AN ABSTRACT OF THE THESIS OF
Aida D. Arik for the degree of Master of Science in Water Resources Engineering
presented on November 8, 2011.
Title: A Study of Stream Temperature Using Distributed Temperature Sensing Fiber
Optics Technology in Big Boulder Creek, a Tributary to the Middle Fork John Day
River in Eastern Oregon
Abstract approved:
John S. Selker
The Middle Fork John Day Basin in Northeastern Oregon is prime habitat for spring
Chinook salmon and Steelhead trout. In 2008, a major tributary supporting rearing
habitat, Big Boulder Creek, was restored to its historic mid-valley channel along a 1
km stretch of stream 800 m upstream of the mouth. Reduction of peak summer stream
temperatures was among the goals of the restoration. Using Distributed Temperature
Sensing (DTS) Fiber Optic Technology, stream temperature was monitored prior to
restoration in June 2008, and after restoration in September 2008, July 2009, and
August 2009. Data gathered was used to determine locations of groundwater and
hyporheic inflow and to form a stream temperature model of the system. The model
was used both to develop an evaluation method to interpret components of model
performance, and to better understand the physical processes important to the study
reach.
A very clear decreasing trend in surface temperature was seen throughout each of the
DTS stream temperature datasets in the downstream 500 m of the study reach.
Observed reduction in temperature was 0.5◦ C (±0.10) in June 2008, 0.3◦ C (±0.37) in
September 2008, 0.6◦ C (±0.25) in July 2009, and 0.2◦ C (±0.08) in August 2009.
Groundwater inflow was calculated to be 3% of the streamflow for July 2009 and 1%
during the August 2009 installation. Statistically significant locations of groundwater
and hyporheic inflow were also determined.
July 2009 data was used to model stream temperature of the 1 km (RMSE 0.28◦ C). The
developed model performance evaluation method measures timelag, offset, and
amplitude at a downstream observed or simulated point compared with the boundary
condition, rather than evaluating the model based on error. These measures are
particularly relevant to small scale models in which error may not be a true reflection of
the ability of a model to correctly predict temperature. Breaking down model
performance into these three predictive measures was a simple and graphic method to
show the model’s predictive capability without sorting through large amounts of
data.To better understand the model and the stream system, a sensitivity analysis was
conducted showing high sensitivity to streamflow, air temperature, groundwater inflow,
and relative humidity. Somewhat surprisingly, solar radiation was among the lowest
sensitivity. Furthermore, three model scenarios were run: a 25% reduction in water
velocity, a 5◦ C increase in air temperature, and no groundwater inflow. Simulations of
removal of groundwater inflows resulted in a 0.5◦ C increase in average temperature
over the modeled time period at the downstream end, further illustrating the importance
of groundwater in this stream system to reduce temperatures.
c
Copyright by Aida D. Arik
November 8, 2011
All Rights Reserved
A Study of Stream Temperature Using Distributed Temperature Sensing
Fiber Optics Technology in Big Boulder Creek, a Tributary to the Middle
Fork John Day River in Eastern Oregon
by
Aida D. Arik
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Presented November 8, 2011
Commencement June 2012
Master of Science thesis of Aida D. Arik presented on November 8, 2011.
APPROVED:
Major Professor, representing Water Resources Engineering
Director of the Water Resources Graduate Program
Dean of the Graduate School
I understand that my thesis will become part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my thesis to any
reader upon request.
Aida D. Arik, Author
ACKNOWLEDGEMENTS
There are so many to thank for the journey to come to this point, to all I offer my
appreciation and gratitude. The bulk of this work was funded by the Bureau of
Reclamation. Thanks to the all the helpful folks out in the MFJD, especially to Les and
Scotta for letting us tromp around their ranch, studying the creek. I had the privilege of
working with brilliant and respectable professors for this thesis. Thanks to John Selker
for his incredible energy, knowledge, and support. Also, thanks to my committee
members Sherri Johnson, Kelly Vaché, and Stanley Gregory for their guidance. The
contributions they have made to this field are truly inspiring. There were many hands
and minds that helped carry out the work that went into this research. Julie Huff, Tara
O’Donnell, and Travis Roth especially deserve great thanks. They have been amazing
colleagues, advisors, and friends. And to all those that helped in the field and
otherwise: Mike Collier, Landon Gryczkowski, Kace Fujiwara, Matthieu Mollet, Jim
Wagner, Greg Nagle, Marie Perennes, Eric Johnson, Khalil Yaker, Estefania Elorriaga,
Ryan Stewart, Kyle Chambers, Chris Gregory, Daniel Moreno, and many more. I am
blessed to have my beautiful family shower me with love and support. To Nick, every
time you make me smile, I am evermore thankful for running into you underneath the
tree. Finally, I’m thankful for such an awesome world to learn about and play in!
CONTRIBUTION OF AUTHORS
Julie Huff and Tara O’Donnell have been integral in the collection of data from the
field, as well as advisory on data analysis. Landon Gryczkowski contributed to Chapter
2 and Travis Roth contributed to Chapter 3 with constructive support in developing the
methods. Dr. John Selker has advised this work to its completion.
TABLE OF CONTENTS
Page
1
2
Introduction
1
1.1
Understanding Stream Temperature . . . . . . . . . . . . . . . . . . .
3
1.2
Scope of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
Detecting Groundwater and Hyporheic Inflows in Big Boulder Creek Using Distributed Temperature Sensing Measurements
3
7
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
2.2
Site Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
2.3
Methods . . . . . . . . . . . . . . . . . . . . .
2.3.1 Distributed Temperature Sensing Set-Up
2.3.2 Calibration of DTS Data . . . . . . . .
2.3.3 Statistical Testing . . . . . . . . . . . .
2.3.4 Calculation of Inflow . . . . . . . . . .
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12
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21
2.4
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23
2.5
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
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Evaluating Model Performance Of A Small Scale Physically Based Stream Temperature Model Of Big Boulder Creek
4
44
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
44
3.2
Methods . . . . . . . . . . . . . . . . . .
3.2.1 Stream Temperature Model . . . .
3.2.2 Stream Model Inputs . . . . . . .
3.2.3 Evaluation of Model Performance
3.2.4 Sensitivity Analysis . . . . . . . .
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3.3
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
56
3.4
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 On Model Performance Evaluation . . . . . . . . . . . . . . . .
3.4.2 What the Model Shows About the Stream System . . . . . . . .
65
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Conclusions and Recommendations
4.1
DTS as a Restoration Tool . . . . . . . . . . . . . . . . . . . . . . . .
70
70
TABLE OF CONTENTS (Continued)
Page
4.2
Model Performance Evaluation . . . . . . . . . . . . . . . . . . . . . .
72
Bibliography
74
Appendices
80
LIST OF FIGURES
Figure
Page
2.1
Map of the Big Boulder Creek watershed . . . . . . . . . . . . . . . .
11
2.2
Images of BBC Pre- and Post-restoration . . . . . . . . . . . . . . . . .
13
2.3
Conceptual setup of DTS in stream system . . . . . . . . . . . . . . . .
14
2.4
Picture of solar trailer power system . . . . . . . . . . . . . . . . . . .
16
2.5
Stream temperature averaged over the entire longitudinal profile . . . .
21
2.6
Conceptual model of mixing of surface water with groundwater inflow
of hyporheic inflow . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
2.7
A snapshot of temperature measured on 28 June 2008 . . . . . . . . . .
25
2.8
A snapshot of temperature measured on 5 September 2008 . . . . . . .
25
2.9
Daily maximum stream temperatures for 18-24 July 2009 . . . . . . . .
26
2.10 Daily minimum stream temperatures for 18-24 July 2009 . . . . . . . .
27
2.11 Daily maximum stream temperatures for 19-23 August 2009 . . . . . .
28
2.12 Daily minimum stream temperatures for 19-23 August 2009 . . . . . .
29
2.13 Areas of detectable groundwater and hyporheic inflow located using a
25-m z-test for July 2009 . . . . . . . . . . . . . . . . . . . . . . . . .
32
2.14 Areas of detectable groundwater and hyporheic inflow located using a
25-m z-test for August 2009 . . . . . . . . . . . . . . . . . . . . . . .
33
2.15 Examples groundwater and hyporheic inflow found in the abondoned
channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
2.16 Picture taken in August 2009 of BBC valley . . . . . . . . . . . . . . .
38
2.17 A photo taken of the stream in July 2009 showing another large bedrock
outcrop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
2.18 Areas in the new channel that are probable candidates for hyporheic
exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
2.19 Map of locations of hyporheic and groundwater inflow for July 2009 . .
41
LIST OF FIGURES (Continued)
Figure
Page
2.20 Map of locations of hyporheic and groundwater inflow for August 2009
42
3.1
Velocity and cross-sectional profiles . . . . . . . . . . . . . . . . . . .
50
3.2
Metrics which compare modeled and observed data at a downstream
point to the boundary condition . . . . . . . . . . . . . . . . . . . . . .
54
3.3
Calibrated model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
3.4
Validation of model . . . . . . . . . . . . . . . . . . . . . . . . . . . .
58
3.5
Model performance metrics for calibrated model . . . . . . . . . . . .
60
3.6
Model performance parameters for the validation of the model . . . . .
61
3.7
Simulation run showing reduction of water velocity by 25% . . . . . . .
62
3.8
Model simulation with 5◦ C increase in air temperature . . . . . . . . .
63
3.9
Model simulation with no groundwater inflow . . . . . . . . . . . . . .
64
3.10 Early model simulation . . . . . . . . . . . . . . . . . . . . . . . . . .
66
3.11 Distance dependent parameters input into the model . . . . . . . . . . .
67
3.12 Solar radiation values input into the model . . . . . . . . . . . . . . . .
68
3.13 Time dependent parameters input into the model . . . . . . . . . . . . .
68
LIST OF TABLES
Table
Page
2.1
Temperature sensing instrument specification comparison . . . . . . . .
9
2.2
Daily weather parameters for the hour of maximum stream temperatures
for 18-24 July 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
2.3
Daily weather parameters for the hour of minimum stream temperatures
30
2.4
Measured streamflow throughout July 2009 . . . . . . . . . . . . . . .
30
2.5
July 2009 calculation of percentage of groundwater inflow . . . . . . .
31
2.6
August 2009 calculation of percentage of groundwater inflow . . . . . .
31
2.7
Locations of hyporheic inflow for July 2009 . . . . . . . . . . . . . . .
34
2.8
Locations of hyporheic inflow for August 2009 . . . . . . . . . . . . .
34
2.9
Locations of groundwater inflow for July 2009 . . . . . . . . . . . . . .
35
2.10 Locations of groundwater inflow for August 2009 . . . . . . . . . . . .
35
3.1
48
Model input parameters . . . . . . . . . . . . . . . . . . . . . . . . . .
LIST OF APPENDICES
Page
A An Example Budget for Minimizing the Cost of DTS Instrumentation Use in
Stream Restoration Applications
81
B Data Collected (See Attached CD)
83
C Stream Temperature Model (See Attached CD)
84
LIST OF APPENDIX TABLES
Table
Page
A.1 Investments in riparian and in-stream restoration projects from 1995-2009 82
A.2 An example budget for a 2-day DTS field installation with 4 days of data
analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
Chapter 1 – Introduction
The United States has seen a dramatic shift in attitude towards water resources in the
past century. With the mentality that any drop of freshwater that reaches the ocean is
a waste, leading to an era of development that defined the American West (Reisner,
1993), to the more recent realization of the worth of “ecosystem services” for which
attaching a monetary value to the natural services provided by ecosystems (Costanza
et al., 1987) has, in part, led to the incentive to protect and restore the physical setup
of ecosystems outside of regulatory standards. Thus, there is much money currently
being spent to protect and mitigate water resources by reviving or restoring degraded
ecosystems within a watershed. In Oregon alone, an economy has developed around the
industry of restoration, with $500 million invested in such projects over a period between
1995 and 2007 (Nielsen-Pincus and Moseley, 2010). The government organization,
Oregon Watershed Enhancement Board (OWEB), for example was established in the
late 1980s to distribute dedicated funds to help protect and restore watershed health
and natural habitats around the state. Ultimately, past land use practices and human
activities have led to the degradation of many natural systems, from which the field of
stream restoration is expanding with abundant numbers of projects found all over the
United States (Kondolf and Micheli, 1995, Roni et al., 2002, Bernhardt et al., 2005,
Giller, 2005, Gillian et al., 2005, Jansson et al., 2005, Palmer et al., 2005, Watanbe
et al., 2005). Having a holistic, watershed approach to water resources, which weighs
2
the importance of the interconnectivity and sustainability of natural processes within the
entire watershed, is becoming more commonplace in the United States.
Regionally, the major drive for stream restoration projects in the Pacific Northwest
is the large number of areas listing salmonid species as endangered or threatened under
the U.S. Endangered Species Act (ESA). Restoration projects may range from controlling noxious weeds and establishing native vegetation, to wetland and in-stream habitat
restoration and enhancement, to removal of barriers for fish passage. Endangerment of
salmonid species may stem from a number of factors, but one of the most prevalent
challenges is stream temperature. The US Environmental Protection Agency (EPA) inventories impaired streams under section 303(d) of the Clean Water Act (CWA), under
which 59% of listed reaches in Oregon have an impaired status for temperature (EPA,
2006). Temperature in streams is an important component of water quality and habitat
for biota.
The Middle Fork John Day River (MFJD) in Eastern Oregon is undergoing several strategic restoration projects to improve and protect quality and quantity of salmon
spawning and rearing habitat. The MFJD basin is part of a network of watersheds in the
Pacific Northwest designated as Intensively Monitored Watersheds (IMW), designed to
better understand the response of fish to restoration and management activities. Temperature is a critical factor of the design of the restoration projects for the MFJD, where
high temperatures in combination with low-flow during the summer months can cause
stress on spawning adult salmon. In 2007 for example, nearly half the salmon run died
due to extreme water temperatures in the headwaters of the MFJD. Currently, 364 km
of the MFJD and its tributaries are listed for temperature by the EPA under CWA 303(d)
3
(EPA, 2006). Although many of the restoration projects try to address temperature issues, there is much still to be learned about the direct effects of restoration practices on
stream temperature.
1.1
Understanding Stream Temperature
Heat energy storage of a stream can be described by the sum of net radiation flux, latent
heat flux, sensible heat flux, and streambed conduction (Brown, 1969, Westhoff et al.,
2007). The net radiation flux can further be broken into the sum of shortwave (or solar)
and longwave radiation, of which the input by solar radiation can be reduced based on
a multiplicative factor described by the amount of shade over the stream. It is well
established that solar radiation is the major driving component of energy or temperature
of a stream. The question is how to keep streams cool for the native organisms which
depend on certain temperatures to survive?
Landscape disturbance in various forms resulting in removal, reduction/addition,
or change of riparian vegetation among other important factors, can have an effect on
the temperature regime of the stream. Various studies recognize the key role riparian
vegetation plays in buffering stream temperature by the shade provided (Brown, 1969,
Brown and Kygier, 1970, Swift and Messer, 1971, Ringler and Hall, 1975, McSwain,
1987, Amaranthus et al., 1989, Sugimoto et al., 1997, Hetrick et al., 1998, Zwieniecki
and Newton, 1999, Johnson and Jones, 2000, Johnson, 2004, Young et al., 2005, Webb
and Crisp, 2006, Nelitz et al., 2007, Dent et al., 2008). Whether measured or calculated
using a physical model, there is sufficient evidence that shading provided by streamside
4
vegetation reduces thermal input from solar radiation into small streams, especially on
long stretches. Although vegetation may be the easiest and most visible component
to manage for stream temperature, other components affecting the energy balance can
govern whether the stream can lose heat energy.
Additional processes influencing the energy balance may explain changes in the thermograph of a given stream. Channel morphology, which is interconnected with vegetation, is an important feature that may affect the temperature profile (McSwain, 1987,
Liquori and Jackson, 2001, Dent et al., 2008). The width-to-depth ratio is an important measure of the surface area exposed to sunlight compared to the volume of water
(McSwain, 1987, Liquori and Jackson, 2001), which is why low flow conditions during summer are most vulnerable to shading. Furthermore, air temperature and other
atmospheric conditions, which may also be influenced by shade and riparian vegetation,
are cited by some to be a driver in stream temperature (Hetrick et al., 1998, Larson
et al., 2002, Webb and Crisp, 2006, Nelitz et al., 2007). Velocity or streamflow are other
factors to be considered in the temperature of streams (Hetrick et al., 1998). Furthermore, substrate and geology play a key role in the energy budget of streams (Brown
and Kygier, 1970, Ringler and Hall, 1975, McSwain, 1987, Johnson, 2004, Young et al.,
2005). On a small scale, processes such as hyporheic exchange or groundwater inflow
may have the most impact on reducing temperatures (Torgersen et al., 1999). From a
restoration perspective, knowing locations of cool water inflow within the stream allows
strategic design to enhance such areas to maximize cool water refuges in the stream.
Models can be powerful tools for understanding or predicting the behavior of temperature within a stream system. Physically-based models are generally used to model
5
stream temperature (Johnson, 2003, Boyd and Kasper, 2004, Westhoff et al., 2007).
These temperature models are based on energy and mass balance equations which quantify the energy budget of a stream, or simply the sum of all the energy components:
Φstream = Φradiation + Φconduction + Φlatent + Φsensible
(1.1)
Where the energy of the stream (Φstream ) is the sum of longwave and shortwave radiation (Φradiation ), streambed conduction from the temperature gradient between the
substrate and water interface (Φconduction ), latent heat from evaporation (Φlatent ), and
sensible heat from the air temperature (Φsensible ). This equation is the basis of the HeatSource model commonly used for basin-scale temperature modeling by DEQ (Boyd
and Kasper, 2004). A MATLAB version of the HeatSource model has been produced
for smaller, reach-scale temperature models (Westhoff et al., 2007). Models have great
value as management or restoration design tools, where specific scenarios can be modeled to determine suitable strategies for management relating to stream temperature.
1.2
Scope of Study
A stream temperature study that has been conducted on a tributary to the MFJD, Big
Boulder Creek (BBC), prior to and following major channel restoration is described in
the content of this thesis. The major components of this study were twofold. First,
stream temperature was measured using Fiber Optic Distributed Temperature Sensing
(DTS) technology on four separate occasions during two summers for a 1 km stretch
6
of BBC including approximately 1 km of which was restored. Secondly, a calibrated
model has been produced for the same monitored stretch of the Creek. The model can
be used as a future management planning tool for BBC.
The second and third chapter of this thesis detail major results of the study conducted on BBC. A statistical analysis was used for identifying locations of groundwater
and hyporheic inflow from the stream temperature data collected, described in the second Chapter. From this information, groundwater inflow can be quantified and relative
impacts of hyporheic exchange can be estimated. Knowing such locations enable strategic planning for future adjustments to the channel to capitalize on cool water inflow into
the stream. The third Chapter explains new methods for model performance evaluation
on small-scale models, which have been developed out of the produced stream temperature model. The methods of evaluation allow better insight into how specific model
parameters affect the output by attempting to extract the influence of measured inputs.
7
Chapter 2 – Detecting Groundwater and Hyporheic Inflows in Big Boulder
Creek Using Distributed Temperature Sensing Measurements
2.1
Introduction
The scales at which water temperatures of streams vary both spatially and temporally
play an essential role in the survival of many stream organisms. The importance of such
spatiotemporal scales is reflected in the life cycle of the salmon, for example. Stream
temperature can be viewed at a sub-reach scale by looking at the temperature differences
between pools and riffles, or at a broader basin-scale by examining the temperature in
the headwaters versus the mouth of a river. Timescales of stream temperature changes
may vary from a diel scale to inter-annually or longer term changes.
All stream organisms survive over a specific range of temperatures, and are dependent on the heterogeneity of stream temperatures on a small scale during periods when
the average temperature of the stream moves outside their survivable range. Small scale
thermal refuges are therefore of critical importance for fish seeking cooler water during summer low-flows when peak stream temperatures can exceed the lethal temperature threshold for salmon (Torgersen et al., 1999, Collier, 2008). Subtle temperature
changes in streams are detectable by fish and other stream organisms when survival is in
question, however the accurate measurement of such changes has proven more difficult
to capture using available technology.
8
Various technologies have enabled significant progress in the understanding of the
thermal regimes of streams with capabilities for measurements with increasing accuracy.
In addition to simple thermometers, an array of instruments enables the acquisition of
multiple temporal or spatially variable measurements of temperature. With the ability
to record changing temperatures at one point over time, programmable temperature data
loggers have significantly advanced the understanding of stream temperatures. Affordable water temperature loggers can be found having good accuracy, long battery life,
easy programming, downloading capability, and designs that can be rugged or small.
The affordability of such temperature loggers offer the capability to easily install loggers at multiple points along a river system (Johnson, 2003), although spatial resolution
with this technique is still low since it takes a large number to closely examine subtle
temperature differences along a stream.
The advent of remote sensing technology allowed capture of higher resolution spatial
measurements of temperature. Airborne remote sensing technologies such as Forward
Looking Infrared (FLIR) allow tremendous insights into the thermal regime of a river
with a high resolution of spatially distributed surface temperatures (Torgersen et al.,
1999, 2001, Loheide and Gorelick, 2006). Airborne FLIR allows depiction of surface
stream temperatures at a reasonable level of accuracy along long stretches of river. However, such technology only measures surface temperatures of streams and temperatures
are only recorded at one time, usually at midday.
More recently, the science of stream temperature has been enhanced to a new level of
resolution with Distributed Temperature Sensing Fiber Optic Technology (DTS) (Selker
et al., 2006a,b). This technology is the first method by which temperature can be mea-
9
Table 2.1: Comparison of specifications for selected stream temperature sensing instruments. *Accuracy of DTS instruments is dependent on calibration (Tyler et al., 2009).
Instrument
Temperature
Logger (HOBO/
Tidbit)
Accuracy/Resolution
Spatial Resolution
±0.2◦ C/0.02◦ C
N/A
Airborne FLIR
±0.5◦ C/0.1◦ C
Fiber Optic DTS
±0.05-0.5◦ C*/0.01◦ C
0.2-0.4 m (for 50-60 km
lengths at 2-110 m width)
0.25-1 m (for lengths of up
to 8 km)
Citation
www.onsetcomp.com/
water-temperaturedata-loggers
(Torgersen et al.,
2001)
(Selker et al., 2006b,
Huff, 2010)
sured with much higher spatial and temporal resolution. Although both FLIR and DTS
are expensive and require more training than temperature loggers, each can offer useful
insights into stream temperatures otherwise undetectable by point temperature loggers.
Compared to FLIR, DTS can capture temperature at the streambed, but cannot capture
temperature for reaches as long as FLIR accomplishes and requires much more physical labor. Nevertheless, choice of equipment is highly dependent on what is being
questioned about stream temperature in the research reaches with strengths and disadvantages found in each technology (Table 2.1).
With both high spatial and temporal resolution, DTS is a technology capable of
determining locations of cool water inflows along a channel reach. In the energy budget
of a stream, cool water inflows are a key process by which the stream can be cooled in
the downstream direction and provide cold water refuges for biota during summer low
flows. Groundwater maintains a constant temperature and will therefore reduce both the
daily maximum and minimum stream temperatures when colder than the surface water
temperature (Conant, 2004). Hyporheic exchange is a process in which surface water
from the stream goes subsurface via the streambed, acting as a temporary heat storage
(Arrigoni et al., 2008). Therefore, an area with significant hyporheic exchange resulting
10
in cooling can be described by a temperature signal dampening both the maximum and
minimum daily temperatures.
2.2
Site Description
The Middle Fork John Day River (MFJD) in eastern Oregon (Figure 2.1) has been the
location of many restoration projects to improve and protect essential salmon spawning
and rearing habitat. Restoration projects in the area include installation of Engineered
Log Jams (ELJ), digging of scour pools, re-establishing native riparian vegetation, and
even channel realignment. The area is designated as an Intensively Monitored Watershed (IMW), a watershed undergoing habitat improvement with extensive monitoring
for the purpose of improving the understanding of the responses of salmonids to stream
restoration and enhancement work (PNAMP, 2008). Temperature is a critical consideration in the design of many restoration projects in this area, where high air temperatures
in combination with low-flow during the summer months can stress spawning adult
salmon and young fish. In 2007 for example, nearly half the adult spawning salmon run
died due to extreme water temperatures in the headwaters of MFJD (Huff, 2010). The
Upper Incipient Lethal Threshold (UILT) for adult Chinook Salmon is 25◦ C (McCullough, 1999). Currently, 364 km of the MFJD and its tributaries are listed by the US
Environmental Protection Agency (US EPA) as not meeting water quality standards for
temperature under section 303(d) of the Clean Water Act (CWA) (EPA, 2006).
The MFJD sub-basin is a spring-fed system flowing from the slopes of the Blue
Mountains. The basin is characterized by a mildly sloping river fed by higher gradient
11
Figure 2.1: Map of the Big Boulder Creek watershed within the Middle Fork John Day
River subbasin in northeastern Oregon.
tributaries. The area receives most of its precipitation during the winter, mostly as snow,
and stays warm and dry during the summer, usually with little or no rainfall. A mix of
private, tribal, and public lands occupy the valley floor with most of the upper watershed
controlled by the US Forest Service, Confederated Tribes of Warm Springs, or The
Nature Conservancy, with some of the stream channel in the upper basin private lands
fenced to exclude livestock and allow recovery of riparian vegetation. Historically, some
of the valley bottom was dredge-mined after the discovery of gold in the 1860s, and
in the early 1900s the construction of logging railways further impacted the river and
some of its major tributaries (Beschta and Ripple, 2005). Presently, land set aside
for conservation is interspersed among active ranches, where cattle graze streamside
meadows.
Temperatures for this study were measured in a major tributary to the upper MFJD,
Big Boulder Creek (BBC), which runs through private land at its lowest end after flowing
12
through US Forest Service land for most of its length (Figure 2.1). In the mid-twentieth
century, the stream was diverted into a channel close to the eastern valley wall to expand
grazing land, a common practice in the area. The headwaters of the BBC watershed
are the Greenhorn Mountains, flowing roughly south into the MFJD from an elevation
of 2050 m to 1100 m at the mouth. The mostly forested upper watershed was burned
with high stand mortality in the Summit Fire of 1996. The downstream end of BBC
runs through Boulder Creek Ranch with a slope of about 3-4%, providing about 2km
of critical rearing habitat for Endangered Species Act (ESA) listed summer steelhead
and spring Chinook. In conjunction with the landowner and The Nature Conservancy
(TNC), the Bureau of Reclamation (BoR) designed a restoration project for a 1 km
stretch of the stream. In the summer of 2008, three sections of the stream were diverted
into its historical channel with large wood and boulders added to provide low flow habitat during high flows. Monitoring set-ups for temperature using DTS were installed
prior to construction on the channel in June 2008, after construction in September 2008,
and during July and August 2009 (Figure 2.2).
2.3
2.3.1
Methods
Distributed Temperature Sensing Set-Up
Distributed temperature sensing (DTS) is used in a stream setting to determine locations
of areas of groundwater upwelling and hyporheic inflow. The DTS installation requires
the placement of fiber optic cable along the streambed, with one cable laid approxi-
13
Figure 2.2: Images of BBC Pre- and Post-restoration looking upstream. The upper images show the same general valley bottom location before restoration in July of 2008
(left) and after restoration in September of 2008 (right). Where the channel had previously been bifurcated, the east side of the channel was filled to create one flowing
channel. The lower images show roughly the same longitudinal location but different
lateral positions, where the old channel (left) was flowing against the hillside and the
restored channel (right) was diverted to a mid-valley position.
14
Figure 2.3: Conceptual setup of DTS in stream system including three points of known
temperature for calibration.
mately along the thalwag in this study. Within the cable are two optical fibers, with
the two fibers fused together creating a loop at the end of the installation for a doubleended measurement. The optic fibers are fused to E2000 connectors allowing the DTS
instrument to read signals in both fibers. With this installation there are, essentially four
of the same measurements, two of which are reflections (Smolen and van der Spek,
2003). The temperature readings obtained by the DTS must be calibrated, with at least
three measured known temperature points along the cable. Coolers are installed at both
ends of the cable with a 20-30 m coil of cable submerged in each 0◦ C ice bath using
an ice-slush mixture. The third calibration point is also a coil of cable in a well-mixed
cooler of water, or with the cable placed in the stream itself (Figure 2.3). Having two
to four extra temperature loggers installed along the cable improve the validation of the
calibrated DTS temperature data.
During the June 2008 installation, flows on the MFJD were unusually high, but with
only one window of opportunity to collect pre-construction temperature, the cable was
15
installed despite the possibility that temperature signals of groundwater and hyporheic
exchange would be lost in high, well-mixed flows. For this installation a SensorTran
5100 M4 was used, with power supplied by the landowner. The installed cable was
manufactured by AFL Telecommunications with white polyvinylidene fluoride coating
a 1mm diameter stainless steel loose tube, protecting two multimode optical fibers. At
about 7 kg/km, the cable was very light for carrying, however the high flows during
the installation caused a major break in the cable. Breaks in the cable are repairable
using a fusion splicer, but require protection of the splice from water, as well as new
calibration points on either side of the repair. Due to a limited supply of temperature
loggers at the site, new loggers were not installed. Another problem was that the power
supply also proved to be inconsistent during this installation. With no trained personnel
to restart the system when power was lost, data was not being collected. When power
was resumed and the system restarted, the initial calibration parameters were no longer
valid. Furthermore, high air temperatures limit the use of the instrument in the field
during very hot periods. The SensorTran shuts down at a temperature of 35◦ C, and
with the DTS installed in a tin roof barn where ambient temperature regularly exceeded
machine constraints. Due to all these equipment, personnel, weather, and instrument
limitations that were not well understood prior to the installation, including the validity
of the calibration parameters (Tyler et al., 2009), temperature data obtained from the
pre-construction installation amounted to only a few hours.
Lessons learned from the previous installation resulted in several strategic changes
for the September 2008 post-construction installation. The major change was to use
a trailer equipped with solar panels to power the DTS instrumentation (Figure 2.4).
16
Figure 2.4: Flatbed solar trailer used in September 2008 installation to supply power the
DTS instrumentation and in the August 2009 installation for power back-up. Batteries
and other equipment fit on the bed of the trailer underneath the solar panels.
An Agilent N4386A DTS was used as a more robust field instrument in the climate
conditions found in the MFJD area during the summer. Since the flow of the creek had
dropped dramatically since the June installation, the same cable was used since it was
more suitable for low-flow applications. For this installation, a black and white cable
was attached in a separate effort to determine shade along the stream (Petrides et al.,
2011). The two fibers of the black and white cable were fused to the two optical fibers in
the stream cable, with the black and white cable then suspended over the stream. When
the installation was left to collect temperatures, the black and white cable was severed,
possibly by wildlife, with the cable found broken upon return. Although the shade cable
was a separate installation, the severance of the above ground cable also affected the
stream temperature measurements since the DTS no longer able to read the entire length
of the cable. In addition to the break in the cable, the overall poor performance of the
DTS instrument resulted in sub-optimal temperature data.
17
Improvements were made to the DTS installations during the summer of 2009, with
this diligence resulting in a marked improvement in the quality of the data. The main
concern was to have trained personnel onsite to check the installation daily, rather than
installing the instruments and leaving them to collect data as was the practice the previous summer. Thus, when problems arose they could be quickly dealt with rather than
relying on the instruments to collect data if left for a week. Moreover, with daily checks
on the equipment, the ice baths were maintained during the entire installation, ensuring an improved and more reliable calibration process. Each ice bath had a HOBO v2
temperature logger attached to the coil as well as at 3-4 points along the stream for
both calibration and validation purposes. The DTS employed was a SensorTran Gemini
enclosed in a solar trailer for power. The cable was manufactured by Brugg Cables,
LLK-BTSE 2FG5 M 4.60 PUR blue. This cable was designed especially for streams
with rough conditions such as BBC, with a polyurethane coating protecting two stainless
steel loose tubes encasing each optical fiber. Although this cable was heavier, it proved
to be much more reliable, with no breaks during either installation. Two problems encountered were ambient temperature exceeding the limitations of the DTS instrument
and power generation not meeting power consumption. Measures were taken to ensure
proper cooling of the enclosed trailer to prevent ambient temperature from reaching or
exceeding 35◦ C. To ensure no power outage or shortages, a gas powered generator was
installed in July to run when solar power dropped to low levels, particularly at night. A
flatbed solar trailer was used during the August installation in addition to the enclosed
trailer, since power generation was not adequate during the first installation.
18
2.3.2
Calibration of DTS Data
Processing of the DTS temperature data requires the determination of three separate
calibration parameters (Maharana, 2009). The first parameter is a slope correction,
under the assumption that the temperature readings of the DTS increase linearly with
distance. Having 0◦ C ice baths (IB) on both the downstream (DS) and upstream (U S)
ends of the cable enables calculation of the slope, where:
TU SIB = TDSIB = 0
(2.1)
The slope correction (Cslope ) can be calculated accordingly:
Cslope =
r
TUr SIB − TDSIB
xU SIB − xDSIB
(2.2)
Raw DTS temperature of the upstream and downstream ice baths is denoted by T r and
x denotes the position along the cable. If the calibration parameter is not constant with
time, knowing the temperature of the ice baths will enable a time dependent calculation
of slope correction. Temperature corrected for slope (T s ) can be calculated with the
following equation:
T s (x, t) = T r (x, t) − Cslope (t) ∗ x
(2.3)
The next correction is an offset correction, when the entire temperature trace is above
or below the calibrated temperature as indicated by the temperature loggers. Where, for
example, the cable temperature in the ice baths should be at 0◦ C, the DTS can be easily
19
shifted to the correct temperature using the following equation:
Cof f set = TUs SIB − TU SIB
(2.4)
Where the correction for offset (Cof f set ) is given by the error of the DTS temperature
compared to the actual temperature known from the temperature logger, giving a new
correction of temperature (T t ):
T t (x, t) = T s (x, t) − Cof f set (t)
(2.5)
Similarly, the offset calibration parameter may be found to be time dependent and can
be calculated over the entire time of installation. The final calibration parameter is a
correction for gain (Cgain ), which can be calculated knowing at least two distinct temperature points along the cable. Where more than two points of temperature were know
along the cable, those measurements were included in the calculation of the correction
parameter by finding the slope of a best fit line to the actual vs. measured dataset:
Cgain = slope of best fit line[T t (x), T (x)]
(2.6)
The final calibrated DTS temperature dataset will be calculated by:
T f (x, t) = T t (x, t) ∗ Cgain
(2.7)
20
These calibrations for the DTS data are time consuming, however, using more rigorous
measures to collect quality data in the field ensures easier calibration.
2.3.3
Statistical Testing
Using the 2009 maximum and minimum DTS temperature data, a Z-test was performed
with a conservative p-value of 0.005 to determine areas of hyporheic exchange or groundwater inflow, similar to the work conducted by Huff 2010. A distance of 25 m was
chosen for the length of the Z-test, meaning each measured temperature was tested to
determine whether the previous 25 m was significantly cooler. Maximum and minimum temperatures during the day were selected by averaging together the every point
of temperature in the stream to create a time series for which the time of minima and
maxima could be determined (Figure 2.5). Maximum daily stream temperatures at each
point in the stream were analyzed to determine whether a cold water inflow occurs at
each point based the 25 m upstream of that point (p-value <0.005). To determine areas
of groundwater inflow, the same method was used for the minimum daily temperatures
(p-value <0.005). Areas where cold water inflow occurred both during the maximum
and minimum stream temperatures are determined to be cold groundwater inflow. Similarly, areas of hyporheic exchange were determined where the minimum temperatures
are warmed by incoming water (p-value >0.995). Therefore, areas with groundwater
inflow cool both during the maximum and minimum daily temperatures, while areas
of hyporheic exchange cool during the maximum temperatures and warm during the
minimum daily temperatures (Figure 2.6).
21
Figure 2.5: August 2009 measurements of stream temperature averaged over the entire
longitudinal profile to determine occurrence of minimum and maximum stream temperature each day.
2.3.4
Calculation of Inflow
Groundwater inflow as a percentage of the streamflow can be calculated using conservation of mass and energy:
Qus + Qgw = Qds
(2.8)
Tus Qus + Tgw Qgw = Tds Qds
(2.9)
Qgw =
Tus −Tds
Tus −Tgw
∗ Qus
(2.10)
The volumetric flow is denoted by Q and T is the temperature, with the subscripts for
upstream (us), groundwater (gw), and downstream (ds).
Figure 2.6: Conceptual model of mixing of surface water with groundwater inflow of hyporheic inflow (figure from
(Huff, 2010).
22
23
2.4
Results
Temperature decreases in the downstream direction in each of the datasets (Figures 2.72.12). The downstream reduction in surface temperatures was especially apparent during July 2009, where the cooling trend starts around 500 m during the peak daily temperatures (Figure 2.9). The amount of cooling and the start of the cooling trend vary
daily, which could have been influenced by the lowest stream temperatures during the
day (Figure 2.10), weather parameters (Table 2.2, 2.3), or streamflow (Table 2.4).
From 450 m to 50 m during the maximum stream temperature, there was an average reduction in stream temperature of 0.5◦ C during the July 2009 installation period. Using
a groundwater temperature of 6◦ C as used by other researchers in the MFJD (Hopson,
1997, Huff, 2010), the groundwater inflow is calculated to be 3% of the upstream flow
(Table 2.5). For example, the flow on 7/17/2009 was measured at 280 L/s (10 cfs), therefore the amount of groundwater inflow was calculated to be 8.5 L/s (0.3 cfs). Moreover,
on 7/23/2009 streamflow was measured at 200 L/s (7.0 cfs) with a calculated groundwater inflow of 6 L/s (0.21 cfs). During the August 2009 installation, the streamflow
was lower, at 160 L/s (5.6 cfs) on 8/18/2009, showing also a decrease in the percentage
of groundwater inflow into the stream (Table 2.6). With a much lower average cooling
of only about 0.1◦ C over the same stream stretch during the August 2009 installation,
the groundwater inflow is calculated as much less, at about 1.6 L/s (0.06 cfs) with the
percentage of groundwater inflow approximately 1% of the upstream flow.
The changes in temperature due to groundwater inflow are greater than the estimated
error of the DTS measurements (0.25◦ C in July 2009, 0.08◦ C in August 2009), therefore
24
enabling the calculation of the influx of groundwater with this approach. However, the
amount of groundwater inflow is below of the detectable range for the Marsh-McBirney
Flow Meter used to measure streamflow. Accurately pinpointing areas of inflow is
therefore limited if using physical streamflow measurement techniques, and subsurface
in-situ techniques to measure groundwater flows such as driving a piezometer into the
streambed are impossible given the large size of streambed material.
Even at inputs as low as 1-3% of the overall streamflow, statistical methods can still
be used for robust determinations of specific areas of groundwater or hyporheic inflow
based on the maximum and minimum daily stream temperatures seen in the variable
cooling signals (Figures 2.13, 2.14). During July, the amount of cool water inflow is
relatively greater compared to a month later in August when streamflow has decreased.
The type of cool water inflow also varies significantly between the measured periods.
Hyporheic inflow is significantly greater in July with nine areas of inflow greater than
5 m long and only three detected in August (Table 2.7, 2.8). Total number of areas
with groundwater inflow detected are distinctly different between months, with 5 areas
greater than 5 m in length found in July compared with 7 in August. However, the inflow
areas are longer in August, with the longest stretch of groundwater inflow detected at
40 m in August compared to only 16 m for July (Table 2.9, 2.10). Reduction in maximum daily stream temperatures caused by hyporheic and groundwater inflow is further
evident in surficially disconnected pools forming in two seasonally dry side channels,
where temperature signals measured in two separate pools showed distinctly different
signals indicating hyporheic and groundwater flows (Figure 2.15).
25
Figure 2.7: A snapshot of temperature measured on 28 June 2008 before construction
of the channel. Shown is the maximum temperature of the traces taken. A cooling
trend downstream can be seen over the distance shown, which is common throughout
the temperature measurements. The stream cools nearly 0.5◦ C over about 600 m, with
an estimated error of 0.10◦ C. Note that the cable was broken, so the trace shown here is
shorter than the following traces.
Figure 2.8: Stream temperature taken 5 September 2008 after the construction of the
channel. Shown is the maximum daily temperature. A cooling trend can be seen starting
at about 400 m and continuing in the downstream direction. Data taken during September 2008 was not used for statistical analysis due to its high error, 0.37◦ C, attributable
to the poor performance of the Aglilent DTS instrument used to collect the data.
Figure 2.9: Daily maximum stream temperatures for 18-24 July 2009.
26
Figure 2.10: Daily minimum stream temperatures for 18-24 July 2009.
27
Figure 2.11: Daily maximum stream temperatures for 19-23 August 2009.
28
Figure 2.12: Daily minimum stream temperatures for 19-23 August 2009.
29
30
Table 2.2: Daily weather parameters for the hour of maximum stream temperatures
for 18-24 July 2009, with data bars to compare daily parameters relative to each other.
There was no precipitation.
Table 2.3: Daily weather parameters for the hour of minimum stream temperatures.
Table 2.4: Measured streamflow throughout July 2009.
31
Table 2.5: Calculation of percentage of groundwater inflow daily based on maximum
daily temperatures averaged over the July 2009 installation period using a groundwater
temperature of 6◦ C. All temperature values are in degrees Celsius.
Table 2.6: August 2009 calculation of percentage of groundwater inflow.
Figure 2.13: Areas of detectable groundwater and hyporheic inflow located using a 25-m z-test. Averaged over the
duration of the July 2009 installation period, the maximum and minimum temperatures are plotted over locations
of groundwater inflow (green) and hyporheic inflow (purple). The independent axis is the longitudinal distance in
meters starting with upstream on the left.
32
Figure 2.14: Areas of groundwater and hyporheic inflow located using a 25-m z-test for August 2009.
33
34
Table 2.7: Locations of hyporheic inflow for July 2009 as indicated by the 25-m z-test.
Occurrences 5 m or greater are highlighted in bold.
Table 2.8: Locations of hyporheic inflow for August 2009 as indicated by the 25-m
z-test.
35
Table 2.9: Locations of groundwater inflow for July 2009 as indicated by the 25-m
z-test.
Table 2.10: Locations of groundwater inflow for August 2009 as indicated by the 25-m
z-test.
Figure 2.15: Temperature loggers were installed in two separate locations in the pre-restoration channel where
ponded water was observed in the downstream most section of the restoration. Temperature in the active channel
(green) is graphed with the two pools in the abandoned channel showing a signal of hyporheic cooling (red) and
groundwater cooling (blue). Near this segment of channel is a bedrock outcrop (see Figure 2.16, 2.17). The pools
were not more than 10cm deep, and therefore particularly susceptible to shading, which likely explains the daily
pattern in temperature.
36
37
2.5
Conclusions
Distributed Temperature Sensing Fiber Optic technology can be used as a tool for planning restoration by pin pointing areas of cold water inflow which allows for better placement of wood structures or excavated pools to enhance habitat for fish. Groundwater
inflow is mostly seen in the downstream half of the study reach on BBC where the reduction in surface water temperatures occur and provide a detectable amount of flow to
the stream. Groundwater inflow plays a key role in reducing surface temperatures of the
stream system.
An important corroborating observation is that, several bedrock outcrops are seen
on the valley wall as well as anomalous bedrock outcrops in the floodplain in the area
corresponding to about 400-450 of the measured DTS temperature where groundwater
inputs were inferred with the Z-test (Figure 2.13, 2.13). Although depth to bedrock is
unknown, bedrock in the streambed appears to be much shallower in this area, resulting
in localized groundwater upwelling in the stream. Green grasses surrounded by dry
grass in the floodplain also provide clues for areas of shallow water during the driest
periods.
Hyporheic exchange in the stream system also appears to be an important process in
this stream system, especially during higher flows. Cool water inflow during daily maximums driven by observed hyporheic inflow is a critical process similar to groundwater
inflow, although the hyporheic exchange shows greater variability over time with marked
decreases later in the summer as flows decrease. Dry side channels with coarse substrates likely provide a conduit for flows outside the main channel allowing for greater
38
Figure 2.16: Picture taken in August 2009 of BBC valley, a year following restoration.
The dotted-dashed line marks the pre-restoration channel. The bedrock outcrop seen in
the picture corresponds approximately to the 430 meter mark of the temperature profiles.
The extent of this picture is roughly starting at DTS meter 450 on the left to DTS meter
280 on the right.
movement of hyporheic flows, which is the case now with the pre-restoration channel
(Figure 2.18).
The movement of the channel to a more mid-valley position is expected to allow
greater lateral hyporheic exchange than possible when the channel was positioned against
the valley wall. During July, four out of the fourteen locations of hyporheic inflow occur within 30 m of an inlet or outlet of the abandoned channel (Figure 2.19), with one
out of five during August (Figure 2.20). More substantial evidence would be needed
to conclusively demonstrate whether the abandoned channels increase the process of
hyporheic exchange, although hyporheic and groundwater inflow clearly occur in the
39
Figure 2.17: A photo taken of the stream in July 2009 showing another large bedrock
outcrop is visible above the floodplain. The photos also show areas of green grasses
versus brown grasses, which may be indicative of areas of shallow water.
40
Figure 2.18: Areas in the new channel that are probable candidates for hyporheic exchange sites with fine sediment underlain with coarse material.
abandoned segment furthest downstream (Figure 2.15).
The measurement of streamflow using a flow meter involves inherent errors with
detection of inflows as little as 1-3% of the streamflow likely impossible. Estimated error of streamflow measurement based on consecutive streamflow measurements on the
mainstem of the MFJD is about 1%. Inherent measurement error likely increases significantly for BBC, since a cross-sectional profile may include many boulders, which affect
both the cross-sectional area measurement and the velocity measurement. Streamflow
taken during both installations at the upstream and downstream ends counter-intuitively
show a smaller flow at the downstream end, although both measurements were within
the uncertainty of the measurement. The amount of groundwater inflow is notable considering the obvious influence on temperature, however the influence of groundwater
influx on streamflow for the study area appears relatively minor if not undetectable.
Groundwater temperature for the purpose of this study was defined as taking on an
41
Figure 2.19: Map of locations of hyporheic (green) and groundwater inflow (purple) for
the monitored stretch of the creek for July 2009. Each dot represents a length of 10 m.
42
Figure 2.20: Map of locations hyporheic and groundwater inflows for August 2009.
43
average yearly air temperature. The groundwater temperature of 6◦ C was used based on
previous research conducting in the MFJD. Groundwater temperature in BBC, however,
is unknown and assumed for this study. If a higher temperature of groundwater was
assumed, there would be greater inflow of groundwater to achieve the cooling that was
seen in the stream system. Moreover, locations of cool water inflow signals picked up
through statistical testing may include mixing of groundwater and hyporheic inflow.
Therefore, locations found may not be absolute signals for groundwater or hyporheic
inflow but a good indication of relative cooling in temperatures during the summer high
temperature, low-flow conditions, which is critical to survival of salmonid species.
DTS technology is limited to detection of areas of cool water inflows. However, hyporheic exchange is a process through which water can flow both in and out of the system and thus only the inflow process is detectable. Hyporheic exchange likely becomes
a process of greater significance during higher flows when the surface water has access
to a greater area of bank and floodplain to enter the subsurface. The differences seen
in hyporheic inflow from July to August support this interpretation. When streamflow
declines to baseflow in August, groundwater inflow becomes a more important process
relative to hyporheic exchange. The dynamic hydrogeological relationships between the
streamflow, valley forms including substrate and abandoned channels, and the changes
in the quantities of hyporheic and groundwater inflows into the system are areas open to
further detailed investigations.
44
Chapter 3 – Evaluating Model Performance Of A Small Scale Physically
Based Stream Temperature Model Of Big Boulder Creek
3.1
Introduction
Models can be powerful tools to better understand and predict the behavior of a system.
The effective uses of models will vary with the degree of understanding of a system
and the quality of data. As both increase, models are better able to be implemented
with decreased uncertainty in their applications. However with most physically based
hydrologic models, complexity often increases as models attempt to increase in the degree of understanding, as does the problem of calibration (Gupta et al., 1998). Model
parameters which cannot be measured in the studied system must be calibrated so the
modeled response is able to closely match the measured data. Often, calibration refers
to a trial-and-error process through which the parameters are adjusted until the modeled
data best fits the real data. The hypothesis that there exists a unique optimal solution
to the calibration of parameters has been researched and closely discussed in the literature (Efstratiadis and Koutsoyiannis, 2010). However, more complex environmental
models might have many different model parameters which could produce an acceptable model for the system (Beven and Freer, 2001). There are a number of methods
commonly used to measure “goodness of fit” between simulated and observed data for
environmental models.
45
Conventional methods of model performance evaluation employ comparison of simulated to observed data to quantify model performance and behavior. For example, root
mean square error, Nash-Sutcliffe efficiency coefficient, and coefficient of determination
are all methods commonly used in the realm of hydrologic modeling to assess fit of the
model performance to the simulated data (Krause et al., 2005). Other statistical methods used in various hydrologic models to optimize calibration include heteroscedastic
maximum likelihood error (HMLE), coefficient of determination (CD), coefficient of
correlation (CC), and mean square derivative error (MSDE) (Efstratiadis and Koutsoyiannis, 2010). All these methods provide measures of fit of modeled data to observed
data without consideration of the shared data between the datasets, i.e. initial condition
or boundary conditions.
A model functions essentially as a ‘filter’ of input data, using model parameters input data to each point. It is therefore very likely to have output data that mimics the
input data, especially at modeled points close to initial conditions. However, there are
instances when the fit of a model can be considered good when in actuality the “closeness” of the modeled data to the observed data may be attributable to the output data of
the model simply being a transformation of observed input data. When these measures
of goodness of fit are used for model calibration and evaluation, the resulting number
of the summary statistic is a measure which averages over the whole time or distance
domains and often both. Therefore, the evaluation measure includes those output points
close to the boundary or initial condition which inherently have a “good fit” to the observed data. To solve this problem, modelers often use the outlet or furthest point from
the boundary condition as an evaluation point. However, any observational data added as
46
an input (e.g. tributaries, precipitation) to the system will translate through the modeled
data downstream. Moreover, the evaluation measures are simply the sum of discrepancies per data point rather than an evaluation of important aspects of the model such
as minima and maxima, timing, or other unique features to the system being modeled.
Often these points of interest in the model are the important characteristics which need
to be captured by the model. Visual assessment of such items is often used, however,
the common methods of model performance evaluation do not specifically capture nor
evaluate these points of interest.
For this study, a model performance evaluation of specific key features was used to
better understand how model parameters affect the modeled output. The model used
was a small scale stream temperature model of a tributary to the Middle Fork John Day
River called Big Boulder Creek in Eastern Oregon. Model performance was evaluated
using a novel method to attempt to extract the influence of the initial condition from the
modeled data.
3.2
Methods
Calibrated temperature traces from Distributed Temperature Sensing instrumentation as
explained in Chapter 2 were employed for this temperature modeling analysis of Big
Boulder Creek (BBC). The Site Description section of Chapter 2 describes the area
where temperature and weather data used to create a model of stream temperature for a
short just over 1 km reach. The resolution of the observed stream temperature data is
at 10 min at each meter, while the time step and distance step of the model is 10 s and
47
5 m respectively. Since the model is over a short reach, evaluation of the performance
is especially susceptible to the shortcomings of using standard model performance assessment equations. Furthermore, the system experiences distributed groundwater upwelling which decreases stream temperature in the middle of the reach both during the
day and night. The ability for the model to accurately predict the temperature changes
due to groundwater upwelling can be visually assessed but are not reflected in the calculation of RMSE or other assessment tools. Moreover, the decrease in temperature due to
groundwater inflow (0.1 - 0.5◦ C) is slight and within what would be considered a “good
fit” using standard forms of model performance assessment.
3.2.1
Stream Temperature Model
A physically based deterministic model based on Heat Source was developed to model
stream temperature for BBC. Deterministic models are based on mathematical equations
which govern or predict the system being modeled, which enable managers to change
parameters within the model to perform “what-if” scenarios. Heat Source is a modeling
tool used by the Department of Environmental Quality developed by Boyd and Kasper
2004, the interface of which is Microsoft Excel. However, with the large amounts of
data contained over short reaches of stream, the model converted by Westhoff’s research
team into MATLAB programming was a more suitable method for this study (Westhoff
et al., 2007). Rather than globally applying model parameters, such as groundwater or
climate, the Westhoff model allows dynamic model parameters spatially and temporally
enabling better calibration of the model.
48
Table 3.1: Table of model inputs showing value or range of values input into the model,
how the value was determined, and the sensitivity of some of the parameter values.
49
The model first requires determination of topographic shade, and vegetative shade if available, using a TTools extension of ArcGIS (downloadable at
http://www.deq.state.or.us/WQ/TMDLs/tools.htm). TTools runs through a series of six
steps to analyze topography based on a Digital Elevation Model (DEM) of the area. The
process therefore requires a DEM of the study area based on LiDAR and a high resolution set of coordinates of left and right stream banks. The calculated outputs used
for the model include channel depth, cross-sectional area, and multi-directional shade
due to topography. The values for shade from topography must further be analyzed to
determine location and time based shading effects on the stream.
The basic premise of the model is a sum of energy fluxes in or out of the stream
(Φstream ). The energy and mass balance components which quantify the energy budget
of a stream are: longwave and shortwave radiation (Φradiation ), streambed conduction
from the temperature gradient between the substrate and water interface (Φconduction ), latent heat from evaporation (Φlatent ), and sensible heat from the air temperature (Φsensible ).
Or simply Φstream = Φradiation + Φconduction + Φlatent + Φsensible . Each component has
a governing equation used by the model to calculate the energy contributed to or taken
out of the stream.
3.2.2
Stream Model Inputs
The study period used for the stream temperature model was from 17-21 July 2009.
Climate data needed for the model includes solar radiation, air temperature, relative humidity, wind speed, and the height of wind measurements. Air temperature, relative
50
Figure 3.1: Velocity and cross-sectional profiles for the upstream end (top) and downstream end (bottom) of the installation.
51
humidity, and wind speed were all measured locally using two wireless Ko weather
stations by Crossbow Technology at various locations along the stream banks. Solar radiation was determined from a Campbell weather station located on the Nature Conservancy property just downstream of the confluence of BBC with the MFJD. Streamflow
was measured at the inflow and the outflow of the study reach using a Marsh-McBirney
flowmeter. Stream temperature was obtained every 10 minutes in July for each meter
over 1.25 km using Distributed Temperature Sensing technology (for more information,
refer to the Methods section of Chapter 2). Other parameters needed for the model were
either referenced, estimated, or calibrated (Table 3.1). The stream temperature model
was calibrated using the July dataset and validated against the temperature data taken
later on during July.
Calibration was firstly done by minimizing overall RMSE and secondarily guided by
model performance parameters explained further in the next section. Parameters used
to minimize RMSE were fraction of diffuse solar radiation, view to sky coefficient, and
thickness of substrate layer. Although optimizing the model by finding the lowest value
of RMSE returned acceptable values for RMSE, the model simulated stream temperature poorly in initial runs. Because the timing of the temperature in the simulation
indicated that advection of temperature downstream was delayed, the velocity input into
the model was too slow. The streambed is characterized by boulder sized substrate,
therefore it appears that measurement of velocity was affected by the influence of the
streambed structure causing turbulent flow. As seen in the cross-sectional velocity profiles, turbulent flow can lead to the velocity at a deep point to be measured as a minimal
velocity and vice versa (Figure 3.1). Therefore, it was essential to reduce the cross-
52
sectional area of the stream to an effective cross-sectional area to minimize the effect of
turbulent flow on the velocity for the purposes of this model.
3.2.3
Evaluation of Model Performance
To better determine what should be used to measure the accuracy of our simulation of
temperature in both time and space, we looked at how others have analyzed similar
models. Root Mean Square Error (RMSE) is commonly used as a calibration tool for
the Matlab version of the Heat Source temperature model (Westhoff et al., 2007, Huff,
2010, Roth et al., 2010). The Oregon Department of Environmental Quality used the
following measures to evaluate the accuracy of their developed John Day River Heat
Source model both temporally and spatially (ODEQ, 2009) :
Mean Error
1 P
ME =
(xsim − xobs )
N
(3.1)
Mean Absolute Error
1 P
MAE =
|xsim − xobs |
N
(3.2)
Root Mean Square Error
r
1 P
RMSE =
(xsim − xobs )2
N
(3.3)
Nash-Sutcliffe Efficiency Coefficient
P
(xsim − xobs )2
EF = 1 − P
(xsim − xobs )2
(3.4)
53
For all equations, N is the number of samples, x denotes the sample point, and ‘sim’
and ‘obs’ stand for simulated and observed respectively. These equations are noticeable
always a comparison of the sum of errors between a simulated and observed dataset,
while not taking into account the influence observed data on the simulated data. While
equations 3.1 - 3.4 reflect standard methods used across sciences to evaluate model
performance, for small scale models such as the BBC stream temperature model used
in this study, these measures are highly influenced by the known data inputs into the
model. The following equations have been preliminarily developed to evaluate model
performance by removing the influence of the initial condition from the simulated data:
Output(t) = Ω(Input(t + τ ) + ξ)
(3.5)
Equation 3.5 is used for both simulated and observed data at a downstream point, this
point is a function of time and is defined by Output(t). The equation defines how the
Output relates to the Input, which is the boundary condition defined in the model (Figure 3.2). The input is first adjusted for a timelag, τ , which accounts for the difference
in timing between the temperatures of the boundary condition compared to the temperature at a given point downstream. For example, if the timing of the absolute temperature
during the day occurred at a downstream observed point occurred 15 minutes after the
boundary condition, then the actual timelag (τ ) would be 15 minutes. If at the same
point downstream the simulated timelag (τ ∗ ) for the absolute temperature is 20 minutes,
then error in the timing of the model (τ ∗ − τ ) would be 5 minutes. The whole dataset
over the time period was used for calculation rather than solely looking at maximum.
54
Figure 3.2: Metrics which compare modeled and observed data at a downstream point
to the boundary condition.
To adjust for a possible difference in average between the input and output, the
difference in average, ξ, is added to the input, defined by the average temperature of the
Input data (boundary condition), TBC , subtracted out of the Output data (observed or
simulated), T :
ξ = T − TBC
(3.6)
Thus, if the average temperature through time does not differ between the boundary
55
condition and the 1000 m downstream, then ξ = 0◦ C. However, if the model shows a
difference in average of 0.5◦ C at 1000 m downstream compared to the boundary condition, then ξ ∗ = 0.5◦ C and the averaging error for the model (ξ ∗ − ξ) would be 0.5◦ C. The
fitting parameter, , adjusts the difference in the amplitude of the temperature between
the Input and Output functions:
Ω=
ABC + 4Tmax − 4Tmin
ABC
(3.7)
Where ABC is the amplitude of the Input (boundary condition) calculated from the
minimum to the maximum. 4Tmax and 4Tmin reflect the difference between the maximum and minimum between the Input and Output functions. As an example, if the
amplitude of the boundary condition is 10◦ C, and at 1000 m downstream the maximum
temperature is 3◦ C greater and there is no difference in the minimum, then Ω = 1.3.
If the simulated data 1000 m downstream also has the maximum temperature is 3◦ C
greater than the boundary condition and the minimum is 1◦ C less, then Ω ∗ = 1.4. The
error of the model in how the data fits the amplitude (Ω ∗ − Ω) is therefore 0.1.
3.2.4
Sensitivity Analysis
In order to gain a better understanding of how changing parameters affect the model, a
sensitivity analysis was conducted. Parameters calibrated, estimated, or measured were
increased and decreased by 10%. Sensitivity reflects the maximum relative change in
RMSE. Influence on model performance was also considered for the parameters which
56
were chosen for the sensitivity analysis. To further test the model as well as the parameters of model performance evaluation, three scenarios were simulated using the
calibrated model: decreasing water velocity by 25%, adding 5◦ C to the air temperature, and removing all groundwater inflow. These were chosen to see how the model
performance parameters would react and to better understand the stream system.
3.3
Results
A temperature model for BBC was calibrated for the time period from 18:00 on July
17 to 6:00 on July 19, 2009 (Figure 3.3) and validated against temperature data taken
during the time period of 7:00 on July 19 to 5:00 on July 21, 2009. Overall RMSE
was calculated to be 0.28◦ C for the final calibrated model, while RMSE was 0.34◦ C
for the simulation of the validation period (Figure 3.4). Estimated error in temperature
measurement from the DTS instrument is 0.25◦ C for data used for model simulation. As
would be expected, RMSE increases with distance away from the boundary condition
reaching up to 0.4◦ C. There is no visible pattern in RMSE with time, however values
range from 0.1 to up to 0.8◦ C. Strengths and weaknesses of the model are more visible
in looking at the model performance metrics (Figures 3.5, 3.6). Initial model runs
clearly showed the modeled timelag was 20-30 minutes off from the measured timelag.
Therefore, timelag was used as a calibration parameter by raising the water velocity to
minimize the difference in timlag. In the end, timelag for the simulated data is up to 10
minutes slow from about 300 to 800 meters and toward the downstream end. Of note is
that timelag is discretized in 10 minute intervals. The offset for simulated temperature
57
Figure 3.3: July 2009 stream temperature observed (left) compared with simulated
(right), during the period from 18:00 on July 17 to 6:00 on July 19. RMSE is 0.28◦ C
while error in temperature measurement from the DTS is estimated to be 0.25◦ C.
58
Figure 3.4: Validation of the calibrated stream temperature model for the period of 7:00
on July 19 through 5:00 on July 21, 2009. RMSE is calculated to be 0.34◦ C.
59
remains less than observed for the entire length of the stream, reaching as high as 0.15◦ C
difference. Amplitude in July for simulated temperature seems to be “off-phase” with
observed temperature data, the difference in amplitude ranging from -0.03 to 0.05. The
validation results show timelag is slow by 10 minutes over most the distance, offset is
up to 0.2◦ C too cold but the trend is modeled well, and amplitude ranges between -0.04
to 0.02.
The model was tested by conducting a sensitivity analysis and by running three
model scenarios. Results of the parameter sensitivity analysis can be found in Table
3.1. For the scenario of 25% reduction in water velocity (Figure 3.7), timelag becomes
up to 20 minutes too slow but equilibrating with the correct timelag near the end. Offset
ranges from 0.1◦ C too cold to 0.2◦ C too warm. Difference in amplitude peaks around
200 m by 0.04, but fits well from 400 m on. Increasing the air temperature by 5◦ C
(Figure 3.8) results in an offset ranging between -0.1 to 0.1. The peak temperature
along the distance of the stream occurring at about 650 m is 0.1◦ C greater than the
calibrated model and 0.05◦ C greater at the end of the stream reach. The amplitude
of this model scenario differs between -0.02 and 0.04. There is no notable change in
timelag. Taking away groundwater from the system (Figure 3.9) results in an increasing
offset with distance. The increase in temperature of the water is 0.5◦ C. Taking out the
groundwater inflow reduces the amount of streamflow and therefore results in a slight
change in timelag. Amplitude for this scenario ranges -0.03 to 0.05 difference between
the observed temperature.
Figure 3.5: Model performance metrics for calibrated model. The left column of graphs compare metrics for
simulated with observed for τ , timelag (top), ξ, offset (middle), and Ω, amplitude (bottom) with distance. Timelag
is discretized in 10 minute time steps. The middle column displays the difference (observed - simulated) for τ ,
timelag (top), ξ, offset (middle), and Ω, amplitude (bottom). The right column shows RMSE with distance (top),
with time (middle), and overall RMSE at 0.283◦ C.
60
Figure 3.6: Model performance parameters for the validation of the model.
61
Figure 3.7: Simulation run showing reduction of water velocity by 25%.
62
Figure 3.8: Model simulation with 5◦ C increase in air temperature.
63
Figure 3.9: Model simulation with no groundwater inflow.
64
65
3.4
Conclusions
The natural systems which models attempt to simulate have inherent complexities beyond what is measureable or what can be modeled. In the modeling of systems, it
is important to find a balance given the scale of the model between detail and overall
trends. Since the scale of the reach being modeled is just over 1 km, certain details that
were initially thought to be critical turned out to be of low importance. For example,
solar radiation has a very low sensitivity at 0.03, the lowest sensitivity of the weather
parameters, compared with air temperature having the highest sensitivity of the weather
parameters at 1.61. It is possible that over such a short distance certain model parameters
do not exhibit the degree of importance that they might appear on a larger scale.
3.4.1
On Model Performance Evaluation
Having model performance parameters which reflect how temperature changes downstream from the boundary condition give better insight into the shortcomings of the
model. RMSE and other standard methods of calculating model performance indicate
little about how the model is working and fail to properly evaluate the ability of a small
scale model to predict temperature. Sensitivity analyses are effective in showing which
parameters have the largest or smallest influence on the model, however, these can be
time consuming and are usually conducted after a model has been calibrated. Having
model performance measures which compartmentalize data helps guide the modeler to
the cause of the error without delving into fine details. In the case of the BBC model,
early model runs, though producing reasonable values for RMSE at 0.34◦ C, indicated
66
Figure 3.10: Early model simulations with apparent error in timing of heat flow through
the stream, although RMSE is 0.34◦ C. Observed stream temperature is on the left and
simulated temperature is on the right. Although temperatures appear to be predicted
well, there is an apparent ‘skew’ to the temperatures.
a problem with velocity or streamflow inputs (Figure 3.10). Although the calibration
reduced overall RMSE, the timelag performance measure during these early runs led
to the recalculation of effective area to take out the possible influence of turbulent flow
caused by the streambed full of boulders. The overall method not only helps the modeler
discover shortcomings of the model but is also visually evident to others looking at the
model to realize how the model is performing. In the future, it may be useful to calibrate the model according to these parameters by minimizing the sum of the differences
of the three model performance parameters. Other anomalies in data may be found by
looking at distance dependent parameters to see if patterns in model performance pa-
67
Figure 3.11: Distance dependent parameters input into the model.
rameters appear similar to the input data (Figures 3.11- 3.13). For example, the offset
parameter has clear drops in locations where groundwater is input into the model. All
parameters have an overall effect on the average temperature which the model simulates,
however, being able to find specific locations of anomalies and correlating back to parameters which have changed in those areas give a better enables the modeler to discern
parameters affecting undue change.
3.4.2
What the Model Shows About the Stream System
It is clear that for this the 1 km of this stream system which has been restored, groundwater is essential in reducing stream temperatures. Although temperature had a high
sensitivity in the model, increasing the air temperature by 5◦ C resulted in a simulated
68
Figure 3.12: Solar radiation values input into the model, a function of both time and
space.
Figure 3.13: Time dependent parameters input into the model.
69
increase in the average temperature over the time period modeled of only 0.05◦ C at the
downstream end. However, if there were no groundwater, the temperature would increase by 0.5◦ C at the downstream end according to the simulation. From a restoration
perspective, the data shows that it would be more pertinent in terms of the restoration
goal to reduce summer peak temperatures to encourage groundwater inflow than provide
shade for this stretch of stream. This may be accomplished by strategically positioning
installed log jams in known locations of groundwater inflow and excavating pools in
these areas to encourage locations of cool water refuges.
70
Chapter 4 – Conclusions and Recommendations
Research presented in this thesis is derived from a stream temperature study conducted
on a tributary to the Middle Fork John Day River (MFJD), Big Boulder Creek (BBC).
The tributary has historically and currently runs through privately owned ranching land
before reaching the MFJD. In 2008, the stream channel was restored into its historic,
mid-valley channel, where earlier land owners diverted the stream into a channel against
the valley wall to create grazing land. The restoration is less than 1 km in length, downstream of a natural fish passage barrier preventing migrating fish from swimming upstream to the headwaters. Fish found in the restored reach, which is mostly rearing
habitat for juveniles, include steelhead trout and Chinook salmon. Peak summer temperatures are therefore of pressing interest in this tributary because one of the restoration
goals of the project was to reduce in-stream peak summer temperatures for steelhead and
Chinook.
4.1
DTS as a Restoration Tool
Still in its infancy in environmental application at the time of this study, Fiber Optic
Distributed Temperature Sensing (DTS) technology was used to obtain stream temperature. This technology is valuable in environmental research because it obtains high
spatial and temporal resolution temperature data. In Chapter 2, DTS stream tempera-
71
ture measurements were used to determine areas of groundwater and hyporheic inflow.
Groundwater temperature in this study was defined to take on the average annual air temperature, whereas hyporheic water was defined to assume the average daily or weekly
air temperature. With these assumptions, a groundwater signal would result in decreasing both maximum and minimum stream temperature at the point in which groundwater
is entering the stream, while a hyporheic inflow signal would result in a buffering of
temperature, reducing maximum and raising minimum temperatures. Results of this
study showed 1-3% groundwater inflow, an amount undetectable by other methods of
measurement, as well as a great amount of hyporheic inflow. Although DTS technology
is a unique and useful tool, it does have some drawbacks. Instrumentation is expensive,
installation is labor intensive, and data analysis is time consuming because of the large
amount of data collected. With increased use of the technology, some of these drawbacks are declining and the technology likely will become more readily usable. There
are many plausible environment applications of DTS technology, such as planning and
monitoring stream restoration projects. Knowing areas of groundwater inflow prior to
restoration can be a strategic method for determining locations of logjams or excavation
of pools in order to have the highest impact on reducing stream temperatures or creating cool water refuges. In BBC, combined groundwater and hyporheic inflow has a
clear impact on stream temperature reducing peak July and August 2009 temperature by
0.3-0.6◦ C over about 500 m of the study reach. When it comes to knowing how to effectively spend money on restoration objectives, use of DTS could make the difference in
cost-effectiveness (see Appendix A). The importance of groundwater in the BBC study
reach was further demonstrated in the stream temperature model. While stream shading
72
may help prevent localized heating of the stream, groundwater inflow had the highest
impact on stream temperature. Discussed in Chapter 3, simulations indicate that stream
temperature would be 0.5◦ C greater at the downstream end of the reach without any
groundwater inflow. Hyporheic exchange was not considered in model simulations but
is certainly an important process in temperature dynamics of BBC contributing to reduction of peak stream temperatures. Stream shading and tree canopy cover provide are
beneficial to the riparian ecosystem, however limiting the system view to this 1 km reach
of BBC, restoration dollars are best spent towards targeting groundwater inflow into the
stream to provide cool water refuge. Shade would prevent heating of the stream by
blocking incoming solar radiation, while groundwater is contributing to the reduction of
temperatures of the stream. If looking at temperature at a watershed scale, shade would
presumably play more imperative role in preventing heating of the stream if upstream
reaches have sufficient shade.
4.2
Model Performance Evaluation
Further discussed in Chapter 3 is a method for model evaluation, which better indicates
components which a model is able to simulate well or is simulating poorly. Standard
methods of model performance evaluation such as Root Mean Square Error (RMSE),
Mean Error, Mean Absolute Error, or Nash-Sutcliffe Efficiency Coefficient depend on
measures of error (Xobserved − Xsimulated ) to evaluate performance. Measures of error
are biased by inclusion of data points close to the boundary conditions as well as initial
conditions which have inherently low error. For small scale models, such as the BBC
73
model produced in this study, standard methods of model performance evaluation fail
to represent a true assessment of the ability of a model to predict temperature. The developed model evaluation tool compares three different parameters, timelag, offset and
amplitude, between the boundary condition and a point downstream either observed or
simulated. These measures calculate how each parameter is changing downstream away
from the boundary condition, rather than calculating error at each point. The modeler
is therefore better able to understand and present how the simulated temperature is predicting these three components of temperature changes at each point downstream. The
stream temperature model for this study was calibrated by minimizing RMSE. Only
timelag was used as a calibration parameter since it appeared to be the major shortcoming in the model. As this study has demonstrated, these measures are a useful tool to
visually understand what the model has simulated well or poorly. In the future, these
measures may be developed further to use in optimization of models, perhaps by minimizing the sum of the differences between observed and simulated for each component.
For small scale models, in particular models produced using DTS data where spatial
resolution is high but modeled reaches are short, these model performance measures are
a necessary evaluation tool.
74
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APPENDICES
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Appendix A – An Example Budget for Minimizing the Cost of DTS
Instrumentation Use in Stream Restoration Applications
Stream restoration as a field of study and industry has been around for only about 20
years. Money spent towards restoration projects range from thousands to millions of
dollars (Table A.1). To ensure successful use of funds, projects should be tailored
strategically to the specific stream system in which restoration goals have been laid
out, rather than adopting a “one-size-fits-all” model which may or may not effective in
accomplishing broad restoration goals. In the Middle Fork John Day River there are
232 miles of stream listed by EPA for exceeding temperature limits, while in Oregon the
total is 17,253 miles (EPA, 2006). Since temperature is one of the most critical factors
in habitat for salmonid fish species, it is imperative that restoration projects meant to
reduce stream temperatures or provide cool water refuges are successful. Fiber Optic
Distributed Temperature Sensing (DTS) technology is a tool for determining areas of
groundwater and hyporheic inflow, the knowledge of which would enable restoration
planners to act strategically in placement of log-jams or excavation of pools to target
areas of cool water inflow. Table A.2 is an example budget for a DTS installation meant
to obtain such data with trained personnel. A DTS component to a pre-implementation
project would cost $10,000-$12,000, when considering the cost of restoration projects,
on a case specific basis an investigation of groundwater using DTS may be significant
factor in the success of a restoration (Beschta et al., 1994).
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Table A.1: Investments in riparian and in-stream restoration projects from 1995-2009
(Oregon Explorer, http://oe.oregonexplorer.info/RestorationTool/). Riparian restoration
projects may include activities designed to improve riparian habitat or provide bank
stabilization. In-stream activities may include any in-channel activities designed to improve aquatic habitat, which does not include fish passage projects.
Table A.2: An example budget for a 2-day DTS field installation with 4 days of data
analysis. This quick installation is designed to obtain enough temperature data to determine locations of groundwater inflow prior to restoration, which would lead to strategic
placement log-jams or excavation of pools for cool water refuges.
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Appendix B – Data Collected (See Attached CD)
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Appendix C – Stream Temperature Model (See Attached CD)