Glacier Effects on Downstream Water Quality
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
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 i 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.
ii 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
iii 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
iv 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
v 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 vi
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
vii 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 viii
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 ix
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
x 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
1 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
2 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*;
L* = Stream Length Glacier Area 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
3 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.
0.8 White_R Nisqually_R 0.7 Eliot_H Sandy_H
r 0.6 White_H Salmon_H 0.5
0.4
0.3
Fractional Glacier Cove Glacier Fractional 0.2
0.1
0 01020304050 L*
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
4 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.
5 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. 6 Table 1. Glaciers and glacier areas draining to study rivers, listed by region. Glacier Area Mountain Primary Glacier River (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 Non-glacial Gales Creek/Tualatin River 0 Range 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.
7
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.
8
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
9 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
10 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.
11
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,
12 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
13 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
14 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
15 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.
16 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.
17 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 Aletch Glacier Rhone Glacier Scaletta Glacier flow basin basin basin (69% glacierized) (47% glacierized) (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).
18 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).
19 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
20 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
21 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
22 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
23 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
24 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
25 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).
26 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.
27 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 Glacier % Basin Samp. Dist L* Elev River Glacier Area glacier Area IDs (km) values (m) (km2) cover (km2) 1467, 0.25, 0.1, 0.7, 73, 42, 1300, 18, 33, White, Emmon 12.9 to 1, 2, 3, 2.7, 4.8, 1.3, 2.6, 34, 20, 1207, 41, 83, Rainier s 16.4 4, 5, 6 11, 25, 5.9, 8.8 10, 6 1029, 159, 260 36 796, 646 974, Nisqual., Nisquall 8.9 to 1, 2, 3, 4.9, 8.0, 1.6, 2.7, 33, 18, 27, 49, 850, Rainier y 16.1 4 19, 33 4.7, 8.2 9, 5 178, 329 613, 476 0.38, 1903, Eliot Cr., 0.3, 0.6, 64, 62, Eliot 1.9 1, 2, 3 0.75, 1817, 2.9 – 4.6 Hood 5.8 21 7.9 900 1371, 1262, 2, 2, 13, 1.6, 2.2, 2.7, 3.7, 21, 20, 1, 2, 3, 860, 58, 135, Sandy, 0.3 to 6.6, 14, 6.7, 9.2, 8, 4, 2, Sandy 4, 5, 6, 612, 625, Hood 2.5 21, 33, 14, 21, 0.4, 0.2, 7, 8 440, 1238, 83, 91 52, 57 0.2, 291, 19, 1254 6 2.2, 2.8, 3.0, 4.0, 1394, 1, 2, 3, 50, 43, 4.2, 4.4, 5.7, 6.1, 1355, 1, 1, 7, 3b, 4, 7, 7, 7, White, White 4.9, 5.5, 6.7, 7.5, 1305, 8, 9, 10, 0.5 5, 6, 7, 7, 6, 5, Hood River 6.3, 7.0, 8.6, 9.6, 1260, 83, 102, 8, 9, 0.6, 0.5, 21, 25, 28, 34, 900, 672 10 <0.1 74 102 864, 326 2089, 0.95, 2.1, 3.3, 1868, Salmon, 0.2 to 1, 2, 3, 41, 25, 0.5, 1, 7, Palmer 1.8, 8.6, 16, 22, 1120, Hood 0.3 4, 5 4, 1, 0.1 22, 266 12, 52 96 1048, 373 NSantiam, Sant_ various 2.0 78 55 <1 185 1686 Jefferson 8 698, 24, 54, 601, 113, 5.8, 9.2, LN 1, 2, 3, 449, 150, 16, 24, Santiam, - - 4, 5, 6, - - 347, 238, 33, 38, Non-Gl. 7 298, 260, 46 265, 287, 206, 185 1686 838, 1, 2, 3, 1.6, 3.2, 638, 2, 3, 7, Mult., - - 4, 4c, 4.0, 5.2, - - 519, 11, 12, Non-Gl. 5 7.2 417, 14 411, 22 19, 56, 8.12, 276, Tualatin, 1, 2, 3, 171, - - 13, 32, - - 181, 64, Non-Gl. 4, 5 1223, 70, 115 37, 35 1692
28 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
29 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.
30 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.
31 40 35 30 25 20 15 10
Temperature (°C) 5 0 14:00 14:30 15:00 15:30 16:00 16:30 White R., Mt. Hood, 8/3/2005
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
32 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
33 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:
34 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
35 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
36 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,
k 2 1 v = ( )R 3 S 2 , n 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.
37 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
38 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.
20 White R., Mt. Rainier LaGrangian 9/4/05 15 Water
10
Time (hours) 5
0 0246810 L* 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
39 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.
40 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.
41 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. Min. Max. Range Std. Temp Dev. Glacial All Glacial 2752 8.8 0.3 21.0 20.7 4.9 30 sample 0.3km/0.07 L* locations to 78 km/97 L* White (Rainier) 445 6.3 0.9 13.7 12.8 3.3 6 sample locations 0.3 km/0.07 L* to 36 km/8.8 L* Nisqually 385 8.1 4.2 16.4 12.2 3.4 4 sample locations 4.9 km/1.6 L* to 33 km/8.2 L* Eliot Cr. 288 2.3 0.3 11.0 10.7 2.9 3 sample locations 0.4 km/0.3 L* to 8 km/5.8 L* Sandy 385 13.7 7.4 21.0 13.5 4.5 4 sample locations 13 km/9 L* to 83 m/52L* White (Hood) 672 10.2 4.6 19.8 15.2 4.2 7 sample locations 2.9 km/4.0 L* to 25 km/34 L* Salmon 480 8.9 2.9 17.1 14.3 3.7 5 sample locations 1.0 km/ 2 L* to 52 km/97 L* N Santiam 97 samples 13.0 10.2 15.6 5.4 1.8 1 sample location 78 km/55 L* Non-Glacial All non-glacial 1552 samples 14.2 8.6 22.5 13.9 3.1 16 sample 1 to 70 km locations Multnomah Cr. 485 11.9 8.6 14.9 6.2 1.6 5 sample locations 1 to 6.6 km Gales Cr./Tualatin 388 samples 13.0 9.8 16.0 6.2 2.0 4 sample locations 7.8 to70 km Opal Cr./ LNS 679 samples 16.5 11.4 22.5 11.1 2.9 7 sample locations 5.6 to 46 km
L* performed better than other stream distance measures, but not as well as upstream fractional glacier cover, in results from correlation and regression
42 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). Fraction Upstream Distance L* Elevation glacierized 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
43 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. Fraction Upstream Distance L* glacierized Elevation basin area Logarithmic 0.77 0.84 0.82 0.60 0.44 (2.2) (1.8) (1.9) (2.9) (3.4) Quadratic 0.69 0.73 0.78 0.63 0.53 (2.5) (2.4) (2.2) (2.8) (3.2) Cubic 0.76 0.78 0.82 0.65 0.53 (2.3) (2.2) (2.0) (2.8) (3.2) Power (axb) 0.70 0.73 0.49 0.27 0.26 (0.5) (0.5) (0.5) (0.8) (0.8) Exponential 0.28 0.22 0.80 0.45 0.13 (aebx) (0.8) (0.8) (0.4) (0.7) (0.9) Linear 0.61 0.49 0.73 0.60 0.35 (2.8) (3.2) (2.3) (2.8) (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.
44 20 White_R 20 Nisq_R 15 Eliot_H 15 Sandy_H 10 10 White_H White_R Nisq_R 5 Salmon_H 5 Eliot_H Sandy_H White_H Salmon_H
Ave. Temperature (°C) 0 Ave. Temperature (°C) 0 0 20406080100 0 20406080100 Distance (km) L* 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*.
45 20 WhiteR_1 L*=0.07 (a) White R., Mt. Rainier 20 (b) Eliot Cr./Hood R., Mt. Hood WhiteR_3 L*=1.3 WhiteR_4 L*=2.6 EliotH_1 L*=0.3 15 WhiteR_5 L*=5.9 15 EliotH_2 L*=0.5 WhiteR_6 L*=8.8 EliotH_3 L*=5.8 10 10
5 5 Temperature (°C) Temperature (°C) 0 0 9/7/05 9/7/05 9/7/05 9/8/05 9/8/05 8/11/05 8/11/05 8/11/05 8/11/05 8/12/05 6:00 12:00 18:00 0:00 6:00 0:00 6:00 12:00 18:00 0:00
25 (c) Sandy R., Mt. Hood 25 Salmon_2 L*=3 (d) Salmon R. Salmon_3 L*=16 20 20 Salmon_4 L*=22 Salmon_5 L*=97 15 15 Sandy_4 L*=9 10 10 Sandy_5 L*=14 5 Sandy_6 L*=21 5 Temperature (°C) Temperature Temperature (°C) Temperature Sandy_7 L*=52 0 0 8/3/05 8/3/05 8/3/05 8/3/05 8/4/05 8/20/05 8/20/05 8/20/05 8/20/05 8/21/05 0:00 6:00 12:00 18:00 0:00 0:00 6:00 12:00 18:00 0:00
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.
46
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.
47 . White_R . White_R 12 12 Nisq_R Nisq_R 10 Eliot_H 10 Eliot_H Sandy_H 8 8 Sandy_H White_H White_H 6 Salmon_H 6 Salmon_H 4 4 2 2 Temperature range (°C)
0 Temperature (°C) range 0 0 102030405060 0 102030405060 L* Stream distance (km) . 12 White_R . 12 Nisq_R 10 Eliot_H 10 Sandy_H 8 White_H 8 6 Salmon_H 6 White_R Nisq_R 4 4 Eliot_H Sandy_H 2 2 White_H Salmon_H Temperature range (°C) 0 Temperature range (°C) 0 0.00.20.40.60.8 0 50 100 150 200 250 300 350 400 2 Fraction glacierized Upstream basin area (km ) 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.
20 20 (b) . (a) 15 15
10 10 8/21/05 Sampling Hobo 8/21/05 5 Hobo 8/4/05 5 Hobo 8/20/05 Temperature (°C) Temperature 8/3/05 Sampling Temperature (°C) 0 0 0 10203040 0 20406080100 L* L* 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.
48 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*.
15 (a) 20 (b)
15 10 10 5 LaGrangian 9/4/05 Hobo 9/7/05 5 LaGrangian 9/5/05 Temperature (°C) Temperature (°C) Hobo 9/6/05 0 0 0123456789 0246810 L* L* 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 49 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.
25 20 15
(°C) 10 5
Average Temperature 0 0 102030405060708090 Stream distance (km) 25 25 White_R Nisq_R Eliot_H Sandy_H 20 20 White_H Salmon_H 15 15 Tualatin Multnomah LN Santiam N Santiam (°C) (°C) 10 10 5 5 Average Temperature Average Temperature 0 0 0 102030405060 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 L* Fractional glacier cover 25 25 20 20 15 15 (°C) 10 (°C) 10 5 5 Average Temperature 0 Average Temperature 0 0 500 1000 1500 2000 2500 0 1000 2000 3000 2 Elevation (m) Upstream basin area (km )
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).
50 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
51 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. Fraction Upstream Distance L* glacierized 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
52 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. Fraction Upstream Distance L* glacierized basin area Turbidity averages – 25 sample locations (significant R2 values bold) Logarithmic 0.13 0.047 0.08 0.23 (245.1, 0.07) (257.8, 0.33) (252.8, 0.18) (231.1) Quadratic 0.18 0.05 0.07 0.19 (243.6, 0.11) (262.0, 0.55) (258.83, 0.43) (241.7, 0.09) Cubic 0.43 0.05 0.08 0.22 (249.0, 0.23) 268.1, 0.76) (263.57, 0.60) (242.7, 0.14) Power (axb) 0.28 0.23 0.35 0.32 (1.0) (1.0) (0.9) (1.0) Exponential 0.47 0.41 0.16 0.44 (aebx) (0.8) (0.9) (1.1) (0.9) Linear 0.15 0.05 0.07 0.14 (242.6, 0.06) (256.3, 0.27) (253.3, 0.19) (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. 53 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.
1200 . 1.0 White_R White_R 1000 Nisqually_R 0.8 Nisqually_R 800 Eliot_H Eliot_H Sandy_H 0.6 Sandy_H 600 White_H White_H 0.4 400 Salmon_H Salmon_H
200 (NTU) turbidity 0.2
0 maximum Normalized 0.0 Maximum turbidity (NTU) turbidity Maximum 0204060 0204060 Distance (km) Distance (km)
1200 . 1.0 White_R 1000 White_R 0.8 Nisqually_R 800 Eliot_H Nisqually_R 0.6 Eliot_H Sandy_H 600 White_H Sandy_H 0.4 400 White_H Salmon_H
200 Salmon_H turbidity (NTU) 0.2
0 maximum Normalized 0.0 Maximum turbidity (NTU) turbidity Maximum 0 5 10 15 20 25 30 0 50 100 150 L* L*
1200 . 1.0 1000 White_R Nisqually_R 0.8 Eliot_H Sandy_H 800 White_R White_H Salmon_H 0.6 600 Nisqually_R 0.4 Eliot_H 400 Sandy_H
200 turbidity (NTU) 0.2 White_H Salmon_H 0 maximum Normalized 0.0 Maximum turbidity (NTU) turbidity Maximum 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Fraction glacierized Fraction glacierized
White_R 1200 Nisqually_R . 1.0 Eliot_H 1000 0.8 White_R Sandy_H 800 Nisqually_R White_H 0.6 Eliot_H 600 Salmon_H 0.4 Sandy_H 400 White_H
200 turbidity (NTU) 0.2 Salmon_H
0 maximum Normalized 0.0 Maximum turbidity (NTU) turbidity Maximum 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350
Basin area (km2) Basin area (km2)
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
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.
55 250 White R., Mt. Rainier 50 9/4/2005
200 . 40
150 30
100 20 Turbidity (NTU) 50 Turbidity (NTU) 10 Nisqually R., Mt. Rainier 9/05/05 sampling 0 0 05100246810 L* L* 90 300 Sandy R., Mt. Hood White R., Mt. Hood 80 250 70 7/25/2005 12:20-20:45 8/3/2005 10:30-17:10 60 8/4/2005 10:30-18:15 200 8/5/2005 8:00-14:30 50 150 40 30 100 Turbidity (NTU) Turbidity (NTU) Turbidity 20 50 10 0 0 010203040 0 20406080 L* L* 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.
56 800 WhiteR_1 L*=0.1 700 WhiteR_4 L*=2.6 . 600 WhiteR_5 L*=5.9 500 400 300
Turbidity (NTU) 200 100 White R., Mt. Rainier, 9/7-8/2005 0 8:00 2:00 8:00 14:00 20:00 9/7/05 9/7/05 9/7/05 9/8/05 9/8/05
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 57 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.
White_R Nisq_R White_R Nisq_R 10000 Eliot_H Sandy_H 10000 Eliot_H Sandy_H White_H Salmon_H 1000 1000 White_H Salmon_H N Santiam Tualatin N Santiam Tualatin 100 Multnomah LN Santiam 100 Multnomah LN Santiam Filtered Agricultural Filtered Agricultural 10 10 Turbidity (NTU) 1 Turbidity (NTU) 1 0.1 0 20 40 60 80 100 120 140 0.1 0 20 40 60 80 100 120 140 Distance (km) L*
White_R Nisq_R 10000 10000 Eliot_H Sandy_H White_H Salmon_H 1000 1000 N Santiam Tualatin 100 100 Multnomah LN Santiam Filtered Agricultural 10 10
Turbidity (NTU) 1 Turbidity (NTU) 1 0 500 1000 1500 2000 0.10.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.1 Fraction glacierized Basin Area (km2)
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).
58 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). No. Std. Stream Name Samples Ave. Median Min. Max. Range Dev. All Glacial 202 0.89 0.11 0.001 24.89 24.89 2.57 White 76 0.24 0.17 0.015 1.07 1.05 0.24 (Rainier) 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
59 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.
7 0.5 White_R White_H Nisqually 6 Salmon_H 0.4 Eliot_H 5 Sandy_H 4 0.3 3 0.2 2 0.1 Average SSC (mg/L) 1 Average SSC (mg/L) 0 0.0 01020304050 0 5 10 15 20 25 L* L*
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. Fraction Upstream Distance L* glacierized 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
60 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. Fraction Upstream Distance L* glacierized basin area All glacial - 187 data values Logarithmic 0.02 <0.01 <0.01 0.14 (2.5, 0.06) (2.5, 0.55) (2.5, 0.34) (2.3) Quadratic 0.06 <0.01 <0.01 0.07 (2.5) (2.5, 0.65) (2.5, 0.41) (2.4) Cubic 0.06 <0.01 0.03 0.10 (2.5) (2.5, 0.59) (2.5, 0.13) (2.4) Power (axb) 0.18 0.05 0.11 0.31 (1.7) (1.8) (1.7) (1.5) Exponential 0.30 0.13 0.05 0.27 (aebx) (1.5) (1.7) (1.8) (1.6) Linear 0.04 <0.01 <0.01 0.03 (2.5) (2.5) (2.5, 0.84) (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).
61 1.2 White R., Mt. Rainier 1.0 WhiteR_1 0.8 WhiteR_4 WhiteR_5 0.6 0.4 SSC (mg/L) SSC 0.2 0.0 2:00 6:00 10:00 14:00 18:00 22:00 10:00 9/7/05 9/7/05 9/7/05 9/7/05 9/8/05 9/8/05 9/8/05 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.
25 White R., Mt. Rainier 1.2 White R., Mt. Rainier Nisqually R., Mt. Rainier 1.0 Nisqually R., Mt. Rainier 20 Sandy R., Mt. Hood Sandy R., Mt. Hood White R., Mt. Hood 0.8 15 0.6 10 0.4
5 Range SSC (mg/L) Range SSC (mg/L) 0.2
0 0.0 0102030 0 5 10 15 L* L* 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.
62 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 AM Range 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.
63 0.10 White R., Mt. Rainier 0.025
0.08 9/4/2005 12:00-16:50 0.020
0.06 0.015
0.04 0.010 SSC (mg/L) SSC (mg/L) 0.02 0.005 Nisqually R., Mt. Rainier 9/5/2005 11:15-13:50 0.00 0.000 0246810 0246810 L* L*
0.18 0.10 Sandy R., Mt. Rainier 0.16 0.09 0.08 7/25/2005 12:20-20:45 0.14 8/4/2005 10:30-18:15 0.07 0.12 0.06 0.10 0.05 0.08 0.04 Eliot Cr., Mt. Hood SSC (mg/L) SSC (mg/L) 0.06 0.03 0.04 8/10/2005 15:40-18:15 0.02 8/12/2005 11:50-14:00 0.02 0.01 0.00 0.00 0204060 0246810L* L*
1.8 0.8 White R., Mt. Hood White R., Mt. Hood 1.6 0.7 7/27/05 15:00-21:00 8/3/2005 10:30-14:30 1.4 8/5/2005 8:00-13:10 0.6 1.2 0.5 1.0 0.4 0.8 0.3 SSC (mg/L) 0.6 (mg/L) SSC 0.4 0.2 0.2 0.1 0.0 0.0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 L* L* 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.
64 100.0000 White_R Nisq_R 100.0000 White_R Nisq_R Eliot_H Sandy_H Eliot_H Sandy_H White_H Salmon_H White_H Salmon_H 10.0000 Mult_NG Tual_NG 10.0000 Mult_NG Tual_NG 1.0000 1.0000 0 25 50 75 100 0 50 100 150 0.1000 0.1000
0.0100 SSC (mg/L) SSC
SSC (mg/L) SSC 0.0100
0.0010 0.0010
0.0001 L* 0.0001 Stream distance (km)
100.0000 100.0000 White_R Nisq_R Eliot_H Sandy_H White_H Salmon_H 10.0000 10.0000 Mult_NG Tual_NG 1.0000 1.0000 0.00 0.02 0.04 0.06 0.08 0.10 0 500 1000 1500 0.1000 0.1000 SSC (mg/L) SSC
0.0100 White_R Nisq_R (mg/L) SSC 0.0100 Eliot_H Sandy_H 0.0010 White_H Salmon_H 0.0010 Mult_NG Tual_NG 0.0001 Fraction glacierized 0.0001 Basin Area (km2)
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.
65 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). No. Std. Stream Name Samples Average Median Min. Max. Range Dev. All Glacial, 150 47.5 39.0 3.9 148.7 144.9 27.2 but Sandy White 61 42.4 31.6 22.0 104.5 82.5 23.8 (Rainier) 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- 35 66.1 40.6 22.9 316.5 293.6 67.1 glacial 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
66 300 White_R 100 Nisqually_R 250 Eliot_H Sandy_H 80 200 White_H Salmon_H 60 150 White_R 40 100 Nisqually_R Eliot_H Median Specific 50 Median Specific 20 White_H Conductance (uS/cm) Conductance (uS/cm) Salmon_H 0 0 0 102030405060 0 10203040 L* L* 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. Fraction Upstream Distance L* glacierized 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
67 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. Fraction Upstream Distance L* glacierized basin area All glacial conductivity data, except Sandy River’s – 147 values Logarithmic 0.27 0.34 0.49 0.12 (22.8) (21.6) (19.0) (24.9) Quadratic 0.27 0.47 0.64 0.12 (22.7) (19.3) (15.9) (25.0) Cubic 0.31 0.58 0.65 0.13 (22.3) (17.3) (15.9) (25.0) Power (axb) 0.29 0.28 0.47 0.23 (0.5) (0.5) (0.4) (0.5) Exponential 0.25 0.13 0.43 0.14 (aebx) (0.5) (0.6) (0.5) (0.6) Linear 0.23 0.16 0.41 0.12 (23.3) (24.4) (20.5) (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) 68 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).
120 WhiteR_1 300 Sandy R., Mt. Hood 100 WhiteR_4 250 80 WhiteR_5 200 60 150 (uS/cm)
(uS/cm) 100 40 Sandy_2 50 Sandy_3 20 Specific Conductance Specific Specific Conductance White R., Mt. Rainier. 0 0 8/13/05 8/13/05 8/13/05 8/14/05 8/14/05 9/7/05 9/7/05 9/7/05 9/8/05 9/8/05 9:00 15:00 21:00 3:00 9:00 9:00 15:00 21:00 3:00 9:00
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.
69 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*.
120 300 Sandy R., Mt. Hood 100 250 7/25/2005, 12:20-20:45 8/4/2005, 10:30-18:15 80 200 White R., Mt. Hood 60 7/27/2005, 15:00-21:00 150 (µS/cm) (µS/cm) 40 8/3/2005, 10:30-17:10 100 8/5/2005, 8:00-14:30 Specific conductance Specific 20 conductance Specific 50 8/5/2005, 15:50 0 0 0 20406080100120 0204060 L* L* 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*.
70 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.
100 100
80 80 60 60 40
(µS/cm) White_R Nisq_R 40 Eliot_H White_H (µS/cm) 20 White_R Nisq_R Salmon_H Multnomah Eliot_H White_H Specific conductivitySpecific Tualatin 20
0 Specific conductivity Salmon_H Multnomah Tualatin 0 1020304050 0 0102030 Stream distance (km) L*
100 White_R Nisq_R Eliot_H White_H 100 White_R Salmon_H Multnomah Nisq_R 80 Tualatin 80 Eliot_H 60 60 White_H 40 40 (µS/cm) Salmon_H (µS/cm) 20 20 Multnomah Specific conductivitySpecific Specific conductivity Tualatin 0 0 0.0 0.2 0.4 0.6 0.8 0 50 100 150 200 2 Fraction glacierized Basin Area (km )
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.
71 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).
White_R Conductivity 8/17/5 rain 12 Nisqually_R 160 Conductivity 8/21/5 dry 14 . 10 Eliot_H 140 Chloride 8/21/5 12 Sandy_H . 120 8 White_H 10 100 8 6 Salmon_H 80 60 6 4 (µS/cm) 4
Conductivity 40
2 Chloride (mg/L) Chloride (mg/L) 20 2 0 0 0 0 20 40 60 80 100 120 0 20 40 60 80 100 L* L* 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
72 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.
120 White_R 10 Nisqually_R 9 100 8 80 Eliot_H 7 Sandy_H 6 60 White_H 5 40 4 Salmon_H 3 Sulfate (mg/L) Sulfate 20 (mg/L) Sulfate 2 0 1 0 0 102030405060 0 20406080100 L* 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
73 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.
74 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
75 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
76 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.
L* L* 0 5 10 15 20 25 30 0 102030405060 . 0 0 2 4 5 6 8 10 10 12 15 14 Average temperature (°C) Ave. Temperature (°C) 20 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
77 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 78 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.
79 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,
80 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
81 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
82 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.
83 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).
84 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
85 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.
86 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
87 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.
88 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
89 water quality values, and sediment availability may be estimated using aerial photography or satellite imagery.
90 References
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98 Appendix A. Assessment of Lagrangian sampling
Figures A1-A10. Calculated water speeds compared to Lagrangian sampling. 20 White R., Mt. Rainier 8 Nisqually R., Mt. Rainier LaGrangian 9/4/05 7 LaGrangian 9/5/05 15 6 Water Water 5 10 4 3 Time (hours)
5 (hours) Time 2 1 0 0 0246810 0510 L* L* 5 Eliot Cr. 24 Sandy R. LaGrangian 8/10/05 4 20 LaGrangian 7/25/05 LaGrangian 8/12/05 LaGrangian 8/4/05 3 Water 16 Water 2 12
Time (hours) 8 1 Time (hours) Time 4 0 024680 0 204060 L* L* 30 White R., Mt. Hood 30 White R., Mt. Hood 25 LaGrangian 7/27/05 25 LaGraqngian 8/3/05 Water LaGrangian 8/5/05 20 20 Water 15 15 10 10 Time (hours) Time (hours) Time 5 5 0 0 0 20 40 60 80 100 120 0 20406080100120 L* L* 24 Salmon R. 8 Multnomah Creek, Non-glacial 20 LaGrangian 8/17/05 7 LaGrangian 8/25/05 LaGrangian 8/21/05 6 Water 16 Water 5 12 4 8 3
Time (hours) 2 Time (hours) Time 4 1 0 0 0 20406080100 012345678 L* L* (median) 20 Gales Cr./Tualatin R., non-glacial 30 Opal Cr. to North Santiam River LaGrangian 9/14/05 25 LaGrangian 8/13/06 15 LaGrangian 9/18/05 Water Water 20 10 15 10
Time (hours) 5 Time (hours) Time 5 0 0 0 20 40 60 80 100 120 140 0204060 L* L*
99 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. River Date Mean Max. Min. Range (m) 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
White Sandy Eliot Salmon Mult. Cr.
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.
100 Nisq. White
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. Fraction Upstream Distance L* Elevation glacierized 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
101 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. Fraction Upstream Distance L* Elevation glacierized 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
102 Linear Exponential Power 25 y = 0.15x + 5.94 y = 4.45e0.02x y = 2.06x0.54 2 2 R2 = 0.61 20 R = 0.45 R = 0.25
15
10 Logarithmic
Temperature (°C) 5 y = 2.68Ln(x) + 2.91 R2 = 0.56 0 020406080100 Distance (km) 25 Linear Exponential Linear Logarithmic 25 y = 0.14x + 6.6 y = 4.94e0.02x y = -18.47x + 11.79 y = -2.03Ln(x) + 2.85 2 20 2 2 R = 0.37 R = 0.20 20 R2 = 0.53 Power R = 0.61 y = 2.54x-0.33 15 15 R2 = 0.44 Exponential 10 10 y = 13.17e-4.14x Power Logarithmic 2 5 R = 0.71 Temperature (°C) Temperature y = 2.74Ln(x) + 3.61 5 y = 2.44x0.54 (°C) Temperature 2 R2 = 0.65 R = 0.64 0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 20406080100 L* Fractional glacierization
25 Linear Power 25 Linear Logarithmic y = 0.007x + 7.52 y = 2.98x0.23 y = -0.006x + 14.90 y = -3.59Ln(x) + 32.69 2 2 2 2 20 R = 0.27 R = 0.26 20 R = 0.44 R = 0.45 Exponential 15 15 y = 20.84e-0.001x R2 = 0.41 10 10 Exponential Logarithmic Power Temperature (°C) Temperature 5 y = 5.68e0.0009x y = 1.37Ln(x) + 3.94 (°C) Temperature 5 y = 212.09x-0.52 R2 = 0.12 R2 = 0.35 R2 = 0.25 0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 2500 2 Basin Area (km ) Elevation (m) 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. Fraction Upstream Distance L* glacierized Elevation 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 0.27 (0.81) 0.19 (0.9) 0.71 (0.5) 0.40 (0.7) 0.15 (0.9) (aebx) Linear 0.50 (3.5) 0.36 (3.9) 0.52 (3.4) 0.43 (3.7) 0.37 (3.9)
103 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. Fraction Upstream Distance L* Elevation glacierized 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
104
20 20 White_R 15 Nisq_R 15 Eliot_H 10 10 Sandy_H White_R Nisq_R 5 White_H 5 Eliot_H Sandy_H Salmon_H White_H Salmon_H Ave. Temperature (°C) 0 Ave. Temperature (°C) 0 0 20406080100 0 20 40 60 80 100 Distance (km) L* 20 White_R 20 Nisq_R 15 Eliot_H 15 Sandy_H 10 10 White_H White_R Nisq_R 5 Salmon_H 5 Eliot_H Sandy_H White_H Salmon_H Ave. Temperature(°C) Ave. Temperature(°C) 0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 500 1000 1500 2 Fractional glacier cover Basin Area (km ) 20 White_R Nisq_R Eliot_H Sandy_H 15 White_H Salmon_H
10
5
Ave. Temperature(°C) 0 0 400 800 1200 1600 2000 2400 Elevation (m) Figures B8 – B12. Average temperatures from 24-hour logger data plotted against distance measures (distance, L*, fractional glacier cover, upstream basin area, and elevation).
25 Opal Cr./Little North Santiam R.
) 20
15
10 Sant_1 L*=7 Sant_2 L*=11
Temperature (°C Temperature Sant_3 L*=20 Sant_4 L*=28 5 Sant_5 L*=39 Sant_6 L*=44 Sant_7 L*=54 Sant_8 L*=55 0 8/18/06 8/18/06 8/18/06 8/18/06 8/19/06 0:00 6:00 12:00 18:00 0:00 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. 105 B14. White R., WhiteR_1 L*=0.07 30 Mt. Rainier WhiteR_3 L*=1.30 30 25 WhiteR_4 L*=2.64 25 WhiteR_5 L*=5.93 20 20 WhiteR_6 L*=8.78 15 15 Reg. Air Temp. 10 10 5 5 Temperature (° C) 0 0 9/7/05 9/7/05 9/8/05 9/8/05 9/8/05 12:00 18:00 0:00 6:00 12:00 30 B15. Nisqually R. Mt. Rainier 30 30 B16. 30 EliotH_1 L*=0.3 Eliot Cr., Mt. Hood 25 NisqR_1 L*=1.6 25 NisqR_2 L*=2.7 25 EliotH_2 L*=0.5 25 20 NisqR_3 L*=4.7 20 20 EliotH_3 L*=5.8 20 NisqR_4 L*=8.2 15 15 Air Temp Reg. Air Temp. 15 15 10 10 10 10 Temperature (°C) Temperature 5 5 (°C) Temperature 5 5 0 0 0 0 9/7/05 9/7/05 9/7/05 9/7/05 9/8/05 8/11/05 8/11/05 8/11/05 8/11/05 8/12/05 0:00 6:00 12:00 18:00 0:00 0:00 6:00 12:00 18:00 0:00 30 Sandy_4 L*=9 Sandy_5 L*=14 30 WhiteH_2 L*=4.0 WhiteH_3 L*=5.7 Sandy_6 L*=21 Sandy_7 L*=52 WhiteH_5 L*=7.5 WhiteH_6 L*=8.6 25 25 30 30 WhiteH_7 L*=9.6 WhiteH_8 L*=28 Air Temp 25 25 20 20 WhiteH_9 L*=34 Reg. Air Temp. 20 20 15 15 15 15 10 10 B17. 10 10 Temperature (°C) 5 Sandy R., Mt. Hood 5 B18.
Temperature (°C) 5 5 White R., Mt. Hood 0 0 0 0 8/3/05 8/3/05 8/3/05 8/3/05 8/4/05 8/4/05 8/4/05 8/4/05 8/4/05 8/5/05 0:00 6:00 12:00 18:00 0:00 0:00 6:00 12:00 18:00 0:00 Salmon_1 B19. B20. Multnomah Cr., non-glacial 35 35 Salmon_2 Salmon R., Mt. Hood 25 35 30 Salmon_3 30 30 Salmon_4 20 25 25 25 Salmon_5 15 20 Air Temp. 20 20 15 15 10 15 10 10 Mult_1 L*=1.2 Mult_2 L*=3.1 10 5 Mult_3 L*=4.0 Mult_4 L*=5.5 Temperature (°C) 5 5 5 Mult_5 L*=7.8 Air Temp. Temperature (°C) Air 0 0 (°C) Temperature Water 0 0 8/27/05 8/27/05 8/27/05 8/27/05 8/28/05 8/20/05 8/20/05 8/20/05 8/20/05 8/21/05 0:00 6:00 12:00 18:00 0:00 0:00 6:00 12:00 18:00 0:00 25 Tual_1 L*=9.2 30 30 B22. Opal Cr., L.N. Santiam R. 30 Tual_2 L*=15.6 Tual_3 L*=37.5 25 25 25 20 Tual_4 L*=82.3 Air Temp. 20 20 20 15 15 15 15 10(°C) 10 10 Sant_1 L*=7 Sant_2 L*=11 10 B21. Sant_3 L*=20 Sant_4 L*=28 5 Temperature (°C) Sant_5 L*=39 Sant_6 L*=44 Water Temperature Temperature Water 5 5 Gales Cr., Tualatin R. 5 (°C) Temperature Air Sant_7 L*=54 Sant_8 L*=55 Air Temp. 0 0 0 0 9/15/05 9/15/05 9/15/05 9/15/05 9/16/05 8/18/06 8/18/06 8/18/06 8/18/06 8/19/06 0:00 6:00 12:00 18:00 0:00 0:00 6:00 12:00 18:00 0:00 Figures B14-B22. Diurnal temperature variation from 24-hour data loggers. Hourly average air temperatures from a regional RAWS station are included.
106
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).
107 White_R . 12 Nisq_R 10 Eliot_H Sandy_H 8 White_H 6 Salmon_H 4 2
Temperature range (°C) range Temperature 0 0 102030405060 Stream distance (km)
White_R . White_R . 12 Nisq_R 12 Nisq_R 10 Eliot_H 10 Eliot_H Sandy_H Sandy_H 8 White_H 8 White_H Salmon_H 6 Salmon_H 6 4 4 2 2 0 Temperature range (°C) range Temperature
0 Temperature range (°C) 0 102030405060 0.0 0.2 0.4 0.6 0.8 L* Fraction glacierized .
. 12 12 10 10 8 8 White_R 6 Nisq_R 6 Eliot_H 4 4 Sandy_H 2 White_H 2 Salmon_H
Temperature range (°C) Temperature 0
0 Temperature range (°C) 0 50 100 150 200 250 300 350 400 0 600 1200 1800 2400 2 Upstream basin area (km ) Elevation (m) Figures B28-B32. Daily temperature range plotted against distance measures.
108 16 LaGrangian 9/4/05 18 Nisqually R. Hobo 9/7/05 16 14 11:15AM-1:50PM 12 14 10 12 10 8 8 6 6 4 Temperature (°C)
White R., Mt. Rainier, Temperature (°C) 4 LaGrangian 9/5/05 2 10:30AM-4:50PM 2 Hobo 9/6/05 0 0 0123456789 0246810 L* L* 14 20 Eliot Cr. 12 11:50 -14:00 10 15 8 6 10 Sandy R. 10:30 -16:55 4 LaGrangian 8/12/05 LaGrangian 8/4/05
Temperature (°C) 5 2 Temperature (°C) Hobo 8/12/05 Hobo 8/3/05 0 0 02468 0 5 10 15 20 25 L* L*
20 20 White R., Mt. Hood
. 8:00 -13:10 . 15 15
10 10 White R., Mt. Hood Hobo 8/4/5 10:30-20:15 5 5 Hobo8/5/5 Temperature (°C) Hobo 8/4/05 (°C) Temperature 8/3/05 Sampling 8/5/5 Sampling 0 0 010203040 0 10203040 L* L*
18 Salmon R. 25 Opal Cr./Little N. Santiam R./N. Santiam 16 14:40 -18:55 14 20 12 15 10 8 LaGrangian 8/21/05 10 6 Hobo 10AM start Hobo 8/21/05
Temperature (°C) 4 Temperature (°C) 5 Hobo 8 AM start 2 Hobo 8/20/05 Hobo 6 AM start 0 0 0 20406080100 0204060 L* Distance (km)
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.
109 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. Fraction Upstream Distance L* glacierized 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. Fraction Upstream Distance L* glacierized 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
110 Table C3. Regression coefficients for regression of all turbidity values on stream location variables. P-values are <0.05 unless given in parentheses. Fraction Upstream Distance L* glacierized 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) 0.98 111 Exponential Logarithmic 1200 Exponential Logarithmic 1200 y = 311.29e-0.055x y = -80.32Ln(x) + 457.52 y = 226.02e-0.045x y = -24.13Ln(x) + 338.24 2 2 1000 R2 = 0.35 R = 0.11 1000 R2 = 0.24 R = 0.01 Quadratic Quadratic 800 y = 0.16x2 - 18.43x + 487.04 800 y = -0.02x2 - 1.55x + 325.22 R2 = 0.16 2 600 Power 600 R = 0.02 Power -0.32 y = 349.87x-0.44 y = 234.83x 400 2 400 2 R = 0.23 R = 0.15 Turbidity (NTU) Turbidity (NTU) Turbidity 200 200 0 0 0 20406080100 0 20 40 60 80 100 120 Distance (km) L* 1200 Power Logarithmic 1200 Exponential Logarithmic y = 453.91x0.47 y = 43.338Ln(x) + 407.24 y = 225.27e-0.004x y = -106.56Ln(x) + 676.76 2 2 1000 R2 = 0.26 R = 0.03 1000 R2 = 0.32 R = 0.26 Quadratic Quadratic 800 2 800 y = -50.08x + 332.54x + 244.43 y = 0.001x2 - 1.69x + 427.99 2 600 R = 0.03 600 R2 = 0.16 Power 400 400 y = 623.89x-0.40 Exponential 2 Turbidity (NTU) Turbidity (NTU) Turbidity R = 0.26 200 y = 97.76e2.26x 200 R2 = 0.14 0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 200 400 600 800 1000 1200 1400 2 Fractional glacier cover Basin area (km )
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.
1.0 Power Logarithmic 1.0 Power Logarithmic y = 0.54x-0.33 y = -0.09Ln(x) + 0.62 y = 0.43x-0.29 y = -0.05Ln(x) + 0.51 2 2 2 2 0.8 R = 0.17 R = 0.17 0.8 R = 0.17 R = 0.06 Quadratic Quadratic y = 0.0001x2 - 0.02x + 0.63 2 0.6 0.6 y = 6E-05x - 0.01x + 0.52 R2 = 0.20 R2 = 0.08 Exponential Exponential 0.4 -0.05x y = 0.52e-0.05x 0.4 y = 0.43e 2 R2 = 0.29 R = 0.33 0.2 0.2 Normalized Turbidity Normailized Turbidity 0.0 0.0 0 20406080100 0 20 40 60 80 100 120 Distance (km) L* 1.0 Power Logarithmic 1.0 Power Logarithmic y = 0.79x0.44 y = 0.08Ln(x) + 0.62 y = 0.75x-0.27 y = -0.10Ln(x) + 0.78 Exponential 2 2 R2 = 0.29 R = 0.11 R2 = 0.16 R = 0.26 0.8 1.86x 0.8 y = 0.20e Quadratic 2 2 0.6 R = 0.13 0.6 y = 1E-06x - 0.002x + 0.56 R2 = 0.16 0.4 0.4 Exponential Quadratic y = 0.39e-0.003x 0.2 y = -0.77x2 + 0.99x + 0.31 0.2 R2 = 0.19 Normalized Turbidity R2 = 0.10 Normalized Turbidity 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 200 400 600 800 1000 1200 1400 2 Fractional glacier cover Basin area (km ) 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. 112 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. Fraction Upstream Distance L* glacierized basin area 211 turbidity values at 36 sample locations Logarithmic 0.11 (331.5) 0.01 0.03 (345.9) 0.26 (302.9) (349.1, 0.14) Quadratic 0.15 (323.6) 0.01 0.03 (345.8) 0.17 (320.4) (349.3, 0.23) Cubic 0.16 (323.8) 0.01 0.05 (343.3) 0.20 (314.5) (350.1, 0.40) Power (axb) 0.22 (1.1) 0.14 (1.2) 0.23 (1.1) 0.24 (1.1) Exponential 0.31 (1.0) 0.20 (1.1) 0.14 (1.2) 0.25 (1.1) (aebx) Linear 0.12 (329.7) 0.01 0.03 (345.0) 0.09 (334.3) (348.6, 0.10)
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. Fraction Upstream Distance L* glacierized basin area Average normalized turbidity – 25 sample locations Logarithmic 0.35 0.30 0.40 0.32 (0.21) (0.22) (0.21) (0.22) Quadratic 0.34 0.30 0.39 0.23 (0.22) (0.23) (0.21) (0.24, 0.06) Cubic 0.37 0.34 0.39 0.36 (0.22) (0.23) (0.22) (0.22) Power 0.32 0.39 0.58 0.28 (0.99) (0.94) (0.78) (1.02) Exponential 0.58 0.67 0.22 0.48 (0.78) (0.68) (1.06) (0.87) Linear 0.30 0.22 0.34 0.21 (0.22) (0.24) (0.22) (0.24)
113 White_R 1200 Nisqually_R 1200 1000 Eliot_H 1000 Sandy_H 800 800 White_H White_R Nisqually_R Salmon_H 600 600 Eliot_H Sandy_H 400 400 White_H Salmon_H 200 200 Average turbidity(NTU) Average turbidity (NTU) 0 0 0204060 0 5 10 15 20 25 30 Distance (km) L* White_R 1200 1200 Nisqually_R 1000 1000 Eliot_H Sandy_H 800 White_R Nisqually_R 800 Eliot_H Sandy_H White_H 600 White_H Salmon_H 600 Salmon_H 400 400 200 200 Average turbidity (NTU) Average turbidity (NTU) 0 0 0.00.10.20.30.40.50.60.70.8 0 50 100 150 200 250 300 350 2 Fraction glacierized Basin area (km ) Figures C9 – C12. Average turbidity per location is graphed against the four distance measures.
1.0 1.0 White_R White_R 0.8 Nisqually_R 0.8 Nisqually_R Eliot_H Eliot_H 0.6 Sandy_H 0.6 Sandy_H White_H White_H 0.4 0.4 Salmon_H Salmon_H
turbidity (NTU) 0.2 turbidity (NTU) 0.2 Normalized average Normalized average 0.0 0.0 0 5 10 15 20 25 30 0204060 L* Distance (km)
1.0 1.0 White_R 0.8 0.8 Nisqually_R White_R Eliot_H 0.6 0.6 Nisqually_R Sandy_H White_H 0.4 Eliot_H 0.4 Sandy_H Salmon_H turbidity (NTU) turbidity (NTU) 0.2 White_H 0.2 Normalized average Normalized average Salmon_H 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 250 300 350
Fraction glacierized Basin area (km2)
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.
114 250 White R., Mt. Rainier 50 200 9/4/2005 40
150 30
100 20
Turbidity (NTU) Nisqually R., Mt. Rainier 50 Turbidity (NTU) 10 9/5/2005 0 0 05100510 L* L*
450 90 Eliot Creek, Mt. Hood Sandy R., Mt. Hood 400 80 7/25/2005 12:20-20:45 350 70 300 60 8/4/2005 10:30-18:15 250 50 200 40 150 8/10/05 15:40-18:15 30 Turbidity (NTU) Turbidity 100 8/12/2005 11:50-14:00 (NTU) Turbidity 20 50 10 0 0 02468 020406080 L* L*
1200 White R., Mt. Hood 300 White R., Mt. Hood 250 1000 7/27/2005 15:00-21:00 8/3/2005 10:30-17:10 800 8/3/2005 10:30-17:10 200 8/5/2005 8:00-14:30 8/5/2005 8:00-14:30 600 8/5/2005 15:50 150 400 100 Turbidity (NTU) Turbidity (NTU) 200 50 0 0 0 50 100 150 0 10203040 L* L*
1200 Salmon R., Mt. Hood 12 Gales Cr./Tualatin R. 1000 8/21/2005 14:40-18:55 10 9/14/05 2:00 PM - 7:30 PM 800 8 9/18/05 10:25 AM - 3:30 PM 600 6 400 4 Turbidity (NTU). Turbidity (NTU) 200 2 0 0 020406080100 020406080100120 L* Distance (km) 3.0 Multnomah Cr., non-glacial 1.4 2.5 8/25/2005 11:20-18:20 1.2 2.0 1.0 0.8 1.5 0.6 1.0 0.4 Turbidity (NTU) Turbidity
Turbidity (NTU) Turbidity Opal Cr., Little N. and N. Santiam R. 0.5 0.2 8/13/2005 12:35-18:40 0.0 0.0 02468 0 20406080100 Distance (km) Distance (km) Figures C17 – C26. Turbidity values from Lagrangian sampling.
115 1200 800 WhiteR_1 L*=0.1 700 WhiteR_4 L*=2.6 1000 WhiteH_2 L*=3.97 600 WhiteR_5 L*=5.9 800 WhiteH_3 L*=5.74 500 400 600 WhiteH_6 L*=8.62 WhiteH_8 L*=28.26 300 400 Turbidity (NTU) 200 Turbidity (NTU) 100 200 White R., Mt. Rainier, 9/7-8/2005 White R., Mt. Hood, 8/20/2005 0 0 8/20/05 8/20/05 8/20/05 8/20/05 8/21/05 8:00 2:00 8:00 14:00 20:00 9/7/05 9/7/05 9/7/05 9/8/05 9/8/05 0:00 6:00 12:00 18:00 0:00
300 Sandy_2 L*=3.7 Sandy R., 8/13/2005 1.4 Mult_1 L* med=1.2 Sandy_3 L*=6.7 Multnomah Cr. 250 1.2 Mult_2 L*med=3.1 Sandy_4 L*=9.2 200 .. 1.0 Mult_4a L*med=5.5 Sandy_5 L*=14 Mult_4c L*med=5.6 150 0.8 0.6 100 0.4 Turbidity (NTU) Turbidity (NTU) Turbidity 50 0.2 0 0.0 7:00 9:00 0:00 6:00 0:00 11:00 13:00 15:00 17:00 19:00 12:00 18:00 1/0/00 1/0/00 1/0/00 1/0/00 1/1/00 8/28/05 8/28/05 8/28/05 8/28/05 8/28/05 8/28/05 8/28/05
Figures C27– C30. Synoptic turbidity results.
White_R Nisq_R 10000 10000 White_R Nisq_R Eliot_H Sandy_H Eliot_H Sandy_H . 1000 White_H Salmon_H . 1000 White_H Salmon_H N Santiam Tualatin N Santiam Tualatin 100 Multnomah LN Santiam 100 Multnomah LN Santiam Filtered Agricultural Filtered Agricultural 10 10 1 1 Turbidity (NTU) Turbidity (NTU) 0.1 0 20 40 60 80 100 120 140 0.1 0 20 40 60 80 100 120 140 Distance (km) L*
White_R Nisq_R 10000 10000 Eliot_H Sandy_H . . White_H Salmon_H 1000 1000 N Santiam Tualatin 100 Multnomah LN Santiam 100 Filtered Agricultural 10 10 1
1 Turbidity (NTU) Turbidity (NTU) 0 500 1000 1500 2000 0.10.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.1 Fraction glacierized Basin Area (km2)
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). 116 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. Fraction Upstream Distance L* glacierized 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
117 Table D2. Regression coefficients, R2, for SSC on distance measures on rivers. P- values given in parentheses when >0.05. Fraction Upstream basin Distance L* glacierized 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)
118 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. Fraction Upstream Distance L* glacierized basin area All glacial - 187 datavalues Logarithmic 0.02 <0.01 0.01 0.14 (1.8, 0.43) (7.8, 0.90) (1.8, 0.57) (1.7,0.06) Quadradic 0.07 0.01 0.02 0.10 (1.7, 0.38) (1.8, 0.86) (1.8, 0.81) (1.7, 0.29) Cubic 0.08 0.03 0.03 0.13 (1.8, 0.60) (1.8, 0.87) (1.8, 0.87) (1.7, 0.36) Power (axb) 0.17 0.07 0.17 0.30 (1.7) (1.8, 0.18) (1.7) (1.5) Exponential 0.37 0.25 0.05 0.35 (aebx) (1.5) (1.6) (1.8, 0.26) (1.5) Linear 0.05 0.01 <0.01 0.05 (1.7, 0.24) (1.8, 0.58) (1.8, 0.86) (1.7, 0.25)
119 7 White_H 0.45 White_R 6 Salmon_H 0.40 Nisqually 0.35 5 Eliot_H 0.30 Sandy_H 4 0.25 3 0.20 2 0.15 0.10
Average SSC (mg/L) 1 Average SSC (mg/L) 0.05 0 0.00 0 102030405060 0 5 10 15 20 25 30 35 40 Distance (km) Distance (km) 7 White_H 0.5 White_R 6 Salmon_H 0.4 Nisqually 0.4 Eliot_H 5 0.3 Sandy_H 4 0.3 3 0.2 2 0.2 0.1 Average SSC (mg/L) 1 Average SSC (mg/L) 0.1 0 0.0 0 1020304050 0 5 10 15 20 25 L* L*
7 0.5 White_R 6 0.4 Nisqually 0.4 5 White_H Eliot_H 0.3 4 Salmon_H Sandy_H 0.3 3 0.2 2 0.2 0.1 Average SSC (mg/L) Average SSC
1 Average SSC(mg/L) 0.1 0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0 5 10 15 20 25 Fraction glacierized L*
0.5 7 White_H White_R Salmon_H 0.4 6 Nisqually 0.4 5 Eliot_H 0.3 4 0.3 Sandy_H 3 0.2 0.2 2 0.1 Average SSC (mg/L) Average SSC (mg/L) 1 0.1 0 0.0 0 50 100 150 0 100 200 300 400 500 600 700 2 2 Basin Area (km ) Basin Area (km )
Figures D1-D8. Average suspended sediment concentration against distance variables.
120 1.2 White R., Mt. Rainier 0.16 Nisqually R., Mt. Rainier 0.14 Nisq_1 1.0 WhiteR_1 0.12 Nisq_2 WhiteR_2 Nisq_3 0.8 0.1 WhiteR_4 Nisq_4 0.6 0.08 WhiteR_5 0.06
0.4 SSC (mg/L) SSC (mg/L) 0.04 0.2 0.02 0 0.0 8:00 14:00 20:00 2:00 8:00 9/9/05 9/9/05 9/9/05 4:00 10:00 16:00 22:00 10:00 9/7/05 9/7/05 9/7/05 9/8/05 9/8/05 9/10/05 9/10/05 0.4 25 Sandy R., Mt. Hood White R., Mt. Hood 0.35 Sandy_2 20 WhiteH_2 0.3 Sandy_3 WhiteH_3 0.25 Sandy_4 15 WhiteH_4 0.2 Sandy_5 WhiteH_8 0.15 10 SSC (mg/L) SSC (mg/L) 0.1 5 0.05 0 0 0:00 6:00 0:00 0:00 6:00 0:00 12:00 18:00 12:00 18:00 8/20/05 8/20/05 8/13/05 8/20/05 8/20/05 8/21/05 8/13/05 8/13/05 8/13/05 8/14/05 0.0035 Multnomah Cr., non-glacial 0.003 Mult_1 0.0025 Mult_2 0.002 Mult_4 Mult_5 0.0015
SSC (mg/L) SSC 0.001 0.0005 0 0:00 6:00 0:00 12:00 18:00 8/28/05 8/28/05 8/28/05 8/28/05 8/29/05
Figures D9-D13. Variation of SSC over time through synoptic sampling.
121 0.10 White R., Mt. Rainier 0.025 0.08 9/4/2005 12:00-16:50 0.020 0.06 0.015 0.04 0.010 Nisqually R., Mt. Rainier SSC (mg/L) 0.02 (mg/L) SSC 0.005 9/5/2005 11:15-13:50 0.00 0.000 0246810 0246810 L* L* 0.20 Eliot Cr., Mt. Hood 0.10 7/25/2005 12:20-20:45 8/4/2005 10:30-18:15 0.15 0.08 0.06 Sandy R., Mt. Hood 0.10 0.04
SSC (mg/L) SSC 8/10/2005 15:40-18:15 0.05 SSC (mg/L) 8/12/2005 11:50-14:00 0.02 0.00 0.00 0123456 0204060 L* L*
0.8 2.0 White R., Mt. Hood White R., Mt. Hood 0.7 7/27/05 15:00-21:00 0.6 8/3/2005 10:30-14:30 1.5 8/5/2005 8:00-13:10 0.5 1.0 0.4 0.3
SSC (mg/L) SSC 0.2 SSC (mg/L) 0.5 0.1 0.0 0.0 0 20406080100120 0 20 40 60 80 100 120 L* L* 3.0 Salmon R., Mt. Hood 0.0020 Multnomah Cr., non-glacial 2.5 8/25/2005 11:20-18:20 8/21/2005 14:40-18:55 2.0 0.0015 1.5 0.0010 1.0 SSC (mg/L) SSC SSC (mg/L) 0.0005 0.5 0.0 0.0000 0 20406080100 02468 L* Distance (km) 0.005 Tualatin R., non-glacial 0.004 9/14/2005 14:00-19:30 9/18/2005 10:25-15:30 0.003 0.002
SSC (mg/L) 0.001 0.000 0 20 40 60 80 100 120 Distance (km)
Figures D14-D22. Graphs of Lagrangian sampled SSC. 122 Appendix E. Electrical conductivity
Table E1. Correlation coefficients from non-parametric tests, with p-values in parentheses if >0.05. Fraction Upstream Distance L* glacierized 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)
123 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. Fraction Upstream Distance L* glacierized basin area All glacial, except Sandy River – 28 sample location medians Logarithmic 0.42 0.43 0.54 0.12 (21.6) (21.4) (19.1) (0.8) Quadratic 0.30 0.45 0.76 0.14 (24.1) (21.5) (14.1) (26.7, 0.14) Cubic 0.49 0.58 0.76 0.24 (21.0) (19.1) (14.3) (25.7, 0.08) Power (axb) 0.41 0.28 0.42 0.44 (0.6) (0.7) (0.6) (1.5) Exponential 0.20 0.13 0.50 0.35 (aebx) (0.8) (0.8, 0.06) (0.6) (0.6) Linear 0.24 0.19 0.60 0.12 (24.7) (25.5) (17.8) (0.8, 0.07)
124 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.) Fraction Upstream basin Distance L* glacierized 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)
125 120 WhiteR_1 70 Nisqually R., Mt. Rainier 100 WhiteR_4 60 50 80 WhiteR_5 40 Nisqually_1 60 30 Nisqually_2 (uS/cm) (uS/cm) 40 20 Nisqually_3 20 10 Nisqually_4
Specific Conductance White R., Mt. Rainier. Specific Conductance Specific 0 0 9/7/05 9/7/05 9/7/05 9/8/05 9/8/05 9/9/05 9/9/05 9/9/05 9/10/05 9/10/05 9:00 15:00 21:00 3:00 9:00 9:00 15:00 21:00 3:00 9:00 300 Sandy R., Mt. Hood 50 Multnomah Creek 250 40 200 30 Mult_1 150 Sandy_2 20 Mult_4 (uS/cm)
100 (uS/cm) Sandy_3 Mult_4c 50 10 Specific Conductivity Specific Conductance Specific 0 0 8/13/05 8/13/05 8/13/05 8/14/05 8/14/05 8/28/05 8/28/05 8/28/05 8/29/05 8/29/05 9:00 15:00 21:00 3:00 9:00 9:00 15:00 21:00 3:00 9:00
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.
126 60 White R., Mt. Rainier 60 White R., Mt. Rainier
. 9/4/05 11:30-16:50 50 9/4/05 11:30-16:50 50
40 40
30 30 (µS/cm) (µS/cm) 20 20 Specific conductance Specific conductance conductance Specific 10 10
0 0 0 10203040 0123456789 Distance (km) L*
60 60
50 50
40 40
30 30 (µS/cm) (µS/cm) 20 20 Nisqually R., Mt. Rainier Nisqually R., Mt. Rainier Specific conductance Specific conductance 10 9/5/05 11:15-13:50 10 9/5/05 11:15-13:50 0 0 0 5 10 15 20 25 30 35 0123456789 Distance (km) L*
30 30
25 25
20 20
15 15 (µS/cm) (µS/cm) 10 Eliot Cr., Mt. Hood 10 Eliot Cr., Mt. Hood 8/10/2005, 15:40-18:15 8/10/2005, 15:40-18:15 Specific conductance 5 conductance Specific 5 8/12/2005, 11:50-14:00 8/12/2005, 11:50-14:00 0 0 0246810 02468 Distance (km) L* 300 Sandy R., Mt. Hood 300 Sandy R., Mt. Hood 250 7/25/2005, 12:20-20:45 250 7/25/2005, 12:20-20:45 8/4/2005, 10:30-18:15 8/4/2005, 10:30-18:15 200 200
150 150 (µS/cm) (µS/cm) 100 100 Specific conductance Specific 50 conductance Specific 50
0 0 0 20406080100 0204060 Distance (km) L* Figures E5-E8. Lagrangian samplilng results plotted against L* and distance (km).
127 120 120
100 100
80 80 White R., Mt. Hood 60 White R., Mt. Hood 60 7/27/2005, 15:00-21:00 (µS/cm) (µS/cm) 40 7/27/2005, 15:00-21:00 40 8/3/2005, 10:30-17:10 8/3/2005, 10:30-17:10 8/5/2005, 8:00-14:30 8/5/2005, 8:00-14:30 Specific conductance Specific Specific conductanceSpecific 20 20 8/5/2005, 15:50 8/5/2005, 15:50 0 0 0 20406080 0 20406080100120 Distance (km) L*
160 Salmon R., Mt. Hood 160 Salmon R., Mt. Hood 140 140 120 120 100 100 80 80 (µS/cm) (µS/cm) 60 60 40 40 8/17/2005, 12:47-19:50 (rain)
Specific conductanceSpecific 8/17/2005, 12:47-19:50 (raining) 20 20 Specific conductance 8/21/2005, 14:40-18:55 (dry) 8/21/2005, 14:40-18:55 (dry) 0 0 0 102030405060 0 20406080100 Distance (km) L* 50 Multnomah Cr., non-glacial 350 Gales Cr./Tualatin R., non-glacial 300 40 9/14/2005, 14:00-19:30 250 9/18/2005, 10:25-15:30 30 200 20 150 (µS/cm) (µS/cm) 100 10 8/25/2005, 11:20-18:20 50 Specific conductance Specific conductance conductance Specific 0 0 012345678 0 20406080100120 Distance (km) Distance (km) Figures E9-E13. Lagrangian sampling results plotted against L* and distance (km).
128 Appendix F: Anions
White_R Nisqually_R White_R Nisqually_R 14 Eliot_H Sandy_H 12 Eliot_H Sandy_H 12 White_H Salmon_H . . Mult_ng Tual_ng 10 White_H Salmon_H 10 8 Mult_ng Tual_ng 8 6 6 4 4 Chloride (mg/L) Chloride 2 Chloride (mg/L) 2 0 0 0 20406080100 0 20 40 60 80 100 120 140 Distance (km) L* White_R White_R Nisqually_R 14 Nisqually_R 14 Eliot_H Sandy_H 12 Eliot_H 12 White_H Salmon_H Mult_ng Tual_ng 10 Sandy_H 10 8 White_H 8 6 Salmon_H 6 Mult_ng 4 Tual_ng 4
Chloride (mg/L) Chloride 2 (mg/L) Chloride 2 0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 100 200 300 400 2 Fraction glacierized Basin Area (km ) Figure F1-F4. Chloride concentrations compared with distance measures.
120 White_R Nisqually_R 120 White_R 100 Eliot_H Sandy_H 100 Nisqually_R White_H Salmon_H Eliot_H 80 Mult_ng Tual_ng 80 Sandy_H 60 60 White_H 40 40 Salmon_H Sulfate (mg/L) Sulfate 20 (mg/L) Sulfate 20 0 0 0 20406080100 0 102030405060 Distance (km) L* 120 White_R 120 White_R Nisqually_R Nisqually_R 100 100 Eliot_H Sandy_H Eliot_H White_H Salmon_H 80 Sandy_H 80 Mult_ng Tual_ng 60 White_H 60 Salmon_H 40 Mult_ng 40 Sulfate (mg/L) Sulfate Sulfate (mg/L) Sulfate 20 Tual_ng 20 0 0 0.00.10.20.30.40.50.60.70.8 0 400 800 1200 1600 2000 Fraction glacierized Basin Area (km2)
Figures F5-F8. Sulfate concentrations compared with distance measures.
129 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. Distance Fraction Location L* NO NH SRP SO (km) glacierized 3 4 4 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
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