005), using L*;

AreaGlacier Length Stream*=L

where ‘Stream Length’ is the distance from the glacier to a point downstream from

the glacier, and ‘Glacier Area’ is the total ice-covered area upstream from that

point. A plot of glacial cover as a function of normalized distance from the glacier

(Figure 1) shows a point at about 5% to 10% glacier cover, or 5 to 10 L*, where

the steeply descending curve changes slope to a gradual decline, and which I will

4

refer to as the break point of the curve. As mentioned, Fountain and Tangborn

(1985) show for glacier-induced streamflow variations, when the fractional glacier

cover in a watershed is less than 10% there is no difference from a non-glacial

watershed. My study assesses whether this is also true for physical water quality

characteristics.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 10 20 30 40 50

L*

Frac

tiona

l Gla

cier

Cov

er

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

Figure 1. Decrease in fractional glacier cover with increasing stream distance normalized by glacier area (L*). Rivers are listed by name followed by _H or _R for Mount Hood and Mount Rainier, respectively.

I have three goals for my thesis. The first is to determine how water quality

characteristics change with distance from glaciers of different size. The second is

to determine whether stream length normalized by glacier area (L*) is a better

indicator of changing water quality characteristics than other indicators, such as

5

fractional glacier cover, upstream basin area, and, for temperature, elevation. The

third is to determine the distance at which glacial water quality characteristics

become like non-glacial water. Examining a large range of glacier areas is

important because if they were of similar size, differences among streams at

similar distances would be small. My hypothesis is that glacial water quality

characteristics sourced from the glacier will change rapidly until 10% glacier

cover, about 5 to 10 L*, then change more slowly. I expect that fractional glacier

cover will be a good predictor of downstream water quality changes, and that L*

will also be good. I also hypothesize that glacial and non-glacial water quality will

become similar at about 10% glacier cover (5 to 10 L*). The water quality

characteristics I will test are temperature, ionic concentration, and suspended

sediment concentration. Suspended sediment is known to travel long distances in

glacial streams, so I do not expect this characteristic to become like non-glacial

water at 10% glacier cover, but much farther.

6

Chapter 2: Study Area

Mount Hood, Oregon, and Mount Rainier, Washington, were selected as

the primary study areas because they support a range of glacier sizes, are

geologically similar, and are exposed to similar regional climate patterns. Mount

Rainier is farther north, peaks at a higher elevation (4392 m), and supports larger

glaciers than Mount Hood (3426 m). Two non-glacial streams were selected for

comparison along the Cascade Range north and south of Mount Hood in Oregon,

with a third draining east from Oregon’s Coast Range. The location of each

watershed is shown in Figure 2. Rivers included in this study drain from glaciers

that range from small glaciers or snowfields, such as Palmer Snowfield, Mount

Hood, to much larger alpine glaciers, such as Emmons Glacier, Mount Rainier.

Rivers and contributing glacier areas are given in Table 1. Glacier area is derived

from GIS shapefiles that include glaciers and permanent ice (www.glaciers.us).

Figure 2. Regional study area map with glacial watersheds on Mount Hood, Oregon, and Mount Rainier, Washington. Non-glacial watersheds are shown in black.

7

Table 1. Glaciers and glacier areas draining to study rivers, listed by region.

Mountain Primary Glacier River Glacier Area (km2)

Rainier Emmons White River 12.9 - 16.4

Rainier Nisqually Nisqually River 8.9 – 16.1

Hood Eliot Eliot Cr./Hood River 1.9

Hood Sandy Sandy River 0.5 – 2.3

Hood White River White River 0.5

Hood Palmer Salmon River 0.2 – 0.3

Hood Non-glacial Multnomah Creek 0

Coast Range Non-glacial Gales Creek/Tualatin River 0

Jefferson Various N. Santiam R. 2.0

Jefferson Non-Glacial Opal Cr./Little N. Santiam R. 0

On Mount Rainier, the two rivers examined were the White and Nisqually

Rivers (Figure 3). The White River drains Emmons Glacier and flows north and

then west to the Puget Sound. The Nisqually River drains Nisqually Glacier, and

flows southwest and then north to the Puget Sound. Vegetation in the parts of these

rivers included in this study is Douglas fir dominated forest. Both rivers were

sampled to outside of the boundary of Mount Rainier National Park. Within the

park there is little development other than a few roads, trails and campgrounds,

except by the second sample location along Nisqually River, where there is a

lodge, visitor services and employee housing. A bridge was under construction

during the period of time Nisqually River was being sampled and may have

affected downstream water quality.

8

Figure 3. Map of sample locations on White River and Nisqually River in the Mount Rainier study area.

Sampled streams on Mount Hood (Figure 4) include, (1) the Eliot branch of

the Middle fork of the Hood River, which drains Eliot Glacier, and which I refer to

as Eliot Creek; (2) the Muddy fork of the Sandy River and the Sandy River, which

drain Sandy Glacier; (3) the White River, which drains White River Glacier, and

(4) the Salmon River, which drains Palmer Snowfield and flows to the Sandy

River. Eliot Creek basin drains Eliot Glacier and flows to the Hood River, which

drains Eliot and Coe Glaciers.

9

Figure 4. Map of Mount Hood study area showing Eliot Creek, Sandy River, White River, and Salmon River with non-glacial Multnomah Creek watershed outlined in black. The inset shows Mount Hood’s glaciers and permanent ice with primary glaciers named.

The Hood River flows through forest and agricultural land in the Hood

River Valley, then to the Columbia River. Eliot Creek was not sampled to lower

elevations with recreational, agricultural and urban land cover because flow was

diverted for irrigation at high elevation where land cover is rocky and forested.

Sandy River begins at Sandy Glacier and flows through Douglas fir forests,

grading to tree farms, small family farms, and nurseries, without complete

disappearance of forested area. Marmot Dam diverts some of the flow along the

Sandy River approximately 41 km downstream from Sandy Glacier. That flow re-

enters the Sandy River by way of the Little Sandy River.

The Salmon River, a tributary of the Sandy, drains Palmer Snowfield, part

of a year-round ski resort at Timberline Lodge. The Salmon River flows through

10

Douglas fir forest and the Salmon River Meadows before joining the Sandy River

near the village of Brightwood.

White River drains White River Glacier on the southeast face of Mount

Hood and flows east to join the Deschutes River. The vegetational gradient is steep

as the river flows to lower elevations on the arid east side of the Cascade Range.

Vegetation is sparse at high elevation and among the lahar deposits on either side

of Highway 35. The White River then flows through dense forests of Douglas fir.

As elevation decreases, the surrounding forest grades from Douglas fir dominated

to Ponderosa pine dominated, and then to open rangeland at the lowest elevations.

Three non-glacial streams were included in the study to provide a control.

They include (1) Multnomah Creek, which drains Larch Mountain, just north of

Mount Hood (Figure 4); (2) Tualatin River and its tributary Gales Creek, which

flow east from the Coast Range (Figure 5); and (3) Little North Santiam River,

which drains the Cascade Range south of Mount Hood (Figure 6). When sampling

Little North Santiam River, I also measured its tributary, Opal Creek, and one

glacial sample point on the North Santiam River, with a small glacial contribution

from Mount Jefferson; about 0.12% of the basin is glacierized (having glaciers).

All basins were sampled in the summer of 2005, except the Little North Santiam

River, which was sampled in the summer of 2006. Multnomah Creek flows north

through a mixed Douglas fir and deciduous forest to the Columbia River from the

eroded crater of Larch Mountain. Larch Mountain is north of Mount Hood along

the Cascade Range. The Tualatin River flows east from the Oregon Coast Range.

This is the only basin included in the study that does not drain from the Cascade

Range. The headwaters begin with Gales Creek, which flows through heavily

11

forested areas of Douglas fir and mixed deciduous trees with some logging roads

and a few small towns. Lower elevations of Gales Creek and the Tualatin River

flow through agricultural land with several suburban cities. This is the most

developed of the watersheds. The non-glacial Little North Santiam River has

headwaters in Opal Creek. The Opal Creek watershed is a protected wilderness,

but supports s a small environmental center with housing for fewer than 100

people. Most of the watershed is forested in Douglas fir. The final sample point in

this watershed drains from the glaciers on Mount Jefferson, flowing first through

Detroit Lake, a dammed reservoir used recreationally and surrounded by

campgrounds, then past a second dam slightly farther downstream.

Figure 5. Map of sample locations on non-glacial Gales Creek and Tualatin River. Rivers, streams, and sample locations are shown. The Tualatin River is a tributary of the Willamette River.

12

Figure 6. Map of sample locations on non-glacial Opal Creek and Little North Santiam River, with one sample location on the glacial North Santiam River.

2.1 Geology The Cascade Range is a north-south trending volcanic range formed as a

magmatic arc resulting from subduction of the Juan de Fuca tectonic plate beneath

the North American continent (Wood and Kienle, 1990). The High Cascade

volcanoes span a distance from northern California to northern Washington. In

Oregon they are built upon the older Western Cascade Range. All are composite

volcanoes consisting of extruded lavas and pyroclastic materials that are primarily

andesitic, with some rhyolites and basalts (Wise, 1969).

Mount Hood (3,426 m) is an andesitic composite volcano (Cameron and

Pringle, 1987). Approximately 70% of Mount Hood is formed of andesite and

dacite flows interbedded with pyroclastic deposits (Wise, 1969). Mount Hood was

formed on the Yakima Formation (13.5 – 6 Mya) of the Columbia River Basalts,

13

which are overlain by the andesitic Rhododendron and Dalles formations of the

late Miocene, 11.6 to 5.3 Mya (Wise, 1969). Mount Hood’s volcanic activity

started in the Pliocene, 5.3 to 1.8 Mya, but the bulk of Mount Hood was not

formed until the Quaternary, primarily about 0.73 million years ago to present

(Wise, 1969; Wood and Kienle, 1990). Towards the end of the Fraser Glaciation,

which peaked about 18,000 years ago, volcanic activity declined (Crandell, 1980).

Mount Hood’s crumbly appearance results from recent eruptions of ash and breccia

between 1,400 and 200 years ago (Crandell, 1980; Scott et al., 1997) resulting in a

fairly unconsolidated fan of pyroclastic and lahar deposits on Mount Hood’s south

flank (Scott, et al., 1997). The north flank has not had volcanic deposition since

before the last ice age, ending about 10,000 years ago, so it is relatively free of

volcanic debris (Gardner et al., 2000; Scott et al., 1997). Abundant debris on

southern slopes has resulted in mass wasting events. Glacier outburst floods or

debris flows broke out from White River Glacier in 1926, 1931, 1946, 1949, 1959,

1968, and 2006 each time destroying the Highway 35 bridge at the White River

crossing (Kenneth A. Cameron, Oregon Department of Environmental Quality,

personal communication, January 2007). Fumaroles at the head of the glacier near

Crater Rock may contribute to these floods (Cameron, 1988). Lahars also flood

Sandy River, most recently in 2002 (Soren Clark and Tom Pierson, personal

communication, June 2005).

Mount Rainier is the highest of the Cascade Range volcanoes at 4,392 m

elevation. Fiske et al. (1963) describe Mount Rainier as a composite volcano built

primarily of andesite lava flows. The bulk of the mountain-building flows that

form Mount Rainier erupted during the Pleistocene (1.8 Mya – 11,000 ya), with the

14

last major eruption occurring only 550 to 600 years ago. Initially, thick lava flows

filled canyons. The older rock eroded to form new canyons, leaving rock from the

lava flows as ridges. Breccias, formed where lava came in contact with water or

ice, are interbedded with lava flows and mudflows. Glaciers from the Pleistocene

to the present reshaped Mount Rainier, leaving steep escarpments and deep

incisions. At lower elevations of Mount Rainier, glacial till and lahar deposits

constitute a greater part of the geologic environment.

Non-glacial Multnomah Creek drains from the glacially eroded crater of a

volcanic cone named Larch Mountain (Allen, 1989). Larch Mountain is one of the

many volcanic cones and shield volcanoes that compose the Boring Lava Field,

which was active during the late Tertiary and early Quaternary (Trimble, 1963).

The Boring lavas are composed of basalt flows and pyroclastic material, so they

are more mafic than most of the rock types found on Mount Hood, but are about

the same age.

The Gales Creek headwaters of the Tualatin River drain from the Oregon

Coast Range. The headwaters begin in the Tillamook Highlands, which are

composed of sub-aerial and sub-marine basalt flows and breccia interbedded with

basaltic sandstones, siltstones and conglomerate (Snavely et al., 1993). Lower

reaches of Gales Creek flow across thick to thinly bedded marine sandstones,

mudstones and siltstones of the late Eocene.

Opal Creek and the Little North Santiam River drain from a portion of the

Western Cascades, whereas Mount Hood and Mount Rainier are part of the High

Cascades. Opal Creek and the Little North Santiam River flow through the Sardine

Formation (Pollack and Cummings, 1985.) The Sardine Formation comprises two

15

large units. The upper unit is composed of andesite and is gently folded, faulted

and intruded with dikes and plugs ranging in composition from andesite to quartz

monzanite. Contact metamorphism from the intrusions rendered the area profitable

for mining, and the area has been mined for gold, copper, zinc, and lead (Mason et

al., 1977). One location is sampled along the North Santiam River, which drains

from glaciers on Mount Jefferson (3,199 m). Mount Jefferson is contemporaneous

with Mount Hood and Mount Rainier, and is primarily andesitic (Green, 1968).

2.2 Glaciers and glaciation

During the Pleistocene, the Cascade Range was repeatedly mantled by ice

caps and ice fields (Crandell, 1964). Glaciers retreated during the Holocene with

some temporary advances. The most recent advance was the Little Ice Age, which

reached maximum extents from the middle of the 14th through the middle of the

19th centuries (Crandell, 1964). Glaciers in western North America have generally

been retreating since the Little Ice Age (Nylen, 2002; Granshaw and Fountain,

2006; Jackson, 2007; Jackson and Fountain, 2007). Glacier area on Mt Rainier is

87.4 km2 (Nylen, 2002). Mount Hood supports twelve glaciers and named

snowfields with an area of about 8.9 km2 (Jackson, 2007).

2.3 Climate

The Cascades Range study region has a maritime Mediterranean-like

climate with mild, wet winters and warm, dry summers (Melack et al., 1997).

Prevailing westerly winds bring moist air from the Pacific Ocean. Orographic

effects on precipitation cause it to increase with elevation as storms rise to cross

the Cascade Range and decrease, creating a rainshadow, to the east. Average

annual maximum and minimum air temperatures at the Paradise Ranger Station on

16

Mount Rainier (1665 m elevation) for the years 1948 to 2006 were 7.2 and -1.0°C,

respectively, while annual average precipitation for that period was 2956 mm

(Western Regional Climate Center, wwwlwrcc.dri.edu/Climsum.html). Average

annual maximum and minimum air temperatures at Government Camp (1185 m

elevation) on Mount Hood for the years 1951 to 2006 were 10.2 and 1.1°C,

respectively, with 2214 mm of precipitation. Precipitation along the Cascade crest

averages about 2500mm/yr with about 80% falling as snow in October through

April.

17

Chapter 3: Previous Work

Water quality of glacial streams depends on glacier processes, in-stream

processes, and natural and anthropogenic influences on adjacent landscapes.

Glacier processes include glacier movement and glacier hydrology. In-stream

processes include deposition and entrainment of sediment and water exchange with

the hyporheic zone and groundwater. Biogeochemical reactions and local geology

influence stream water chemistry. Anthropogenic influences include increases in

turbidity from development and nutrient increases from agricultural runoff.

Glaciers affect annual and seasonal flow patterns of rivers by serving as

frozen reservoirs of water. Fountain and Tangborn (1985) showed that annual and

month-to-month variation in flow is modulated by glacier runoff because more

runoff is produced in warm, dry conditions and is reduced when conditions are

cool and wet. Verbunt et al. (2003) calculated contributions to flow in glacierized

catchments from glacier melt and non-glacial runoff, and found that the fractional

contribution of glacier runoff to annual flow is significantly greater than the

basin’s fractional glacier cover (Table 2). During summer months, the fractional

contribution of glacier melt to flow becomes more significant, even for basins with

small glacierized fractions.

18

Table 2. Contributions to annual flow from glacier and non-glacial sources for three basins with different glacial cover (Verbunt et al., 2003). The contribution from non-glacial sources is divided into surface-, inter-, and base-flow. The glacial component to flow during the ablation season is italicized in the bottom row.

Source of flow

Aletch Glacier basin (69% glacierized)

Rhone Glacier basin (47% glacierized)

Scaletta Glacier basin (2.1% glacierized)

Glacier 85 62 6 Non-glacial 15 38 94

Surface runoff 10 14 16 Interflow 2 14 60 Baseflow 3 10 18

Summer 90 70 30

Fountain and Walder (1998) developed a conceptual model of water

routing through a glacier. In the accumulation zone (snow-covered), melt water

percolates through seasonal snow and firn (old snow that has survived a summer

melt season) to perch atop the impermeable ice layer. Perched water forms a water

table that drains into the glacier via crevasses. Most likely this water is the source

of base flow from a glacier. Melt water on the ablation zone (ice-covered) flows

over the ice surface to crevasses and moulins and enters the glacier. Inside the

glacier, conduits or fractures drain water to the base where subglacial flow occurs

in two general systems (Fountain et al., 2005). The first system is characterized as

channelized or fast flow through converging fractures and channels similar to a

stream network. The second system conveys water slowly and probably covers a

much larger fraction of the glacier bed. The slow system includes a network of

cavities created in the lee of bedrock bumps, flow through subglacial sediment, and

flow through channels incised into the bed. Water quality is affected by each

system since rock-water contact times are different (Anderson et al., 2003).

19

Glacial sliding erodes the bed through abrasion, which produces fine

sediment, and quarrying, which produces coarse sediment and cobbles. Abrasion

rates depend on sliding rates (Riihimaki et al., 2005), which in turn depend on

subglacial water pressure, and hence, its hydrology (Anderson et al., 2004).

Patterns of glacial sediment discharge mirror the development of glacier

hydrologic pathways. Swift et al. (2005) and Clifford et al. (1995) divided the melt

season into two periods. The first period extends roughly from May to late July.

Discharge and suspended sediment concentrations are low, and then increase,

developing stronger diurnal variations. The second period, from late July through

September, is characterized by coincidental high amplitude diurnal cycles of

discharge and suspended sediment superimposed on a declining base flow. Diurnal

variation results from the daily cycle of solar energy (Østrem, 1975; Gurnell, 1982;

Hammer and Smith, 1983; Clifford et al., 1995; Anderson et al., 2004). Suspended

sediment concentrations increase relative to discharge during the second period,

when data were collected for this thesis.

Proglacial regions can exert significant influence on sediment concentration

because of the capacity to store and release sediment (Gurnell, 1982; Hammer and

Smith, 1983; Warburton, 1990; Orwin and Smart, 2004). Proglacial regions often

have large stores of sediment in the form of proglacial plains and moraines.

Unstable and braided channels easily erode as channels migrate (Gurnell, 1982).

Studies of proglacial channels have revealed downstream enrichment of suspended

sediment relative to concentrations at the glacier terminus (Gurnell, 1982;

Warburton, 1990; Hammer and Smith, 1983).

20

3.1 Physical water quality

In general, suspended sediment concentration and turbidity decrease with

distance from glaciers because particles settle. The finest sediment can remain in

suspension as far as a thousand kilometers as wash load even in low turbulence

flows (Chikita et al., 2002). The distance suspended sediment persists will depend

on a combination of global and local factors, such as distance, for the former, and

sediment availability, gradient, discharge, discharge variability, tributary stream

contributions, and so on, for local factors.

Turbidity is an optical property of water measured by the attenuation of

light scattered or absorbed by suspended mineral or organic particles, typically

measured in nephelometric turbidity units (NTU). Turbidity is highly correlated

with suspended sediment concentration (Kunkle and Comer, 1971). However,

suspended sediment concentration and turbidity are not equivalent, as a mass of

fine clay suspended in water will scatter more light than the same mass of coarse

sand.

Electrical conductivity is the measure of a solution’s ability to conduct a

current and is often used as an indicator of concentration of soluble ions (Fenn,

1987). Melt from firn and glacial ice is relatively free of solutes (Fountain, 1996)

which are derived primarily from precipitation (Collins, 1979). Glacial water gains

solutes at the glacier bed where recently ground rock reacts with water (Collins,

1981). The conductivity of glacial melt is about 101 µS cm-1 and for subglacial

water it is about 102 µS cm-1, hence conductivity can be used as an indicator of

water source in a glacier (Collins, 1979). Near the glacier outlet, conductivity is

21

inversely related to flow, both seasonally and diurnally (Fenn, 1987; Fountain,

1992) due to dilution (Collins, 1979; Collins, 1981; Fenn, 1987).

In proglacial regions, glacial stream water can gain ionic concentration

(Fenn, 1987). Dilute glacial melt coming in contact with clays in proglacial

sediments is enriched through cation exchange, where hydrogen ions from the

water exchange places with cations sorbed to negatively charged clay particles

(Souchez and Lemmens, 1987). Enrichment rates decrease as water flows through

older proglacial sediments, which have become increasingly denuded of cations

over time (Anderson et al., 2000). Downstream flows will also gain cations from

ionically enriched groundwater entering through tributaries or the hyporheic zone.

Ionic concentrations and sodium and chloride concentrations were

monitored monthly at five locations along the Salmon River, Mount Hood, from

1988 to 1995, primarily to investigate the influence of salting the Palmer

Snowfield for spring and summer skiing, which was applied at a rate of 600,000 to

700,000 pounds per year (U.S.D.A. Forest Service, 1995). Conductivity values

were determined to be two to three times the values from other snow and glacier

fed streams in the Mount Hood area, and sodium concentrations were more than 10

to more than100 times higher. These levels remain below thresholds of concern

suggested by the U. S. Environmental Protection Agency, but possibly above

levels for which species have been adapted.

Water temperature increases downstream at a rate that depends on the

fraction of glacial contribution, distance from the glacier (Uehlinger et al., 2003;

Brown et al, 2006), and on discharge volume (Uehlinger et al., 2003; Milner and

Petts, 1994), which relates to glacier size. Water temperature at the glacier portal is

22

close to 0°C because the source is melting ice. Mean ablation season temperature

from the portal of the 6.2 km2 Tschierva Glacier (Switzerland) to a distance of 11.5

km downstream increased at an average rate of 0.5°C km-1 (Uehlinger et al., 2003),

while increase in mean temperature with similar distance from the 0.22 km2

Taillon Glacier (French Pyrénées) was about 10°C km-1 (Brown et al., 2006). Other

factors may have differed, but the magnitude of the difference in warming rates

indicates that glacier size makes a difference.

Departures from the usual pattern of temperature increase downstream

occur where water ponds or receives significant contributions of non-glacial water

(Uehlinger et al., 2003, Brown et al., 2006). Roseg and Tschierva Glaciers (Swiss

Alps) are adjacent and similar in size, but Roseg flows into a lake and Tschierva

flows to a stream. Water temperatures at the lake outlet average 3.3°C warmer than

a similar distance from the glacier along the river (Uehlinger et al., 2003).

Groundwater can also increase or decrease temperatures; influx from warm

hillslope groundwater and tributaries increased temperatures in streams draining

the Taillon and Gabiétous Glaciers (French Pyrénées), while colder karstic

groundwater and tributaries decreased temperatures (Brown et al., 2006).

High amplitude diurnal temperature variation is typical of glacial rivers

during the summer, and water temperature correlates positively with discharge

(Smith et al., 2001; Uehlinger et al., 2003), solar insolation, and air temperature

(Uehlinger et al., 2003; Malard et al., 2001; Ward et al., 1999). Sensitivity to

atmospheric controls is enhanced by the large surface area to volume ratios of

glacial headwater streams (Webb and Walling, 1993). While 46% of diurnal

variation in glacial water temperature 900 m from the Roseg Glacier was explained

23

by air temperature, 82% was explained at about 11.5 km from the glacier

(Uehlinger et al., 2003). The degree of correlation between air temperature and

water temperature increases with distance (Uehlinger et al., 2003; Zappa et al.,

2000). At greater distances from the glacier, maximum diurnal temperature was

influenced primarily by elevation, azimuth, and slope (Arscott et al., 2001).

Diurnal patterns of temperature variation can be dampened by the influx of

groundwater (Brown et al., 2006), which is usually within 1°C of mean annual air

temperature (Allan, 1995).

3.2 Nitrogen

The only source of nitrogen to high alpine basins is atmospheric, primarily

as nitrate (NO3-) and ammonium (NH4

+), and secondarily as dissolved and

particulate organic nitrogen (Williams et al., 2001). Nitrates in the atmosphere are

more easily absorbed in liquid than solid precipitation (Williams et al., 1993), so

atmospheric loading of nitrogen in the Cascades should be low, as most

precipitation falls as snow in winter. Convective storms contribute higher

concentrations of anthropogenic nitrogen from inland valleys, as occurs in the

Colorado Front Range (Hood and Williams, 2001), but summer rainstorms in the

Cascades are infrequent.

Solute elution during spring snowmelt renders the remaining ice and snow

more dilute than the original snow (Hodson et al., 2005), but once snowmelt has

occurred, nitrogen remaining on glacier surfaces is available for microbial

transformation. In an interesting study, Hodson et al. (2005) compared nutrient

budgets for two glacier basins in Arctic Svalbard, Norway, where nutrient inputs

are presumably similar. Hodson et al. (2005) showed that ammonium is consumed

24

and nitrate released in oxidation-reduction reactions that provide energy for

microbial growth on the top surface of glaciers. Beneath the glacier, anaerobic

microbes reduced nitrate by sulfate reduction, consuming nitrate received from

upper glacier surfaces and producing sulfate. The influence on glacial runoff is

nitrate concentrations consistent with and sulfate concentrations higher than those

from atmospheric deposition. These results are supported by experiments where

basal glacier ice was subjected to anaerobic conditions and nitrate depletion

occurred (Skidmore et al., 2000).

Non-glacial runoff also contributes nutrients to glacial streams. Steep rock,

sparse soils, and limited vegetation are characteristic of alpine basins. Soils and

plants are known sinks for nitrogen. Ammonium is immobilized by soil microbes

and converted to nitrate or used for growth even under winter snow (Brooks and

Williams, 1999). While massive rock is not generally thought of as a sink,

Williams et al. (2001) found that a stream draining talus contained higher nitrate

concentrations than a stream draining alpine tundra. Runoff from ice-free areas of

alpine glacial basins should provide nitrate but little ammonium.

Variation in inorganic nitrogen concentration along proglacial streams will

depend on the proportion of tributary and glacial contributions. Glacial water is

dilute, whereas soil solution may have much higher nitrogen concentrations due to

microbial activity. Hodson et al. (2005) found that the proglacial floodplain of

Arctic glaciers is a sink for dissolved organic nitrogen, ammonium and to a lesser

extent, nitrate, while it is a source for particulate nitrogen. Since glacial floodplains

have abundant sediment, ammonium can be captured at cation exchange sites and

25

both nitrate and ammonium can be used by microbes. This may also apply to

alpine basins outside the Arctic.

3.3 Glacial stream macroinvertebrates

In terms of macroinvertebrate habitat, Milner and Petts (1994) listed six

characteristics that distinguish glacial stream habitat from habitats in snowmelt-

and rainfall-fed rivers: (1) seasonal ice melt that causes peak discharge during

summer, (2) diurnal fluctuations in summer flow, (3) summer water temperatures

not generally exceeding 10 ºC, (4) turbidity above 30 NTU, (5) specific

conductance below 50 µS cm-1, and (6) unstable channel morphology that becomes

more stable with distance from the glacier. Of these, temperature and bed stability

were determined to be the most influential. The dominant macroinvertebrates

found in the upper reaches of glacier-fed streams (meta-kryal habitat) are from

among the genus Diamesa, family Chironomidae (Ward, 1994). Diamesa are

dominant in water up to 2 ºC, and even up to 4°C if channel instability prevents

other species from successfully colonizing (Milner and Petts, 1994). These are

favorable conditions for Diamesa because other taxa are not tolerant of them; only

downstream with warmer water and/or more stable substrate can other taxa

compete with Diamesa for habitat (Milner and Petts, 1994).

3.4 Climate change effects on glaciers and glacial rivers

Glaciers in all parts of the world have been generally receding since the end

of the Little Ice Age in the mid-19th century, with accelerated retreat after the mid-

1970’s (Dyurgerov and Meier, 2000; Meier et al., 2003). Glaciers of the Pacific

Northwest have also retreated during this time (Hodge et al., 1998; Jackson 2007;

Jackson and Fountain, 2007). Between 1901 and 2004, Mount Hood’s glaciers

26

have shrunk an average of 34%, with Eliot Glacier losing 19%, Sandy Glacier

losing 40% and White River Glacier losing 61% (Jackson, 2007).

Climate warming has increased the melting of glaciers (Dyrgerov and

Meier, 2000), altering local hydrology. Glacier retreat could cause stream

temperatures to either increase or decrease. Increased volumes of meltwater cause

streams to become colder (Melack et al., 1997). Sustained deglaciation will

eventually lead to lower summer flows and warmer temperatures, which will

negatively affect Diamesa populations and those of spring and summer chinook

and sockeye salmon (Onchorhynchus tshawytscha and O. nerka, respectively;

Melack, et al., 1997; Neitzal, et al., 1991).

27

Chapter 4: Methods

All rivers, glacial and non-glacial, were sampled at three to ten locations

each for temperature, electrical conductivity, turbidity, suspended sediment, total

nitrogen, total phosphorus, soluble reactive phosphorus, ammonium, and major

anions. Four variables, temperature, turbidity, electrical conductivity, and

suspended sediment concentration, were measured and sampled at least once, but

often two or three times at each location to obtain replicate samples. The remaining

variables were sampled only once at each location. Chloride was of particular

interest in the Salmon River because it drains Palmer Snowfield, which is salted

for use as a summer ski area (U.S.D.A. Forest Service, 2001), and higher levels of

chloride are expected in runoff. Chloride is a conservative tracer (Schlesinger,

1997), and its dilution with distance will help test L* as a model for changing

water quality. Macroinvertebrates were sampled at two locations on White River,

Mount Rainier, and four locations on White River, Mount Hood.

4.1 Sampling locations

Water quality measurements were made at various distances from glaciers.

Since the fraction of basin covered by glaciers varies most rapidly for L* less than

about 7, more samples were collected for L* < 10 and fewer samples collected for

L* > 10. Each site was selected based on accessibility and its location was

determined using topographic maps, and a GPS position was taken. Sampling

distances from the primary glacier feeding each stream are given in Table 3, and

locations are plotted on maps (Figures 3 – 6). The glacier area upstream from

sample sites varies as downstream locations drain larger basins. Sampling

distances along non-glacial streams mimic those along glacial streams.

28

Table 3. Sampling locations and measures for each river, listed in order of decreasing contributing glacier area, given as a range. The Sample ID is bold for locations with synoptic sampling and underlined where temperature was recorded by loggers. All locations were sampled with the Lagrangian method.

Study Area Sample Location Measures

River Glacier Glacier

Area (km2)

Samp. IDs

Dist (km)

L* values

% glacier cover

Elev (m)

Basin Area (km2)

White, Rainier

Emmons

12.9 to 16.4

1, 2, 3, 4, 5, 6

0.25, 2.7, 4.8, 11, 25,

36

0.1, 0.7, 1.3, 2.6, 5.9, 8.8

73, 42, 34, 20, 10, 6

1467, 1300, 1207, 1029,

796, 646

18, 33, 41, 83,

159, 260

Nisqual., Rainier

Nisqually

8.9 to 16.1

1, 2, 3, 4

4.9, 8.0, 19, 33

1.6, 2.7, 4.7, 8.2

33, 18, 9, 5

974, 850,

613, 476

27, 49, 178, 329

Eliot Cr., Hood Eliot 1.9 1, 2, 3

0.38, 0.75, 7.9

0.3, 0.6, 5.8

64, 62, 21

1903, 1817, 900

2.9 – 4.6

Sandy, Hood Sandy 0.3 to

2.5

1, 2, 3, 4, 5, 6,

7, 8

1.6, 2.2, 6.6, 14, 21, 33, 83, 91

2.7, 3.7, 6.7, 9.2, 14, 21, 52, 57

21, 20, 8, 4, 2,

0.4, 0.2, 0.2,

1371, 1262, 860, 612, 440,

291, 19, 6

2, 2, 13, 58, 135,

625, 1238, 1254

White, Hood

White River 0.5

1, 2, 3, 3b, 4, 5, 6, 7, 8, 9, 10

2.2, 2.8, 4.2, 4.4, 4.9, 5.5, 6.3, 7.0, 21, 25,

74

3.0, 4.0, 5.7, 6.1, 6.7, 7.5, 8.6, 9.6, 28, 34,

102

50, 43, 7, 7, 7, 7, 6, 5,

0.6, 0.5, <0.1

1394, 1355, 1305, 1260, 900,

864, 326

1, 1, 7, 8, 9, 10, 83, 102,

672

Salmon, Hood Palmer 0.2 to

0.3 1, 2, 3,

4, 5

0.95, 1.8, 8.6, 12, 52

2.1, 3.3, 16, 22,

96

41, 25, 4, 1, 0.1

2089, 1868, 1120, 1048, 373

0.5, 1, 7, 22, 266

NSantiam, Jefferson various 2.0 Sant_

8 78 55 <1 185 1686

LN Santiam, Non-Gl.

- - 1, 2, 3, 4, 5, 6,

7

5.8, 9.2, 16, 24, 33, 38,

46

- -

698, 601, 449, 347, 298, 265,

206, 185

24, 54, 113, 150, 238, 260, 287, 1686

Mult., Non-Gl. - -

1, 2, 3, 4, 4c,

5

1.6, 3.2, 4.0, 5.2,

7.2 - -

838, 638, 519, 417,

411, 22

2, 3, 7, 11, 12,

14

Tualatin, Non-Gl. - - 1, 2, 3,

4, 5

8.12, 13, 32, 70, 115

- - 276,

181, 64, 37, 35

19, 56, 171,

1223, 1692

29

4.2 Sampling methods

Three different sampling methods were used (Tables 3 and 4). Lagrangian

sampling attempted to follow the same parcel of water as it moved downstream. I

began sampling in the headwaters as near to the glacier as safe access allowed,

then walked or drove to each successive downstream sample location.

Temperature, turbidity and electrical conductivity were measured using field

probes and a turbidimeter, and grab samples for suspended sediment were

collected at each location at least once. Grab samples for nutrients and chloride

were collected at every location only once.

Synoptic sampling, the second type of sampling method, is intended to

define the diurnal variation in water quality along the rivers and collect a larger

number of samples at a range of times during the day. Teams of three or four

people were stationed at different locations along each stream, with more sample

points upstream from L* = 10 and fewer downstream. Each person sampled hourly

for suspended sediment, and measured temperature, turbidity and electrical

conductivity, as equipment availability permitted. Multnomah Creek and two

rivers each on Mt. Hood and Mt. Rainier were sampled synoptically (Table 4).

Sampling lasted for as few as 6 hours along the Sandy River, Mount Hood, to as

many as 24 hours on the White River, Mt. Rainier.

Water temperature along each stream was logged at 15 minute intervals

over a period of at least two days using Hobo temperature data loggers. Each

temperature data logger was sealed in a water-tight plastic (PVC) capsule in which

gravel was added for ballast. Capsules were tethered with a metal cable and

anchored to large boulders or trees on the stream banks. Remaining air within the

30

capsules caused them to float, so capsules were either secured to boulders or large

cobbles under water.

Table 4. Sampling dates, locations, duration listed by sampling method. Synoptic

River Date(s) No. sites sampled Hours sampled White (Rainier) 9/7-8 4 24 (9:00 on) Nisqually (Rainier) 9/9 4 7-10 (10:00 on) Sandy (Hood) 8/13 4 5-9 (8:30 on) White (Hood) 8/20 4 5-6 (15:30 on) Multnomah Cr. 8/28 3 5-7 (10:00 on)

Lagrangian River Dates Time spans White (Rainier) 9/4 11:30-17:30 Nisqually (Rainier) 9/5 11:15-13:50 Eliot Cr. (Hood) 8/10, 8/12 11:15-18:15, 11:50-14:00 Sandy (Hood) 7/25, 8/2, 8/4 12:20-20:45, 8:50-18:45, 10:30-18:15 White (Hood) 7/27, 8/3, 8/5 15:00-21:00, 10:30-17:10, 8:00-15:50 Salmon (Hood) 8/17, 8/21 12:47-19:50, 14:40-18:55 Multnomah Cr. 8/25 11:20-18:20 Gales Cr./Tualatin 9/14, 9/18 14:00-19:30, 10:25-15:30 Opal Cr./LNS 8/13/2006 12:35-18:40

Continuous temperature data collection with loggers River Dates Selected 24-hour time span White (Rainier) 9/4-9/8 9/7 9:00 – 9/8 10:00 Nisqually (Rainier) 9/5-9/10 9/7 0:00 – 9/8 0:00 (all day 9/7) Eliot Cr. (Hood) 8/10-8/12 8/11 0:00 – 8/12 0:00 (all day 8/11) White (Hood) 8/3–8/5 8/4 0:00 – 8/5 0:00 (all day 8/4) Sandy (Hood) 8/2-8/4 8/3 0:00 – 8/4 0:00 (all day 8/3) Salmon (Hood) 8/17-8/21 8/20 0:00 – 8/21 0:00 (all day 8/20) Multnomah Cr. 8/25-8/28 8/27 0:00 – 8/28 0:00 (all day 8/27) Gales Cr./Tualatin 9/14-9/18 9/15 0:00 – 9/16 0:00 (all day 9/15) Opal Cr./LNS 8/13-8/20/2006 8/18 0:00 – 8/19/2006 0:00 (all of 8/18) 4.3 Sampling chronology

I tried to sample runoff from glaciers in order from smallest to largest

glacier to minimize the influence of snow melt. Larger glaciers include high

elevations with more persistent seasonal snow. Sampling began at the end of July

and early August, when the smaller south facing glaciers of Mt. Hood tend to be

snow-free. I also sampled on cloud-free days to minimize the effects of rain.

31

4.4 Field and laboratory methods

Laboratory methods followed The Standard Methods for Examining Water

and Wastewater, 20th edition (Clesceri et al., 1998; this title is abbreviated as

Standard Methods through the remainder of this section.) All measurements were

taken where turbulence ensured mixing. Where water appeared to be more poorly

mixed, samples were taken by wading toward the center of the stream.

Temperature and conductivity were measured using a variety of probes.

Two educational-grade digital water temperature probes measured to the nearest

0.1°C and were accurate to ±0.1°C. A YSI Model 85 field probe, measured

temperature to the nearest 0.01°C with ±2% accuracy, and measured conductivity

to the nearest 0.1 µS cm-1 with ±1% accuracy. A TI-83 calculator with a Vernier

software interface and educational grade probe measured temperature to the

nearest 0.1°C and conductivity to the nearest 0.1 µS cm-1, with ±1% accuracy. Two

additional conductivity meters with temperature functions were also used at Mount

Rainier (Hach and YSI). Conductivity measurements were recorded after readings

stabilized. Some conductivity meters reported specific conductance and others did

not. Values were converted to specific conductance at 25°C and a secondary

calibration was applied to align measurements with the YSI Model 85 field probe.

Temperature data loggers exhibited a delay between temperature change

and response. The relatively large volume of air surrounding Hobos in the PVC

capsules is the most likely cause. It took about 45 minutes after capsules were

placed under water for the data logger temperatures to reflect stream temperature

(Figure 7). It is assumed that subsequent temperature changes experienced some

delay, as well, though possibly not as long as 45 minutes.

32

05

10152025303540

14:00 14:30 15:00 15:30 16:00 16:30White R., Mt. Hood, 8/3/2005

Tem

pera

ture

(°C

)

Figure 7. Graph of temperature data logger equilibration over time. Equilibration after submersion took about 45 minutes – first circled data point (3:15 PM) to second (4:00 PM).

Turbidity was measured using four Hach model 2100P turbidimeters,

calibrated with commercial 0.1 and 1000 NTU formazin standards and accurate to

0.1 NTU at less than 10 NTU and accurate to 1 NTU when higher. Turbidity

measurements are based on methods in the turbidity chapter of the U.S. Geologic

Survey Field Manual for the Collection of Water Quality Data (Anderson, 2004),

which recommended taking the median of three consecutive turbidity

measurements that are within 10% during an interval of 30 seconds to a minute. I

was unable to get three consecutive readings within 10% of each other because of

high rates of sediment settling. I altered the method by using the median value of

three consecutive measurements within 10% of each other where the sample was

inverted several times before each measurement. Turbidimeter readings that

exceeded 1000 NTU, the maximum limit of the turbidimeters, were recorded as

1100 NTU.

Suspended sediment concentration (SSC) was sampled according to

methods recommended for small mountain streams (Thomas, 1983). Highly

turbulent reaches of meltwater streams are well mixed (Østrem, 1975) and do not

33

require depth integrated sampling. I used a 250 mL wide-mouth Nalgene sample

bottle held at a 45° to the surface and lowered. Samples were taken close to the

surface in shallow streams to avoid capturing bed load. In deeper water, samples

were taken near mid-depth. Pre-dried and weighed 47mm glass fiber filters

(Whatman GF/F, 0.6 – 0.8 µm particle retention) were used to filter stream water.

The pre-weighed glass fiber filters were fitted to a pump and a sample size of 250

mL was filtered in the field, usually on one filter, but with two or three if sediment

clogged the filter. I tried to flush as much sediment from the walls of the crucible

onto the filter as possible. Filters and sediment were individually stored in small

zip-locking plastic bags.

Laboratory analysis was completed at the Portland State University

Geology Department’s soils laboratory. Sediment remaining in the plastic storage

bags was recovered as completely as possible using a small metal spatula. Filters

with samples were dried at 103-105°C for three hours. After cooling in a

desiccator, the filter and sediment were reweighed. The mass of the sediment is the

difference between the total mass of sediment and filter, and the pre-weighed filter

(Standard Methods 2540 D, 2-57).

Stream water samples were collected in the field and analyzed for nutrients

in the laboratories of Dr. Yangdong Pan and Dr. Mark Sytsma in the

Environmental Sciences and Resources Program at Portland State University.

Water samples were collected in or towards the middle of the stream. Two water

samples were taken at each sample location, using 250 ml acid washed Nalgene

bottles. One sample was filtered on site (Whatman GF/F, 0.6 – 0.8 µm particle

retention) to test for soluble reactive phosphorus and ammonium. Unfiltered

34

samples were tested for total nitrogen and total phosphorus. All samples were

stored on ice until returned to the laboratory for analysis.

Anions were analyzed in order to look at the downstream effects of salting

Palmer Snowfield for summer skiing. The Timberline ski area applies 500,000 kg

of sodium chloride to the ski slopes on the Palmer Snowfield during the summer to

improve snow conditions (Nathenson, 2004). Chloride is a conservative tracer

(Schlesinger, 1997; Waiser, 2006) and its dilution with distance will help test L* as

a model for changing water quality. Unused portions of filtered water collected for

nutrient analysis were used for anion analysis in Dr. Ben Perkin’s laboratory in the

Geology Department of Portland State University. Anions were measured using a

Dionex ICS-2500 ion chromatograph, and included chloride, fluoride, bromide,

nitrate and sulfate.

Total nitrogen and total phosphorus concentrations were estimated from

persulfate oxidation and colorimetry. Water samples were analyzed for

concentrations of nitrate and nitrite (EPA methods 300.0, 1979, 353.2, 1993).

These two constituents’ values were added together for total nitrogen. Soluble

reactive phosphorus (SRP) concentrations were determined using colorimetric

methods (EPA method 365.1, 1993). Total phosphorus (TP) concentrations were

determined using persulfate digestion and colorimetric methods (EPA method

365.1, 1993). Because of interference from suspended sediment in the samples for

total nitrogen and total phosphorus, results are suspect. Concentrations of soluble

reactive phosphorus and nitrate-nitrogen are more likely to be accurate. Because of

the uncertainty in the total nitrogen and total phosphorus results, nutrient data is

not reported in Chapter 5: Results and Analysis, nor discussed in Chapter 6:

35

Discussion. Instead, results are included in tabular form in the appendix (Appendix

G, Table G1).

4.5 Macroinvertebrate sampling

Macroinvertebrates were sampled by Dr. Jean Jacoby, Seattle University,

and Joy Michaud at White River, Mount Rainier, 0.15, 0.25, and 2.7 km from the

glacier (0.04, 0.07, and 0.7 L*). I sampled four locations on White River, Mount

Hood, between 2.9 and 6.7 km from the glacier (4.0-8.6 L*). All macroinvertebrate

samples were identified in the laboratory by Dr. Jacoby.

4.6 Geographic Information System analysis

To define sample location along each river and to calculate distance values

and fractional glacier cover, I used ESRI ArcGIS version 9.1. The base layer for

each watershed was a 10m digital elevation model acquired from the U. S. Bureau

of Land Management’s GIS data portal (http://www.blm.gov/or/gis/index.php).

Elevation accuracy is estimated by the Bureau of Land Management to be in the

range of 7 to 15 m root mean square error. Watershed boundaries and streamlines

were generated with ESRI’s Spatial Analyst hydrology tools. Sample locations

were added, and upstream watersheds were generated to determine the upstream

basin area, glacier area, and stream distance to the glacier source for each location.

4.7 Data analysis

Water quality variation with distance from glaciers was analyzed using four

different methods. First, water quality data were plotted against L* and other

distance measures to examine trends. Other distance measures include stream

distance from glaciers, fractional glacierization, upstream basin area, and, in the

case of temperature, elevation. Second, the degree of correlation was determined

36

between water quality data and distance measures. Kendall’s τ and Spearman’s ρ

were calculated for each combination of water quality value and distance measure.

These nonparametric tests were applied because the data is for the most part not

normal. The exception was temperature, for which there was a large sample size

that was close to normal; temperature correlation with distance measures was also

calculated using the parametric Pearson’s product moment. Third, water quality

values were regressed against distance measures to determine which measure

explains the most variation in water quality and to determine which mathematical

models best describe downstream changes. Correlation and regression were

calculated using average water quality values for sample locations and using all

data collected. Although logarithmic, power and exponential models are more

common in hydrology and fluvial morphology, I used several regression fit models

with the anticipation that one fit would not work for glacial streams for those

distances where water quality characteristics are distinctly glacial and farther

downstream where they become like non-glacial water. Linear and non-linear

regressions were performed, although water quality characteristics were expected

to change non-linearly with distance. If a water quality characteristic scales with

glacier size, I expect that L* and fractional glacierization would result in larger

regression and correlation coefficients than other measures. Fourth, glacial and

non-glacial characteristics were plotted against distance measures and compared

graphically to see if there is a distance at which they become indistinguishable.

Non-glacial stream distances were measured from stream inception points on

1:24,000 USGS topographic maps. This happened to result in upstream non-glacial

contributing source areas approximately equal in area to the smallest glacier in the

37

study, Palmer Snowfield (0.2 km2). Finally, diurnal variation in water quality

values was also examined because it is biologically influential and may scale with

glacier size.

4.8 Assessment of Lagrangian method

Lagrangian sampling was intended to simulate the observation of

temperature change in a parcel of water as it moved downstream. To assess this

method I used the Manning equation (Ward and Trimble, 2004) for estimating

stream velocity,

21

32

)( SRnkv = ,

where v is velocity, k is a conversion factor that depends on whether metric or

English units are used, n is the Manning roughness coefficient, R is the hydraulic

radius and S is the slope. Width and average depth were estimated, and hydraulic

radius was calculated as twice the depth plus the width, since sediment-laden

glacial streams are typically broad and shallow. Most locations had beds of gravel

and cobble, sometimes with boulders, for which Manning’s coefficients of

roughness are about 0.04 and 0.05, respectively (Ward and Trimble, 2004). I

estimated roughness to the thousandths place by comparing photographs of my

sampling locations with those in Roughness Characteristics of Natural Channels

(Barnes, 1967). Slope was calculated using stream distance and elevation values

from GIS topographic layers. The Manning velocity was calculated at each

location and the average from two sampling locations was applied to the stream

segment between them. Finally, travel time was estimated using the calculated

velocities and GIS generated stream distances.

38

Slope calculations were based on stream distance between points 10, 20,

30, 50, 100, and 1000 meters upstream and downstream from each sample point

and the elevation at those points in a GIS. A minimum of 10 m upstream and

downstream from a sample location (20 m distance) was selected because the grid

size of the DEM is 10 meters. A shorter distance could result in elevation values

being calculated from the same pixel, resulting in zero slope. From the pairs of

elevation and distance values, six corresponding slope values and six velocity

values were calculated. The median of the resulting non-zero velocities was used to

calculate travel time to avoid the influence of extreme values and minimize

influence from abrupt changes in elevation with distance. The 100 and 1000 m

distances allowed for velocity calculations in low gradient reaches where shorter

distances resulted in zero slope.

Uncertainty in stream velocity was calculated by estimating error at 25% in

stream width and depth, and as 0.01 in Manning’s roughness. Maximum hydraulic

radius was calculated using an increased depth and decreased width, and visa versa

for minimum hydraulic radius. Maximum velocity error was estimated using the

larger hydraulic radius and smaller roughness value; minimum velocity was

estimated using the smaller hydraulic radius and the larger roughness value. The

range of slope values was not included in error calculations.

Results show that Lagrangian sampling roughly matched the travel time of

a parcel of water (Figure 8, Appendix A). Sampling times lag water travel time

where sample locations were reached by trail (all less than 10 km). Sampling kept

pace with, and then exceeded the travel time of the water when downstream

locations were driven to by car. Fifty-five percent of Lagrangian samples were

39

taken within 2 hours of estimated water arrival time (Table 5). Lagrangian

sampling times were worse for non-glacial streams than for glacial streams. When

glacial samples are considered alone, 76% of samples were taken within two hours

of water arrival times.

White R., Mt. Rainier

0

5

10

15

20

0 2 4 6 8 10L*

Tim

e (h

ours

)

LaGrangian 9/4/05Water

Figure 8. Estimated water travel time and actual sampling times are plotted against L* for September 4, 2005 Lagrangian sampling on White River, Mount Hood. Points depicting calculated travel are shown with error bars.

Table 5. Number of hours Lagrangian sampling was conducted before or after estimated water travel times. Stream location identification numbers are listed below the nearest number of hours difference between estimated water arrival times and actual Lagrangian sampling times. Locations IDs preceded by a negative sign indicate the sample was taken before the parcel of water arrived. Lagrangian Sampling Hours water arrived ahead of sampling (+) or

after sampling (-) River Date <1 1-2 2-3 3-4 4-5 >5 White (Rainier) 9/4/2005 1, 5 2, 3, 4, -6 Nisqually 9/5/2005 1, 2, -3 -4 Eliot Cr. 8/10/2005 1, 2 3 Eliot Cr. 8/12/2005 1, 2 3 Sandy 7/25/2005 2 5 4 -8 Sandy 8/4/2005 1, 2, 6 4, 5 -7 White (Hood) 7/27/2005 1, 2 3, 8 5 -10 White (Hood) 8/3/2005 2, 3, 7 3.5, 8 4 9 White (Hood) 8/5/2005 2 3, 4, 5, 8, 9 6, 7 -10 Salmon 8/17/2005 1, 2, 4 3 -5 Salmon 8/21/2005 1 2, 3, 4 -5 Multnomah 8/25/2005 1 2 3 4 -5 Gales/Tualatin 9/14/2005 1, 2 -3 -4, -5 Gales/Tualatin 9/18/2005 1, -2 -3, -4, -5 Opal/Santiam 8/13/2006 1, -2, -3 -4 -5, -6, -7,

-8

40

Chapter 5: Results and Analysis

Sampling by synoptic and Lagrangian methods was completed along each

river during the summer of 2005, with additional sampling on non-glacial Little

North Santiam River during the summer of 2006 (Table 4). Synoptic sampling

duration varied, with White River, Mount Rainier, sampled for 24 hours, and the

remainder sampled for fewer hours because of a lack of personnel. Sampling

duration also varied by site, as some locations required hiking, while others were

accessible by car. Sampling on Nisqually River was halted after 16 hours due to

heavy rains. Lagrangian sampling was completed along each river at least once.

Fewer sets of Lagrangian samples were collected than originally intended because

of time constraints.

Automatic temperature sampling allowed me to sample temperatures at 15-

minute intervals over periods of more than 24-hours at multiple locations on each

stream. Equipment failure resulted in no data for locations 1 and 4 on White River,

Mount Rainier; locations 1 and 2, Sandy River, Mount Hood; and location 5 on the

non-glacial Tualatin River. One data logger in Salmon River became buried in

sediment, but data were used anyway when the graph appeared not to deviate too

much from expected. Overall, automated temperature data capture was successful

for 51 of 56 sample sites.

Analyses of each water quality variable, presented in the following

sections, test whether the rate of change in fractional glacierization with increasing

L* (Figure 1) is a useful model for downstream change in water quality.

41

5.1 Temperature

Temperature analyses are based on synoptic data logger samples. To verify

that ambient air temperatures were comparable, meteorological data were acquired

from the Western Regional Climate Center’s Remote Automated Weather Station

(RAWS) network and temperatures were compared between sample dates

(Appendix B, Table B1, Figures B1 and B2). Five RAWS stations were used. After

accounting for elevation differences, I decided that air temperatures were

reasonably comparable, except for the data from non-glacial Gales Creek and the

Tualatin River when there were clouds but no rain.

Glacial and non-glacial stream water temperature increased with distance

and varied diurnally. A summary of 24-hour logger temperature data for each

stream is provided in Table 6, though it should be noted that rivers were sampled at

different distances and on different days, resulting in differences in temperatures.

As a rule, minimum temperatures per stream correspond to the closest sample

location to the glacier, and maximum values correspond to the farthest sample

locations. Temperature minima reach near 0°C along glacial streams, while the

coldest non-glacial temperature was 8.6°C. Stream temperature was low along

non-glacial Gales Creek and Tualatin River because of overcast weather.

42

Table 6. Statistical summary of temperature data (°C) from data loggers. The number of sample sites follows the stream name, and the distances sampled follow the total number of samples. White River sampling includes grab sample data for locations 1 and 4 where logger data was not available. Little North Santiam River is abbreviated LNS.

Stream Name No. Samples Ave. Temp

Min. Max. Range Std. Dev.

Glacial All Glacial 30 sample locations

2752 0.3km/0.07 L* to 78 km/97 L*

8.8 0.3 21.0 20.7 4.9

White (Rainier) 6 sample locations

445 0.3 km/0.07 L* to 36 km/8.8 L*

6.3 0.9 13.7 12.8 3.3

Nisqually 4 sample locations

385 4.9 km/1.6 L* to 33 km/8.2 L*

8.1 4.2 16.4 12.2 3.4

Eliot Cr. 3 sample locations

288 0.4 km/0.3 L* to 8 km/5.8 L*

2.3 0.3 11.0 10.7 2.9

Sandy 4 sample locations

385 13 km/9 L* to 83 m/52L*

13.7 7.4 21.0 13.5 4.5

White (Hood) 7 sample locations

672 2.9 km/4.0 L* to 25 km/34 L*

10.2 4.6 19.8 15.2 4.2

Salmon 5 sample locations

480 1.0 km/ 2 L* to 52 km/97 L*

8.9 2.9 17.1 14.3 3.7

N Santiam 1 sample location

97 samples 78 km/55 L*

13.0 10.2 15.6 5.4 1.8

Non-Glacial All non-glacial 16 sample locations

1552 samples 1 to 70 km

14.2 8.6 22.5 13.9 3.1

Multnomah Cr. 5 sample locations

485 1 to 6.6 km

11.9 8.6 14.9 6.2 1.6

Gales Cr./Tualatin 4 sample locations

388 samples 7.8 to70 km

13.0 9.8 16.0 6.2 2.0

Opal Cr./ LNS 7 sample locations

679 samples 5.6 to 46 km

16.5 11.4 22.5 11.1 2.9

L* performed better than other stream distance measures, but not as well as

upstream fractional glacier cover, in results from correlation and regression

43

analyses. Temperature correlated more highly with distance, L* and fractional

glacierization than with elevation and upstream basin area when location

temperature averages were used (Table 7), and when all data were used (Appendix

B, Table B2). Regression coefficients, R2, for regressions of sample location

average temperatures on distance measures were generally highest when the

fraction glacierized was plotted on the x-axis (Table 8). After fraction glacierized,

regression coefficients were highest for L*, then distance and elevation, followed

by upstream basin area. Results were similar for regression of all temperature data

on distance measures (Appendix B, Table B4).

Table 7. Correlation coefficients from comparisons of location temperature averages to distance measures. Correlation coefficients stronger than 0.75 are bold. All results are statistically significant (p-values<0.05).

Distance L* Fraction glacierized Elevation Upstream

basin area Temperature averages from sample locations – n = 30 Pearson’s 0.78 0.70 - 0.86 - 0.78 0.59 Kendall’s τ 0.71 0.81 - 0.83 - 0.60 0.51 Spearman’s ρ 0.87 0.94 - 0.94 - 0.76 0.69

No single type of regression equation described the variation in temperature

data when plotted against the five distance measures equally well. Logarithmic

regressions resulted in the largest regression coefficients, R2, for distance, L*, and

fractional glacier cover (Table 8, Appendix B, Figures B3- B7). Cubic equations

provided the next best fit and R2 values for distance, L*, and fractional glacier

cover, and the best fits for elevation and upstream basin area. Linear, exponential

and power equations generally fit more poorly and resulted in lower R2 values.

Correlation and regression analyses for individual streams using both location

averages and all temperature data generally resulted in very similar results no

44

matter which distance measure was considered (Appendix B, Tables B3 and B5).

Root mean square errors (RMSE) were low for all regressions, and lowest for

power and exponential equations.

Table 8 Regression coefficients, R2, from comparisons of sample location average temperatures on the five distance measures. R2> 0.75 are bold. RMSE (°C) follows in parentheses. All results are statistically significant with p-values<0.05.

Distance L* Fraction

glacierized Elevation Upstream basin area

Logarithmic 0.77 (2.2)

0.84 (1.8)

0.82 (1.9)

0.60 (2.9)

0.44 (3.4)

Quadratic 0.69 (2.5)

0.73 (2.4)

0.78 (2.2)

0.63 (2.8)

0.53 (3.2)

Cubic 0.76 (2.3)

0.78 (2.2)

0.82 (2.0)

0.65 (2.8)

0.53 (3.2)

Power (axb) 0.70 (0.5)

0.73 (0.5)

0.49 (0.5)

0.27 (0.8)

0.26 (0.8)

Exponential (aebx)

0.28 (0.8)

0.22 (0.8)

0.80 (0.4)

0.45 (0.7)

0.13 (0.9)

Linear 0.61 (2.8)

0.49 (3.2)

0.73 (2.3)

0.60 (2.8)

0.35 (3.6)

To graphically examine whether temperature scales with glacier size, I

plotted average temperatures for each location against distance measures (Figure 9,

Appendix B, Figures B8-B12). Plotted against stream distance, curves from

individual rivers are roughly parallel with curves from larger glaciers shifted to

lower temperatures and crossing a horizontal line at 10°C at greater distances. The

threshold of 10°C is the threshold below which macroinvertebrates adapted to

glacial streams can colonize during summer months. When plotted against L*,

curves from individual rivers coincide until about 5 to 10 L* when temperatures of

individual streams begin to diverge.

45

0

5

10

15

20

0 20 40 60 80 100Distance (km)

Ave.

Tem

pera

ture

(°C

) White_RNisq_REliot_HSandy_HWhite_HSalmon_H

0

5

10

15

20

0 20 40 60 80 100L*

Ave.

Tem

pera

ture

(°C

)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_H

Figure 9. Average glacial stream temperatures are plotted against stream distance (left) and L* (right). The legend lists streams in order of decreasing contributing glacier area followed by _R or _H for Mount Rainier or Mount Hood. A horizontal line at 10°C is added as the upper limit for glacier stream habitat (Milner and Petts, 1994)

Diurnal variations in temperature also change with distance from glacier

sources (Figure 10, Appendix B, Figures B13-B22). Diurnal temperature variation

decreases both near the glacier and far from the glacier. Close to the glacier, at less

than about 2 L*, temperatures remain close to 0°C with little to no variation.

Rivers sampled far from glacial sources, more than 20 L*, show decreasing diurnal

variation with distance. Maximum diurnal temperature variation appears to occur

between about 6 and 10 L*.

46

(a) White R., Mt. Rainier

0

5

10

15

20

9/7/056:00

9/7/0512:00

9/7/0518:00

9/8/050:00

9/8/056:00

Tem

pera

ture

(°C

)

WhiteR_1 L*=0.07WhiteR_3 L*=1.3WhiteR_4 L*=2.6WhiteR_5 L*=5.9WhiteR_6 L*=8.8

(b) Eliot Cr./Hood R., Mt. Hood

0

5

10

15

20

8/11/050:00

8/11/056:00

8/11/0512:00

8/11/0518:00

8/12/050:00

Tem

pera

ture

(°C

) EliotH_1 L*=0.3EliotH_2 L*=0.5EliotH_3 L*=5.8

(c) Sandy R., Mt. Hood

0

5

10

15

20

25

8/3/050:00

8/3/056:00

8/3/0512:00

8/3/0518:00

8/4/050:00

Tem

pera

ture

(°C

)

Sandy_4 L*=9Sandy_5 L*=14Sandy_6 L*=21Sandy_7 L*=52

(d) Salmon R.

0

5

10

15

20

25

8/20/050:00

8/20/056:00

8/20/0512:00

8/20/0518:00

8/21/050:00

Tem

pera

ture

(°C

)

Salmon_2 L*=3Salmon_3 L*=16Salmon_4 L*=22Salmon_5 L*=97

Figure 10. Twenty-four hour temperature variation plotted against time at locations along (a) White River, Mount Rainier, (b) Eliot Creek, (c) Sandy River, and (d) Salmon River. Figures (a) and (b) show minimum variation close to the glacier and (c) and (d) show minimum variation at distance.

Box plots of 24-hour temperature averages ordered by increasing L* and

fractional glacierization show an orderly trend of increasing temperature minima

and maxima, while the box plot ordered by increasing stream distance, elevation,

and basin area show erratic increases (Figure 11; Appendix B, Figures B23-B27).

Boxes display temperatures between the 25th and 75th percentiles (the interquartile

range), with the median indicated by a horizontal bar within the box. The range is

displayed by the distance between the upper and lower stem ends. At distances less

than 4 L*, diurnal variation is low and temperatures remain below 10°C (glacial

environment). Temperatures remain below 2°C, the upper temperature boundary

for Diamesa, at less than 0.7 L*. Diurnal variation begins to decrease after about

10 L* except for those locations along the White River, Mount Hood.

47

Figure 11. Box plots of 24-hour temperature data are plotted in order of increasing L* (left) and stream distance in km (right). A horizontal line at 2°C sets the lower limit for Diamesa and one at 10°C indicates the maximum summer temperature for glacial stream environments (Milner and Petts, 1994).

Temperature ranges were calculated for each 24-hour interval and plotted

against distance measures (Figure 12; Appendix B, Figures B28-B32). Rivers from

the three largest glaciers, White and Nisqually Rivers, Mount Rainier, and Eliot

Creek, Mount Hood, appear to behave similarly with little temperature variation

near the glacier (L*<5 to 10). Diurnal temperature variation increases rapidly with

distance from a glacial source, then decreases with greater distance (L*>10).

Maximum variation appears to occur at about L*=10 and less than 10%

glacierization. Salmon River behaves exceptionally, with initial downstream

decreases in temperature variation.

48

0

2

4

6

8

10

12

0 10 20 30 40 50 60L*

Tem

pera

ture

rang

e (°

C). White_R

Nisq_REliot_HSandy_HWhite_HSalmon_H

02

46

810

12

0 10 20 30 40 50 60Stream distance (km)

Tem

pera

ture

rang

e (°

C). White_R

Nisq_REliot_HSandy_HWhite_HSalmon_H

0

2

4

6

8

10

12

0.0 0.2 0.4 0.6 0.8Fraction glacierized

Tem

pera

ture

rang

e (°

C). White_R

Nisq_REliot_HSandy_HWhite_HSalmon_H

02

46

81012

0 50 100 150 200 250 300 350 400

Upstream basin area (km2)Te

mpe

ratu

re ra

nge

(°C

).

White_RNisq_REliot_HSandy_HWhite_HSalmon_H

Figure 12. Daily temperature range plotted against the distance measures L*, stream distance (km), fraction glacierized, and upstream basin area (km2).

Using water temperature, I tested the actual Lagrangian sampling with an

ideal Lagrangian method derived from the Hobo temperature loggers. Ideal and

actual methods compare favorably, as shown by representative samples in Figure

13 (Appendix B, Figures B33-B40). These show variation in downstream

temperature change, possibly because of groundwater influences.

(a)

0

5

10

15

20

0 10 20 30 40L*

Tem

pera

ture

(°C

).

Hobo 8/4/058/3/05 Sampling

(b)

0

5

10

15

20

0 20 40 60 80 100L*

Tem

pera

ture

(°C

)

8/21/05 SamplingHobo 8/21/05Hobo 8/20/05

Figure 13. A comparison of temperature plotted against distance using actual and idealized Lagrangian sampling. (a) White River, Mount Hood 8/3/2005, 10:30 - 20:15. (b) Salmon River, Mount Hood, 8/5/2005, 8:00 – 17:45.

49

All Lagrangian samples were collected beginning in daylight hours, mostly

at different times in the morning, and some continued past nightfall. In every case,

water temperatures rise more rapidly near glacial headwaters than downstream

where warming rates decrease (Figure 14; Appendix B, Figures B33-B40). In each

sample set, the break point between rapid temperature change and more gradual

change occurs at between 5 and 10 L*.

(a)

0

5

10

15

0 1 2 3 4 5 6 7 8 9L*

Tem

pera

ture

(°C

)

LaGrangian 9/4/05Hobo 9/7/05

(b)

0

5

10

15

20

0 2 4 6 8 10L*

Tem

pera

ture

(°C

)LaGrangian 9/5/05Hobo 9/6/05

Figure 14. Lagrangian temperature changes observed with distance on (a) White River, Mount Rainier, September 4, 2005, and (b) Nisqually River, Mount Rainier, September 5, 2005.

Twenty-four hour logger averages for glacial and non-glacial streams were

plotted against distance measures, with a horizontal line at 10°C, the temperature

boundary for glacial habitat (Milner and Petts, 1994), to determine the distance at

which glacial stream temperatures become like non-glacial (Figure 15). Non-

glacial temperatures were plotted at 0 L* and 0 glacial cover, since those rivers do

not have glaciers. Plotted against stream distance, glacial temperatures begin

colder, but warm at a rate similar to non-glacial streams, so there is no distance at

which both glacial and non-glacial temperatures coincide. This same pattern was

seen when temperature was graphed against upstream basin area. Glacial water

temperatures reached 10°C at between about 7 and 36 km, 5 and 22 L*, and less

than 5% glacierized. Salmon River average temperatures remain below 10 °C

50

much farther when plotted against L* than other glacial streams do. If this is

anomalous behavior, then the range of L* distances that glacial stream

temperatures reach 10 °C is 5 to 10 L*. The plot of temperatures on elevation only

showed that non-glacial streams were not sampled at elevations as high as glacial

streams were. Where glacial streams were sampled at elevations where non-glacial

streams were also sampled, both glacial and non-glacial temperatures were present

at all elevation ranges.

0

5

10

15

20

25

0 10 20 30 40 50 60 70 80 90

Stream distance (km)

Aver

age

Tem

pera

ture

(°

C)

0

5

10

15

20

25

0 10 20 30 40 50 60

L*

Aver

age

Tem

pera

ture

(°

C)

0

5

10

15

20

25

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fractional glacier cover

Aver

age

Tem

pera

ture

(°

C)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HTualatin MultnomahLN Santiam N Santiam

0

5

10

15

20

25

0 500 1000 1500 2000 2500

Elevation (m)

Aver

age

Tem

pera

ture

(°

C)

0

5

10

15

20

25

0 1000 2000 3000

Upstream basin area (km2)

Aver

age

Tem

pera

ture

(°

C)

Figure 15. Glacial and non-glacial temperature averages plotted against distance measures. L* and fractional glacier cover for non-glacial streams are set to 0, and distance was measured from the stream inception point on 1:24,000 U. S. Geological Survey topographic maps. A horizontal line marks the upper threshold of glacial stream habitat (Milner and Petts, 1994).

51

5.2 Turbidity

Turbidity is high near glaciers and generally decreases with distance, and

levels vary over the summer melt season, on a daily scale, and abruptly with

sudden sediment discharge from the glacier. Turbidity in the highest reaches of the

Salmon River was less than 100 NTU during reconnaissance on July 23rd, 2005,

but on August 21st turbidity was greater than 1000 NTU. White River, Mount

Hood, at State Highway 35 exhibited 47.3 NTU at 10:50 AM, August 5, 2005, and

was over1000 NTU by 3:50 PM. A statistical summary of turbidity on each river is

provided in Table 9. Of the 232 samples, 18 samples exceeded the1000 NTU limit

of the instrument. Fifteen of these samples were from the White River, Mount

Hood. Only 5 turbidity readings were taken from the Salmon River, and 3 of these

readings exceeded instrument capability.

Table 9. Statistical summary of turbidity data (NTU). Asterisks (*) indicate turbidity values above 1000 NTU were set to 1100 NTU. Little North Santiam River is abbreviated LNS and the North Santiam River is abbreviated NS. The single turbidity value measured on North Santiam River was 0.6 NTU.

Stream Name No. Samples Ave. Min. Max. Range Std. Dev. All Glacial 212 307* 0.6 1100* 1099* 350* White (Rainier) 66 270 56 754 698 175 Nisqually 37 104 34 222 188 40 Eliot Cr. 8 313 253 404 151 46 Sandy 41 84 4.2 277 273 70 White (Hood) 54 616* 5.0 1100* 1095* 483* Salmon 5 833* 3.3 1100* 1097* 475* Multnomah Cr. 31 0.5 0.2 2.7 2.5 0.5 Gales Cr./Tualatin 10 3.5 0.6 11.6 10.6 3.4 Opal Cr./ LNS/NS 8 0.9 0.6 1.2 0.6 0.2

Correlation analysis between turbidity and distance measures did not result

in clear differences between measures (Table 10; Appendix C, Tables C1-C2).

Correlation was analyzed with two non-parametric tests, Kendall’s τ and

52

Spearman’s ρ; parametric tests were not used because the data were not normal.

Distance, L*, and upstream basin area correlated negatively with turbidity, while

fractional glacierization correlated positively. Correlation is generally weak, with

the correlation for L* the weakest, similar to fractional glacier cover. Correlation

of turbidity with distance and with upstream basin area was stronger than

correlation with other measures. Results were similar when turbidity was

normalized by the maximum of average values per stream (Table 10, bottom half).

Table 10. Correlation coefficients for correlation between turbidity values and distance measures. Results are significant at p<0.01.

Distance L* Fraction glacierized

Upstream basin area

All glacial data - 232 values Kendall’s τ -0.31 -0.27 0.31 -0.32 Spearman’s ρ -0.44 -0.35 0.40 -0.44 All glacial data – 232 values normalized by stream maxima Kendall’s τ -0.34 -0.25 0.25 -0.34 Spearman’s ρ -0.48 -0.35 0.35 -0.45

Regression analysis also did not indicate a superior distance measure

(Table 11). Regression coefficients from polynomial equations (linear, quadratic,

and cubic) are generally weak with large RMSE (root mean square error), while

coefficients from logarithmic equations are stronger, but also have larger RMSE.

Power and exponential equations explain more of the variation in turbidity with

smaller error, but do not indicate the superiority of any distance measure. When all

glacial turbidity data are considered together, upstream basin area performed better

as a predictor of turbidity (Table 11, Appendix C, Figures C1-C4), although often

with large RMSE. Results were similar on regressions of all turbidity values

(Appendix C, Table C4), and when turbidity values were normalized by stream

53

maxima (Appendix C, Table C5, Figures C5-C8). For regressions on data from

individual rivers, all measures perform about equally well (Appendix C, Table C3).

Table 11. Regression coefficients from regressions of average turbidity values per sample location on distance measures. RMSE (NTU), followed by p-values in parentheses, unless <0.05. Only power and exponential models resulted in p-values <0.05 for averaged data.

Distance L* Fraction glacierized

Upstream basin area

Turbidity averages – 25 sample locations (significant R2 values bold) Logarithmic 0.13

(245.1, 0.07) 0.047

(257.8, 0.33) 0.08

(252.8, 0.18) 0.23

(231.1) Quadratic 0.18

(243.6, 0.11) 0.05

(262.0, 0.55) 0.07

(258.83, 0.43) 0.19

(241.7, 0.09) Cubic 0.43

(249.0, 0.23) 0.05

268.1, 0.76) 0.08

(263.57, 0.60) 0.22

(242.7, 0.14) Power (axb) 0.28

(1.0) 0.23 (1.0)

0.35 (0.9)

0.32 (1.0)

Exponential (aebx)

0.47 (0.8)

0.41 (0.9)

0.16 (1.1)

0.44 (0.9)

Linear 0.15 (242.6, 0.06)

0.05 (256.3, 0.27)

0.07 (253.3, 0.19)

0.14 (243.7, 0.06)

Average and maximum turbidity values per sample site were compared to

distance measures graphically. In order to make better comparisons despite orders

of magnitude difference, average turbidity values for each location were

normalized by the largest average value per stream, and maximum values per

sample location were normalized by the maximum per stream. Graphs of

maximum and normalized maximum turbidity are shown here, since they exhibit a

less variable pattern with distance (Figure 16) and graphs of average and

normalized average show less pattern (Appendix C, Figures C9-C16). Only data

from locations where turbidity was tested at least twice were included. While

turbidity generally decreased with distance, Nisqually River being a notable

exception, no other obvious pattern emerges from plots without normalization.

54

Plots with normalized turbidity reveal similar rates of decrease as sets of nearly

parallel lines for White River, Mount Rainier, and Sandy and Salmon Rivers,

Mount Hood, when plotted against distance.

0200

400600800

10001200

0 20 40 60

Distance (km)

Max

imum

tur

bidi

ty (N

TU)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60

Distance (km)

Nor

mal

ized

max

imum

.

turb

idity

(NTU

)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0200

400600800

10001200

0 5 10 15 20 25 30

L*

Max

imum

tur

bidi

ty (N

TU)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

L*

Nor

mal

ized

max

imum

.

turb

idity

(NTU

)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0

200

400600

800

1000

1200

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fraction glacierized

Max

imum

tur

bidi

ty (N

TU)

White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_H

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fraction glacierized

Nor

mal

ized

max

imum

.

turb

idity

(NTU

)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0200400

600

8001000

1200

0 50 100 150 200 250 300 350

Basin area (km2)

Max

imum

tur

bidi

ty (N

TU) White_R

Nisqually_REliot_HSandy_HWhite_HSalmon_H

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150 200 250 300 350

Basin area (km2)

Nor

mal

ized

max

imum

.

turb

idity

(NTU

) White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

Figure 16. Maximum turbidity (left) and normalized maximum values (right) are plotted against stream distance (km, top), L* (second row), fraction glacierized (third row), and upstream basin area (km2, bottom row).

55

Lagrangian sampling revealed that downstream change in turbidity in Eliot

Creek declined slowly with distance, while turbidity in White and Nisqually

Rivers, Mt. Rainier, and White and Sandy Rivers, Mount Hood, increased in

turbidity at some point along the stream, indicating downstream sediment

enrichment through non-glacial sources (Figure 17; Appendix C, Figures C17-

C26). Break points between rapid decrease in turbidity and more gradual decrease

did not generally occur between 5 and 10 L*, but between a range of about 5 to 30

L*. Non-glacial turbidity increased, decreased or vacillated with distance, though

at much lower levels than most glacial locations. Synoptic and Lagrangian

sampling revealed downstream increases in turbidity along Nisqually River,

contrary to expectation. Recent and on-going construction at Longmire may

provide part of the explanation. Stream banks at Longmire had been augmented to

help prevent flooding of employee housing and park facilities. A bridge was also

under construction to additional employee housing. Lower turbidities were

recorded at Longmire than other sites, but the sampling location was not far from

the bridge and perhaps upstream from locations where sediment was added to the

stream. Data show that turbidity increased downstream from the Longmire

sampling site, so there may have been additional disturbance farther downstream.

56

White R., Mt. Rainier9/4/2005

0

50

100

150

200

250

0 5 10L*

Turb

idity

(NTU

)

Nisqually R., Mt. Rainier9/05/05 sampling

0

10

20

30

40

50

0 2 4 6 8 10L*

Turb

idity

(NTU

).

Sandy R., Mt. Hood

0102030405060708090

0 20 40 60 80L*

Turb

idity

(NTU

) 7/25/2005 12:20-20:458/4/2005 10:30-18:15

White R., Mt. Hood

050

100150

200250

300

0 10 20 30 40L*

Turb

idity

(NTU

) 8/3/2005 10:30-17:108/5/2005 8:00-14:30

Figure 17. Lagrangian turbidity results plotted against L*. Turbidity generally declined with distance. Nisqually River, Mount Rainier, and White River, Mount Hood, show downstream sediment enrichment.

Stream turbidity displayed a roughly sinusoidal daily pattern. Near the

glacier, turbidity generally peaked in the early afternoon and declined towards

evening. One-day sampling of White River, Mount Rainier, revealed two turbidity

peaks at about 4PM and 11PM near the glacier portal. These peaks appeared later

and attenuated farther downstream (Figure 18; Table; Appendix C, Figures C27-

C30). Close to the Sandy Glacier, Sandy River turbidity peaked at about 1PM,

decreased for about an hour and was increasing again when sampling ended. Close

to the glacier (L*<9) on the White River, Mount Hood, turbidity exceeded the

maximum value of the turbidimeter for the five hours of sampling. Non-glacial

Multnomah Creek decreased in turbidity during the day and increased in the

afternoon, but values were very low.

57

White R., Mt. Rainier, 9/7-8/20050

100200300400500600700800

9/7/

058:

00

9/7/

0514

:00

9/7/

0520

:00

9/8/

052:

00

9/8/

058:

00

Turb

idity

(NTU

).

WhiteR_1 L*=0.1

WhiteR_4 L*=2.6

WhiteR_5 L*=5.9

Figure 18. Turbidity changes over a 24-hour period at three locations on White River, Mount Rainier.

Glacial and non-glacial turbidity were plotted together against stream

distance measures to determine the distance they became indistinguishable, and

compared to a line at 30 NTU, the threshold established for glacial stream habitat

(Milner and Petts, 1994). Since L* and fractional glacier cover cannot be

calculated for non-glacial streams, non-glacial turbidity values are stacked at zero

for those graphs. Non-glacial stream distance is calculated from the stream

inception point on 1:24,000 scale U.S. Geologic Survey topographic maps.

Turbidity was also compared by upstream basin area. Graphs are semi-log to

accommodate the range of turbidity values (Figure 19; Appendix C, Figures C31-

C34).

Glacial turbidity first approaches non-glacial values at 51 km (96 L*) on

the Salmon River, or 78 km (55 L*) on the North Santiam River. The North

Santiam River location is downstream from Detroit Lake and two dams. The low

turbidity site on the Salmon River point lies downstream from the low gradient

Sandy River Meadows. These data points are noticeably less turbid than glacial

stream locations without such sediment filters. If these locations are disregarded,

glacial stream turbidity values are not regularly less than the highest non-glacial

58

turbidity values until more than 60 km (or 50L*) from glacier sources. However,

the non-glacial values approached are downstream from agricultural regions on the

Tualatin River, where added nutrients may increase turbidity. Certainly, glacial

water is not regularly like non-glacial until basins have less than 1% glacier cover.

0.1

1

10

100

1000

10000

0 20 40 60 80 100 120 140

Distance (km)

Turb

idity

(NTU

)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HN Santiam TualatinMultnomah LN SantiamFiltered Agricultural

0.1

1

10

100

1000

10000

0 20 40 60 80 100 120 140

L*

Turb

idity

(NTU

)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HN Santiam TualatinMultnomah LN SantiamFiltered Agricultural

0.1

1

10

100

1000

10000

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fraction glacierized

Turb

idity

(NTU

)

0.1

1

10

100

1000

10000

0 500 1000 1500 2000

Basin Area (km2)

Turb

idity

(NTU

)White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HN Santiam TualatinMultnomah LN SantiamFiltered Agricultural

Figure 19. Glacial and non-glacial turbidity values plotted against distance measures. Non-glacial L* and fractional glacier cover are set to 0, while stream distance is measured from the inception point on 1:24,000 scale U.S. Geologic Survey topographic maps. A threshold line is drawn at 30 NTU, the minimum turbidity level for glacial stream habitat (Milner and Petts, 1994).

59

5.3 Suspended sediment concentration

Patterns in suspended sediment concentration mirrored turbidity patterns.

Suspended sediment concentration (SSC) was high near the glacier and decreased

with distance. Like turbidity, SSC varied tremendously over the summer melt

season and on a daily scale, increasing through the summer, and through the day.

Maximum concentrations were found closest to glaciers in the afternoon or

evening. Downstream enrichment was observed on Nisqually River, Mount

Rainier, and Eliot Creek, Sandy and White Rivers, Mount Hood. Average and

maximum SSC along glacial rivers were significantly higher than on non-glacial

rivers. Minimum SSC along Sandy, White and Salmon Rivers, Mount Hood, were

similar to maximum concentrations on non-glacial streams, but only far from

glaciers, at 83 km/52 L* for Sandy River (0.001 mg/L), 74 km/102 L* for White

River (0.001 gm/L), and 52 km/97 L* for Salmon River (0.005 mg/L). A statistical

summary of suspended sediment concentrations is given in Table 12.

Table 12. Statistical summary of suspended sediment concentrations (mg/L).

Stream Name No. Samples Ave. Median Min. Max. Range

Std. Dev.

All Glacial 202 0.89 0.11 0.001 24.89 24.89 2.57 White (Rainier) 76 0.24 0.17 0.015 1.07 1.05 0.24

Nisqually 30 0.07 0.06 0.013 0.14 0.13 0.04 Eliot Cr. 7 0.14 0.10 0.098 0.27 0.17 0.06 Sandy 38 0.07 0.06 0.001 0.35 0.35 0.07 White (Hood) 46 3.26 0.89 0.001 24.89 24.89 4.65 Salmon 5 0.03 1.01 0.005 2.88 2.87 1.34 Multnomah 25 0.001 0.001 <0.0001 0.003 0.003 0.001 Gales/Tualatin 10 0.001 0.001 <0.0001 0.005 0.005 0.001

Average suspended sediment concentration varied greatly among rivers

(Figure 20; Appendix D, Figures D1-D8). The two smallest glaciers generated the

60

highest concentrations of suspended sediment (White and Salmon Rivers, Mount

Hood). Of the remaining, the most sediment-laden stream was White River, Mount

Rainier, which drains the largest glacier included in the study. All rivers except for

the Nisqually River decrease in suspended sediment concentration with distance.

0

1

2

3

4

5

6

7

0 10 20 30 40 50L*

Ave

rage

SS

C (m

g/L)

White_HSalmon_H

0.0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25L*

Ave

rage

SS

C (m

g/L)

White_RNisquallyEliot_HSandy_H

Figure 20. Average suspended sediment concentration plotted against L* for White and Salmon Rivers, Mount Hood (left), and remaining glacial rivers (right).

Using the non-parametric measures Kendall’s τ and Spearman’s ρ

correlation between SSC and different distance measures is generally weak when

data are analyzed together (Table 13) and by individual stream (Appendix D, Table

D1). Correlations between SSC values and upstream basin area result in the

strongest coefficients, followed closely by distance. Fraction glacierized and L*

correlated more poorly, but similarly. Distance, L*, and upstream basin area

resulted in negative correlation with SSC, while fractional glacierization resulted in

positive correlation.

Table 13. Correlation coefficients for correlation between suspended sediment concentrations and distance measures. Results are significant at p-value <0.05.

Distance L* Fraction glacierized

Upstream basin area

All glacial - 187 data values Kendall’s τ -0.33 -0.15 0.17 -0.36 Spearman’s ρ -0.48 -0.21 0.24 -0.51

61

Regression analysis resulted in no strong relationships between suspended

sediment concentration and any distance measure, although distance and upstream

basin area explained slightly more of the variation in SSC than did fractional

glacier cover or L* (Table14). The same relationships resulted when sample

location averages were regressed, rather than all SSC data values (Appendix D,

Table D3). Root mean square error was large for all regressions. Regressions on

White River, Mount Rainier, were the only regressions to pass all tests of

significance among individual rivers (Appendix D, Table D2).

Table 14. Regression coefficients from regressions of all suspended sediment concentrations on distance measures. Parentheses contain RMSE (mg/L) followed by p-values if >0.05.

Distance L* Fraction glacierized

Upstream basin area

All glacial - 187 data values Logarithmic 0.02

(2.5, 0.06) <0.01

(2.5, 0.55) <0.01

(2.5, 0.34) 0.14 (2.3)

Quadratic 0.06 (2.5)

<0.01 (2.5, 0.65)

<0.01 (2.5, 0.41)

0.07 (2.4)

Cubic 0.06 (2.5)

<0.01 (2.5, 0.59)

0.03 (2.5, 0.13)

0.10 (2.4)

Power (axb) 0.18 (1.7)

0.05 (1.8)

0.11 (1.7)

0.31 (1.5)

Exponential (aebx)

0.30 (1.5)

0.13 (1.7)

0.05 (1.8)

0.27 (1.6)

Linear 0.04 (2.5)

<0.01 (2.5)

<0.01 (2.5, 0.84)

0.03 (2.5)

Suspended sediment concentration, like turbidity, displayed a roughly

sinusoidal daily pattern over time, with peaks near the glacier in the early

afternoon migrating to downstream locations later in the day. The two turbidity

peaks observed during the one-day sampling of White River, Mount Rainier, are

also evident in the SSC data (Figure 21; Appendix D, Figures D9-D13).

62

White R., Mt. Rainier

0.0

0.2

0.4

0.6

0.8

1.0

1.2

9/7/

0510

:00

9/7/

0514

:00

9/7/

0518

:00

9/7/

0522

:00

9/8/

052:

00

9/8/

056:

00

9/8/

0510

:00

SSC

(mg/

L)

WhiteR_1WhiteR_4WhiteR_5

Figure 21. Suspended sediment concentration plotted against time for three synoptically sampled locations on the White River, Mount Rainier.

Diurnal variation was estimated for all streams sampled synoptically,

showing the greatest variation generally closer to the glacier (Figure 22). Twenty-

four hour sampling on White River, Mount Rainier, provides the most detailed

picture of diurnal variation in this study (Table 15). The greatest variation was

observed on the White River, Mount Hood, where there was a difference of more

than 23 grams of sediment per liter of water between the most dilute sample and

the sample with the highest concentration.

0

5

10

15

20

25

0 10 20 30L*

Ran

ge S

SC (m

g/L)

White R., Mt. RainierNisqually R., Mt. RainierSandy R., Mt. HoodWhite R., Mt. Hood

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 5 10 15L*

Ran

ge S

SC (m

g/L)

White R., Mt. Rainier

Nisqually R., Mt. RainierSandy R., Mt. Hood

Figure 22. Range of suspended sediment concentration plotted against L* for all synoptically sampled streams (left) and without White River, Mount Hood (right). The maximum range of suspended sediment concentration observed at each sample location indicates the possible diurnal range.

63

Table 15. Downstream migration in SSC maxima observed during synoptic sampling on White River, Mount Rainier. The two SSC maxima (mg/L) seen at the closest sample location to Emmons Glacier in the synoptic graphs decline with distance along White River, Mount Rainier, at downstream locations. Diurnal variation (range) also declines with distance.

Sample location WhiteR_1 WhiteR_4 WhiteR_5 1st Maximum / time 1.07 / 4:00 PM Not visible 0.15 / 8:00 PM 2nd Maximum / time 0.77 / 8:00 PM Not visible 0.16 / 3:00 AM Minimum / time 0.12 / 8:00 AM 0.03 / 10:00 AM 0.02 / 11:00 AMRange 0.95 --- 0.13 Distance (km) 0.25 10.7 5.9 L* 0.07 2.6 24.0

Lagrangian sampling showed SSC declining with distance until break

points of between about 2 L* and 30 L*. At distances of less than about 20 to 30

L*, downstream increases and decreases were observed (Figure 22; Appendix D,

Figures D14-D22). Concentrations increased over some distances of Nisqually

River, Mount Rainier, and Eliot Creek, Sandy and White Rivers, Mount Hood,

indicating inputs of sediment. In non-glacial streams, SSC was less than 0.01 mg/L

for all samples and exhibited no consistent patterns.

64

White R., Mt. Rainier

0.00

0.02

0.04

0.06

0.08

0.10

0 2 4 6 8 10L*

SS

C (m

g/L)

9/4/2005 12:00-16:50

Nisqually R., Mt. Rainier

0.000

0.005

0.010

0.015

0.020

0.025

0 2 4 6 8 10L*

SS

C (m

g/L)

9/5/2005 11:15-13:50

Eliot Cr., Mt. Hood

0.000.020.040.060.080.100.120.140.160.18

0 2 4 6 8 10L*

SSC

(mg/

L)

8/10/2005 15:40-18:158/12/2005 11:50-14:00

Sandy R., Mt. Rainier

0.000.010.020.030.040.050.060.070.080.090.10

0 20 40 60L*

SSC

(mg/

L)

7/25/2005 12:20-20:458/4/2005 10:30-18:15

White R., Mt. Hood

0.00.20.40.60.81.01.21.41.61.8

0 20 40 60 80 100 120L*

SS

C (m

g/L)

7/27/05 15:00-21:008/5/2005 8:00-13:10

White R., Mt. Hood

0.00.10.20.30.40.50.60.70.8

0 20 40 60 80 100 120L*

SSC

(mg/

L)

8/3/2005 10:30-14:30

Figure 23. Lagrangian sampled suspended sediment concentrations are plotted against L* for all glacial streams. Downstream enrichment was observed along Nisqually River, Mount Rainier, and Eliot Creek, Sandy and White Rivers, Mount Hood.

Glacial and non-glacial suspended sediment concentrations appear to

become indistinguishable at about 30 km, 20 L* (Figure 23), and less than 1%

glacierized or a basin area of about 600 km2. Analyses of turbidity indicate that

turbidity in glacial and non-glacial rivers might never become similar except for

filtered glacial and agricultural non-glacial turbidity values. These exceptions

appear to play no part in differences based on suspended sediment. Sandy River’s

distant locations had lower SSC than the most distant point along Salmon River,

which may be filtered by Salmon River Meadows.

65

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0 25 50 75 100

L*

SS

C (m

g/L)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HMult_NG Tual_NG

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0 50 100 150

Stream distance (km)

SS

C (m

g/L)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HMult_NG Tual_NG

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.00 0.02 0.04 0.06 0.08 0.10

Fraction glacierized

SS

C (m

g/L)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HMult_NG Tual_NG

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0 500 1000 1500

Basin Area (km2)S

SC

(mg/

L)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HMult_NG Tual_NG

Figure 24. Glacial and non-glacial suspended sediment concentrations plotted against distance measures. Non-glacial L* and fractional glacier cover are set to 0, while stream distance is measured from the inception point on 1:24,000 scale US Geologic Survey topographic maps.

66

5.4 Electrical conductivity

Electrical conductivity was similar among glacial rivers, except for Sandy

River, Mount Hood (Table 16). The conductance of glacial rivers other than Sandy

River averaged 46 µS cm-1, while the average for Sandy River averaged three

times higher. Sandy River data is omitted from remaining analyses and dealt with

separately in order to look for common trends among the non-anomalous rivers.

Conductivity in non-glacial rivers was generally higher than in glacial rivers. The

highest values observed were in the Tualatin River. Electrical conductivity for both

glacial and non-glacial rivers, except Sandy River, was lower in headwaters and

increased downstream (Figure 16).

Table 16. Statistical summary of specific conductivity by river (µS cm-1).

Stream Name No. Samples Average Median Min. Max. Range

Std. Dev.

All Glacial, but Sandy 150 47.5 39.0 3.9 148.7 144.9 27.2

White (Rainier) 61 42.4 31.6 22.0 104.5 82.5 23.8

Nisqually 31 46.1 39.2 27.6 114.3 86.7 20.8 Eliot Cr. 8 20.2 20.2 17.4 25.5 8.1 2.6 Sandy 28 146.3 124.7 66.4 278.6 212.2 68.7 White (Hood) 40 58.6 62.3 9.4 102.2 92.9 27.7 Salmon 10 60.6 53.2 3.9 148.7 144.9 46.4 All non-glacial 35 66.1 40.6 22.9 316.5 293.6 67.1

Multnomah 25 34.4 33.9 22.9 46.8 23.8 8.5 Gales/Tualatin 10 145.6 106.1 96.7 316.5 224.8 83.5

67

0

50

100

150

200

250

300

0 10 20 30 40 50 60L*

Med

ian

Spec

ific

Con

duct

ance

(uS/

cm)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0

20

40

60

80

100

0 10 20 30 40L*

Med

ian

Spe

cific

C

ondu

ctan

ce (u

S/cm

)

White_RNisqually_REliot_HWhite_HSalmon_H

Figure 25. Median glacial specific conductivity for each location plotted against L* contrasted with those along Sandy River (left), and without the Sandy River (right).

Correlation between specific conductivity and distance measures resulted in

a clear difference between measures when all glacial data, except from Sandy

River, are considered (Table 17). Fractional glacierization resulted in the strongest

correlations, closely followed by L*. Correlation is significant for individual rivers

except Eliot Creek and Salmon River, which have too few samples (Appendix E,

Table E1).

Table 17. Correlation coefficients for correlation between electrical conductivity and distance measures, excluding Sandy River. All results are significant to p<0.05.

Distance L* Fraction glacierized

Upstream basin area

All glacial data, except from Sandy River – 147 data values Kendall’s τ 0.40 0.55 -0.60 0.31 Spearman’s ρ 0.59 0.75 -0.80 0.41

Regression between specific conductivity with distance measures shows a

clear difference between measures when all glacial data, except from Sandy River,

are considered together, and when Sandy River data are considered separately

(Table 18). Power and exponential models provided the lowest error, and between

those, fractional glacierization explains most of the variability in conductivity,

followed by L*, then distance and, explaining the least, upstream basin area. Cubic

68

and quadratic equations resulted in the largest R2 for all measures with the same

order of influence. Regression results on median values per sample site were

similar (Appendix E, Table E2). Differences among regression coefficients for

different distance measures are smaller when rivers are considered individually

(Appendix E, Table E3). Most regressions on Eliot Creek and Salmon River failed

tests of significance. Results when only Sandy River data were regressed on

distance measures were much stronger. Fractional glacier cover explained much

more of the variability in the data than any other distance measure. Regression

coefficients for distance and L* were about the same, and both were larger than

those for upstream basin area.

Table 18. Regression coefficients, R2, from regressions of specific conductance on distance measures using all data but Sandy River (top) and Sandy River data alone (bottom). Results are significant to p-value<0.05. RMSE (µS cm-1) are in parentheses.

Distance L* Fraction glacierized

Upstream basin area

All glacial conductivity data, except Sandy River’s – 147 values Logarithmic 0.27

(22.8) 0.34

(21.6) 0.49

(19.0) 0.12

(24.9) Quadratic 0.27

(22.7) 0.47

(19.3) 0.64

(15.9) 0.12

(25.0) Cubic 0.31

(22.3) 0.58

(17.3) 0.65

(15.9) 0.13

(25.0) Power (axb) 0.29

(0.5) 0.28 (0.5)

0.47 (0.4)

0.23 (0.5)

Exponential (aebx)

0.25 (0.5)

0.13 (0.6)

0.43 (0.5)

0.14 (0.6)

Linear 0.23 (23.3)

0.16 (24.4)

0.41 (20.5)

0.12 (25.0)

Sandy R., Mt. Hood, conductivity – 28 values Linear 0.32 (57.6) 0.31 (58.4) 0.85 (27.3) 0.21 (62.1) Logarithmic 0.77 (33.5) 0.68 (39.8) 0.64 (42.2) 0.75 (35.1) Quadratic 0.70 (39.3) 0.72 (38.0) 0.85 (27.7) 0.40 (55.2) Cubic 0.77 (35.0) 0.80 (32.8) 0.86 (27.2) 0.59 (46.7) Power 0.87 (0.2) 0.79 (0.2) 0.77 (0.2) 0.87 (0.2) Exponential 0.44 (0.4) 0.41 (0.4) 0.90 (0.2) 0.31 (0.4)

69

Conductivity displayed a roughly sinusoidal diurnal pattern on White

River, Mount Rainier (Figure 26). Conductivity was lowest near the glacier and

with night-time low flows, increasing downstream. Conductivity patterns from

daytime sampling on Nisqually (Appendix E, Figure E2) and Sandy Rivers (Figure

26) appear to be special cases. Nisqually River conductivity increased during the

day, and was lowest close to the glacier, but intermittent rain early in the day

became heavy at night when sampling was discontinued. Conductivity on Sandy

River decreased with daytime increases in flow, but was highest near the glacier.

Non-glacial conductivity was high in Tualatin and Little North Santiam Rivers.

Conductivity in non-glacial Multnomah Creek was as low as glacial rivers, without

consistent change over time or distance (Appendix E, Figure E4).

White R., Mt. Rainier.0

20

40

60

80

100

120

9/7/059:00

9/7/0515:00

9/7/0521:00

9/8/053:00

9/8/059:00

Spec

ific

Con

duct

ance

(u

S/cm

)

WhiteR_1WhiteR_4WhiteR_5

Sandy R., Mt. Hood

050

100150200250300

8/13/059:00

8/13/0515:00

8/13/0521:00

8/14/053:00

8/14/059:00

Spe

cific

Con

duct

ance

(uS

/cm

)

Sandy_2Sandy_3

Figure 26. Specific conductance plotted over time for White River, Mount Rainier (left), and Sandy River, Mount Hood (right). Conductivity on White River, Mount Rainier is low near the glacier and increases with distance. Conductivity on Sandy River, Mount Hood begins high near the glacier and decreases with distance.

Diurnal variation in conductivity based on samples collected all in one day

was only determined for White River, Mount Rainier (Table 19). Conductivity was

lowest close to the glacier in the evening and highest in the morning, 10 hours

apart. Variation increased downstream. At location 4, maximum and minimum

were only 4.5 hours apart, and at location 5 they were 12 hours apart.

70

Table 19. Downstream migration in electrical conductivity maxima observed on White River, Mount Rainier. Diurnal variation in conductivity was low near the glacier and increased downstream.

Sample location WhiteR_1 WhiteR_4 WhiteR_5 Maximum / time 35.4 / 8:00 AM 36.2 / 3:30 PM 104.5 / 1:00 PM Minimum / time 22.0 / 6:00 PM 22.8 / 8:00 PM 38.1 / 1:00 AM Range 18.5 13.4 66.4 Distance (km) 0.25 10.7 24.0 L* 0.07 2.6 5.9

Specific conductance generally increased with distance, although the

pattern of downstream increase revealed by Lagrangian sampling varied (Appendix

E, Figures E5-E14). Two rivers showed a rapid rate of change in conductivity until

about 10 L* (Figure 27). Conductivity along White River, Mount Hood, increased

steeply until about 5-6 km/10 L* when the rate of increase slowed and steadied.

The opposite pattern was observed along Sandy River, with high conductivity

declining steeply until about 14 km/9 L*, then declining at a much slower rate.

Other rivers did not conform to the pattern observed on the White and Sandy

Rivers. Nisqually River conductivity increased until about 20km/5 L* and then

decreased. White River, Mount Rainier, and Eliot Creek, Mount Hood, were only

measured to less than 10 L*.

White R., Mt. Hood

0

20

40

60

80

100

120

0 20 40 60 80 100 120L*

Spec

ific

cond

ucta

nce

(µS/

cm)

7/27/2005, 15:00-21:008/3/2005, 10:30-17:108/5/2005, 8:00-14:308/5/2005, 15:50

Sandy R., Mt. Hood

0

50

100

150

200

250

300

0 20 40 60L*

Spec

ific

cond

ucta

nce

(µS/

cm)

7/25/2005, 12:20-20:458/4/2005, 10:30-18:15

Figure 27. Specific conductance from Lagrangian sampling of White River, Mount Hood (left) and Sandy River (right) plotted against L*. Change in conductance along both rivers is rapid until about 10 L*.

71

Conductivity values plotted against distance measures showed no distinct

separation between glacial and non-glacial streams (Figure 28). All values on non-

glacial Multnomah Creek were below the 50 µS cm-1 threshold suggested by

Milner and Petts (1994), and values from Tualatin River far exceeded it. Glacial

conductivity does not exceed 50 µS cm-1 until between 3 and 9 L*, or 2 to 24 km.

0

20

40

60

80

100

0 10 20 30 40 50Stream distance (km)

Spec

ific

cond

uctiv

ity

(µS/

cm)

White_R Nisq_REliot_H White_HSalmon_H MultnomahTualatin

0

20

40

60

80

100

0 10 20 30L*

Spec

ific

cond

uctiv

ity

(µS/

cm)

White_R Nisq_REliot_H White_HSalmon_H MultnomahTualatin

0

20

40

60

80

100

0.0 0.2 0.4 0.6 0.8Fraction glacierized

Spec

ific

cond

uctiv

ity

(µS/

cm)

White_R Nisq_REliot_H White_HSalmon_H MultnomahTualatin

0

20

40

60

80

100

0 50 100 150 200Basin Area (km2)

Spec

ific

cond

uctiv

ity

(µS/

cm)

White_RNisq_REliot_HWhite_HSalmon_HMultnomahTualatin

Figure 28. Glacial and non-glacial conductivity plotted together against distance measures. The glacial habitat threshold value of 50µScm-1 suggested by Milner and Petts (1994) is marked by a horizontal line.

72

5.5 Anion concentrations

One set of Lagrangian samples was collected from each stream and tested

for major anions, although only chloride and sulfate were detected at levels that

revealed patterns in downstream change in concentration. Lagrangian conductivity

mirrored chloride concentration on the Salmon River (Figure 29). Conductivity on

the Salmon River was sampled twice, once during a storm event and once during

dry weather. Maximum conductivity was about 3 times higher during the rain

event, so chloride concentration was probably similarly magnified. While elevated

chloride concentration was observed for some distance along Salmon River, farther

downstream chloride concentration was dilute and resembled other glacial streams.

The average chloride concentration for glacial streams other than Salmon River

was 2.3 mg/L, yet the average for Salmon River was 6.1 mg/L with a maximum of

11.6 mg/L. Plots of chloride concentration against L* for other glacial streams are

in the Appendix (Appendix F, Figures F1-F4).

02468

1012

0 20 40 60 80 100 120L*

Chl

orid

e (m

g/L)

.

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

020406080

100120140160

0 20 40 60 80 100L*

Con

duct

ivity

(µ

S/cm

).

02468101214

Chl

orid

e (m

g/L)

Conductivity 8/17/5 rainConductivity 8/21/5 dryChloride 8/21/5

Figure 29. Chloride concentrations are plotted against L* (left) for all rivers, and conductivity and chloride concentrations for Salmon River are plotted against L* (right). Note the difference in scales on sulfate concentration between plots.

Sandy River remained something of a mystery because it exhibited high

electrical conductivity, yet did not receive flow from salted ski areas nearby and

was expected to have low conductivity. However, sulfate concentrations at the

73

headwaters of the Sandy River were much higher than in any other rivers (Figure

30; Appendix F, Figures F5-F8). The highest concentration of sulfate observed

was 100.9 mg/L at the nearest sample location to the glacier (1.6 km, 2.7 L*). The

observed concentration decreased steeply until it reached 19.2 mg/L at a distance

of 13.7 km or 9.2 L*, after which it decreased at a slower rate, reaching 4.6 mg/L

at the most distant sample location (91 km, 57 L*). Salmon River had a

comparatively low sulfate concentration (maximum=9.9 mg/L), although the

downstream pattern resembled the one it had for chloride (Figure 30). For

comparison, maximum sulfate concentration in other glacial streams was 6.6 mg/L,

and the average was 4.7 mg/L.

020406080

100120

0 10 20 30 40 50 60L*

Sul

fate

(mg/

L)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0123456789

10

0 20 40 60 80 100L*

Sul

fate

(mg/

L)

Figure 30. Sandy River sulfate concentrations (left), and Salmon and White River sulfate concentrations (right). Other rivers’ sulfate concentrations are not connected by lines. Note the difference in scales on sulfate concentration between plots.

5.6 Macroinvertebrates

No macroinvertebrates were found at White River, Mount Rainer, 0.15 km

(0.04 L*); at 0.25 km (0.07 L*) only supported Chironomids, probably of the

genus Diamesa (only 6 larvae and 5 adults were collected). Shrubby alder grew

along the banks between the first and second sample location, and at 2.7 km (0.7

L*) many twigs and leaves were found in with the cobble and gravel, which would

74

provide nutrients for some types of macroinvertebrates. At this site 16 larvae, 3

pupae, and 1 adult Chironomid (probably Diamesa) were identified. Also, two

Ephemeroptera (two-tailed Baetis) and two small Trichoptera instars, possibly

Polycentropodida, were identified. In White River Mount Hood, no

macroinvertebrates were found until 6.7 km (8.6 L*). The three individuals found

were identified by Dr. Jacoby as Chironomid, probably Diamesa. Diamesa habitat

is determined by both colder water and less stable substrate than the

macroinvertebrates it would otherwise compete with. It may be that the substrate

was more stable close to the glacier at White River, Mount Rainier, than it was at

White River, Mount Hood.

75

Chapter 6: Discussion

The goals of this study were to determine how water quality characteristics

change with distance from glaciers of different size, to test whether stream length

normalized by glacier area (L*) is a better indicator of downstream changes in

water quality characteristics than other measures, and to determine the distance

where glacial water quality appears to be no different from non-glacial water. My

hypothesis was that the water quality characteristics of glacial streamflow will

decrease rapidly with distance from a glacier, according to the change in fractional

glacier cover within the upstream watershed (Figure 1). Fountain and Tangborn

(1985) showed that glacier-induced variation in downstream discharge becomes

negligible in basins with less than 10% glacier cover, which occurs at between

about 5 and 10 L*. I tested whether water quality variables also change rapidly to 5

to 10 L*, after which non-glacial influences would dominate. Verbunt (2003)

suggests that the mean annual fraction of glacier contribution to flow is greater

than the basin’s fractional glacier cover, especially during summer (Table 2), and

results suggest that glacier melt may strongly influence water quality for basins

with at least 1% glacier cover during peak melt. As the fraction of glacier cover

decreases, the fractions of baseflow and interflow components increase, increasing

the influence from groundwater on downstream water quality. In my study a 1%

glacier cover corresponds to an L* between 15 and 40, and for 10% glacier cover

L* is between 5 and 10. Temperature, electrical conductivity, and sulfate

concentrations in this study showed that the point at which glacial water quality

rapidly loses influence from glaciers is about 10 L*, which corresponds with about

76

3 to 10% glacier cover, supporting both Verbunt et al. (2003), and Fountain and

Tangborn (1985).

This thesis shows that water quality characteristics controlled primarily by

the glacier change according to the L* model. Water quality characteristics

affected by downstream influences do not follow the L* model. The degree of

deviation from the model depends on the influence of local factors. Temperature

appears to be influenced primarily by the glacier via melting ice with few or no

sources of cold water in the downstream environment. Similarly, ice melt is very

low in electrical conductivity relative to downstream processes. I did not measure

low conductivity sources other than the glacier in glacial watersheds, but non-

glacial Multnomah Creek also had low ionic concentration. I presume this is from

snowmelt water percolating through the alpine meadows with little or no rock or

biologic interaction that would increase the conductivity. Dowsntream changes in

suspended sediment concentration and turbidity appear to reflect strong local

influences along the stream channel. Although glacial streams convey vast

amounts of sediment from the subglacial environment, and affect SSC and

turbidity for long distances downstream, unstable stream channels and weak lahar

deposits, common especially to the volcanic environment on the south flank of

Mount Hood, combine to create sediment sources along the stream channel for

long distances as well.

Temperature

Fractional glacier cover was a somewhat better predictor of downstream

temperature change than L*, as indicated by higher correlation and regression

77

coefficients. A logarithmic equation best accounted for the variation in temperature

for L* and fractional glacier cover (Table 8). Glacial stream temperature appears to

become like non-glacial stream temperature at about 5 to 22 L*, but only 5 to 10

L* if Salmon River is excluded (Figure 32). Salmon River may stay colder farther

because of snowmelt flow through the abundance of poorly consolidated sediment

on Mount Hood’s south-facing debris fan.

02468

101214

0 5 10 15 20 25 30L*

Aver

age

tem

pera

ture

(°C

). 0

5

10

15

20

0 10 20 30 40 50 60L*

Ave.

Tem

pera

ture

(°C

)

Figure 31. Average temperature per glacial sample location plotted against L* (left) and with lines connecting points on the same rivers (right). Y-axes are reversed to facilitate comparison with the model (Figure 31). Temperatures increase rapidly with distance until about 5 to 10 L* before beginning to increase less rapidly and deviate by stream, from about 3 to 9 L*.

Previous authors have suggested that downstream rates of temperature

increase may depend on the fraction of glacial contribution and distance from the

glacier (Uehlinger et al., 2003; Brown et al, 2006). Other authors have noted that

downstream temperature increase may also depend on stream discharge from the

glacier (Uehlinger et al., 2003; Milner and Petts, 1994), and discharge is related to

glacier size. Given the same subaerial conditions (e.g. air temperature, solar

insolation) the water from larger glaciers stays colder farther downstream than

from smaller glaciers due to the difference in discharge. Variations in the rate of

warming can occur because of differences in insolation due to aspect and shading

(Arscott et al., 2001), and because of differences in downstream non-glacial

78

contributions to flow. Arscott et al. (2001) showed that at longer distances on

glacial streams local water temperatures correlate primarily with elevation, then

with azimuth, with deviations due to upwelling groundwater and variation in

weather (Uehlinger et al., 2003; Brown et al., 2006). Results from this thesis

support and advance these findings.

Electrical conductivity

Correlation and regression analyses indicated that fractional glacierization

was marginally better than L* for describing and predicting variation in

conductivity downstream, and both are far better than distance or upstream basin

area. Cubic and quadratic equations resulted in the largest R2 values. Glacial and

non-glacial conductivity values were indistinguishable at all distances. Milner and

Petts (1994) suggested 50 µS cm-1 as a threshold below which stream water could

be considered glacial. The lowest conductivity value measured from non-glacial

Gales Creek and the Tualatin River was, in fact, 91.7 µS cm-1. However, all 25

measurements on Multnomah Creek were between 22.9 and 46.8 µS cm-1. Every

glacial river other than the Sandy River had median conductivity values below 30

µS cm-1 at distances of less than 10 L*. Hence, 30 µS cm-1 might be a more

accurate threshold for the streams I studied.

Sandy River was unique among those measured because its EC decreased

with distance, following the L* pattern nicely, and with a break point at 9 L*. This

unexpected result is probably due to a sulfate concentration in the headwaters

diluting with distance. Sulfates can derive from hydrothermal alteration of minerals

(David Clow, U.S. Geological Survey, personal communication, September 2006).

Sandy Glacier rests on the 1.3 to 3.2 million year old Sandy Glacier volcano, older

79

rock that was buried by Mount Hood and is now exposed (Thouret, 2005; Wise,

1969; Wood and Kienle, 1990). Elevated electrical conductivity and sulfate

concentrations in the headwaters of Sandy River are likely due to the chemical

weathering of rock from Sandy Glacier volcano.

Electrical conductivity at headwaters other than the Sandy River could be

divided into two groups. Salmon and White Rivers, Mount Hood, had conductivity

values of less than 10 µScm-1 close to the glacier, while White and Nisqually

Rivers, Mount Rainier, and Eliot Creek and White River, Mount Hood, had values

for specific conductance that were near or greater than 20 µS cm-1. Collins (1979)

found that conductivity of glacial melt is only a few µS cm-1, while that of

subglacial water can be on the order of 102 µS cm-1, and that conductivity near the

glacier portal can be used to determine the fraction from each. If this is the case,

then headwater samples from White and Salmon Rivers, near the two smallest

glaciers have a higher proportion of glacial melt than subglacial water. The closest

sample location to White River Glacier is 2.2 km/ 3.0 L*, so it is surprising with

water flowing over or through sediment for such a long distance that conductivity

is not higher. Greater downstream increases in conductivity along White and

Salmon Rivers, Mount Hood, may be due to the greater abundance of downstream

proglacial sediment there. Samples from Eliot Creek and White River, Mount

Rainier, were taken with glacial ice in sight. Specific conductance at those

locations ranged from 17-21 µS cm-1 for Eliot Creek, and from 22 to 41 µS cm-1

for White River. Perhaps these larger glaciers have a larger fraction of subglacial

water because there is a greater distance to flow subglacially than there is for the

smaller glaciers.

80

There is very little literature on downstream changes in electrical

conductivity that I can compare and contrast my results with. Most studies have

been focused on temporal variation in conductivity along proglacial reaches

(Lafrenière and Sharp, 2005; Swift et al., 2005; Smith et al., 2001). Others have

focused on conductivity differences with emphasis on habitats provided by main

stem and disconnected channels (Malard et al., 2000). Anderson et al. (2000)

showed that the rate of downstream increases in conductivity along the main stem

slow as water comes in contact with sediments that have been exposed to chemical

weathering for more time. This thesis supports their conclusions.

Anion concentrations

Downstream change in sulfate concentration along the Sandy River is

reflected in the conductivity changes discussed previously. Results from ionic

analyses on other rivers neither supported my hypothesis, nor provided evidence

which might refute it. I was unable to determine whether chloride from the salting

of Palmer Snowfield dilutes downstream in the Salmon River according to the L*

model. I suspect the most direct flow path from the ski area enters the east fork

between my second (1.8 km, 3.3 L*) and third (8.6 km, 16.6 L*) sample locations

on the west fork (Figure 34, right). I expect that if this river were resampled on a

more direct flow path, results would support my hypothesis.

Turbidity and suspended sediment

Correlation and regression analyses on turbidity and SSC data indicate

upstream basin area is the most influential among distance measures tested,

81

followed closely by stream distance. This indicates the importance of in stream

processes rather than source at the headwaters. Power and exponential equations

accounted for the greatest variation in the results. Correlation and regression

results on individual stream data were similar (Appendix D, Table D3; Appendix

E, Table E2). Turbidity and SSC data plotted against distance measures revealed

no patterns, but when turbidity averages for each sample location were normalized

by the highest average per stream, trends with distance and basin area appeared

showing a common rate of decrease (Appendix C, Figures C13-C16).

Although turbidity and SSC were high at the glacier portal and decreased

with distance, proglacial sediment deposits and erosion of stream channels also

contributed sediment. Proglacial regions can be degrading (Hammer and Smith,

1983; Warburton, 1990; Orwin and Smart, 2004) and aggrading (Maizels, 1978,

Fahnestock, 1963), and some areas can alternate between periods of aggradation

and degradation (Fahnestock, 1963). The turbidity and SSC data I collected

support this idea by revealing turbidity and SSC declines and increases (Figure 17;

Figure 22; Appendix D, Figures D17-26; Appendix E, Figures E13-21).

Glacial stream turbidity and SSC were quite different from non-glacial

values except for large distances from glaciers, or where glacial water passed

through low gradient areas (Salmon River) or was retained behind dams (North

Santiam River). Lakes in proglacial environments significantly decrease suspended

sediment by allowing sediment to settle (Østrem et al., 2005; Uehlinger et al.,

2003; Benn, 1998). Results from the North Santiam and Salmon Rivers showed the

effects of this kind of settling. While many glacial streams produced low turbidity

at low flows, the only sample locations never observed to exceed 30 NTU, the

82

threshold suggested by Milner and Petts (1994) to distinguish glacial and non-

glacial rivers, were on the Sandy, White and Salmon Rivers, Mount Hood.

Turbidity remained below this threshold on the Sandy River at 21 L*, on the White

River at more than 34 L*, and on the Salmon River at 96 L*, which in reality must

have occurred at some point beyond 22L*. In contrast, the highest turbidity values

among non-glacial streams was found in downstream reaches of the Tualatin River

(11.2 NTU), probably because of periphyton growth caused by insolation and

nutrients from agricultural runoff. I was not able to determine a consistent break

point when values were plotted against stream distance or upstream basin area, due

to large variability, probably due to sediment sources and sinks along the channel.

Macroinvertebrates

Results from macroinvertebrate sampling showed that kryal habitat, where

temperatures are from 2-4°C and Diamesa predominate, extends to between 0.25

and 2.7 km (0.07 and 0.7 L*) on the White River, Mount Rainier, and may extend

beyond 6.7 km (8.6 L*) on the White River, Mount Hood. It may be that

temperature is the determining factor of habitat threshold on White River, Mount

Rainier, as the maximum temperature was 1.9°C where only Diamesa were found,

while the maximum temperature downstream was 5.0°C where there was a greater

diversity of macroinvertebrates. These distances are in accord with thresholds

suggested by Milner and Petts (1994). Habitat limits on White River, Mount Hood,

may be determined by channel instability, since the one location where Diamesa

was found had a maximum stream temperature of 14.5°C. Milner and Petts (1994)

noted that temperature and substrate stability were the two dominant factors in the

83

determination of glacial macroinvertebrate populations. It may be that the White

River, Mount Hood, has such an unstable channel that Diamesa are able to

dominate even to such high temperatures. Milner et al. (2001) found that Diamesa

primarily feed on diatoms and algae attached to rocks. All Diamesa samples found

in my study locations were on larger rocks at locations on channel margins that

were away from stronger flows. These locations provided more protection from

scouring and larger rocks are less likely to move.

Climate change

Climate warming has accelerated glacier shrinkage (Dyrgerov and Meier,

1997). Glacier recession could initially cause increases in the volume of cold melt

water contributed to streams (Melack, 1997), causing streams to be colder further

downstream and increasing the extent of glacial macroinvertebrate habitat.

Reduced downstream temperatures along with increased channel instability with

greater diurnal flow variation could cause an increase in habitat for kryal taxa such

as Diamesa. However, sustained deglaciation will eventually lead to lower summer

flows, which may already be the case. Stahl and Moore (2006) have shown that

most glaciers in British Columbia, Canada, are past the point where an increased

warming rate results in increases in runoff and that glaciers are now producing

reduced runoff. If this is also true for glaciers in the Cascade Range, then, given

predictions for continued warming (Ferguson, 1997; Barnett et al., 2004), runoff

from Mount Hood and Mount Rainier may already be decreasing rather than

increasing.

84

My results indicate that temperature and electrical conductivity extend

shorter distances downstream for smaller glaciers. A space for time substitution

would then imply that as glaciers shrink, the extent of downstream water quality

will decrease and glacial stream habitat will shrink. Habitat loss could affect not

only glacial macroinvertebrates and downstream fish populations, but could

adversely affect salmon populations, and thereby, recreational and commercial

fishing. Turbidity and suspended sediment concentration are not likely to change

rapidly as glaciers recede and disappear because of the abundant sediment that will

remain in the form of outwash plains and moraines. High turbidity and SSC values

from White and Salmon Rivers, Mount Hood, will persist as long as there is flow

from glaciers because they drain a fairly unconsolidated fan of debris formed

during the past 1500 years and the supply of sediment is extremely large (Scott et

al., 1997; Figure 32).

Figure 32. Glaciers on the south flank of Mount Hood rest upon a wide debris fan formed during eruptive periods during the past 1,500 years (photo: Scott et al., 1997).

85

Chapter 7: Conclusions

The results from my thesis show that downstream changes in temperature

and, to some extent, electrical conductivity follow the model of changing fractional

glacier cover with increasing L*. Fractional glacier cover is a slightly better

predictor than L* of downstream change in these characteristics, although

fractional glacier cover does not explicitly measure distance. The distance measure

L* is useful and works well for describing water quality change with distance from

glaciers for those water quality characteristics that originate at the glacier. Sulfate

concentrations on the Sandy River also followed the pattern, probably because the

source of sulfate was subglacial. In contrast, turbidity and suspended sediment,

although originated from glaciers, was picked up in streamflow away from the

glacier. Consequently, turbidity and suspended sediment concentrations probably

depend on many local influences rather than glacier size, including in-channel

erosion due to sediment availability, stream gradient, and discharge.

As mentioned, Fountain and Tangborn (1985) show glacier-induced

reduction in streamflow variation, when the fractional glacier cover in a watershed

is less than 10% (5 to 10 L*), displays no difference from a non-glacial watershed.

My results show that this limit also applies to glacier influence on temperature and,

to some extent, on electrical conductivity. Milner and Petts’ (1994) proposed

glacial temperature threshold of 10°C was reached at distances of 5 to 22 L* with

Salmon River, and 5 to 10 L* without Salmon River which had anomalously low

temperature probably influenced by groundwater. Suspended sediment

concentration and turbidity did not follow the model of diminishing fractional

86

glacier cover with increasing L*; this supports my hypothesis since these have a

source that is distributed in space and not solely at the glacier.

Climate warming has accelerated glacier shrinkage (Dyrgerov and Meier,

1997). Glacier recession could initially cause increases in the volume of cold melt

water contributed to streams (Melack, 1997), causing streams to be colder further

downstream and increasing the extent of glacial macroinvertebrate habitat.

Reduced downstream temperatures along with increased channel instability with

greater diurnal flow variation could cause an increase in habitat for kryal taxa such

as Diamesa. However, sustained deglaciation will eventually lead to lower summer

flows. This may already be the case. Stahl and Moore (2006) have shown that most

glaciers in British Columbia, Canada, are past the point where an increased

warming rate results in increases in runoff and that glaciers are now producing

reduced runoff. If this is also true for glaciers in the Cascade Range, then, given

predictions for continued warming (Ferguson, 1997; Barnett et al., 2004), runoff

from Mount Hood and Mount Rainier may already be decreasing rather than

increasing, and glacial stream habitat will shrink. As habitat shrinks, downstream

temperatures will become warmer, adversely affecting populations of spring and

summer chinook and sockeye salmon (Melack, 1997; Neitzal, et al., 1991).

Optimal temperatures for most salmonid species is about 12 to 14°C, while

spawning coho and steelhead may require temperatures less than 10°C (Beschta et

al., 1987). Whether habitat disappears entirely will depend on whether glaciers can

retreat to a position of equilibrium or whether they simply melt away.

87

Suggestions for future research

Automated sampling was the most efficient and productive method in my

study, and I recommend more automated data collection for future studies. Even if

a Lagrangian perspective on downstream change is desired, Lagrangian sampling

can be culled from synoptic samples. Long-term automated sampling would be the

most valuable. Organizing volunteers for simultaneous synoptic sampling was

difficult and resulted in more human error and lost data for time spent than

automated methods. Results show that Lagrangian sampling can roughly simulate

sampling the same parcel of water as it makes its way downstream along some

rivers, but that these calculations should be made ahead of time to facilitate both

keeping up with and not getting ahead of that parcel. The White and Nisqually

Rivers, Mount Rainier, and Sandy and Salmon Rivers, Mount Hood, could have

been sampled with less than 2 hours error if I had waited to do downstream

sampling. Lagrangian sampling is limited in that it only provides a snapshot view

of stream water quality, and does not reveal the range of values on any stream

unless many Lagrangian sample sets are taken at many different times of day.

Without automated sampling equipment, regular sampling by field assistants for

24-hour periods would improve the analysis.

In terms of good rivers to sample, while Nisqually River was accessible

and simple to sample at a pace near the water speed, Nisqually did not exhibit

expected changes in water quality with distance. Suspended sediment

concentration, turbidity and conductivity increased rather than decreased with

distance, suggesting water quality characteristics may be influenced by human

work on the channel and banks, especially near the bridge at Longmire and

88

possibly elsewhere downstream. White, Salmon, and Sandy Rivers, Mount Hood,

are all accessible and further study of temperature, conductivity and ionic

concentrations could help refine determinations of how glacier size affects water

quality extent. Sampling conductivity and ions on the headwaters of Salmon River

should be done on the west branch despite lower flows, since I did not find high

conductivity or ionic concentrations on the east branch.

My suspended sediment concentration and turbidity results suggest many

opportunities for research. Research has shown that proglacial regions are

primarily aggradational (Maizels, 1978, Fahnestock, 1963), but the bulk of this

research has occurred during periods of widespread negative glacier mass balance.

It would be interesting to pursue the idea of inversely related glacier mass balance

and proglacial sediment budget. However, opportunities to study proglacial

sediments for glaciers with positive mass balance may be limited.

Macroinvertebrate sampling in relation to water quality along glacial

streams on Mount Hood and Mount Rainier could determine glacial and kryal

habitat boundaries. It would also be interesting to determine where the greatest

diurnal temperature variation occurs along these glacial streams. Vannote et al.

(1980) observed that the greatest macroinvertebrate diversity occurs where diurnal

temperature variation is greatest. My results indicate these ranges lie at some

distance less than roughly 6 to 10 L*, so greater distances need not be sampled.

Milner and Brittain (2001) suggest that glacial macroinvertebrate populations

could be used as sensitive bioindicators of climate change in the same way they

indicate anthropogenic influence on stream water quality at lower elevations, so

their long-term monitoring may be useful.

89

Anomalous findings would be interesting to investigate. For example, it is

still unclear how chloride is transported from the ski area to Salmon River, and

what in-stream biological consequences there might be from elevated chloride

concentrations. While the east branch of the Salmon River is the largest stream

draining the Palmer Snowfield, upstream concentrations are lower than

concentrations at 8.6-16.2 km (11.9-22.3 L*) downstream. It could be that salts are

primarily transported from the ski area though the West Fork of the Salmon River.

Water temperature remains lower than for similar distances as measured by L*

along other rivers in this study (Figure 9), so perhaps chloride travels to the

channel through groundwater. Farther downstream, at 52 km (96 L*) chloride

concentration is again as low as in other glacial streams. Salting of roads may

complicate this picture. Determination of the extent of salinity and a survey of in-

stream macroinvertebrate populations along Salmon River’s gradient of change

would make an interesting study. A similar study of high sulfate concentrations

along Sandy River would also be interesting, and could include a determination of

the source of the sulfate.

Finally, a more purely GIS approach to the analysis of my data set could

prove very illuminating. The long, narrow shape of the Eliot Creek basin has

obvious influence on downstream temperature, but less obvious is the long, narrow

shape of Salmon basin, which may have similar influence. Turbidity and

suspended sediment concentration were determined in this thesis to result from

local factors, such as gradient and sediment availability, rather than more global

factors, such as distance and glacier size. It might be useful to generate stream

gradients for the lengths of each river to see how well they correlate with these

90

water quality values, and sediment availability may be estimated using aerial

photography or satellite imagery.

91

References

Allan, D. J. 1995. Stream Ecology: Structure and function of running waters, Kluwer Academic Publishers, Dordrecht, The Netherlands.

Allen, J. E. 1989. Ice-Age glaciers and lakes south of the Columbia River Gorge: Oregon Department of Geology and Mineral Industries, Portland, OR, United States, Oregon Geology, 51(1): 12-14.

Anderson, C. W. 2004. Chapter 6.7, Turbidity, in: National Field Manual for the Collection of Water-Quality Data, U. S. Geological Survey Techniques of Water-Resources Investigations, Book 9, Chapter 6.7, version 2.0, http://water.usgs.gov/owq/FieldManual/Chapter6/Archive/6.7_ver2.0_8-04.pdf, last accessed April 20, 2006.

Anderson, R. S., Anderson, S. P., MacGregor, K. R., Waddington, E. D., O’Neel, S., Riihimaki, C. A., Loso, M. G. 2004. Strong feedbacks between hydrology and sliding of a small alpine glacier, Journal of Geophysical Research, 109(F03005): 1-17.

Anderson, S. P., Drever, J. I., Frost, C. D., Holden, P. 2000. Chemical weathering in the foreland of a retreating glacier, Geochemica et Cosmochemica Acta, 64(7): 1173-1189.

Anderson, S. P., Longacre, S. A., Kraal, E. R. 2003. Patterns of water chemistry and discharge in the glacier-fed Kennicott River, Alaska: evidence for subglacial water storage cycles, Chemical Geology, 202(3/4): 297-312.

Arscott, D.B., Tockner, K. & Ward, J.V. 2001. Thermal heterogeneity along a braided floodplain river (Tagliamento River, northeastern Italy). Canadian Journal of Fisheries and Aquatic Sciences 58: 2359-2373.

Barnes, H. H. 1967. Roughness Characteristics of Natural Channels: U. S. Geological Survey Water-Supply Paper 1849, United States Government Printing Office, Washington.

Barnett, T., Malone, R., Pennell, W., Stammer, D., Semtner, B., Washington, W. 2004. The effects of climate change on water resources in the west: Introduction and overview, Climatic Change, 62(1-3): 1-11.

Benn, D. I. and Evans, D. J. A., 1998. Glaciers and Glaciation. John Wiley & Sons, London; New York, New York, 734 pp.

Beschta, R.L., R.E. Bilby, G.W. Brown, L.B. Holtby, and T.D. Hofstra. 1987. Stream Temperature and Aquatic Habitat: Fisheries and Forestry Interactions. In: Salo, E.O. and T.W. Cundy, eds. Streamside Management: Forestry and Fishery Interactions. Institute of Forest Resources University of Washington, Seattle, Washington. 57:191-232.

Brittain, J. E. and Milner, A. M. 2001. Ecology of glacier-fed rivers: current status and concepts, Freshwater Biology, 46(12): 1571-1578.

92

Brooks, P. D. and Williams, M. W. 1999. Snowpack controls on nitrogen cycling and export in seasonally snow-covered catchments, Hydrological Processes, 13(14): 2177-2190.

Brown, L. E., Hannah, D. M., Milner, A. M. 2006. Hydroclimatological influences on water column and streambed thermal dynamics in an alpine river system, Journal of Hydrology, 325(1-4): 1-20.

Burgherr, P. and Ward, J. V. 2001. Longitudinal and seasonal distribution patterns of the benthic fauna of an alpine glacial stream (Val Roseg, Swiss Alps), Freshwater Biology, 46(12): 1705-1721.

Cameron, Ken. 1988. Abstract, Northwest Science, vol. 62, no. 2, p. 82 (http://vulcan.wr.usgs.gov/Volcanoes/Hood/Glaciers/description_hood_glaciers.html)

Cameron, K.A. and Pringle, P.T., 1986. Post-Glacial Lahars of the Sandy River Basin, Mount Hood, Oregon: Northwest Science, 60(4): 225-237

Chikita, K. A., Kemnitz, R., Kumai, R. 2002. Characteristics of sediment discharge in the subarctic Yukon River, Alaska, Catena, 48(4): 235-253.

Clesceri, L. S., Greenberg, A. E., Eaton, A. D. 1998. Standard Methods for the Examination of Water and Wastewater, 20th edition. American Public Health Association, American Water Works Association, Water Environment Federation. Baltimore, Maryland: United Book Press.

Collins, D. N. 1979. Hydrochemistry of meltwaters draining from an alpine glacier, Arctic and Alpine Research, 13(3): 307-324.

Collins, D. N. 1981. Seasonal variation of solute concentration in melt waters draining from an alpine glacier, Annals of Glaciology, 2: 11-16.

Crandell, D. R. and Miller, R. D. 1964. Post-Hypsithermal glacier advances at Mount Rainier, Washington, U.S. Geological Survey Professional Paper, 501-D, D110-D114, in Davis, P. T. 1988. Holocene glacier fluctuations in the American Cordillera, Quaternary Science Reviews, 7(2): 129-157.

Crandell, D. R.1980. Recent Eruptive History of Mount Hood, Oregon, and Potential Hazards from Future Eruptions: U.S. Geological Survey Bulletin 1492, 81 p.

Dyurgerov, M.B. and Meier, M.F. 1997. Mass balance of mountain and subpolar glaciers: a new global assessment for 1961-1990. Arctic and Alpine Research, 29(3): 379-391.

Dyurgerov, M. B., Meier, M. F. 2000. Twentieth century climate change: evidence from small glaciers, Proceedings of the National Academy of Sciences of the United States of America, 97(4): 1406-1411.

Fahnestock, R. K. 1963. Morphology and hydrology of a glacial stream – White River, Mt. Rainier, Washington, U. S. Geologic Survey Professional Paper, 422-A, 70 p.

93

Fenn, C. R. 1987. Chapter 14: Electrical conductivity, in Gurnell, A. M. 1987. Fluvial sediment yield from alpine glacierized catchments. In Glacio-fluvial sediment transfer, Gurnell, A. M., Clark, M. J. (eds.), Wiley, Chichester, pp. 415-420.

Fiske, R.S., Hopson, C.A., and Waters, A.C., 1963, Geology of Mount Rainier National Park, Washington: U. S. Geological Survey Professional Paper 444, 93 p.

Fleming, S. W. 2005. Comparative analysis of glacial and nival streamflow regimes with implications for lotic habitat and fish species richness, River Research and Applications, 21: 363-379.

Fountain, A. G. 1992. Subglacial water flow inferred from stream measurements at South Cascade Glacier, Washington, U.S.A., Journal of Glaciology, 38(128): 51-64.

Fountain, A. G. 1996. Effect of snow and firn hydrology on the physical and chemical characteristics of glacial runoff, Hydrological Processes, 10(4): 509-521.

Fountain, A. G., Jacobel, R. W., Schlichting, R., Jannson, Peter. 2005. Fractures as the main pathways of water flow in glaciers, Nature, 433(7026): 618-621.

Fountain, A. G. and Tangborn, W. V. 1985. The effects of glaciers on streamflow variations, Water Resources Research, 21(4): 579-586.

Fountain, A.G. and J.S. Walder. 1998. Water flow through temperate glaciers. Review of Geophysics, 36(3): 299–328.

Füreder, L., Wallinger, M., Burger, R. 2005, Longitudinal and seasonal pattern of insect emergence in alpine streams, Aquatic Ecology, 39(1): 67-78.

Furgeson, S. A. 1997. A climate-change scenario for the Columbia River Basin, PNR-GTR-445, In: Interior Columbia Basin Ecosystem Management Project: Scientific Assessment, Quigley, T. M., ed., U. S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Research Paper PNR-RP-499, 9 p.

Gardner, C. A., Scott, W. E., Major, J. J., Pierson, T. C. 2000. Mount Hood – History and hazards of Oregon’s most recently active volcano, U. S. Geologic Survey Factsheet 060-00, 3p.

Granshaw, F. D. and Fountain, A. G. 2006. Glacier change (1958-1998) in the North Cascades National Park Complex, Washington, U.S.A., Journal of Glaciology, 52(177): 251-256.

Greene, R. C. 1968. Petrography and petrology of volcanic rocks in the Mount Jefferson area, High Cascade Range, Oregon, U. S. Geological Survey Bulletin 1251-6, 48 p.

Gurnell, A.M. 1982. The dynamics of suspended sediment concentration in an Alpine pro-glacial stream network, Hydrological Aspects of Alpine and High Mountain Areas (Proceedings of the Exeter Symposium, July, 1982). International Association of Hydrological Sciences, Publication No. 138.

94

Hammer, K. M. and Smith, N. D. 1983. Sediment production and transport in a proglacial stream: Hilda Glacier, Alberta, Canada, Boreas, 12: 91-106.

Hodge, S. M., Trabant, D. C., Krimmel, R. M., Heinrichs, T. A., March, R. S., Josberger, E. G. 1998. Climate variations and changes in mass of three glaciers in Western North America, Journal of Climate, 11(9): 2161-2179.

Hodson, A. J., Mumford, P. N., Kohler, J., Wynn, P. M. 2005. The high arctic glacial ecosystem: new insights from nutrient budgets, Biogeochemistry, 72(2): 233-256.

Hood, E. W., Williams, M. W., Caine, N. 2003. Landscape controls on organic and inorganic nitrogen leaching across an alpine/subablpine ecotone, Green Lakes Valley, Colorado Front Range, Ecosystems, 6(1): 31-45.

Irvine-Flynn, T. D. L., Moorman, B. J., Willis, I. C., Sjogren, D. B., Hodson, A. J., Mumford, P. N., Walter, F. S. A., Williams, J. L., M. 2005. Geocryological processes linked to high arctic proglacial stream suspended sediment dynamics: examples from Bylot Island, Nunavut, and Spitsbergen, Svalbard, Hydrologic Processes, 19(1): 115-135.

Jackson, K. 2007. Spatial and morphological change on Eliot Glacier, Mount Hood, Oregon, M. S. Thesis: Portland State University, Portland, Oregon, 108 p.

Jackson, K. M. and Fountain, A. G. 2007. Spatial and morphological change on Eliot Glacier, Mount Hood, Oregon, USA, Annals of Glaciology, 46: 167-170.

Key, C.H., D.B. Fagre, R.K. Menicke. 2002. Glacier retreat in Glacier National Park, Montana. in R.S. Williams and J.G. Ferrigno, eds., Satellite image atlas of glaciers of the world: North America. U.S. Geological Survey Professional Paper 1386-J, U.S. Government Printing Office, Washington D.C. p 365-381.

Kunkle, S. H. and Comer, G. H. 1971. Estimating suspended sediment concentrations in streams by turbidity measurements, Journal of Soil and Water Conservation, 36: 18-20.

Lafrenière. M. J. and Sharp, M. J. 2005. A comparison of solute fluxes and sources from glacial and non-glacial catchments over contrasting melt seasons, Hydrological Processes, 19: 2991-3012.

Maizels, J. K. 1978. Debit des eaux de fonte, charges sedimentaires et taux d’erosion dans le massif du Mont Blanc, Revue Geographie Alpine, 66 : 65-91. Cited in: Warburton, J. 1990. An alpine proglacial fluvial sediment budget, Geografiska Annaler. Series A, Physical Geography, 72(3/4): 261-272.

Malard, F., Tockner, K., Ward, J. V. 2000. Physico-chemical heterogeneity in a glacial riverscape, Landscape Ecology, 15(8): 679-695.

Malard, F., Mangin, A., Uehlinger, U., Ward, J. V. 2001. Thermal heterogeneity in the hyporheic zone of a glacial floodplain, Canadian Journal of Fisheries and Aquatic Sciences, 58(7): 1319-1335.

95

Meier, M. F., Dyurgerov, M. B., McCabe, G. J. 2003. The health of glaciers: recent changes in glacier regime, Climatic Change, 59(1-2): 123-159.

Melack, J. M., Dozier, J., Goldman, C. R., Greenland, D., Milner, A. M, Naiman, R. J. 1997. Effects of climate change on inland waters of the Pacific coastal mountains and the western Great Basin of North America, Hydrological Processes, 11(8): 971-992.

Milner, A. M. and Petts, G. E. 1994. Glacial rivers: physical habitat and ecology, Freshwater Biology, 32(2): 295-307.

Nathenson, M. 2004. Springs on and in the vicinity of Mount Hood volcano, Oregon, U.S. Geological Survey Open File Report 2004-1298, U. S. Geological Survey, Washington, D.C.

Neitzal, D. A., Scott, M. J., Shankle, S. A., Chatters, J. C. 1991. The effect of climate change on stream environments: the salmonid resource of the Columbia River basin, The Northwest Environmental Journal, 7: 271-293.

Nylen, T. 2002. Spatial and temporal variations of glaciers (1913 and 1994) on Mt. Rainier and the relation with climate. M.S. Thesis, Portland State University, Portland, Oregon, 128 p.

Orwin, J. F. and Smart, C. C. 2004. Short-term spatial and temporal patterns of suspended sediment transfer in proglacial channels, Small River Glacier, Canada, Hydrological Processes, 18(9): 1521-1542.

Østrem, G., Haakenson, N., Olsen, H. C. 2005. Sediment transport, delta growth and sedimentation in Lake Nigardsvatn, Norway, Geografiska Annaler, Series A, Physical Geography, vol. 87A (1): 243-258.

Østrem, G. 1975. Sediment transport in glacial meltwater streams,” in Glaciofluvial and glaciolacustrine sedimentation, A. V. Jopling and B. C. McDonald (eds.) Los Angeles, CA: Society for Economic Paleontology and Mineralogy, Special Publication 23, pp. 101-122.

Riihimaki, C. A., MacGregor, K. R., Anderson, R. S., Anderson, S. P., Loso, M. G. 2005. Sediment evacuation and glacial erosion rates at a small alpine glacier, Journal of Geophysical Research, 110(F03003): p1-17.

Robinson, C. T., Uehlinger, U., Hieber, M. 2001. Spatio-temporal variation in macroinvertebrate assemblages of glacial streams in the Swiss Alps, Freshwater Biology, 46(12): 1663-1672.

Schlesinger, W. H., 1997. Biogeochemistry: An Analysis of Global Change. San Diego: Academic Press. 159 p.

Scott, W. E., Pierson, T. C., Schilling, S. P., Costa, J. E., Gardner, C. A., Vallance, J. W., Major, J. J. 1997. Volcano hazards in the Mount Hood region, Oregon, U. S. Geologic Survey Open File Report 97-89, 14 p.

Skidmore, M. L., Foght, J. M., Sharp, M. J. 2000. Microbial life beneath a High-Arctic glacier, Applied and Environmental Microbiology, 66(8): 3214-3220.

96

Smith, B. P. G., Hannah, D. M., Gurnell, A. M., Petts, G. E. 2001. A hydrogeomorphological context for ecological research on alpine glacial rivers, Freshwater Biology, 46(12): 1579-1596.

Snavely, P. D., Jr., Wells, R., Minasian, D. L. 1993. The Cenozoic geology of the Oregon and Washington Coast Range, U. S. Geological Survey Open File Report 93-189, 40 p.

Souchez, R. A. and Lemmens, M. M 1987. Chapter 11, Solutes, in Gurnell, A. M. 1987. Fluvial sediment yield from alpine glacierized catchments. In Glacio-fluvial sediment transfer, Gurnell, A. M., Clark, M. J. (eds.), Wiley, Chichester, pp. 415-420.

Stahl, K., Moore, R. D. 2006. Influence of watershed glacier coverage on summer streamflow in British Columbia, Canada, Water Resources Research, 42(6), (W06201)

Steffan, A. W. 1971. Chironomid (Diptera) biocenoses in Scandinavian glacier brooks, Canadian Entomologist, 103(3): 477-486.

Swift, D. A., Nienow, P. W., Hoey, T. B. 2005. Basal sediment evacuation by subglacial meltwater, Earth Surface Processes and Landforms, 30(7): 867-883.

Thomas, R. B. 1983. Measuring suspended sediment in small mountain streams, United States Forest Service General Technical Report PSW-83.

Thouret, J. C. 2005. The stratigraphy, depositional processes, and environment of the late Pleistocene Pollalie-period deposits at Mount Hood Volcano, Oregon, USA, Geomorphology, 70(1-2): 12-32.

Tockner, K., Malard, F., Uehlinger, U. and J.V. Ward. 2002. Nutrients and organic matter in a glacial river–floodplain system (Val Roseg, Switzerland). Limnology and Oceanography 47(1): 266-277.

Trimble, D.E. 1963. Geology of Portland, Oregon and Adjacent Areas: U.S. Geological Survey Bulletin 1119, 119p.

Uehlinger, U., Malard, F., Ward, J. V. 2003. Thermal patterns in the surface waters of a glacial river corridor (Val Roseg, Switzerland), Freshwater Biology, 48(2): 284-300.

U.S. Department of Agriculture. Forest Service. 1995. Salmon River Watershed Analysis. Mt. Hood National Forest. Oregon.

U.S. Department of Agriculture. Forest Service. 2001. Mount Hood National Forest 2001, Land and Resource Management Plan, Monitoring Report, Fiscal Year 2000, U.S. Forest Service, 166p. In: Nathenson, M. 2004. Springs on and in the Vicinity of Mount Hood Volcano, Oregon, U.S.G.S Open File Report 2004-1298, U. S. Geological Survey, Washington D.C.

Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R., Cushing, C. E. 1980. The river continuum concept, Canadian Journal of Fisheries and Aquatic Sciences, 37: 130-137.

97

Verbunt, M., Gurtz, J., Jasper, K., Lang, H., Warmerdam, P., Zappa, M. 2003. The hydrological role of snow and glaciers in alpine river basins and their distributed modeling, Journal of Hydrology, 282(1-4): 36-55.

Waiser, M. J. 2006. Relationship between hydrological characteristics and dissolved organic carbon concentration and mass in northern prairie wetlands using a conservative tracer approach, Journal of Geophysical Research, vol. 111(G02024).

Warburton, J. 1990. An alpine proglacial fluvial sediment budget, Geografiska Annaler. Series A, Physical Geography, 72(3/4): 261-272.

Ward, A. D., Trimble, S. W. 2004. Environmental Hydrology, 2nd ed., Boca Raton: Lewis Publishers. 475 p.

Ward, J. V. 1994. Ecology of alpine streams, Freshwater Biology, 32: 277-294.

Ward, J. V., Malard, F., Tockner, K., Uehlinger, U. 1999. Influence of ground water on surface water conditions in a glacial floodplain of the Swiss Alps, Hydrological Processes, 13(3): 277-293. In: Uehlinger, U., Malard, F., Ward, J. V. 2003. Thermal patterns in the surface waters of a glacial river corridor (Val Roseg, Switzerland), Freshwater Biology, 48(2): 284-300.

Webb, B. W., Walling, D. E. 1993. Longer-term water temperature behaviour in an upland stream, Hydrological Processes, 17(1): 19-32. In: Brown, L. E., Hannah, D. M., Milner, A. M. 2006. Hydroclimatological influences on water column and streambed thermal dynamics in an alpine river system, Journal of Hydrology, 325(1-4): 1-20.

Western Regional Climate Center, http://www.wrcc.dri.edu/wraws/waF.html for Washington, and http://www.wrcc.dri.edu/wraws/orF.html for Oregon.

Williams, M. W., Brown, A. D., Melack, J. M. 1993. Geochemical and hydrologic controls on the composition of surface water in a high elevation basin, Sierra Nevada, California, Limnology and Oceanography, 38(4): 775-797.

Williams, M. W., Hood, E., Caine, N. 2001. The role of inorganic nitrogen in the nitrogen cycle of a high-elevation catchment, Colorado Front Range, USA, Water Resources Research, 37(10): 2569-2582.

Wise, W.S., 1969. Geology and petrology of the Mt. Hood area—a study of high cascade volcanism. Geological Society of America Bulletin 80(6): 969– 1006.

Wood, C. A. and Kienle, J., 1990, Volcanoes of North America: United States and Canada: Cambridge University Press, 354p.

Zappa, M., Badoux, A. Gurtz, J. 2000. The application of a complex distributed hydrological model in a highly glaciated in alpine river catchment. In: Limnological Reports, 33rd Conference of the International Association for Danube Research, Osijek, Croatia, 3-9 September 2000 (Ed. J. Horvatic), pp. 23-28. International Association for Danube Research, Vienna. In: Uehlinger, U., Malard, F., Ward, J. V. 2003. Thermal patterns in the surface

98

waters of a glacial river corridor (Val Roseg, Switzerland), Freshwater Biology, 48(2): 284-300.

99

Appendix A. Assessment of Lagrangian sampling Figures A1-A10. Calculated water speeds compared to Lagrangian sampling.

White R., Mt. Rainier

0

5

10

15

20

0 2 4 6 8 10L*

Tim

e (h

ours

) LaGrangian 9/4/05Water

Nisqually R., Mt. Rainier

012345678

0 5 10L*

Tim

e (h

ours

)

LaGrangian 9/5/05Water

Eliot Cr.

0

1

2

3

4

5

0 2 4 6 8L*

Tim

e (h

ours

)

LaGrangian 8/10/05LaGrangian 8/12/05Water

Sandy R.

0

4

8

12

16

20

24

0 20 40 60L*

Tim

e (h

ours

)

LaGrangian 7/25/05LaGrangian 8/4/05Water

White R., Mt. Hood

0

510

15

2025

30

0 20 40 60 80 100 120L*

Tim

e (h

ours

)

LaGrangian 7/27/05Water

White R., Mt. Hood

0

5

10

15

20

25

30

0 20 40 60 80 100 120L*

Tim

e (h

ours

)

LaGraqngian 8/3/05LaGrangian 8/5/05Water

Salmon R.

048

12162024

0 20 40 60 80 100L*

Tim

e (h

ours

)

LaGrangian 8/17/05LaGrangian 8/21/05Water

Multnomah Creek, Non-glacial

012345678

0 1 2 3 4 5 6 7 8L* (median)

Tim

e (h

ours

)

LaGrangian 8/25/05Water

Gales Cr./Tualatin R., non-glacial

0

5

10

15

20

0 20 40 60 80 100 120 140L*

Tim

e (h

ours

)

LaGrangian 9/14/05LaGrangian 9/18/05Water

Opal Cr. to North Santiam River

05

1015202530

0 20 40 60L*

Tim

e (h

ours

) LaGrangian 8/13/06Water

100

Appendix B. Temperature Table B1. Ambient regional temperature from Remote Automated Weather Stations during Hobo data logger temperature sampling intervals.

Sampling data Air Temperature (°C) RAWS Elev.

(m) River Date Mean Max. Min. Range

Blue Ridge 1,152 Sandy 8/3/05 19.2 25.0 12.2 12.8 Blue Ridge 1,152 White 8/4/05 23.0 30.6 16.7 13.9 Blue Ridge 1,152 Eliot 8/11/05 14.0 20.0 9.4 10.6 Blue Ridge 1,152 Salmon 8/20/05 21.5 30.0 13.9 16.1 Locks 39 Multnomah 8/27/05 20.7 29.4 14.4 15.0 Ohanapecosh 503 White, Nisqually 9/7/05 15.6 27.8 7.2 20.6 Ohanapecosh 503 White (Rainier) 9/8/05 15.7 27.8 6.7 21.1 South Fork 682 Gales/Tualatin 9/15/05 10.5 15.0 7.2 7.8 Horse Creek 610 Opal/Santiam 8/18/06 23.4 29.4 17.8 11.6

San

dyW

hite

Elio

t

Sal

mon

Mul

t. C

r.

Figure B1. August, 2005, average air temperatures at Blue Ridge RAWS station, Mount Hood, are shown with 24-hour temperature logger dates labelled by river.

101

Nis

q.W

hite

Figure B2. August and September, 2005, air temperatures at Ohanpecosh RAWS station, Mount Rainier, are shown with 24-hour temperature logger days labelled by river.

Table B2. Correlation coefficients from parametric and non-parametric correlation for all 24-hour logger temperature data on distance measures. Coefficients stronger than 0.70 are bold. All correlations were statistically significant with p-value<0.01.

Distance L* Fraction glacierized Elevation Upstream

basin area Correlation coefficients, all temperature data, n = 2655 data values Pearson’s 0.71 0.60 -0.72 -0.65 0.61 Kendall’s τ 0.56 0.62 -0.62 -0.46 0.40 Spearman’s 0.72 0.79 -0.79 -0.61 0.56

102

Table B3. Correlation coefficients for the relationship between individual stream temperature data from selected 24-our periods and distance measures show that each measure correlates about equally well. All correlations were significant with p-value<0.01.

Distance L* Fraction glacierized Elevation Upstream

basin area White R., Mt. Rainier – 445 data points (6 locations) Pearson’s 0.77 0.77 -0.75 -0.78 0.76 Kendall’s τ 0.63 0.63 -0.60 -0.63 0.63 Spearman’s ρ 0.81 0.81 -0.78 -0.81 0.81 Nisqually R., Mt. Rainier – 385 data points (4 locations) Pearson’s 0.73 0.73 -0.73 -0.75 0.73 Kendall’s τ 0.65 0.65 -0.65 -0.65 0.65 Spearman’s ρ 0.79 0.79 -0.79 -079 0.79 Eliot Cr., Mt. Hood –288 data points (3 locations) Pearson’s 0.91 0.91 -0.91 -0.91 0.91 Kendall’s τ 0.81 0.81 -0.81 -0.81 0.81 Spearman’s ρ 0.89 0.89 -0.89 -0.89 0.89 Sandy R., Mt. Hood – 385 data points (4 locations) Pearson’s 0.86 0.86 -0.81 -0.91 0.88 Kendall’s τ 0.79 0.79 -0.79 -0.79 0.79 Spearman’s ρ 0.91 0.91 -0.91 -0.91 0.91 White R., Mt. Hood – 672 data points (7 locations) Pearson’s 0.47 0.47 -0.25 -0.46 0.46 Kendall’s τ 0.34 0.34 -0.33 -0.34 0.34 Spearman’s ρ 0.45 0.45 -0.44 -0.45 0.45 Salmon R., Mt. Hood – 480 data points (5 locations) Pearson’s 0.85 0.85 -0.76 -0.88 0.80 Kendall’s τ 0.72 0.72 -0.72 -0.72 0.72 Spearman’s ρ 0.85 0.85 -0.85 -0.85 0.84

103

Lineary = 0.15x + 5.94

R2 = 0.45

Logarithmicy = 2.68Ln(x) + 2.91

R2 = 0.56

Powery = 2.06x0.54

R2 = 0.61

Exponentialy = 4.45e0.02x

R2 = 0.25

0

5

10

15

20

25

0 20 40 60 80 100Distance (km)

Tem

pera

ture

(°C

)

Linear

y = 0.14x + 6.6R2 = 0.37

Logarithmicy = 2.74Ln(x) + 3.61

R2 = 0.64

Powery = 2.44x0.54

R2 = 0.65

Exponentialy = 4.94e0.02x

R2 = 0.20

0

5

10

15

20

25

0 20 40 60 80 100L*

Tem

pera

ture

(°C

)

Lineary = -18.47x + 11.79

R2 = 0.53

Logarithmicy = -2.03Ln(x) + 2.85

R2 = 0.61Powery = 2.54x-0.33

R2 = 0.44 Exponentialy = 13.17e-4.14x

R2 = 0.71

0

5

10

15

20

25

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fractional glacierizationTe

mpe

ratu

re (°

C)

Lineary = 0.007x + 7.52

R2 = 0.27

Logarithmicy = 1.37Ln(x) + 3.94

R2 = 0.35

Powery = 2.98x0.23

R2 = 0.26

Exponentialy = 5.68e0.0009x

R2 = 0.120

5

10

15

20

25

0 500 1000 1500 2000Basin Area (km2)

Tem

pera

ture

(°C

)

Lineary = -0.006x + 14.90

R2 = 0.44

Logarithmicy = -3.59Ln(x) + 32.69

R2 = 0.45

Powery = 212.09x-0.52

R2 = 0.25

Exponentialy = 20.84e-0.001x

R2 = 0.41

0

5

10

15

20

25

0 500 1000 1500 2000 2500Elevation (m)

Tem

pera

ture

(°C

)

Figures B3 – B7. Graphs of regression equations on all 24-hour Hobo temperature data against five distance measures (distance, L*, fraction glacierized, upstream basin area and elevation). Some points have been colored using the scheme in other legends used throughout this thesis. Table B4. Regression coefficients, R2, from regressions on all 24-hour logger temperature data. R2> 0.60 are bold. RMSE follows in parentheses. All results are statistically significant with p- value<0.01.

Distance L* Fraction

glacierized Elevation Upstream basin area

Logarithmic 0.55 (3.3) 0.63 (3.0) 0.62 (3.0) 0.44 (3.7) 0.33 (4.0) Quadratic 0.52 (3.4) 0.58 (3.2) 0.58 (3.2) 0.45 (3.6) 0.40 (3.8) Cubic 0.56 (3.3) 0.58 (3.2) 60 (3.1) 0.49 (3.5) 0.41 (3.7) Power (axb) 0.61 (0.6) 0.56 (0.6) 0.45 (0.7) 0.24 (0.8) 0..25 (0.8) Exponential (aebx)

0.27 (0.81) 0.19 (0.9) 0.71 (0.5) 0.40 (0.7) 0.15 (0.9)

Linear 0.50 (3.5) 0.36 (3.9) 0.52 (3.4) 0.43 (3.7) 0.37 (3.9)

104

Table B5. Regression coefficients, R2, for regression of 24-hour stream temperature data by individual streams. All values are significant at p-values<0.05.

Distance L* Fraction glacierized Elevation Upstream

basin area White R., Mt. Rainier – 445 data points (6 locations)

Linear 0.59 0.59 0.57 0.61 0.57 Logarithmic 0.56 0.51 0.61 0.60 0.62 Quadratic 0.60 0.60 0.60 0.61 0.61 Cubic 0.61 0.60 0.61 0.61 0.62 Power 0.70 0.70 0.66 0.64 0.68 Exponential 0.61 0.61 0.70 0.66 0.58 Nisqually R., Mt. Rainier – 385 data points (4 locations) Linear 0.53 0.52 0.49 0.56 0.54 Logarithmic 0.56 0.55 0.55 0.56 0.56 Quadratic 0.56 0.56 0.56 0.56 0.56 Cubic 0.56 0.56 0.56 0.56 0.56 Power 0.62 0.60 0.61 0.61 0.62 Exponential 0.57 0.55 0.57 0.62 0.57 Eliot Cr., Mt. Hood – 288 data points (3 locations) Linear 0.82 0.82 0.82 0.82 0.82 Logarithmic 0.80 0.80 0.82 0.82 0.81 Quadratic 0.82 0.82 0.82 0.82 0.82 Cubic 0.82 0.82 0.82 0.82 0.82 Power 0.93 0.93 0.91 0.91 0.91 Exponential 0.91 0.91 0.91 0.92 0.90 Sandy R., Mt. Hood – 385 data points (4 locations) Linear 0.74 0.74 0.66 0.82 0.77 Logarithmic 0.81 0.81 0.77 0.72 0.76 Quadratic 0.81 0.82 0.71 0.82 0.77 Cubic 0.81 0.82 0.71 0.82 0.82 Power 0.78 0.78 0.77 0.64 0.77 Exponential 0.67 0.67 0.72 0.80 0.72 White R., Mt. Hood – 672 data points (7 locations) Linear 0.22 0.22 0.06 0.21 0.21 Logarithmic 0.22 0.22 0.19 0.22 0.19 Quadratic 0.22 0.22 0.22 0.22 0.21 Cubic 0.22 0.22 0.22 0.22 0.22 Power 0.21 0.21 0.19 0.22 0.19 Exponential 0.22 0.22 0.06 0.21 0.21 Salmon R., Mt. Hood – 480 data points (5 locations) Linear 0.72 0.72 0.58 0.77 0.64 Logarithmic 0.78 0.77 0.77 0.80 0.78 Quadratic 0.78 0.78 0.59 0.80 0.72 Cubic 0.78 0.78 0.71 0.82 0.72 Power 0.76 0.75 0.71 0.70 0.74 Exponential 0.57 0.57 0.68 0.75 0.47

105

0

5

10

15

20

0 20 40 60 80 100Distance (km)

Ave.

Tem

pera

ture

(°C

)White_RNisq_REliot_HSandy_HWhite_HSalmon_H

0

5

10

15

20

0 20 40 60 80 100L*

Ave.

Tem

pera

ture

(°C

)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_H

0

5

10

15

20

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Fractional glacier cover

Ave.

Tem

pera

ture

(°C

) White_RNisq_REliot_HSandy_HWhite_HSalmon_H

0

5

10

15

20

0 500 1000 1500Basin Area (km2)

Ave.

Tem

pera

ture

(°C

)

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_H

0

5

10

15

20

0 400 800 1200 1600 2000 2400Elevation (m)

Ave.

Tem

pera

ture

(°C

) White_R Nisq_REliot_H Sandy_HWhite_H Salmon_H

Figures B8 – B12. Average temperatures from 24-hour logger data plotted against distance measures (distance, L*, fractional glacier cover, upstream basin area, and elevation).

Opal Cr./Little North Santiam R.

0

5

10

15

20

25

8/18/060:00

8/18/066:00

8/18/0612:00

8/18/0618:00

8/19/060:00

Tem

pera

ture

(°C

)

Sant_1 L*=7 Sant_2 L*=11Sant_3 L*=20 Sant_4 L*=28Sant_5 L*=39 Sant_6 L*=44Sant_7 L*=54 Sant_8 L*=55

Figure B13. Temperature collected at 15 minute intervals along Opal Creek and Little North Santiam River. The temperature data logger placed farthest downstream (Sant_8) was beyond the confluence with the glacial North Santiam River, resulting in an abrupt decrease in downstream temperature.

106

B14. White R., Mt. Rainier

0

5

10

1520

25

30

9/7/0512:00

9/7/0518:00

9/8/050:00

9/8/056:00

9/8/0512:00

Tem

pera

ture

(° C

)

0

5

10

1520

25

30WhiteR_1 L*=0.07WhiteR_3 L*=1.30WhiteR_4 L*=2.64WhiteR_5 L*=5.93WhiteR_6 L*=8.78Reg. Air Temp.

B15. Nisqually R. Mt. Rainier

0

5

10

15

20

25

30

9/7/050:00

9/7/056:00

9/7/0512:00

9/7/0518:00

9/8/050:00

Tem

pera

ture

(°C

)

0

5

10

15

20

25

30NisqR_1 L*=1.6NisqR_2 L*=2.7NisqR_3 L*=4.7NisqR_4 L*=8.2Reg. Air Temp.

B16. Eliot Cr., Mt. Hood

0

5

10

15

20

25

30

8/11/050:00

8/11/056:00

8/11/0512:00

8/11/0518:00

8/12/050:00

Tem

pera

ture

(°C

)

0

5

10

15

20

25

30EliotH_1 L*=0.3EliotH_2 L*=0.5EliotH_3 L*=5.8Air Temp

B17. Sandy R., Mt. Hood

0

5

10

15

20

25

30

8/3/050:00

8/3/056:00

8/3/0512:00

8/3/0518:00

8/4/050:00

Tem

pera

ture

(°C

)

0

5

10

15

20

25

30Sandy_4 L*=9 Sandy_5 L*=14Sandy_6 L*=21 Sandy_7 L*=52Air Temp

B18. White R., Mt. Hood

05

1015

202530

8/4/050:00

8/4/056:00

8/4/0512:00

8/4/0518:00

8/5/050:00

Tem

pera

ture

(°C

)

05

1015

202530

WhiteH_2 L*=4.0 WhiteH_3 L*=5.7WhiteH_5 L*=7.5 WhiteH_6 L*=8.6WhiteH_7 L*=9.6 WhiteH_8 L*=28WhiteH_9 L*=34 Reg. Air Temp.

B19.

Salmon R., Mt. Hood

05

101520253035

8/20/050:00

8/20/056:00

8/20/0512:00

8/20/0518:00

8/21/050:00

Tem

pera

ture

(°C

)

05101520253035

Salmon_1Salmon_2Salmon_3Salmon_4Salmon_5Air Temp.

B20. Multnomah Cr., non-glacial

0

5

10

15

20

25

8/27/050:00

8/27/056:00

8/27/0512:00

8/27/0518:00

8/28/050:00

Wat

er T

empe

ratu

re (°

C)

05101520253035

Air T

empe

ratu

re (°

C)

Mult_1 L*=1.2 Mult_2 L*=3.1Mult_3 L*=4.0 Mult_4 L*=5.5Mult_5 L*=7.8 Air Temp.

B21. Gales Cr., Tualatin R.

0

5

10

15

20

25

9/15/050:00

9/15/056:00

9/15/0512:00

9/15/0518:00

9/16/050:00

Wat

er T

empe

ratu

re

(°C

)

0

5

10

15

20

25

30

Air T

empe

ratu

re (°

C)

Tual_1 L*=9.2Tual_2 L*=15.6Tual_3 L*=37.5Tual_4 L*=82.3Air Temp.

B22. Opal Cr., L.N. Santiam R.

0

5

10

15

20

25

30

8/18/060:00

8/18/066:00

8/18/0612:00

8/18/0618:00

8/19/060:00

Tem

pera

ture

(°C

)

0

5

10

15

20

25

30

Sant_1 L*=7 Sant_2 L*=11Sant_3 L*=20 Sant_4 L*=28Sant_5 L*=39 Sant_6 L*=44Sant_7 L*=54 Sant_8 L*=55Air Temp.

Figures B14-B22. Diurnal temperature variation from 24-hour data loggers. Hourly average air temperatures from a regional RAWS station are included.

107

Figures B23-B27. Averages from 24-hour temperature logger data plotted in order

of increasing L* (Figure B1), Distance (Figure B2), percent glacierized (Figure B3), elevation (Figure B4), and upstream basin area (km2; Figure B5).

108

02468

1012

0 600 1200 1800 2400Elevation (m)

Tem

pera

ture

rang

e (°

C).

Figures B28-B32. Daily temperature range plotted against distance measures.

0

2

4

6

8

10

12

0 50 100 150 200 250 300 350 400

Upstream basin area (km2)

Tem

pera

ture

rang

e (°

C).

White_RNisq_REliot_HSandy_HWhite_HSalmon_H

02468

1012

0.0 0.2 0.4 0.6 0.8

Fraction glacierizedTe

mpe

ratu

re ra

nge

(°C

). White_RNisq_REliot_HSandy_HWhite_HSalmon_H

0

2

4

6

8

10

12

0 10 20 30 40 50 60L*

Tem

pera

ture

rang

e (°

C).

White_RNisq_REliot_HSandy_HWhite_HSalmon_H

0

2

4

6

8

10

12

0 10 20 30 40 50 60Stream distance (km)

Tem

pera

ture

rang

e (°

C).

White_RNisq_REliot_HSandy_HWhite_HSalmon_H

109

White R., Mt. Rainier, 10:30AM-4:50PM

02468

10121416

0 1 2 3 4 5 6 7 8 9L*

Tem

pera

ture

(°C

)

LaGrangian 9/4/05Hobo 9/7/05

Nisqually R.11:15AM-1:50PM

02468

1012141618

0 2 4 6 8 10L*

Tem

pera

ture

(°C

)

LaGrangian 9/5/05Hobo 9/6/05

Eliot Cr.

11:50 -14:00

02468

101214

0 2 4 6 8

L*

Tem

pera

ture

(°C

)

LaGrangian 8/12/05

Hobo 8/12/05

Sandy R.10:30 -16:55

0

5

10

15

20

0 5 10 15 20 25L*

Tem

pera

ture

(°C

)

LaGrangian 8/4/05

Hobo 8/3/05

White R., Mt. Hood10:30-20:15

0

5

10

15

20

0 10 20 30 40

L*

Tem

pera

ture

(°C

).

Hobo 8/4/058/3/05 Sampling

White R., Mt. Hood8:00 -13:10

0

5

10

15

20

0 10 20 30 40

L*

Tem

pera

ture

(°C

).

Hobo 8/4/5Hobo8/5/5 8/5/5 Sampling

Salmon R.14:40 -18:55

02468

1012141618

0 20 40 60 80 100L*

Tem

pera

ture

(°C

)

LaGrangian 8/21/05

Hobo 8/21/05

Hobo 8/20/05

Opal Cr./Little N. Santiam R./N. Santiam

0

5

10

15

20

25

0 20 40 60Distance (km)

Tem

pera

ture

(°C

)

Hobo 10AM startHobo 8 AM startHobo 6 AM start

Figures B33 – B40. Comparison of Lagrangian and Hobo-derived Lagrangian temperatures. Figure B40 shows three Hobo-derived Lagrangian sample sets with temperature drop after flow enters glacial North Santiam River, from non-glacial Little North Santiam River.

110

Appendix C. Turbidity Table C1. This table shows non-parametric correlation coefficients for tests of correlation between turbidity and distance measures. P-values are <0.05 unless shown in parentheses. Distance L* Fraction

glacierized Upstream basin area

All glacial - 232 values Kendall’s τ -0.31 -0.27 0.31 -0.32 Spearman’s ρ -0.44 -0.35 0.40 -0.44 White R., Mt. Rainier – 87 values Kendall’s τ -0.37 -0.37 0.37 -0.37 Spearman’s ρ -0.48 -0.48 0.48 -0.48 Nisqually R., Mt. Rainier – 37 values Kendall’s τ 0.04 (0.75) 0.04 (0.75) -0.04 (0.75) 0.04 (0.75) Spearman’s ρ 0.07 (0.68) 0.07 (0.68) -0.07 (0.68) 0.07 (0.687) Eliot Cr., Mt. Hood – 8 values Kendall’s τ 0.04 (0.89) 0.04 (0.89) -0.04 (0.89) 0.04 (0.89) Spearman’s ρ -0.03 (0.95) -0.03 (0.95) 0.03 (0.95) -0.03 (0.95) Sandy R., Mt. Hood – 41 values Kendall’s τ -0.53 -0.53 0.53 -0.53 Spearman’s ρ -0.68 -0.67 0.67 -0.67 White R., Mt. Hood – 54 values Kendall’s τ -0.36 -0.37 0.36 -0.35 Spearman’s ρ -0.50 -0.47 0.47 -0.46 Salmon R., Mt. Hood – 5 values Kendall’s τ -0.84 (0.03) -0.84 (0.05) 0.84 (0.05) -0.84 (0.05) Spearman’s ρ -0.89 (0.04) -0.89 (0.04) 0.89 (0.04) -0.89 (0.04) Table C2. Correlation coefficients for location average turbidity values (top) and location average normalized turbidity (bottom) from the 25 glacial stream locations where turbidity was sampled at least twice. All p-values < 0.05. Distance L* Fraction

glacierized Upstream basin area

Average turbidity per glacial sample location, n = 25 Kendall’s τ -0.40 -0.36 0.39 -0.38 Spearman’s -0.54 -0.40 0.46 -0.52 Location averages of turbidity normalized by stream maxima, n = 25 Kendall’s τ -0.48 -0.46 0.48 -0.43 Spearman’s -0.63 -0.61 0.63 -0.55

111

Table C3. Regression coefficients for regression of all turbidity values on stream location variables. P-values are <0.05 unless given in parentheses.

Distance L* Fraction glacierized

Upstream basin area

White R., Mt. Rainier – 87 values Logarithmic 0.32 0.33 0.24 0.23 Quadratic 0.19 0.19 0.37 0.19 Cubic 0.22 0.23 0.45 0.22 Power 0.28 0.29 0.23 0.22 Exponential 0.17 0.19 0.27 0.19 Linear 0.17 0.17 0.31 0.17

Nisqually R., Mt. Rainier – 37 values Logarithmic 0.03 (0.29) 0.02 (0.36) 0.03 (0.34) 0.04 (0.25) Quadratic 0.04 (0.49) 0.03 (0.61) 0.07 (0.29) 0.05 (0.41 Cubic 0.11 (0.26) 0.11 (0.23) 0.11 (0.26) 0.11 (0.26( Power 0.01 (0.66) <0.01 (0.78) <0.01 (0.75) 0.01 (0.59) Exponential 0.01 (0.67) <0.01 (0.77) <0.01 (0.88) 0.01 (0.61) Linear 0.03 (0.28) 0.03 (0.34) 0.02 (0.88) 0.04 (0.25)

Eliot Cr., Mt. Hood – 8 data points Logarithmic 0.08 (0.50) 0.08 (0.50) 0.08 (0.49) 0.08 (0.49) Quadratic 0.08 (0.80) 0.08 (0.80) 0.08 (0.80) 0.08 (0.49) Cubic 0.08 (0.80) 0.08 (0.80) 0.08 (0.80) 0.08 (0.80) Power 0.08 (0.49) 0.08 (0.49) 0.09 (0.46) 0.09 (0.46) Exponential 0.05 (0.46) 0.09 (0.46) 0.09 (0.46) 0.09 (0.46) Linear 0.08 (0.49) 0.08 (0.49) 0.08 (0.49) 0.08 (0.49)

Sandy R., Mt. Hood – 41 data points Logarithmic 0.34 0.31 0.36 0.37 Quadratic 0.38 0.35 0.41 0.30 (0.01) Cubic 0.38 0.35 0.45 0.37 (0.01) Power 0.48 0.55 0.60 0.50 Exponential 0.61 0.58 0.29 0.58 Linear 0.22 0.20 0.30 0.15 (0.01)

White R., Mt. Hood – 54 data points Logarithmic 0.18 0.18 0.18 0.18 Quadratic 0.17 (0.01) 0.17 (0.01) 0.15 0.16 (0.01) Cubic 0.17 (0.03) 0.17 (0.03) 0.17 (0.02) 0.16 (0.01) Power 0.24 0.24 0.24 0.24 Exponential 0.27 0.27 0.11 (0.01) 0.24 Linear 0.14 0.14 0.11 (0.02) 0.10 (0.01)

Salmon R., Mt. Hood – 5 data points Logarithmic 0.68 (0.09) 0.69 (0.08) 0.80 (0.04) 0.78 (0.05) Quadratic 0.98 (0.02) 0.98 (0.02) 0.43 (0.59) 1.00 Cubic 0.98 (0.02) 0.98 (0.02) 0.84 (0.50) 1.00 Power 0.58 (0.13) 0.60 (0.13) 0.69 (0.08) 0.67 (0.08) Exponential 0.96 0.96 0.21 (0.44) 1.00 Linear 0.98 0.98 0.30 (0.34) 0.98

112

Logarithmicy = -80.32Ln(x) + 457.52

R2 = 0.11Quadratic

y = 0.16x2 - 18.43x + 487.04R2 = 0.16 Power

y = 349.87x-0.44

R2 = 0.23

Exponentialy = 311.29e-0.055x

R2 = 0.35

0

200

400

600

800

1000

1200

0 20 40 60 80 100Distance (km)

Turb

idity

(NTU

)

Logarithmicy = -24.13Ln(x) + 338.24

R2 = 0.01Quadratic

y = -0.02x2 - 1.55x + 325.22R2 = 0.02 Power

y = 234.83x-0.32

R2 = 0.15

Exponentialy = 226.02e-0.045x

R2 = 0.24

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120L*

Turb

idity

(NTU

)

Logarithmic

y = 43.338Ln(x) + 407.24R2 = 0.03

Quadraticy = -50.08x2 + 332.54x + 244.43

R2 = 0.03

Powery = 453.91x0.47

R2 = 0.26

Exponentialy = 97.76e2.26x

R2 = 0.140

200

400

600

800

1000

1200

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Fractional glacier cover

Turb

idity

(NTU

)

Logarithmicy = -106.56Ln(x) + 676.76

R2 = 0.26Quadratic

y = 0.001x2 - 1.69x + 427.99R2 = 0.16

Powery = 623.89x-0.40

R2 = 0.26

Exponentialy = 225.27e-0.004x

R2 = 0.32

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200 1400Basin area (km2)

Turb

idity

(NTU

)

Figures C1 – C4. Graphs of regressions of distance measures on turbidity. Logarithmic, power and exponential regressions appear to fit all turbidity data, while linear and polynomial did not.

Logarithmicy = -0.09Ln(x) + 0.62

R2 = 0.17Quadratic

y = 0.0001x2 - 0.02x + 0.63R2 = 0.20

Powery = 0.54x-0.33

R2 = 0.17

Exponentialy = 0.52e-0.05x

R2 = 0.29

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100Distance (km)

Nor

mal

ized

Tur

bidi

ty

Logarithmicy = -0.05Ln(x) + 0.51

R2 = 0.06Quadratic

y = 6E-05x2 - 0.01x + 0.52R2 = 0.08

Powery = 0.43x-0.29

R2 = 0.17

Exponentialy = 0.43e-0.05x

R2 = 0.33

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100 120L*

Nor

mai

lized

Tur

bidi

ty

Logarithmic

y = 0.08Ln(x) + 0.62R2 = 0.11

Quadraticy = -0.77x2 + 0.99x + 0.31

R2 = 0.10

Powery = 0.79x0.44

R2 = 0.29 Exponentialy = 0.20e1.86x

R2 = 0.13

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Fractional glacier cover

Nor

mal

ized

Tur

bidi

ty

Logarithmicy = -0.10Ln(x) + 0.78

R2 = 0.26Quadratic

y = 1E-06x2 - 0.002x + 0.56R2 = 0.16

Powery = 0.75x-0.27

R2 = 0.16

Exponentialy = 0.39e-0.003x

R2 = 0.19

0.0

0.2

0.4

0.6

0.8

1.0

0 200 400 600 800 1000 1200 1400Basin area (km2)

Nor

mal

ized

Tur

bidi

ty

Figures C5 – C8. Graphs of regressions of distance measures on turbidity values normalized by stream maxima. Logarithmic, power and exponential regressions appear to fit all turbidity data normalized by stream turbidity maxima, while linear and polynomial did not.

113

Table C4. Regression coefficients, R2, from regressions of all turbidity values on distance measures. RMSE (NTU) are followed by p-values in parentheses, unless p-value<0.05.

Distance L* Fraction glacierized

Upstream basin area

211 turbidity values at 36 sample locations Logarithmic 0.11 (331.5) 0.01

(349.1, 0.14) 0.03 (345.9) 0.26 (302.9)

Quadratic 0.15 (323.6) 0.01 (349.3, 0.23)

0.03 (345.8) 0.17 (320.4)

Cubic 0.16 (323.8) 0.01 (350.1, 0.40)

0.05 (343.3) 0.20 (314.5)

Power (axb) 0.22 (1.1) 0.14 (1.2) 0.23 (1.1) 0.24 (1.1) Exponential (aebx)

0.31 (1.0) 0.20 (1.1) 0.14 (1.2) 0.25 (1.1)

Linear 0.12 (329.7) 0.01 (348.6, 0.10)

0.03 (345.0) 0.09 (334.3)

Table C5. Regression coefficients from regressions of average normalized turbidity values per location on distance measures. RMSE, followed by p-values if >0.05 in parentheses.

Distance L* Fraction glacierized

Upstream basin area

Average normalized turbidity – 25 sample locations Logarithmic 0.35

(0.21) 0.30

(0.22) 0.40

(0.21) 0.32

(0.22) Quadratic 0.34

(0.22) 0.30

(0.23) 0.39

(0.21) 0.23

(0.24, 0.06) Cubic 0.37

(0.22) 0.34

(0.23) 0.39

(0.22) 0.36

(0.22) Power 0.32

(0.99) 0.39

(0.94) 0.58

(0.78) 0.28

(1.02) Exponential 0.58

(0.78) 0.67

(0.68) 0.22

(1.06) 0.48

(0.87) Linear 0.30

(0.22) 0.22

(0.24) 0.34

(0.22) 0.21

(0.24)

114

0

200

400

600

800

1000

1200

0 20 40 60Distance (km)

Aver

age

turb

idity

(NTU

)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0

200

400

600

800

1000

1200

0 5 10 15 20 25 30L*

Aver

age

turb

idity

(NTU

)

White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_H

0

200

400

600

800

1000

1200

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Fraction glacierized

Aver

age

turb

idity

(NTU

)

White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_H

0

200

400

600

800

1000

1200

0 50 100 150 200 250 300 350Basin area (km2)

Ave

rage

turb

idity

(NTU

)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

Figures C9 – C12. Average turbidity per location is graphed against the four distance measures.

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60Distance (km)

Nor

mal

ized

ave

rage

tu

rbid

ity (N

TU)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0.0

0.2

0.4

0.6

0.8

1.0

0 5 10 15 20 25 30

L*

Nor

mal

ized

ave

rage

tu

rbid

ity (N

TU)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fraction glacierized

Nor

mal

ized

ave

rage

tu

rbid

ity (N

TU)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150 200 250 300 350

Basin area (km2)

Nor

mal

ized

ave

rage

tu

rbid

ity (N

TU)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

Figures C13 – C16. Average turbidity value per sample location is normalized by the highest average turbidity value among each river’s sampling locations is graphed against the four distance measures.

115

White R., Mt. Rainier9/4/2005

0

50

100

150

200

250

0 5 10L*

Turb

idity

(NTU

)

Nisqually R., Mt. Rainier9/5/2005

0

10

20

30

40

50

0 5 10L*

Turb

idity

(NTU

)

Eliot Creek, Mt. Hood

050

100150200250300350400450

0 2 4 6 8L*

Turb

idity

(NTU

)

8/10/05 15:40-18:158/12/2005 11:50-14:00

Sandy R., Mt. Hood

0102030405060708090

0 20 40 60 80L*

Turb

idity

(NTU

) 7/25/2005 12:20-20:458/4/2005 10:30-18:15

White R., Mt. Hood

0

200

400

600

800

1000

1200

0 50 100 150L*

Turb

idity

(NTU

) 7/27/2005 15:00-21:008/3/2005 10:30-17:108/5/2005 8:00-14:308/5/2005 15:50

White R., Mt. Hood

050

100150

200250

300

0 10 20 30 40L*

Turb

idity

(NTU

) 8/3/2005 10:30-17:108/5/2005 8:00-14:30

Salmon R., Mt. Hood

0

200

400

600

800

1000

1200

0 20 40 60 80 100L*

Turb

idity

(NTU

) 8/21/2005 14:40-18:55Gales Cr./Tualatin R.

0

2

4

6

8

10

12

0 20 40 60 80 100 120Distance (km)

Turb

idity

(NTU

). 9/14/05 2:00 PM - 7:30 PM

9/18/05 10:25 AM - 3:30 PM

Multnomah Cr., non-glacial

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 2 4 6 8Distance (km)

Turb

idity

(NTU

) 8/25/2005 11:20-18:20

Opal Cr., Little N. and N. Santiam R.

0.00.20.40.60.81.01.21.4

0 20 40 60 80 100Distance (km)

Turb

idity

(NTU

)

8/13/2005 12:35-18:40

Figures C17 – C26. Turbidity values from Lagrangian sampling.

116

White R., Mt. Rainier, 9/7-8/20050

100200300400500600700800

9/7/

058:

00

9/7/

0514

:00

9/7/

0520

:00

9/8/

052:

00

9/8/

058:

00

Turb

idity

(NTU

)

WhiteR_1 L*=0.1

WhiteR_4 L*=2.6

WhiteR_5 L*=5.9

White R., Mt. Hood, 8/20/20050

200

400

600

800

1000

1200

8/20/050:00

8/20/056:00

8/20/0512:00

8/20/0518:00

8/21/050:00

Turb

idity

(NTU

) WhiteH_2 L*=3.97WhiteH_3 L*=5.74WhiteH_6 L*=8.62WhiteH_8 L*=28.26

Sandy R., 8/13/2005

0

50

100

150

200

250

300

1/0/

000:

00

1/0/

006:

00

1/0/

0012

:00

1/0/

0018

:00

1/1/

000:

00

Turb

idity

(NTU

)

Sandy_2 L*=3.7Sandy_3 L*=6.7Sandy_4 L*=9.2Sandy_5 L*=14

Figures C27– C30. Synoptic turbidity results.

0.1

1

10

100

1000

10000

0 20 40 60 80 100 120 140

Distance (km)

Turb

idity

(NTU

).

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HN Santiam TualatinMultnomah LN SantiamFiltered Agricultural

0.1

1

10

100

1000

10000

0 20 40 60 80 100 120 140

L*

Turb

idity

(NTU

).

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HN Santiam TualatinMultnomah LN SantiamFiltered Agricultural

0.1

1

10

100

1000

10000

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fraction glacierized

Turb

idity

(NTU

).

0.1

1

10

100

1000

10000

0 500 1000 1500 2000

Basin Area (km2)

Turb

idity

(NTU

).

White_R Nisq_REliot_H Sandy_HWhite_H Salmon_HN Santiam TualatinMultnomah LN SantiamFiltered Agricultural

Figures C31-C34. Comparison of glacial and non-glacial turbidity. Non-glacial distances are measured from the inception point on U. S. Geological Survey 1:24,000 scale topographic maps, and fractional glacier cover are plotted at 0. Agricultural non-glacial are enclosed in a second diamond, and glacial water that was filtered by settling behind dams or in low gradient meadows are squared. A threshold line is drawn at 30 NTU, the minimum turbidity level for glacial stream habitat (Milner and Petts, 1994).

Multnomah Cr.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

8/28

/05

7:00

8/28

/05

9:00

8/28

/05

11:0

0

8/28

/05

13:0

0

8/28

/05

15:0

0

8/28

/05

17:0

0

8/28

/05

19:0

0

Turb

idity

(NTU

)..

Mult_1 L* med=1.2Mult_2 L*med=3.1Mult_4a L*med=5.5Mult_4c L*med=5.6

117

Appendix D. Suspended sediment concentration Table D1. Non-parametric correlation analyses for suspended sediment concentration and distance measures resulted in the following coefficients of correlation. Results are significant at p-value <0.01 unless noted in parentheses. Distance L* Fraction

glacierized Upstream basin area

All glacial - 187 data points Kendall’s τ -0.33 -0.15 0.17 -0.36 Spearman’s ρ -0.48 -0.21 0.24 -0.51 White R., Mt. Rainier – 61 data points Kendall’s τ -0.54 -0.54 0.54 -0.54 Spearman’s ρ -0.69 -0.69 0.69 -0.69 Nisqually R., Mt. Rainier – 30 data points Kendall’s τ 0.38 0.38 -0.38 0.38 Spearman’s ρ 0.46 (0.01) 0.46 (0.01) -0.46 (0.01) 0.46 (0.01) Eliot Cr., Mt. Hood – 7 data points Kendall’s τ 0.11 (0.75) 0.11 (0.75) -0.11 (0.75) 0.11 (0.75) Spearman’s ρ 0.21 (0.66) 0.21 (0.66) -0.21 (0.66) 0.21 (0.66) Sandy R., Mt. Hood – 38 data points Kendall’s τ -0.16 (0.18) -0.16 (0.18) 0.17 (0.18) -0.16 (0.18) Spearman’s ρ -0.21 (0.22) -0.21 (0.22) 0.21 (0.22) -0.21 (0.22) White R., Mt. Hood – 46 data points Kendall’s τ -0.46 -0.46 0.46 -0.46 Spearman’s ρ -0.51 -0.51 0.51 -0.51 Salmon R., Mt. Hood – 5 data points Kendall’s τ -1.0 -1.0 1.0 -1.0 Spearman’s ρ -1.0 -1.0 1.0 -1.0

118

Table D2. Regression coefficients, R2, for SSC on distance measures on rivers. P-values given in parentheses when >0.05.

Distance L* Fraction glacierized

Upstream basin area

White R., Mt. Rainier – 61 data points Linear 0.33 0.33 0.42 0.32 Logarithmic 0.42 0.42 0.40 0.40 Quadratic 0.38 0.38 0.42 0.37 Cubic 0.39 0.40 0.43 0.39 Power 0.44 0.44 0.46 0.45 Exponential 0.44 0.44 0.43 0.43 Nisqually R., Mt. Rainier – 30 data points Linear 0.15 0.15 0.24 0.15 Logarithmic 0.21 0.20 0.21 0.21 Quadratic 0.25 0.26 0.24 0.23 Cubic 0.26 0.26 (0.05) 0.26 (0.05) 0.26 (0.05) Power 0.18 0.18 0.18 0.18 Exponential 0.13 (0.06) 0.12 (0.06) 0.22 0.12 (0.06) Eliot Cr., Mt. Hood – 7 data points Linear 0.13 (0.43) 0.13 (0.43) 0.13 (0.43) 0.14 (0.41) Logarithmic 0.06 (0.59) 0.06 (0.59) 0.14 (0.42) 0.14 (0.42) Quadratic 0.48 (0.27) 0.48 (0.27) 0.48 (0.27) 0.14 (0.40) Cubic 0.48 (0.27) 0.48 (0.27) 0.48 0.27) 0.48 (0.27) Power 0.08 (0.54) 0.08 (0.54) 0.16 (0.37) 0.16 (0.38) Exponential 0.15 (0.39) 0.15 (0.39) 0.15 (0.38) 0.17 (0.37) Sandy R., Mt. Hood – 38 data points Linear 0.10 (0.05) 0.10 (0.09) 0.11 0.09 (0.07) Logarithmic 0.12 0.12 0.13 0.13 Quadratic 0.12 (0.10) 0.12 (0.11) 0.11 (0.12) 0.11 (0.13) Cubic 0.12 (0.21) 0.12 (0.22) 0.12 (0.24) 0.11 (0.25) Power 0.22 0.30 0.32 0.22 Exponential 0.48 0.48 0.10 (0.06) 0.56 White R., Mt. Hood – 46 data points Linear 0.10 0.10 0.10 0.06 (0.11) Logarithmic 0.19 0.19 0.18 0.18 Quadratic 0.19 0.19 0.23 0.17 Cubic 0.22 0.22 0.24 0.17 Power 0.55 0.55 0.48 0.48 Exponential 0.55 0.55 0.13 0.45 Salmon R., Mt. Hood – 5 data points Linear 0.54 (0.16) 0.54 (0.16) 0.88 0.39 (0.26) Logarithmic 0.92 0.92 0.88 0.89 Quadratic 1.00 1.00 0.98 0.92 (0.08) Cubic 1.00 1.00 1.00 (0.06) 0.92 (0.08) Power 0.84 0.85 0.93 0.92 Exponential 0.96 0.96 0.48 (0.20) 0.90 (0.02)

119

Table D3. Regression coefficients with p-values given in parentheses for regressions of average suspended sediment concentration for each of 27 sample locations with at least 2 samples on distance variables. Parentheses contain RMSE followed by p-values if >0.05.

Distance L* Fraction glacierized

Upstream basin area

All glacial - 187 datavalues Logarithmic 0.02

(1.8, 0.43) <0.01

(7.8, 0.90) 0.01

(1.8, 0.57) 0.14

(1.7,0.06) Quadradic 0.07

(1.7, 0.38) 0.01

(1.8, 0.86) 0.02

(1.8, 0.81) 0.10

(1.7, 0.29) Cubic 0.08

(1.8, 0.60) 0.03

(1.8, 0.87) 0.03

(1.8, 0.87) 0.13

(1.7, 0.36) Power (axb) 0.17

(1.7) 0.07

(1.8, 0.18) 0.17 (1.7)

0.30 (1.5)

Exponential (aebx)

0.37 (1.5)

0.25 (1.6)

0.05 (1.8, 0.26)

0.35 (1.5)

Linear 0.05 (1.7, 0.24)

0.01 (1.8, 0.58)

<0.01 (1.8, 0.86)

0.05 (1.7, 0.25)

120

01234567

0 10 20 30 40 50 60

Distance (km)

Aver

age

SSC

(mg/

L)

White_HSalmon_H

0.000.050.100.150.200.250.300.350.400.45

0 5 10 15 20 25 30 35 40

Distance (km)

Aver

age

SSC

(mg/

L)

White_RNisquallyEliot_HSandy_H

01

234

5

6

7

0 10 20 30 40 50

L*

Ave

rage

SSC

(mg/

L)

White_HSalmon_H

0.00.10.10.20.20.30.30.40.40.5

0 5 10 15 20 25L*

Ave

rage

SS

C (m

g/L)

White_RNisquallyEliot_HSandy_H

0

1

2

3

4

5

6

7

0.0 0.1 0.2 0.3 0.4 0.5

Fraction glacierized

Aver

age

SSC

(mg/

L)

White_HSalmon_H

0.00.10.10.20.20.30.30.40.40.5

0 5 10 15 20 25L*

Aver

age

SSC

(mg/

L)

White_RNisquallyEliot_HSandy_H

0

1

2

3

4

5

6

7

0 50 100 150Basin Area (km2)

Aver

age

SSC

(mg/

L)

White_HSalmon_H

0.00.10.10.20.20.30.30.40.40.5

0 100 200 300 400 500 600 700Basin Area (km2)

Ave

rage

SSC

(mg/

L)

White_RNisquallyEliot_HSandy_H

Figures D1-D8. Average suspended sediment concentration against distance variables.

121

White R., Mt. Rainier

0.0

0.2

0.4

0.6

0.8

1.0

1.2

9/7/

0510

:00

9/7/

0516

:00

9/7/

0522

:00

9/8/

054:

00

9/8/

0510

:00

SS

C (m

g/L)

WhiteR_1WhiteR_2WhiteR_4WhiteR_5

Nisqually R., Mt. Rainier

00.020.040.060.080.1

0.120.140.16

9/9/

058:

00

9/9/

0514

:00

9/9/

0520

:00

9/10

/05

2:00

9/10

/05

8:00

SS

C (m

g/L)

Nisq_1Nisq_2Nisq_3Nisq_4

Sandy R., Mt. Hood

00.050.1

0.150.2

0.250.3

0.350.4

8/13

/05

0:00

8/13

/05

6:00

8/13

/05

12:0

0

8/13

/05

18:0

0

8/14

/05

0:00

SS

C (m

g/L)

Sandy_2Sandy_3Sandy_4Sandy_5

White R., Mt. Hood

0

5

10

15

20

25

8/20

/05

0:00

8/20

/05

6:00

8/20

/05

12:0

0

8/20

/05

18:0

0

8/21

/05

0:00

SS

C (m

g/L)

WhiteH_2WhiteH_3WhiteH_4WhiteH_8

Multnomah Cr., non-glacial

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

8/28

/05

0:00

8/28

/05

6:00

8/28

/05

12:0

0

8/28

/05

18:0

0

8/29

/05

0:00

SSC

(mg/

L)

Mult_1Mult_2Mult_4Mult_5

Figures D9-D13. Variation of SSC over time through synoptic sampling.

122

White R., Mt. Hood

0.00.10.20.30.40.50.60.70.8

0 20 40 60 80 100 120L*

SS

C (m

g/L) 8/3/2005 10:30-14:30

Salmon R., Mt. Hood

0.00.51.01.52.02.53.0

0 20 40 60 80 100L*

SSC

(mg/

L) 8/21/2005 14:40-18:55

Multnomah Cr., non-glacial

0.0000

0.0005

0.0010

0.0015

0.0020

0 2 4 6 8Distance (km)

SSC

(mg/

L)

8/25/2005 11:20-18:20

Tualatin R., non-glacial

0.000

0.001

0.002

0.003

0.004

0.005

0 20 40 60 80 100 120Distance (km)

SSC

(mg/

L)

9/14/2005 14:00-19:309/18/2005 10:25-15:30

Figures D14-D22. Graphs of Lagrangian sampled SSC.

Eliot Cr., Mt. Hood

0.00

0.05

0.10

0.15

0.20

0 1 2 3 4 5 6L*

SS

C (m

g/L)

8/10/2005 15:40-18:158/12/2005 11:50-14:00

Sandy R., Mt. Hood

0.00

0.02

0.04

0.06

0.08

0.10

0 20 40 60L*S

SC

(mg/

L)

7/25/2005 12:20-20:458/4/2005 10:30-18:15

White R., Mt. Hood

0.0

0.5

1.0

1.5

2.0

0 20 40 60 80 100 120L*

SSC

(mg/

L)

7/27/05 15:00-21:008/5/2005 8:00-13:10

White R., Mt. Rainier

0.00

0.02

0.04

0.06

0.08

0.10

0 2 4 6 8 10L*

SSC

(mg/

L)

9/4/2005 12:00-16:50

Nisqually R., Mt. Rainier

0.000

0.005

0.010

0.015

0.020

0.025

0 2 4 6 8 10L*

SSC

(mg/

L)

9/5/2005 11:15-13:50

123

Appendix E. Electrical conductivity

Table E1. Correlation coefficients from non-parametric tests, with p-values in parentheses if >0.05.

Distance L* Fraction glacierized

Upstream basin area

All glacial, excluding Sandy River - 147 data values Kendall’s τ 0.40 0.55 -0.60 0.31 Spearman’s ρ 0.59 0.75 -0.80 0.41 White R., Mt. Rainier – 61 data values Kendall’s τ 0.56 0.56 -0.56 0.56 Spearman’s ρ 0.72 0.72 -0.72 0.72 Nisqually R., Mt. Rainier – 28 data values Kendall’s τ 0.63 0.63 -0.63 0.63 Spearman’s ρ 0.82 0.82 -0.82 0.82 Eliot Cr., Mt. Hood – 8 data values Kendall’s τ 0.21 (0.51) 0.21 (0.51) -0.21 (0.51) 0.21 (0.51) Spearman’s ρ 0.21 (0.61) 0.21 (0.61) -0.21 (0.61) 0.21 (0.61) Sandy R., Mt. Hood – 28 data values Kendall’s τ -0.87 -0.87 0.88 -0.89 Spearman’s ρ -0.96 -0.96 0.96 -0.97 White R., Mt. Hood – 40 data values Kendall’s τ 0.82 0.82 -0.82 0.82 Spearman’s ρ 0.95 0.95 -0.95 0.95 Salmon R., Mt. Hood – 5 data values Kendall’s τ 0.42 (0.10) 0.42 (0.10) -0.42 (0.10) 0.42 (0.10) Spearman’s ρ 0.62 (0.06) 0.62 (0.06) -0.62 (0.06) 0.62 (0.06)

124

Table E2. Regression coefficients (R2) for regressions of median specific conductance on all rivers except Sandy River. The upper value in each cell is the R2 value. Parentheses contain RMSE followed by p-values if >0.05.

Distance L* Fraction glacierized

Upstream basin area

All glacial, except Sandy River – 28 sample location medians Logarithmic 0.42

(21.6) 0.43

(21.4) 0.54

(19.1) 0.12 (0.8)

Quadratic 0.30 (24.1)

0.45 (21.5)

0.76 (14.1)

0.14 (26.7, 0.14)

Cubic 0.49 (21.0)

0.58 (19.1)

0.76 (14.3)

0.24 (25.7, 0.08)

Power (axb) 0.41 (0.6)

0.28 (0.7)

0.42 (0.6)

0.44 (1.5)

Exponential (aebx)

0.20 (0.8)

0.13 (0.8, 0.06)

0.50 (0.6)

0.35 (0.6)

Linear 0.24 (24.7)

0.19 (25.5)

0.60 (17.8)

0.12 (0.8, 0.07)

125

Table E3. Regression coefficients, R2, from regressions of conductivity on distance measures for individual rivers. Regression coefficients, R2, greater than 0.75 are bold. (P-values <0.05 unless provide.)

Distance L* Fraction glacierized

Upstream basin area

White R., Mt. Rainier – 61 data values Linear 0.54 0.54 0.28 0.51 Logarithmic 0.30 0.30 0.40 0.38 Quadratic 0.57 0.57 0.61 0.51 Cubic 0.68 0.68 0.61 0.68 Power 0.34 0.34 0.46 0.43 Exponential 0.61 0.61 0.32 0.58

Nisqually R., Mt. Rainier – 28 data values Linear 0.54 0.50 0.77 0.54 Logarithmic 0.73 0.68 0.71 0.77 Quadratic 0.92 0.89 0.77 0.92 Cubic 0.92 0.92 0.92 0.92 Power 0.75 0.71 0.74 0.78 Exponential 0.56 0.53 0.81 0.56

Eliot Cr., Mt. Hood – 8 data values Linear 0.50 (0.05) 0.50 (0.05) 0.50 (0.05) 0.51 (0.05) Logarithmic 0.42 (0.08) 0.42 (0.08) 0.51 (0.05) 0.51 (0.05) Quadratic 0.55 (0.13) 0.55 (0.13) 0.55 (0.13) 0.51 (0.05) Cubic 0.55 (0.13) 0.55 (0.13) 0.55 (0.13) 0.55 (0.14) Power 0.40 (0.09) 0.40 (0.09) 0.49 (0.05) 0.49 (0.05) Exponential 0.48 (0.06) 0.48 (0.06) 0.49 (0.05) 0.49 (0.05)

Sandy R., Mt. Hood – 28 data values, all with p-values,0.05 (RMSE) Linear 0.32 (57.6) 0.31 (58.4) 0.85 (27.3) 0.21 (62.1) Logarithmic 0.77 (33.5) 0.68 (39.8) 0.64 (42.2) 0.75 (35.1) Quadratic 0.70 (39.3) 0.72 (38.0) 0.85 (27.7) 0.40 (55.2) Cubic 0.77 (35.0) 0.80 (32.8) 0.86 (27.2) 0.59 (46.7) Power 0.87 (0.2) 0.79 (0.2) 0.77 (0.2) 0.87 (0.2) Exponential 0.44 (0.4) 0.41 (0.4) 0.90 (0.2) 0.31 (0.4)

White R., Mt. Hood – 40 data values Linear 0.24 0.24 0.83 0.14 (0.02) Logarithmic 0.55 0.55 0.69 0.69 Quadratic 0.47 0.47 0.88 0.38 Cubic 0.82 0.82 0.89 0.38 Power 0.40 0.40 0.59 0.59 Exponential 0.14 (0.02) 0.14 (0.02) 0.94 0.08 (0.07)

Salmon R., Mt. Hood – 10 data points Linear 0.01 (0.75) 0.01 (0.75) 0.38 (0.06) <0.01 (>0.99) Logarithmic 0.21 (0.19) 0.20 (0.20) 0.14 (0.29) 0.15 (0.27) Quadratic 0.52 (0.08) 0.52 (0.08 0.39 (0.18) 0.35 (0.22) Cubic 0.52 (0.08) 0.52 (0.08) 0.50 (0.22 ) 0.34 (0.23) Power 0.36 (0.07) 0.35 (0.07) 0.29 (0.11) 0.30 (0.10) Exponential 0.10 (0.39) 0.09 (0.39) 0.51 (0.02) 0.04 (0.58)

126

White R., Mt. Rainier.0

20

40

60

80

100

120

9/7/059:00

9/7/0515:00

9/7/0521:00

9/8/053:00

9/8/059:00

Spec

ific

Con

duct

ance

(u

S/cm

)

WhiteR_1WhiteR_4WhiteR_5

Nisqually R., Mt. Rainier

010203040506070

9/9/059:00

9/9/0515:00

9/9/0521:00

9/10/053:00

9/10/059:00

Spe

cific

Con

duct

ance

(uS

/cm

)

Nisqually_1Nisqually_2Nisqually_3Nisqually_4

Sandy R., Mt. Hood

050

100150200250300

8/13/059:00

8/13/0515:00

8/13/0521:00

8/14/053:00

8/14/059:00

Spe

cific

Con

duct

ance

(uS

/cm

)

Sandy_2Sandy_3

Multnomah Creek

0

10

20

30

40

50

8/28/059:00

8/28/0515:00

8/28/0521:00

8/29/053:00

8/29/059:00

Spe

cific

Con

duct

ivity

(u

S/cm

) Mult_1Mult_4Mult_4c

Figures E1-E4. White River, Mount Rainier (upper left), was sampled over a 24-hour period. Conductivity on Nisqually River increased during the day (upper right) and may have been affected by rain. Sandy River conductivity (lower left) begins high and decreases with distance and time during the day. Non-glacial Multnomah Creek is at the lower right.

127

White R., Mt. Rainier9/4/05 11:30-16:50

0

10

20

30

40

50

60

0 10 20 30 40Distance (km)

Spe

cific

con

duct

ance

. (µ

S/c

m)

Nisqually R., Mt. Rainier9/5/05 11:15-13:50

0

10

20

30

40

50

60

0 5 10 15 20 25 30 35Distance (km)

Spe

cific

con

duct

ance

(µ

S/c

m)

Nisqually R., Mt. Rainier9/5/05 11:15-13:50

0

10

20

30

40

50

60

0 1 2 3 4 5 6 7 8 9L*

Spe

cific

con

duct

ance

(µ

S/c

m)

Eliot Cr., Mt. Hood

0

5

10

15

20

25

30

0 2 4 6 8 10Distance (km)

Spe

cific

con

duct

ance

(µ

S/c

m)

8/10/2005, 15:40-18:158/12/2005, 11:50-14:00

Eliot Cr., Mt. Hood

0

5

10

15

20

25

30

0 2 4 6 8L*

Spec

ific

cond

ucta

nce

(µS/

cm)

8/10/2005, 15:40-18:158/12/2005, 11:50-14:00

Sandy R., Mt. Hood

0

50

100

150

200

250

300

0 20 40 60 80 100Distance (km)

Spec

ific

cond

ucta

nce

(µS/

cm)

7/25/2005, 12:20-20:458/4/2005, 10:30-18:15

Sandy R., Mt. Hood

0

50

100

150

200

250

300

0 20 40 60L*

Spec

ific

cond

ucta

nce

(µS/

cm)

7/25/2005, 12:20-20:458/4/2005, 10:30-18:15

Figures E5-E8. Lagrangian samplilng results plotted against L* and distance (km).

White R., Mt. Rainier9/4/05 11:30-16:50

0

10

20

30

40

50

60

0 1 2 3 4 5 6 7 8 9L*

Spe

cific

con

duct

ance

(µ

S/c

m)

128

White R., Mt. Hood

0

20

40

60

80

100

120

0 20 40 60 80Distance (km)

Spec

ific

cond

ucta

nce

(µS/

cm)

7/27/2005, 15:00-21:008/3/2005, 10:30-17:108/5/2005, 8:00-14:308/5/2005, 15:50

White R., Mt. Hood

0

20

40

60

80

100

120

0 20 40 60 80 100 120L*

Spec

ific

cond

ucta

nce

(µS/

cm)

7/27/2005, 15:00-21:008/3/2005, 10:30-17:108/5/2005, 8:00-14:308/5/2005, 15:50

Salmon R., Mt. Hood

020406080

100120140160

0 10 20 30 40 50 60Distance (km)

Spec

ific

cond

ucta

nce

(µS/

cm)

8/17/2005, 12:47-19:50 (raining)8/21/2005, 14:40-18:55 (dry)

Salmon R., Mt. Hood

020406080

100120140160

0 20 40 60 80 100L*

Spe

cific

con

duct

ance

(µ

S/c

m)

8/17/2005, 12:47-19:50 (rain)8/21/2005, 14:40-18:55 (dry)

Multnomah Cr., non-glacial

0

10

20

30

40

50

0 1 2 3 4 5 6 7 8Distance (km)

Spe

cific

con

duct

ance

(µ

S/c

m)

8/25/2005, 11:20-18:20

Gales Cr./Tualatin R., non-glacial

050

100150200250300350

0 20 40 60 80 100 120Distance (km)

Spec

ific

cond

ucta

nce

(µS/

cm)

9/14/2005, 14:00-19:309/18/2005, 10:25-15:30

Figures E9-E13. Lagrangian sampling results plotted against L* and distance (km).

129

Appendix F: Anions

02468

101214

0 20 40 60 80 100Distance (km)

Chl

orid

e (m

g/L)

.White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_HMult_ng Tual_ng

02468

1012

0 20 40 60 80 100 120 140L*

Chl

orid

e (m

g/L)

.

White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_HMult_ng Tual_ng

02468

101214

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Fraction glacierized

Chl

orid

e (m

g/L)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_HMult_ngTual_ng

02468

101214

0 100 200 300 400Basin Area (km2)

Chl

orid

e (m

g/L)

White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_HMult_ng Tual_ng

Figure F1-F4. Chloride concentrations compared with distance measures.

020406080

100120

0 20 40 60 80 100Distance (km)

Sul

fate

(mg/

L)

White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_HMult_ng Tual_ng

020406080

100120

0 10 20 30 40 50 60

L*

Sul

fate

(mg/

L)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_H

020406080

100120

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fraction glacierized

Sul

fate

(mg/

L)

White_RNisqually_REliot_HSandy_HWhite_HSalmon_HMult_ngTual_ng

020406080

100120

0 400 800 1200 1600 2000

Basin Area (km2)

Sul

fate

(mg/

L)

White_R Nisqually_REliot_H Sandy_HWhite_H Salmon_HMult_ng Tual_ng

Figures F5-F8. Sulfate concentrations compared with distance measures.

130

Appendix G: Nutrients

Table G1. Results from nutrient analyses for those locations that are in the proglacial region of each river are presented with distance measures. Sulfate is included as a possible indicator of subglacial nitrate reduction. Winter snow sampled from a snow pit above treeline near Eliot Glacier in March 2006 had nitrate concentrations ranging from 00.1 – 0.36 mg/l (Sarah Fortner, Ohio State University, personal communication, April 2007). Concentrations from atmospheric deposition were not likely more than 0.36 mg/L. Nitrate concentrations close to glaciers were generally higher than expected from atmospheric deposition, so nitrification is probably occurring.

Location Distance (km) L* Fraction

glacierized NO3 NH4 SRP SO4

WhiteR_1 0.2 0.04 0.73 26 11 38 6.3 WhiteR_2 2.7 0.7 0.42 24 0 32 6.1

Eliot_1 0.4 0.3 0.64 13 6 36 4.6 Sandy_1 1.6 2.7 0.21 0 0 39 100.9 Sandy_2 2.2 3.7 0.20 0 2 35 97.0

WhiteH_1 2.2 3.0 0.50 1 24 29 0.3 WhiteH_3 4.2 5.7 0.07 1 43 19 4.5 WhiteH_5 5.5 7.5 0.07 8 3 24 6.1 Salmon_1 1.0 2.1 0.41 3 0 1 0.7 Salmon_2 1.8 3.3 0.25 12 0 2 0.4