USING THERMAL TRACERS TO DETERMINE FALL-RUN CHINOOK
SPAWNING SITE SELECTION PREFERENCES ON THE LOWER AMERICAN
RIVER, CALIFORNIA, USA
A Thesis
Presented to the faculty of the Department of Geology
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
Geology
by
Michael Edward O’Connor
SPRING
2014
© 2014
Michael Edward O’Connor
ALL RIGHTS RESERVED
ii
USING THERMAL TRACERS TO DETERMINE FALL-RUN CHINOOK
SPAWNING SITE SELECTION PREFERENCES ON THE LOWER AMERICAN
RIVER, CALIFORNIA, USA
A Thesis
by
Michael Edward O’Connor
Approved by:
__________________________________, Committee Chair
Tim Horner
__________________________________, Second Reader
Kevin Cornwell
__________________________________, Third Reader
Celia Zamora
____________________________
Date
iii
Student: Michael Edward O’Connor
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Department Chair ___________________
Tim Horner
Date
Department of Geology
iv
Abstract
of
USING THERMAL TRACERS TO DETERMINE FALL-RUN CHINOOK
SPAWNING SITE SELECTION PREFERENCES ON THE LOWER AMERICAN
RIVER, CALIFORNIA, USA
by
Michael Edward O’Connor
Thermal tracers were used to characterize two adjacent salmonid spawning habitat
sites on the Lower American River: a natural spawning feature heavily used by fall-run
Chinook salmon and a less utilized site that was enhanced with spawning gravels. A
network of monitoring wells were installed at the sites to monitor stream and subsurface
water temperatures coupled with pressure to determine subsurface flow characteristics.
Data was qualitatively analyzed to investigate differences in subsurface flow paths using
temperature gradients. Additionally, hydraulic conductivity and seepage discharge
values were estimated at the monitoring wells using 1DTempPro, a recently developed
graphical user interface for VS2DH that facilitates the one-dimensional energy transport
model.
Qualitative and quantitative results show clear differences between the two study
sites. The natural spawning site showed more temperature variation with depth from
surface water temperature while the enhanced site’s subsurface temperatures closely
followed stream temperature variations. Furthermore, estimated hydraulic conductivity
v
and specific discharge values at the natural site were one to two orders of magnitude
lower than those estimated at the enhanced spawning site. Specific discharge values also
showed a mix of upwelling and downwelling conditions at the natural spawning site
while downwelling dominated the enhanced spawning site.
Qualitative and quantitative results suggest spawning fall-run Chinook salmon in
the Lower American River prefer spawning features that have a mix of downwelling and
upwelling flow conditions with relatively lower hydraulic conductivity values which
allow adequate mixing of groundwater and surface water in the subsurface. These
conditions create a temperature signal in the shallow subsurface that is distinctly different
than the surface water temperature signal.
This study also shows the utility of employing heat as a tracer to characterize
spawning features in streams. Spawning habitat enhancement projects are likely to
increase in the future in response to salmonid population vulnerability in rivers.
Therefore, qualitative and quantitative evaluation of habitat quality before and after
project completion is crucial for improving this restoration technique. Thermal tracers
provide a relatively simple, low-cost, low-maintenance method for determining key
habitat characteristics over long time scales and at potentially high spatial resolutions.
_______________________, Committee Chair
Tim Horner
_______________________
Date
vi
ACKNOWLEDGEMENTS
I would like to thank Dr. Tim Horner for his guidance and support on this manuscript
and for helping build my interest in the wonderful world of groundwater-surface water
interactions. I would also like to thank Celia Zamora for providing additional support
with field instrumentation and many useful comments on using heat as a tracer and
modeling. You greatly improved this manuscript. Thanks! Dr. Kevin Cornwell also
provided important feedback on this thesis project as a second faculty reader. Installation
and maintenance of the streambed monitoring network would have been impossible
without the support of burly graduate and undergraduate students. I would like to thank
Joe Rosenberry, Lewis Lummen, Jay Heffernan, Katy Janes, Jessica Bean, and anyone
else I failed to mention for flexing their muscles.
I would also like to acknowledge funding from the Sacramento Water Forum and
U.S. Bureau of Reclamation that support salmonid restoration assessments on the Lower
American River. Without their support, this project and other studies would not be
possible.
Finally, I would like to thank my partner, Elise Fitzgerald, and my family for their
endless love and support through this epic education journey. I can’t imagine where I
would be without them. You guys rock!
vii
TABLE OF CONTENTS
Page
Acknowledgements ..................................................................................................... vii
List of Tables ................................................................................................................ x
List of Figures ............................................................................................................. xi
Chapter
1. INTRODUCTION .................................................................................................. 1
Purpose.............................................................................................................. 1
Background ....................................................................................................... 2
The decline of Central Valley salmon and steelhead ............................ 2
Chinook salmon and steelhead in the American River ......................... 3
Salmonid habitat restoration ................................................................. 5
Physical Setting ................................................................................................. 8
Lower American River ......................................................................... 8
Study Area: Upper Sunrise restoration area........................................ 10
2. METHODS ........................................................................................................... 18
Monitoring Well Installation and Instrumentation ......................................... 18
Heat as a Tracer .............................................................................................. 21
Salmonid Redd Density .................................................................................. 25
1D Energy Transport Modeling ...................................................................... 26
Modeling framework .......................................................................... 27
viii
Model calibration ................................................................................ 28
Sensitivity analysis.............................................................................. 29
Model limitations ................................................................................ 32
3. RESULTS AND DISCUSSION ........................................................................... 33
Qualitative Analysis of Temperature .............................................................. 33
Temperature response to flow ............................................................. 34
Seasonal temperature trends ............................................................... 35
Spawning season temperature trends (October 15, 2013 –
January 1, 2014) .................................................................................. 38
Diurnal temperature trends at the Gravel Spit .................................... 42
Diurnal temperature trends at Upper Sunrise...................................... 47
Quantitative Analysis: 1DTempPro Model Results ........................................ 51
Model results and discussion: hydraulic conductivity ........................ 51
Model results and discussion: specific discharge ............................... 57
Salmonid Spawning Habitat Preferences on the LAR .................................... 58
4. CONCLUSION ..................................................................................................... 63
References ................................................................................................................... 65
ix
LIST OF TABLES
Tables
Page
1. Monthly total accumulated precipitation averages at Folsom Dam......................... 11
2. AFRP flow objectives (cfs) for the Lower American River based on water year type
...................................................................................................................................... 13
3. Redd count data at the study area for fall-run Chinook spawning seasons 2010 through
2013.............................................................................................................................. 17
4. Analysis of model calibration at varying timescales at the Gravel Spit monitoring well
GS-1 ............................................................................................................................. 32
5. One-dimensional model results for monitoring wells over a one-week period from
October 15 through October 21, 2013 ......................................................................... 52
6. Comparison of modeled and measured hydraulic conductivity (K) ........................ 57
7. Specific discharge (q) results estimated in 1DTempPro .......................................... 58
x
LIST OF FIGURES
Figures
Page
1. American River watershed map ................................................................................. 4
2. Lower American River location map ......................................................................... 9
3. Comparison of flow in the American River before and after construction of Folsom
and Nimbus Dams ........................................................................................................ 12
4. Hydrograph for the LAR at USGS Gage 11446500 ................................................ 14
5. Daily mean surface water temperature for the LAR at USGS Gage 11446500 from WY
2011 into the beginning of WY 2014 .......................................................................... 15
6. Study area location map ........................................................................................... 16
7. Fall-run Chinook salmon redd locations at the study area for spawning year 2013
...................................................................................................................................... 17
8. Monitoring well schematic with instrumentation and photos of well installation ... 19
9. Typical streamflow and temperature profiles for a) losing (downwelling) and b)
gaining (upwelling) stream reaches ............................................................................. 23
10. Vertical temperature profiles for a) losing, b) gaining, and c) neutral stream reaches
...................................................................................................................................... 24
11. Model design in 1DTempPro ................................................................................. 28
12. Model sensitivity analysis for 1DTempPro at monitoring well GS-1 (0.9 m below the
streambed) with varying hydraulic conductivity (Kz).................................................. 30
xi
13. Daily mean surface water temperature (blue line), moving average air temperature
(orange line), and daily mean flow (black line) measured on the LAR ....................... 35
14. Seasonal changes in streambed temperature at monitoring well GS-4 .................. 37
15. Zoom-in of subsurface water temperatures at monitoring well US-1.................... 38
16. Box-plot of surface and shallow subsurface (0.3 m depth) water temperature data at
the Gravel Spit and Upper Sunrise study sites during the spawning season (October 15,
2013 – January 1, 2014) ............................................................................................... 39
17. Vertical temperature gradients showing the 5th and 95th temperature percentiles
through the streambed measured at the (a) Gravel Spit and (b) Upper Sunrise sites .. 41
18. Close-up of the Gravel Spit site ............................................................................. 43
19. Daily thermographs for monitoring wells at the Gravel Spit on October 15, 2013
...................................................................................................................................... 44
20. Daily thermographs for various monitoring depths below the streambed at the Gravel
Spit on October 15, 2013 ............................................................................................. 46
21. Close-up of the Upper Sunrise site ........................................................................ 48
22. Daily thermographs for monitoring wells at Upper Sunrise on October 15, 2013
...................................................................................................................................... 49
23. Daily thermographs for various monitoring depths below the streambed at Upper
Sunrise on October 15, 2013 ........................................................................................ 50
24. Example of 1DTempPro model results for the Gravel Spit at monitoring well GS-1
over the one-week analysis period ............................................................................... 53
xii
25. Example of the decay in model fit with depth at Gravel Spit monitoring well GS-1
...................................................................................................................................... 54
26. Example of 1DTempPro model results for Upper Sunrise at monitoring well US-1
...................................................................................................................................... 55
27. Standpipe drawdown test locations at the Gravel Spit and Upper Sunrise ............ 56
xiii
1
INTRODUCTION
Purpose
The purpose of this study is to qualitatively and quantitatively characterize two
fall-run Chinook salmon spawning sites on the Lower American River (LAR) to
determine why salmon preferentially choose a natural spawning site over an adjacent,
restored habitat site. Temperature gradients were analyzed to qualitatively characterize
downwelling and upwelling conditions at each spawning feature and investigate
longitudinal flow. Additionally, temperature and head data were inputted into
1DTempPro to estimate hydraulic conductivity and specific discharge at the two sites.
1DTempPro (Voytek et al., 2013) is a recently developed graphical user interface, which
facilitates the one-dimensional heat-transport model in VS2DH (Healy and Ronan, 1996).
Furthermore, this study illustrates the utility of monitoring long-term temperature
gradients to assess salmonid spawning habitat and evaluate restoration projects.
Incorporation of continuous, in-situ streambed temperature monitoring in pre- and postrestoration assessments improves understanding of available spawning habitat and
effectiveness of restoration projects. Coupled with user-friendly one-dimensional
modeling, subsurface temperature gradients provide valuable qualitative and quantitative
characterization of spawning habitat features.
2
Background
The decline of Central Valley salmon and steelhead
Chinook salmon Oncorynchus tshawytscha and anadromous steelhead O. mykiss
populations were once renowned in the streams that drain the Central Valley; however,
the four seasonal runs (spring, fall, late-fall, and winter) that once dominated the Central
Valley system have been greatly diminished and in some streams completely decimated
over the last 150 years (Yoshiyama et al., 1998: Yoshiyama et al., 2001). Central Valley
steelhead have also been severely impacted (McEwan, 2001). The drastic reduction of
Chinook salmon from historic numbers is the result of numerous factors including
overfishing; blockage and degradation of habitat and stream quality by mining activities;
construction of dams and water diversions which reduced available spawning habitat,
restricted downstream transport of suitable spawning gravels, and greatly altered
streamflow regimes (Yoshiyama et al., 1998; Kondolf, 1998; Kondolf, 2000). It is
estimated that approximately 9200 km of Central Valley salmon and steelhead spawning
habitat was lost due to dam construction (Reynolds et al., 1993).
Furthermore, the alteration of flow regimes by dams and water diversions has
increased flows during the irrigation season (mid-April to mid-September) and has
reduced the historically higher flows in the fall, winter, and early spring (Reynolds et al.,
1993). Alteration and restriction of streamflows due to dams and water diversions can
have multiple effects including elevated water temperatures, highly variable water levels,
increased siltation of streambeds, and the exacerbation of pollution effects (Yoshiyama et
al., 1998), which can further stress anadromous fish populations.
3
These major shifts in flow regimes are mainly in response to high water demands
of California’s Central Valley which relies heavily on surface-water diversions and
groundwater pumpage to irrigate approximately 52,000 km2 of agricultural land (Faunt
and others, 2009). A multitude of crops are grown in the Central Valley (with an
estimated worth of $17 billion per year). Coupled with expanding human population
growth in the region, the competition for water resources within the Central Valley is a
critical topic which affects many political, economic, and environmental components in
the state, including adequate water supply for aquatic species such as salmonids.
Chinook salmon and steelhead in the American River
The American River (Figure 1) was noted by the California State Board of Fish
Commissioners (CFC) in 1886 as one of the best salmon streams in California prior to
mining with spring, fall, and possibly late-fall run Chinook salmon, as well as steelhead,
migrating upstream to distal reaches on the mainstem and its branches (Yoshiyama et al.,
2001; Snider et al., 2001; Williams, 2001). However, extensive human modifications
beginning in the 1800s with gold mining in the Sierra foothills subjected the American
River to a large influx (approximately 257 million cubic yards) of gravel, silt, and debris
which nearly exterminated the salmon runs due to increased siltation of spawning beds
(Yoshiyama et al., 2001; Zeug et al., 2013).
4
Figure 1. American River watershed map.
Salmon runs went through a period of recovery after the gold rush, but substantial
water development in the first half of the 20th century on the American River for storage,
flood control, and hydropower resulted in the construction of dams with inadequate fish
passage (Zeug et al., 2013; Yoshiyama et al., 2011; Water Forum, 2001). These barriers
halted the downstream transport of suitable spawning gravels from upstream sources and
cut-off a large proportion of spawning habitat (Yoshiyama et al., 2001; Reynolds et al.,
1993; Fairman, 2007; Snider et al., 2001). The spring run was completely lost during the
construction of Folsom and Nimbus Dams with remaining the salmonid species being
restricted to the lowermost 37 km of the American River known as the Lower American
5
River (LAR) (Figure 1), an area that had been rarely used for spawning and rearing prior
to dam construction (Yoshiyama et al., 2001; Snider et al., 2001; Water Forum, 2001).
Currently, fall-run Chinook salmon and steelhead spawn below Nimbus Dam in
the LAR, and can only access approximately 17% of their historic spawning habitat in the
watershed (Yoshiyama et al., 2001). Additionally, salmon and steelhead produced by the
Nimbus Hatchery comprise a considerable portion of the population in the LAR.
Between the period 1990 to 1997, Yoshiyama et al. (2001) estimated that Nimbus
Hatchery salmon accounted for approximately 9 to 59% of the spawning runs in the
LAR, and it has been noted that most steelhead observed spawning in the LAR are
hatchery fish (Williams, 2006; Hannon et al., 2003). More recent estimates suggest that
hatchery salmonids may account for approximately 90% of the LAR fall-run (Personal
communication, Tim Horner, CSUS, Geology Department Chair, May 2014). This
increasing presence of hatchery fish may have major implications on the naturally
spawning salmonid populations (Yoshiyama et al., 1998; Williams, 2006; Williams,
2001) including decreased fecundity and effects on otolith composition (Williams, 2001).
Salmonid habitat restoration
Numerous projects have been planned and implemented in the Central Valley
since the 1970’s to help maintain and restore indigenous salmonid populations (USFWS,
2013; Merz et al., 2004; Kondolf et al., 1996; Reynolds et al., 1993). The degradation
and armoring of spawning gravels is recognized as a primary contributing factor in the
decline of salmon and steelhead populations (Kondolf et al., 2008; Fairman, 2007;
Vyverberg et al., 1997; Horner, 2005). Accordingly, salmonid spawning habitat
6
rehabilitation (e.g. gravel augmentation and spawning bed enhancement) has received
considerable attention as a viable restoration tool (Merz et al., 2004; Merz and Setka,
2004; Flosi et al., 2010; Horner 2005). However, salmon habitat restoration project
effectiveness is not always adequately evaluated and some projects have proven to be
ineffective or detrimental due to poor project planning (Kondolf, 2000; Kondolf et al.,
1996; Kondolf, 1998). Such issues have resulted in the recommendation and
incorporation of comprehensive pre- and post-evaluations of spawning habitat restoration
projects to quantify their effectiveness using various assessment tools including grainsize analysis and intragravel physical and geochemical characterization (Kondolf, 2000;
Kondolf et al., 2008; Bean, 2013; Janes et al., 2013; Redd, 2010; Horner et al., 2004;
Horner, 2005, Flosi et al., 2010; Geist and Dauble, 1998).
Habitat restoration work on the LAR began in the mid-1990’s and included
evaluation of the pre-existing spawning habitat conditions, reasons for poor quality
spawning habitat, and delineating high-priority restoration areas (Vyverberg et al., 1997;
Horner et al., 2004). Redd surveys conducted by the Department of Fish and Wildlife
(DFW) indicated that the majority of spawning occurred along the 10 km reach
immediately downstream of Nimbus Dam (Horner, 2005). Furthermore, this upper reach
of the LAR produces approximately one-third of the salmon in Northern California
(Horner et al., 2004).
However, Folsom and Nimbus Dams have choked off the LAR’s annual gravel
deposition due to the restriction and management of high-flow events. Fairman (2007)
calculated that approximately 1400 m3 of gravel is lost annually due to upstream barriers,
7
which prevents natural gravel replenishment downstream in the LAR. Furthermore,
increased stream incision related to sediment starvation has in turn affected habitat by
armoring the streambed, making many reaches downstream of Nimbus Dam unsuitable
for spawning.
As a result, enhancement of spawning habitat on the LAR began with gravel
manipulation and augmentation in the 1990’s (Vyverberg et al., 1997; Horner, 2005).
Additional spawning habitat restoration projects were completed in 2008-2009 along the
Upper Sailor Bar reach adjacent to Nimbus Hatchery (Redd, 2010; Janes et al., 2013),
2008 at the Lower Sunrise side channel (Redd, 2010), 2010-2011 at Upper Sunrise (Janes
et al., 2013), 2012 at Lower Sailor Bar, and most recently 2013 at Riverbend Park
(Horner, 2013).
As noted above, numerous studies have been implemented on the LAR to assess
pre- and post-restoration spawning habitat conditions. Common parameters assessed
include grain size, grain mobility, depth and velocity, gravel permeability, hyporheic
head, water quality, and spawning data (e.g. Horner, 2005; Redd, 2010; Bean, 2013;
Janes et al., 2013; Fairman, 2007; Silver, 2007; Morita, 2005; Vyverberg et al., 1997;
Zeug et al., 2013). These studies have provided valuable information on the effectiveness
of spawning habitat restoration; however, many of these assessments focus on seasonal
discrete sampling events and do not monitor continuous, long-term conditions. The
utilization of heat as a tracer to monitor subsurface temperature gradients coupled with
changes in head allows long-term monitoring to qualitatively and quantitatively
characterize salmonid spawning habitat.
8
Physical Setting
Lower American River
The American River is the second largest tributary of the Sacramento River and
supports a mixed run of natural and hatchery-produced Chinook salmon and steelhead
(Williams, 2001; Williams, 2006; McEwan, 2001). The American River can be divided
into two watersheds: the Upper American River and Lower American River (Figure 1).
The Upper American River watershed originates in the Sierra Nevada (2,440 m
elevation) and terminates approximately 80 km downstream at Folsom Reservoir (120 m
elevation). The watershed covers an area of approximately 5.000 km2 and includes the
North, Middle, and South Forks. The completion of Folsom Dam in 1955 effectively
blocked upward migration of salmonids and cut-off a majority of the historical spawning
habitat (Yoshiyama et al., 2001; Snider et al., 2001).
The Lower American River watershed (Figure 2) is approximately 250 km2 in
size and comprises a highly urbanized 50 km reach immediately below Folsom Dam (120
m elevation) downstream to its confluence with the Sacramento River (7 m elevation).
9
Figure 2. Lower American River location map. This study focuses on salmon spawning
habitat at the Upper Sunrise restoration area downstream of Nimbus Dam.
The LAR is confined by resistant Pleistocene fan deposits and levees, restricting
the stream to a narrow floodplain that has been aggraded by debris from hydraulic mining
(Williams, 2001). The floodplain is inset into older Tertiary to Quaternary alluvial
deposits, which form steep bluffs along the north bank of the river from Folsom Dam
downstream several miles (Fugro, 2012). The Fair Oaks (Pliocene to Pleistocene) and
the Riverbank and Modesto Formations (Pleistocene) are found underlying and adjacent
to the LAR (Fugro, 2012). There are two potentially erosion-resistant units within the
local units that are dissected by the LAR: a spatially limited, moderately cohesive silt and
sandy interbed found within loose Holocene sediments; and a much thicker, more
10
widespread resistant unit associated with the Fair Oaks Formation (Fugro, 2012;
Shlemon, 1967).
Study Area: Upper Sunrise restoration area
The current study was conducted on the first 3 km immediately downstream of
Nimbus dam which has been the focus of several spawning habitat restoration projects on
the LAR (Figure 2). The LAR along this reach is a single-thread channel with an average
streambed gradient of approximately 0.001 ft/ft and is characterized by long pools
separated by riffles (Zeug et al., 2013; Williams, 2001; Snider et al., 1992). The type
section for the Fair Oaks Formation (Shlemon, 1967) is found just downstream of the
study site. An outcrop of resistant clay is located on the north bank of the study site and
is presumed to be the Fair Oaks Formation.
Precipitation in the study area is generally greatest from late fall through early
spring (Table 1). On average, approximately 23.5 inches of precipitation accumulates at
the study area with January typically being the wettest month. However, precipitation
totals have been relatively low the past two years due to drought conditions with water
year (WY) 2012 and 2013 being characterized as below normal and dry (DWR, 2014).
Severe drought conditions have persisted through the beginning of WY 2014 (DWR,
2014). Normally wet months December and January were critically dry in WY 2014
with predictions for the driest year in state history (DWR, 2014).
11
Table 1. Monthly total accumulated precipitation averages at Folsom Dam*
Month
Average (inches)
WY 2014 (inches)
October
1.30
0.00
November
2.77
0.95
December
3.91
0.39
January
4.73
0.51
February
3.98
7.37
March
3.44
April
1.94
May
0.81
June
0.21
July
0.07
August
0.06
September
0.24
Yearly
*
23.46
9.22
†
Data source: California Data Exchange Center (DWR, 2014), Station FLD
†
Yearly total for WY 2014 through February 2014
Flow at the study area is regulated by several dams upstream, with Folsom Dam
and its regulating facility downstream (Nimbus Dam), having the strongest influence on
the hydrological regime. Annual peak flows on the LAR at Fair Oaks stream gage
(USGS Gage 11446500) (Figure 2) ranged from 9,900 to 180,000 cfs before construction
of Folsom Dam and 1,920 to 134,000 cfs after dam completion. Due to Folsom
Reservoir’s relatively small size compared to mean annual flows, reductions in peak
flows during wet years has been moderate (Williams, 2001) although effects can be more
pronounced during low to moderate peak flows (Fairman, 2007). Perhaps more
importantly, the management of Folsom and other smaller dams has changed the variance
and timing of runoff due to attenuation of winter and spring pulses for reservoir storage
and elevated baseflows during the summer, primarily for irrigation (Williams, 2001;
Zeug et al., 2013). Figure 3 depicts a comparison of flow in the LAR before and after
12
construction of Folsom and Nimbus Dams for two dry water years with approximately
equal total discharge. The two hydrographs illustrate the effects of regulation on the
seasonality and variability of flow after dam completion (Figure 3). Currently, the U.S.
Bureau of Reclamation (USBR) operates Folsom and Nimbus Dams following flood
control objectives, irrigation needs, and flow objectives for spawning salmonids set by
the Anadromous Fish Restoration Plan (AFRP), which varies based on month and water
year conditions (Williams, 2001) (Table 2).
Figure 3. Comparison of flow in the American River before and after construction of
Folsom and Nimbus Dams (from Williams, 2001).
13
Table 2. AFRP flow objectives (cfs) for the Lower American River based on water year type
Above and
Dry and
Critical
Month
Wet
Below Normal Critically Dry
Relaxation
October
2,500
2,000
1,750
800
November to February
2,500
2,000
1,750
1,200
March to May
4,500
3,000
2,000
1,500
June
4,500
3,000
2,000
500
July
2,500
2,000
1,500
500
August
2,500
2,000
1,000
500
September
2,500
1,500
500
500
*
*
Modified from Williams (2001)
As previously mentioned, drought conditions which began in WY 2012 have
intensified into WY 2014 with record dry months being reported in most of California for
December and January (DWR, 2014). This is reflected in the hydrograph at the Fair
Oaks stream gage which shows major reductions in streamflow since wet WY 2011
(Figure 4.). Flows in WY 2014 were reduced to critical relaxation flow objectives (Table
2) due to critically dry conditions.
14
35,000
WY 2011
WY 2012
WY 2013
30,000
WY
2014
Discharge (cfs)
25,000
20,000
15,000
10,000
5,000
0
10/1/2010
10/1/2011
10/1/2012
Date
10/1/2013
Figure 4. Hydrograph for the LAR at USGS Gage 11446500. WY 2011 was designated
a wet year while WY 2012 and WY 2013 were below normal and dry years respectively.
WY 2014 began as one of the driest year on record in California.
In the LAR, the bulk of the fall-run Chinook salmon migration occurs from midOctober through December, although there is high year-to-year variability with fry
emergence from approximately January through mid-April (Williams, 2001). The ideal
temperature range for egg survival in the LAR is approximately 6.1 to 14.3°C (43 to
58°F) (Williams, 2001). Daily mean surface water temperatures recorded on the LAR at
USGS Gage 11446500 display relatively consistent seasonal variability despite water
year types with coldest temperatures typically occurring in the winter and warmest
temperatures in the late summer and early fall (Figure 5).
15
Figure 5. Daily mean surface water temperature for the LAR at USGS Gage 11446500
from WY 2011 into the beginning of WY 2014. Dashed gray lines represent the
estimated start and end of fall-run Chinook salmon spawning season.
Spawning habitat enhancement at the Upper Sunrise study site occurred in two
phases. In 2010, approximately 9,700 metric tons of gravel were placed at the Upper
Sunrise site on the north bank of the LAR (Figure 6). Grain sizes ranged from 8 to 178
mm with a D50 of approximately 30 mm (Zeug et al., 2013). In 2011, an additional 8,100
metric tons of spawning gravels were added to the site with a D50 of approximately 64
mm (Zeug et al., 2013). Adjacent to the restoration feature is a natural gravel bar
(referred to in this study as the “Gravel Spit”) which projects northwest from the south
bank of the reach (Figure 6).
16
Figure 6. Study area location map. Five monitoring wells were installed at the Upper
Sunrise (US) and Gravel Spit (GS) sites respectively.
Although both sites have suitable spawning gravels and have seen increased use
by fall-run Chinook salmon since the completion of the restoration work, redd surveys
conducted by the U.S. Bureau of Reclamation and the Geology Department at
Sacramento State (CSUS) consistently show significantly greater spawning densities at
the Gravel Spit habitat feature (Figure 7, Table 3).
17
Figure 7. Fall-run Chinook salmon redd locations at the study area for spawning year
2013. Redds are generally clustered along the Gravel Spit feature which extends out into
the middle of the stream while significantly fewer utilize the Upper Sunrise gravels.
Table 3. Redd count data at the study area for fall-run Chinook spawning seasons 2010
through 2013.
Number of Redds Observed
Year
Upper Sunrise
Gravel Spit
Data Source
2010
1
U.S. Bureau of Reclamation
2011
7
49
U.S. Bureau of Reclamation
2012
6
157
Sacramento State
2013
11
168
Sacramento State
It should be noted that the Gravel Spit study site (Figure 6) only includes a
portion of the spawning feature due to monitoring well access issues with water depth.
The salmonid habitat feature extends out further into the stream channel as seen in the
aerial photography (Figure 7).
18
METHODS
Monitoring Well Installation and Instrumentation
Five monitoring wells were installed at Upper Sunrise and the Gravel Spit
spawning habitat sites respectively (Figure 6). Two monitoring wells were positioned at
the upstream end of the study sites while two wells were installed downstream. The
upstream end for each site was considered the downwelling side while the upstream end
was considered the upwelling side, which is the case in most pool and riffle stream
reaches (Bencala, 2005; Woessner, 2000; Sophocleous, 2002). A fifth monitoring well
was positioned between the upstream and downstream boundaries to increase resolution
of temperature profiles within the streambed features at the two study sites.
Monitoring wells were constructed from 3.175 cm diameter schedule 40 PVC
pipes with a 10 cm screened interval near the bottom of the well (Figure 8). Monitoring
wells were set into the streambed using a rod and casing apparatus (Figure 8). Wells
were inserted approximately 1.2 m into the streambed at the Gravel Spit and
approximately 1.1 m at Upper Sunrise due to the presence of a resistant streambed layer
that could not be penetrated. Each monitoring well was developed using a peristaltic
pump. Anchor chains similar to those from Nawa and Frissel (1993) and Janes et al.
(2013) were secured onto the monitoring wells with hose clamps and inserted
approximately 0.75 m into the streambed to help prevent equipment loss from streamflow
conditions and vandalism. Additionally, well caps were camouflaged with brown paint to
further prevent vandalism.
19
Figure 8. Monitoring well schematic with instrumentation and photos of well
installation. Open circles are temperature loggers. The black circle is a pressure
transducer that also measures temperature. A temperature logger was set at the surface
water-streambed interface (gray circle) at US-1 and GS-1 to monitor surface water
temperature.
Temperature was recorded continuously at 15-minute intervals from April 2013
through January 2014 using Hobo Water Temp Pro and Tidbit water temperature data
loggers (Onset Computer Corporation, Bourne, MA). Hobo temperature loggers are
capable of measuring temperatures between -40° to 50°C with an accuracy of ±0.2°C.
Tidbit temperature loggers measure temperature over a range from -20° to 30°C with an
accuracy of ±0.2°C.
20
Temperature data loggers were positioned below the surface water-streambed
interface at 0.3 m intervals using 14-gauge metal wire which was inserted into the PVC
housing (Figure 8). At monitoring wells US-1 and GS-1 (Figure 6), a temperature logger
was installed immediately above the streambed to monitor surface water temperatures at
the Upper Sunrise and Gravel Spit sites. At the mid-stream monitoring wells (US-5 and
GS-5, Figure 6), a fifth temperature data logger was installed at the screened interval to
record temperature at the bottom boundary. Pressure was not measured at these two
monitoring wells.
Levelogger pressure transducers (Solinst Canada LTD, Georgetown, ON)
recorded continuous streambed water levels at 15-minute intervals from April 2013
through January 2014. Pressure transducers were positioned within the screened interval
of the monitoring wells at Upper Sunrise (US) and the Gravel Spit (GS) 1.1 m and 1.2 m
below the surface water-streambed interface respectively. The pressure transducers have
a full scale accuracy of ±0.05% and measure temperature with a range of -20° to 80°C at
an accuracy of ±0.05°C.
Barometric pressure was measured at the study area using a Barologger Edge
(Solinst Canada Ltd., Georgetown, ON) to correct water level readings in the monitoring
wells. The barometric pressure sensor has an accuracy of ±0.05 kPa.
Monitoring well elevations were surveyed in reference to a staff gage installed at
the site (Figure 6). Stream stage was recorded at each field visit and related to an
upstream gage operated by the USGS on the LAR at Fair Oaks (USGS 11446500). The
rating curve developed was used to estimate 15-minute stage data at each monitoring well
21
at the study site. Stream stage and water pressure measured at the lower boundary of
each monitoring well was used to calculate change in head.
Heat as a Tracer
The area immediately below the streambed where groundwater and surface water
interact, is crucial for ecosystem functions. Upwelling and downwelling transfers
oxygenated water, nutrients, and organic matter through this vital zone, and mediates
biogeochemical transformations (Brunke and Gonser, 1997; Jones and Mulholland,
2000). Dynamic subsurface flow has been shown to be a key criteria for salmonid
spawning site selection (Geist and Dauble, 1998; Geist et al., 2002; Geist et al., 2008;
Brunke and Gonser, 1997), although it has been overlooked in the past (Geist and
Dauble, 1998). The area below the streambed is also important for additional life stages
of salmonids by regulating temperature, providing protection against predators, and
delivering dissolved oxygen during egg development and juvenile stages (Brunke and
Gonser, 1997).
A variety of methods exist to investigate subsurface flow conditions and
groundwater-surface water interactions in streams (Rosenberry and LaBaugh, 2008;
Stonestrom and Constantz, 2003; Kondolf et al., 2008; Jones and Mulholland, 2000).
The use of heat as a tracer to assess subsurface flow regimes in streams has proven to be
a powerful method to characterize groundwater-surface water interactions (Constantz,
1998; Stonestrom and Constantz, 2003; Rosenberry and LaBaugh, 2008; Constantz,
2008; Conant Jr., 2004; O'Driscoll and DeWalle, 2004; Anderson, 2005; Van Grinsven et
al., 2012).
22
When temperature differences exist between two points, heat is transported due to
advective heat flow and thermal conduction through non-moving solids and fluids
(Stonestrom and Constantz, 2003). The movement of heat is traced by continuously
monitoring temperature profiles in the stream and streambed. Heat transport can then be
used to calculate groundwater-surface water exchanges (e.g. specific discharge or
seepage velocity) and hydraulic conductivity using analytical (Stallman, 1965) and
numerical (Lapham, 1989) solutions, which have been aided with the development of
energy transport models such as VS2DH (Healy and Ronan, 1996), SUTRA (Voss and
Provost, 2002), and VFLUX (Gordon et al., 2012). Temperature profiles in the stream
and through the streambed also provide qualitative information on the general character
of the subsurface flow and temperature regimes (Stonestrom and Constantz, 2003).
For example, a stream that is losing water into the streambed (downwelling) will
show diurnal water temperature cycles through the streambed profile in response to
fluctuating air temperature and incident solar radiation (Figure 9a). In contrast, a stream
that is gaining water from a groundwater source (upwelling) will show a dampened
diurnal temperature signal since the upwelling groundwater temperature is constant on a
daily timescale (Figure 9b; Stonestrom and Constantz, 2003)
23
Figure 9. Typical streamflow and temperature profiles for a) losing (downwelling) and b)
gaining (upwelling) stream reaches (from Stonestrom and Constantz, 2004).
Heat is transported between the stream and streambed through advection and
conduction under losing (downwelling) and gaining (upwelling) conditions, which results
in distinct vertical temperature signatures (Figure 10a-b). Vertical temperature signals
will vary seasonally and with different degrees of groundwater-surface water mixing in
the streambed. In streams with no significant groundwater-surface water exchanges
(neutral reach), heat will primarily be transported via conduction (Figure 10c).
24
Figure 10. Vertical temperature profiles for a) losing, b) gaining, and c) neutral stream
reaches. Colored lines (numbered 1 through 8) show general temperature profiles
through one daily or annual temperature cycle (from Stonestrom and Constantz, 2004).
Kondolf et al. (2008) noted that the use of heat as a tracer has distinctive
advantages over other restoration evaluation tools; however, they have not been applied
extensively to habitat assessments. Heat as a tracer can be used to estimate seepage
fluxes and hydraulic conductivity in the subsurface and distinguish between vertical and
longitudinal flow in the subsurface, which are important factors for salmonid spawning
site selection (Geist and Dauble, 1998).
Although studies have used water temperature to characterize hydraulic gradients
in streams and its relation to salmonid spawning habitat (Geist et al., 2002; Geist et al.,
25
2008; Alexander and Caissie, 2003; Zimmerman and Finn, 2012), very few studies have
implemented high resolution temperature monitoring to quantify longer-term subsurface
flow conditions related to spawning habitat (Van Grinsven et al., 2012). A recent study
by Van Grinsven et al. (2012) used high-resolution temperature data in a stream reach to
quantify groundwater-surface water interactions and characterize areas of groundwater
discharge at sites that support spawning brook trout.
Silver (2007) investigated using heat as a tracer to examine flow in the streambed
on the LAR. The study focused on modeling one-dimension flow in the streambed. The
study showed that hydraulic conductivity varied between sites and vertically in the
streambed and promoted the utility of this method for characterizing salmonid spawning
habitat. Silver (2007) also noted the influence of longitudinal flow in the LAR and
recommended that it be considered in future studies.
The present study expands on Silver’s (2007) work on the LAR and uses heat as a
tracer to compare two spawning habitat sites by qualitatively characterize downwelling
and upwelling conditions, longitudinal flow, and utilizing one-dimensional modeling to
estimate hydraulic conductivity and groundwater-surface water exchange rates (specific
discharge) in the subsurface.
Salmonid Redd Density
Chinook and steelhead redd density data was obtained from the U.S. Bureau of
Reclamation (USBR). Redd surveys were conducted by boat, snorkeling, and wading
and encompassed the entire LAR from Nimbus Dam downstream to Paradise Beach
(Hannon and Deason, 2005). Boat surveys consisted of maneuvering diagonally back
26
and forth along the river reach to examine all potential spawning habitat. Shallow
sections were surveyed by wading or snorkeling (Hannon and Deason, 2005).
Additionally, aerial photography can be utilized to estimate redd density on the
LAR because the water is clear enough to identify where female salmonids have
disturbed gravels for redds (Zeug et al., 2013; Williams, 2001). High-resolution aerial
photographs of the LAR are taken yearly by the USBR. Students at CSUS visually
analyzed the aerial photography to estimate redd densities at the Upper Sunrise
restoration area. Redd density data from the USBR and CSUS were utilized in this study
for spawning years 2010 through 2013.
1D Energy Transport Modeling
Continuous (15-minute interval) temperature and head data was analyzed in
1DTempPro (Voytek et al., 2013) to estimate hydraulic conductivity at each monitoring
well. In addition to hydraulic conductivity estimates, 1DTempPro can estimate specific
discharge in the subsurface using temperature data if head data is not inputted into the
model. Specific discharge, also known as the Darcy flux, provides an estimate of
groundwater-surface water exchange rates and its direction with positive and negative
values reflecting downwelling and upwelling conditions respectively. Temperature data
was analyzed in 1DTempPro to estimate specific discharge at each monitoring well.
1DTempPro is a relatively new graphical user interface (GUI) that facilitates onedimensional energy transport modeling in VS2DH (Healy and Ronan, 1996). VS2DH
uses the finite difference method to solve the advection-dispersion (energy transport)
equation for single phase liquid to describe energy transport through porous media (Healy
27
and Ronan, 1996). The energy transport equation with temperature as the dependent
variable is
d/dt [θCw + (1-φ)Cs] T = ∇·KT(θ) ∇T + ∇θCw DH ∇T – ∇·θCw vT + qCwT*
(1)
where t is time; θ is volumetric moisture content; Cw is heat capacity of water; φ
is porosity; Cs is heat capacity of the dry solid; T is temperature, KT is thermal
conductivity of the water and solid matrix; DH is hydrodynamic dispersion tensor; v is
water velocity; q is rate of fluid source; and T* is temperature of fluid source (Healy and
Ronan; 1996).
The left side of Equation 1 represents the change in energy stored in a volume
over time. The first term on the right side of Equation 1 describes energy transport by
thermal conduction. The second term accounts for transport due to thermo-mechanical
dispersion. The third term describes advective energy transport. The last term represents
heat sources or sinks (Healy and Ronan, 1996). The model was specifically designed to
model temperature time-series data from different depths below a sediment-water
interface and provides an efficient method for analyzing 1D groundwater-surface water
exchanges. Model results are immediately displayed after each run allowing iterative
manual adjustment of model parameters to fit observed data.
Modeling framework
1DTempPro assumes saturated flow conditions and applies no-flow boundaries
around the active model cells. Boundary conditions of specified temperature and
28
specified-head difference are applied to the top and bottom cells which correspond to
upper and lower temperature loggers (observation points). A temperature profile is
interpolated linearly between sensor locations to set initial conditions (Voytek et al.,
2013) (Figure 11). Temperature and head data collected at the beginning of the salmonid
spawning season were analyzed in 1DTempPro to estimate hydraulic conductivity at each
monitoring well. Additionally, temperature data was analyzed in 1DTempPro to estimate
specific discharges at the monitoring wells.
Figure 11. Model design in 1DTempPro (modified from Voytek et al., 2013).
Model calibration
Model calibration was conducted using a trial-and-error calibration approach that
yielded simulated streambed temperatures that visually fit the observed streambed
temperatures at depth. The streambed temperatures measured at 0, 0.3, 0.6, 0.9, and 1.2 m
29
(Gravel Spit) or 1.1 m (Upper Sunrise) below the streambed were used to calibrate each
of the models. The primary measure of model fit was the quantitative comparison
between measured and simulated streambed temperature using the root-mean-square
error (RMS) value that is calculated by 1DTempPro for each model run. Initial estimates
of streambed hydraulic conductivity based on measured (Rosenberry, 2014) and
published (Silver, 2007) data were inputted into the model until simulations visually fit
the observed streambed temperatures. The streambed hydraulic conductivity ranges for
gravel were based on published values (Stonestrom and Constantz, 2003; Silver, 2007).
Model calibration using specific discharge followed the same methods as hydraulic
conductivity.
Porosity, thermal conductivity, dispersivity, and sediment heat capacity can also
be adjusted in the model based on sediment or rock type. These input parameters were
set within the ranges published in the literature (Stonestrom and Constantz, 2003; Silver,
2007) and held constant for all model runs. The values were 0.2 for porosity, 0.4 m for
dispersivity, 1.8 W/(m °C) for thermal conductivity, and 1.3 x 10-6 J/(m3 °C) for sediment
heat capacity.
Sensitivity analysis
The sensitivity of the model to the calibration parameters (hydraulic conductivity
and specific discharge) and data-series time length (e.g. day, week, month) were
evaluated. Sensitivity analysis of the 1DTempPro model provided insight into model fit
response with varying parameter values and temporal scales. The best model fit was
based on graphical interpretation of modeled versus measured temperature and
30
corresponding root-mean-square error (RMS) values, with lower RMS values indicating a
better fit.
The model was most sensitive to hydraulic conductivity (Kz) and specific
discharge (q), the primary input parameters that are adjusted depending on input of head
data. Increasing Kz above the best model fit resulted in an earlier phase and higher
amplitude for modeled results (Figure 12). Decreasing Kz below the best model fit
resulted in a later phase and lower amplitude for modeled results (Figure 12). Generally
speaking, model fits stopped responding to changes in Kz at orders of magnitude greater
than 10-3 or less than 10-6 m/s. Model response to changes in q showed the same trends as
Kz (Figure 12).
Figure 12. Model sensitivity analysis for 1DTempPro at monitoring well GS-1 (0.9 m
below the streambed) with varying hydraulic conductivity (Kz). Changes in specific
discharge (q) showed similar trends as Kz.
31
Silver (2007) examined the sensitivity of the one-dimensional model in VS2DH,
which 1DTempPro utilizes, and found similar results; however, a smaller range of Kz
values was suggested by Silver (2007) than this study. Nonetheless, phase and amplitude
responses to modeled results with modification of Kz values agreed with findings in
Silver (2007).
Model sensitivity was also examined on varying temporal scales. Initial model
calibration was conducted on a 24-hour timescale (Figure 12). Once model-fits were
successful, 15-minute temperature and pressure data for one week of data (October 15 –
October 21, 2013) were inputted into the model for analysis. This date was chosen
because it is the approximate beginning of the fall-run Chinook salmon spawning season
on the LAR. Finally, the entire approximate spawning season (October 15 – December
31, 2013) was inputted into the model. Hydraulic conductivity values were within the
same range on all three timescales (Table 4). Therefore, one-week data from the
beginning of the spawning season (October 15 – October 21, 2013) was used to shorten
model-run analysis times and improve graphical visualization of modeled versus
measured temperature. This one week of data at the beginning of the spawning season is
considered representative of the entire spawning season based on the sensitivity analysis.
32
Table 4. Analysis of model calibration at varying timescales at the Gravel Spit monitoring
well GS-1.
Date Range
Timescale
October 15, 2013
24-hour
October 15 - October 21, 2013
One week
October 15 - December 31, 2013 Spawning season
Kz (m/s)
2.2 - 2.4 x 10
2.2 x 10
RMS (°C)
-4
-4
2.1 - 2.3 x 10
0.164
0.181
-4
0.122
Kz = hydraulic conductivity
RMS = Root-mean-square error for the model run
Model limitations
There are several limitations to 1DTempPro. First, the model is restricted to onedimensional vertical flow for saturated porous media. Silver (2007) noted a longitudinal
flow component at depth on the LAR near the study area, which 1DTempPro cannot
quantify. However, the presence of longitudinal flow is detectable in 1DTempPro as a
decay in model fit with depth (Voytek et al., 2013). For this report, the longitudinal flow
component will be restricted to the qualitative analysis. Second, homogeneous and
isotropic soil hydraulic and thermal properties are assumed (Voytek et al., 2013). Third, it
is assumed that there is negligible feedback between temperature variation and
hydraulic/thermal properties (Voytek et al., 2013).
33
RESULTS AND DISCUSSION
Qualitative analysis of collected temperature data collected from the Gravel Spit
and Upper Sunrise sites is presented for the entire study period (June 4, 2013 – January 1,
2014), and for the time period that coincides with the approximate start of the spawning
season on the LAR, defined as October 15th for this study. The start of the spawning
season was chosen for analysis in order to evaluate the conditions salmon experience
when they first arrive at the Gravel Spit and Upper Sunrise spawning features and to
avoid the influence of redds clustering on site selection as the salmon season progresses.
Data analyzed at the beginning of the spawning season is considered representative of the
entire spawning season, October 15, 2013 – January 1, 2014, (see Sensitivity Analysis
above). Additionally, quantitative results from the vertical one-dimensional heat transport
modeling are presented for a one week period that coincides with the approximate start of
the spawning season, October 15 – October 21, 2013.
Qualitative Analysis of Temperature
Collected temperature data were qualitatively analyzed for temperature responses
to flow variability and seasonal temperature trends over the entire study period. A
qualitative comparison of temperature trends between the two sites is presented for the
approximate spawning season (October 15, 2013 – January 1, 2014) and for a one day
period at the approximate start of the spawning season, defined as October, 15, 2013.
The one day time scale examined diurnal temperature trends in order to qualitatively
characterize vertical and longitudinal flow components in the subsurface at the Gravel
Spit and Upper Sunrise sites.
34
Temperature response to flow
Daily mean discharge on the LAR measured at the USGS streamflow gage at Fair
Oaks (USGS 11446500) is heavily influenced by Nimbus Dam, which is the regulating
facility for Folsom Dam operations upstream (Zeug et al., 2013). During the study
period, maximum flows occurred in the summer, which is related to irrigation water
transfers. After the irrigation season ended in mid-September, flows remained relatively
constant at approximately 1250 cfs until early January when flows were reduced to
approximately 500 cfs due to low reservoir levels from critically dry winter conditions
(Figure 13; Table 1).
Temperature data on the LAR suggests minimal influence from changes in flow
(Figure 13). Surface and subsurface water temperature trends at the two study sites
generally followed mean surface water temperature trends at the USGS streamflow gage;
therefore, this data was used to illustrate general responses to flow for the two study sites.
The most noticeable change in temperature occurred in late June/early July when
irrigation flow releases peaked. This caused a significant drop in water temperatures at
both sites when water temperatures are generally elevated due to high air temperature
(Figure 13). As the peak flow receded, water temperatures increased back to elevated
summer levels.
Surface and subsurface water temperatures declined steadily from mid-September
through January as days grew shorter and cooler. A sharper drop in temperature is
apparent in mid-December, but does not appear to be related to flow (Figure 15). Flow
data for the study period implies dam releases can affect temperatures more significantly
35
during the warmer summer months from the release of deep, cold reservoir water into the
LAR. Nonetheless, air temperature seems to generally drive water temperature
fluctuations in the stream although flow pulses from the reservoir can cause temporary
shifts in water temperature.
35
4000
30
3500
3000
25
20
2500
Water
2000
15
10
1500
Flow
1000
5
0
5/1/2013
Daily Mean Flow (cfs)
Temperature (°C)
Air
500
7/1/2013
9/1/2013 11/1/2013
Date
1/1/2014
0
3/4/2014
Figure 13. Daily mean surface water temperature (blue line), moving average air
temperature (orange line), and daily mean flow (black line) measured on the LAR.
Surface water temperature and flow were measured at the Fair Oaks streamflow gage
(USGS 11446500) and air temperature data was measured at the Folsom Dam CDEC
station (FLD).
Seasonal temperature trends
Seasonal water temperature trends for the study sites followed expected patterns
of warming from spring through summer and cooling from fall through winter (Figure
13). Additionally, subsurface water temperatures showed a seasonal change that
36
occurred on approximately September 15 where deep subsurface water temperatures
became warmer compared to shallower subsurface and surface water temperatures
(Figure 14). This distinct seasonal change in subsurface water temperature typically
occurs in the fall and persists until the spring when deep subsurface water temperatures
eventually become cooler than shallower subsurface temperatures (Tim Horner, personal
communication).
This distinct seasonal change in subsurface water temperature was observed at
most of the monitoring wells to some degree; however, the signal was more prominent at
the Gravel Spit (Figure 14) than the Upper Sunrise site. For example, the water
temperature at 1.1 m depth at monitoring well US-1 did not become noticeably warmer in
relation to shallower subsurface temperatures on approximately September 15, 2013
(Figure 15).
It is unclear if this deep, subsurface water temperature warming signal observed at
the monitoring wells influences fall-run Chinook spawning site selection; however, it is
apparent that this seasonal water temperature signal flip is stronger at the Gravel Spit.
This is likely related to varying subsurface flow paths at the two spawning sites which is
explored in more detail in the following sections.
37
Figure 14. Seasonal changes in streambed temperature at monitoring well GS-4. Deeper
streambed temperatures become warmer relative to shallow streambed temperatures on
approximately September 15 (circle and zoom-in graph).
38
Figure 15. Zoom-in of subsurface water temperatures at monitoring well US-1. The
seasonal shift in deep subsurface water temperature signals on September 15, 2013 is not
readily apparent.
Spawning season temperature trends (October 15, 2013 – January 1, 2014)
Figure 16 summarizes surface water temperatures and shallow subsurface water
temperatures measured at 0.3 m below the streambed in the Gravel Spit and Upper
Sunrise monitoring wells from October 15, 2013 – January 1, 2014. Salmon generally
scour gravel to 0.3 m depth when constructing redds in the LAR (DeVries, 1997),
therefore shallow subsurface water temperature is an important parameter for
characterizing spawning habitat.
Median shallow subsurface water temperatures did not vary significantly between
the two study sites (Figure 16). However, when comparing the range (minimum and
39
maximum) of subsurface temperature data between the two sites; the Gravel Spit site
displayed greater variability in temperature data collected over the study period. The
subsurface water temperature range for the Gravel Spit was greater than the Upper
Sunrise site (2.15 °C compared to 0.69 °C, respectively). Furthermore, the surface water
temperature range between the two sites was also greater for the Gravel Spit site than the
Upper Sunrise site (10.323 °C and 8.942 °C, respectively). This greater variability in
surface and subsurface water temperatures at the Gravel Spit suggests potentially
different degrees of groundwater-surface water exchanges at the two sites.
Figure 16. Box-plot of surface and shallow subsurface (0.3 m depth) water temperature
data at the Gravel Spit and Upper Sunrise study sites during the spawning season
(October 15, 2013 – January 1, 2014).
Vertical temperature gradients through the streambed provide additional insight
into potential groundwater-surface water exchanges at the two study sites (Figure 10).
40
Figure 17 illustrates the general vertical temperature gradients observed at the Gravel Spit
(Figure 17a) and Upper Sunrise (Figure 17b) over the first two weeks of the spawning
season which approximately begins on October 15, 2013. At the Gravel Spit, two general
vertical temperature gradients can be distinguished by plotting monitoring wells GS-1
and GS-4 (Figure 17a; bold text). At monitoring well GS-1, the vertical temperature
gradient remains open, which suggests downwelling at this site (Figure 17a). Monitoring
well GS-5 showed a similar trend as GS-1.
The vertical temperature gradient at GS-4 shows a different trend, with
temperature bottlenecking within the first 0.3 m below the streambed and remaining
relatively constant with depth (Figure 17a). This suggests subsurface water is upwelling,
significantly dampening the surface water temperature signal. Monitoring wells GS-2
and GS-3 showed similar trends as monitoring well GS-4 (Figure 17a). Vertical
temperature gradients at the Gravel Spit suggest a mix of downwelling and upwelling
conditions through the habitat feature (Figure 17a)
Vertical temperature gradients at Upper Sunrise monitoring wells show less
subsurface temperature variation from surface water temperature (Figure 17b). The
vertical temperature gradient at monitoring well US-1 remains open and does not dampen
with depth, suggesting surface water is downwelling into the deeper subsurface (Figure
17b; bold text). Monitoring wells US-2, US-3, and US-5 showed similar trends as US-1.
Only monitoring well US-4 displayed an upwelling temperature gradient signal at the
Upper Sunrise site, with water temperature beginning to bottleneck with depth (Figure
41
17b). Vertical temperature gradient at Upper Sunrise suggest downwelling surface water
dominates the subsurface water temperature signal through most of the site.
Figure 17. Vertical temperature gradients showing the 5th and 95th temperature
percentiles through the streambed measured at the (a) Gravel Spit and (b) Upper Sunrise
sites. Numbers correspond to the first and second week at the beginning of the spawning
season. Refer to text above for additional explanation and Figure 10 for reference.
42
Diurnal temperature trends at the Gravel Spit
Diurnal surface and subsurface water temperature trends can be used to infer
upwelling and downwelling conditions, variations in permeability, and potential
influences from longitudinal flow (Stonestrom and Constantz, 2003). It is likely that a
lateral flow component is also present at the two study sites; however, data collected in
this study cannot effectively characterize this flow direction.
At the Gravel Spit (Figure 18), surface water temperature exhibits strong diurnal
fluctuations for temperature data collected on October 15, 2013 (Figure 19), the
approximate beginning of the fall-run Chinook salmon spawning season.
At monitoring well GS-1, a strong diurnal signal is apparent through the
subsurface, with the surface water temperature signal propagating through the streambed
(Figure 19). This suggests downwelling conditions at monitoring well GS-1. At
monitoring wells GS-2, GS-3, GS-4, and GS-5, the strong diurnal surface water signal
significantly dampens in the subsurface and is not readily apparent at these monitoring
wells 0.6 m below the streambed or deeper (Figure 19). The relatively constant
temperatures seen in the subsurface at these monitoring wells suggests strong upwelling
conditions. Monitoring well GS-5 shows a moderate diurnal signal 0.3 m in the
subsurface which suggests a mix of downwelling in the shallow subsurface and upwelling
with depth at this site which is located at the mid-point of the habitat feature (Figure 19).
All monitoring wells at the Gravel Spit show considerable lag times before the
diurnal surface water temperature signal is seen in the subsurface (Figure 19). At
monitoring well GS-1, it takes 3 to 6 hours before the diurnal signal is observed 0.6 to 1.2
43
m below the streambed (Figure 19). At the other Gravel Spit monitoring wells, the
surface water diurnal signal is seen 4 to 5 hours later within the first 0.3 m below the
streambed (Figure 19). This lag time suggests longitudinal subsurface flow paths at the
Gravel Spit. Lag times show that GS-1 is influenced by deep longitudinal flow paths
while the rest of the monitoring wells may only experience shallow longitudinal flow
paths before upwelling groundwater dominates the temperatures signal (Figure 19). The
considerable lag time also suggests relatively low permeability and long subsurface
residence times at the Gravel Spit.
Figure 18. Close-up of the Gravel Spit site. Surface water flow direction along with
upstream, mid-point, and downstream sides of the habitat feature are labelled for spatial
reference.
44
Figure 19. Daily thermographs for monitoring wells at the Gravel Spit on October 15,
2013. Surface water temperature (0 m) was measured at GS-1.
Diurnal water temperature signals at different streambed depths were further
investigated to characterize longitudinal flow at the Gravel Spit based on temperature
signal lag times (Figure 20). Water temperature measured 0.3 m below the streambed
show the strong diurnal surface water signal on the upstream side of the Gravel Spit (GS-
45
1), propagating longitudinally through the mid-point (GS-5) to the downstream side (GS2) of the habitat feature (Figure 20). Monitoring wells GS-3 and GS-4 also show a weak
diurnal temperature signal but it is unclear if the signal is related to longitudinal flow in
the feature. Nonetheless, shallow subsurface temperatures at the Gravel Spit suggest the
presence of longitudinal flow.
At 0.6 m below the streambed, there is evidence of weak longitudinal flow from
the upstream side of the habitat feature (GS-1) to the mid-point (GS-5) of the Gravel Spit
(Figure 20). Subsurface water temperature at the monitoring wells GS-2, GS-3, and GS-4
are relatively constant suggests upwelling flows begin to dominate the temperature signal
at this depth. At depths greater than 0.6 m below the streambed, upwelling flows
dominate the temperature signal and there is no discernable evidence of longitudinal flow
at the Gravel Spit (Figure 20).
46
Figure 20. Daily thermographs for various monitoring depths below the streambed at the
Gravel Spit on October 15, 2013.
Qualitative analysis of temperature data at the Gravel Spit shows a complex mix
of downwelling, upwelling, and longitudinal flow paths through the spawning habitat
feature. Additionally, the considerable lag times of the diurnal water temperature signal
with depth suggests relatively low permeability and long subsurface residence time at the
47
Gravel Spit. Finally, it should be reiterated that this analysis cannot characterize lateral
flow although this flow direction is likely present in the spawning feature.
Diurnal temperature trends at Upper Sunrise
Similar to the Gravel Spit, diurnal surface and subsurface water temperature
trends at the Upper Sunrise habitat feature (Figure 21) were qualitatively analyzed to
characterize upwelling and downwelling conditions, variations in permeability, and
potential influences from longitudinal flow. Strong diurnal surface water temperature
fluctuations are apparent at Upper Sunrise (Figure 22). This strong diurnal signal is
propagated through the streambed at monitoring wells US-1, US-2, and US-3 to 1.1 m
below the streambed (Figure 22). This suggests strong downwelling at these three
monitoring wells with the surface water signal only beginning to dampen 1.1 m below the
streambed.
The surface water diurnal signal is also observed at monitoring wells US-4 and
US-5; however, the signal only propagates into the first 0.3 m of the subsurface (Figure
22). Deeper subsurface water temperatures at these two wells do not exhibit the diurnal
surface water signal. This suggests a mix of downwelling surface water in the shallow
subsurface with deep upwelling water at monitoring wells US-4 and US-5 (Figure 22).
Water temperature signal lag times at monitoring wells US-1, US-2, and US-3
only register at the deepest temperature depth (1.1 m) with a lag time of approximately 1
hour. This suggests only a slight influence from longitudinal flow at depth. In contrast,
monitoring wells US-4 exhibits a 1-hour lag time in the shallow subsurface (0.3 m),
which suggests some longitudinal flow in the shallow subsurface (Figure 22).
48
Monitoring well US-5 shows potential longitudinal in the shallow streambed to 0.6 m
depth (Figure 22). Nonetheless, the absence of appreciable lag times in the shallow
subsurface at most of the Upper Sunrise wells suggest strong downwelling flow paths.
Figure 21. Close-up of the Upper Sunrise site. Surface water flow direction along with
upstream, mid-point, and downstream sides of the habitat feature are labelled for spatial
reference.
49
Figure 22. Daily thermographs for monitoring wells at Upper Sunrise on October 15,
2013. Surface water temperature (0 m) was measured at US-1.
Diurnal temperature signals at streambed monitoring depths were further
investigated to characterize longitudinal flow at Upper Sunrise (Figure 23). In the
shallow subsurface (0.3 m), only monitoring well US-4 displayed a 1-hour lag time of the
strong diurnal signal (Figure 23), which suggests strong downwelling surface water
50
dominates the water temperature signal at most of the monitoring wells. Diurnal water
temperature signals at monitoring wells US-4 and US-5 dampen considerably at depth
(0.6 – 1.1 m) which suggests upwelling begins to dominate the temperature signal at
these monitoring wells (Figures 22 and 23).
Figure 23. Daily thermographs for various monitoring depths below the streambed at
Upper Sunrise on October 15, 2013.
51
Qualitative analysis of temperature data at Upper Sunrise suggests strong
downwelling through much of the habitat feature. Significant upwelling is only seen at
depth at monitoring wells US-4 and US-5. Furthermore, the strong propagation of the
surface water diurnal signal through the streambed suggests a relatively high permeability
which facilitates rapid downwelling flows at Upper Sunrise. Longitudinal flow at Upper
Sunrise appears to be over-powered by the strong downwelling water temperature signal
at most of the monitoring wells with only monitoring wells US-4 and US-5 showing
some evidence of longitudinal flow in the shallow subsurface. As noted previously, the
lateral flow component could not be characterized with this analysis although it is likely
present at the study site.
Quantitative Analysis: 1DTempPro Model Results
Water temperature was modeled in 1DTempPro over a one-week period at the
approximate beginning of the spawning season (October 15 – October 21, 2013) to
estimate hydraulic conductivity (Kz) and specific discharge (q) at the two study sites.
This date was chosen to characterize habitat conditions when fall-run Chinook salmon
begin to spawn on the LAR to avoid the potential influence of preferential redd clustering
as the spawning season. The sensitivity analysis confirmed that this one week of data is
representative of the full spawning season.
Model results and discussion: hydraulic conductivity
One-dimensional model results show clear differences in hydraulic conductivity
between the two study sites (Table 5). At the Gravel Spit, Kz values ranged from 4.0 x
52
10-6 to 2.1 x 10-4 m/s (Table 5). Monitoring well GS-1, exhibited the largest hydraulic
conductivity while monitoring wells GS-2, GS-3, and GS-4 Kz values were two orders of
magnitude smaller (Table 5). These differences in Kz values correspond to downwelling
(GS-1) and upwelling (GS-2, GS-3, and GS-4) conditions discussed in the Qualitative
Analysis section (Figure 19). Additionally, monitoring wells GS-1, GS-2, and GS-4
displayed a decay in the model fit with depth (Table 5, Figures 24 and 25), which
indicates an influence from longitudinal flow (Voytek et al., 2013). The influence of
longitudinal flow was also supported by thermographs discussed in the Qualitative
Analysis section (Figures 19 and 20).
Table 5. One-dimensional model results for monitoring wells over a one-week period
from October 15 through October 21, 2013.
Monitoring Well
Kz (m/s)
RMS (°C)
Comments
Gravel Spit (GS) Site:
GS-1
2.1 x 10
-4
0.181
Model fit decays with depth Longitudinal flow influence
GS-2
7.5 x 10-6
0.099
Model fit decays with depth Longitudinal flow influence
GS-3
8.0 x 10
-6
0.068
GS-4
4.0 x 10
-6
0.087
US-1
9.0 x 10
-3
0.077
US-2
US-3
7.0 x 10
-3
5.0 x 10-3
0.093
0.075
US-4
2.0 x 10-5
0.113
Model fit decays with depth Longitudinal flow influence
Upper Sunrise (US) Site:
Kz = hydraulic conductivity
RMS = Root-mean-square error for the model run
Model fit decays with depth Longitudinal flow influence
53
Figure 24. Example of 1DTempPro model results for the Gravel Spit at monitoring well
GS-1 over the one-week analysis period. Note the decay in the model fit with depth (see
Figure 21 for zoom in view).
54
Figure 25. Example of the decay in model fit with depth at Gravel Spit monitoring well
GS-1. Monitoring wells GS-1, GS-2, and GS-4 at the Gravel Spit as well as monitoring
well US-4 at Upper Sunrise exhibited this decay in model fit with depth.
At Upper Sunrise, Kz ranged from 2.0 x 10-5 to 9.0 x 10-3 m/s (Table 5).
Monitoring well US-4 exhibited the lowest Kz value while monitoring wells US-1, US-2,
and US-3 Kz values were two orders of magnitude greater (Table 5). The lower Kz value
at monitoring well US-4 corresponds to upwelling conditions while the larger Kz values
at US-1, US-2, and US-3 correspond to strong downwelling conditions previously
discussed in the Qualitative Analysis section (Figure 22). A decay in the model fit with
depth was observed at monitoring well US-4 (see Figure 25 for example of model fit
decay) but not at the other Upper Sunrise wells (Figure 26), suggesting minimal influence
from longitudinal flow.
55
Figure 26. Example of 1DTempPro model results for Upper Sunrise at monitoring well
US-1. Note the much better fit of modeled and measured temperature with depth and
lack of model decay.
Rosenberry (2014) measured hydraulic conductivity in the shallow subsurface at
the Gravel Spit and Upper Sunrise using a standpipe drawdown test (Bean, 2013).
Eleven drawdown tests were conducted at each site (Figure 27). Hydraulic conductivity
values measured by Rosenberry (2014) near the monitoring well locations show
relatively good agreement with Kz values estimated with 1DTempPro (Table 6);
however, drawdown test K values, which are measuring three-dimensional radial flow,
are approximately two orders of magnitude larger than modeled results, which are
estimated in one-dimension. Nonetheless, both estimated and measured hydraulic
conductivity results show lower K values at the Gravel Spit with significantly greater K
values at Upper Sunrise. It should be noted that drawdown test K values at Upper
56
Sunrise reached maximum pumping rates and therefore K values may actually be greater
than 95,000 cm/hr (Bean, 2012).
Figure 27. Standpipe drawdown test locations at the Gravel Spit and Upper Sunrise
(From Bean, 2012).
57
Table 6. Comparison of modeled and measured hydraulic conductivity (K).
Drawdown K
Modeled Kz
Drawdown
Monitoring Well
*
Test Number
(cm/hr)
(cm/hr)
Gravel Spit (GS) Site
GS-1
75
5
33
GS-2
3
3
1,205
GS-3
3
8
9,330
1.5
11
305
US-1
3,250
4
95,000
US-2
2,500
3
95,000‡
US-3
1,800
11
95,000‡
US-4†
7
-
-
GS-4
Upper Sunrise (US) Site
*
‡
Data from Bean (2013)
†
Drawdown test not conducted near monitoring well US-4
‡
Maximum K value for drawdown test
Model results and discussion: specific discharge
Specific discharge (q) was also estimated using 1DTempPro to quantify
groundwater-surface water exchanges in the shallow subsurface at the Gravel Spit and
Upper Sunrise habitat features. To estimate shallow subsurface q fluxes, the lower
boundary of the model was set at 0.9 m below the streambed.
At the Gravel Spit, estimated q fluxes show a mix of downwelling (positive q)
and upwelling (negative q) conditions (Table 7), which supports results found in the
Qualitative Analysis section. At monitoring well GS-3, a good model fit was established
with both positive and negative q values, suggesting a mix of downwelling in the shallow
subsurface and upwelling conditions with depth. Specific discharge values ranged from
10-5 to 10-6 m/s at the Gravel Spit.
58
At Upper Sunrise, estimated q fluxes were positive at all of the monitoring wells
(Table 7), which suggests downwelling dominates the shallow subsurface flow paths at
the site. Similar to hydraulic conductivity estimates, specific discharge values were
generally one to two orders of magnitude greater than q estimates at the Gravel Spit site
(Table 7).
Table 7. Specific discharge (q) results estimated in 1DTempPro.
Monitoring Well
Gravel Spit (GS) Site:
GS-1
Qualitative Flow
Direction
q (m/s)
Downwelling
3.0 x 10
Quantitative Flow
Direction
-5
Downwelling
-6
Upwelling
GS-2
Upwelling
-2.0 x 10
GS-3
GS-4
Mix
Upwelling
1.0 x 10-6 ; -2.0 x 10-6
-1.0 x 10-6
Mix
Upwelling
GS-5
Upper Sunrise (US) Site:
Upwelling
-4.0 x 10-6
Upwelling
US-1
Downwelling
5.0 x 10-4
Downwelling
US-2
US-3
Downwelling
Downwelling
5.0 x 10-4
5.0 x 10
-4
Downwelling
Downwelling
US-4
Mix
3.0 x 10
-6
Downwelling
US-5
Mix
5.0 x 10
-5
Downwelling
Salmonid Spawning Habitat Preferences on the LAR
Theoretically, the Gravel Spit and Upper Sunrise habitat sites should be suitable
for spawning salmonids; however, there is a distinct preference for fall-run Chinook
salmon to select the Gravel Spit over Upper Sunrise. Even though these sites are directly
adjacent to each other, fall-run Chinook salmon regularly cluster and even super-impose
redds at the Gravel Spit instead of utilizing Upper Sunrise gravels. Preferential spawning
59
site selection by salmonids has been noted in other studies (Geist and Dauble, 1998; Geist
et al., 2002; Van Grinsven et al., 2012) with intragravel permeability and subsurface flow
paths in response to complex channel patterns being considered as important factors
(Geist and Dauble, 1998; Geist et al., 2002; Water Forum, 2001).
Qualitative and quantitative results show clear differences between the natural
spawning feature at the Gravel Spit and the enhanced spawning habitat at Upper Sunrise.
Extended lag times in subsurface temperatures coupled with lower hydraulic conductivity
and varying specific discharge estimates suggest a mix of upwelling and downwelling
conditions at the Gravel Spit, which likely promotes groundwater-surface water mixing in
the subsurface. This creates a distinct difference of shallow subsurface (0.3 m depth)
water temperatures from the surface water temperature signal which may attract
spawning fall-run Chinook salmon.
Conversely, greater hydraulic conductivity and specific discharge estimates along
with less variable subsurface temperature gradients at the Upper Sunrise feature suggest
rapid downwelling persists throughout most of the site. This provides no definitive
temperature signal in the shallow subsurface, which may explain why fall-run Chinook
salmon do not use the site excessively.
Geist and Dauble (1998), along with other studies they cited, found that upwelling
subsurface flow was commonly associated with spawning locations of salmon.
Upwelling areas would tend to improve survival of eggs and emergent fry by providing
more stable conditions. However, other studies have also found the opposite trend, with
downwelling sites being preferentially selected (Geist et al., 2002). Qualitative results
60
show a mix of upwelling and downwelling conditions at the Gravel Spit with upwelling
temperature signals observed at all of the monitoring wells except GS-1. Results at the
Gravel Spit show complex groundwater-surface water exchanges and longitudinal flow
paths which promote both upwelling and downwelling conditions, which appear to be
preferred by LAR fall-run Chinook salmon.
Alexander and Caissie (2003) found that upwelling conditions interpreted from
seepage meters were dominated by surface water signatures, suggesting that although an
upward flux was being measured, it did not necessary equate to groundwater discharge to
the stream. They suggested a mixing model where different proportions of groundwater
and surface water would mix in the subsurface. This may be the scenario occurring at the
two spawning habitat sites in the present study. At the Gravel Spit, lower hydraulic
conductivity coupled with upwelling subsurface water would promote groundwatersurface water mixing which would result in the variation of shallow subsurface
temperature signals from surface water temperature. In contrast, rapid downwelling
surface water at Upper Sunrise would suppress mixing of deeper and warmer subsurface
water resulting in the marginal variation from stream temperatures seen at most of the
Upper Sunrise monitoring wells, especially within the shallow subsurface where salmon
spawn.
Additionally, grain size distributions at the two study sites are dissimilar, which
affects spawning site selection and observed differences in subsurface flow paths. The
enhanced spawning habitat at Upper Sunrise has considerably larger grain sizes with a
D50 of approximately 30 mm with a range from 8 to 178 mm (Zeug et al., 2013). In
61
contrast, grain size analyses conducted at the Gravel Spit show much smaller grain size
distributions with grain sizes generally less than 64 mm (Rosenberry, 2014). Salmonids
can move substrate up to approximately 10% of their length (Kondolf, 2000); therefore, it
may be that the smaller grain size distributions at the Gravel Spit provide habitat for a
larger group of salmonids of varying size compared to Upper Sunrise. Furthermore, the
distribution of different grain sizes would also influence the hydraulic conductivity
values estimated at the two sites. The mix of small gravels and fine-grained material
likely influences the subsurface flow paths at the Gravel Spit that spawning salmonids
prefer. Zeug et al. (2013) found that a spawning habitat site with the smallest D50 out of
the other enhanced spawning sites on the LAR attracted greater numbers of fish, which
would support preferential spawning at the Gravel Spit over Upper Sunrise.
It should also be noted that redd clustering is a typical occurrence on the LAR
(Zeug et al., 2013; Water Forum, 2001) along with other streams (Geist et al., 2002; Van
Grinsven, 2012; Geist and Dauble 1998). Zeug et al. (2013) found that redds were more
likely to be constructed near habitat features or other redds, which could simply reflect
superior spawning habitat. Redd clustering is highly evident at the Gravel Spit, with fallrun Chinook salmon super-imposing redds on each other rather than utilize spawning
gravels at Upper Sunrise. Geist and Dauble (1998) and The Water Forum (2001) suggest
that redd clustering may be related to intragravel flow, which suggests that the habitat
characteristics of the Gravel Spit described above are preferred by fall-run Chinook on
the LAR. Other studies have also suggested that salmonids have a behavioral preference
for spawning in the proximity of previously constructed redds (Essington et al., 1998;
62
Youngsen et al., 2011). This may also play an important factor in observed redd
clustering at the Gravel Spit. Nonetheless, early arriving salmonids preferentially choose
the Gravel Spit over Upper Sunrise, likely due to the subsurface flow and temperature
characteristics discussed above.
63
CONCLUSION
Water temperature measured in streambed monitoring wells on the LAR showed
distinct subsurface flow path differences between an enhanced salmonid spawning habitat
site (Upper Sunrise) and an adjacent, naturally-occurring habitat feature (Gravel Spit).
Qualitative analysis of temperature gradients revealed a mix of downwelling and
upwelling subsurface flow paths at the Gravel Spit. Modeling results for the Gravel Spit
provided quantitative estimates of hydraulic conductivity and specific discharge which
were approximately two orders of magnitude lower than those seen at Upper Sunrise and
further supported the presence of both downwelling and upwelling conditions at the
Gravel Spit. In contrast, qualitative and quantitative results at Upper Sunrise revealed
predominantly downwelling conditions with greater hydraulic conductivity and specific
discharge estimates. This results in a strong diurnal temperature signal that propagates
from the stream through the subsurface.
Mixing of groundwater and surface water in the shallow subsurface appears to be
an important factor for redd site selection by fall-run Chinook salmon on the LAR.
Thermal tracers provide a relatively low-cost method for characterizing specific salmon
spawning features and their intragravel properties.
Spawning habitat enhancement projects are likely to increase in the future in
response to salmonid population vulnerability in rivers. Therefore, qualitative and
quantitative evaluation of habitat quality before and after project completion is crucial for
improving habitat restoration techniques and evaluating project effectiveness. Thermal
tracers provide a relatively simple, low-cost, low-maintenance method for determining
64
key habitat characteristics over long time scales and at potentially high spatial
resolutions. It is highly recommended that thermal tracers be incorporated as a standard
tool for habitat assessments.
65
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